From 12455d6ece1a58fe274905824ca5a66cdf963e27 Mon Sep 17 00:00:00 2001
From: Paul David Harris <hifret@neomailbox.com>
Date: Sun, 4 Sep 2022 11:06:24 +0300
Subject: [PATCH 001/142] Fixed nsalex group data replicating first stream
 issue

---
 fretbursts/burstlib_ext.py | 4 ++--
 fretbursts/loader.py       | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/fretbursts/burstlib_ext.py b/fretbursts/burstlib_ext.py
index 27ccb1d..33bef41 100644
--- a/fretbursts/burstlib_ext.py
+++ b/fretbursts/burstlib_ext.py
@@ -887,7 +887,7 @@ def group_data(d_list):
     new_d.add(nch = sum([d.nch for d in d_list]))
     new_d.add(name = 'Joined data of\n' + '\n'.join(d.name for d in d_list))
     for field in ('_leakage', '_dir_ex', '_gamma', '_beta', 'clk_p'):
-        if np.any([d[field] != new_d[field] for d in d_list]):
+        if not np.all([np.allclose(d[field], new_d[field])  for d in d_list]):
             raise ValueError(f"Different {field} corrections, ensure data sets are technical repeats")
     if hasattr(d_list[0], 'nanotimes_params'):
         if not dict_equal(*[d.nanotimes_params[0] for d in d_list]):
@@ -896,7 +896,7 @@ def group_data(d_list):
         new_d.add(det_donor_accept=[l for l in chain.from_iterable(d.det_donor_accept for d in d_list)])
     # Check that burst metadata is also consistent, so analysis, if done conducted equally
     meta_data = dict()
-    for field in chain(Data.ph_fields, Data.burst_fields, Data.bg_fields, Data.burst_metadata):
+    for field in chain(Data.ph_fields, Data.burst_fields, Data.bg_fields, Data.burst_metadata, ('ph_times_t','det_t')):
         if hasattr(d_list[0], field):
             if np.any([not hasattr(d, field) for d in d_list[1:]]):
                 raise RuntimeError(f"Inconsistent analysis of {field}")
diff --git a/fretbursts/loader.py b/fretbursts/loader.py
index 4c78000..23f8ceb 100644
--- a/fretbursts/loader.py
+++ b/fretbursts/loader.py
@@ -614,7 +614,7 @@ def _nsalex_apply_period_1ch(d, i, delete_ph_t=True):
 
     *See also:* :func:`alex_apply_period`.
     """
-    ich = 0   # we only support single-spot here
+    ich = i
     donor_ch, accept_ch = d._det_donor_accept_multich[ich]
     D_ON_multi, A_ON_multi = d._D_ON_multich[ich], d._A_ON_multich[ich]
     D_ON = [(D_ON_multi[i], D_ON_multi[i + 1])

From 2e17aa698b9dee7853b33b64f43cfc115a0f136f Mon Sep 17 00:00:00 2001
From: Paul David Harris <hifret@neomailbox.com>
Date: Sun, 4 Sep 2022 14:38:45 +0300
Subject: [PATCH 002/142] Fixed issues with burstlib_ext

---
 fretbursts/burstlib_ext.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/fretbursts/burstlib_ext.py b/fretbursts/burstlib_ext.py
index 33bef41..f12b78a 100644
--- a/fretbursts/burstlib_ext.py
+++ b/fretbursts/burstlib_ext.py
@@ -885,7 +885,8 @@ def group_data(d_list):
             raise RuntimeError("Inconsistent background estimation")
     new_d = Data(**dict(d_list[0]))
     new_d.add(nch = sum([d.nch for d in d_list]))
-    new_d.add(name = 'Joined data of\n' + '\n'.join(d.name for d in d_list))
+    new_d.add(name = 'Grouped data starting with ' + d_list[0].name)
+    new_d.add(fname = [d.fname for d in d_list])
     for field in ('_leakage', '_dir_ex', '_gamma', '_beta', 'clk_p'):
         if not np.all([np.allclose(d[field], new_d[field])  for d in d_list]):
             raise ValueError(f"Different {field} corrections, ensure data sets are technical repeats")

From 7d5ac8fad37487f139e2a69f103f33a391a81c55 Mon Sep 17 00:00:00 2001
From: Paul David Harris <hifret@neomailbox.com>
Date: Mon, 19 Sep 2022 02:33:57 +0300
Subject: [PATCH 003/142] Full plotting updates and general bug fixex

---
 .github/workflows/tests.yml           |   8 +
 fretbursts/background.py              |   4 +-
 fretbursts/burst_plot.py              | 328 +++++++++++++++++++-------
 fretbursts/burstlib.py                |  71 +++---
 fretbursts/burstlib_ext.py            |  65 ++---
 fretbursts/fit/exp_fitting.py         |   3 +-
 fretbursts/fit/weighted_kde.py        |   2 +-
 fretbursts/loader.py                  |  29 ++-
 fretbursts/mfit.py                    |  60 ++++-
 fretbursts/phtools/burstsearch.py     |   6 +-
 fretbursts/phtools/phrates.py         |   7 +-
 fretbursts/tests/test_burst_plot.py   | 235 ++++++++++++++++++
 fretbursts/tests/test_burstlib.py     | 109 +++------
 fretbursts/tests/test_burstlib_ext.py | 232 ++++++++++++++++++
 14 files changed, 907 insertions(+), 252 deletions(-)
 create mode 100644 fretbursts/tests/test_burst_plot.py
 create mode 100644 fretbursts/tests/test_burstlib_ext.py

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index c0210b8..600d133 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -27,11 +27,19 @@ jobs:
         run: |
           wget -N http://files.figshare.com/2182604/12d_New_30p_320mW_steer_3.hdf5
           wget -N http://files.figshare.com/2182601/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5
+          wget -N https://zenodo.org/record/5902313/files/HP3_TE150_SPC630.hdf5
+          wget -N https://zenodo.org/record/5902313/files/HP3_TE200_SPC630.hdf5
+          wget -N https://zenodo.org/record/5902313/files/HP3_TE250_SPC630.hdf5
+          wget -N https://zenodo.org/record/5902313/files/HP3_TE300_SPC630.hdf5
       - name: Downlaod files Windows
         if: runner.os == 'Windows'
         run: |
           curl.exe --output 2182604/12d_New_30p_320mW_steer_3.hdf5 --url http://files.figshare.com/2182604/12d_New_30p_320mW_steer_3.hdf5
           curl.exe --output 0023uLRpitc_NTP_20dT_0.5GndCl.hdf5 --url http://files.figshare.com/2182601/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5
+          curl.exe --output HP3_TE150_SPC630.hdf5 --url https://zenodo.org/record/5902313/files/HP3_TE150_SPC630.hdf5
+          curl.exe --output HP3_TE200_SPC630.hdf5 --url https://zenodo.org/record/5902313/files/HP3_TE200_SPC630.hdf5
+          curl.exe --output HP3_TE250_SPC630.hdf5 --url https://zenodo.org/record/5902313/files/HP3_TE250_SPC630.hdf5
+          curl.exe --output HP3_TE300_SPC630.hdf5 --url https://zenodo.org/record/5902313/files/HP3_TE300_SPC630.hdf5
       - name: Upgrade pip
         run: python -m pip install --upgrade pip
       - name: Windows 3.6 Oddities
diff --git a/fretbursts/background.py b/fretbursts/background.py
index aea9b65..6f6c0d6 100644
--- a/fretbursts/background.py
+++ b/fretbursts/background.py
@@ -225,9 +225,9 @@ def fit_varying_min_delta_ph(d, min_delta_ph_list, bg_fit_fun=exp_fit,
         (nch, nperiods, len(min_delta_ph_list)).
     """
 
-    BG = np.zeros((d.nch, d.nperiods, np.size(min_delta_ph_list)))
+    BG = np.zeros((d.nch, d.nperiods.max(), np.size(min_delta_ph_list)))
     BG_err = np.zeros_like(BG)
-    BG[:], BG_err[:] = None, None
+    BG[:], BG_err[:] = np.nan, np.nan
 
     for ich in range(d.nch):
         for period, ph in enumerate(
diff --git a/fretbursts/burst_plot.py b/fretbursts/burst_plot.py
index e4fb489..ede3753 100644
--- a/fretbursts/burst_plot.py
+++ b/fretbursts/burst_plot.py
@@ -33,6 +33,7 @@
 
 import warnings
 from itertools import cycle
+from collections.abc import Iterable
 
 # Numeric imports
 import numpy as np
@@ -456,7 +457,7 @@ def _has_cache_for(binwidth, tmin, tmax):
             plt.ylim(bottom=-100)
         _plot_status['timetrace_single'] = {'autoscale': False}
 
-
+# do not concatenate, timetrace should always be shown per channel
 def timetrace(d, i=0, binwidth=1e-3, bins=None, tmin=0, tmax=200,
               bursts=False, burst_picker=True, scroll=False,
               show_rate_th=True, F=None, rate_th_style={'label': None},
@@ -556,7 +557,7 @@ def ratetrace_single(d, i=0, m=None, max_num_ph=1e6, tmin=0, tmax=200,
     iph1 = np.searchsorted(ph_times, tmin_clk)
     iph2 = np.searchsorted(ph_times, tmax_clk)
     if iph2 - iph1 > max_num_ph:
-        iph2 = iph1 + max_num_ph
+        iph2 = iph1 + int(max_num_ph)
         tmax = ph_times[iph2] * d.clk_p
         warnings.warn(('Max number of photons reached in ratetrace_single().'
                        '\n    tmax is reduced to %ds. To plot a wider '
@@ -596,7 +597,7 @@ def ratetrace_single(d, i=0, m=None, max_num_ph=1e6, tmin=0, tmax=200,
             plt.ylim(bottom=-100)
         _plot_status['ratetrace_single'] = {'autoscale': False}
 
-
+# same, must be plotted per channel always
 def ratetrace(d, i=0, m=None, max_num_ph=1e6, tmin=0, tmax=200,
               bursts=False, burst_picker=True, scroll=False,
               show_rate_th=True, F=None, rate_th_style={'label': None},
@@ -674,10 +675,11 @@ def sort_burst_sizes(sizes, levels=np.arange(1, 102, 20)):
     masks.append(sizes >= level2)
     return masks
 
+# plot per channel always
 def timetrace_fret(d, i=0, gamma=1., **kwargs):
     """Timetrace of burst FRET vs time. Uses `plot`."""
     b = d.mburst[i]
-    bsizes = bl.select_bursts.get_burst_size(d, ich=i, gamma=gamma)
+    bsizes = d.burst_sizes_ich(ich=i, gamma=gamma)
 
     style_kwargs = dict(marker='o', mew=0.5, color=blue, mec='grey',
                         alpha=0.4, ls='')
@@ -690,19 +692,20 @@ def timetrace_fret(d, i=0, gamma=1., **kwargs):
                  **style_kwargs)
     plt.plot(b.start*d.clk_p, d.E[i], '-k', alpha=0.1, lw=1)
     xlabel('Time (s)'); ylabel('E')
-    _gui_timetrace_burst_sel(d, i, timetrace_fret, gcf(), gca())
+    _gui_timetrace_burst_sel(d, gcf(), gca())
 
+# plot per channel always
 def timetrace_fret_scatter(d, i=0, gamma=1., **kwargs):
     """Timetrace of burst FRET vs time. Uses `scatter` (slow)."""
     b = d.mburst[i]
-    bsizes = d.burst_sizes_ich(d, ich=i, gamma=gamma)
+    bsizes = d.burst_sizes_ich(ich=i, gamma=gamma)
 
     style_kwargs = dict(s=bsizes, marker='o', alpha=0.5)
     style_kwargs.update(**kwargs)
     plt.scatter(b.start*d.clk_p, d.E[i], **style_kwargs)
     xlabel('Time (s)'); ylabel('E')
 
-
+# plot per channel always
 def timetrace_bg(d, i=0, nolegend=False, ncol=2, plot_style={}, show_da=False):
     """Timetrace of background rates."""
     bg = d.bg_from(Ph_sel('all'))
@@ -733,7 +736,7 @@ def timetrace_bg(d, i=0, nolegend=False, ncol=2, plot_style={}, show_da=False):
     plt.grid(True)
     plt.ylim(bottom=0)
 
-
+# plot per channel always
 def timetrace_b_rate(d, i=0):
     """Timetrace of bursts-per-second in each period."""
     t = arange(d.bg[i].size)*d.bg_time_s
@@ -748,11 +751,13 @@ def timetrace_b_rate(d, i=0):
     xlabel("Time (s)"); ylabel("Burst per second"); grid(True)
     plt.ylim(bottom=0)
 
+# plot per channel always
 def time_ph(d, i=0, num_ph=1e4, ph_istart=0):
     """Plot 'num_ph' ph starting at 'ph_istart' marking burst start/end.
     TODO: Update to use the new matplotlib eventplot.
     """
     b = d.mburst[i]
+    num_ph = int(num_ph)
     SLICE = slice(ph_istart, ph_istart+num_ph)
     ph_d = d.ph_times_m[i][SLICE][~d.A_em[i][SLICE]]
     ph_a = d.ph_times_m[i][SLICE][d.A_em[i][SLICE]]
@@ -779,7 +784,7 @@ def _bins_array(bins):
         bins = np.arange(*bins)
     return bins
 
-
+# not channel specific hidden function
 def _hist_burst_taildist(data, bins, pdf, weights=None, yscale='log',
                          color=None, label=None, plot_style=None, vline=None):
     hist = HistData(*np.histogram(data[~np.isnan(data)],
@@ -820,8 +825,11 @@ def hist_width(d, i=0, bins=(0, 10, 0.025), pdf=True, weights=None,
         vline (float): If not None, plot vertical line at the specified x
             position.
     """
-    weights = weights[i] if weights is not None else None
-    burst_widths = d.mburst[i].width * d.clk_p * 1e3
+    if i is None:
+        burst_widths = np.concatenate([mb.width for mb in d.mburst]) * d.clk_p * 1e3
+    else:
+        weights = weights[i] if weights is not None else None
+        burst_widths = d.mburst[i].width * d.clk_p * 1e3
 
     _hist_burst_taildist(burst_widths, bins, pdf, weights=weights, vline=vline,
                          yscale=yscale, color=color, plot_style=plot_style)
@@ -830,8 +838,8 @@ def hist_width(d, i=0, bins=(0, 10, 0.025), pdf=True, weights=None,
 
 
 def hist_brightness(d, i=0, bins=(0, 60, 1), pdf=True, weights=None,
-                    yscale='log', gamma=1, add_naa=False, beta=1.,
-                    donor_ref=True, add_aex=True, aex_corr=True,
+                    yscale='log', gamma=1, add_naa=False, ph_sel=Ph_sel('all'), beta=1.,
+                    donor_ref=True, naa_aexonly=False, naa_comp=False, na_comp=False,
                     label_prefix=None, color=None, plot_style=None, vline=None):
     """Plot histogram of burst brightness, i.e. burst size / duration.
 
@@ -845,12 +853,17 @@ def hist_brightness(d, i=0, bins=(0, 60, 1), pdf=True, weights=None,
         add_naa (bool): if True, include `naa` to the total burst size.
         donor_ref (bool): convention used for corrected burst size computation.
             See :meth:`fretbursts.burstlib.Data.burst_sizes_ich` for details.
-        add_aex (bool): *PAX-only*. Whether to add signal from Aex laser period
-            to the burst size. Default True.
-            See :meth:`fretbursts.burstlib.Data.burst_sizes_pax_ich`.
-        aex_corr (bool): *PAX-only*. If True, do duty-cycle correction
-            when adding the DAexAem term `naa`.
-            See :meth:`fretbursts.burstlib.Data.burst_sizes_pax_ich`.
+        na_comp (bool): **[PAX-only]** If True, multiply the `na` term
+            by `(1 + Wa/Wd)`, where Wa and Wd are the D and A alternation
+            durations (typically Wa/Wd = 1).
+        naa_aexonly (bool): **[PAX-only]** if True, the `naa` term is
+            corrected to include only A emission due to A excitation.
+            If False, the `naa` term includes all the counts in DAexAem.
+            The `naa` term also depends on the `naa_comp` argument.
+        naa_comp (bool): **[PAX-only]** If True, multiply the `naa` term by
+            `(1 + Wa/Wd)` where Wa and Wd are the D and A alternation
+            durations (typically Wa/Wd = 1). The `naa` term also depends on
+            the `naa_aexonly` argument.
         label_prefix (string or None): a custom prefix for the legend label.
         color (string or tuple or None): matplotlib color used for the plot.
         pdf (bool): if True, normalize the histogram to obtain a PDF.
@@ -863,10 +876,14 @@ def hist_brightness(d, i=0, bins=(0, 60, 1), pdf=True, weights=None,
     if plot_style is None:
         plot_style = {}
 
-    burst_widths = d.mburst[i].width * d.clk_p * 1e3
+    if i is None:
+        burst_widths = np.concatenate([mb.width for mb in d.mburst]) * d.clk_p * 1e3
+    else:
+        burst_widths = d.mburst[i].width * d.clk_p * 1e3
     sizes, label = _get_sizes_and_formula(
         d=d, ich=i, gamma=gamma, beta=beta, donor_ref=donor_ref,
-        add_naa=add_naa, add_aex=add_aex, aex_corr=aex_corr)
+        add_naa=add_naa, ph_sel=ph_sel, naa_aexonly=naa_aexonly,
+        naa_comp=naa_comp, na_comp=na_comp)
     brightness = sizes / burst_widths
     label = '$(' + label[1:-1] + ') / w$'
     if label_prefix is not None:
@@ -891,10 +908,17 @@ def _get_sizes_and_formula(d, ich, gamma, beta, donor_ref, add_naa,
     if 'PAX' in d.meas_type and ph_sel is not None:
         kws_pax = dict(ph_sel=ph_sel, naa_aexonly=naa_aexonly,
                        naa_comp=naa_comp, na_comp=na_comp)
-        sizes = d.burst_sizes_pax_ich(ich=ich, **dict(kws, **kws_pax))
+        if ich is None:
+            sizes = np.concatenate([d.burst_sizes_pax_ich(ich=i, **dict(kws, **kws_pax)) for i in range(d.nch)])
+        else:
+            sizes = d.burst_sizes_pax_ich(ich=ich, **dict(kws, **kws_pax))
         label = '$ %s $' % d._burst_sizes_pax_formula(**dict(kws, **kws_pax))
     else:
-        sizes = d.burst_sizes_ich(ich=ich, add_naa=add_naa, **kws)
+        if ich is None:
+            sizes = np.concatenate([d.burst_sizes_ich(ich=i, add_naa=add_naa, **kws) 
+                                    for i in range(d.nch)])
+        else:
+            sizes = d.burst_sizes_ich(ich=ich, add_naa=add_naa, **kws)
         label = label.format(FD='n_d', FA='n_a')
         if add_naa:
             corr = '(\\gamma\\beta) ' if donor_ref else '\\beta '
@@ -902,6 +926,7 @@ def _get_sizes_and_formula(d, ich, gamma, beta, donor_ref, add_naa,
     return sizes, label
 
 
+# dependent on _hist_burst_taildist
 def hist_size(d, i=0, which='all', bins=(0, 600, 4), pdf=False, weights=None,
               yscale='log', gamma=1, beta=1, donor_ref=True, add_naa=False,
               ph_sel=None, naa_aexonly=False, naa_comp=False, na_comp=False,
@@ -961,7 +986,7 @@ def hist_size(d, i=0, which='all', bins=(0, 600, 4), pdf=False, weights=None,
             add_naa=add_naa, ph_sel=ph_sel, naa_aexonly=naa_aexonly,
             naa_comp=naa_comp, na_comp=na_comp)
     else:
-        sizes = d[which][i]
+        sizes = np.concatenate(d[which]) if i is None else d[which][i]
         label = which
 
     # Use default label (with optional prefix) only if not explicitly
@@ -983,7 +1008,7 @@ def hist_size(d, i=0, which='all', bins=(0, 600, 4), pdf=False, weights=None,
     if legend:
         plt.legend(loc='upper right')
 
-
+# depends on  _hist_burst_taildist
 def hist_size_all(d, i=0, **kwargs):
     """Plot burst sizes for all the combinations of photons.
 
@@ -1152,10 +1177,10 @@ def hist_burst_data(
     fitter.histogram(binwidth=binwidth, bins=bins, verbose=verbose)
     if pdf:
         ylabel('PDF')
-        hist_vals = fitter.hist_pdf[i]
+        hist_vals = fitter.hist_pdf_tot if i is None else fitter.hist_pdf[i]
     else:
         ylabel('# Bursts')
-        hist_vals = fitter.hist_counts[i]
+        hist_vals = fitter.hist_counts_tot if i is None else fitter.hist_counts[i]
     xlabel(data_name)
     if data_name in ['E', 'S']:
         xlim(-0.19, 1.19)
@@ -1181,13 +1206,16 @@ def hist_burst_data(
         if pdf:
             scale = 1
         else:
-            scale = fitter.hist_binwidth * d.num_bursts[i]
+            if i is None:
+                scale = fitter.hist_binwidth * sum(d.num_bursts)
+            else:
+                scale = fitter.hist_binwidth * d.num_bursts[i]
 
     if show_model:
         model_plot_style_ = dict(color='k', alpha=0.8, label='Model')
         model_plot_style_.update(_normalize_kwargs(model_plot_style,
                                                    kind='line2d'))
-        fit_res = fitter.fit_res[i]
+        fit_res = fitter.fit_res_tot if i is None else fitter.fit_res[i]
         x = fitter.x_axis
         y = fit_res.model.eval(x=x, **fit_res.values)
         xx, yy = (y, x) if vertical else (x, y)
@@ -1213,7 +1241,10 @@ def hist_burst_data(
         xx, yy = (y, x) if vertical else (x, y)
         ax.plot(xx, yy, **kde_plot_style_)
     if show_kde_peak:
-        pline(fitter.kde_max_pos[i], ls='--', color='orange')
+        if i is None:
+            pline(fitter.kde_max_pos[-1], ls='--', color='orange')
+        else:
+            pline(fitter.kde_max_pos[i], ls='--', color='orange')
 
     if show_fit_value or show_fit_stats:
         if fit_from == 'kde':
@@ -1229,6 +1260,7 @@ def hist_burst_data(
         if show_fit_value:
             _plot_fit_text_ch(fit_arr, i, ax=ax)
 
+# depends on hist_burst_data
 def hist_fret(
         d, i=0, ax=None, binwidth=0.03, bins=None, pdf=True,
         hist_style='bar',
@@ -1257,6 +1289,7 @@ def hist_fret(
         model_plot_style=model_plot_style, kde_plot_style=kde_plot_style,
         verbose=verbose)
 
+# depends on hist_burst_data
 def hist_S(
         d, i=0, ax=None, binwidth=0.03, bins=None, pdf=True,
         hist_style='bar',
@@ -1284,6 +1317,7 @@ def hist_S(
         model_plot_style=model_plot_style, kde_plot_style=kde_plot_style,
         verbose=verbose)
 
+# should not depend on channel
 def _get_fit_text_stats(fit_arr, pylab=True):
     """Return a formatted string for mean E and max delta-E."""
     delta = (fit_arr.max() - fit_arr.min())*100
@@ -1292,6 +1326,7 @@ def _get_fit_text_stats(fit_arr, pylab=True):
     if pylab: fit_text = r'$'+fit_text+r'$'
     return fit_text
 
+
 def _plot_fit_text_ch(
         fit_arr, ich, fmt_str="CH%d: $E_{fit} = %.3f$", ax=None,
         bbox=dict(boxstyle='round', facecolor='#dedede', alpha=0.5),
@@ -1312,7 +1347,8 @@ def hist2d_alex(d, i=0, vmin=2, vmax=0, binwidth=0.05, S_max_norm=0.8,
     """
     ax = plt.gca()
     d._calc_alex_hist(binwidth)
-    ES_hist, E_bins, S_bins, S_ax = d.ES_hist[i], d.E_bins, d.S_bins, d.S_ax
+    ES_hist = np.sum(d.ES_hist, axis=0) if i is None else d.ES_hist[i]
+    E_bins, S_bins, S_ax = d.E_bins, d.S_bins, d.S_ax
 
     colormap = plt.get_cmap(cmap)
     # Heuristic for colormap range
@@ -1322,7 +1358,9 @@ def hist2d_alex(d, i=0, vmin=2, vmax=0, binwidth=0.05, S_max_norm=0.8,
         if vmax <= vmin: vmax = 10*vmin
 
     if scatter:
-        ax.plot(d.E[i], d.S[i], 'o', mew=0, ms=scatter_ms,
+        E = np.concatenate(d.E) if i is None else d.E[i]
+        S = np.concatenate(d.S) if i is None else d.S[i]
+        ax.plot(E, S, 'o', mew=0, ms=scatter_ms,
                 alpha=scatter_alpha, color=scatter_color)
     im = ax.imshow(ES_hist[:, ::-1].T, interpolation=interp,
                    extent=(E_bins[0], E_bins[-1], S_bins[0], S_bins[-1]),
@@ -1347,18 +1385,22 @@ def hexbin_alex(d, i=0, vmin=1, vmax=None, gridsize=80, cmap='Spectral_r',
                 E_name='E', S_name='S', **hexbin_kwargs):
     """Plot an hexbin 2D histogram for E-S.
     """
-    if d.num_bursts[i] < 1:
+    if i is None:
+        E, S = np.concatenate(d[E_name]), np.concatenate(d[S_name])
+    else:
+        E, S = d[E_name][i], d[S_name][i]
+    if E.size < 1:
         return
     hexbin_kwargs_ = dict(edgecolor='none', linewidth=0.2, gridsize=gridsize,
                           cmap=cmap, extent=(-0.2, 1.2, -0.2, 1.2), mincnt=1)
     if hexbin_kwargs is not None:
         hexbin_kwargs_.update(_normalize_kwargs(hexbin_kwargs))
-    poly = plt.hexbin(d[E_name][i], d[S_name][i], **hexbin_kwargs_)
+    poly = plt.hexbin(E, S, **hexbin_kwargs_)
     poly.set_clim(vmin, vmax)
     plt.xlabel('E')
     plt.ylabel('S')
 
-
+# channel independent
 def plot_ES_selection(ax, E1, E2, S1, S2, rect=True, **kwargs):
     """Plot an overlay ROI on top of an E-S plot (i.e. ALEX histogram).
 
@@ -1398,6 +1440,7 @@ def plot_ES_selection(ax, E1, E2, S1, S2, rect=True, **kwargs):
     ax.add_patch(ellips)
     return rect, ellips
 
+# channel independent
 def get_ES_range():
     """Get the range of ES histogram selected via GUI.
 
@@ -1409,7 +1452,6 @@ def get_ES_range():
         print('E1={E1:.3}, E2={E2:.3}, S1={S1:.3}, S2={S2:.3}'.format(**sel))
     return sel
 
-
 def hist_interphoton_single(d, i=0, binwidth=1e-4, tmax=None, bins=None,
                             ph_sel=Ph_sel('all'), period=None,
                             yscale='log', xscale='linear', xunit='ms',
@@ -1450,8 +1492,13 @@ def hist_interphoton_single(d, i=0, binwidth=1e-4, tmax=None, bins=None,
 
     # Compute interphoton delays
     if period is None:
-        ph_times = d.get_ph_times(ich=i, ph_sel=ph_sel)
+        if i is None:
+            ph_times = np.concatenate([d.get_ph_times(ich=j,ph_sel=ph_sel) for j in range(d.nch)])
+        else:
+            ph_times = d.get_ph_times(ich=i, ph_sel=ph_sel)
     else:
+        if i is None:
+            raise RuntimeError("Must specify channel/spot when specifying period")
         ph_times = d.get_ph_times_period(ich=i, period=period, ph_sel=ph_sel)
     delta_ph_t = np.diff(ph_times) * d.clk_p
     if tmax is None:
@@ -1538,7 +1585,8 @@ def hist_interphoton(d, i=0, binwidth=1e-4, tmax=None, bins=None, period=None,
             ph_sel_list.append(Ph_sel(Aex='Dem'))
 
     for ix, ph_sel in enumerate(ph_sel_list):
-        if not bl.mask_empty(d.get_ph_mask(i, ph_sel=ph_sel)):
+        sl = range(d.nch) if i is None else (i, )
+        if not np.all([bl.mask_empty(d.get_ph_mask(j, ph_sel=ph_sel)) for j in sl]):
             hist_interphoton_single(d, i=i, binwidth=binwidth, tmax=tmax,
                                     bins=bins, period=period, ph_sel=ph_sel,
                                     yscale=yscale, xscale=xscale, xunit=xunit,
@@ -1549,7 +1597,7 @@ def hist_interphoton(d, i=0, binwidth=1e-4, tmax=None, bins=None, period=None,
     if yscale == 'log' or xscale == 'log':
         _plot_status['hist_interphoton'] = {'autoscale': False}
 
-
+# TODO: condsider better method for displaying all channel, total bg histogram
 def hist_bg_single(d, i=0, binwidth=1e-4, tmax=0.01, bins=None,
                    ph_sel=Ph_sel('all'), period=0,
                    yscale='log', xscale='linear', xunit='ms', plot_style=None,
@@ -1642,31 +1690,63 @@ def hist_bg(d, i=0, binwidth=1e-4, tmax=0.01, bins=None, period=0,
 def hist_ph_delays(
         d, i=0, time_min_s=0, time_max_s=30, bin_width_us=10, mask=None,
         yscale='log', hfit_bin_ms=1, efit_tail_min_us=1000, **kwargs):
-    """Histog. of ph delays and comparison with 3 BG fitting functions.
+    """Histogram of ph delays and comparison with 3 BG fitting functions.
     """
-    ph = d.ph_times_m[i].copy()
-    if mask is not None: ph = ph[mask[i]]
-    ph = ph[(ph < time_max_s/d.clk_p)*(ph > time_min_s/d.clk_p)]
+    if i is None:
+        if mask is None:
+            mask = (slice(None) for _ in range(d.nch))
+        ph = (times[msk].copy() for times, msk in zip(d.ph_times_m, mask))
+        print("next")
+        if not isinstance(time_min_s, Iterable):
+            time_min_sg = (time_min_s for _ in range(d.nch))
+            print("next")
+        if not isinstance(time_max_s, Iterable):
+            time_max_sg = (time_max_s for _ in range(d.nch))
+            print("next")
+        ph = np.concatenate([p[(p < tmax/d.clk_p)*(p > tmin/d.clk_p)] 
+                             for p, tmax, tmin in zip(ph, time_max_sg, time_min_sg)])
+    else:
+        ph = d.ph_times_m[i].copy()
+        if mask is not None: 
+            ph = ph[mask[i]]
+        ph = ph[(ph < time_max_s/d.clk_p)*(ph > time_min_s/d.clk_p)]
     dph = np.diff(ph)*d.clk_p
     H = hist(dph*1e6, bins=r_[0:1200:bin_width_us], histtype='step', **kwargs)
     gca().set_yscale('log')
     xlabel(u'Ph delay time (μs)'); ylabel("# Ph")
+    F = 1 if 'normed' in kwargs else H[0].sum()*(bin_width_us)
 
     efun = lambda t, r: np.exp(-r*t)*r
-    re = bg.exp_fit(ph, tail_min_us=efit_tail_min_us)
-    rg = bg.exp_hist_fit(ph, tail_min_us=efit_tail_min_us, binw=hfit_bin_ms*1e3)
-    rc = bg.exp_cdf_fit(ph, tail_min_us=efit_tail_min_us)
+    try:
+        re = bg.exp_fit(ph, tail_min_us=efit_tail_min_us)[0]
+        re_do = True
+    except:
+        re_do = False
+    try:
+        rg = bg.exp_hist_fit(ph, tail_min_us=efit_tail_min_us, binw=hfit_bin_ms*1e-3)[0]
+        rg_do = True
+    except:
+        rg_do = False
+    try:
+        rc = bg.exp_cdf_fit(ph, tail_min_us=efit_tail_min_us)[0]
+        rc_do = True
+    except:
+        rc_do = False
     t = r_[0:1200]*1e-6
-    F = 1 if 'normed' in kwargs else H[0].sum()*(bin_width_us)
-    plot(t*1e6, 0.65*F*efun(t, rc)*1e-6, lw=3, alpha=0.5, color=purple,
-         label="%d cps - Exp CDF (tail_min_p=%.2f)" % (rc, efit_tail_min_us))
-    plot(t*1e6, 0.65*F*efun(t, re)*1e-6, lw=3, alpha=0.5, color=red,
-         label="%d cps - Exp ML (tail_min_p=%.2f)" % (re, efit_tail_min_us))
-    plot(t*1e6, 0.68*F*efun(t, rg)*1e-6, lw=3, alpha=0.5, color=green,
-         label=u"%d cps - Hist (bin_ms=%d) [Δ=%d%%]" % (hfit_bin_ms, rg,
-                                                        100*(rg-re)/re))
+    
+    if rc_do:
+        plot(t*1e6, 0.65*F*efun(t, rc)*1e-6, lw=3, alpha=0.5, color=purple,
+             label="%d cps - Exp CDF (tail_min_p=%.2f)" % (rc, efit_tail_min_us))
+    if re_do:
+        plot(t*1e6, 0.65*F*efun(t, re)*1e-6, lw=3, alpha=0.5, color=red,
+             label="%d cps - Exp ML (tail_min_p=%.2f)" % (re, efit_tail_min_us))
+    if re_do and rg_do:
+        plot(t*1e6, 0.68*F*efun(t, rg)*1e-6, lw=3, alpha=0.5, color=green,
+             label=u"%d cps - Hist (bin_ms=%d) [Δ=%d%%]" % (hfit_bin_ms, rg,
+                                                            100*(rg-re)/re))
     plt.legend(loc='best', fancybox=True)
 
+# TODO: update for concatenated data, probably fix bext.calc_mdelays_hist
 def hist_mdelays(d, i=0, m=10, bins_s=(0, 10, 0.02), period=0,
                  hold=False, bg_ppf=0.01, ph_sel=Ph_sel('all'), spline=True,
                  s=1., bg_fit=True, bg_F=0.8):
@@ -1677,9 +1757,15 @@ def hist_mdelays(d, i=0, m=10, bins_s=(0, 10, 0.02), period=0,
         #ax.clear()
         for _ind in range(len(ax.lines)): ax.lines.pop()
 
-    results = bext.calc_mdelays_hist(
-        d=d, ich=i, m=m, period=period, bins_s=bins_s,
-        ph_sel=ph_sel, bursts=True, bg_fit=bg_fit, bg_F=bg_F)
+    if i is None:
+        results = np.concatenate([bext.calc_mdelays_hist(d, ich=j, m=m, period=period, 
+                                                          bins_s=bins_s,ph_sel=ph_sel, 
+                                                          bursts=True, bg_fit=bg_fit, 
+                                                          bg_F=bg_F) 
+                                  for j in range(d.nch)])
+    else:
+        results = bext.calc_mdelays_hist(d, ich=i, m=m, period=period, bins_s=bins_s,
+                                         ph_sel=ph_sel, bursts=True, bg_fit=bg_fit, bg_F=bg_F)
     bin_x, histog_y = results[:2]
     bg_dist = results[2]
     rate_ch_kcps = 1./bg_dist.kwds['scale']  # extract the rate
@@ -1746,11 +1832,18 @@ def hist_mrates(d, i=0, m=10, bins=(0, 4000, 100), yscale='log', pdf=False,
                 dense=True, plot_style=None):
     """Histogram of m-photons rates. See also :func:`hist_mdelays`.
     """
-    ph = d.get_ph_times(ich=i)
-    if dense:
-        ph_mrates = 1.*m/((ph[m-1:]-ph[:ph.size-m+1])*d.clk_p*1e3)
+    if i is None:
+        ph = [d.get_ph_times(ich=j) for j in range(d.nch)]
+        if dense:
+            ph_mrates = np.concatenate([1.*m/(p[m-1:]-p[:p.size-m+1]*1e3*d.clk_p) for p in ph])
+        else:
+            ph_mrates = np.concatenate([1.*m/(np.diff(p[::m])*1e3*d.clk_p) for p in ph])
     else:
-        ph_mrates = 1.*m/(np.diff(ph[::m])*d.clk_p*1e3)
+        ph = d.get_ph_times(ich=i)
+        if dense:
+            ph_mrates = 1.*m/((ph[m-1:]-ph[:ph.size-m+1])*d.clk_p*1e3)
+        else:
+            ph_mrates = 1.*m/(np.diff(ph[::m])*d.clk_p*1e3)
 
     hist = HistData(*np.histogram(ph_mrates, bins=_bins_array(bins)))
     ydata = hist.pdf if pdf else hist.counts
@@ -1765,10 +1858,14 @@ def hist_sbr(d, i=0, bins=(0, 30, 1), pdf=True, weights=None, color=None,
              plot_style=None):
     """Histogram of per-burst Signal-to-Background Ratio (SBR).
     """
-    weights = weights[i] if weights is not None else None
+    if i is None:
+        weights = np.concatenate(weights) if weights is not None else None
+    else:
+        weights = weights[i] if weights is not None else None
     if 'sbr' not in d:
         d.calc_sbr()
-    _hist_burst_taildist(d.sbr[i], bins, pdf, weights=weights, color=color,
+    sbr = np.concatenate(d.sbr) if i is None else d.sbr[i]
+    _hist_burst_taildist(sbr, bins, pdf, weights=weights, color=color,
                          plot_style=plot_style)
     plt.xlabel('SBR')
 
@@ -1783,8 +1880,9 @@ def hist_burst_phrate(d, i=0, bins=(0, 1000, 20), pdf=True, weights=None,
     else:
         if 'max_rate' not in d:
             d.calc_max_rate(m=10)
-        max_rate = d.max_rate
-    _hist_burst_taildist(max_rate[i] * 1e-3, bins, pdf, weights=weights,
+        max_rate = np.concatenate(d.max_rate) if i is None else d.max_rate[i]
+    
+    _hist_burst_taildist(max_rate * 1e-3, bins, pdf, weights=weights,
                          color=color, plot_style=plot_style, vline=vline)
     plt.xlabel('Peak rate (kcps)')
 
@@ -1793,8 +1891,12 @@ def hist_burst_delays(d, i=0, bins=(0, 10, 0.2), pdf=False, weights=None,
                       color=None, plot_style=None):
     """Histogram of waiting times between bursts.
     """
-    weights = weights[i] if weights is not None else None
-    bdelays = np.diff(d.mburst[i].start*d.clk_p)
+    if i is None:
+        weights = np.concatenate(weights) if weights is not None else None
+        bdelays = np.concatenate([np.diff(mburst.start*d.clk_p) for mburst in d.mburst])
+    else:
+        weights = weights[i] if weights is not None else None
+        bdelays = np.diff(d.mburst[i].start*d.clk_p)
 
     _hist_burst_taildist(bdelays, bins, pdf, weights=weights, color=color,
                          plot_style=plot_style)
@@ -1802,10 +1904,13 @@ def hist_burst_delays(d, i=0, bins=(0, 10, 0.2), pdf=False, weights=None,
 
 ## Burst internal "symmetry"
 def hist_asymmetry(d, i=0, bin_max=2, binwidth=0.1, stat_func=np.median):
-    burst_asym = bext.asymmetry(d, ich=i, func=stat_func)
+    if i is None:
+        burst_asym = np.concatenate([bext.asymmetry(d, ich=j, func=stat_func) for j in range(d.nch)])
+    else:
+        burst_asym = bext.asymmetry(d, ich=i, func=stat_func)
     bins_pos = np.arange(0, bin_max+binwidth, binwidth)
     bins = np.hstack([-bins_pos[1:][::-1], bins_pos])
-    izero = (bins.size - 1)/2.
+    izero = int((bins.size - 1)/2)
     assert izero == np.where(np.abs(bins) < 1e-8)[0]
 
     counts, _ = np.histogram(burst_asym, bins=bins)
@@ -1832,69 +1937,105 @@ def hist_asymmetry(d, i=0, bin_max=2, binwidth=0.1, stat_func=np.median):
 
 def scatter_width_size(d, i=0):
     """Scatterplot of burst width versus size."""
-    b = d.mburst[i]
-    plot(b.width*d.clk_p*1e3, d.nt[i], 'o', mew=0, ms=3, alpha=0.7,
-         color='blue')
     t_ms = arange(0, 50)
-    plot(t_ms, ((d.m)/(d.T[i]))*t_ms*1e-3, '--', lw=2, color='k',
-         label='Slope = m/T = min. rate = %1.0f cps' % (d.m/d.T[i]))
-    plot(t_ms, d.bg_mean[Ph_sel('all')][i]*t_ms*1e-3, '--', lw=2, color=red,
+    if i is None:
+        b = np.concatenate([bb.width*d.clk_p*1e3 for bb in d.mburst])
+        nt = np.concatenate(d.nt)
+        T = np.average(d.T, weights=[ph.max()-ph.min() for ph in d.ph_times_m])
+        bg_mean = np.average(d.bg_mean[Ph_sel('all')], weights=[ph.max()-ph.min() for ph in d.ph_times_m])
+        bg_mean = bg_mean*t_ms*1e-3
+    else:
+        b = d.mburst[i].width*d.clk_p*1e3
+        nt = d.nt[i]
+        T = d.T[i]
+        bg_mean = d.bg_mean[Ph_sel('all')][i]*t_ms*1e-3
+    plot(b, nt, 'o', mew=0, ms=3, alpha=0.7,
+         color='blue')
+    
+    plot(t_ms, ((d.m)/(T))*t_ms*1e-3, '--', lw=2, color='k',
+         label='Slope = m/T = min. rate = %1.0f cps' % (d.m/T))
+    plot(t_ms, bg_mean, '--', lw=2, color=red,
          label='Noise rate: BG*t')
     xlabel('Burst width (ms)'); ylabel('Burst size (# ph.)')
     plt.xlim(0, 10); plt.ylim(0, 300)
     legend(frameon=False)
 
+
 def scatter_rate_da(d, i=0):
     """Scatter of nd rate vs na rate (rates for each burst)."""
-    b = d.mburst[i]
-    Rate = lambda nX: nX[i]/b.width/d.clk_p*1e-3
-    plot(Rate(d.nd), Rate(d.na), 'o', mew=0, ms=3, alpha=0.1, color='blue')
+    bw = np.concatenate([burst.width for burst in d.mburst]) if i is None else d.mburst[i].width
+    nd = np.concatenate(d.nd) if i is None else d.nd[i]
+    na = np.concatenate(d.na) if i is None else d.na[i]
+    Rate = lambda nX: nX/bw/d.clk_p*1e-3
+    plot(Rate(nd), Rate(na), 'o', mew=0, ms=3, alpha=0.1, color='blue')
     xlabel('D burst rate (kcps)'); ylabel('A burst rate (kcps)')
     plt.xlim(-20, 100); plt.ylim(-20, 100)
     legend(frameon=False)
 
+
 def scatter_fret_size(d, i=0, which='all', gamma=1, add_naa=False,
                       plot_style=None):
     """Scatterplot of FRET efficiency versus burst size.
     """
     if which == 'all':
-        size = d.burst_sizes_ich(ich=i, gamma=gamma, add_naa=add_naa)
+        if i is None:
+            size = np.concatenate([d.burst_sizes_ich(ich=j, gamma=gamma, add_naa=add_naa) 
+                                   for j in range(d.nch)])
+        else:
+            size = d.burst_sizes_ich(ich=i, gamma=gamma, add_naa=add_naa)
     else:
         assert which in d
-        size = d[which][i]
+        size = np.concatenate([d[which][j] for j in range(d.nch)]) if i is None else d[which][i]
 
     plot_style_ = dict(linestyle='', alpha=0.1, color=blue,
                        marker='o', markeredgewidth=0, markersize=3)
     plot_style_.update(_normalize_kwargs(plot_style, kind='line2d'))
-    plot(d.E[i], size, **plot_style_)
+    E = np.concatenate(d.E) if i is None else d.E[i]
+    plot(E, size, **plot_style_)
     xlabel("FRET Efficiency (E)")
     ylabel("Corrected Burst size (#ph)")
 
+
 def scatter_fret_nd_na(d, i=0, gamma=1., **kwargs):
     """Scatterplot of FRET versus gamma-corrected burst size."""
     default_kwargs = dict(mew=0, ms=3, alpha=0.3, color=blue)
     default_kwargs.update(**kwargs)
-    plot(d.E[i], gamma*d.nd[i]+d.na[i], 'o', **default_kwargs)
+    E = np.concatenate(d.E) if i is None else d.E[i]
+    nd = np.concatenate(d.nd) if i is None else d.nd[i]
+    na = np.concatenate(d.na) if i is None else d.na[i]
+    plot(E, gamma*nd+na, 'o', **default_kwargs)
     xlabel("FRET Efficiency (E)")
     ylabel("Burst size (#ph)")
 
+
 def scatter_fret_width(d, i=0):
     """Scatterplot of FRET versus burst width."""
-    b = d.mburst[i]
-    plot(d.E[i], (b[:, 1]*d.clk_p)*1e3, 'o', mew=0, ms=3, alpha=0.1,
+    if i is None:        
+        b = np.concatenate([mburst.width for mburst in d.mburst])*d.clk_p*1e3
+        E = np.concatenate(d.E)
+    else:
+        b = d.mburst[i].width*d.clk_p*1e3
+        E = d.E[i]
+    plot(E, b, 'o', mew=0, ms=3, alpha=0.1,
          color="blue")
     xlabel("FRET Efficiency (E)")
     ylabel("Burst width (ms)")
 
+
 def scatter_da(d, i=0, alpha=0.3):
     """Scatterplot of donor vs acceptor photons (nd, vs na) in each burst."""
-    plot(d.nd[i], d.na[i], 'o', mew=0, ms=3, alpha=alpha, color='blue')
+    nd = np.concatenate(d.nd) if i is None else d.nd[i]
+    na = np.concatenate(d.na) if i is None else d.na[i]
+    plot(nd, na, 'o', mew=0, ms=3, alpha=alpha, color='blue')
     xlabel('# donor ph.'); ylabel('# acceptor ph.')
     plt.xlim(-5, 200); plt.ylim(-5, 120)
 
+
 def scatter_naa_nt(d, i=0, alpha=0.5):
     """Scatterplot of nt versus naa."""
-    plot(d.nt[i], d.naa[i], 'o', mew=0, ms=3, alpha=alpha, color='blue')
+    nt = np.concatenate(d.nt) if i is None else d.nt[i]
+    naa = np.concatenate(d.naa) if i is None else d.naa[i]
+    plot(nt, naa, 'o', mew=0, ms=3, alpha=alpha, color='blue')
     plot(arange(200), color='k', lw=2)
     xlabel('Total burst size (nd+na+naa)'); ylabel('Accept em-ex BS (naa)')
     plt.xlim(-5, 200); plt.ylim(-5, 120)
@@ -1905,7 +2046,9 @@ def scatter_alex(d, i=0, **kwargs):
                       alpha=0.1)
     plot_style = _normalize_kwargs(plot_style, 'line2d')
     plot_style.update(_normalize_kwargs(kwargs, 'line2d'))
-    plot(d.E[i], d.S[i], 'o', **plot_style)
+    E = np.concatenate(d.E) if i is None else d.E[i]
+    S = np.concatenate(d.S) if i is None else d.S[i]
+    plot(E, S, 'o', **plot_style)
     xlabel("E"); ylabel('S')
     plt.xlim(-0.2, 1.2); plt.ylim(-0.2, 1.2)
 
@@ -2130,7 +2273,7 @@ def dplot(d, func, **kwargs):
         nch = d.shape[1]
     else:
         nch = d.nch
-    if nch == 1:
+    if "i" in kwargs or nch == 1:
         return dplot_1ch(d=d, func=func, **kwargs)
     elif nch <= 8:
         return dplot_8ch(d=d, func=func, **kwargs)
@@ -2265,6 +2408,9 @@ def alex_jointplot(d, i=0, gridsize=50, cmap='Spectral_r', kind='hex',
     ax_horiz = g.add_subplot(gs[0,0:3],sharex=ax_joint)
     ax_verti = g.add_subplot(gs[1:4,3],sharey=ax_joint)
     
+    E = np.concatenate(d[E_name]) if i is None else d[E_name][i]
+    S = np.concatenate(d[S_name]) if i is None else d[S_name][i]
+    
     if isinstance(marginal_color, int):
         histcolor = sns.color_palette(cmap, 100)[marginal_color]
     else:
@@ -2280,13 +2426,13 @@ def alex_jointplot(d, i=0, gridsize=50, cmap='Spectral_r', kind='hex',
         joint_kws_ = dict(s=40, color=histcolor, alpha=0.1, linewidths=0)
         if joint_kws is not None:
             joint_kws_.update(_normalize_kwargs(joint_kws))
-        jplot = ax_joint.scatter(d[E_name][i], d[S_name][i], **joint_kws_)
+        jplot = ax_joint.scatter(E, S, **joint_kws_)
     elif kind.startswith('hex'):
         joint_kws_ = dict(edgecolor='none', linewidth=0.2, gridsize=gridsize,
                           cmap=cmap, extent=(-0.2, 1.2, -0.2, 1.2), mincnt=1)
         if joint_kws is not None:
             joint_kws_.update(_normalize_kwargs(joint_kws))
-        jplot = ax_joint.hexbin(d[E_name][i], d[S_name][i], **joint_kws_)
+        jplot = ax_joint.hexbin(E, S, **joint_kws_)
 
         # Set the vmin and vmax values for the colormap
         if vmax is None:
@@ -2297,7 +2443,7 @@ def alex_jointplot(d, i=0, gridsize=50, cmap='Spectral_r', kind='hex',
                           cmap=cmap, clip=(-0.4, 1.4), bw=0.03)
         if joint_kws is not None:
             joint_kws_.update(_normalize_kwargs(joint_kws))
-        jplot = sns.kdeplot(x=d[E_name][0], y= d[S_name][0], ax=ax_joint, 
+        jplot = sns.kdeplot(x=E, y=S, ax=ax_joint, 
                             **joint_kws_)
     anno_str = ''
     for mburst in d.mburst:
diff --git a/fretbursts/burstlib.py b/fretbursts/burstlib.py
index 23c8246..4ad0a3b 100644
--- a/fretbursts/burstlib.py
+++ b/fretbursts/burstlib.py
@@ -570,8 +570,9 @@ class Data(DataContainer):
             for the different photon streams and channels. Keys are `Ph_sel`
             objects and values are lists (one element per channel) of
             background rates.
-        nperiods (int): number of periods in which timestamps are split for
-            background calculation
+        nperiods (array): number of periods in which timestamps are split for
+            background calculation, given per channel
+            **NOTE: this is changed from previous versions, to support grouped experiments**
         bg_fun (function): function used to compute the background rates
         Lim (list): each element of this list is a list of index pairs for
             `.ph_times_m[i]` for **first** and **last** photon in each period.
@@ -940,7 +941,7 @@ def iter_ph_times_period(self, ich=0, ph_sel=Ph_sel('all')):
         """Iterate through arrays of ph timestamps in each background period.
         """
         mask = self.get_ph_mask(ich=ich, ph_sel=ph_sel)
-        for period in range(self.nperiods):
+        for period in range(self.nperiods[ich]):
             yield self.get_ph_times_period(period, ich=ich, mask=mask)
 
     def get_ph_times_period(self, period, ich=0, ph_sel=Ph_sel('all'),
@@ -1636,6 +1637,7 @@ def _get_auto_bg_th_arrays(self, F_bg=2, tail_min_us0=250):
 
         The keys are the ph selections in self.ph_streams and the values
         are 1-D arrays of size nch.
+        # TODO: this looks deprecated, consider removing
         """
         Th_us = {}
         for ph_sel in self.ph_streams:
@@ -1655,6 +1657,7 @@ def _get_bg_th_arrays(self, tail_min_us, nperiods):
         The keys are the ph selections in self.ph_streams and the values
         are 1-D arrays of size nch.
         """
+        print("get bg th arrays")
         n_streams = len(self.ph_streams)
 
         if np.size(tail_min_us) == 1:
@@ -1666,7 +1669,7 @@ def _get_bg_th_arrays(self, tail_min_us, nperiods):
                              len(tail_min_us))
         th_us = {}
         for i, key in enumerate(self.ph_streams):
-            th_us[key] = np.ones(nperiods) * tail_min_us[i]
+            th_us[key] = [np.ones(nper) * tail_min_us[i] for nper in nperiods]
         # Save the input used to generate Th_us
         self.add(bg_th_us_user=tail_min_us)
         return th_us
@@ -1690,17 +1693,21 @@ def _clean_bg_data(self):
     def _get_num_periods(self, time_s):
         """Return the number of periods using `time_s` as period duration.
         """
-        duration = self.time_max - self.time_min
+        ph_field = 'ph_times_m' if hasattr(self, 'ph_times_m') else 'ph_times_t'
+        mins = np.array([min(time) for time in self[ph_field]]) * self.clk_p
+        maxs = np.array([max(time) for time in self[ph_field]]) * self.clk_p
+        duration = maxs - mins
         # Take the ceil to have at least 1 periods
-        nperiods = np.ceil(duration / time_s)
+        nperiods = np.ceil(duration / time_s).astype(int)
         # Discard last period if negligibly small to avoid problems with
         # background fit with very few photons.
-        if nperiods > 1:
-            last_period = self.time_max - time_s * (nperiods - 1)
-            # Discard last period if smaller than 3% of the bg period
-            if last_period < time_s * 0.06:
-                nperiods -= 1
-        return int(nperiods)
+        for i in range(self.nch):
+            if nperiods[i] > 1:
+                last_period = maxs[i] - time_s * (nperiods[i] - 1)
+                # Discard last period if smaller than 3% of the bg period
+                if last_period < time_s * 0.06:
+                    nperiods[i] -= 1
+        return nperiods
 
     def calc_bg(self, fun, time_s=60, tail_min_us=500, F_bg=2,
                 error_metrics=None, fit_allph=True):
@@ -1745,6 +1752,7 @@ def calc_bg(self, fun, time_s=60, tail_min_us=500, F_bg=2,
         Returns:
             None, all the results are saved in the object itself.
         """
+        ph_field = 'ph_times_m' if hasattr(self, 'ph_times_m') else 'ph_times_t'
         pprint(" - Calculating BG rates ... ")
         self._clean_bg_data()
         kwargs = dict(clk_p=self.clk_p, error_metrics=error_metrics)
@@ -1758,34 +1766,33 @@ def calc_bg(self, fun, time_s=60, tail_min_us=500, F_bg=2,
             auto_th_kwargs = dict(clk_p=self.clk_p, tail_min_us=tail_min_us0)
             th_us = {}
             for key in self.ph_streams:
-                th_us[key] = np.zeros(nperiods)
+                th_us[key] = [np.zeros(nper) for nper in nperiods]
         else:
             th_us = self._get_bg_th_arrays(tail_min_us, nperiods)
 
-        # Note: histogram bins are half-open, e.g. [a, b)
-        bins = ((np.arange(nperiods + 1) * time_s + self.time_min) /
-                self.clk_p)
-
         Lim, Ph_p = [], []
         BG, BG_err = [], []
         Th_us = []
         for ich, ph_ch in enumerate(self.iter_ph_times()):
+            print(f"Channel {ich}")
             masks = {sel: self.get_ph_mask(ich, ph_sel=sel)
                      for sel in self.ph_streams}
 
+            # Note: histogram bins are half-open, e.g. [a, b)
+            bins = (np.arange(nperiods[ich]+1) * time_s + min(self[ph_field][ich])*self.clk_p) / self.clk_p
             counts, _ = np.histogram(ph_ch, bins=bins)
             lim, ph_p = [], []
-            bg = {sel: np.zeros(nperiods) for sel in self.ph_streams}
-            bg_err = {sel: np.zeros(nperiods) for sel in self.ph_streams}
+            bg = {sel: np.zeros(nperiods[ich]) for sel in self.ph_streams}
+            bg_err = {sel: np.zeros(nperiods[ich]) for sel in self.ph_streams}
             if ph_ch.size == 0:
                 Lim.append(lim)
                 Ph_p.append(ph_p)
                 BG.append(bg)
                 BG_err.append(bg_err)
-                Th_us.append(th_us)
+                Th_us.append({key:val[ich] for key, val in th_us.items()})
                 continue
             i1 = 0
-            for ip in range(nperiods):
+            for ip in range(nperiods[ich]):
                 i0 = i1
                 i1 += counts[ip]
                 lim.append((i0, i1 - 1))
@@ -1796,9 +1803,9 @@ def calc_bg(self, fun, time_s=60, tail_min_us=500, F_bg=2,
                     sel = Ph_sel('all')
                     if bg_auto_th:
                         _bg, _ = fun(ph_i, **auto_th_kwargs)
-                        th_us[sel][ip] = 1e6 * F_bg / _bg
+                        th_us[sel][ich][ip] = 1e6 * F_bg / _bg
                     bg[sel][ip], bg_err[sel][ip] = \
-                        fun(ph_i, tail_min_us=th_us[sel][ip], **kwargs)
+                        fun(ph_i, tail_min_us=th_us[sel][ich][ip], **kwargs)
 
                 for sel in streams_noall:
                     # This supports cases of D-only or A-only timestamps
@@ -1816,9 +1823,9 @@ def calc_bg(self, fun, time_s=60, tail_min_us=500, F_bg=2,
                     if ph_i_sel.size > 10:
                         if bg_auto_th:
                             _bg, _ = fun(ph_i_sel, **auto_th_kwargs)
-                            th_us[sel][ip] = 1e6 * F_bg / _bg
+                            th_us[sel][ich][ip] = 1e6 * F_bg / _bg
                         bg[sel][ip], bg_err[sel][ip] = \
-                            fun(ph_i_sel, tail_min_us=th_us[sel][ip], **kwargs)
+                            fun(ph_i_sel, tail_min_us=th_us[sel][ich][ip], **kwargs)
 
             if not fit_allph:
                 bg[Ph_sel('all')] += sum(bg[s] for s in streams_noall)
@@ -1827,7 +1834,7 @@ def calc_bg(self, fun, time_s=60, tail_min_us=500, F_bg=2,
             Ph_p.append(ph_p)
             BG.append(bg)
             BG_err.append(bg_err)
-            Th_us.append(th_us)
+            Th_us.append({key:val[ich] for key, val in th_us.items()})
 
         # Make Dict Of Lists (DOL) from Lists of Dicts
         BG_dol, BG_err_dol, Th_us_dol = {}, {}, {}
@@ -1841,8 +1848,8 @@ def calc_bg(self, fun, time_s=60, tail_min_us=500, F_bg=2,
         for i, (lim, ph_p) in enumerate(zip(Lim, Ph_p)):
             if len(lim) == 0:
                 assert len(ph_p) == 0
-                Lim[i] = [(0, 0)] * nperiods
-                Ph_p[i] = [(0, 0)] * nperiods
+                Lim[i] = [(0, 0)] * nperiods[i]
+                Ph_p[i] = [(0, 0)] * nperiods[i]
 
         self.add(bg=BG_dol, bg_err=BG_err_dol, bg_th_us=Th_us_dol,
                  Lim=Lim, Ph_p=Ph_p,
@@ -1854,7 +1861,7 @@ def calc_bg(self, fun, time_s=60, tail_min_us=500, F_bg=2,
 
     @property
     def nperiods(self):
-        return len(self.bg[Ph_sel('all')][0])
+        return np.array([len(self.bg[Ph_sel('all')][i]) for i in range(self.nch)])
 
     @property
     def bg_mean(self):
@@ -1878,13 +1885,13 @@ def recompute_bg_lim_ph_p(self, ph_sel, mute=False):
                str(ph_sel), mute)
         bg_time_clk = self.bg_time_s / self.clk_p
         Lim, Ph_p = [], []
-        for ph_ch, lim in zip(self.iter_ph_times(ph_sel), self.Lim):
-            bins = np.arange(self.nperiods + 1) * bg_time_clk
+        for i, (ph_ch, lim) in enumerate(zip(self.iter_ph_times(ph_sel), self.Lim)):
+            bins = np.arange(self.nperiods[i] + 1) * bg_time_clk
             # Note: histogram bins are half-open, e.g. [a, b)
             counts, _ = np.histogram(ph_ch, bins=bins)
             lim, ph_p = [], []
             i1 = 0
-            for ip in range(self.nperiods):
+            for ip in range(self.nperiods[i]):
                 i0 = i1
                 i1 += counts[ip]
                 lim.append((i0, i1 - 1))
diff --git a/fretbursts/burstlib_ext.py b/fretbursts/burstlib_ext.py
index f12b78a..bb3c179 100644
--- a/fretbursts/burstlib_ext.py
+++ b/fretbursts/burstlib_ext.py
@@ -309,9 +309,9 @@ def calc_bg_brute(dx, min_ph_delay_list=None, return_all=False,
 
     index = pd.MultiIndex.from_product([list(range(dx.nch)), ph_sel_labels],
                                        names=['CH', 'ph_sel'])
-    best_th = pd.DataFrame(index=index, columns=np.arange(dx.nperiods))
+    best_th = pd.DataFrame(index=index, columns=np.arange(dx.nperiods.max()))
     best_th.columns.name = 'period'
-    best_th.sortlevel(inplace=True)
+    best_th.sort_index(inplace=True)
     best_bg = best_th.copy()
 
     for ph_sel in dx.ph_streams:
@@ -323,7 +323,7 @@ def calc_bg_brute(dx, min_ph_delay_list=None, return_all=False,
 
         # Compute the best Th and BG estimate for all ch and periods
         for ich in range(dx.nch):
-            for period in range(dx.nperiods):
+            for period in range(dx.nperiods[ich]):
                 b = BG_data_e[ph_sel][ich, period, :]
                 i_best_th = b[-np.isnan(b)].argmin()
                 best_th.loc[(ich, str(ph_sel)), period] = \
@@ -496,7 +496,8 @@ def fit_bursts_kde_peak(dx, burst_data='E', bandwidth=0.03, weights=None,
 
 def bursts_fitter(dx, burst_data='E', save_fitter=True,
                   weights=None, gamma=1, add_naa=False, skip_ch=None,
-                  binwidth=None, bandwidth=None, model=None, verbose=False):
+                  binwidth=None, bandwidth=None, model=None, verbose=False,
+                  fit_tot=True):
     """Create a mfit.MultiFitter object (for E or S) add it to `dx`.
 
     A MultiFitter object allows to fit multi-channel data with the same
@@ -518,6 +519,8 @@ def bursts_fitter(dx, burst_data='E', save_fitter=True,
         model (lmfit.Model object or None): lmfit Model used for histogram
             fitting. If None the histogram is not fitted.
         verbose (bool): if False avoids printing any output.
+        fit_tot (bool): whether to perform histogram fitting on combination of
+            all arrays
 
     Returns:
         The `mfit.MultiFitter` object with the specified burst-size weights.
@@ -542,7 +545,7 @@ def bursts_fitter(dx, burst_data='E', save_fitter=True,
     if binwidth is not None:
         fitter.histogram(binwidth=binwidth)
         if model is not None:
-            fitter.fit_histogram(model=model, verbose=verbose)
+            fitter.fit_histogram(model=model, verbose=verbose, fit_tot=fit_tot)
     if save_fitter:
         # Save fitter variables as normal attributes (not with Data.add)
         # so they are not copied when copying Data (e.g. burst selection)
@@ -556,14 +559,14 @@ def _get_bg_distrib_erlang(d, ich=0, m=10, ph_sel=Ph_sel('all'),
     """Return a frozen (scipy) erlang distrib. with rate equal to the bg rate.
     """
     assert ph_sel in [Ph_sel('all'), Ph_sel(Dex='Dem'), Ph_sel(Dex='Aem')]
-
+    # fix negative periods so wrapping occurs coorectly
+    parr = np.array(period)
+    for i, p in enumerate(parr):
+        if p < 0:
+            parr[i] = len(d.Lim[ich]) - p + 1
+    period = tuple(parr)
     # Compute the BG distribution
-    if ph_sel == Ph_sel('all'):
-        bg_ph = d.bg_dd[ich] + d.bg_ad[ich]
-    elif ph_sel == Ph_sel(Dex='Dem'):
-        bg_ph = d.bg_dd[ich]
-    elif ph_sel == Ph_sel(Dex='Aem'):
-        bg_ph = d.bg_ad[ich]
+    bg_ph = d.bg[ph_sel][ich]
 
     rate_ch_kcps = bg_ph[period[0]:period[1]+1].mean()/1e3  # bg rate in kcps
     bg_dist = erlang(a=m, scale=1./rate_ch_kcps)
@@ -578,7 +581,7 @@ def _get_bg_erlang(d, ich=0, m=10, ph_sel=Ph_sel('all'), period=0):
     bg_dist = erlang(a=m, scale=1./bg_rate)
     return bg_dist
 
-
+# TODO: add test
 def histogram_mdelays(d, ich=0, m=10, ph_sel=Ph_sel('all'),
                       binwidth=1e-3, dt_max=10e-3, bins=None,
                       inbursts=False):
@@ -608,6 +611,7 @@ def histogram_mdelays(d, ich=0, m=10, ph_sel=Ph_sel('all'),
     return hist
 
 
+# TODO: add tests beyond simple smoke tests
 def calc_mdelays_hist(d, ich=0, m=10, period=(0, -1), bins_s=(0, 10, 0.02),
                       ph_sel=Ph_sel('all'), bursts=False, bg_fit=True,
                       bg_F=0.8):
@@ -643,17 +647,17 @@ def calc_mdelays_hist(d, ich=0, m=10, period=(0, -1), bins_s=(0, 10, 0.02),
     if ph_sel == Ph_sel('all'):
         ph = d.ph_times_m[ich][periods]
         if bursts:
-            phb = ph[d.ph_in_burst[ich][periods]]
+            phb = ph[d.ph_in_bursts_mask_ich(ich=ich)[periods]]
     elif ph_sel == Ph_sel(Dex='Dem'):
-        donor_ph_period = -d.A_em[ich][periods]
+        donor_ph_period = ~d.A_em[ich][periods]
         ph = d.ph_times_m[ich][periods][donor_ph_period]
         if bursts:
-            phb = ph[d.ph_in_burst[ich][periods][donor_ph_period]]
+            phb = ph[d.ph_in_bursts_mask(ich=ich)[periods][donor_ph_period]]
     elif ph_sel == Ph_sel(Dex='Aem'):
         accept_ph_period = d.A_em[ich][periods]
         ph = d.ph_times_m[ich][periods][accept_ph_period]
         if bursts:
-            phb = ph[d.ph_in_burst[ich][periods][accept_ph_period]]
+            phb = ph[d.ph_in_bursts_mask(ich=ich)[periods][accept_ph_period]]
 
     ph_mdelays = np.diff(ph[::m])*d.clk_p*1e3        # millisec
     if bursts:
@@ -661,7 +665,7 @@ def calc_mdelays_hist(d, ich=0, m=10, period=(0, -1), bins_s=(0, 10, 0.02),
         phb_mdelays = phb_mdelays[phb_mdelays < 5]
 
     # Compute the PDF through histograming
-    hist_kwargs = dict(bins=bins, normed=True)
+    hist_kwargs = dict(bins=bins, density=True)
     mdelays_hist_y, _ = np.histogram(ph_mdelays, **hist_kwargs)
     bin_x = bins[:-1] + 0.5*(bins[1] - bins[0])
     if bursts:
@@ -684,7 +688,7 @@ def calc_mdelays_hist(d, ich=0, m=10, period=(0, -1), bins_s=(0, 10, 0.02),
         # Fitting the BG portion of the PDF to an Erlang
         _x = bin_x[bin_x > bg_mean*bg_F]
         _y = mdelays_hist_y[bin_x > bg_mean*bg_F]
-
+        
         def fit_func(x, a, rate_kcps):
             return a * erlang.pdf(x, a=m, scale=1./rate_kcps)
 
@@ -692,7 +696,6 @@ def err_func(p, x, y):
             return fit_func(x, p[0], p[1]) - y
 
         p, flag = leastsq(err_func, x0=[0.9, 3.], args=(_x, _y))
-        print(p, flag)
         a, rate_kcps = p
         results.extend([a, rate_kcps])
 
@@ -713,10 +716,11 @@ def burst_data_period_mean(dx, burst_data):
     Example:
         burst_period_mean(dx, dx.nt)
     """
-    mean_burst_data = np.zeros((dx.nch, dx.nperiods))
+    mean_burst_data = np.empty((dx.nch, dx.nperiods.max()))
+    mean_burst_data[:] = np.nan
     for ich, (b_data_ch, period) in enumerate(zip(burst_data, dx.bp)):
-        for iperiod in range(dx.nperiods):
-            mean_burst_data[ich, iperiod] = b_data_ch[period == iperiod].mean()
+        for iperiod in range(dx.nperiods[ich]):
+            mean_burst_data[ich, iperiod] = np.nanmean(b_data_ch[period == iperiod])
     return mean_burst_data
 
 
@@ -799,14 +803,14 @@ def join_data(d_list, gap=0):
                 assert new_d[name][ich].size == new_size
 
     # Set the background fields by concatenation along axis = 0
-    new_nperiods = sum((d.nperiods for d in d_list))
+    new_nperiods = np.sum([d.nperiods for d in d_list], axis=0)
     for name in ('Lim', 'Ph_p'):
         if name in new_d:
             new_d.add(**{name: []})
             for ich in range(nch):
                 value = np.concatenate([d[name][ich] for d in d_list])
                 new_d[name].append(value)
-                assert new_d[name][ich].shape[0] == new_nperiods
+                assert new_d[name][ich].shape[0] == new_nperiods[ich]
     if 'bg' in new_d:
         new_d.add(bg={})
         for sel in d.bg:
@@ -814,7 +818,7 @@ def join_data(d_list, gap=0):
             for ich in range(nch):
                 value = np.concatenate([d.bg[sel][ich] for d in d_list])
                 new_d.bg[sel].append(value)
-                assert new_d.bg[sel][ich].shape[0] == new_nperiods
+                assert new_d.bg[sel][ich].shape[0] == new_nperiods[ich]
 
     # Set the i_origin burst attribute
     new_d.add(i_origin=[])
@@ -825,13 +829,13 @@ def join_data(d_list, gap=0):
 
     # Update the `bp` attribute to refer to the background period in
     # the new concatenated background arrays.
-    sum_nperiods = np.cumsum([d.nperiods for d in d_list])
+    sum_nperiods = np.cumsum([d.nperiods for d in d_list], axis=0)
     for i_d, d in islice(enumerate(d_list), 1, None):
         for ich in range(nch):
             # Burst "slice" in new_d coming from current d
             b_mask = new_d.i_origin[ich] == i_d
             # Add the nperiods of all the previous measurements
-            new_d.bp[ich][b_mask] = new_d.bp[ich][b_mask] + sum_nperiods[i_d-1]
+            new_d.bp[ich][b_mask] = new_d.bp[ich][b_mask] + sum_nperiods[i_d-1, ich]
 
     # Modify the new mburst so the time of burst start/end is monotonic
     offset_clk = 0
@@ -1063,7 +1067,6 @@ def ph_burst_stats(d, ich=0, func=np.mean, ph_sel=Ph_sel('all')):
     stats = [func(times) for times in burst_photons]
     return np.array(stats)
 
-
 def asymmetry(dx, ich=0, func=np.mean, dropnan=True):
     """Compute an asymmetry index for each burst in channel `ich`.
 
@@ -1081,9 +1084,11 @@ def asymmetry(dx, ich=0, func=np.mean, dropnan=True):
     Returns:
         An arrays of photon timestamps (one array per burst).
     """
+    if ich is None:
+        burst_asym = [asymmetry(dx, ich=i, func=func, dropnan=dropnan) for i in range(dx.nch)]
     stats_d = ph_burst_stats(dx, ich=ich, func=func, ph_sel=Ph_sel(Dex='Dem'))
     stats_a = ph_burst_stats(dx, ich=ich, func=func, ph_sel=Ph_sel(Dex='Aem'))
     burst_asym = (stats_d - stats_a) * dx.clk_p * 1e3
     if dropnan:
-        burst_asym = burst_asym[-np.isnan(burst_asym)]
+        burst_asym = burst_asym[~np.isnan(burst_asym)]
     return burst_asym
diff --git a/fretbursts/fit/exp_fitting.py b/fretbursts/fit/exp_fitting.py
index 95b603c..c3c69ae 100644
--- a/fretbursts/fit/exp_fitting.py
+++ b/fretbursts/fit/exp_fitting.py
@@ -140,8 +140,9 @@ def expon_fit_hist(s, bins, s_min=0, weights=None, offset=0.5,
     """
     if s_min > 0: s = s[s >= s_min] - s_min
     assert s.size > 10
-
+    
     counts, bins = np.histogram(s, bins=bins, density=True)
+    
     x = bins[:-1] + 0.5*(bins[1] - bins[0])  # bin center position
     y = counts
     x = x[y > 0]
diff --git a/fretbursts/fit/weighted_kde.py b/fretbursts/fit/weighted_kde.py
index 14f00a4..01f1b0b 100644
--- a/fretbursts/fit/weighted_kde.py
+++ b/fretbursts/fit/weighted_kde.py
@@ -18,7 +18,7 @@
 
 
 
-class gaussian_kde_w(object):
+class gaussian_kde_w:
     """
     Representation of a kernel-density estimate using Gaussian kernels.
 
diff --git a/fretbursts/loader.py b/fretbursts/loader.py
index 23f8ceb..e1488e9 100644
--- a/fretbursts/loader.py
+++ b/fretbursts/loader.py
@@ -318,7 +318,7 @@ def _photon_hdf5_multich(h5data, data, ondisk=True):
                          loadspecs=False)
 
 
-def photon_hdf5(filename, ondisk=False, require_setup=True, validate=False):
+def photon_hdf5(filename, ondisk=False, require_setup=True, validate=False, fix_order=True):
     """Load a data file saved in Photon-HDF5 format version 0.3 or higher.
 
     Photon-HDF5 is a format for a wide range of timestamp-based
@@ -336,6 +336,8 @@ def photon_hdf5(filename, ondisk=False, require_setup=True, validate=False):
             DCR files.
         validate (bool): if True validate the Photon-HDF5 file on loading.
             If False skip any validation.
+        fix_order (bool): if True fix the order of photons so all are monotonically
+            increaseing in macrotime.
 
     Returns:
         :class:`fretbursts.burstlib.Data` object containing the data.
@@ -375,7 +377,8 @@ def photon_hdf5(filename, ondisk=False, require_setup=True, validate=False):
         _photon_hdf5_multich(h5data, d, ondisk=ondisk)
     else:
         _photon_hdf5_1ch(h5data, d, ondisk=ondisk)
-
+    if fix_order:
+        sort_photon_times(d)
     return d
 
 
@@ -784,3 +787,25 @@ def sm_single_laser(fname):
               ch_labels=labels)
     dx.add(acquisition_duration=np.round(dx.time_max - dx.time_min, 1))
     return dx
+
+def sort_photon_times(d):
+    for i in range(d.nch):
+        if hasattr(d, 'ph_times_t'):
+            sort_t = np.argsort(d.ph_times_t[i])
+            d.ph_times_t[i] = d.ph_times_t[i][sort_t]
+            d.det_t[i] = d.det_t[i][sort_t]
+            if hasattr(d, 'nanotimes_t'):
+                d.nanotimes_t[i] = d.nanotimes_t[i][sort_t]
+        if hasattr(d, 'ph_times_m'):
+            sort_m = np.argsort(d.ph_times_m[i])
+            d.ph_times_m[i] = d.ph_times_m[i][sort_m]
+            if hasattr(d, 'nanotimes'):
+                d.nanotimes[i] = d.nanotimes[i][sort_m]
+            if hasattr(d, 'A_em'):
+                d.A_em[i] = d.A_em[i][sort_m]
+            if hasattr(d, 'D_em'):
+                d.D_em[i] = d.D_em[i][sort_m]
+            if hasattr(d, 'A_ex'):
+                d.A_ex[i] = d.A_ex[i][sort_m]
+            if hasattr(d, 'D_ex'):
+                d.D_ex[i] = d.D_ex[i][sort_m]
\ No newline at end of file
diff --git a/fretbursts/mfit.py b/fretbursts/mfit.py
index dfab1d4..b6a02c1 100644
--- a/fretbursts/mfit.py
+++ b/fretbursts/mfit.py
@@ -295,7 +295,7 @@ def factory_two_asym_gaussians(add_bridge=False, p1_center=0.1, p2_center=0.9,
 ##
 # Classes to perform fit-related operations on multi-channel data
 #
-class FitterBase(object):
+class FitterBase:
     """Base class for histogramming a dataset.
 
     To set weights assign attribute an array with same size as `data` to
@@ -323,7 +323,7 @@ def _set_hist_data(self, hist_counts, bins):
         self.hist_axis = bins[:-1] + 0.5*self.hist_binwidth
         self.hist_counts = np.array(hist_counts)
         self.hist_pdf = np.array(hist_counts, dtype=np.float)
-        self.hist_pdf /= self.hist_counts.sum(1)[:, np.newaxis]
+        self.hist_pdf /= self.hist_counts.sum(axis=1)[:, np.newaxis]
         self.hist_pdf /= self.hist_binwidth
         self._hist_computed = True
 
@@ -335,7 +335,7 @@ def x_axis(self):
         return self._x_axis
 
 
-class MultiFitter(FitterBase):
+class MultiFitter:
     """A class handling a list of 1-D datasets for histogramming, KDE, fitting.
 
     This class takes a list of 1-D arrays of samples (such as E values
@@ -366,6 +366,13 @@ def __init__(self, data_list, skip_ch=None):
         self.skip_ch = []
         if skip_ch is not None:
             self.skip_ch = skip_ch
+    
+    def _set_hist_data(self, hist_counts, bins):
+        self.hist_bins = bins
+        self.hist_binwidth = (bins[1] - bins[0])
+        self.hist_axis = bins[:-1] + 0.5*self.hist_binwidth
+        self.hist_counts = np.array(hist_counts)
+        self._hist_computed = True
 
     @property
     def weights(self):
@@ -378,6 +385,26 @@ def weights(self, values):
         except TypeError:
             # values is not iterable
             self._weights = [values]*self.ndata
+            
+    @property
+    def hist_counts_tot(self):
+        return self.hist_counts.sum(axis=0)
+    
+    @property
+    def hist_pdf(self):
+        return self.hist_counts.astype(float) / self.hist_counts.sum(axis=1)[:, np.newaxis] / self.hist_binwidth
+    
+    @property
+    def hist_pdf_tot(self):
+        return self.hist_counts_tot / self.hist_counts_tot.sum() / self.hist_binwidth
+
+
+    @property
+    def x_axis(self):
+        if not hasattr(self, '_x_axis'):
+            self._x_axis = np.linspace(self.hist_axis[0],
+                                       self.hist_axis[-1], 1000)
+        return self._x_axis
 
     def histogram(self, binwidth=0.03, bins=None, verbose=False, **kwargs):
         """Compute the histogram of the data for each channel.
@@ -433,7 +460,7 @@ def set_weights_func(self, weight_func, weight_kwargs=None):
                            for k, v in weight_kwargs.items()}
             self.weights.append(weight_func(**weight_kw_i))
 
-    def fit_histogram(self, model=None, pdf=True, **fit_kwargs):
+    def fit_histogram(self, model=None, pdf=True, fit_tot=True, **fit_kwargs):
         """Fit the histogram of each channel using the same lmfit model.
 
         A list of `lmfit.Minimizer` is stored in `.fit_res`.
@@ -446,6 +473,7 @@ def fit_histogram(self, model=None, pdf=True, **fit_kwargs):
             pdf (bool): if True fit the normalized histogram (.hist_pdf)
                 otherwise fit the raw counts (.hist_counts).
             fit_kwargs (dict): keyword arguments passed to `model().fit`.
+            fit_tot (bool): if True then fit the sum of the data as well
         """
         if model is not None:
             self.model = model
@@ -453,8 +481,11 @@ def fit_histogram(self, model=None, pdf=True, **fit_kwargs):
             self.histogram()
 
         data_list = self.hist_pdf if pdf else self.hist_counts
+        if fit_tot:
+            data_sum = self.hist_pdf_tot if pdf else self.hist_counts_tot
+            self.fit_res_tot = self.model.fit(data_sum, x=self.hist_axis, **fit_kwargs)
         self.fit_res = []
-        #init_params = copy.deepcopy(self.model.params)
+        # init_params = copy.deepcopy(self.model.params)
         for i, data in enumerate(data_list):
             self.fit_res.append(
                 self.model.fit(data, x=self.hist_axis, **fit_kwargs))
@@ -463,7 +494,7 @@ def fit_histogram(self, model=None, pdf=True, **fit_kwargs):
         self.params = self.params[sorted(self.params.columns.tolist())]
         
 
-    def calc_kde(self, bandwidth=0.03):
+    def calc_kde(self, bandwidth=0.03, calc_tot=True):
         """Compute the list of kde functions and save it in `.kde`.
         """
         if self._kde_computed and self.kde_bandwidth == bandwidth:
@@ -480,20 +511,33 @@ def calc_kde(self, bandwidth=0.03):
                 kde = gf.gaussian_kde_w(data, bw_method=bandwidth,
                                         weights=weights_i)
             self.kde.append(kde)
+        if calc_tot:
+            data = np.concatenate([data for i, data in enumerate(self.data_list) 
+                                   if i not in self.skip_ch])
+            if np.any([w is not None for w in self.weights]):
+                weights = np.concatenate([w for i, w in enumerate(self.weights) 
+                                          if i not in self.skip_ch])
+            else:
+                weights = [None]
+            kde = gf.gaussian_kde_w(data, bw_method=bandwidth, weights=weights)
+            self.kde_tot = kde
         self._kde_computed = True
 
-    def find_kde_max(self, x_kde, xmin=None, xmax=None):
+    def find_kde_max(self, x_kde, xmin=None, xmax=None, calc_tot=True):
         """Finds the peak position of kde functions between `xmin` and `xmax`.
 
         Results are saved in the list `.kde_max_pos`.
         """
         if not hasattr(self, 'kde'):
-            self.calc_kde()
+            self.calc_kde(calc_tot=calc_tot)
         self.kde_max_pos = np.zeros(self.ndata) * np.nan
         for ich, kde in enumerate(self.kde):
             if ich not in self.skip_ch or len(kde) == 0:
                 self.kde_max_pos[ich] = find_max(x_kde, kde(x_kde),
                                                  xmin=xmin, xmax=xmax)
+        if calc_tot:
+            self.kde_max_pos_tot = find_max(x_kde, self.kde_tot(x_kde), 
+                                            xmin=xmin, xmax=xmax)
 
 
 def plot_mfit(fitter, ich=0, residuals=False, ax=None, plot_kde=False,
diff --git a/fretbursts/phtools/burstsearch.py b/fretbursts/phtools/burstsearch.py
index aa38013..d1e30a5 100644
--- a/fretbursts/phtools/burstsearch.py
+++ b/fretbursts/phtools/burstsearch.py
@@ -118,7 +118,9 @@ def bsearch_py(times, L, m, T, slice_=None,
         # Correct burst-stop off by 1 when last burst does not finish
         i_stop += 1
         if i_stop - i_start + 1 >= L:
-            bursts.pop()
+            # check if the last burst was recorded, or if off-by one resulted in 1 less than necessary
+            if bursts[-1][0] == i_start:
+                bursts.pop()
             bursts.append((i_start, i_stop, times[i_start], times[i_stop]))
 
     bursts = np.array(bursts, dtype='int64')
@@ -247,7 +249,7 @@ def counts(self):
         return self.istop - self.istart + 1 - self.gap_counts
 
 
-class Bursts(object):
+class Bursts:
     """A container for burst data.
 
     This class provides a container for burst data. It has a
diff --git a/fretbursts/phtools/phrates.py b/fretbursts/phtools/phrates.py
index 221a7bd..e0e2779 100644
--- a/fretbursts/phtools/phrates.py
+++ b/fretbursts/phtools/phrates.py
@@ -30,7 +30,12 @@
 
 import numpy as np
 
-import phrates_c as cy
+try:
+    import phrates_c as cy
+except ImportError:
+    has_cython = False
+else:
+    has_cython = True
 try:
     from . import phrates_numba as nb
 except ImportError:
diff --git a/fretbursts/tests/test_burst_plot.py b/fretbursts/tests/test_burst_plot.py
new file mode 100644
index 0000000..1b7653e
--- /dev/null
+++ b/fretbursts/tests/test_burst_plot.py
@@ -0,0 +1,235 @@
+# Author: Paul David Harris
+# Purpose: Unit tests for burst_plot.py functions
+# Created: 13 Sept 2022
+"""
+Tests of plotting functions
+
+These are mostly smoke tests
+"""
+
+from collections import namedtuple
+from itertools import product
+import pytest
+import numpy as np
+
+
+try:
+    import matplotlib
+except ImportError:
+    has_matplotlib = False  # OK to run tests without matplotlib
+else:
+    has_matplotlib = True
+    matplotlib.use('Agg')  # but if matplotlib is installed, use Agg
+
+try:
+    import numba
+except ImportError:
+    has_numba = False
+else:
+    has_numba = True
+
+
+import fretbursts.background as bg
+import fretbursts.burstlib as bl
+import fretbursts.burstlib_ext as bext
+from fretbursts.burstlib import Data
+from fretbursts import loader
+from fretbursts import select_bursts
+from fretbursts.ph_sel import Ph_sel
+from fretbursts.phtools import phrates
+if has_matplotlib:
+    import fretbursts.burst_plot as bplt
+
+
+# data subdir in the notebook folder
+DATASETS_DIR = u'data/'
+
+
+def _alex_process(d):
+    loader.alex_apply_period(d)
+    d.calc_bg(bg.exp_fit, time_s=30, tail_min_us=300)
+    d.burst_search(L=10, m=10, F=7)
+
+def load_dataset_1ch(process=True):
+    fn = "0023uLRpitc_NTP_20dT_0.5GndCl.hdf5"
+    fname = DATASETS_DIR + fn
+    d = loader.photon_hdf5(fname)
+    if process:
+        _alex_process(d)
+    return d
+
+def load_dataset_1ch_nsalex(process=True):
+    fn = "dsdna_d7_d17_50_50_1.hdf5"
+    fname = DATASETS_DIR + fn
+    d = loader.photon_hdf5(fname)
+    if process:
+        _alex_process(d)
+    return d
+
+def load_dataset_8ch():
+    fn = "12d_New_30p_320mW_steer_3.hdf5"
+    fname = DATASETS_DIR + fn
+    d = loader.photon_hdf5(fname)
+    d.calc_bg(bg.exp_fit, time_s=30, tail_min_us=300)
+    d.burst_search(L=10, m=10, F=7)
+    return d
+
+def load_fake_pax():
+    fn = "0023uLRpitc_NTP_20dT_0.5GndCl.hdf5"
+    fname = DATASETS_DIR + fn
+    d = loader.photon_hdf5(fname)
+    d.add(ALEX=False, meas_type='PAX')
+    loader.alex_apply_period(d)
+    d.calc_bg(bg.exp_fit, time_s=30, tail_min_us='auto')
+    d.burst_search(L=10, m=10, F=6, pax=True)
+    return d
+
+    
+def load_dataset_grouped(process=True):
+    fn = ['HP3_TE150_SPC630.hdf5', 'HP3_TE200_SPC630.hdf5', 'HP3_TE250_SPC630.hdf5', 'HP3_TE300_SPC630.hdf5']
+    fn = [DATASETS_DIR + f for f in fn]
+    d = loader.photon_hdf5(fn)
+    if process:
+        _alex_process(d)
+    return d
+
+
+@pytest.fixture(scope="module")
+def data_8ch(request):
+    d = load_dataset_8ch()
+    return d
+
+@pytest.fixture(scope="module")
+def data_1ch(request):
+    d = load_dataset_1ch()
+    return d
+
+@pytest.fixture(scope="module")
+def data_1ch_nsalex(request):
+    d = load_dataset_1ch_nsalex()
+    return d
+
+@pytest.fixture(scope="module")
+def data_grouped(request):
+    d = load_dataset_grouped()
+    return d
+
+@pytest.fixture(scope="module", params=[
+                                    load_dataset_1ch,
+                                    load_dataset_1ch_nsalex,
+                                    load_dataset_8ch,
+                                    load_dataset_grouped
+                                    ])
+def data(request):
+    load_func = request.param
+    d = load_func()
+    return d
+
+@pytest.fixture(scope="module", params=[
+                                    load_dataset_8ch,
+                                    load_dataset_grouped
+                                    ])
+def data_mch(request):
+    load_func = request.param
+    d = load_func()
+    return d
+
+@pytest.fixture(scope='module', params=[load_dataset_1ch,
+                                        load_dataset_1ch_nsalex,
+                                        load_dataset_grouped])
+def data_alex(request):
+    load_func = request.param
+    d = load_func()
+    return d
+
+##
+#  Multi-channel plot functions
+#
+
+def test_mch_plot_bg(data_mch):
+    d = data_mch
+    bplt.mch_plot_bg(d)
+
+def test_mch_plot_bg_ratio(data_mch):
+    d = data_mch
+    bplt.mch_plot_bg_ratio(d)
+
+def test_mch_plot_bsize(data_mch):
+    d = data_mch
+    bplt.mch_plot_bsize(d)
+
+##
+#  Timetrace plots
+#
+@pytest.fixture(scope='module', params = (bplt.timetrace_single, bplt.ratetrace_single,
+                                          bplt.hist_interphoton_single))
+def ratetraces(request):
+    return request.param
+
+def test_trace_single(data, ratetraces):
+    """Test plotting functions for time traces that do so on single Ph_sel"""
+    d = data
+    ph_sel_alex = (Ph_sel('all'), Ph_sel(Dex='Dem'), Ph_sel(Dex='Aem'), Ph_sel(Aex='Dem'), Ph_sel(Aex='Aem'))
+    ph_sel_single = (Ph_sel('all'), Ph_sel(Dex='Dem'), Ph_sel(Dex='Aem'))
+    ph_sel_list = ph_sel_alex if d.alternated else ph_sel_single
+    for i in range(d.nch):
+        for ph_sel in ph_sel_list:
+            bplt.dplot(d, ratetraces, i=i, ph_sel=ph_sel)
+
+@pytest.fixture(scope='module', params = (bplt.timetrace, bplt.ratetrace, 
+                                          bplt.timetrace_bg, bplt.timetrace_fret,
+                                          bplt.timetrace_fret_scatter, bplt.time_ph))
+def timetraces(request):
+    return request.param
+
+def test_trace(data, timetraces):
+    """Test general time trace type functions"""
+    d = data
+    for i in range(d.nch):
+            bplt.dplot(d, timetraces, i=i)
+
+
+@pytest.fixture(scope='module', params = (bplt.hist_size, bplt.hist_width,
+                                          bplt.hist_brightness, bplt.hist_size_all,
+                                          bplt.hist_fret, bplt.hist_interphoton,
+                                          bplt.hist_ph_delays, bplt.hist_mdelays,
+                                          bplt.hist_mrates, bplt.hist_sbr, 
+                                          bplt.hist_burst_phrate, bplt.hist_burst_delays,
+                                          bplt.hist_asymmetry))
+def hists(request):
+    return request.param
+
+def test_hist(data, hists):
+    d = data
+    bplt.dplot(d, hists, i=None)
+    for i in range(d.nch):
+        bplt.dplot(d, hists, i=i)
+
+def test_hist_S(data_alex):
+    d = data_alex
+    bplt.dplot(d, bplt.hist_S, i=None)
+    for i in range(d.nch):
+        bplt.dplot(d, bplt.hist_S)
+
+@pytest.fixture(scope='module', params = (bplt.hist2d_alex, bplt.hexbin_alex,
+                                          bplt.scatter_alex, bplt.scatter_naa_nt))
+def ES_plots(request):
+    return request.param
+
+def test_ES_plots(data_alex, ES_plots):
+    d = data_alex
+    bplt.dplot(d, ES_plots, i=None)
+    for i in range(d.nch):
+        bplt.dplot(d, ES_plots, i=i)
+
+@pytest.fixture(scope='module', params = (bplt.scatter_width_size, bplt.scatter_rate_da,
+                                          bplt.scatter_fret_size, bplt.scatter_fret_nd_na,
+                                          bplt.scatter_fret_width, bplt.scatter_da,))
+def scatterplots(request):
+    return request.param
+
+def test_scatterplots(data, scatterplots):
+    d = data
+    bplt.dplot(d, scatterplots, i=None)
+    for i in range(d.nch):
+        bplt.dplot(d, scatterplots, i=i)
\ No newline at end of file
diff --git a/fretbursts/tests/test_burstlib.py b/fretbursts/tests/test_burstlib.py
index 36a9db5..b4a06d5 100644
--- a/fretbursts/tests/test_burstlib.py
+++ b/fretbursts/tests/test_burstlib.py
@@ -61,7 +61,8 @@ def load_dataset_1ch(process=True):
     return d
 
 def load_dataset_1ch_nsalex(process=True):
-    fn = "dsdna_d7_d17_50_50_1.hdf5"
+    # fn = "dsdna_d7_d17_50_50_1.hdf5"
+    fn = 'HP3_TE150_SPC630.hdf5'
     fname = DATASETS_DIR + fn
     d = loader.photon_hdf5(fname)
     if process:
@@ -86,6 +87,14 @@ def load_fake_pax():
     d.burst_search(L=10, m=10, F=6, pax=True)
     return d
 
+def load_dataset_grouped(process=True):
+    fn = ['HP3_TE150_SPC630.hdf5', 'HP3_TE200_SPC630.hdf5', 'HP3_TE250_SPC630.hdf5', 'HP3_TE300_SPC630.hdf5']
+    fn = [DATASETS_DIR + f for f in fn]
+    d = loader.photon_hdf5(fn)
+    if process:
+        _alex_process(d)
+    return d
+
 def normpdf(x, c=0, mu=0.5):
     return np.exp(-(x-c)**2/((mu**2)*2))/(mu*np.sqrt(2*np.pi))
 
@@ -104,39 +113,12 @@ def data_1ch_nsalex(request):
     d = load_dataset_1ch_nsalex()
     return d
 
-@pytest.mark.parametrize("data_ch, process", product([load_dataset_1ch, 
-                                             load_dataset_1ch_nsalex, 
-                                             load_dataset_8ch],
-                                            [True, False]))
-def test_group_data(data_ch, process):
-    if data_ch is load_dataset_8ch:
-        orig_d = data_ch()
-    else:
-        orig_d = data_ch(process=process)
-    n = orig_d.nch
-    d = bext.group_data([orig_d for _ in range(8)])
-    assert n*8 == d.nch
-    for field in Data.ph_fields:
-        if hasattr(d, field):
-            assert len(d[field]) == 8*n, f"{field} not correct length"
-    if not process and orig_d.alternated:
-        loader.alex_apply_period(d)
-        for field in Data.ph_fields:
-            if hasattr(d, field):
-                assert len(d[field]) == 8*n, f"{field} not correct length"
-    d.calc_bg(bg.exp_fit, time_s=30, tail_min_us='auto')
-    d.burst_search(m=10, F=7, L=10)
-    for field in Data.burst_fields:
-        if hasattr(d, field):
-            assert len(d[field]) == 8*n, f'{field} not correct length'
-
-def load_dataset_grouped(process=True):
-    d = load_dataset_1ch_nsalex(process=False)
-    d = bext.group_data([d for _ in range(8)])
-    if process:
-        _alex_process(d)
+@pytest.fixture(scope="module")
+def data_grouped(request):
+    d = load_dataset_grouped()
     return d
 
+
 @pytest.fixture(scope="module", params=[
                                     load_dataset_1ch,
                                     load_dataset_1ch_nsalex,
@@ -148,7 +130,14 @@ def data(request):
     d = load_func()
     return d
 
-
+@pytest.fixture(scope="module", params=[
+                                    load_dataset_8ch,
+                                    load_dataset_grouped
+                                    ])
+def data_mch(request):
+    load_func = request.param
+    d = load_func()
+    return d
 
 
 ##
@@ -158,7 +147,7 @@ def data(request):
 def list_equal(list1, list2):
     """Test numerical equality of all the elements in the two lists.
     """
-    return np.all([val1 == val2 for val1, val2 in zip(list1, list2)])
+    return np.all([np.all(val1 == val2) for val1, val2 in zip(list1, list2)])
 
 def list_array_equal(list1, list2):
     """Test numerical equality between two lists of arrays.
@@ -227,9 +216,9 @@ def test_time_min_max():
     assert d.time_min == d.mburst[0].start[0] * d.clk_p
 
 
-def test_time_min_max_multispot(data_8ch):
+def test_time_min_max_multispot(data_mch):
     """Test time_min and time_max for multi-spot data."""
-    d = data_8ch
+    d = data_mch
     assert d.time_max == max(t[-1] for t in d.ph_times_m) * d.clk_p
     assert d.time_min == min(t[0] for t in d.ph_times_m) * d.clk_p
 
@@ -479,9 +468,11 @@ def test_burst_search_py_cy(data):
     """Test python and cython burst search with background-dependent threshold.
     """
     data.burst_search(pure_python=True)
+    data = data.fuse_bursts(ms=0)
     mburst1 = [b.copy() for b in data.mburst]
     num_bursts1 = data.num_bursts
     data.burst_search(pure_python=False)
+    data = data.fuse_bursts(ms=0)
     assert np.all(num_bursts1 == data.num_bursts)
     assert mburst1 == data.mburst
     data.burst_search(L=30, pure_python=True)
@@ -564,34 +555,6 @@ def test_burst_search(data):
     data.burst_search(L=10, m=10, F=7)
 
 
-def test_burst_search_and_gate(data_1ch):
-    """Test consistency of burst search and gate."""
-    d = data_1ch
-    assert d.alternated
-
-    # Smoke tests
-    bext.burst_search_and_gate(d, F=(6, 8))
-    bext.burst_search_and_gate(d, m=(12, 8))
-    bext.burst_search_and_gate(d, min_rate_cps=(60e3, 40e3))
-    if d.nch > 1:
-        mr1 = 35e3 + np.arange(d.nch) * 1e3
-        mr2 = 30e3 + np.arange(d.nch) * 1e3
-        bext.burst_search_and_gate(d, min_rate_cps=(mr1, mr2))
-
-    # Consistency test
-    d_dex = d.copy()
-    d_dex.burst_search(ph_sel=Ph_sel(Dex='DAem'))
-    d_aex = d.copy()
-    d_aex.burst_search(ph_sel=Ph_sel(Aex='Aem'))
-    d_and = bext.burst_search_and_gate(d)
-    for bursts_dex, bursts_aex, bursts_and, ph in zip(
-            d_dex.mburst, d_aex.mburst, d_and.mburst, d.iter_ph_times()):
-        ph_b_mask_dex = bl.ph_in_bursts_mask(ph.size, bursts_dex)
-        ph_b_mask_aex = bl.ph_in_bursts_mask(ph.size, bursts_aex)
-        ph_b_mask_and = bl.ph_in_bursts_mask(ph.size, bursts_and)
-        assert (ph_b_mask_and == ph_b_mask_dex * ph_b_mask_aex).all()
-
-
 def test_mch_count_ph_num_py_c(data):
     na_py = bl.bslib.mch_count_ph_in_bursts_py(data.mburst, data.A_em)
     na_c = bl.bslib.mch_count_ph_in_bursts_c(data.mburst, data.A_em)
@@ -973,14 +936,6 @@ def test_calc_max_rate(data):
             data.calc_max_rate(m=10, ph_sel=Ph_sel(Dex='DAem'), compact=True)
 
 
-def test_burst_data(data):
-    """Test for bext.burst_data()"""
-    bext.burst_data(data, include_bg=True, include_ph_index=True)
-    bext.burst_data(data, include_bg=False, include_ph_index=True)
-    bext.burst_data(data, include_bg=True, include_ph_index=False)
-    bext.burst_data(data, include_bg=False, include_ph_index=False)
-
-
 def test_print_burst_stats(data):
     """Smoke test for burstlib.print_burst_stats()"""
     bl.print_burst_stats(data)
@@ -1180,16 +1135,6 @@ def test_burst_selection_ranges(data):
             assert ((selected >= range_.min) * (selected <= range_.max)).all()
 
 
-def test_join_data(data):
-    """Smoke test for bext.join_data() function.
-    """
-    d = data
-    dj = bext.join_data([d, d.copy()])
-    assert (dj.num_bursts == 2 * d.num_bursts).all()
-    for bursts in dj.mburst:
-        assert (np.diff(bursts.start) > 0).all()
-
-
 def test_collapse(data_8ch):
     """Test the .collapse() method that joins the ch.
     """
diff --git a/fretbursts/tests/test_burstlib_ext.py b/fretbursts/tests/test_burstlib_ext.py
new file mode 100644
index 0000000..ac50db1
--- /dev/null
+++ b/fretbursts/tests/test_burstlib_ext.py
@@ -0,0 +1,232 @@
+# Author: Paul David Harris
+# Purpose: Unit tests for burstlib_ext.py functions
+# Created: 13 Sept 2022
+"""
+Unit tests for the burstlib_ext module (bext)
+
+Currently mostly just smoke tests
+
+Running tests requires pytest
+"""
+
+from collections import namedtuple
+from itertools import product
+import pytest
+import numpy as np
+
+
+try:
+    import matplotlib
+except ImportError:
+    has_matplotlib = False  # OK to run tests without matplotlib
+else:
+    has_matplotlib = True
+    matplotlib.use('Agg')  # but if matplotlib is installed, use Agg
+
+try:
+    import numba
+except ImportError:
+    has_numba = False
+else:
+    has_numba = True
+
+
+import fretbursts.background as bg
+import fretbursts.burstlib as bl
+import fretbursts.burstlib_ext as bext
+from fretbursts.burstlib import Data
+from fretbursts import loader
+from fretbursts import select_bursts
+from fretbursts.ph_sel import Ph_sel
+from fretbursts.phtools import phrates
+if has_matplotlib:
+    import fretbursts.burst_plot as bplt
+
+
+# data subdir in the notebook folder
+DATASETS_DIR = u'data/'
+
+
+def _alex_process(d):
+    loader.alex_apply_period(d)
+    d.calc_bg(bg.exp_fit, time_s=30, tail_min_us=300)
+    d.burst_search(L=10, m=10, F=7)
+
+def load_dataset_1ch(process=True):
+    fn = "0023uLRpitc_NTP_20dT_0.5GndCl.hdf5"
+    fname = DATASETS_DIR + fn
+    d = loader.photon_hdf5(fname)
+    if process:
+        _alex_process(d)
+    return d
+
+def load_dataset_1ch_nsalex(process=True):
+    fn = "dsdna_d7_d17_50_50_1.hdf5"
+    fname = DATASETS_DIR + fn
+    d = loader.photon_hdf5(fname)
+    if process:
+        _alex_process(d)
+    return d
+
+def load_dataset_8ch():
+    fn = "12d_New_30p_320mW_steer_3.hdf5"
+    fname = DATASETS_DIR + fn
+    d = loader.photon_hdf5(fname)
+    d.calc_bg(bg.exp_fit, time_s=30, tail_min_us=300)
+    d.burst_search(L=10, m=10, F=7)
+    return d
+
+def load_fake_pax():
+    fn = "0023uLRpitc_NTP_20dT_0.5GndCl.hdf5"
+    fname = DATASETS_DIR + fn
+    d = loader.photon_hdf5(fname)
+    d.add(ALEX=False, meas_type='PAX')
+    loader.alex_apply_period(d)
+    d.calc_bg(bg.exp_fit, time_s=30, tail_min_us='auto')
+    d.burst_search(L=10, m=10, F=6, pax=True)
+    return d
+
+def test_load_group():
+    """Smoke test to see if loading groupd data works"""
+    fn = ['HP3_TE150_SPC630.hdf5', 'HP3_TE200_SPC630.hdf5', 'HP3_TE250_SPC630.hdf5', 'HP3_TE300_SPC630.hdf5']
+    fn = [DATASETS_DIR + f for f in fn]
+    d = loader.photon_hdf5(fn)
+    assert d.nch == len(fn)
+    
+def load_dataset_grouped(process=True):
+    fn = ['HP3_TE150_SPC630.hdf5', 'HP3_TE200_SPC630.hdf5', 'HP3_TE250_SPC630.hdf5', 'HP3_TE300_SPC630.hdf5']
+    fn = [DATASETS_DIR + f for f in fn]
+    d = loader.photon_hdf5(fn)
+    if process:
+        _alex_process(d)
+    return d
+
+
+@pytest.fixture(scope="module")
+def data_8ch(request):
+    d = load_dataset_8ch()
+    return d
+
+@pytest.fixture(scope="module")
+def data_1ch(request):
+    d = load_dataset_1ch()
+    return d
+
+@pytest.fixture(scope="module")
+def data_1ch_nsalex(request):
+    d = load_dataset_1ch_nsalex()
+    return d
+
+@pytest.fixture(scope="module")
+def data_grouped(request):
+    d = load_dataset_grouped()
+    return d
+
+@pytest.fixture(scope="module", params=[
+                                    load_dataset_1ch,
+                                    load_dataset_1ch_nsalex,
+                                    load_dataset_8ch,
+                                    load_dataset_grouped
+                                    ])
+def data(request):
+    load_func = request.param
+    d = load_func()
+    return d
+
+@pytest.mark.parametrize("data_ch, process", product([load_dataset_1ch, 
+                                             load_dataset_1ch_nsalex, 
+                                             load_dataset_8ch],
+                                            [True, False]))
+def test_group_data(data_ch, process):
+    if data_ch is load_dataset_8ch:
+        orig_d = data_ch()
+    else:
+        orig_d = data_ch(process=process)
+    n = orig_d.nch
+    d = bext.group_data([orig_d for _ in range(8)])
+    assert n*8 == d.nch
+    for field in Data.ph_fields:
+        if hasattr(d, field):
+            assert len(d[field]) == 8*n, f"{field} not correct length"
+    if not process and orig_d.alternated:
+        loader.alex_apply_period(d)
+        for field in Data.ph_fields:
+            if hasattr(d, field):
+                assert len(d[field]) == 8*n, f"{field} not correct length"
+    d.calc_bg(bg.exp_fit, time_s=30, tail_min_us='auto')
+    d.burst_search(m=10, F=7, L=10)
+    for field in Data.burst_fields:
+        if hasattr(d, field):
+            assert len(d[field]) == 8*n, f'{field} not correct length'
+
+def test_join_data(data):
+    """Smoke test for bext.join_data() function.
+    """
+    d = data
+    dj = bext.join_data([d, d.copy()])
+    assert (dj.num_bursts == 2 * d.num_bursts).all()
+    for bursts in dj.mburst:
+        assert (np.diff(bursts.start) > 0).all()
+
+def test_burst_search_and_gate(data_1ch):
+    """Test consistency of burst search and gate."""
+    d = data_1ch
+    assert d.alternated
+
+    # Smoke tests
+    bext.burst_search_and_gate(d, F=(6, 8))
+    bext.burst_search_and_gate(d, m=(12, 8))
+    bext.burst_search_and_gate(d, min_rate_cps=(60e3, 40e3))
+    if d.nch > 1:
+        mr1 = 35e3 + np.arange(d.nch) * 1e3
+        mr2 = 30e3 + np.arange(d.nch) * 1e3
+        bext.burst_search_and_gate(d, min_rate_cps=(mr1, mr2))
+
+    # Consistency test
+    d_dex = d.copy()
+    d_dex.burst_search(ph_sel=Ph_sel(Dex='DAem'))
+    d_aex = d.copy()
+    d_aex.burst_search(ph_sel=Ph_sel(Aex='Aem'))
+    d_and = bext.burst_search_and_gate(d)
+    for bursts_dex, bursts_aex, bursts_and, ph in zip(
+            d_dex.mburst, d_aex.mburst, d_and.mburst, d.iter_ph_times()):
+        ph_b_mask_dex = bl.ph_in_bursts_mask(ph.size, bursts_dex)
+        ph_b_mask_aex = bl.ph_in_bursts_mask(ph.size, bursts_aex)
+        ph_b_mask_and = bl.ph_in_bursts_mask(ph.size, bursts_and)
+        assert (ph_b_mask_and == ph_b_mask_dex * ph_b_mask_aex).all()
+
+def test_burst_data(data):
+    """Test for bext.burst_data()"""
+    bext.burst_data(data, include_bg=True, include_ph_index=True)
+    bext.burst_data(data, include_bg=False, include_ph_index=True)
+    bext.burst_data(data, include_bg=True, include_ph_index=False)
+    bext.burst_data(data, include_bg=False, include_ph_index=False)
+
+
+
+
+def test_asymmetry(data):
+    d = data
+    for i in range(data.nch):
+        asym = bext.asymmetry(data, i, dropnan=False)
+        assert len(asym) == d.mburst[i].size
+        if np.any(np.isnan(asym)):
+            nan_count = np.isnan(asym).sum()
+            asym = bext.asymmetry(d, i)
+            assert len(asym) == d.mburst[i].size - nan_count
+
+
+def test_calc_mdelays_hist(data):
+    """Smoke test for calc_mdelays_hist"""
+    d = data
+    for i in range(d.nch):
+        for ph_sel in [Ph_sel('all'), Ph_sel(Dex='Dem'), Ph_sel(Dex='Aem')]:
+            bext.calc_mdelays_hist(d, ich=i)
+
+def test_burst_fitter(data):
+    d = data
+    bext.bursts_fitter(d)
+    assert hasattr(d, 'E_fitter')
+    if d.alternated:
+        bext.bursts_fitter(d, burst_data='S')
+        assert hasattr(d, 'S_fitter')
\ No newline at end of file

From 789f3dd04253203fd2b39d0c55f202b43eae16a1 Mon Sep 17 00:00:00 2001
From: Paul David Harris <hifret@neomailbox.com>
Date: Mon, 19 Sep 2022 08:04:31 +0300
Subject: [PATCH 004/142] Fix notebook kdeplot call

---
 notebooks/Example - 2CDE Method.ipynb | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/notebooks/Example - 2CDE Method.ipynb b/notebooks/Example - 2CDE Method.ipynb
index ddfc19a..c990980 100644
--- a/notebooks/Example - 2CDE Method.ipynb	
+++ b/notebooks/Example - 2CDE Method.ipynb	
@@ -430,7 +430,7 @@
     "                facecolor=sns.color_palette('Spectral_r', 100)[7])\n",
     "\n",
     "valid = np.isfinite(fret_2cde)\n",
-    "sns.kdeplot(ds.E[0][valid], fret_2cde[valid],\n",
+    "sns.kdeplot(x=ds.E[0][valid], y=fret_2cde[valid],\n",
     "            cmap='Spectral_r', shade=True, shade_lowest=False, n_levels=20)\n",
     "plt.xlabel('E', fontsize=16)\n",
     "plt.ylabel('FRET-2CDE', fontsize=16);\n",
@@ -617,9 +617,9 @@
  "metadata": {
   "hide_input": false,
   "kernelspec": {
-   "display_name": "Python [conda env:fretbursts-dev]",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "conda-env-fretbursts-dev-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -631,7 +631,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.7.0"
   },
   "toc": {
    "colors": {

From ca679eb0470f3f344c274c3302aa4f2d9b967bfa Mon Sep 17 00:00:00 2001
From: Paul David Harris <hifret@neomailbox.com>
Date: Mon, 19 Sep 2022 20:43:21 +0300
Subject: [PATCH 005/142] Fixed latest seaborn compatibility issues

---
 README.md                                     |   1 +
 docs/source/releasenotes.rst                  |   3 +
 fretbursts/burst_plot.py                      |  29 ++-
 fretbursts/mfit.py                            |   1 +
 notebooks/Example - 2CDE Method.ipynb         |   4 +-
 .../Example - Background estimation.ipynb     |   2 +-
 .../Example - Burst Variance Analysis.ipynb   |  12 +-
 ...le - Customize the us-ALEX histogram.ipynb |  28 +--
 ...ting Burst Data Including Timestamps.ipynb |   4 +-
 .../Example - FRET histogram fitting.ipynb    |   4 +-
 ...le - Plotting timetraces with bursts.ipynb |   6 +-
 ...Example - Selecting FRET populations.ipynb |  19 +-
 ...- Working with timestamps and bursts.ipynb |   4 +-
 ...Example- Combining Technical Repeats.ipynb | 214 ++++++++++++++++++
 ...ursts - 8-spot smFRET burst analysis.ipynb |  11 +-
 notebooks/FRETBursts - ns-ALEX example.ipynb  |  36 +--
 ...rsts - us-ALEX smFRET burst analysis.ipynb |  11 +-
 17 files changed, 282 insertions(+), 107 deletions(-)
 create mode 100644 notebooks/Example- Combining Technical Repeats.ipynb

diff --git a/README.md b/README.md
index 3d63efa..5eefd2d 100644
--- a/README.md
+++ b/README.md
@@ -3,6 +3,7 @@ FRETBursts
 
 [![DOI](https://zenodo.org/badge/5991/tritemio/FRETBursts.svg)](https://zenodo.org/badge/latestdoi/5991/tritemio/FRETBursts)
 [![Tests](https://github.com/OpenSMFS/FRETBursts/actions/workflows/tests.yml/badge.svg)](https://github.com/OpenSMFS/FRETBursts/actions)
+[![Documentation Status](https://readthedocs.org/projects/fretbursts/badge/?version=latest)](https://fretbursts.readthedocs.io/en/latest/?badge=latest)
 > *Quick links: [Reference documentation](http://fretbursts.readthedocs.org/), [FRETBursts tutorials](https://github.com/OpenSMFS/FRETBursts_notebooks#fretbursts-notebooks), [FRETBursts paper](http://dx.doi.org/10.1101/039198)*
 
 Latest News
diff --git a/docs/source/releasenotes.rst b/docs/source/releasenotes.rst
index bdff827..bf6d7ef 100644
--- a/docs/source/releasenotes.rst
+++ b/docs/source/releasenotes.rst
@@ -11,6 +11,9 @@ Version 0.7.1 (unreleased)
 - More fixes for PIE file with polarization, thanks to Christian Gebhardt 
   for reporting the problem and suggesting solutions 
   (`issue <https://github.com/OpenSMFS/FRETBursts/issues/25>`__)
+- Passing list of strings to `loader.photon_hdf5()` loads each file into the same data object as an excitation spot.
+- dplot function keyword argument `i=None` now plots concatenated data from all excitation spots. Does not apply to trace-based plots
+- Fitter attributes relating to fit values now have parallel attributes ending in `_tot` which are for concatentated data across all spots.
 
 
 Version 0.7 (Jul. 2018)
diff --git a/fretbursts/burst_plot.py b/fretbursts/burst_plot.py
index ede3753..162d440 100644
--- a/fretbursts/burst_plot.py
+++ b/fretbursts/burst_plot.py
@@ -1237,21 +1237,22 @@ def hist_burst_data(
                                label='KDE')
         kde_plot_style_.update(_normalize_kwargs(kde_plot_style,
                                                  kind='line2d'))
-        y = scale * fitter.kde[i](x)
+        kde = fitter.kde_tot(x) if i is None else fitter.kde[i](x)
+        y = scale * kde
         xx, yy = (y, x) if vertical else (x, y)
         ax.plot(xx, yy, **kde_plot_style_)
     if show_kde_peak:
         if i is None:
-            pline(fitter.kde_max_pos[-1], ls='--', color='orange')
+            pline(fitter.kde_max_pos_tot, ls='--', color='orange')
         else:
             pline(fitter.kde_max_pos[i], ls='--', color='orange')
 
     if show_fit_value or show_fit_stats:
         if fit_from == 'kde':
-            fit_arr = fitter.kde_max_pos
+            fit_arr = fitter.kde_max_pos_tot if i is None else fitter.kde_max_pos
         else:
             assert fit_from in fitter.params
-            fit_arr = fitter.params[fit_from]
+            fit_arr = fitter.params_tot[fit_from] if i is None else fitter.params[fit_from]
 
         if i == 0:
             if show_fit_stats:
@@ -1333,7 +1334,10 @@ def _plot_fit_text_ch(
         xtext_low=0.2, xtext_high=0.6, fontsize=16):
     """Plot a text box with ch and fit value."""
     if ax is None: ax = gca()
-    xtext = xtext_high if fit_arr[ich] < xtext_high else xtext_low
+    if ich is None:
+        xtext = xtext_high if fit_arr[0] < xtext_high else xtext_low
+    else:
+        xtext = xtext_high if fit_arr[ich] < xtext_high else xtext_low
     ax.text(xtext, 0.81, fmt_str % (ich+1, fit_arr[ich]),
             transform=ax.transAxes, fontsize=fontsize, bbox=bbox)
 
@@ -2289,6 +2293,7 @@ def dplot(d, func, **kwargs):
 def _alex_plot_style(g, colorbar=True,cmap=None, vmin=1, vmax=1000):
     """Set plot style and colorbar for an ALEX joint plot.
     """
+    print(type(g))
     dummy, ax_joint, ax_x, ax_y = g.get_children()
     ax_joint.set_xlabel("E")
     ax_joint.set_ylabel("S")
@@ -2304,10 +2309,10 @@ def _alex_plot_style(g, colorbar=True,cmap=None, vmin=1, vmax=1000):
     ax_y.locator_params(axis='x', tight=True, nbins=3)
     if colorbar:
         pos = ax_joint.get_position().get_points()
-        #X, Y = pos[:, 0], pos[:, 1]
-        #cax = plt.axes([1., Y[0], (X[1] - X[0]) * 0.045, Y[1] - Y[0]])
+        X, Y = pos[:, 0], pos[:, 1]
+        cax = g.add_axes([1., Y[0], (X[1] - X[0]) * 0.045, Y[1] - Y[0]])
         norm = Normalize(vmin=vmin, vmax=vmax)
-        g.colorbar(ScalarMappable(norm=norm, cmap=cmap))
+        g.colorbar(ScalarMappable(norm=norm, cmap=cmap), cax=cax)
 
 
 def _hist_bursts_marg( dx, i, E_name='E', S_name='S', **kwargs):
@@ -2439,12 +2444,12 @@ def alex_jointplot(d, i=0, gridsize=50, cmap='Spectral_r', kind='hex',
             vmax = _alex_hexbin_vmax(jplot, vmax_fret=vmax_fret)
         jplot.set_clim(vmin, vmax)
     elif kind.startswith("kde"):
-        joint_kws_ = dict(shade=True, shade_lowest=False, n_levels=30,
-                          cmap=cmap, clip=(-0.4, 1.4), bw=0.03)
+        joint_kws_ = dict(fill=True, thresh=0.05, levels=30,
+                          cmap=cmap, clip=(-0.4, 1.4), bw_adjust=0.1)
         if joint_kws is not None:
             joint_kws_.update(_normalize_kwargs(joint_kws))
-        jplot = sns.kdeplot(x=E, y=S, ax=ax_joint, 
-                            **joint_kws_)
+        data = {'E':E, 'S':S}
+        jplot = sns.kdeplot(data=data, x='E', y='S', ax=ax_joint, **joint_kws_)
     anno_str = ''
     for mburst in d.mburst:
         anno_str = anno_str + f'# Bursts: {mburst.size}\n'
diff --git a/fretbursts/mfit.py b/fretbursts/mfit.py
index b6a02c1..9f330e9 100644
--- a/fretbursts/mfit.py
+++ b/fretbursts/mfit.py
@@ -484,6 +484,7 @@ def fit_histogram(self, model=None, pdf=True, fit_tot=True, **fit_kwargs):
         if fit_tot:
             data_sum = self.hist_pdf_tot if pdf else self.hist_counts_tot
             self.fit_res_tot = self.model.fit(data_sum, x=self.hist_axis, **fit_kwargs)
+            self.params_tot = pd.DataFrame(self.fit_res_tot.values, index=[0])
         self.fit_res = []
         # init_params = copy.deepcopy(self.model.params)
         for i, data in enumerate(data_list):
diff --git a/notebooks/Example - 2CDE Method.ipynb b/notebooks/Example - 2CDE Method.ipynb
index c990980..ad797ff 100644
--- a/notebooks/Example - 2CDE Method.ipynb	
+++ b/notebooks/Example - 2CDE Method.ipynb	
@@ -617,7 +617,7 @@
  "metadata": {
   "hide_input": false,
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -631,7 +631,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.6.13"
   },
   "toc": {
    "colors": {
diff --git a/notebooks/Example - Background estimation.ipynb b/notebooks/Example - Background estimation.ipynb
index 5f2e8ed..3049701 100644
--- a/notebooks/Example - Background estimation.ipynb	
+++ b/notebooks/Example - Background estimation.ipynb	
@@ -375,7 +375,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.0"
+   "version": "3.6.13"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/Example - Burst Variance Analysis.ipynb b/notebooks/Example - Burst Variance Analysis.ipynb
index 58f4db3..93f2c6f 100644
--- a/notebooks/Example - Burst Variance Analysis.ipynb	
+++ b/notebooks/Example - Burst Variance Analysis.ipynb	
@@ -92,7 +92,7 @@
    "outputs": [],
    "source": [
     "dx=ds_FRET\n",
-    "alex_jointplot(dx)"
+    "alex_jointplot(dx);"
    ]
   },
   {
@@ -221,8 +221,8 @@
     "x = np.arange(0,1.01,0.01)\n",
     "y = np.sqrt((x*(1-x))/n)\n",
     "plt.plot(x, y, lw=2, color='k', ls='--')\n",
-    "im = sns.kdeplot(ds_FRET.E[0], np.asfarray(E_sub_std), \n",
-    "                 shade=True, cmap='Spectral_r', shade_lowest=False, n_levels=20)\n",
+    "im = sns.kdeplot(data={'E':ds_FRET.E[0], 'sigma':np.asfarray(E_sub_std)}, x='E', y='sigma', \n",
+    "                 fill=True, cmap='Spectral_r', thresh=0.05, levels=20)\n",
     "plt.xlim(0,1)\n",
     "plt.ylim(0,np.sqrt(0.5**2/7)*2)\n",
     "plt.xlabel('E', fontsize=16)\n",
@@ -244,7 +244,7 @@
     "                facecolor=sns.color_palette('Spectral_r', 100)[10])\n",
     "\n",
     "g = sns.JointGrid(x=x, y=y, ratio=3)\n",
-    "g.plot_joint(sns.kdeplot, cmap='Spectral_r', shade=True, shade_lowest=False, n_levels=20)\n",
+    "g.plot_joint(sns.kdeplot, cmap='Spectral_r', fill=True, thresh=0.05, levels=20)\n",
     "g.ax_marg_x.hist(x, bins=np.arange(-0.2, 1.2, 0.025), **hist_kws)\n",
     "g.ax_marg_y.hist(y, bins=50, orientation=\"horizontal\", **hist_kws)\n",
     "\n",
@@ -266,7 +266,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [default]",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -280,7 +280,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.6.13"
   },
   "toc": {
    "colors": {
diff --git a/notebooks/Example - Customize the us-ALEX histogram.ipynb b/notebooks/Example - Customize the us-ALEX histogram.ipynb
index e4ed2e8..06b6150 100644
--- a/notebooks/Example - Customize the us-ALEX histogram.ipynb	
+++ b/notebooks/Example - Customize the us-ALEX histogram.ipynb	
@@ -90,7 +90,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "alex_jointplot(ds)"
+    "alex_jointplot(ds);"
    ]
   },
   {
@@ -132,7 +132,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "alex_jointplot(ds, vmax_fret=False)"
+    "alex_jointplot(ds, vmax_fret=False);"
    ]
   },
   {
@@ -141,7 +141,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "alex_jointplot(ds, vmax_fret=False, marginal_color=8)"
+    "alex_jointplot(ds, vmax_fret=False, marginal_color=8);"
    ]
   },
   {
@@ -150,7 +150,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "alex_jointplot(ds, vmax_fret=False, marginal_color=7)"
+    "alex_jointplot(ds, vmax_fret=False, marginal_color=7);"
    ]
   },
   {
@@ -159,7 +159,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "alex_jointplot(ds, kind='kde')"
+    "alex_jointplot(ds, kind='kde');"
    ]
   },
   {
@@ -175,7 +175,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "alex_jointplot(ds, vmax=40)"
+    "alex_jointplot(ds, vmax=40);"
    ]
   },
   {
@@ -191,7 +191,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "alex_jointplot(ds, cmap='plasma')"
+    "alex_jointplot(ds, cmap='plasma');"
    ]
   },
   {
@@ -207,7 +207,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "alex_jointplot(ds, cmap='plasma', marginal_color=83)"
+    "alex_jointplot(ds, cmap='plasma', marginal_color=83);"
    ]
   },
   {
@@ -225,7 +225,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "alex_jointplot(ds, kind='scatter')"
+    "alex_jointplot(ds, kind='scatter');"
    ]
   },
   {
@@ -234,7 +234,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "alex_jointplot(ds, kind='kde')"
+    "alex_jointplot(ds, kind='kde');"
    ]
   },
   {
@@ -245,7 +245,7 @@
    "source": [
     "dsf = ds.select_bursts(select_bursts.naa, th1=40)\n",
     "alex_jointplot(dsf, kind='kde',\n",
-    "               joint_kws={'shade': False, 'n_levels': 12, 'bw': 0.04})"
+    "               joint_kws={'fill': True, 'levels': 12, 'bw': 0.04});"
    ]
   },
   {
@@ -371,7 +371,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "alex_jointplot(ds, marginal_kws={'show_kde': False})"
+    "alex_jointplot(ds, marginal_kws={'show_kde': False});"
    ]
   },
   {
@@ -448,7 +448,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -462,7 +462,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.12"
+   "version": "3.6.13"
   },
   "toc": {
    "colors": {
diff --git a/notebooks/Example - Exporting Burst Data Including Timestamps.ipynb b/notebooks/Example - Exporting Burst Data Including Timestamps.ipynb
index 311dc47..564ede2 100644
--- a/notebooks/Example - Exporting Burst Data Including Timestamps.ipynb	
+++ b/notebooks/Example - Exporting Burst Data Including Timestamps.ipynb	
@@ -764,7 +764,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -778,7 +778,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.12"
+   "version": "3.6.13"
   },
   "nav_menu": {},
   "toc": {
diff --git a/notebooks/Example - FRET histogram fitting.ipynb b/notebooks/Example - FRET histogram fitting.ipynb
index bf1a149..092fec2 100644
--- a/notebooks/Example - FRET histogram fitting.ipynb	
+++ b/notebooks/Example - FRET histogram fitting.ipynb	
@@ -298,7 +298,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [default]",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -312,7 +312,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.6.13"
   },
   "toc": {
    "colors": {
diff --git a/notebooks/Example - Plotting timetraces with bursts.ipynb b/notebooks/Example - Plotting timetraces with bursts.ipynb
index 16e6a02..754c41d 100644
--- a/notebooks/Example - Plotting timetraces with bursts.ipynb	
+++ b/notebooks/Example - Plotting timetraces with bursts.ipynb	
@@ -159,9 +159,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:fretbursts-dev]",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "conda-env-fretbursts-dev-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -173,7 +173,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.6.13"
   },
   "toc": {
    "colors": {
diff --git a/notebooks/Example - Selecting FRET populations.ipynb b/notebooks/Example - Selecting FRET populations.ipynb
index 6f56698..af91990 100644
--- a/notebooks/Example - Selecting FRET populations.ipynb	
+++ b/notebooks/Example - Selecting FRET populations.ipynb	
@@ -98,7 +98,7 @@
     "ds_fret = ds_no_Aonly.select_bursts(select_bursts.naa, th1=30)\n",
     "\n",
     "alex_jointplot(ds)\n",
-    "alex_jointplot(ds_fret)"
+    "alex_jointplot(ds_fret);"
    ]
   },
   {
@@ -167,7 +167,7 @@
    "outputs": [],
    "source": [
     "ds_fret_2 = d_fret_2.select_bursts(select_bursts.size, th1=30)\n",
-    "alex_jointplot(ds_fret_2)"
+    "alex_jointplot(ds_fret_2);"
    ]
   },
   {
@@ -177,22 +177,15 @@
    "outputs": [],
    "source": [
     "ds_fret_22 = d_fret_22.select_bursts(select_bursts.size, th1=30)\n",
-    "alex_jointplot(ds_fret_22)"
+    "alex_jointplot(ds_fret_22);"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:fretbursts-dev]",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "conda-env-fretbursts-dev-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -204,7 +197,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.6.13"
   },
   "toc": {
    "colors": {
diff --git a/notebooks/Example - Working with timestamps and bursts.ipynb b/notebooks/Example - Working with timestamps and bursts.ipynb
index 8fbb373..9291913 100644
--- a/notebooks/Example - Working with timestamps and bursts.ipynb	
+++ b/notebooks/Example - Working with timestamps and bursts.ipynb	
@@ -672,7 +672,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [default]",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -686,7 +686,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.6.13"
   },
   "toc": {
    "colors": {
diff --git a/notebooks/Example- Combining Technical Repeats.ipynb b/notebooks/Example- Combining Technical Repeats.ipynb
new file mode 100644
index 0000000..9b1ea75
--- /dev/null
+++ b/notebooks/Example- Combining Technical Repeats.ipynb	
@@ -0,0 +1,214 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "58b73b78",
+   "metadata": {},
+   "source": [
+    "# Example: Combining Technical Repeats into Single Data object\n",
+    "\n",
+    "You can now store multiple technical repeats into a single data object.\n",
+    "\n",
+    "> **Note**:\n",
+    "> You can combine any two hdf5 files into technical repeats so long as their base acquisition parameters are the same.\n",
+    "> In this example we will use 4 different files, which are not actually technical repeats.\n",
+    "> This is for demonstration purposes, in general, you should only combine files that are actually acquired with the same sample and measuremtn parameters.\n",
+    "\n",
+    "So first let's import FRETBursts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3d144fba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from fretbursts import *\n",
+    "sns = init_notebook(apionly=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f49a1da3",
+   "metadata": {},
+   "source": [
+    "Now we download the files:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "47a95281",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "urls = ['https://zenodo.org/record/5902313/files/HP3_TE150_SPC630.hdf5',\n",
+    "        'https://zenodo.org/record/5902313/files/HP3_TE200_SPC630.hdf5',\n",
+    "        'https://zenodo.org/record/5902313/files/HP3_TE250_SPC630.hdf5',\n",
+    "        'https://zenodo.org/record/5902313/files/HP3_TE300_SPC630.hdf5']\n",
+    "for url in urls:\n",
+    "    download_file(url, save_dir='./data')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b27535d6",
+   "metadata": {},
+   "source": [
+    "## Loading muliple photon-HDF5 files\n",
+    "\n",
+    "To load mutliple files into a single data object (as technical repeats), simple provide the filenames as a list to `loader.photon_hdf5()`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "38cbd342",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filenames = [\"data/HP3_TE150_SPC630.hdf5\", \n",
+    "             \"data/HP3_TE200_SPC630.hdf5\", \n",
+    "             \"data/HP3_TE250_SPC630.hdf5\", \n",
+    "             \"data/HP3_TE300_SPC630.hdf5\"]\n",
+    "d = loader.photon_hdf5(filenames)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ff5c9cf",
+   "metadata": {},
+   "source": [
+    "Now the rest of the analysis can proceed, what you do to one technical repeat, you do to the other."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b6230e45",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader.alex_apply_period(d)\n",
+    "d.calc_bg(bg.exp_fit, time_s=1000, tail_min_us=(800, 4000, 1500, 1000, 3000))\n",
+    "d.burst_search(L=10, m=10, F=6)\n",
+    "ds = d.select_bursts(select_bursts.size, add_naa=True, th1=30)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "09236ac7",
+   "metadata": {},
+   "source": [
+    "The technical repeats are stored like separate spots in a multi-spot experiment, so now if you want to look at the E-values of a given repeat, it looks like `d.E[i]` where `i` is the index of the repeat in the data, as you supplied it to `loader.photon_hdf5`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f2fe0787",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds.E"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c3c69093",
+   "metadata": {},
+   "source": [
+    "When you plot with `dplot()` the default will be to display each separately, in a 8-channel plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6a40f648",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dplot(ds, hist2d_alex)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aba26b56",
+   "metadata": {},
+   "source": [
+    "If you want to plot the combined results, just add the keyword argument `i=None`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a07bb81f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dplot(ds, hist2d_alex, i=None)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9538bc71",
+   "metadata": {},
+   "source": [
+    "The fitter objects also now fit not just individual repeats, but the entire concatenated set:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "21b860ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "E_fitter = bext.bursts_fitter(ds, 'E', binwidth=0.03)\n",
+    "model = mfit.factory_two_gaussians()\n",
+    "E_fitter.fit_histogram(model=model, pdf=False, method='leastsq')\n",
+    "dplot(ds, hist_fret, show_model=True, pdf=False, i=0);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c60f829",
+   "metadata": {},
+   "source": [
+    "Finally, in the fitter object, new attributes have been added, basically, if there is a per-channel parameter, then the same name with `_tot` added to the end will contain the summed counts, pdf, or fitting object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "539904a5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "E_fitter.fit_res_tot"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/FRETBursts - 8-spot smFRET burst analysis.ipynb b/notebooks/FRETBursts - 8-spot smFRET burst analysis.ipynb
index 9c1ec81..c865622 100644
--- a/notebooks/FRETBursts - 8-spot smFRET burst analysis.ipynb	
+++ b/notebooks/FRETBursts - 8-spot smFRET burst analysis.ipynb	
@@ -594,19 +594,12 @@
     "> On the contrary the 2-Gaussians histogram fit tends to follows \n",
     "> more the peak position an less the \"asymmetric\" tails.  "
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [default]",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -620,7 +613,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.6.13"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/FRETBursts - ns-ALEX example.ipynb b/notebooks/FRETBursts - ns-ALEX example.ipynb
index 0bd01cb..abb7ab3 100644
--- a/notebooks/FRETBursts - ns-ALEX example.ipynb	
+++ b/notebooks/FRETBursts - ns-ALEX example.ipynb	
@@ -280,7 +280,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "alex_jointplot(ds)"
+    "alex_jointplot(ds);"
    ]
   },
   {
@@ -298,7 +298,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "alex_jointplot(ds)"
+    "alex_jointplot(ds);"
    ]
   },
   {
@@ -495,39 +495,11 @@
    "source": [
     "When loaded in MATLAB the arrays will be named `bn_t`, `bn_d` and `bn_a`."
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [default]",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -541,7 +513,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.6.13"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/FRETBursts - us-ALEX smFRET burst analysis.ipynb b/notebooks/FRETBursts - us-ALEX smFRET burst analysis.ipynb
index 9224b77..0e48a66 100644
--- a/notebooks/FRETBursts - us-ALEX smFRET burst analysis.ipynb	
+++ b/notebooks/FRETBursts - us-ALEX smFRET burst analysis.ipynb	
@@ -1897,18 +1897,11 @@
     "\n",
     "*Please send feedback and/or question by opening a new [GitHub Issue](https://github.com/OpenSMFS/FRETBursts/issues).*"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -1922,7 +1915,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.12"
+   "version": "3.6.13"
   }
  },
  "nbformat": 4,

From 21bf0dd06cd49c62e44aa39bcecb558db7e69c8a Mon Sep 17 00:00:00 2001
From: Paul David Harris <hifret@neomailbox.com>
Date: Tue, 20 Sep 2022 09:15:25 +0300
Subject: [PATCH 006/142] Update readthedocs configurations

---
 docs/environment_py2.yml        | 47 ---------------------------------
 docs/source/burst_selection.rst |  2 +-
 docs/source/conf.py             |  2 +-
 docs/source/releasenotes.rst    |  2 +-
 readthedocs.yml                 |  2 +-
 5 files changed, 4 insertions(+), 51 deletions(-)
 delete mode 100644 docs/environment_py2.yml

diff --git a/docs/environment_py2.yml b/docs/environment_py2.yml
deleted file mode 100644
index d2eb0dc..0000000
--- a/docs/environment_py2.yml
+++ /dev/null
@@ -1,47 +0,0 @@
-name: fretbursts_env_py2
-
-channels:
-- tritemio
-
-dependencies:
-- cycler=0.10.0=py27_0
-- enum34=1.1.2=py27_0
-- freetype=2.5.5=0
-- funcsigs=0.4=py27_0
-- future=0.15.2=py27_0
-- hdf5=1.8.15.1=2
-- libpng=1.6.17=0
-- llvmlite=0.9.0=py27_0
-- matplotlib=1.5.1=np110py27_0
-- mkl=11.3.1=0
-- mock=1.3.0=py27_0
-- nose=1.3.7=py27_0
-- numba=0.24.0=np110py27_0
-- numexpr=2.4.6=np110py27_1
-- numpy=1.10.4=py27_0
-- openssl=1.0.2g=0
-- pandas=0.18.0=np110py27_0
-- pbr=1.3.0=py27_0
-- phconvert=0.6.9=py27_0
-- pip=8.1.0=py27_0
-- pyparsing=2.0.3=py27_0
-- pyqt=4.11.4=py27_1
-- pytables=3.2.2=np110py27_0
-- python=2.7.11=0
-- python-dateutil=2.4.2=py27_0
-- pytz=2015.7=py27_0
-- qt=4.8.7=1
-- readline=6.2=2
-- scipy=0.17.0=np110py27_0
-- seaborn=0.7.0=py27_0
-- setuptools=20.2.2=py27_0
-- singledispatch=3.4.0.3=py27_0
-- sip=4.16.9=py27_0
-- six=1.10.0=py27_0
-- sqlite=3.9.2=0
-- tk=8.5.18=0
-- wheel=0.29.0=py27_0
-- zlib=1.2.8=0
-- pip:
-  - tables==3.2.2
-  - lmfit==0.9.2
diff --git a/docs/source/burst_selection.rst b/docs/source/burst_selection.rst
index a88bf7e..266f701 100644
--- a/docs/source/burst_selection.rst
+++ b/docs/source/burst_selection.rst
@@ -3,7 +3,7 @@
 Burst selection
 ===============
 
-.. module:: fretbursts.burstlib
+.. currentmodule:: fretbursts.burstlib
 
 After performing a burst search is common to select bursts according to
 different criteria (burst size, FRET efficiency, etc...).
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 1a217d0..2f8fa2a 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -366,4 +366,4 @@
 # examples, originally taken from scikit-learn's doc/conf.py
 def setup(app):
     #app.add_javascript('copybutton.js')
-    app.add_stylesheet('style.css')
+    app.add_css_file('style.css')
diff --git a/docs/source/releasenotes.rst b/docs/source/releasenotes.rst
index bf6d7ef..f98f77c 100644
--- a/docs/source/releasenotes.rst
+++ b/docs/source/releasenotes.rst
@@ -2,7 +2,7 @@ FRETBursts Release Notes
 ========================
 
 Version 0.7.1 (unreleased)
--------------------------
+--------------------------
 
 - Require Python 3.6+. Python 2.7 is not supported anymore.
 - Fix deprecation warning when plotting timetraces. Now matplotlib 3+ is required.
diff --git a/readthedocs.yml b/readthedocs.yml
index d029b0a..ea69474 100644
--- a/readthedocs.yml
+++ b/readthedocs.yml
@@ -2,7 +2,7 @@ conda:
     file: docs/environment.yml
 
 python:
-   version: 3.5
+   version: 3.8
    setup_py_install: true
 
 formats:

From f1e7de5133b605481627c95a2bbe8d58a5d824a8 Mon Sep 17 00:00:00 2001
From: Paul David Harris <hifret@neomailbox.com>
Date: Thu, 2 Mar 2023 12:59:53 +0200
Subject: [PATCH 007/142] Minor plotting updates

---
 fretbursts/burst_plot.py | 23 +++++++++++++++++------
 1 file changed, 17 insertions(+), 6 deletions(-)

diff --git a/fretbursts/burst_plot.py b/fretbursts/burst_plot.py
index 162d440..4eae0dc 100644
--- a/fretbursts/burst_plot.py
+++ b/fretbursts/burst_plot.py
@@ -49,7 +49,7 @@
 from matplotlib.patches import Rectangle, Ellipse
 from matplotlib.collections import PatchCollection, PolyCollection
 from matplotlib.offsetbox import AnchoredText
-from matplotlib.gridspec import GridSpec
+from matplotlib.gridspec import GridSpec, GridSpecFromSubplotSpec
 from matplotlib.cm import ScalarMappable
 from matplotlib.colors import Normalize
 import seaborn as sns
@@ -2351,7 +2351,8 @@ def _alex_hexbin_vmax(patches, vmax_fret=True, Smax=0.8):
 def alex_jointplot(d, i=0, gridsize=50, cmap='Spectral_r', kind='hex',
                    vmax_fret=True, vmin=1, vmax=None,
                    joint_kws=None, marginal_kws=None, marginal_color=10,
-                   rightside_text=False, E_name='E', S_name='S'):
+                   rightside_text=False, E_name='E', S_name='S',
+                   ax=None, fig=None):
     """Plot an ALEX join plot: an E-S 2D histograms with marginal E and S.
 
     This function plots a jointplot: an inner 2D E-S distribution plot
@@ -2407,8 +2408,15 @@ def alex_jointplot(d, i=0, gridsize=50, cmap='Spectral_r', kind='hex',
     """
     #g = sns.JointGrid(x=d[E_name][i], y=d[S_name][i], ratio=3, space=0.2,
     #                  xlim=(-0.2, 1.2), ylim=(-0.2, 1.2))
-    g = plt.figure(figsize=(7,7))
-    gs = GridSpec(figure=g,nrows=4,ncols=4)
+    if ax is None and fig is None:        
+        g = plt.figure(figsize=(7,7))
+    else:
+        g = fig
+    if g is not None:
+        gs = GridSpec(figure=g,nrows=4,ncols=4)
+    elif ax is not None:
+        g = ax.figure
+        gs = GridSpecFromSubplotSpec(subplot_spec=ax, nrows=4, ncols=4)
     ax_joint = g.add_subplot(gs[1:4,0:3])
     ax_horiz = g.add_subplot(gs[0,0:3],sharex=ax_joint)
     ax_verti = g.add_subplot(gs[1:4,3],sharey=ax_joint)
@@ -2451,8 +2459,11 @@ def alex_jointplot(d, i=0, gridsize=50, cmap='Spectral_r', kind='hex',
         data = {'E':E, 'S':S}
         jplot = sns.kdeplot(data=data, x='E', y='S', ax=ax_joint, **joint_kws_)
     anno_str = ''
-    for mburst in d.mburst:
-        anno_str = anno_str + f'# Bursts: {mburst.size}\n'
+    if i is None:
+        for mburst in d.mburst:
+            anno_str += anno_str + f'# Bursts: {mburst.size}\n'
+    else:
+        anno_str += f'# Bursts: {d.mburst[i].size}' 
     anno_box = AnchoredText(anno_str,loc='upper right',frameon=False)
     ax_joint.add_artist(anno_box)
     ax_joint.set_xlim(-0.19, 1.19)

From 1e3bda0f3159238afe49fba4ba771cdf15e603a5 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Thu, 2 May 2024 18:55:14 +0300
Subject: [PATCH 008/142] Revert "Minor plotting updates"

This reverts commit f1e7de5133b605481627c95a2bbe8d58a5d824a8.
---
 fretbursts/burst_plot.py | 23 ++++++-----------------
 1 file changed, 6 insertions(+), 17 deletions(-)

diff --git a/fretbursts/burst_plot.py b/fretbursts/burst_plot.py
index 4eae0dc..162d440 100644
--- a/fretbursts/burst_plot.py
+++ b/fretbursts/burst_plot.py
@@ -49,7 +49,7 @@
 from matplotlib.patches import Rectangle, Ellipse
 from matplotlib.collections import PatchCollection, PolyCollection
 from matplotlib.offsetbox import AnchoredText
-from matplotlib.gridspec import GridSpec, GridSpecFromSubplotSpec
+from matplotlib.gridspec import GridSpec
 from matplotlib.cm import ScalarMappable
 from matplotlib.colors import Normalize
 import seaborn as sns
@@ -2351,8 +2351,7 @@ def _alex_hexbin_vmax(patches, vmax_fret=True, Smax=0.8):
 def alex_jointplot(d, i=0, gridsize=50, cmap='Spectral_r', kind='hex',
                    vmax_fret=True, vmin=1, vmax=None,
                    joint_kws=None, marginal_kws=None, marginal_color=10,
-                   rightside_text=False, E_name='E', S_name='S',
-                   ax=None, fig=None):
+                   rightside_text=False, E_name='E', S_name='S'):
     """Plot an ALEX join plot: an E-S 2D histograms with marginal E and S.
 
     This function plots a jointplot: an inner 2D E-S distribution plot
@@ -2408,15 +2407,8 @@ def alex_jointplot(d, i=0, gridsize=50, cmap='Spectral_r', kind='hex',
     """
     #g = sns.JointGrid(x=d[E_name][i], y=d[S_name][i], ratio=3, space=0.2,
     #                  xlim=(-0.2, 1.2), ylim=(-0.2, 1.2))
-    if ax is None and fig is None:        
-        g = plt.figure(figsize=(7,7))
-    else:
-        g = fig
-    if g is not None:
-        gs = GridSpec(figure=g,nrows=4,ncols=4)
-    elif ax is not None:
-        g = ax.figure
-        gs = GridSpecFromSubplotSpec(subplot_spec=ax, nrows=4, ncols=4)
+    g = plt.figure(figsize=(7,7))
+    gs = GridSpec(figure=g,nrows=4,ncols=4)
     ax_joint = g.add_subplot(gs[1:4,0:3])
     ax_horiz = g.add_subplot(gs[0,0:3],sharex=ax_joint)
     ax_verti = g.add_subplot(gs[1:4,3],sharey=ax_joint)
@@ -2459,11 +2451,8 @@ def alex_jointplot(d, i=0, gridsize=50, cmap='Spectral_r', kind='hex',
         data = {'E':E, 'S':S}
         jplot = sns.kdeplot(data=data, x='E', y='S', ax=ax_joint, **joint_kws_)
     anno_str = ''
-    if i is None:
-        for mburst in d.mburst:
-            anno_str += anno_str + f'# Bursts: {mburst.size}\n'
-    else:
-        anno_str += f'# Bursts: {d.mburst[i].size}' 
+    for mburst in d.mburst:
+        anno_str = anno_str + f'# Bursts: {mburst.size}\n'
     anno_box = AnchoredText(anno_str,loc='upper right',frameon=False)
     ax_joint.add_artist(anno_box)
     ax_joint.set_xlim(-0.19, 1.19)

From d9560bb60bd313a9ca116b84726867686dc64859 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Thu, 2 May 2024 21:32:09 +0300
Subject: [PATCH 009/142] Update all np.flot to np.float64

---
 fretbursts/fit/weighted_kde.py | 2 +-
 fretbursts/mfit.py             | 2 +-
 fretbursts/utils/misc.py       | 2 +-
 setup.py                       | 4 +++-
 4 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/fretbursts/fit/weighted_kde.py b/fretbursts/fit/weighted_kde.py
index 01f1b0b..982ad35 100644
--- a/fretbursts/fit/weighted_kde.py
+++ b/fretbursts/fit/weighted_kde.py
@@ -72,7 +72,7 @@ def evaluate(self, points):
                         (d, self.d)
                 raise ValueError(msg)
 
-        result = zeros((m,), dtype=np.float)
+        result = zeros((m,), dtype=np.float64)
 
         if m >= self.n:
             # there are more points than data, so loop over data
diff --git a/fretbursts/mfit.py b/fretbursts/mfit.py
index 9f330e9..5a6ba13 100644
--- a/fretbursts/mfit.py
+++ b/fretbursts/mfit.py
@@ -322,7 +322,7 @@ def _set_hist_data(self, hist_counts, bins):
         self.hist_binwidth = (bins[1] - bins[0])
         self.hist_axis = bins[:-1] + 0.5*self.hist_binwidth
         self.hist_counts = np.array(hist_counts)
-        self.hist_pdf = np.array(hist_counts, dtype=np.float)
+        self.hist_pdf = np.array(hist_counts, dtype=np.float64)
         self.hist_pdf /= self.hist_counts.sum(axis=1)[:, np.newaxis]
         self.hist_pdf /= self.hist_binwidth
         self._hist_computed = True
diff --git a/fretbursts/utils/misc.py b/fretbursts/utils/misc.py
index 9fbd51a..3ad001f 100644
--- a/fretbursts/utils/misc.py
+++ b/fretbursts/utils/misc.py
@@ -64,7 +64,7 @@ def bincenters(self):
     @property
     def pdf(self):
         if not hasattr(self, '_pdf'):
-            self._pdf = np.array(self.counts, dtype=np.float)
+            self._pdf = np.array(self.counts, dtype=np.float64)
             self._pdf /= (self.counts.sum() * self.binwidth)
         return self._pdf
 
diff --git a/setup.py b/setup.py
index dcbf603..c5bdcb8 100644
--- a/setup.py
+++ b/setup.py
@@ -73,8 +73,10 @@
       classifiers=['Intended Audience :: Science/Research',
                    'Operating System :: OS Independent',
                    'Programming Language :: Python',
-                   'Programming Language :: Python :: 3.6',
                    'Programming Language :: Python :: 3.7',
+                   'Programming Language :: Python :: 3.8',
+                   'Programming Language :: Python :: 3.9',
+                   'Programming Language :: Python :: 3.10',
                    'Topic :: Scientific/Engineering',
                    ],
       packages = ['fretbursts', 'fretbursts.utils', 'fretbursts.fit',

From a7c7dfe60a9a00adfc76e53f4e088a14b1aa387c Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Tue, 7 May 2024 13:17:16 +0300
Subject: [PATCH 010/142] Fixed np.float -> float data type change

---
 fretbursts/burst_plot.py              | 12 +----
 fretbursts/burstlib.py                |  5 +-
 fretbursts/burstlib_ext.py            | 74 +++++++++++++++------------
 fretbursts/tests/test_burst_plot.py   | 14 ++---
 fretbursts/tests/test_burstlib.py     |  5 +-
 fretbursts/tests/test_burstlib_ext.py | 12 ++---
 6 files changed, 63 insertions(+), 59 deletions(-)

diff --git a/fretbursts/burst_plot.py b/fretbursts/burst_plot.py
index 162d440..a09086e 100644
--- a/fretbursts/burst_plot.py
+++ b/fretbursts/burst_plot.py
@@ -1761,17 +1761,9 @@ def hist_mdelays(d, i=0, m=10, bins_s=(0, 10, 0.02), period=0,
         #ax.clear()
         for _ind in range(len(ax.lines)): ax.lines.pop()
 
-    if i is None:
-        results = np.concatenate([bext.calc_mdelays_hist(d, ich=j, m=m, period=period, 
-                                                          bins_s=bins_s,ph_sel=ph_sel, 
-                                                          bursts=True, bg_fit=bg_fit, 
-                                                          bg_F=bg_F) 
-                                  for j in range(d.nch)])
-    else:
-        results = bext.calc_mdelays_hist(d, ich=i, m=m, period=period, bins_s=bins_s,
+    results = bext.calc_mdelays_hist(d, ich=i, m=m, period=period, bins_s=bins_s,
                                          ph_sel=ph_sel, bursts=True, bg_fit=bg_fit, bg_F=bg_F)
-    bin_x, histog_y = results[:2]
-    bg_dist = results[2]
+    bin_x, histog_y, bg_dist = results[:3]
     rate_ch_kcps = 1./bg_dist.kwds['scale']  # extract the rate
     if bg_fit:
         a, rate_kcps = results[3:5]
diff --git a/fretbursts/burstlib.py b/fretbursts/burstlib.py
index 4ad0a3b..095ee05 100644
--- a/fretbursts/burstlib.py
+++ b/fretbursts/burstlib.py
@@ -789,7 +789,8 @@ def get_ph_mask(self, ich=0, ph_sel=Ph_sel('all')):
             ph_sel (Ph_sel object): object defining the photon selection.
                 See :mod:`fretbursts.ph_sel` for details.
         """
-        isinstance(ich, numbers.Integral)
+        if not isinstance(ich, numbers.Integral):
+            raise TypeError(f"channel must be integer value, got {type(ich)}")
 
         if self._is_allph(ph_sel):
             # Note that slice(None) is equivalent to [:].
@@ -1273,7 +1274,7 @@ def burst_sizes(self, gamma=1., add_naa=False, beta=1., donor_ref=True):
                       donor_ref=donor_ref)
         bsize_list = [self.burst_sizes_ich(ich, **kwargs) for ich in
                       range(self.nch)]
-        return np.array(bsize_list)
+        return bsize_list
 
     def iter_bursts_ph(self, ich=0):
         """Iterate over (start, stop) indexes to slice photons for each burst.
diff --git a/fretbursts/burstlib_ext.py b/fretbursts/burstlib_ext.py
index bb3c179..1b7c666 100644
--- a/fretbursts/burstlib_ext.py
+++ b/fretbursts/burstlib_ext.py
@@ -554,22 +554,17 @@ def bursts_fitter(dx, burst_data='E', save_fitter=True,
     return fitter
 
 
-def _get_bg_distrib_erlang(d, ich=0, m=10, ph_sel=Ph_sel('all'),
+def _get_bg_distrib_erlang(d, ich=None, m=10, ph_sel=Ph_sel('all'),
                            period=(0, -1)):
     """Return a frozen (scipy) erlang distrib. with rate equal to the bg rate.
     """
+    if ich is None:
+        ich = tuple(range(d.nch))
     assert ph_sel in [Ph_sel('all'), Ph_sel(Dex='Dem'), Ph_sel(Dex='Aem')]
     # fix negative periods so wrapping occurs coorectly
-    parr = np.array(period)
-    for i, p in enumerate(parr):
-        if p < 0:
-            parr[i] = len(d.Lim[ich]) - p + 1
-    period = tuple(parr)
     # Compute the BG distribution
-    bg_ph = d.bg[ph_sel][ich]
-
-    rate_ch_kcps = bg_ph[period[0]:period[1]+1].mean()/1e3  # bg rate in kcps
-    bg_dist = erlang(a=m, scale=1./rate_ch_kcps)
+    rate_ch_kcps = np.concatenate([d.bg[ph_sel][i][p[0]:p[1]] for i, p in zip(ich, period)]).mean() / 1e3
+    bg_dist = erlang(a=m, scale=1.0/rate_ch_kcps)
     return bg_dist
 
 
@@ -611,6 +606,25 @@ def histogram_mdelays(d, ich=0, m=10, ph_sel=Ph_sel('all'),
     return hist
 
 
+def _get_mdelay_channel(d, ph_sel, i, period, bursts):
+    if ph_sel == Ph_sel('all'):
+        ph = d.ph_times_m[i][period]
+        if bursts:
+            phb = ph[d.ph_in_bursts_mask_ich(ich=i)[period]]
+    elif ph_sel == Ph_sel(Dex='Dem'):
+        donor_ph_period = ~d.A_em[i][period]
+        ph = d.ph_times_m[i][period][donor_ph_period]
+        if bursts:
+            phb = ph[d.ph_in_bursts_mask(ich=i)[period][donor_ph_period]]
+    elif ph_sel == Ph_sel(Dex='Aem'):
+        accept_ph_period = d.A_em[i][period]
+        ph = d.ph_times_m[i][period][accept_ph_period]
+        if bursts:
+            phb = ph[d.ph_in_bursts_mask(ich=i)[period][accept_ph_period]]
+    if not bursts:
+        phb = None
+    return ph, phb
+
 # TODO: add tests beyond simple smoke tests
 def calc_mdelays_hist(d, ich=0, m=10, period=(0, -1), bins_s=(0, 10, 0.02),
                       ph_sel=Ph_sel('all'), bursts=False, bg_fit=True,
@@ -620,6 +634,7 @@ def calc_mdelays_hist(d, ich=0, m=10, period=(0, -1), bins_s=(0, 10, 0.02),
     Arguments:
         dx (Data object): contains the burst data to process.
         ich (int): the channel number. Default 0.
+        period (tuple): tuple of the range of periods for calculating
         m (int): number of photons used to compute each delay.
         period (int or 2-element tuple): index of the period to use. If
             tuple, the period range between period[0] and period[1]
@@ -639,29 +654,24 @@ def calc_mdelays_hist(d, ich=0, m=10, period=(0, -1), bins_s=(0, 10, 0.02),
           bin_x > bg_mean*bg_F. Returned only if `bg_fit` is True.
     """
     assert ph_sel in [Ph_sel('all'), Ph_sel(Dex='Dem'), Ph_sel(Dex='Aem')]
-    if np.size(period) == 1:
-        period = (period, period)
-    periods = slice(d.Lim[ich][period[0]][0], d.Lim[ich][period[1]][1] + 1)
+    if ich is None:
+        ich = tuple(range(d.nch))
+    elif np.issubdtype(type(ich), np.integer):
+        ich = (ich, )
+    elif np.size(ich) == 2:
+        ich = tuple(range(ich[0], ich[1]))
+    if np.issubdtype(type(ich), np.integer):
+        period = (ich, ich+1)
+    if np.issubdtype(type(period), np.integer):
+        period = (period, period+1)
+    if np.issubdtype(type(period[0]), np.integer):
+        period = tuple(period for _ in ich)
+    periods = tuple(slice(d.Lim[i][p[0]][0], d.Lim[i][p[1]][1] + 1) for i, p in zip(ich, period))
     bins = np.arange(*bins_s)
-
-    if ph_sel == Ph_sel('all'):
-        ph = d.ph_times_m[ich][periods]
-        if bursts:
-            phb = ph[d.ph_in_bursts_mask_ich(ich=ich)[periods]]
-    elif ph_sel == Ph_sel(Dex='Dem'):
-        donor_ph_period = ~d.A_em[ich][periods]
-        ph = d.ph_times_m[ich][periods][donor_ph_period]
-        if bursts:
-            phb = ph[d.ph_in_bursts_mask(ich=ich)[periods][donor_ph_period]]
-    elif ph_sel == Ph_sel(Dex='Aem'):
-        accept_ph_period = d.A_em[ich][periods]
-        ph = d.ph_times_m[ich][periods][accept_ph_period]
-        if bursts:
-            phb = ph[d.ph_in_bursts_mask(ich=ich)[periods][accept_ph_period]]
-
-    ph_mdelays = np.diff(ph[::m])*d.clk_p*1e3        # millisec
+    ph, phb = zip(*(_get_mdelay_channel(d, ph_sel, i, prds, bursts) for i, prds, in zip(ich, periods)))
+    ph_mdelays = np.concatenate([np.diff(ph_[::m])*d.clk_p*1e3 for ph_ in ph])       # millisec
     if bursts:
-        phb_mdelays = np.diff(phb[::m])*d.clk_p*1e3  # millisec
+        phb_mdelays = np.concatenate([np.diff(phb_[::m])*d.clk_p*1e3 for phb_ in phb])  # millisec
         phb_mdelays = phb_mdelays[phb_mdelays < 5]
 
     # Compute the PDF through histograming
@@ -697,7 +707,7 @@ def err_func(p, x, y):
 
         p, flag = leastsq(err_func, x0=[0.9, 3.], args=(_x, _y))
         a, rate_kcps = p
-        results.extend([a, rate_kcps])
+        results += [a, rate_kcps]
 
     return results
 
diff --git a/fretbursts/tests/test_burst_plot.py b/fretbursts/tests/test_burst_plot.py
index 1b7653e..12e2e05 100644
--- a/fretbursts/tests/test_burst_plot.py
+++ b/fretbursts/tests/test_burst_plot.py
@@ -21,12 +21,12 @@
     has_matplotlib = True
     matplotlib.use('Agg')  # but if matplotlib is installed, use Agg
 
-try:
-    import numba
-except ImportError:
-    has_numba = False
-else:
-    has_numba = True
+# try:
+#     import numba
+# except ImportError:
+#     has_numba = False
+# else:
+#     has_numba = True
 
 
 import fretbursts.background as bg
@@ -59,7 +59,7 @@ def load_dataset_1ch(process=True):
     return d
 
 def load_dataset_1ch_nsalex(process=True):
-    fn = "dsdna_d7_d17_50_50_1.hdf5"
+    fn = "HP3_TE150_SPC630.hdf5"
     fname = DATASETS_DIR + fn
     d = loader.photon_hdf5(fname)
     if process:
diff --git a/fretbursts/tests/test_burstlib.py b/fretbursts/tests/test_burstlib.py
index b4a06d5..ecd2830 100644
--- a/fretbursts/tests/test_burstlib.py
+++ b/fretbursts/tests/test_burstlib.py
@@ -790,6 +790,7 @@ def test_phrates_mtuple(data):
 
 
 if has_numba:
+# if True:
     def test_phrates_kde(data):
         d = data
         tau = 5000  # 5000 * 12.5ns = 6.25 us
@@ -800,12 +801,12 @@ def test_phrates_kde(data):
             ratesl, nph = phrates.nb.kde_laplace_nph(ph, tau)
             assert (rates == ratesl).all()
             assert (nph == nrect).all()
-
+    
             # Test consistency of kde_laplace and _kde_laplace_self_numba
             ratesl2, nph2 = phrates.nb.kde_laplace_self_numba(ph, tau)
             assert (nph2 == nrect).all()
             assert (ratesl2 == rates).all()
-
+    
             # Smoke test laplace, gaussian, rect with time_axis
             ratesl = phrates.kde_laplace(ph, tau, time_axis=ph+1)
             assert ((ratesl >= 0) * (ratesl < 5e6)).all()
diff --git a/fretbursts/tests/test_burstlib_ext.py b/fretbursts/tests/test_burstlib_ext.py
index ac50db1..5753ac8 100644
--- a/fretbursts/tests/test_burstlib_ext.py
+++ b/fretbursts/tests/test_burstlib_ext.py
@@ -23,12 +23,12 @@
     has_matplotlib = True
     matplotlib.use('Agg')  # but if matplotlib is installed, use Agg
 
-try:
-    import numba
-except ImportError:
-    has_numba = False
-else:
-    has_numba = True
+# try:
+#     import numba
+# except ImportError:
+#     has_numba = False
+# else:
+#     has_numba = True
 
 
 import fretbursts.background as bg

From 00cf09414614a31e60a78d3e56f302b6374cafbe Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Tue, 7 May 2024 13:23:04 +0300
Subject: [PATCH 011/142] Update tests.yml

---
 .github/workflows/tests.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 600d133..a835c94 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -14,7 +14,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest, windows-latest, macos-latest]
-        python-version: ["3.6", "3.7", "3.8", "3.9", "3.10"]
+        python-version: ["3.7", "3.8", "3.9", "3.10", "3.11"]
 
     steps:
       - uses: actions/checkout@v3

From 68fe13de97850d1e4cf8a270558039b072c837a9 Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Tue, 7 May 2024 14:54:42 +0300
Subject: [PATCH 012/142] Update tests.yml

---
 .github/workflows/tests.yml | 50 ++++++++++++++++++++++++++++++++++---
 1 file changed, 47 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index a835c94..9c7ff90 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -13,9 +13,53 @@ jobs:
     runs-on: ${{matrix.os}}
     strategy:
       matrix:
-        os: [ubuntu-latest, windows-latest, macos-latest]
-        python-version: ["3.7", "3.8", "3.9", "3.10", "3.11"]
-
+        # os: [ubuntu-latest, windows-latest, macos-latest]
+        # python-version: ["3.7", "3.8", "3.9", "3.10", "3.11"]
+        include:
+           - os: ubuntu-20.04
+             python-version: "3.6"
+           - os: ubuntu-latest
+             python-version: "3.7"
+           - os: ubuntu-latest
+             python-version: "3.8"
+           - os: ubuntu-latest
+             python-version: "3.9"
+           - os: ubuntu-latest
+             python-version: "3.10"
+           - os: ubuntu-latest
+             python-version: "3.11"
+           - os: ubuntu-latest
+             python-version: "3.12"
+           - os: windows-latest
+             python-version: "3.6"
+           - os: windows-latest
+             python-version: "3.7"
+           - os: windows-latest
+             python-version: "3.8"
+           - os: windows-latest
+             python-version: "3.9"
+           - os: windows-latest
+             python-version: "3.10"
+           - os: windows-latest
+             python-version: "3.11"
+           - os: windows-latest
+             python-version: "3.12"
+           - os: macOS-12
+             python-version: "3.6"
+           - os: macOS-12
+             python-version: "3.7"
+           - os: macOS-latest
+             python-version: "3.8"
+           - os: macOS-latest
+             python-version: "3.9"
+           - os: macOS-latest
+             python-version: "3.10"
+           - os: macOS-latest
+             python-version: "3.11"
+           - os: macOS-latest
+             python-version: "3.12"
+          
+          
     steps:
       - uses: actions/checkout@v3
       - name: Setup Python ${{matrix.python-version}}

From 905613d34890a56bd2471af49f15d988e4e61092 Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Tue, 7 May 2024 15:39:56 +0300
Subject: [PATCH 013/142] Update tests.yml

---
 .github/workflows/tests.yml | 8 +-------
 1 file changed, 1 insertion(+), 7 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 9c7ff90..e124e25 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -28,8 +28,6 @@ jobs:
              python-version: "3.10"
            - os: ubuntu-latest
              python-version: "3.11"
-           - os: ubuntu-latest
-             python-version: "3.12"
            - os: windows-latest
              python-version: "3.6"
            - os: windows-latest
@@ -42,8 +40,6 @@ jobs:
              python-version: "3.10"
            - os: windows-latest
              python-version: "3.11"
-           - os: windows-latest
-             python-version: "3.12"
            - os: macOS-12
              python-version: "3.6"
            - os: macOS-12
@@ -56,9 +52,7 @@ jobs:
              python-version: "3.10"
            - os: macOS-latest
              python-version: "3.11"
-           - os: macOS-latest
-             python-version: "3.12"
-          
+           
           
     steps:
       - uses: actions/checkout@v3

From ca859b3ab1a05937a518dfd4c1b7ba5184304da1 Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Tue, 7 May 2024 17:03:58 +0300
Subject: [PATCH 014/142] Shrink test environments

---
 .github/workflows/tests.yml | 77 +++++++++++++++++++------------------
 1 file changed, 39 insertions(+), 38 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index e124e25..7aae2e5 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -16,42 +16,42 @@ jobs:
         # os: [ubuntu-latest, windows-latest, macos-latest]
         # python-version: ["3.7", "3.8", "3.9", "3.10", "3.11"]
         include:
-           - os: ubuntu-20.04
-             python-version: "3.6"
-           - os: ubuntu-latest
-             python-version: "3.7"
+#           - os: ubuntu-20.04
+#             python-version: "3.6"
+#           - os: ubuntu-latest
+#             python-version: "3.7"
            - os: ubuntu-latest
              python-version: "3.8"
-           - os: ubuntu-latest
-             python-version: "3.9"
-           - os: ubuntu-latest
-             python-version: "3.10"
-           - os: ubuntu-latest
-             python-version: "3.11"
-           - os: windows-latest
-             python-version: "3.6"
-           - os: windows-latest
-             python-version: "3.7"
+           # - os: ubuntu-latest
+           #   python-version: "3.9"
+           # - os: ubuntu-latest
+           #   python-version: "3.10"
+           # - os: ubuntu-latest
+           #   python-version: "3.11"
+           # - os: windows-latest
+           #   python-version: "3.6"
+           # - os: windows-latest
+             # python-version: "3.7"
            - os: windows-latest
              python-version: "3.8"
-           - os: windows-latest
-             python-version: "3.9"
-           - os: windows-latest
-             python-version: "3.10"
-           - os: windows-latest
-             python-version: "3.11"
-           - os: macOS-12
-             python-version: "3.6"
-           - os: macOS-12
-             python-version: "3.7"
-           - os: macOS-latest
-             python-version: "3.8"
+           # - os: windows-latest
+           #   python-version: "3.9"
+           # - os: windows-latest
+           #   python-version: "3.10"
+           # - os: windows-latest
+           #   python-version: "3.11"
+           # - os: macOS-12
+           #   python-version: "3.6"
+           # - os: macOS-12
+           #   python-version: "3.7"
+           # - os: macOS-latest
+           #   python-version: "3.8"
            - os: macOS-latest
              python-version: "3.9"
-           - os: macOS-latest
-             python-version: "3.10"
-           - os: macOS-latest
-             python-version: "3.11"
+           # - os: macOS-latest
+           #   python-version: "3.10"
+           # - os: macOS-latest
+           #   python-version: "3.11"
            
           
     steps:
@@ -60,6 +60,14 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: ${{matrix.python-version}}
+      - name: Install Dependencies
+        run: |
+          python -m pip install pytest cython numpy scipy pandas matplotlib seaborn
+          python -m pip install jupyter nbconvert lmfit phconvert pybroom
+      - name: Install project
+        run: |
+          # python setup.py sdist
+          python -m pip install .
       - name: Download files Unix
         if: runner.os != 'Windows'
         run: |
@@ -83,14 +91,7 @@ jobs:
       - name: Windows 3.6 Oddities
         if: matrix.python-version == 3.6 && runner.os == 'Windows'
         run: python -m pip install pywinpty==1.1.6
-      - name: Install Dependencies
-        run: |
-          python -m pip install pytest cython numpy scipy pandas matplotlib seaborn
-          python -m pip install jupyter nbconvert lmfit phconvert pybroom
-      - name: Install project
-        run: |
-          python setup.py sdist
-          python -m pip install .
+      
       - name: Test project
         run: |
           python fretbursts/tests/nbrun.py notebooks

From ec6cab75d3ce039a65367d2ccae882a7103d4352 Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Tue, 7 May 2024 17:21:30 +0300
Subject: [PATCH 015/142] Update setup.cfg

---
 setup.cfg | 13 +++++++++++++
 1 file changed, 13 insertions(+)

diff --git a/setup.cfg b/setup.cfg
index 9a2af6e..3391c16 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -5,3 +5,16 @@ versionfile_source = fretbursts/_version.py
 versionfile_build = fretbursts/_version.py
 tag_prefix =
 parentdir_prefix = fretbursts-
+
+[options]
+packages = find:
+python_requires = >=3.6
+install_requires = 
+    numpy >= 1.19
+    cython >= 0.29
+    matplotlib >= 3.0
+    scipy >= 1.11
+    pandas >= 1.5
+    seaborn >= 0.11.1
+    phconvert >= 0.9
+    lmfit >= 1.0.1

From 589553347fa7e23fb2a2b61948de7a66a36a0c54 Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Tue, 7 May 2024 17:25:15 +0300
Subject: [PATCH 016/142] Update tests.yml

---
 .github/workflows/tests.yml | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 7aae2e5..759f1fe 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -60,6 +60,12 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: ${{matrix.python-version}}
+      - name: Upgrade pip
+        run: python -m pip install --upgrade pip
+      - name: Windows 3.6 Oddities
+        if: matrix.python-version == 3.6 && runner.os == 'Windows'
+        run: python -m pip install pywinpty==1.1.6
+      
       - name: Install Dependencies
         run: |
           python -m pip install pytest cython numpy scipy pandas matplotlib seaborn
@@ -86,12 +92,6 @@ jobs:
           curl.exe --output HP3_TE200_SPC630.hdf5 --url https://zenodo.org/record/5902313/files/HP3_TE200_SPC630.hdf5
           curl.exe --output HP3_TE250_SPC630.hdf5 --url https://zenodo.org/record/5902313/files/HP3_TE250_SPC630.hdf5
           curl.exe --output HP3_TE300_SPC630.hdf5 --url https://zenodo.org/record/5902313/files/HP3_TE300_SPC630.hdf5
-      - name: Upgrade pip
-        run: python -m pip install --upgrade pip
-      - name: Windows 3.6 Oddities
-        if: matrix.python-version == 3.6 && runner.os == 'Windows'
-        run: python -m pip install pywinpty==1.1.6
-      
       - name: Test project
         run: |
           python fretbursts/tests/nbrun.py notebooks

From 3e55b911c3a0dd535919147aa3dbfa5d8eda0184 Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Tue, 7 May 2024 17:29:52 +0300
Subject: [PATCH 017/142] Update tests.yml

---
 .github/workflows/tests.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 759f1fe..7b8bc56 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -68,7 +68,7 @@ jobs:
       
       - name: Install Dependencies
         run: |
-          python -m pip install pytest cython numpy scipy pandas matplotlib seaborn
+          python -m pip install pytest cython numpy scipy pandas matplotlib seaborn tables
           python -m pip install jupyter nbconvert lmfit phconvert pybroom
       - name: Install project
         run: |

From 4f46778fb5b926375c361941d35446a20fd411e8 Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Tue, 7 May 2024 17:32:59 +0300
Subject: [PATCH 018/142] Update setup.cfg

---
 setup.cfg | 1 +
 1 file changed, 1 insertion(+)

diff --git a/setup.cfg b/setup.cfg
index 3391c16..4177243 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -16,5 +16,6 @@ install_requires =
     scipy >= 1.11
     pandas >= 1.5
     seaborn >= 0.11.1
+    tables >= 3.5
     phconvert >= 0.9
     lmfit >= 1.0.1

From 60a79c21707fb0d0d78949330be90b7d4f59f1e3 Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Tue, 7 May 2024 17:34:40 +0300
Subject: [PATCH 019/142] Update tests.yml

---
 .github/workflows/tests.yml | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 7b8bc56..7559107 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -32,8 +32,8 @@ jobs:
            #   python-version: "3.6"
            # - os: windows-latest
              # python-version: "3.7"
-           - os: windows-latest
-             python-version: "3.8"
+           # - os: windows-latest
+           #   python-version: "3.8"
            # - os: windows-latest
            #   python-version: "3.9"
            # - os: windows-latest
@@ -46,8 +46,8 @@ jobs:
            #   python-version: "3.7"
            # - os: macOS-latest
            #   python-version: "3.8"
-           - os: macOS-latest
-             python-version: "3.9"
+           # - os: macOS-latest
+           #   python-version: "3.9"
            # - os: macOS-latest
            #   python-version: "3.10"
            # - os: macOS-latest

From 393f387ae1cc86b65e5be0b9522be5795a23f910 Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Wed, 8 May 2024 06:28:20 +0300
Subject: [PATCH 020/142] Update tests.yml

---
 .github/workflows/tests.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 7559107..a17afd9 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -20,7 +20,7 @@ jobs:
 #             python-version: "3.6"
 #           - os: ubuntu-latest
 #             python-version: "3.7"
-           - os: ubuntu-latest
+           - os: ubuntu-20.04
              python-version: "3.8"
            # - os: ubuntu-latest
            #   python-version: "3.9"

From 17035cea488a9e409db823743f4f86d932d78bb4 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Wed, 8 May 2024 07:41:26 +0300
Subject: [PATCH 021/142] Added pyproject.toml

---
 fretbursts/phtools/setup.py | 13 -------------
 pyproject.toml              | 15 +++++++++++++++
 setup.cfg                   |  6 +++---
 3 files changed, 18 insertions(+), 16 deletions(-)
 delete mode 100644 fretbursts/phtools/setup.py
 create mode 100644 pyproject.toml

diff --git a/fretbursts/phtools/setup.py b/fretbursts/phtools/setup.py
deleted file mode 100644
index 136488a..0000000
--- a/fretbursts/phtools/setup.py
+++ /dev/null
@@ -1,13 +0,0 @@
-from distutils.core import setup
-from distutils.extension import Extension
-from Cython.Distutils import build_ext
-import numpy as NP
-
-ext_modules = [Extension("burstsearch_c", ["burstsearch_c.pyx"])]
-
-setup(
-  name = 'Burst search',
-  cmdclass = {'build_ext': build_ext},
-  include_dirs = [NP.get_include()],
-  ext_modules = ext_modules
-)
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..a0b68d5
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,15 @@
+[build-system]
+requires = [
+    "setuptools>=42",
+    "cython>=0.29",
+    "numpy>=1.19",
+    "matplotlib>=3.0",
+    "scipy>=1.2",
+    "pandas>=0.23",
+    "seaborn>=0.11.1",
+    "tables>=3.5",
+    "phconvert>=0.8",
+    "lmfit>=1.0.1",
+    "versioneer"
+]
+build-backend = "setuptools.build_meta"
diff --git a/setup.cfg b/setup.cfg
index 4177243..d86bb82 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -13,9 +13,9 @@ install_requires =
     numpy >= 1.19
     cython >= 0.29
     matplotlib >= 3.0
-    scipy >= 1.11
-    pandas >= 1.5
+    scipy >= 1.2
+    pandas >= 0.23
     seaborn >= 0.11.1
     tables >= 3.5
-    phconvert >= 0.9
+    phconvert >= 0.8
     lmfit >= 1.0.1

From 3cef9739256d8fe8a61e28088cb90b5885387b67 Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Wed, 8 May 2024 11:21:03 +0300
Subject: [PATCH 022/142] Update tests.yml

---
 .github/workflows/tests.yml | 68 ++++++++++++++++++-------------------
 1 file changed, 34 insertions(+), 34 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index a17afd9..18ef174 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -16,42 +16,42 @@ jobs:
         # os: [ubuntu-latest, windows-latest, macos-latest]
         # python-version: ["3.7", "3.8", "3.9", "3.10", "3.11"]
         include:
-#           - os: ubuntu-20.04
-#             python-version: "3.6"
-#           - os: ubuntu-latest
-#             python-version: "3.7"
+          - os: ubuntu-20.04
+            python-version: "3.6"
+          - os: ubuntu-latest
+            python-version: "3.7"
            - os: ubuntu-20.04
              python-version: "3.8"
-           # - os: ubuntu-latest
-           #   python-version: "3.9"
-           # - os: ubuntu-latest
-           #   python-version: "3.10"
-           # - os: ubuntu-latest
-           #   python-version: "3.11"
-           # - os: windows-latest
-           #   python-version: "3.6"
-           # - os: windows-latest
-             # python-version: "3.7"
-           # - os: windows-latest
-           #   python-version: "3.8"
-           # - os: windows-latest
-           #   python-version: "3.9"
-           # - os: windows-latest
-           #   python-version: "3.10"
-           # - os: windows-latest
-           #   python-version: "3.11"
-           # - os: macOS-12
-           #   python-version: "3.6"
-           # - os: macOS-12
-           #   python-version: "3.7"
-           # - os: macOS-latest
-           #   python-version: "3.8"
-           # - os: macOS-latest
-           #   python-version: "3.9"
-           # - os: macOS-latest
-           #   python-version: "3.10"
-           # - os: macOS-latest
-           #   python-version: "3.11"
+           - os: ubuntu-latest
+             python-version: "3.9"
+           - os: ubuntu-latest
+             python-version: "3.10"
+           - os: ubuntu-latest
+             python-version: "3.11"
+           - os: windows-latest
+             python-version: "3.6"
+           - os: windows-latest
+             python-version: "3.7"
+           - os: windows-latest
+             python-version: "3.8"
+           - os: windows-latest
+             python-version: "3.9"
+           - os: windows-latest
+             python-version: "3.10"
+           - os: windows-latest
+             python-version: "3.11"
+           - os: macOS-12
+             python-version: "3.6"
+           - os: macOS-12
+             python-version: "3.7"
+           - os: macOS-latest
+             python-version: "3.8"
+           - os: macOS-latest
+             python-version: "3.9"
+           - os: macOS-latest
+             python-version: "3.10"
+           - os: macOS-latest
+             python-version: "3.11"
            
           
     steps:

From aa30cd0547021f6c4cccf3215d06d227c54f0867 Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Wed, 8 May 2024 11:24:18 +0300
Subject: [PATCH 023/142] Update tests.yml

---
 .github/workflows/tests.yml | 65 ++++++++++++++++++-------------------
 1 file changed, 32 insertions(+), 33 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 18ef174..60f5538 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -20,39 +20,38 @@ jobs:
             python-version: "3.6"
           - os: ubuntu-latest
             python-version: "3.7"
-           - os: ubuntu-20.04
-             python-version: "3.8"
-           - os: ubuntu-latest
-             python-version: "3.9"
-           - os: ubuntu-latest
-             python-version: "3.10"
-           - os: ubuntu-latest
-             python-version: "3.11"
-           - os: windows-latest
-             python-version: "3.6"
-           - os: windows-latest
-             python-version: "3.7"
-           - os: windows-latest
-             python-version: "3.8"
-           - os: windows-latest
-             python-version: "3.9"
-           - os: windows-latest
-             python-version: "3.10"
-           - os: windows-latest
-             python-version: "3.11"
-           - os: macOS-12
-             python-version: "3.6"
-           - os: macOS-12
-             python-version: "3.7"
-           - os: macOS-latest
-             python-version: "3.8"
-           - os: macOS-latest
-             python-version: "3.9"
-           - os: macOS-latest
-             python-version: "3.10"
-           - os: macOS-latest
-             python-version: "3.11"
-           
+          - os: ubuntu-20.04
+            python-version: "3.8"
+          - os: ubuntu-latest
+            python-version: "3.9"
+          - os: ubuntu-latest
+            python-version: "3.10"
+          - os: ubuntu-latest
+            python-version: "3.11"
+          - os: windows-latest
+            python-version: "3.6"
+          - os: windows-latest
+            python-version: "3.7"
+          - os: windows-latest
+            python-version: "3.8"
+          - os: windows-latest
+            python-version: "3.9"
+          - os: windows-latest
+            python-version: "3.10"
+          - os: windows-latest
+            python-version: "3.11"
+          - os: macOS-12
+            python-version: "3.6"
+          - os: macOS-12
+            python-version: "3.7"
+          - os: macOS-latest
+            python-version: "3.8"
+          - os: macOS-latest
+            python-version: "3.9"
+          - os: macOS-latest
+            python-version: "3.10"
+          - os: macOS-latest
+            python-version: "3.11" 
           
     steps:
       - uses: actions/checkout@v3

From 4f652c595f513dce7ea2db01c0a3e620b89bff40 Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Wed, 8 May 2024 12:36:22 +0300
Subject: [PATCH 024/142] Update tests.yml

---
 .github/workflows/tests.yml | 72 +++++++++++++++++++------------------
 1 file changed, 37 insertions(+), 35 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 60f5538..681b2e1 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -16,42 +16,42 @@ jobs:
         # os: [ubuntu-latest, windows-latest, macos-latest]
         # python-version: ["3.7", "3.8", "3.9", "3.10", "3.11"]
         include:
-          - os: ubuntu-20.04
-            python-version: "3.6"
-          - os: ubuntu-latest
-            python-version: "3.7"
-          - os: ubuntu-20.04
-            python-version: "3.8"
-          - os: ubuntu-latest
-            python-version: "3.9"
-          - os: ubuntu-latest
-            python-version: "3.10"
-          - os: ubuntu-latest
-            python-version: "3.11"
-          - os: windows-latest
-            python-version: "3.6"
-          - os: windows-latest
-            python-version: "3.7"
-          - os: windows-latest
-            python-version: "3.8"
-          - os: windows-latest
-            python-version: "3.9"
-          - os: windows-latest
-            python-version: "3.10"
-          - os: windows-latest
-            python-version: "3.11"
-          - os: macOS-12
-            python-version: "3.6"
-          - os: macOS-12
-            python-version: "3.7"
-          - os: macOS-latest
-            python-version: "3.8"
+          # - os: ubuntu-20.04
+          #   python-version: "3.6"
+          # - os: ubuntu-latest
+          #   python-version: "3.7"
+          # - os: ubuntu-20.04
+          #   python-version: "3.8"
+          # - os: ubuntu-latest
+          #   python-version: "3.9"
+          # - os: ubuntu-latest
+          #   python-version: "3.10"
+          # - os: ubuntu-latest
+          #   python-version: "3.11"
+          # - os: windows-latest
+          #   python-version: "3.6"
+          # - os: windows-latest
+          #   python-version: "3.7"
+          # - os: windows-latest
+          #   python-version: "3.8"
+          # - os: windows-latest
+          #   python-version: "3.9"
+          # - os: windows-latest
+          #   python-version: "3.10"
+          # - os: windows-latest
+          #   python-version: "3.11"
+          # - os: macOS-12
+          #   python-version: "3.6"
+          # - os: macOS-12
+          #   python-version: "3.7"
+          # - os: macOS-latest
+          #   python-version: "3.8"
           - os: macOS-latest
             python-version: "3.9"
-          - os: macOS-latest
-            python-version: "3.10"
-          - os: macOS-latest
-            python-version: "3.11" 
+          # - os: macOS-latest
+          #   python-version: "3.10"
+          # - os: macOS-latest
+          #   python-version: "3.11" 
           
     steps:
       - uses: actions/checkout@v3
@@ -67,7 +67,9 @@ jobs:
       
       - name: Install Dependencies
         run: |
-          python -m pip install pytest cython numpy scipy pandas matplotlib seaborn tables
+          python -m pip install pytest cython numpy scipy pandas matplotlib 
+          python -m pip install seaborn 
+          python -m pip install tables
           python -m pip install jupyter nbconvert lmfit phconvert pybroom
       - name: Install project
         run: |

From bc68b3f42777eeb3b731b9ca10b7c8b0fd5b602a Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Wed, 8 May 2024 12:45:03 +0300
Subject: [PATCH 025/142] Update tests.yml

---
 .github/workflows/tests.yml | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 681b2e1..b957954 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -68,7 +68,15 @@ jobs:
       - name: Install Dependencies
         run: |
           python -m pip install pytest cython numpy scipy pandas matplotlib 
-          python -m pip install seaborn 
+          python -m pip install seaborn
+      - name: MacOS install hdf5 dependencies
+        if: runner.os == 'maxOS'
+        run: |
+          brew install hdf5
+          export HDF5_DIR=/usr/local/
+          export BLOSC_DIR=/usr/local/
+      - name: Install tables
+        run: |
           python -m pip install tables
           python -m pip install jupyter nbconvert lmfit phconvert pybroom
       - name: Install project

From bb862018beaa00db420f21003e8ab535967a6381 Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Wed, 8 May 2024 12:52:35 +0300
Subject: [PATCH 026/142] Update tests.yml

---
 .github/workflows/tests.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index b957954..470ba93 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -70,7 +70,7 @@ jobs:
           python -m pip install pytest cython numpy scipy pandas matplotlib 
           python -m pip install seaborn
       - name: MacOS install hdf5 dependencies
-        if: runner.os == 'maxOS'
+        if: runner.os == 'macOS'
         run: |
           brew install hdf5
           export HDF5_DIR=/usr/local/

From f869008aa265867a401f0ea8dfafa12a1253a2fc Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Wed, 8 May 2024 13:12:04 +0300
Subject: [PATCH 027/142] Fix np.hstack of iterable

---
 fretbursts/burstlib_ext.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/fretbursts/burstlib_ext.py b/fretbursts/burstlib_ext.py
index 1b7c666..6838438 100644
--- a/fretbursts/burstlib_ext.py
+++ b/fretbursts/burstlib_ext.py
@@ -434,9 +434,9 @@ def burst_photons(dx, skip_ch=None):
     else:
         stream = dx.A_em[ich].view('int8')
     times_arr = np.hstack(
-        burstlib.iter_bursts_ph(dx.ph_times_m[ich], dx.mburst[ich]))
+        list(burstlib.iter_bursts_ph(dx.ph_times_m[ich], dx.mburst[ich])))
     stream_arr = np.hstack(
-        burstlib.iter_bursts_ph(stream, dx.mburst[ich]))
+        list(burstlib.iter_bursts_ph(stream, dx.mburst[ich])))
 
     burst_id, ph_id = [], []
     for i, arr in enumerate(burstlib.iter_bursts_ph(stream, dx.mburst[ich])):
@@ -449,7 +449,7 @@ def burst_photons(dx, skip_ch=None):
     columns = ['timestamp', 'stream']
     if dx.lifetime:
         nanot_arr = np.hstack(
-            burstlib.iter_bursts_ph(dx.nanotimes[ich], dx.mburst[ich]))
+            list(burstlib.iter_bursts_ph(dx.nanotimes[ich], dx.mburst[ich])))
         bph['nanotime'] = nanot_arr
         columns = ['timestamp', 'nanotime', 'stream']
     burstph = pd.DataFrame(bph, index=[burst_id, ph_id], columns=columns)

From 046a855d8945f229695a06aa6d4f4009a0825533 Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Wed, 8 May 2024 13:35:08 +0300
Subject: [PATCH 028/142] Update tests.yml

---
 .github/workflows/tests.yml | 68 ++++++++++++++++++-------------------
 1 file changed, 34 insertions(+), 34 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 470ba93..0c53aaa 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -16,42 +16,42 @@ jobs:
         # os: [ubuntu-latest, windows-latest, macos-latest]
         # python-version: ["3.7", "3.8", "3.9", "3.10", "3.11"]
         include:
-          # - os: ubuntu-20.04
-          #   python-version: "3.6"
-          # - os: ubuntu-latest
-          #   python-version: "3.7"
-          # - os: ubuntu-20.04
-          #   python-version: "3.8"
-          # - os: ubuntu-latest
-          #   python-version: "3.9"
-          # - os: ubuntu-latest
-          #   python-version: "3.10"
-          # - os: ubuntu-latest
-          #   python-version: "3.11"
-          # - os: windows-latest
-          #   python-version: "3.6"
-          # - os: windows-latest
-          #   python-version: "3.7"
-          # - os: windows-latest
-          #   python-version: "3.8"
-          # - os: windows-latest
-          #   python-version: "3.9"
-          # - os: windows-latest
-          #   python-version: "3.10"
-          # - os: windows-latest
-          #   python-version: "3.11"
-          # - os: macOS-12
-          #   python-version: "3.6"
-          # - os: macOS-12
-          #   python-version: "3.7"
-          # - os: macOS-latest
-          #   python-version: "3.8"
+          - os: ubuntu-20.04
+            python-version: "3.6"
+          - os: ubuntu-latest
+            python-version: "3.7"
+          - os: ubuntu-20.04
+            python-version: "3.8"
+          - os: ubuntu-latest
+            python-version: "3.9"
+          - os: ubuntu-latest
+            python-version: "3.10"
+          - os: ubuntu-latest
+            python-version: "3.11"
+          - os: windows-latest
+            python-version: "3.6"
+          - os: windows-latest
+            python-version: "3.7"
+          - os: windows-latest
+            python-version: "3.8"
+          - os: windows-latest
+            python-version: "3.9"
+          - os: windows-latest
+            python-version: "3.10"
+          - os: windows-latest
+            python-version: "3.11"
+          - os: macOS-12
+            python-version: "3.6"
+          - os: macOS-12
+            python-version: "3.7"
+          - os: macOS-latest
+            python-version: "3.8"
           - os: macOS-latest
             python-version: "3.9"
-          # - os: macOS-latest
-          #   python-version: "3.10"
-          # - os: macOS-latest
-          #   python-version: "3.11" 
+          - os: macOS-latest
+            python-version: "3.10"
+          - os: macOS-latest
+            python-version: "3.11" 
           
     steps:
       - uses: actions/checkout@v3

From 1bbffda658b86eb1878e085345af182dbd48c552 Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Wed, 8 May 2024 15:16:35 +0300
Subject: [PATCH 029/142] Remove Python 3.6 tests

---
 .github/workflows/tests.yml | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 0c53aaa..54bc7e7 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -16,8 +16,8 @@ jobs:
         # os: [ubuntu-latest, windows-latest, macos-latest]
         # python-version: ["3.7", "3.8", "3.9", "3.10", "3.11"]
         include:
-          - os: ubuntu-20.04
-            python-version: "3.6"
+          # - os: ubuntu-20.04
+          #   python-version: "3.6"
           - os: ubuntu-latest
             python-version: "3.7"
           - os: ubuntu-20.04
@@ -28,8 +28,8 @@ jobs:
             python-version: "3.10"
           - os: ubuntu-latest
             python-version: "3.11"
-          - os: windows-latest
-            python-version: "3.6"
+          # - os: windows-latest
+          #   python-version: "3.6"
           - os: windows-latest
             python-version: "3.7"
           - os: windows-latest
@@ -40,8 +40,8 @@ jobs:
             python-version: "3.10"
           - os: windows-latest
             python-version: "3.11"
-          - os: macOS-12
-            python-version: "3.6"
+          # - os: macOS-12
+          #   python-version: "3.6"
           - os: macOS-12
             python-version: "3.7"
           - os: macOS-latest

From 30af7f92a2bc690814e4e6d3928afd7d9b3ddb03 Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Thu, 9 May 2024 07:12:48 +0300
Subject: [PATCH 030/142] Update tests.yml

---
 .github/workflows/tests.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 54bc7e7..85356a3 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -46,7 +46,7 @@ jobs:
             python-version: "3.7"
           - os: macOS-latest
             python-version: "3.8"
-          - os: macOS-latest
+          - os: macOS-12
             python-version: "3.9"
           - os: macOS-latest
             python-version: "3.10"

From 1379bc2ffe50ed2d121c6307e3975f5d6154497e Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Thu, 9 May 2024 07:46:16 +0300
Subject: [PATCH 031/142] Update tests.yml

---
 .github/workflows/tests.yml | 89 ++++++++++++++++++++-----------------
 1 file changed, 48 insertions(+), 41 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 85356a3..862d357 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -18,47 +18,52 @@ jobs:
         include:
           # - os: ubuntu-20.04
           #   python-version: "3.6"
-          - os: ubuntu-latest
-            python-version: "3.7"
-          - os: ubuntu-20.04
-            python-version: "3.8"
-          - os: ubuntu-latest
-            python-version: "3.9"
+          # - os: ubuntu-latest
+          #   python-version: "3.7"
+          # - os: ubuntu-20.04
+          #   python-version: "3.8"
+          # - os: ubuntu-latest
+          #   python-version: "3.9"
           - os: ubuntu-latest
             python-version: "3.10"
-          - os: ubuntu-latest
-            python-version: "3.11"
+          # - os: ubuntu-latest
+          #   python-version: "3.11"
           # - os: windows-latest
           #   python-version: "3.6"
-          - os: windows-latest
-            python-version: "3.7"
-          - os: windows-latest
-            python-version: "3.8"
-          - os: windows-latest
-            python-version: "3.9"
+          # - os: windows-latest
+          #   python-version: "3.7"
+          # - os: windows-latest
+          #   python-version: "3.8"
+          # - os: windows-latest
+          #   python-version: "3.9"
           - os: windows-latest
             python-version: "3.10"
-          - os: windows-latest
-            python-version: "3.11"
+          # - os: windows-latest
+          #   python-version: "3.11"
           # - os: macOS-12
           #   python-version: "3.6"
-          - os: macOS-12
-            python-version: "3.7"
+          # - os: macOS-12
+          #   python-version: "3.7"
           - os: macOS-latest
             python-version: "3.8"
-          - os: macOS-12
-            python-version: "3.9"
-          - os: macOS-latest
-            python-version: "3.10"
-          - os: macOS-latest
-            python-version: "3.11" 
+          # - os: macOS-latest
+          #   python-version: "3.9"
+          # - os: macOS-latest
+          #   python-version: "3.10"
+          # - os: macOS-latest
+          #   python-version: "3.11" 
           
     steps:
-      - uses: actions/checkout@v3
-      - name: Setup Python ${{matrix.python-version}}
-        uses: actions/setup-python@v4
+    #   - uses: actions/checkout@v3
+    #   - name: Setup Python ${{matrix.python-version}}
+    #     uses: actions/setup-python@v4
+    #     with:
+    #       python-version: ${{matrix.python-version}}
+      - uses: conda-incubator/setup-miniconda@v3
         with:
-          python-version: ${{matrix.python-version}}
+          auto-update-conda: true
+          channels: conda-forge
+          python-version: ${{ matrix.python-version }}
       - name: Upgrade pip
         run: python -m pip install --upgrade pip
       - name: Windows 3.6 Oddities
@@ -67,21 +72,23 @@ jobs:
       
       - name: Install Dependencies
         run: |
-          python -m pip install pytest cython numpy scipy pandas matplotlib 
-          python -m pip install seaborn
-      - name: MacOS install hdf5 dependencies
-        if: runner.os == 'macOS'
-        run: |
-          brew install hdf5
-          export HDF5_DIR=/usr/local/
-          export BLOSC_DIR=/usr/local/
-      - name: Install tables
-        run: |
-          python -m pip install tables
-          python -m pip install jupyter nbconvert lmfit phconvert pybroom
+          conda install pytest cython numpy jupyter nbconvert phconvert
+          conda install scipy pandas matplotlib tables
+          conda install seaborn
+          python -m install lmfit pybroom
+      # - name: MacOS install hdf5 dependencies
+      #   if: runner.os == 'macOS'
+      #   run: |
+      #     brew install hdf5
+      #     export HDF5_DIR=/usr/local/
+      #     export BLOSC_DIR=/usr/local/
+      # - name: Install tables
+      #   run: |
+      #     python -m pip install tables
+      #     python -m pip install jupyter nbconvert lmfit phconvert pybroom
       - name: Install project
         run: |
-          # python setup.py sdist
+          python setup.py sdist
           python -m pip install .
       - name: Download files Unix
         if: runner.os != 'Windows'

From 744d04b34a28590fe5404218d99a56f2db5818fb Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Thu, 9 May 2024 07:51:41 +0300
Subject: [PATCH 032/142] Update tests.yml

---
 .github/workflows/tests.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 862d357..f339fbc 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -74,8 +74,8 @@ jobs:
         run: |
           conda install pytest cython numpy jupyter nbconvert phconvert
           conda install scipy pandas matplotlib tables
-          conda install seaborn
-          python -m install lmfit pybroom
+          python -m pip install seaborn
+          python -m pip install lmfit pybroom
       # - name: MacOS install hdf5 dependencies
       #   if: runner.os == 'macOS'
       #   run: |

From 31fda6424b54671b2e1099da90b16b2dca5f0e5c Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Thu, 9 May 2024 08:16:16 +0300
Subject: [PATCH 033/142] Update tests.yml

---
 .github/workflows/tests.yml | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index f339fbc..824e2f7 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -36,16 +36,16 @@ jobs:
           #   python-version: "3.8"
           # - os: windows-latest
           #   python-version: "3.9"
-          - os: windows-latest
-            python-version: "3.10"
+          # - os: windows-latest
+          #   python-version: "3.10"
           # - os: windows-latest
           #   python-version: "3.11"
           # - os: macOS-12
           #   python-version: "3.6"
           # - os: macOS-12
           #   python-version: "3.7"
-          - os: macOS-latest
-            python-version: "3.8"
+          # - os: macOS-latest
+          #   python-version: "3.8"
           # - os: macOS-latest
           #   python-version: "3.9"
           # - os: macOS-latest
@@ -73,7 +73,7 @@ jobs:
       - name: Install Dependencies
         run: |
           conda install pytest cython numpy jupyter nbconvert phconvert
-          conda install scipy pandas matplotlib tables
+          conda install scipy pandas matplotlib pytables
           python -m pip install seaborn
           python -m pip install lmfit pybroom
       # - name: MacOS install hdf5 dependencies

From 700d0c50108ac4ed98877d0571fd7047c1e2cda6 Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Thu, 9 May 2024 08:21:16 +0300
Subject: [PATCH 034/142] Update tests.yml

---
 .github/workflows/tests.yml | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 824e2f7..83c5cfc 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -86,6 +86,10 @@ jobs:
       #   run: |
       #     python -m pip install tables
       #     python -m pip install jupyter nbconvert lmfit phconvert pybroom
+      - name: Locate project
+        run: |
+          pwd
+          ls
       - name: Install project
         run: |
           python setup.py sdist

From a487c5ac2e03c2a35ee0cd08f9b5d733f3ae6df0 Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Thu, 9 May 2024 08:25:24 +0300
Subject: [PATCH 035/142] Update tests.yml

---
 .github/workflows/tests.yml | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 83c5cfc..b12633b 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -54,7 +54,7 @@ jobs:
           #   python-version: "3.11" 
           
     steps:
-    #   - uses: actions/checkout@v3
+      - uses: actions/checkout@v3
     #   - name: Setup Python ${{matrix.python-version}}
     #     uses: actions/setup-python@v4
     #     with:
@@ -86,10 +86,6 @@ jobs:
       #   run: |
       #     python -m pip install tables
       #     python -m pip install jupyter nbconvert lmfit phconvert pybroom
-      - name: Locate project
-        run: |
-          pwd
-          ls
       - name: Install project
         run: |
           python setup.py sdist

From 01027f6faabe00a1d763a9c88570c446d7d3933f Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Thu, 9 May 2024 08:38:12 +0300
Subject: [PATCH 036/142] Update tests.yml

---
 .github/workflows/tests.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index b12633b..73b4bd3 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -72,8 +72,8 @@ jobs:
       
       - name: Install Dependencies
         run: |
-          conda install pytest cython numpy jupyter nbconvert phconvert
-          conda install scipy pandas matplotlib pytables
+          conda install pytest cython numpy ipython jupyter nbconvert
+          conda install scipy pandas matplotlib pytables phconvert
           python -m pip install seaborn
           python -m pip install lmfit pybroom
       # - name: MacOS install hdf5 dependencies

From e3008c6dfefc805bbe08bc8d4dd8ade35b82d25e Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Thu, 9 May 2024 11:18:35 +0300
Subject: [PATCH 037/142] Update tests.yml

---
 .github/workflows/tests.yml | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 73b4bd3..f910d7b 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -54,7 +54,7 @@ jobs:
           #   python-version: "3.11" 
           
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
     #   - name: Setup Python ${{matrix.python-version}}
     #     uses: actions/setup-python@v4
     #     with:
@@ -72,7 +72,8 @@ jobs:
       
       - name: Install Dependencies
         run: |
-          conda install pytest cython numpy ipython jupyter nbconvert
+          conda install pytest cython numpy nbconvert
+          python -m pip install ipython jupyter
           conda install scipy pandas matplotlib pytables phconvert
           python -m pip install seaborn
           python -m pip install lmfit pybroom

From e67756611c0dd3a76f32785197c43b038da0d264 Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Thu, 9 May 2024 14:13:12 +0300
Subject: [PATCH 038/142] Update tests.yml

---
 .github/workflows/tests.yml | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index f910d7b..ba377ab 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -24,8 +24,8 @@ jobs:
           #   python-version: "3.8"
           # - os: ubuntu-latest
           #   python-version: "3.9"
-          - os: ubuntu-latest
-            python-version: "3.10"
+          # - os: ubuntu-latest
+          #   python-version: "3.10"
           # - os: ubuntu-latest
           #   python-version: "3.11"
           # - os: windows-latest
@@ -44,8 +44,8 @@ jobs:
           #   python-version: "3.6"
           # - os: macOS-12
           #   python-version: "3.7"
-          # - os: macOS-latest
-          #   python-version: "3.8"
+          - os: macOS-latest
+            python-version: "3.8"
           # - os: macOS-latest
           #   python-version: "3.9"
           # - os: macOS-latest
@@ -72,8 +72,8 @@ jobs:
       
       - name: Install Dependencies
         run: |
-          conda install pytest cython numpy nbconvert
-          python -m pip install ipython jupyter
+          conda activate test
+          conda install pytest cython numpy nbconvert ipython jupyter
           conda install scipy pandas matplotlib pytables phconvert
           python -m pip install seaborn
           python -m pip install lmfit pybroom

From 9c9dec43d522c0ad4c1370ec6c2a55f7baaaa7c8 Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Thu, 9 May 2024 14:15:39 +0300
Subject: [PATCH 039/142] Update tests.yml

---
 .github/workflows/tests.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index ba377ab..3378a7d 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -72,6 +72,7 @@ jobs:
       
       - name: Install Dependencies
         run: |
+          conda init
           conda activate test
           conda install pytest cython numpy nbconvert ipython jupyter
           conda install scipy pandas matplotlib pytables phconvert

From 6991064346a730538164f7c95f951c91616b74b7 Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Thu, 9 May 2024 14:20:23 +0300
Subject: [PATCH 040/142] Update tests.yml

---
 .github/workflows/tests.yml | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 3378a7d..f0437a4 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -62,6 +62,7 @@ jobs:
       - uses: conda-incubator/setup-miniconda@v3
         with:
           auto-update-conda: true
+          activate-environment: test
           channels: conda-forge
           python-version: ${{ matrix.python-version }}
       - name: Upgrade pip
@@ -72,8 +73,6 @@ jobs:
       
       - name: Install Dependencies
         run: |
-          conda init
-          conda activate test
           conda install pytest cython numpy nbconvert ipython jupyter
           conda install scipy pandas matplotlib pytables phconvert
           python -m pip install seaborn

From af2bdccbaaf3d74a4fb3aff104217ee85b766105 Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Thu, 9 May 2024 14:36:51 +0300
Subject: [PATCH 041/142] Update tests.yml

---
 .github/workflows/tests.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index f0437a4..89134a8 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -73,7 +73,7 @@ jobs:
       
       - name: Install Dependencies
         run: |
-          conda install pytest cython numpy nbconvert ipython jupyter
+          conda install pytest cython numpy nbconvert ipython jupyter setuptools
           conda install scipy pandas matplotlib pytables phconvert
           python -m pip install seaborn
           python -m pip install lmfit pybroom

From 5093c0b3fa4efac64b2fd4e20c7ebcb2d16bc6f7 Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Thu, 9 May 2024 14:43:47 +0300
Subject: [PATCH 042/142] Update tests.yml

---
 .github/workflows/tests.yml | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 89134a8..33e3b6b 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -32,8 +32,8 @@ jobs:
           #   python-version: "3.6"
           # - os: windows-latest
           #   python-version: "3.7"
-          # - os: windows-latest
-          #   python-version: "3.8"
+          - os: windows-latest
+            python-version: "3.8"
           # - os: windows-latest
           #   python-version: "3.9"
           # - os: windows-latest
@@ -44,8 +44,8 @@ jobs:
           #   python-version: "3.6"
           # - os: macOS-12
           #   python-version: "3.7"
-          - os: macOS-latest
-            python-version: "3.8"
+          # - os: macOS-latest
+          #   python-version: "3.8"
           # - os: macOS-latest
           #   python-version: "3.9"
           # - os: macOS-latest

From db6f64f7ff1c452290323fe629b1551fcd3525db Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Thu, 9 May 2024 15:24:13 +0300
Subject: [PATCH 043/142] Update tests.yml

---
 .github/workflows/tests.yml | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 33e3b6b..974ca0d 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -20,8 +20,8 @@ jobs:
           #   python-version: "3.6"
           # - os: ubuntu-latest
           #   python-version: "3.7"
-          # - os: ubuntu-20.04
-          #   python-version: "3.8"
+          - os: ubuntu-20.04
+            python-version: "3.8"
           # - os: ubuntu-latest
           #   python-version: "3.9"
           # - os: ubuntu-latest
@@ -32,8 +32,8 @@ jobs:
           #   python-version: "3.6"
           # - os: windows-latest
           #   python-version: "3.7"
-          - os: windows-latest
-            python-version: "3.8"
+          # - os: windows-latest
+          #   python-version: "3.8"
           # - os: windows-latest
           #   python-version: "3.9"
           # - os: windows-latest

From 4157af953c8546c422f9570bfc6a493c28ad4b2c Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Thu, 9 May 2024 15:48:12 +0300
Subject: [PATCH 044/142] Update tests.yml

---
 .github/workflows/tests.yml | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 974ca0d..12ade26 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -44,8 +44,8 @@ jobs:
           #   python-version: "3.6"
           # - os: macOS-12
           #   python-version: "3.7"
-          # - os: macOS-latest
-          #   python-version: "3.8"
+          - os: macOS-latest
+            python-version: "3.8"
           # - os: macOS-latest
           #   python-version: "3.9"
           # - os: macOS-latest
@@ -73,9 +73,9 @@ jobs:
       
       - name: Install Dependencies
         run: |
-          conda install pytest cython numpy nbconvert ipython jupyter setuptools
+          conda install pytest cython numpy nbconvert setuptools
           conda install scipy pandas matplotlib pytables phconvert
-          python -m pip install seaborn
+          python -m pip install seaborn ipython jupyter
           python -m pip install lmfit pybroom
       # - name: MacOS install hdf5 dependencies
       #   if: runner.os == 'macOS'

From 4f31a636769275e913c5ae7df6dab2b447730421 Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Fri, 10 May 2024 11:03:22 +0300
Subject: [PATCH 045/142] Update tests.yml

---
 .github/workflows/tests.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 12ade26..ea38344 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -73,10 +73,10 @@ jobs:
       
       - name: Install Dependencies
         run: |
+          python -m pip install setuptools ipython jupyter
           conda install pytest cython numpy nbconvert setuptools
           conda install scipy pandas matplotlib pytables phconvert
-          python -m pip install seaborn ipython jupyter
-          python -m pip install lmfit pybroom
+          python -m pip install seaborn lmfit pybroom
       # - name: MacOS install hdf5 dependencies
       #   if: runner.os == 'macOS'
       #   run: |

From ccb565e47ce4425e5b95c9cad2fd94c4d3f44585 Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Fri, 10 May 2024 11:30:21 +0300
Subject: [PATCH 046/142] Update versioneer.py

---
 versioneer.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/versioneer.py b/versioneer.py
index 181766a..a0852d9 100644
--- a/versioneer.py
+++ b/versioneer.py
@@ -395,7 +395,7 @@ def get_config_from_root(root):
     # configparser.NoOptionError (if it lacks "VCS="). See the docstring at
     # the top of versioneer.py for instructions on writing your setup.cfg .
     setup_cfg = os.path.join(root, "setup.cfg")
-    parser = configparser.SafeConfigParser()
+    parser = configparser.RawConfigParser()
     with open(setup_cfg, "r") as f:
         parser.readfp(f)
     VCS = parser.get("versioneer", "VCS")  # mandatory

From 54cb83343af02052a26f25891245fca38a8a0312 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Fri, 10 May 2024 12:50:23 +0300
Subject: [PATCH 047/142] Upaded versioneer.py to v 0.19

---
 versioneer.py | 839 ++++++++++++++++++++++++++++++--------------------
 1 file changed, 498 insertions(+), 341 deletions(-)

diff --git a/versioneer.py b/versioneer.py
index a0852d9..5f8d8ec 100644
--- a/versioneer.py
+++ b/versioneer.py
@@ -1,21 +1,18 @@
 
-# Version: 0.15
+# Version: 0.19
+
+"""The Versioneer - like a rocketeer, but for versions.
 
-"""
 The Versioneer
 ==============
 
 * like a rocketeer, but for versions!
-* https://github.com/warner/python-versioneer
+* https://github.com/python-versioneer/python-versioneer
 * Brian Warner
 * License: Public Domain
-* Compatible With: python2.6, 2.7, 3.2, 3.3, 3.4, and pypy
-* [![Latest Version]
-(https://pypip.in/version/versioneer/badge.svg?style=flat)
-](https://pypi.python.org/pypi/versioneer/)
-* [![Build Status]
-(https://travis-ci.org/warner/python-versioneer.png?branch=master)
-](https://travis-ci.org/warner/python-versioneer)
+* Compatible with: Python 3.6, 3.7, 3.8, 3.9 and pypy3
+* [![Latest Version][pypi-image]][pypi-url]
+* [![Build Status][travis-image]][travis-url]
 
 This is a tool for managing a recorded version number in distutils-based
 python projects. The goal is to remove the tedious and error-prone "update
@@ -26,9 +23,10 @@
 
 ## Quick Install
 
-* `pip install versioneer` to somewhere to your $PATH
-* add a `[versioneer]` section to your setup.cfg (see below)
+* `pip install versioneer` to somewhere in your $PATH
+* add a `[versioneer]` section to your setup.cfg (see [Install](INSTALL.md))
 * run `versioneer install` in your source tree, commit the results
+* Verify version information with `python setup.py version`
 
 ## Version Identifiers
 
@@ -60,7 +58,7 @@
 for example `git describe --tags --dirty --always` reports things like
 "0.7-1-g574ab98-dirty" to indicate that the checkout is one revision past the
 0.7 tag, has a unique revision id of "574ab98", and is "dirty" (it has
-uncommitted changes.
+uncommitted changes).
 
 The version identifier is used for multiple purposes:
 
@@ -87,125 +85,7 @@
 
 ## Installation
 
-First, decide on values for the following configuration variables:
-
-* `VCS`: the version control system you use. Currently accepts "git".
-
-* `style`: the style of version string to be produced. See "Styles" below for
-  details. Defaults to "pep440", which looks like
-  `TAG[+DISTANCE.gSHORTHASH[.dirty]]`.
-
-* `versionfile_source`:
-
-  A project-relative pathname into which the generated version strings should
-  be written. This is usually a `_version.py` next to your project's main
-  `__init__.py` file, so it can be imported at runtime. If your project uses
-  `src/myproject/__init__.py`, this should be `src/myproject/_version.py`.
-  This file should be checked in to your VCS as usual: the copy created below
-  by `setup.py setup_versioneer` will include code that parses expanded VCS
-  keywords in generated tarballs. The 'build' and 'sdist' commands will
-  replace it with a copy that has just the calculated version string.
-
-  This must be set even if your project does not have any modules (and will
-  therefore never import `_version.py`), since "setup.py sdist" -based trees
-  still need somewhere to record the pre-calculated version strings. Anywhere
-  in the source tree should do. If there is a `__init__.py` next to your
-  `_version.py`, the `setup.py setup_versioneer` command (described below)
-  will append some `__version__`-setting assignments, if they aren't already
-  present.
-
-* `versionfile_build`:
-
-  Like `versionfile_source`, but relative to the build directory instead of
-  the source directory. These will differ when your setup.py uses
-  'package_dir='. If you have `package_dir={'myproject': 'src/myproject'}`,
-  then you will probably have `versionfile_build='myproject/_version.py'` and
-  `versionfile_source='src/myproject/_version.py'`.
-
-  If this is set to None, then `setup.py build` will not attempt to rewrite
-  any `_version.py` in the built tree. If your project does not have any
-  libraries (e.g. if it only builds a script), then you should use
-  `versionfile_build = None` and override `distutils.command.build_scripts`
-  to explicitly insert a copy of `versioneer.get_version()` into your
-  generated script.
-
-* `tag_prefix`:
-
-  a string, like 'PROJECTNAME-', which appears at the start of all VCS tags.
-  If your tags look like 'myproject-1.2.0', then you should use
-  tag_prefix='myproject-'. If you use unprefixed tags like '1.2.0', this
-  should be an empty string.
-
-* `parentdir_prefix`:
-
-  a optional string, frequently the same as tag_prefix, which appears at the
-  start of all unpacked tarball filenames. If your tarball unpacks into
-  'myproject-1.2.0', this should be 'myproject-'. To disable this feature,
-  just omit the field from your `setup.cfg`.
-
-This tool provides one script, named `versioneer`. That script has one mode,
-"install", which writes a copy of `versioneer.py` into the current directory
-and runs `versioneer.py setup` to finish the installation.
-
-To versioneer-enable your project:
-
-* 1: Modify your `setup.cfg`, adding a section named `[versioneer]` and
-  populating it with the configuration values you decided earlier (note that
-  the option names are not case-sensitive):
-
-  ````
-  [versioneer]
-  VCS = git
-  style = pep440
-  versionfile_source = src/myproject/_version.py
-  versionfile_build = myproject/_version.py
-  tag_prefix = ""
-  parentdir_prefix = myproject-
-  ````
-
-* 2: Run `versioneer install`. This will do the following:
-
-  * copy `versioneer.py` into the top of your source tree
-  * create `_version.py` in the right place (`versionfile_source`)
-  * modify your `__init__.py` (if one exists next to `_version.py`) to define
-    `__version__` (by calling a function from `_version.py`)
-  * modify your `MANIFEST.in` to include both `versioneer.py` and the
-    generated `_version.py` in sdist tarballs
-
-  `versioneer install` will complain about any problems it finds with your
-  `setup.py` or `setup.cfg`. Run it multiple times until you have fixed all
-  the problems.
-
-* 3: add a `import versioneer` to your setup.py, and add the following
-  arguments to the setup() call:
-
-        version=versioneer.get_version(),
-        cmdclass=versioneer.get_cmdclass(),
-
-* 4: commit these changes to your VCS. To make sure you won't forget,
-  `versioneer install` will mark everything it touched for addition using
-  `git add`. Don't forget to add `setup.py` and `setup.cfg` too.
-
-## Post-Installation Usage
-
-Once established, all uses of your tree from a VCS checkout should get the
-current version string. All generated tarballs should include an embedded
-version string (so users who unpack them will not need a VCS tool installed).
-
-If you distribute your project through PyPI, then the release process should
-boil down to two steps:
-
-* 1: git tag 1.0
-* 2: python setup.py register sdist upload
-
-If you distribute it through github (i.e. users use github to generate
-tarballs with `git archive`), the process is:
-
-* 1: git tag 1.0
-* 2: git push; git push --tags
-
-Versioneer will report "0+untagged.NUMCOMMITS.gHASH" until your tree has at
-least one tag in its history.
+See [INSTALL.md](./INSTALL.md) for detailed installation instructions.
 
 ## Version-String Flavors
 
@@ -226,6 +106,10 @@
 * `['full-revisionid']`: detailed revision identifier. For Git, this is the
   full SHA1 commit id, e.g. "1076c978a8d3cfc70f408fe5974aa6c092c949ac".
 
+* `['date']`: Date and time of the latest `HEAD` commit. For Git, it is the
+  commit date in ISO 8601 format. This will be None if the date is not
+  available.
+
 * `['dirty']`: a boolean, True if the tree has uncommitted changes. Note that
   this is only accurate if run in a VCS checkout, otherwise it is likely to
   be False or None
@@ -264,8 +148,8 @@
 software (exactly equal to a known tag), the identifier will only contain the
 stripped tag, e.g. "0.11".
 
-Other styles are available. See details.md in the Versioneer source tree for
-descriptions.
+Other styles are available. See [details.md](details.md) in the Versioneer
+source tree for descriptions.
 
 ## Debugging
 
@@ -275,47 +159,83 @@
 display the full contents of `get_versions()` (including the `error` string,
 which may help identify what went wrong).
 
-## Updating Versioneer
+## Known Limitations
 
-To upgrade your project to a new release of Versioneer, do the following:
+Some situations are known to cause problems for Versioneer. This details the
+most significant ones. More can be found on Github
+[issues page](https://github.com/python-versioneer/python-versioneer/issues).
 
-* install the new Versioneer (`pip install -U versioneer` or equivalent)
-* edit `setup.cfg`, if necessary, to include any new configuration settings
-  indicated by the release notes
-* re-run `versioneer install` in your source tree, to replace
-  `SRC/_version.py`
-* commit any changed files
+### Subprojects
+
+Versioneer has limited support for source trees in which `setup.py` is not in
+the root directory (e.g. `setup.py` and `.git/` are *not* siblings). The are
+two common reasons why `setup.py` might not be in the root:
+
+* Source trees which contain multiple subprojects, such as
+  [Buildbot](https://github.com/buildbot/buildbot), which contains both
+  "master" and "slave" subprojects, each with their own `setup.py`,
+  `setup.cfg`, and `tox.ini`. Projects like these produce multiple PyPI
+  distributions (and upload multiple independently-installable tarballs).
+* Source trees whose main purpose is to contain a C library, but which also
+  provide bindings to Python (and perhaps other languages) in subdirectories.
+
+Versioneer will look for `.git` in parent directories, and most operations
+should get the right version string. However `pip` and `setuptools` have bugs
+and implementation details which frequently cause `pip install .` from a
+subproject directory to fail to find a correct version string (so it usually
+defaults to `0+unknown`).
+
+`pip install --editable .` should work correctly. `setup.py install` might
+work too.
 
-### Upgrading to 0.15
+Pip-8.1.1 is known to have this problem, but hopefully it will get fixed in
+some later version.
 
-Starting with this version, Versioneer is configured with a `[versioneer]`
-section in your `setup.cfg` file. Earlier versions required the `setup.py` to
-set attributes on the `versioneer` module immediately after import. The new
-version will refuse to run (raising an exception during import) until you
-have provided the necessary `setup.cfg` section.
+[Bug #38](https://github.com/python-versioneer/python-versioneer/issues/38) is tracking
+this issue. The discussion in
+[PR #61](https://github.com/python-versioneer/python-versioneer/pull/61) describes the
+issue from the Versioneer side in more detail.
+[pip PR#3176](https://github.com/pypa/pip/pull/3176) and
+[pip PR#3615](https://github.com/pypa/pip/pull/3615) contain work to improve
+pip to let Versioneer work correctly.
 
-In addition, the Versioneer package provides an executable named
-`versioneer`, and the installation process is driven by running `versioneer
-install`. In 0.14 and earlier, the executable was named
-`versioneer-installer` and was run without an argument.
+Versioneer-0.16 and earlier only looked for a `.git` directory next to the
+`setup.cfg`, so subprojects were completely unsupported with those releases.
 
-### Upgrading to 0.14
+### Editable installs with setuptools <= 18.5
 
-0.14 changes the format of the version string. 0.13 and earlier used
-hyphen-separated strings like "0.11-2-g1076c97-dirty". 0.14 and beyond use a
-plus-separated "local version" section strings, with dot-separated
-components, like "0.11+2.g1076c97". PEP440-strict tools did not like the old
-format, but should be ok with the new one.
+`setup.py develop` and `pip install --editable .` allow you to install a
+project into a virtualenv once, then continue editing the source code (and
+test) without re-installing after every change.
 
-### Upgrading from 0.11 to 0.12
+"Entry-point scripts" (`setup(entry_points={"console_scripts": ..})`) are a
+convenient way to specify executable scripts that should be installed along
+with the python package.
 
-Nothing special.
+These both work as expected when using modern setuptools. When using
+setuptools-18.5 or earlier, however, certain operations will cause
+`pkg_resources.DistributionNotFound` errors when running the entrypoint
+script, which must be resolved by re-installing the package. This happens
+when the install happens with one version, then the egg_info data is
+regenerated while a different version is checked out. Many setup.py commands
+cause egg_info to be rebuilt (including `sdist`, `wheel`, and installing into
+a different virtualenv), so this can be surprising.
 
-### Upgrading from 0.10 to 0.11
+[Bug #83](https://github.com/python-versioneer/python-versioneer/issues/83) describes
+this one, but upgrading to a newer version of setuptools should probably
+resolve it.
 
-You must add a `versioneer.VCS = "git"` to your `setup.py` before re-running
-`setup.py setup_versioneer`. This will enable the use of additional
-version-control systems (SVN, etc) in the future.
+
+## Updating Versioneer
+
+To upgrade your project to a new release of Versioneer, do the following:
+
+* install the new Versioneer (`pip install -U versioneer` or equivalent)
+* edit `setup.cfg`, if necessary, to include any new configuration settings
+  indicated by the release notes. See [UPGRADING](./UPGRADING.md) for details.
+* re-run `versioneer install` in your source tree, to replace
+  `SRC/_version.py`
+* commit any changed files
 
 ## Future Directions
 
@@ -330,19 +250,30 @@
 direction and include code from all supported VCS systems, reducing the
 number of intermediate scripts.
 
+## Similar projects
+
+* [setuptools_scm](https://github.com/pypa/setuptools_scm/) - a non-vendored build-time
+  dependency
+* [minver](https://github.com/jbweston/miniver) - a lightweight reimplementation of
+  versioneer
 
 ## License
 
-To make Versioneer easier to embed, all its code is hereby released into the
-public domain. The `_version.py` that it creates is also in the public
-domain.
+To make Versioneer easier to embed, all its code is dedicated to the public
+domain. The `_version.py` that it creates is also in the public domain.
+Specifically, both are released under the Creative Commons "Public Domain
+Dedication" license (CC0-1.0), as described in
+https://creativecommons.org/publicdomain/zero/1.0/ .
+
+[pypi-image]: https://img.shields.io/pypi/v/versioneer.svg
+[pypi-url]: https://pypi.python.org/pypi/versioneer/
+[travis-image]:
+https://img.shields.io/travis/com/python-versioneer/python-versioneer.svg
+[travis-url]: https://travis-ci.com/github/python-versioneer/python-versioneer
 
 """
 
-try:
-    import configparser
-except ImportError:
-    import ConfigParser as configparser
+import configparser
 import errno
 import json
 import os
@@ -352,12 +283,15 @@
 
 
 class VersioneerConfig:
-    pass
+    """Container for Versioneer configuration parameters."""
 
 
 def get_root():
-    # we require that all commands are run from the project root, i.e. the
-    # directory that contains setup.py, setup.cfg, and versioneer.py .
+    """Get the project root directory.
+
+    We require that all commands are run from the project root, i.e. the
+    directory that contains setup.py, setup.cfg, and versioneer.py .
+    """
     root = os.path.realpath(os.path.abspath(os.getcwd()))
     setup_py = os.path.join(root, "setup.py")
     versioneer_py = os.path.join(root, "versioneer.py")
@@ -381,7 +315,9 @@ def get_root():
         # os.path.dirname(__file__), as that will find whichever
         # versioneer.py was first imported, even in later projects.
         me = os.path.realpath(os.path.abspath(__file__))
-        if os.path.splitext(me)[0] != os.path.splitext(versioneer_py)[0]:
+        me_dir = os.path.normcase(os.path.splitext(me)[0])
+        vsr_dir = os.path.normcase(os.path.splitext(versioneer_py)[0])
+        if me_dir != vsr_dir:
             print("Warning: build in %s is using versioneer.py from %s"
                   % (os.path.dirname(me), versioneer_py))
     except NameError:
@@ -390,14 +326,15 @@ def get_root():
 
 
 def get_config_from_root(root):
+    """Read the project setup.cfg file to determine Versioneer config."""
     # This might raise EnvironmentError (if setup.cfg is missing), or
     # configparser.NoSectionError (if it lacks a [versioneer] section), or
     # configparser.NoOptionError (if it lacks "VCS="). See the docstring at
     # the top of versioneer.py for instructions on writing your setup.cfg .
     setup_cfg = os.path.join(root, "setup.cfg")
-    parser = configparser.RawConfigParser()
+    parser = configparser.ConfigParser()
     with open(setup_cfg, "r") as f:
-        parser.readfp(f)
+        parser.read_file(f)
     VCS = parser.get("versioneer", "VCS")  # mandatory
 
     def get(parser, name):
@@ -410,13 +347,16 @@ def get(parser, name):
     cfg.versionfile_source = get(parser, "versionfile_source")
     cfg.versionfile_build = get(parser, "versionfile_build")
     cfg.tag_prefix = get(parser, "tag_prefix")
+    if cfg.tag_prefix in ("''", '""'):
+        cfg.tag_prefix = ""
     cfg.parentdir_prefix = get(parser, "parentdir_prefix")
     cfg.verbose = get(parser, "verbose")
     return cfg
 
 
 class NotThisMethod(Exception):
-    pass
+    """Exception raised if a method is not valid for the current scenario."""
+
 
 # these dictionaries contain VCS-specific tools
 LONG_VERSION_PY = {}
@@ -424,7 +364,9 @@ class NotThisMethod(Exception):
 
 
 def register_vcs_handler(vcs, method):  # decorator
+    """Create decorator to mark a method as the handler of a VCS."""
     def decorate(f):
+        """Store f in HANDLERS[vcs][method]."""
         if vcs not in HANDLERS:
             HANDLERS[vcs] = {}
         HANDLERS[vcs][method] = f
@@ -432,14 +374,17 @@ def decorate(f):
     return decorate
 
 
-def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False):
+def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False,
+                env=None):
+    """Call the given command(s)."""
     assert isinstance(commands, list)
     p = None
     for c in commands:
         try:
             dispcmd = str([c] + args)
             # remember shell=False, so use git.cmd on windows, not just git
-            p = subprocess.Popen([c] + args, cwd=cwd, stdout=subprocess.PIPE,
+            p = subprocess.Popen([c] + args, cwd=cwd, env=env,
+                                 stdout=subprocess.PIPE,
                                  stderr=(subprocess.PIPE if hide_stderr
                                          else None))
             break
@@ -450,20 +395,21 @@ def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False):
             if verbose:
                 print("unable to run %s" % dispcmd)
                 print(e)
-            return None
+            return None, None
     else:
         if verbose:
             print("unable to find command, tried %s" % (commands,))
-        return None
-    stdout = p.communicate()[0].strip()
-    if sys.version_info[0] >= 3:
-        stdout = stdout.decode()
+        return None, None
+    stdout = p.communicate()[0].strip().decode()
     if p.returncode != 0:
         if verbose:
             print("unable to run %s (error)" % dispcmd)
-        return None
-    return stdout
-LONG_VERSION_PY['git'] = '''
+            print("stdout was %s" % stdout)
+        return None, p.returncode
+    return stdout, p.returncode
+
+
+LONG_VERSION_PY['git'] = r'''
 # This file helps to compute a version number in source trees obtained from
 # git-archive tarball (such as those provided by githubs download-from-tag
 # feature). Distribution tarballs (built by setup.py sdist) and build
@@ -471,7 +417,9 @@ def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False):
 # that just contains the computed version number.
 
 # This file is released into the public domain. Generated by
-# versioneer-0.15 (https://github.com/warner/python-versioneer)
+# versioneer-0.19 (https://github.com/python-versioneer/python-versioneer)
+
+"""Git implementation of _version.py."""
 
 import errno
 import os
@@ -481,21 +429,24 @@ def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False):
 
 
 def get_keywords():
+    """Get the keywords needed to look up the version information."""
     # these strings will be replaced by git during git-archive.
     # setup.py/versioneer.py will grep for the variable names, so they must
     # each be defined on a line of their own. _version.py will just call
     # get_keywords().
     git_refnames = "%(DOLLAR)sFormat:%%d%(DOLLAR)s"
     git_full = "%(DOLLAR)sFormat:%%H%(DOLLAR)s"
-    keywords = {"refnames": git_refnames, "full": git_full}
+    git_date = "%(DOLLAR)sFormat:%%ci%(DOLLAR)s"
+    keywords = {"refnames": git_refnames, "full": git_full, "date": git_date}
     return keywords
 
 
 class VersioneerConfig:
-    pass
+    """Container for Versioneer configuration parameters."""
 
 
 def get_config():
+    """Create, populate and return the VersioneerConfig() object."""
     # these strings are filled in when 'setup.py versioneer' creates
     # _version.py
     cfg = VersioneerConfig()
@@ -509,7 +460,7 @@ def get_config():
 
 
 class NotThisMethod(Exception):
-    pass
+    """Exception raised if a method is not valid for the current scenario."""
 
 
 LONG_VERSION_PY = {}
@@ -517,7 +468,9 @@ class NotThisMethod(Exception):
 
 
 def register_vcs_handler(vcs, method):  # decorator
+    """Create decorator to mark a method as the handler of a VCS."""
     def decorate(f):
+        """Store f in HANDLERS[vcs][method]."""
         if vcs not in HANDLERS:
             HANDLERS[vcs] = {}
         HANDLERS[vcs][method] = f
@@ -525,14 +478,17 @@ def decorate(f):
     return decorate
 
 
-def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False):
+def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False,
+                env=None):
+    """Call the given command(s)."""
     assert isinstance(commands, list)
     p = None
     for c in commands:
         try:
             dispcmd = str([c] + args)
             # remember shell=False, so use git.cmd on windows, not just git
-            p = subprocess.Popen([c] + args, cwd=cwd, stdout=subprocess.PIPE,
+            p = subprocess.Popen([c] + args, cwd=cwd, env=env,
+                                 stdout=subprocess.PIPE,
                                  stderr=(subprocess.PIPE if hide_stderr
                                          else None))
             break
@@ -543,37 +499,48 @@ def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False):
             if verbose:
                 print("unable to run %%s" %% dispcmd)
                 print(e)
-            return None
+            return None, None
     else:
         if verbose:
             print("unable to find command, tried %%s" %% (commands,))
-        return None
-    stdout = p.communicate()[0].strip()
-    if sys.version_info[0] >= 3:
-        stdout = stdout.decode()
+        return None, None
+    stdout = p.communicate()[0].strip().decode()
     if p.returncode != 0:
         if verbose:
             print("unable to run %%s (error)" %% dispcmd)
-        return None
-    return stdout
+            print("stdout was %%s" %% stdout)
+        return None, p.returncode
+    return stdout, p.returncode
 
 
 def versions_from_parentdir(parentdir_prefix, root, verbose):
-    # Source tarballs conventionally unpack into a directory that includes
-    # both the project name and a version string.
-    dirname = os.path.basename(root)
-    if not dirname.startswith(parentdir_prefix):
-        if verbose:
-            print("guessing rootdir is '%%s', but '%%s' doesn't start with "
-                  "prefix '%%s'" %% (root, dirname, parentdir_prefix))
-        raise NotThisMethod("rootdir doesn't start with parentdir_prefix")
-    return {"version": dirname[len(parentdir_prefix):],
-            "full-revisionid": None,
-            "dirty": False, "error": None}
+    """Try to determine the version from the parent directory name.
+
+    Source tarballs conventionally unpack into a directory that includes both
+    the project name and a version string. We will also support searching up
+    two directory levels for an appropriately named parent directory
+    """
+    rootdirs = []
+
+    for i in range(3):
+        dirname = os.path.basename(root)
+        if dirname.startswith(parentdir_prefix):
+            return {"version": dirname[len(parentdir_prefix):],
+                    "full-revisionid": None,
+                    "dirty": False, "error": None, "date": None}
+        else:
+            rootdirs.append(root)
+            root = os.path.dirname(root)  # up a level
+
+    if verbose:
+        print("Tried directories %%s but none started with prefix %%s" %%
+              (str(rootdirs), parentdir_prefix))
+    raise NotThisMethod("rootdir doesn't start with parentdir_prefix")
 
 
 @register_vcs_handler("git", "get_keywords")
 def git_get_keywords(versionfile_abs):
+    """Extract version information from the given file."""
     # the code embedded in _version.py can just fetch the value of these
     # keywords. When used from setup.py, we don't want to import _version.py,
     # so we do it with a regexp instead. This function is not used from
@@ -590,6 +557,10 @@ def git_get_keywords(versionfile_abs):
                 mo = re.search(r'=\s*"(.*)"', line)
                 if mo:
                     keywords["full"] = mo.group(1)
+            if line.strip().startswith("git_date ="):
+                mo = re.search(r'=\s*"(.*)"', line)
+                if mo:
+                    keywords["date"] = mo.group(1)
         f.close()
     except EnvironmentError:
         pass
@@ -598,8 +569,22 @@ def git_get_keywords(versionfile_abs):
 
 @register_vcs_handler("git", "keywords")
 def git_versions_from_keywords(keywords, tag_prefix, verbose):
+    """Get version information from git keywords."""
     if not keywords:
         raise NotThisMethod("no keywords at all, weird")
+    date = keywords.get("date")
+    if date is not None:
+        # Use only the last line.  Previous lines may contain GPG signature
+        # information.
+        date = date.splitlines()[-1]
+
+        # git-2.2.0 added "%%cI", which expands to an ISO-8601 -compliant
+        # datestamp. However we prefer "%%ci" (which expands to an "ISO-8601
+        # -like" string, which we must then edit to make compliant), because
+        # it's been around since git-1.5.3, and it's too difficult to
+        # discover which version we're using, or to work around using an
+        # older one.
+        date = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
     refnames = keywords["refnames"].strip()
     if refnames.startswith("$Format"):
         if verbose:
@@ -620,7 +605,7 @@ def git_versions_from_keywords(keywords, tag_prefix, verbose):
         # "stabilization", as well as "HEAD" and "master".
         tags = set([r for r in refs if re.search(r'\d', r)])
         if verbose:
-            print("discarding '%%s', no digits" %% ",".join(refs-tags))
+            print("discarding '%%s', no digits" %% ",".join(refs - tags))
     if verbose:
         print("likely tags: %%s" %% ",".join(sorted(tags)))
     for ref in sorted(tags):
@@ -631,41 +616,46 @@ def git_versions_from_keywords(keywords, tag_prefix, verbose):
                 print("picking %%s" %% r)
             return {"version": r,
                     "full-revisionid": keywords["full"].strip(),
-                    "dirty": False, "error": None
-                    }
+                    "dirty": False, "error": None,
+                    "date": date}
     # no suitable tags, so version is "0+unknown", but full hex is still there
     if verbose:
         print("no suitable tags, using unknown + full revision id")
     return {"version": "0+unknown",
             "full-revisionid": keywords["full"].strip(),
-            "dirty": False, "error": "no suitable tags"}
+            "dirty": False, "error": "no suitable tags", "date": None}
 
 
 @register_vcs_handler("git", "pieces_from_vcs")
 def git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command):
-    # this runs 'git' from the root of the source tree. This only gets called
-    # if the git-archive 'subst' keywords were *not* expanded, and
-    # _version.py hasn't already been rewritten with a short version string,
-    # meaning we're inside a checked out source tree.
-
-    if not os.path.exists(os.path.join(root, ".git")):
-        if verbose:
-            print("no .git in %%s" %% root)
-        raise NotThisMethod("no .git directory")
+    """Get version from 'git describe' in the root of the source tree.
 
+    This only gets called if the git-archive 'subst' keywords were *not*
+    expanded, and _version.py hasn't already been rewritten with a short
+    version string, meaning we're inside a checked out source tree.
+    """
     GITS = ["git"]
     if sys.platform == "win32":
         GITS = ["git.cmd", "git.exe"]
-    # if there is a tag, this yields TAG-NUM-gHEX[-dirty]
-    # if there are no tags, this yields HEX[-dirty] (no NUM)
-    describe_out = run_command(GITS, ["describe", "--tags", "--dirty",
-                                      "--always", "--long"],
-                               cwd=root)
+
+    out, rc = run_command(GITS, ["rev-parse", "--git-dir"], cwd=root,
+                          hide_stderr=True)
+    if rc != 0:
+        if verbose:
+            print("Directory %%s not under git control" %% root)
+        raise NotThisMethod("'git rev-parse --git-dir' returned error")
+
+    # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty]
+    # if there isn't one, this yields HEX[-dirty] (no NUM)
+    describe_out, rc = run_command(GITS, ["describe", "--tags", "--dirty",
+                                          "--always", "--long",
+                                          "--match", "%%s*" %% tag_prefix],
+                                   cwd=root)
     # --long was added in git-1.5.5
     if describe_out is None:
         raise NotThisMethod("'git describe' failed")
     describe_out = describe_out.strip()
-    full_out = run_command(GITS, ["rev-parse", "HEAD"], cwd=root)
+    full_out, rc = run_command(GITS, ["rev-parse", "HEAD"], cwd=root)
     if full_out is None:
         raise NotThisMethod("'git rev-parse' failed")
     full_out = full_out.strip()
@@ -716,27 +706,37 @@ def git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command):
     else:
         # HEX: no tags
         pieces["closest-tag"] = None
-        count_out = run_command(GITS, ["rev-list", "HEAD", "--count"],
-                                cwd=root)
+        count_out, rc = run_command(GITS, ["rev-list", "HEAD", "--count"],
+                                    cwd=root)
         pieces["distance"] = int(count_out)  # total number of commits
 
+    # commit date: see ISO-8601 comment in git_versions_from_keywords()
+    date = run_command(GITS, ["show", "-s", "--format=%%ci", "HEAD"],
+                       cwd=root)[0].strip()
+    # Use only the last line.  Previous lines may contain GPG signature
+    # information.
+    date = date.splitlines()[-1]
+    pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
+
     return pieces
 
 
 def plus_or_dot(pieces):
+    """Return a + if we don't already have one, else return a ."""
     if "+" in pieces.get("closest-tag", ""):
         return "."
     return "+"
 
 
 def render_pep440(pieces):
-    # now build up version string, with post-release "local version
-    # identifier". Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you
-    # get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty
+    """Build up version string, with post-release "local version identifier".
 
-    # exceptions:
-    # 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]
+    Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you
+    get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty
 
+    Exceptions:
+    1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]
+    """
     if pieces["closest-tag"]:
         rendered = pieces["closest-tag"]
         if pieces["distance"] or pieces["dirty"]:
@@ -754,30 +754,31 @@ def render_pep440(pieces):
 
 
 def render_pep440_pre(pieces):
-    # TAG[.post.devDISTANCE] . No -dirty
-
-    # exceptions:
-    # 1: no tags. 0.post.devDISTANCE
+    """TAG[.post0.devDISTANCE] -- No -dirty.
 
+    Exceptions:
+    1: no tags. 0.post0.devDISTANCE
+    """
     if pieces["closest-tag"]:
         rendered = pieces["closest-tag"]
         if pieces["distance"]:
-            rendered += ".post.dev%%d" %% pieces["distance"]
+            rendered += ".post0.dev%%d" %% pieces["distance"]
     else:
         # exception #1
-        rendered = "0.post.dev%%d" %% pieces["distance"]
+        rendered = "0.post0.dev%%d" %% pieces["distance"]
     return rendered
 
 
 def render_pep440_post(pieces):
-    # TAG[.postDISTANCE[.dev0]+gHEX] . The ".dev0" means dirty. Note that
-    # .dev0 sorts backwards (a dirty tree will appear "older" than the
-    # corresponding clean one), but you shouldn't be releasing software with
-    # -dirty anyways.
+    """TAG[.postDISTANCE[.dev0]+gHEX] .
 
-    # exceptions:
-    # 1: no tags. 0.postDISTANCE[.dev0]
+    The ".dev0" means dirty. Note that .dev0 sorts backwards
+    (a dirty tree will appear "older" than the corresponding clean one),
+    but you shouldn't be releasing software with -dirty anyways.
 
+    Exceptions:
+    1: no tags. 0.postDISTANCE[.dev0]
+    """
     if pieces["closest-tag"]:
         rendered = pieces["closest-tag"]
         if pieces["distance"] or pieces["dirty"]:
@@ -796,11 +797,13 @@ def render_pep440_post(pieces):
 
 
 def render_pep440_old(pieces):
-    # TAG[.postDISTANCE[.dev0]] . The ".dev0" means dirty.
+    """TAG[.postDISTANCE[.dev0]] .
 
-    # exceptions:
-    # 1: no tags. 0.postDISTANCE[.dev0]
+    The ".dev0" means dirty.
 
+    Exceptions:
+    1: no tags. 0.postDISTANCE[.dev0]
+    """
     if pieces["closest-tag"]:
         rendered = pieces["closest-tag"]
         if pieces["distance"] or pieces["dirty"]:
@@ -816,12 +819,13 @@ def render_pep440_old(pieces):
 
 
 def render_git_describe(pieces):
-    # TAG[-DISTANCE-gHEX][-dirty], like 'git describe --tags --dirty
-    # --always'
+    """TAG[-DISTANCE-gHEX][-dirty].
 
-    # exceptions:
-    # 1: no tags. HEX[-dirty]  (note: no 'g' prefix)
+    Like 'git describe --tags --dirty --always'.
 
+    Exceptions:
+    1: no tags. HEX[-dirty]  (note: no 'g' prefix)
+    """
     if pieces["closest-tag"]:
         rendered = pieces["closest-tag"]
         if pieces["distance"]:
@@ -835,12 +839,14 @@ def render_git_describe(pieces):
 
 
 def render_git_describe_long(pieces):
-    # TAG-DISTANCE-gHEX[-dirty], like 'git describe --tags --dirty
-    # --always -long'. The distance/hash is unconditional.
+    """TAG-DISTANCE-gHEX[-dirty].
 
-    # exceptions:
-    # 1: no tags. HEX[-dirty]  (note: no 'g' prefix)
+    Like 'git describe --tags --dirty --always -long'.
+    The distance/hash is unconditional.
 
+    Exceptions:
+    1: no tags. HEX[-dirty]  (note: no 'g' prefix)
+    """
     if pieces["closest-tag"]:
         rendered = pieces["closest-tag"]
         rendered += "-%%d-g%%s" %% (pieces["distance"], pieces["short"])
@@ -853,11 +859,13 @@ def render_git_describe_long(pieces):
 
 
 def render(pieces, style):
+    """Render the given version pieces into the requested style."""
     if pieces["error"]:
         return {"version": "unknown",
                 "full-revisionid": pieces.get("long"),
                 "dirty": None,
-                "error": pieces["error"]}
+                "error": pieces["error"],
+                "date": None}
 
     if not style or style == "default":
         style = "pep440"  # the default
@@ -878,10 +886,12 @@ def render(pieces, style):
         raise ValueError("unknown style '%%s'" %% style)
 
     return {"version": rendered, "full-revisionid": pieces["long"],
-            "dirty": pieces["dirty"], "error": None}
+            "dirty": pieces["dirty"], "error": None,
+            "date": pieces.get("date")}
 
 
 def get_versions():
+    """Get version information or return default if unable to do so."""
     # I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have
     # __file__, we can work backwards from there to the root. Some
     # py2exe/bbfreeze/non-CPython implementations don't do __file__, in which
@@ -906,7 +916,8 @@ def get_versions():
     except NameError:
         return {"version": "0+unknown", "full-revisionid": None,
                 "dirty": None,
-                "error": "unable to find root of source tree"}
+                "error": "unable to find root of source tree",
+                "date": None}
 
     try:
         pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose)
@@ -922,12 +933,13 @@ def get_versions():
 
     return {"version": "0+unknown", "full-revisionid": None,
             "dirty": None,
-            "error": "unable to compute version"}
+            "error": "unable to compute version", "date": None}
 '''
 
 
 @register_vcs_handler("git", "get_keywords")
 def git_get_keywords(versionfile_abs):
+    """Extract version information from the given file."""
     # the code embedded in _version.py can just fetch the value of these
     # keywords. When used from setup.py, we don't want to import _version.py,
     # so we do it with a regexp instead. This function is not used from
@@ -944,6 +956,10 @@ def git_get_keywords(versionfile_abs):
                 mo = re.search(r'=\s*"(.*)"', line)
                 if mo:
                     keywords["full"] = mo.group(1)
+            if line.strip().startswith("git_date ="):
+                mo = re.search(r'=\s*"(.*)"', line)
+                if mo:
+                    keywords["date"] = mo.group(1)
         f.close()
     except EnvironmentError:
         pass
@@ -952,8 +968,22 @@ def git_get_keywords(versionfile_abs):
 
 @register_vcs_handler("git", "keywords")
 def git_versions_from_keywords(keywords, tag_prefix, verbose):
+    """Get version information from git keywords."""
     if not keywords:
         raise NotThisMethod("no keywords at all, weird")
+    date = keywords.get("date")
+    if date is not None:
+        # Use only the last line.  Previous lines may contain GPG signature
+        # information.
+        date = date.splitlines()[-1]
+
+        # git-2.2.0 added "%cI", which expands to an ISO-8601 -compliant
+        # datestamp. However we prefer "%ci" (which expands to an "ISO-8601
+        # -like" string, which we must then edit to make compliant), because
+        # it's been around since git-1.5.3, and it's too difficult to
+        # discover which version we're using, or to work around using an
+        # older one.
+        date = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
     refnames = keywords["refnames"].strip()
     if refnames.startswith("$Format"):
         if verbose:
@@ -974,7 +1004,7 @@ def git_versions_from_keywords(keywords, tag_prefix, verbose):
         # "stabilization", as well as "HEAD" and "master".
         tags = set([r for r in refs if re.search(r'\d', r)])
         if verbose:
-            print("discarding '%s', no digits" % ",".join(refs-tags))
+            print("discarding '%s', no digits" % ",".join(refs - tags))
     if verbose:
         print("likely tags: %s" % ",".join(sorted(tags)))
     for ref in sorted(tags):
@@ -985,41 +1015,46 @@ def git_versions_from_keywords(keywords, tag_prefix, verbose):
                 print("picking %s" % r)
             return {"version": r,
                     "full-revisionid": keywords["full"].strip(),
-                    "dirty": False, "error": None
-                    }
+                    "dirty": False, "error": None,
+                    "date": date}
     # no suitable tags, so version is "0+unknown", but full hex is still there
     if verbose:
         print("no suitable tags, using unknown + full revision id")
     return {"version": "0+unknown",
             "full-revisionid": keywords["full"].strip(),
-            "dirty": False, "error": "no suitable tags"}
+            "dirty": False, "error": "no suitable tags", "date": None}
 
 
 @register_vcs_handler("git", "pieces_from_vcs")
 def git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command):
-    # this runs 'git' from the root of the source tree. This only gets called
-    # if the git-archive 'subst' keywords were *not* expanded, and
-    # _version.py hasn't already been rewritten with a short version string,
-    # meaning we're inside a checked out source tree.
-
-    if not os.path.exists(os.path.join(root, ".git")):
-        if verbose:
-            print("no .git in %s" % root)
-        raise NotThisMethod("no .git directory")
+    """Get version from 'git describe' in the root of the source tree.
 
+    This only gets called if the git-archive 'subst' keywords were *not*
+    expanded, and _version.py hasn't already been rewritten with a short
+    version string, meaning we're inside a checked out source tree.
+    """
     GITS = ["git"]
     if sys.platform == "win32":
         GITS = ["git.cmd", "git.exe"]
-    # if there is a tag, this yields TAG-NUM-gHEX[-dirty]
-    # if there are no tags, this yields HEX[-dirty] (no NUM)
-    describe_out = run_command(GITS, ["describe", "--tags", "--dirty",
-                                      "--always", "--long"],
-                               cwd=root)
+
+    out, rc = run_command(GITS, ["rev-parse", "--git-dir"], cwd=root,
+                          hide_stderr=True)
+    if rc != 0:
+        if verbose:
+            print("Directory %s not under git control" % root)
+        raise NotThisMethod("'git rev-parse --git-dir' returned error")
+
+    # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty]
+    # if there isn't one, this yields HEX[-dirty] (no NUM)
+    describe_out, rc = run_command(GITS, ["describe", "--tags", "--dirty",
+                                          "--always", "--long",
+                                          "--match", "%s*" % tag_prefix],
+                                   cwd=root)
     # --long was added in git-1.5.5
     if describe_out is None:
         raise NotThisMethod("'git describe' failed")
     describe_out = describe_out.strip()
-    full_out = run_command(GITS, ["rev-parse", "HEAD"], cwd=root)
+    full_out, rc = run_command(GITS, ["rev-parse", "HEAD"], cwd=root)
     if full_out is None:
         raise NotThisMethod("'git rev-parse' failed")
     full_out = full_out.strip()
@@ -1070,14 +1105,27 @@ def git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command):
     else:
         # HEX: no tags
         pieces["closest-tag"] = None
-        count_out = run_command(GITS, ["rev-list", "HEAD", "--count"],
-                                cwd=root)
+        count_out, rc = run_command(GITS, ["rev-list", "HEAD", "--count"],
+                                    cwd=root)
         pieces["distance"] = int(count_out)  # total number of commits
 
+    # commit date: see ISO-8601 comment in git_versions_from_keywords()
+    date = run_command(GITS, ["show", "-s", "--format=%ci", "HEAD"],
+                       cwd=root)[0].strip()
+    # Use only the last line.  Previous lines may contain GPG signature
+    # information.
+    date = date.splitlines()[-1]
+    pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
+
     return pieces
 
 
 def do_vcs_install(manifest_in, versionfile_source, ipy):
+    """Git-specific installation logic for Versioneer.
+
+    For Git, this means creating/changing .gitattributes to mark _version.py
+    for export-subst keyword substitution.
+    """
     GITS = ["git"]
     if sys.platform == "win32":
         GITS = ["git.cmd", "git.exe"]
@@ -1111,26 +1159,37 @@ def do_vcs_install(manifest_in, versionfile_source, ipy):
 
 
 def versions_from_parentdir(parentdir_prefix, root, verbose):
-    # Source tarballs conventionally unpack into a directory that includes
-    # both the project name and a version string.
-    dirname = os.path.basename(root)
-    if not dirname.startswith(parentdir_prefix):
-        if verbose:
-            print("guessing rootdir is '%s', but '%s' doesn't start with "
-                  "prefix '%s'" % (root, dirname, parentdir_prefix))
-        raise NotThisMethod("rootdir doesn't start with parentdir_prefix")
-    return {"version": dirname[len(parentdir_prefix):],
-            "full-revisionid": None,
-            "dirty": False, "error": None}
+    """Try to determine the version from the parent directory name.
+
+    Source tarballs conventionally unpack into a directory that includes both
+    the project name and a version string. We will also support searching up
+    two directory levels for an appropriately named parent directory
+    """
+    rootdirs = []
+
+    for i in range(3):
+        dirname = os.path.basename(root)
+        if dirname.startswith(parentdir_prefix):
+            return {"version": dirname[len(parentdir_prefix):],
+                    "full-revisionid": None,
+                    "dirty": False, "error": None, "date": None}
+        else:
+            rootdirs.append(root)
+            root = os.path.dirname(root)  # up a level
+
+    if verbose:
+        print("Tried directories %s but none started with prefix %s" %
+              (str(rootdirs), parentdir_prefix))
+    raise NotThisMethod("rootdir doesn't start with parentdir_prefix")
+
 
 SHORT_VERSION_PY = """
-# This file was generated by 'versioneer.py' (0.15) from
+# This file was generated by 'versioneer.py' (0.19) from
 # revision-control system data, or from the parent directory name of an
 # unpacked source archive. Distribution tarballs contain a pre-generated copy
 # of this file.
 
 import json
-import sys
 
 version_json = '''
 %s
@@ -1143,6 +1202,7 @@ def get_versions():
 
 
 def versions_from_file(filename):
+    """Try to determine the version from _version.py if present."""
     try:
         with open(filename) as f:
             contents = f.read()
@@ -1150,12 +1210,16 @@ def versions_from_file(filename):
         raise NotThisMethod("unable to read _version.py")
     mo = re.search(r"version_json = '''\n(.*)'''  # END VERSION_JSON",
                    contents, re.M | re.S)
+    if not mo:
+        mo = re.search(r"version_json = '''\r\n(.*)'''  # END VERSION_JSON",
+                       contents, re.M | re.S)
     if not mo:
         raise NotThisMethod("no version_json in _version.py")
     return json.loads(mo.group(1))
 
 
 def write_to_version_file(filename, versions):
+    """Write the given version number to the given _version.py file."""
     os.unlink(filename)
     contents = json.dumps(versions, sort_keys=True,
                           indent=1, separators=(",", ": "))
@@ -1166,19 +1230,21 @@ def write_to_version_file(filename, versions):
 
 
 def plus_or_dot(pieces):
+    """Return a + if we don't already have one, else return a ."""
     if "+" in pieces.get("closest-tag", ""):
         return "."
     return "+"
 
 
 def render_pep440(pieces):
-    # now build up version string, with post-release "local version
-    # identifier". Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you
-    # get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty
+    """Build up version string, with post-release "local version identifier".
 
-    # exceptions:
-    # 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]
+    Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you
+    get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty
 
+    Exceptions:
+    1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]
+    """
     if pieces["closest-tag"]:
         rendered = pieces["closest-tag"]
         if pieces["distance"] or pieces["dirty"]:
@@ -1196,30 +1262,31 @@ def render_pep440(pieces):
 
 
 def render_pep440_pre(pieces):
-    # TAG[.post.devDISTANCE] . No -dirty
-
-    # exceptions:
-    # 1: no tags. 0.post.devDISTANCE
+    """TAG[.post0.devDISTANCE] -- No -dirty.
 
+    Exceptions:
+    1: no tags. 0.post0.devDISTANCE
+    """
     if pieces["closest-tag"]:
         rendered = pieces["closest-tag"]
         if pieces["distance"]:
-            rendered += ".post.dev%d" % pieces["distance"]
+            rendered += ".post0.dev%d" % pieces["distance"]
     else:
         # exception #1
-        rendered = "0.post.dev%d" % pieces["distance"]
+        rendered = "0.post0.dev%d" % pieces["distance"]
     return rendered
 
 
 def render_pep440_post(pieces):
-    # TAG[.postDISTANCE[.dev0]+gHEX] . The ".dev0" means dirty. Note that
-    # .dev0 sorts backwards (a dirty tree will appear "older" than the
-    # corresponding clean one), but you shouldn't be releasing software with
-    # -dirty anyways.
+    """TAG[.postDISTANCE[.dev0]+gHEX] .
 
-    # exceptions:
-    # 1: no tags. 0.postDISTANCE[.dev0]
+    The ".dev0" means dirty. Note that .dev0 sorts backwards
+    (a dirty tree will appear "older" than the corresponding clean one),
+    but you shouldn't be releasing software with -dirty anyways.
 
+    Exceptions:
+    1: no tags. 0.postDISTANCE[.dev0]
+    """
     if pieces["closest-tag"]:
         rendered = pieces["closest-tag"]
         if pieces["distance"] or pieces["dirty"]:
@@ -1238,11 +1305,13 @@ def render_pep440_post(pieces):
 
 
 def render_pep440_old(pieces):
-    # TAG[.postDISTANCE[.dev0]] . The ".dev0" means dirty.
+    """TAG[.postDISTANCE[.dev0]] .
 
-    # exceptions:
-    # 1: no tags. 0.postDISTANCE[.dev0]
+    The ".dev0" means dirty.
 
+    Exceptions:
+    1: no tags. 0.postDISTANCE[.dev0]
+    """
     if pieces["closest-tag"]:
         rendered = pieces["closest-tag"]
         if pieces["distance"] or pieces["dirty"]:
@@ -1258,12 +1327,13 @@ def render_pep440_old(pieces):
 
 
 def render_git_describe(pieces):
-    # TAG[-DISTANCE-gHEX][-dirty], like 'git describe --tags --dirty
-    # --always'
+    """TAG[-DISTANCE-gHEX][-dirty].
 
-    # exceptions:
-    # 1: no tags. HEX[-dirty]  (note: no 'g' prefix)
+    Like 'git describe --tags --dirty --always'.
 
+    Exceptions:
+    1: no tags. HEX[-dirty]  (note: no 'g' prefix)
+    """
     if pieces["closest-tag"]:
         rendered = pieces["closest-tag"]
         if pieces["distance"]:
@@ -1277,12 +1347,14 @@ def render_git_describe(pieces):
 
 
 def render_git_describe_long(pieces):
-    # TAG-DISTANCE-gHEX[-dirty], like 'git describe --tags --dirty
-    # --always -long'. The distance/hash is unconditional.
+    """TAG-DISTANCE-gHEX[-dirty].
 
-    # exceptions:
-    # 1: no tags. HEX[-dirty]  (note: no 'g' prefix)
+    Like 'git describe --tags --dirty --always -long'.
+    The distance/hash is unconditional.
 
+    Exceptions:
+    1: no tags. HEX[-dirty]  (note: no 'g' prefix)
+    """
     if pieces["closest-tag"]:
         rendered = pieces["closest-tag"]
         rendered += "-%d-g%s" % (pieces["distance"], pieces["short"])
@@ -1295,11 +1367,13 @@ def render_git_describe_long(pieces):
 
 
 def render(pieces, style):
+    """Render the given version pieces into the requested style."""
     if pieces["error"]:
         return {"version": "unknown",
                 "full-revisionid": pieces.get("long"),
                 "dirty": None,
-                "error": pieces["error"]}
+                "error": pieces["error"],
+                "date": None}
 
     if not style or style == "default":
         style = "pep440"  # the default
@@ -1320,16 +1394,19 @@ def render(pieces, style):
         raise ValueError("unknown style '%s'" % style)
 
     return {"version": rendered, "full-revisionid": pieces["long"],
-            "dirty": pieces["dirty"], "error": None}
+            "dirty": pieces["dirty"], "error": None,
+            "date": pieces.get("date")}
 
 
 class VersioneerBadRootError(Exception):
-    pass
+    """The project root directory is unknown or missing key files."""
 
 
 def get_versions(verbose=False):
-    # returns dict with two keys: 'version' and 'full'
+    """Get the project version from whatever source is available.
 
+    Returns dict with two keys: 'version' and 'full'.
+    """
     if "versioneer" in sys.modules:
         # see the discussion in cmdclass.py:get_cmdclass()
         del sys.modules["versioneer"]
@@ -1397,14 +1474,21 @@ def get_versions(verbose=False):
         print("unable to compute version")
 
     return {"version": "0+unknown", "full-revisionid": None,
-            "dirty": None, "error": "unable to compute version"}
+            "dirty": None, "error": "unable to compute version",
+            "date": None}
 
 
 def get_version():
+    """Get the short version string for this project."""
     return get_versions()["version"]
 
 
-def get_cmdclass():
+def get_cmdclass(cmdclass=None):
+    """Get the custom setuptools/distutils subclasses used by Versioneer.
+
+    If the package uses a different cmdclass (e.g. one from numpy), it
+    should be provide as an argument.
+    """
     if "versioneer" in sys.modules:
         del sys.modules["versioneer"]
         # this fixes the "python setup.py develop" case (also 'install' and
@@ -1418,9 +1502,9 @@ def get_cmdclass():
         # parent is protected against the child's "import versioneer". By
         # removing ourselves from sys.modules here, before the child build
         # happens, we protect the child from the parent's versioneer too.
-        # Also see https://github.com/warner/python-versioneer/issues/52
+        # Also see https://github.com/python-versioneer/python-versioneer/issues/52
 
-    cmds = {}
+    cmds = {} if cmdclass is None else cmdclass.copy()
 
     # we add "version" to both distutils and setuptools
     from distutils.core import Command
@@ -1441,6 +1525,7 @@ def run(self):
             print("Version: %s" % vers["version"])
             print(" full-revisionid: %s" % vers.get("full-revisionid"))
             print(" dirty: %s" % vers.get("dirty"))
+            print(" date: %s" % vers.get("date"))
             if vers["error"]:
                 print(" error: %s" % vers["error"])
     cmds["version"] = cmd_version
@@ -1454,8 +1539,19 @@ def run(self):
     #  setuptools/bdist_egg -> distutils/install_lib -> build_py
     #  setuptools/install -> bdist_egg ->..
     #  setuptools/develop -> ?
-
-    from distutils.command.build_py import build_py as _build_py
+    #  pip install:
+    #   copies source tree to a tempdir before running egg_info/etc
+    #   if .git isn't copied too, 'git describe' will fail
+    #   then does setup.py bdist_wheel, or sometimes setup.py install
+    #  setup.py egg_info -> ?
+
+    # we override different "build_py" commands for both environments
+    if 'build_py' in cmds:
+        _build_py = cmds['build_py']
+    elif "setuptools" in sys.modules:
+        from setuptools.command.build_py import build_py as _build_py
+    else:
+        from distutils.command.build_py import build_py as _build_py
 
     class cmd_build_py(_build_py):
         def run(self):
@@ -1472,8 +1568,39 @@ def run(self):
                 write_to_version_file(target_versionfile, versions)
     cmds["build_py"] = cmd_build_py
 
+    if "setuptools" in sys.modules:
+        from setuptools.command.build_ext import build_ext as _build_ext
+    else:
+        from distutils.command.build_ext import build_ext as _build_ext
+
+    class cmd_build_ext(_build_ext):
+        def run(self):
+            root = get_root()
+            cfg = get_config_from_root(root)
+            versions = get_versions()
+            _build_ext.run(self)
+            if self.inplace:
+                # build_ext --inplace will only build extensions in
+                # build/lib<..> dir with no _version.py to write to.
+                # As in place builds will already have a _version.py
+                # in the module dir, we do not need to write one.
+                return
+            # now locate _version.py in the new build/ directory and replace
+            # it with an updated value
+            target_versionfile = os.path.join(self.build_lib,
+                                              cfg.versionfile_source)
+            print("UPDATING %s" % target_versionfile)
+            write_to_version_file(target_versionfile, versions)
+    cmds["build_ext"] = cmd_build_ext
+
     if "cx_Freeze" in sys.modules:  # cx_freeze enabled?
         from cx_Freeze.dist import build_exe as _build_exe
+        # nczeczulin reports that py2exe won't like the pep440-style string
+        # as FILEVERSION, but it can be used for PRODUCTVERSION, e.g.
+        # setup(console=[{
+        #   "version": versioneer.get_version().split("+", 1)[0], # FILEVERSION
+        #   "product_version": versioneer.get_version(),
+        #   ...
 
         class cmd_build_exe(_build_exe):
             def run(self):
@@ -1498,8 +1625,35 @@ def run(self):
         cmds["build_exe"] = cmd_build_exe
         del cmds["build_py"]
 
+    if 'py2exe' in sys.modules:  # py2exe enabled?
+        from py2exe.distutils_buildexe import py2exe as _py2exe
+
+        class cmd_py2exe(_py2exe):
+            def run(self):
+                root = get_root()
+                cfg = get_config_from_root(root)
+                versions = get_versions()
+                target_versionfile = cfg.versionfile_source
+                print("UPDATING %s" % target_versionfile)
+                write_to_version_file(target_versionfile, versions)
+
+                _py2exe.run(self)
+                os.unlink(target_versionfile)
+                with open(cfg.versionfile_source, "w") as f:
+                    LONG = LONG_VERSION_PY[cfg.VCS]
+                    f.write(LONG %
+                            {"DOLLAR": "$",
+                             "STYLE": cfg.style,
+                             "TAG_PREFIX": cfg.tag_prefix,
+                             "PARENTDIR_PREFIX": cfg.parentdir_prefix,
+                             "VERSIONFILE_SOURCE": cfg.versionfile_source,
+                             })
+        cmds["py2exe"] = cmd_py2exe
+
     # we override different "sdist" commands for both environments
-    if "setuptools" in sys.modules:
+    if 'sdist' in cmds:
+        _sdist = cmds['sdist']
+    elif "setuptools" in sys.modules:
         from setuptools.command.sdist import sdist as _sdist
     else:
         from distutils.command.sdist import sdist as _sdist
@@ -1538,7 +1692,7 @@ def make_release_tree(self, base_dir, files):
  style = pep440
  versionfile_source = src/myproject/_version.py
  versionfile_build = myproject/_version.py
- tag_prefix = ""
+ tag_prefix =
  parentdir_prefix = myproject-
 
 You will also need to edit your setup.py to use the results:
@@ -1574,6 +1728,7 @@ def make_release_tree(self, base_dir, files):
 
 
 def do_setup():
+    """Do main VCS-independent setup function for installing Versioneer."""
     root = get_root()
     try:
         cfg = get_config_from_root(root)
@@ -1648,13 +1803,14 @@ def do_setup():
         print(" versionfile_source already in MANIFEST.in")
 
     # Make VCS-specific changes. For git, this means creating/changing
-    # .gitattributes to mark _version.py for export-time keyword
+    # .gitattributes to mark _version.py for export-subst keyword
     # substitution.
     do_vcs_install(manifest_in, cfg.versionfile_source, ipy)
     return 0
 
 
 def scan_setup_py():
+    """Validate the contents of setup.py against Versioneer's expectations."""
     found = set()
     setters = False
     errors = 0
@@ -1689,10 +1845,11 @@ def scan_setup_py():
         errors += 1
     return errors
 
+
 if __name__ == "__main__":
     cmd = sys.argv[1]
     if cmd == "setup":
         errors = do_setup()
         errors += scan_setup_py()
         if errors:
-            sys.exit(1)
+            sys.exit(1)
\ No newline at end of file

From bd87a40b1684cd3c40c172fb6659ccaf9452594f Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Sun, 12 May 2024 06:43:16 +0300
Subject: [PATCH 048/142] Update tests.yml

---
 .github/workflows/tests.yml | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index ea38344..fe2f3ca 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -24,8 +24,8 @@ jobs:
             python-version: "3.8"
           # - os: ubuntu-latest
           #   python-version: "3.9"
-          # - os: ubuntu-latest
-          #   python-version: "3.10"
+          - os: ubuntu-latest
+            python-version: "3.10"
           # - os: ubuntu-latest
           #   python-version: "3.11"
           # - os: windows-latest
@@ -44,8 +44,8 @@ jobs:
           #   python-version: "3.6"
           # - os: macOS-12
           #   python-version: "3.7"
-          - os: macOS-latest
-            python-version: "3.8"
+          # - os: macOS-latest
+          #   python-version: "3.8"
           # - os: macOS-latest
           #   python-version: "3.9"
           # - os: macOS-latest

From c3bac41c4c780728b41d2a2ae1e8e32b977a3c8b Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Sun, 12 May 2024 10:09:39 +0300
Subject: [PATCH 049/142] Update tests.yml

---
 .github/workflows/tests.yml | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index fe2f3ca..b3613e2 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -73,8 +73,8 @@ jobs:
       
       - name: Install Dependencies
         run: |
-          python -m pip install setuptools ipython jupyter
-          conda install pytest cython numpy nbconvert setuptools
+          python -m pip install setuptools ipython jupyter pytest
+          conda install cython numpy nbconvert setuptools
           conda install scipy pandas matplotlib pytables phconvert
           python -m pip install seaborn lmfit pybroom
       # - name: MacOS install hdf5 dependencies
@@ -89,7 +89,6 @@ jobs:
       #     python -m pip install jupyter nbconvert lmfit phconvert pybroom
       - name: Install project
         run: |
-          python setup.py sdist
           python -m pip install .
       - name: Download files Unix
         if: runner.os != 'Windows'
@@ -111,5 +110,4 @@ jobs:
           curl.exe --output HP3_TE300_SPC630.hdf5 --url https://zenodo.org/record/5902313/files/HP3_TE300_SPC630.hdf5
       - name: Test project
         run: |
-          python fretbursts/tests/nbrun.py notebooks
           python -m pytest

From 6b90262684021726a51f65a346eea88ecd6ca5c8 Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Sun, 12 May 2024 11:52:48 +0300
Subject: [PATCH 050/142] Update tests.yml

---
 .github/workflows/tests.yml | 37 +++++++++++++++++++------------------
 1 file changed, 19 insertions(+), 18 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index b3613e2..83ddf0b 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -90,24 +90,25 @@ jobs:
       - name: Install project
         run: |
           python -m pip install .
-      - name: Download files Unix
-        if: runner.os != 'Windows'
-        run: |
-          wget -N http://files.figshare.com/2182604/12d_New_30p_320mW_steer_3.hdf5
-          wget -N http://files.figshare.com/2182601/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5
-          wget -N https://zenodo.org/record/5902313/files/HP3_TE150_SPC630.hdf5
-          wget -N https://zenodo.org/record/5902313/files/HP3_TE200_SPC630.hdf5
-          wget -N https://zenodo.org/record/5902313/files/HP3_TE250_SPC630.hdf5
-          wget -N https://zenodo.org/record/5902313/files/HP3_TE300_SPC630.hdf5
-      - name: Downlaod files Windows
-        if: runner.os == 'Windows'
-        run: |
-          curl.exe --output 2182604/12d_New_30p_320mW_steer_3.hdf5 --url http://files.figshare.com/2182604/12d_New_30p_320mW_steer_3.hdf5
-          curl.exe --output 0023uLRpitc_NTP_20dT_0.5GndCl.hdf5 --url http://files.figshare.com/2182601/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5
-          curl.exe --output HP3_TE150_SPC630.hdf5 --url https://zenodo.org/record/5902313/files/HP3_TE150_SPC630.hdf5
-          curl.exe --output HP3_TE200_SPC630.hdf5 --url https://zenodo.org/record/5902313/files/HP3_TE200_SPC630.hdf5
-          curl.exe --output HP3_TE250_SPC630.hdf5 --url https://zenodo.org/record/5902313/files/HP3_TE250_SPC630.hdf5
-          curl.exe --output HP3_TE300_SPC630.hdf5 --url https://zenodo.org/record/5902313/files/HP3_TE300_SPC630.hdf5
+      # - name: Download files Unix
+      #   if: runner.os != 'Windows'
+      #   run: |
+      #     wget -N http://files.figshare.com/2182604/12d_New_30p_320mW_steer_3.hdf5
+      #     wget -N http://files.figshare.com/2182601/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5
+      #     wget -N https://zenodo.org/record/5902313/files/HP3_TE150_SPC630.hdf5
+      #     wget -N https://zenodo.org/record/5902313/files/HP3_TE200_SPC630.hdf5
+      #     wget -N https://zenodo.org/record/5902313/files/HP3_TE250_SPC630.hdf5
+      #     wget -N https://zenodo.org/record/5902313/files/HP3_TE300_SPC630.hdf5
+      # - name: Downlaod files Windows
+      #   if: runner.os == 'Windows'
+      #   run: |
+      #     curl.exe --output 2182604/12d_New_30p_320mW_steer_3.hdf5 --url http://files.figshare.com/2182604/12d_New_30p_320mW_steer_3.hdf5
+      #     curl.exe --output 0023uLRpitc_NTP_20dT_0.5GndCl.hdf5 --url http://files.figshare.com/2182601/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5
+      #     curl.exe --output HP3_TE150_SPC630.hdf5 --url https://zenodo.org/record/5902313/files/HP3_TE150_SPC630.hdf5
+      #     curl.exe --output HP3_TE200_SPC630.hdf5 --url https://zenodo.org/record/5902313/files/HP3_TE200_SPC630.hdf5
+      #     curl.exe --output HP3_TE250_SPC630.hdf5 --url https://zenodo.org/record/5902313/files/HP3_TE250_SPC630.hdf5
+      #     curl.exe --output HP3_TE300_SPC630.hdf5 --url https://zenodo.org/record/5902313/files/HP3_TE300_SPC630.hdf5
       - name: Test project
         run: |
+          python fretbursts/tests/nbrun.py notebooks
           python -m pytest

From f1b85cad75c01b519b678b44339309ebf2d0683b Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Sun, 12 May 2024 13:16:01 +0300
Subject: [PATCH 051/142] Update tests.yml

---
 .github/workflows/tests.yml | 32 ++++++++++++++++----------------
 1 file changed, 16 insertions(+), 16 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 83ddf0b..354733c 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -20,12 +20,12 @@ jobs:
           #   python-version: "3.6"
           # - os: ubuntu-latest
           #   python-version: "3.7"
-          - os: ubuntu-20.04
-            python-version: "3.8"
+          # - os: ubuntu-20.04
+          #   python-version: "3.8"
           # - os: ubuntu-latest
           #   python-version: "3.9"
-          - os: ubuntu-latest
-            python-version: "3.10"
+          # - os: ubuntu-latest
+          #   python-version: "3.10"
           # - os: ubuntu-latest
           #   python-version: "3.11"
           # - os: windows-latest
@@ -42,8 +42,8 @@ jobs:
           #   python-version: "3.11"
           # - os: macOS-12
           #   python-version: "3.6"
-          # - os: macOS-12
-          #   python-version: "3.7"
+          - os: macOS-12
+            python-version: "3.7"
           # - os: macOS-latest
           #   python-version: "3.8"
           # - os: macOS-latest
@@ -77,16 +77,16 @@ jobs:
           conda install cython numpy nbconvert setuptools
           conda install scipy pandas matplotlib pytables phconvert
           python -m pip install seaborn lmfit pybroom
-      # - name: MacOS install hdf5 dependencies
-      #   if: runner.os == 'macOS'
-      #   run: |
-      #     brew install hdf5
-      #     export HDF5_DIR=/usr/local/
-      #     export BLOSC_DIR=/usr/local/
-      # - name: Install tables
-      #   run: |
-      #     python -m pip install tables
-      #     python -m pip install jupyter nbconvert lmfit phconvert pybroom
+      - name: MacOS install hdf5 dependencies
+        if: runner.os == 'macOS'
+        run: |
+          brew install hdf5
+          export HDF5_DIR=/usr/local/
+          export BLOSC_DIR=/usr/local/
+      - name: Install tables
+        run: |
+          python -m pip install tables
+          python -m pip install jupyter nbconvert lmfit phconvert pybroom
       - name: Install project
         run: |
           python -m pip install .

From ffc9bc6ca132c5a273d84e83e6b060ec10a05927 Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Sun, 12 May 2024 13:21:42 +0300
Subject: [PATCH 052/142] Update tests.yml

---
 .github/workflows/tests.yml | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 354733c..1bfb550 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -77,16 +77,16 @@ jobs:
           conda install cython numpy nbconvert setuptools
           conda install scipy pandas matplotlib pytables phconvert
           python -m pip install seaborn lmfit pybroom
-      - name: MacOS install hdf5 dependencies
-        if: runner.os == 'macOS'
-        run: |
-          brew install hdf5
-          export HDF5_DIR=/usr/local/
-          export BLOSC_DIR=/usr/local/
-      - name: Install tables
-        run: |
-          python -m pip install tables
-          python -m pip install jupyter nbconvert lmfit phconvert pybroom
+      # - name: MacOS install hdf5 dependencies
+      #   if: runner.os == 'macOS'
+      #   run: |
+      #     brew install hdf5
+      #     export HDF5_DIR=/usr/local/
+      #     export BLOSC_DIR=/usr/local/
+      # - name: Install tables
+      #   run: |
+      #     python -m pip install tables
+      #     python -m pip install jupyter nbconvert lmfit phconvert pybroom
       - name: Install project
         run: |
           python -m pip install .

From a21aefe6829c698ca4f176e3cc013bf7faed8db3 Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Sun, 12 May 2024 13:44:16 +0300
Subject: [PATCH 053/142] Update tests.yml

---
 .github/workflows/tests.yml | 52 ++++++++++++++++++-------------------
 1 file changed, 26 insertions(+), 26 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 1bfb550..b9cae29 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -16,34 +16,34 @@ jobs:
         # os: [ubuntu-latest, windows-latest, macos-latest]
         # python-version: ["3.7", "3.8", "3.9", "3.10", "3.11"]
         include:
-          # - os: ubuntu-20.04
-          #   python-version: "3.6"
-          # - os: ubuntu-latest
-          #   python-version: "3.7"
-          # - os: ubuntu-20.04
-          #   python-version: "3.8"
-          # - os: ubuntu-latest
-          #   python-version: "3.9"
-          # - os: ubuntu-latest
-          #   python-version: "3.10"
-          # - os: ubuntu-latest
-          #   python-version: "3.11"
-          # - os: windows-latest
-          #   python-version: "3.6"
-          # - os: windows-latest
-          #   python-version: "3.7"
-          # - os: windows-latest
-          #   python-version: "3.8"
-          # - os: windows-latest
-          #   python-version: "3.9"
-          # - os: windows-latest
-          #   python-version: "3.10"
-          # - os: windows-latest
-          #   python-version: "3.11"
+          - os: ubuntu-20.04
+            python-version: "3.6"
+          - os: ubuntu-latest
+            python-version: "3.7"
+          - os: ubuntu-20.04
+            python-version: "3.8"
+          - os: ubuntu-latest
+            python-version: "3.9"
+          - os: ubuntu-latest
+            python-version: "3.10"
+          - os: ubuntu-latest
+            python-version: "3.11"
+          - os: windows-latest
+            python-version: "3.6"
+          - os: windows-latest
+            python-version: "3.7"
+          - os: windows-latest
+            python-version: "3.8"
+          - os: windows-latest
+            python-version: "3.9"
+          - os: windows-latest
+            python-version: "3.10"
+          - os: windows-latest
+            python-version: "3.11"
           # - os: macOS-12
           #   python-version: "3.6"
-          - os: macOS-12
-            python-version: "3.7"
+          # - os: macOS-12
+          #   python-version: "3.7"
           # - os: macOS-latest
           #   python-version: "3.8"
           # - os: macOS-latest

From 27d0bbe9fb9ae12bc23f572e20d3baecea5fa4f6 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Tue, 14 May 2024 08:28:19 +0300
Subject: [PATCH 054/142] Make pyproject.toml primary config file

---
 LongDescription.md | 20 +++++++++++++
 pyproject.toml     | 53 ++++++++++++++++++++++++++++-------
 setup.cfg          | 26 ++++++++---------
 setup.py           | 70 +++++-----------------------------------------
 4 files changed, 83 insertions(+), 86 deletions(-)
 create mode 100644 LongDescription.md

diff --git a/LongDescription.md b/LongDescription.md
new file mode 100644
index 0000000..851a51d
--- /dev/null
+++ b/LongDescription.md
@@ -0,0 +1,20 @@
+FRETBursts
+==========
+
+**FRETBursts** is a software toolkit for burst analysis of confocal
+single-molecule FRET (smFRET) measurements. It can analyze both single-spot
+and multi-spot smFRET data with or without alternating laser excitation (ALEX).
+
+For more info please refer to:
+
+- **FRETBursts: An Open Source Toolkit for Analysis of Freely-Diffusing Single-Molecule FRET**
+  *Ingargiola et. al.* (2016). PLoS ONE doi: `10.1371/journal.pone.0160716 <10.1371/journal.pone.0160716>`__.
+
+
+Quick links:
+
+- `FRETBursts Homepage <http://opensmfs.github.io/FRETBursts/>`_
+- `FRETBursts Reference Documentation <http://fretbursts.readthedocs.org>`_
+- `FRETBursts Tutorials <https://github.com/OpenSMFS/FRETBursts_notebooks>`_
+
+See also `Release Notes <http://fretbursts.readthedocs.io/en/latest/releasenotes.html>`__.
diff --git a/pyproject.toml b/pyproject.toml
index a0b68d5..c8bfc64 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,15 +1,48 @@
 [build-system]
 requires = [
-    "setuptools>=42",
-    "cython>=0.29",
-    "numpy>=1.19",
-    "matplotlib>=3.0",
-    "scipy>=1.2",
-    "pandas>=0.23",
+    "setuptools>=45", 
+    "cython>=0.29", 
+    "oldest-supported-numpy",
+    "versioneer",
+]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "fretbursts"
+dynamic = ["version", ]
+authors = [
+    {name="Antonio Ingargiola", email="tritemio@gmail.com"}, 
+    {name="Paul David Harris", email="harripd@gmail.com"}]
+description = "Burst analysis toolkit for single and multi-spot smFRET data."
+readme = "README.md"
+license = {file = "LICENSE.txt"}
+keywords = ["single-molecule FRET","smFRET", "burst-analysis", "biophysics"]
+classifiers = [
+    "Intended Audience :: Science/Research",
+    "Operating System :: OS Independent",
+    "Programming Language :: Python",
+    "Programming Language :: Python :: 3.7",
+    "Programming Language :: Python :: 3.8",
+    "Programming Language :: Python :: 3.9",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Topic :: Scientific/Engineering"
+ ]
+requires-python = ">= 3.6"
+dependencies = [
+    "numpy>=1.19", 
+    "matplotlib>=3.0.1", 
+    "scipy>=1.2", 
+    "pandas >= 0.23",
     "seaborn>=0.11.1",
     "tables>=3.5",
-    "phconvert>=0.8",
     "lmfit>=1.0.1",
-    "versioneer"
-]
-build-backend = "setuptools.build_meta"
+    "phconvert>=0.8"
+    ]
+ 
+[project.urls]
+Homepage = "http://opensmfs.github.io/FRETBursts/"
+Documentation = "https://fretbursts.readthedocs.io/en/latest/"
+Issues = "https://github.com/OpenSMFS/FRETBursts/"
+Repository = "https://github.com/OpenSMFS/FRETBursts/issues"
+
diff --git a/setup.cfg b/setup.cfg
index d86bb82..14e44fb 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -6,16 +6,16 @@ versionfile_build = fretbursts/_version.py
 tag_prefix =
 parentdir_prefix = fretbursts-
 
-[options]
-packages = find:
-python_requires = >=3.6
-install_requires = 
-    numpy >= 1.19
-    cython >= 0.29
-    matplotlib >= 3.0
-    scipy >= 1.2
-    pandas >= 0.23
-    seaborn >= 0.11.1
-    tables >= 3.5
-    phconvert >= 0.8
-    lmfit >= 1.0.1
+# [options]
+# packages = find:
+# python_requires = >=3.6
+# install_requires = 
+#     numpy >= 1.19
+#     cython >= 0.29
+#     matplotlib >= 3.0
+#     scipy >= 1.2
+#     pandas >= 0.23
+#     seaborn >= 0.11.1
+#     tables >= 3.5
+#     phconvert >= 0.8
+#     lmfit >= 1.0.1
diff --git a/setup.py b/setup.py
index c5bdcb8..92bbccc 100644
--- a/setup.py
+++ b/setup.py
@@ -5,51 +5,17 @@
 
 ## Metadata
 project_name = 'fretbursts'
-long_description = """
-FRETBursts
-==========
 
-**FRETBursts** is a software toolkit for burst analysis of confocal
-single-molecule FRET (smFRET) measurements. It can analyze both single-spot
-and multi-spot smFRET data with or without alternating laser excitation (ALEX).
-
-For more info please refer to:
-
-- **FRETBursts: An Open Source Toolkit for Analysis of Freely-Diffusing Single-Molecule FRET**
-  *Ingargiola et. al.* (2016). PLoS ONE doi: `10.1371/journal.pone.0160716 <10.1371/journal.pone.0160716>`__.
-
-
-Quick links:
-
-- `FRETBursts Homepage <http://opensmfs.github.io/FRETBursts/>`_
-- `FRETBursts Reference Documentation <http://fretbursts.readthedocs.org>`_
-- `FRETBursts Tutorials <https://github.com/OpenSMFS/FRETBursts_notebooks>`_
-
-See also `Release Notes <http://fretbursts.readthedocs.io/en/latest/releasenotes.html>`__.
-"""
-
-## Configuration to build Cython extensions
-try:
-    from Cython.Distutils import build_ext
-except ImportError:
-    # cython is not installed: do not build extensions
-    has_cython = False
-    ext_modules = []
-else:
-    # cython is installed: register the extensions to be built
-    has_cython = True
-    ext_modules = [Extension("burstsearch_c",
-                             [project_name + "/phtools/burstsearch_c.pyx"]),
-                   Extension("phrates_c",
-                             [project_name + "/phtools/phrates_cy.pyx"],
-                             include_dirs = ["."],)]
+from Cython.Distutils import build_ext
+ext_modules = [Extension("burstsearch_c",
+                         [project_name + "/phtools/burstsearch_c.pyx"]),
+               Extension("phrates_c",
+                         [project_name + "/phtools/phrates_cy.pyx"],
+                          include_dirs = ["."],)]
 
 ## Configure setup.py commands
 cmdclass = versioneer.get_cmdclass()
-if has_cython:
-    cmdclass.update(build_ext=build_ext)
-else:
-    print('WARNING: No cython found. Fast routines will not be installed.')
+cmdclass.update(build_ext=build_ext)
 
 
 setup(name = project_name,
@@ -57,30 +23,8 @@
       cmdclass = cmdclass,
       include_dirs = [np.get_include()],
       ext_modules = ext_modules,
-      author = 'Antonino Ingargiola',
-      author_email = 'tritemio@gmail.com',
-      url          = 'http://opensmfs.github.io/FRETBursts/',
-      download_url = 'http://opensmfs.github.io/FRETBursts/',
-      python_requires='>=3.6',
-      install_requires = ['numpy', 'scipy', 'matplotlib', 'lmfit', 'seaborn',
-                          'phconvert'],
       include_package_data = True,
-      license = 'GPLv2',
-      description = ("Burst analysis toolkit for single and multi-spot "
-                     "smFRET data."),
-      long_description = long_description,
-      platforms = ['Windows', 'Linux', 'Mac OS X'],
-      classifiers=['Intended Audience :: Science/Research',
-                   'Operating System :: OS Independent',
-                   'Programming Language :: Python',
-                   'Programming Language :: Python :: 3.7',
-                   'Programming Language :: Python :: 3.8',
-                   'Programming Language :: Python :: 3.9',
-                   'Programming Language :: Python :: 3.10',
-                   'Topic :: Scientific/Engineering',
-                   ],
       packages = ['fretbursts', 'fretbursts.utils', 'fretbursts.fit',
                   'fretbursts.phtools', 'fretbursts.dataload',
                   'fretbursts.tests'],
-      keywords = 'single-molecule FRET smFRET burst-analysis biophysics',
       )

From dfbe8d9e406e4ddd62a5e78641e256c64a8b2c30 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Mon, 20 May 2024 15:03:19 +0300
Subject: [PATCH 055/142] Updated to setuptools_scm

---
 .gitattributes                                |    1 -
 .gitignore                                    |    1 +
 .travis.yml                                   |   47 -
 MANIFEST.in                                   |    2 -
 appveyor.yml                                  |   58 -
 docs/source/conf.py                           |    4 +-
 fretbursts/__init__.py                        |    7 +-
 fretbursts/_version.py                        |  476 +----
 fretbursts/tests/__init__.py                  |    0
 pyproject.toml                                |   10 +-
 setup.cfg                                     |   21 -
 setup.py                                      |    8 +-
 {fretbursts/tests => tests}/importtest.py     |    0
 {fretbursts/tests => tests}/nbrun.py          |    0
 {fretbursts/tests => tests}/test_Bursts.py    |    0
 .../tests => tests}/test_burst_plot.py        |    0
 {fretbursts/tests => tests}/test_burstlib.py  |    0
 .../tests => tests}/test_burstlib_ext.py      |    0
 {fretbursts/tests => tests}/test_ph_sel.py    |    0
 versioneer.py                                 | 1855 -----------------
 20 files changed, 30 insertions(+), 2460 deletions(-)
 delete mode 100644 .gitattributes
 delete mode 100644 .travis.yml
 delete mode 100644 appveyor.yml
 delete mode 100644 fretbursts/tests/__init__.py
 delete mode 100644 setup.cfg
 rename {fretbursts/tests => tests}/importtest.py (100%)
 rename {fretbursts/tests => tests}/nbrun.py (100%)
 rename {fretbursts/tests => tests}/test_Bursts.py (100%)
 rename {fretbursts/tests => tests}/test_burst_plot.py (100%)
 rename {fretbursts/tests => tests}/test_burstlib.py (100%)
 rename {fretbursts/tests => tests}/test_burstlib_ext.py (100%)
 rename {fretbursts/tests => tests}/test_ph_sel.py (100%)
 delete mode 100644 versioneer.py

diff --git a/.gitattributes b/.gitattributes
deleted file mode 100644
index 36f74eb..0000000
--- a/.gitattributes
+++ /dev/null
@@ -1 +0,0 @@
-fretbursts/_version.py export-subst
diff --git a/.gitignore b/.gitignore
index 8027688..761ae8c 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,6 +1,7 @@
 burstsearch/build/
 docs/source/_themes/
 *ipynb_checkpoints*
+fretbursts/version.py
 
 *build/
 *egg-info/
diff --git a/.travis.yml b/.travis.yml
deleted file mode 100644
index 7dfcf5d..0000000
--- a/.travis.yml
+++ /dev/null
@@ -1,47 +0,0 @@
-language: python
-sudo: required
-dist: xenial
-
-python:
-    - "3.6"
-    - "3.7"
-    - "3.8"
-    - "3.9"
-    - "3.10"
-
-services:
-  - xvfb
-
-before_install:
-    - wget http://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh
-    - chmod +x miniconda.sh
-    - ./miniconda.sh -b
-    - export PATH=/home/travis/miniconda3/bin:$PATH
-
-install:
-    - conda create -n conda_test_env --yes python=$TRAVIS_PYTHON_VERSION
-    - source activate conda_test_env
-    - conda install --yes scipy pandas matplotlib cython numba pytest nbconvert ipykernel ipywidgets seaborn
-    - conda config --append channels conda-forge
-    - conda install --yes lmfit
-    - conda install --yes phconvert
-    - pip install pybroom
-    - python setup.py build
-    - pip install pybroom
-    - pip install .
-    - rm -rf build/
-
-before_script:
-    - mkdir notebooks/data
-    - cd notebooks/data
-    - wget -N http://files.figshare.com/2182604/12d_New_30p_320mW_steer_3.hdf5
-    - wget -N http://files.figshare.com/2182601/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5
-    - cd ../..
-
-script:
-    - python -Wd fretbursts/tests/importtest.py
-    - py.test -v
-    - cd notebooks
-    - python ../fretbursts/tests/nbrun.py --exclude-list dev/exclude-py27.txt .
-
-sudo: false
diff --git a/MANIFEST.in b/MANIFEST.in
index da275dc..6006776 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -1,4 +1,2 @@
-include versioneer.py
-include fretbursts/_version.py
 include LICENSE.txt
 include README.md
diff --git a/appveyor.yml b/appveyor.yml
deleted file mode 100644
index e9c2634..0000000
--- a/appveyor.yml
+++ /dev/null
@@ -1,58 +0,0 @@
-build: false
-
-environment:
-  matrix:
-
-    - PYTHON: "C:\\Python36-x64"
-      PYTHON_VERSION: "3.6"
-      PYTHON_ARCH: "64"
-      MINICONDA: C:\Miniconda36-x64
-
-    - PYTHON: "C:\\Python37-x64"
-      PYTHON_VERSION: "3.7"
-      PYTHON_ARCH: "64"
-      MINICONDA: C:\Miniconda37-x64
-
-init:
-  - "ECHO %PYTHON% %PYTHON_VERSION% %PYTHON_ARCH% %MINICONDA%"
-
-install:
-  - "set PATH=%MINICONDA%;%MINICONDA%\\Scripts;%PATH%"
-  - conda config --set always_yes yes --set changeps1 no
-  - conda update -q conda
-  - conda info -a
-  - conda config --append channels conda-forge
-  - "conda create -q -n test-environment python=%PYTHON_VERSION% pip scipy pandas matplotlib lmfit cython numba nbconvert pytest ipykernel ipywidgets seaborn terminado"
-  - activate test-environment
-  - conda install phconvert
-  - python -m pip install --upgrade pip
-  - pip install pybroom
-  - python --version
-  - cd %APPVEYOR_BUILD_FOLDER%
-  - dir
-  - build.cmd python setup.py build
-  - pip install .
-  - python setup.py clean --all
-
-before_test:
-  - cd %APPVEYOR_BUILD_FOLDER%\notebooks
-  - mkdir data
-  - cd data
-  - dir
-  - ps: wget http://files.figshare.com/2182604/12d_New_30p_320mW_steer_3.hdf5 -OutFile 12d_New_30p_320mW_steer_3.hdf5
-  - ps: wget http://files.figshare.com/2182601/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5 -OutFile 0023uLRpitc_NTP_20dT_0.5GndCl.hdf5
-
-test_script:
-  - cd %APPVEYOR_BUILD_FOLDER%
-  - python -Wd fretbursts/tests/importtest.py
-  - py.test -v
-  - cd %APPVEYOR_BUILD_FOLDER%\notebooks
-  - python ../fretbursts/tests/nbrun.py --exclude-list dev/exclude-py27.txt .
-
-after_test:
-  - cd %APPVEYOR_BUILD_FOLDER%
-  - python setup.py bdist_wheel
-
-artifacts:
-  # bdist_wheel puts your built wheel in the dist directory
-  - path: dist\*
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 2f8fa2a..ba61559 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -54,7 +54,9 @@
 # documentation root, use os.path.abspath to make it absolute, like shown here.
 sys.path.insert(0, os.path.abspath('../..'))
 import fretbursts
-version = fretbursts._version.get_versions()['version'][:13]
+
+from fretbursts._version import version as __version__
+version = ".".join(__version__.split('.')[:2])
 release = version
 
 import sphinx_bootstrap_theme
diff --git a/fretbursts/__init__.py b/fretbursts/__init__.py
index 018378f..e377c09 100644
--- a/fretbursts/__init__.py
+++ b/fretbursts/__init__.py
@@ -5,11 +5,6 @@
 #               Antonino Ingargiola <tritemio@gmail.com>
 #
 
-from ._version import get_versions
-__version__ = get_versions()['version']
-del get_versions
-
-
 ## Citation information
 _CITATION = """
    FRETBursts: An Open Source Toolkit for Analysis of Freely-Diffusing Single-Molecule FRET
@@ -25,7 +20,7 @@ def citation(bar=True):
         cit = ('-' * 62) + '\n' + _INFO_CITATION +  ('-' * 62)
     print(cit)
 
-
+from fretbursts._version import version as __version__
 import warnings
 
 try:
diff --git a/fretbursts/_version.py b/fretbursts/_version.py
index f3bfbd2..7b94310 100644
--- a/fretbursts/_version.py
+++ b/fretbursts/_version.py
@@ -1,460 +1,16 @@
-
-# This file helps to compute a version number in source trees obtained from
-# git-archive tarball (such as those provided by githubs download-from-tag
-# feature). Distribution tarballs (built by setup.py sdist) and build
-# directories (produced by setup.py build) will contain a much shorter file
-# that just contains the computed version number.
-
-# This file is released into the public domain. Generated by
-# versioneer-0.15 (https://github.com/warner/python-versioneer)
-
-import errno
-import os
-import re
-import subprocess
-import sys
-
-
-def get_keywords():
-    # these strings will be replaced by git during git-archive.
-    # setup.py/versioneer.py will grep for the variable names, so they must
-    # each be defined on a line of their own. _version.py will just call
-    # get_keywords().
-    git_refnames = "$Format:%d$"
-    git_full = "$Format:%H$"
-    keywords = {"refnames": git_refnames, "full": git_full}
-    return keywords
-
-
-class VersioneerConfig:
-    pass
-
-
-def get_config():
-    # these strings are filled in when 'setup.py versioneer' creates
-    # _version.py
-    cfg = VersioneerConfig()
-    cfg.VCS = "git"
-    cfg.style = "pep440"
-    cfg.tag_prefix = ""
-    cfg.parentdir_prefix = "fretbursts-"
-    cfg.versionfile_source = "fretbursts/_version.py"
-    cfg.verbose = False
-    return cfg
-
-
-class NotThisMethod(Exception):
-    pass
-
-
-LONG_VERSION_PY = {}
-HANDLERS = {}
-
-
-def register_vcs_handler(vcs, method):  # decorator
-    def decorate(f):
-        if vcs not in HANDLERS:
-            HANDLERS[vcs] = {}
-        HANDLERS[vcs][method] = f
-        return f
-    return decorate
-
-
-def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False):
-    assert isinstance(commands, list)
-    p = None
-    for c in commands:
-        try:
-            dispcmd = str([c] + args)
-            # remember shell=False, so use git.cmd on windows, not just git
-            p = subprocess.Popen([c] + args, cwd=cwd, stdout=subprocess.PIPE,
-                                 stderr=(subprocess.PIPE if hide_stderr
-                                         else None))
-            break
-        except EnvironmentError:
-            e = sys.exc_info()[1]
-            if e.errno == errno.ENOENT:
-                continue
-            if verbose:
-                print("unable to run %s" % dispcmd)
-                print(e)
-            return None
-    else:
-        if verbose:
-            print("unable to find command, tried %s" % (commands,))
-        return None
-    stdout = p.communicate()[0].strip()
-    if sys.version_info[0] >= 3:
-        stdout = stdout.decode()
-    if p.returncode != 0:
-        if verbose:
-            print("unable to run %s (error)" % dispcmd)
-        return None
-    return stdout
-
-
-def versions_from_parentdir(parentdir_prefix, root, verbose):
-    # Source tarballs conventionally unpack into a directory that includes
-    # both the project name and a version string.
-    dirname = os.path.basename(root)
-    if not dirname.startswith(parentdir_prefix):
-        if verbose:
-            print("guessing rootdir is '%s', but '%s' doesn't start with "
-                  "prefix '%s'" % (root, dirname, parentdir_prefix))
-        raise NotThisMethod("rootdir doesn't start with parentdir_prefix")
-    return {"version": dirname[len(parentdir_prefix):],
-            "full-revisionid": None,
-            "dirty": False, "error": None}
-
-
-@register_vcs_handler("git", "get_keywords")
-def git_get_keywords(versionfile_abs):
-    # the code embedded in _version.py can just fetch the value of these
-    # keywords. When used from setup.py, we don't want to import _version.py,
-    # so we do it with a regexp instead. This function is not used from
-    # _version.py.
-    keywords = {}
-    try:
-        f = open(versionfile_abs, "r")
-        for line in f.readlines():
-            if line.strip().startswith("git_refnames ="):
-                mo = re.search(r'=\s*"(.*)"', line)
-                if mo:
-                    keywords["refnames"] = mo.group(1)
-            if line.strip().startswith("git_full ="):
-                mo = re.search(r'=\s*"(.*)"', line)
-                if mo:
-                    keywords["full"] = mo.group(1)
-        f.close()
-    except EnvironmentError:
-        pass
-    return keywords
-
-
-@register_vcs_handler("git", "keywords")
-def git_versions_from_keywords(keywords, tag_prefix, verbose):
-    if not keywords:
-        raise NotThisMethod("no keywords at all, weird")
-    refnames = keywords["refnames"].strip()
-    if refnames.startswith("$Format"):
-        if verbose:
-            print("keywords are unexpanded, not using")
-        raise NotThisMethod("unexpanded keywords, not a git-archive tarball")
-    refs = set([r.strip() for r in refnames.strip("()").split(",")])
-    # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of
-    # just "foo-1.0". If we see a "tag: " prefix, prefer those.
-    TAG = "tag: "
-    tags = set([r[len(TAG):] for r in refs if r.startswith(TAG)])
-    if not tags:
-        # Either we're using git < 1.8.3, or there really are no tags. We use
-        # a heuristic: assume all version tags have a digit. The old git %d
-        # expansion behaves like git log --decorate=short and strips out the
-        # refs/heads/ and refs/tags/ prefixes that would let us distinguish
-        # between branches and tags. By ignoring refnames without digits, we
-        # filter out many common branch names like "release" and
-        # "stabilization", as well as "HEAD" and "master".
-        tags = set([r for r in refs if re.search(r'\d', r)])
-        if verbose:
-            print("discarding '%s', no digits" % ",".join(refs-tags))
-    if verbose:
-        print("likely tags: %s" % ",".join(sorted(tags)))
-    for ref in sorted(tags):
-        # sorting will prefer e.g. "2.0" over "2.0rc1"
-        if ref.startswith(tag_prefix):
-            r = ref[len(tag_prefix):]
-            if verbose:
-                print("picking %s" % r)
-            return {"version": r,
-                    "full-revisionid": keywords["full"].strip(),
-                    "dirty": False, "error": None
-                    }
-    # no suitable tags, so version is "0+unknown", but full hex is still there
-    if verbose:
-        print("no suitable tags, using unknown + full revision id")
-    return {"version": "0+unknown",
-            "full-revisionid": keywords["full"].strip(),
-            "dirty": False, "error": "no suitable tags"}
-
-
-@register_vcs_handler("git", "pieces_from_vcs")
-def git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command):
-    # this runs 'git' from the root of the source tree. This only gets called
-    # if the git-archive 'subst' keywords were *not* expanded, and
-    # _version.py hasn't already been rewritten with a short version string,
-    # meaning we're inside a checked out source tree.
-
-    if not os.path.exists(os.path.join(root, ".git")):
-        if verbose:
-            print("no .git in %s" % root)
-        raise NotThisMethod("no .git directory")
-
-    GITS = ["git"]
-    if sys.platform == "win32":
-        GITS = ["git.cmd", "git.exe"]
-    # if there is a tag, this yields TAG-NUM-gHEX[-dirty]
-    # if there are no tags, this yields HEX[-dirty] (no NUM)
-    describe_out = run_command(GITS, ["describe", "--tags", "--dirty",
-                                      "--always", "--long"],
-                               cwd=root)
-    # --long was added in git-1.5.5
-    if describe_out is None:
-        raise NotThisMethod("'git describe' failed")
-    describe_out = describe_out.strip()
-    full_out = run_command(GITS, ["rev-parse", "HEAD"], cwd=root)
-    if full_out is None:
-        raise NotThisMethod("'git rev-parse' failed")
-    full_out = full_out.strip()
-
-    pieces = {}
-    pieces["long"] = full_out
-    pieces["short"] = full_out[:7]  # maybe improved later
-    pieces["error"] = None
-
-    # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty]
-    # TAG might have hyphens.
-    git_describe = describe_out
-
-    # look for -dirty suffix
-    dirty = git_describe.endswith("-dirty")
-    pieces["dirty"] = dirty
-    if dirty:
-        git_describe = git_describe[:git_describe.rindex("-dirty")]
-
-    # now we have TAG-NUM-gHEX or HEX
-
-    if "-" in git_describe:
-        # TAG-NUM-gHEX
-        mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe)
-        if not mo:
-            # unparseable. Maybe git-describe is misbehaving?
-            pieces["error"] = ("unable to parse git-describe output: '%s'"
-                               % describe_out)
-            return pieces
-
-        # tag
-        full_tag = mo.group(1)
-        if not full_tag.startswith(tag_prefix):
-            if verbose:
-                fmt = "tag '%s' doesn't start with prefix '%s'"
-                print(fmt % (full_tag, tag_prefix))
-            pieces["error"] = ("tag '%s' doesn't start with prefix '%s'"
-                               % (full_tag, tag_prefix))
-            return pieces
-        pieces["closest-tag"] = full_tag[len(tag_prefix):]
-
-        # distance: number of commits since tag
-        pieces["distance"] = int(mo.group(2))
-
-        # commit: short hex revision ID
-        pieces["short"] = mo.group(3)
-
-    else:
-        # HEX: no tags
-        pieces["closest-tag"] = None
-        count_out = run_command(GITS, ["rev-list", "HEAD", "--count"],
-                                cwd=root)
-        pieces["distance"] = int(count_out)  # total number of commits
-
-    return pieces
-
-
-def plus_or_dot(pieces):
-    if "+" in pieces.get("closest-tag", ""):
-        return "."
-    return "+"
-
-
-def render_pep440(pieces):
-    # now build up version string, with post-release "local version
-    # identifier". Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you
-    # get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty
-
-    # exceptions:
-    # 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]
-
-    if pieces["closest-tag"]:
-        rendered = pieces["closest-tag"]
-        if pieces["distance"] or pieces["dirty"]:
-            rendered += plus_or_dot(pieces)
-            rendered += "%d.g%s" % (pieces["distance"], pieces["short"])
-            if pieces["dirty"]:
-                rendered += ".dirty"
-    else:
-        # exception #1
-        rendered = "0+untagged.%d.g%s" % (pieces["distance"],
-                                          pieces["short"])
-        if pieces["dirty"]:
-            rendered += ".dirty"
-    return rendered
-
-
-def render_pep440_pre(pieces):
-    # TAG[.post.devDISTANCE] . No -dirty
-
-    # exceptions:
-    # 1: no tags. 0.post.devDISTANCE
-
-    if pieces["closest-tag"]:
-        rendered = pieces["closest-tag"]
-        if pieces["distance"]:
-            rendered += ".post.dev%d" % pieces["distance"]
-    else:
-        # exception #1
-        rendered = "0.post.dev%d" % pieces["distance"]
-    return rendered
-
-
-def render_pep440_post(pieces):
-    # TAG[.postDISTANCE[.dev0]+gHEX] . The ".dev0" means dirty. Note that
-    # .dev0 sorts backwards (a dirty tree will appear "older" than the
-    # corresponding clean one), but you shouldn't be releasing software with
-    # -dirty anyways.
-
-    # exceptions:
-    # 1: no tags. 0.postDISTANCE[.dev0]
-
-    if pieces["closest-tag"]:
-        rendered = pieces["closest-tag"]
-        if pieces["distance"] or pieces["dirty"]:
-            rendered += ".post%d" % pieces["distance"]
-            if pieces["dirty"]:
-                rendered += ".dev0"
-            rendered += plus_or_dot(pieces)
-            rendered += "g%s" % pieces["short"]
-    else:
-        # exception #1
-        rendered = "0.post%d" % pieces["distance"]
-        if pieces["dirty"]:
-            rendered += ".dev0"
-        rendered += "+g%s" % pieces["short"]
-    return rendered
-
-
-def render_pep440_old(pieces):
-    # TAG[.postDISTANCE[.dev0]] . The ".dev0" means dirty.
-
-    # exceptions:
-    # 1: no tags. 0.postDISTANCE[.dev0]
-
-    if pieces["closest-tag"]:
-        rendered = pieces["closest-tag"]
-        if pieces["distance"] or pieces["dirty"]:
-            rendered += ".post%d" % pieces["distance"]
-            if pieces["dirty"]:
-                rendered += ".dev0"
-    else:
-        # exception #1
-        rendered = "0.post%d" % pieces["distance"]
-        if pieces["dirty"]:
-            rendered += ".dev0"
-    return rendered
-
-
-def render_git_describe(pieces):
-    # TAG[-DISTANCE-gHEX][-dirty], like 'git describe --tags --dirty
-    # --always'
-
-    # exceptions:
-    # 1: no tags. HEX[-dirty]  (note: no 'g' prefix)
-
-    if pieces["closest-tag"]:
-        rendered = pieces["closest-tag"]
-        if pieces["distance"]:
-            rendered += "-%d-g%s" % (pieces["distance"], pieces["short"])
-    else:
-        # exception #1
-        rendered = pieces["short"]
-    if pieces["dirty"]:
-        rendered += "-dirty"
-    return rendered
-
-
-def render_git_describe_long(pieces):
-    # TAG-DISTANCE-gHEX[-dirty], like 'git describe --tags --dirty
-    # --always -long'. The distance/hash is unconditional.
-
-    # exceptions:
-    # 1: no tags. HEX[-dirty]  (note: no 'g' prefix)
-
-    if pieces["closest-tag"]:
-        rendered = pieces["closest-tag"]
-        rendered += "-%d-g%s" % (pieces["distance"], pieces["short"])
-    else:
-        # exception #1
-        rendered = pieces["short"]
-    if pieces["dirty"]:
-        rendered += "-dirty"
-    return rendered
-
-
-def render(pieces, style):
-    if pieces["error"]:
-        return {"version": "unknown",
-                "full-revisionid": pieces.get("long"),
-                "dirty": None,
-                "error": pieces["error"]}
-
-    if not style or style == "default":
-        style = "pep440"  # the default
-
-    if style == "pep440":
-        rendered = render_pep440(pieces)
-    elif style == "pep440-pre":
-        rendered = render_pep440_pre(pieces)
-    elif style == "pep440-post":
-        rendered = render_pep440_post(pieces)
-    elif style == "pep440-old":
-        rendered = render_pep440_old(pieces)
-    elif style == "git-describe":
-        rendered = render_git_describe(pieces)
-    elif style == "git-describe-long":
-        rendered = render_git_describe_long(pieces)
-    else:
-        raise ValueError("unknown style '%s'" % style)
-
-    return {"version": rendered, "full-revisionid": pieces["long"],
-            "dirty": pieces["dirty"], "error": None}
-
-
-def get_versions():
-    # I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have
-    # __file__, we can work backwards from there to the root. Some
-    # py2exe/bbfreeze/non-CPython implementations don't do __file__, in which
-    # case we can only use expanded keywords.
-
-    cfg = get_config()
-    verbose = cfg.verbose
-
-    try:
-        return git_versions_from_keywords(get_keywords(), cfg.tag_prefix,
-                                          verbose)
-    except NotThisMethod:
-        pass
-
-    try:
-        root = os.path.realpath(__file__)
-        # versionfile_source is the relative path from the top of the source
-        # tree (where the .git directory might live) to this file. Invert
-        # this to find the root from __file__.
-        for i in cfg.versionfile_source.split('/'):
-            root = os.path.dirname(root)
-    except NameError:
-        return {"version": "0+unknown", "full-revisionid": None,
-                "dirty": None,
-                "error": "unable to find root of source tree"}
-
-    try:
-        pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose)
-        return render(pieces, cfg.style)
-    except NotThisMethod:
-        pass
-
-    try:
-        if cfg.parentdir_prefix:
-            return versions_from_parentdir(cfg.parentdir_prefix, root, verbose)
-    except NotThisMethod:
-        pass
-
-    return {"version": "0+unknown", "full-revisionid": None,
-            "dirty": None,
-            "error": "unable to compute version"}
+# file generated by setuptools_scm
+# don't change, don't track in version control
+TYPE_CHECKING = False
+if TYPE_CHECKING:
+    from typing import Tuple, Union
+    VERSION_TUPLE = Tuple[Union[int, str], ...]
+else:
+    VERSION_TUPLE = object
+
+version: str
+__version__: str
+__version_tuple__: VERSION_TUPLE
+version_tuple: VERSION_TUPLE
+
+__version__ = version = '0.7.post98+g27d0bbe.d20240520'
+__version_tuple__ = version_tuple = (0, 7, 'g27d0bbe.d20240520')
diff --git a/fretbursts/tests/__init__.py b/fretbursts/tests/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/pyproject.toml b/pyproject.toml
index c8bfc64..16f3056 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,9 +1,9 @@
 [build-system]
 requires = [
-    "setuptools>=45", 
+    "setuptools>=64", 
+    "setuptools_scm>=8",
     "cython>=0.29", 
     "oldest-supported-numpy",
-    "versioneer",
 ]
 build-backend = "setuptools.build_meta"
 
@@ -46,3 +46,9 @@ Documentation = "https://fretbursts.readthedocs.io/en/latest/"
 Issues = "https://github.com/OpenSMFS/FRETBursts/"
 Repository = "https://github.com/OpenSMFS/FRETBursts/issues"
 
+[project.optional-dependencies]
+gui = ["PyQt5"]
+
+[tool.setuptools_scm]
+version_scheme = "post-release"
+write_to = "fretbursts/_version.py"
diff --git a/setup.cfg b/setup.cfg
deleted file mode 100644
index 14e44fb..0000000
--- a/setup.cfg
+++ /dev/null
@@ -1,21 +0,0 @@
-[versioneer]
-VCS = git
-style = pep440
-versionfile_source = fretbursts/_version.py
-versionfile_build = fretbursts/_version.py
-tag_prefix =
-parentdir_prefix = fretbursts-
-
-# [options]
-# packages = find:
-# python_requires = >=3.6
-# install_requires = 
-#     numpy >= 1.19
-#     cython >= 0.29
-#     matplotlib >= 3.0
-#     scipy >= 1.2
-#     pandas >= 0.23
-#     seaborn >= 0.11.1
-#     tables >= 3.5
-#     phconvert >= 0.8
-#     lmfit >= 1.0.1
diff --git a/setup.py b/setup.py
index 92bbccc..5b74e67 100644
--- a/setup.py
+++ b/setup.py
@@ -1,7 +1,6 @@
 from setuptools import setup
 from setuptools.extension import Extension
 import numpy as np
-import versioneer
 
 ## Metadata
 project_name = 'fretbursts'
@@ -14,17 +13,12 @@
                           include_dirs = ["."],)]
 
 ## Configure setup.py commands
-cmdclass = versioneer.get_cmdclass()
-cmdclass.update(build_ext=build_ext)
 
 
 setup(name = project_name,
-      version = versioneer.get_version(),
-      cmdclass = cmdclass,
       include_dirs = [np.get_include()],
       ext_modules = ext_modules,
       include_package_data = True,
       packages = ['fretbursts', 'fretbursts.utils', 'fretbursts.fit',
-                  'fretbursts.phtools', 'fretbursts.dataload',
-                  'fretbursts.tests'],
+                  'fretbursts.phtools', 'fretbursts.dataload'],
       )
diff --git a/fretbursts/tests/importtest.py b/tests/importtest.py
similarity index 100%
rename from fretbursts/tests/importtest.py
rename to tests/importtest.py
diff --git a/fretbursts/tests/nbrun.py b/tests/nbrun.py
similarity index 100%
rename from fretbursts/tests/nbrun.py
rename to tests/nbrun.py
diff --git a/fretbursts/tests/test_Bursts.py b/tests/test_Bursts.py
similarity index 100%
rename from fretbursts/tests/test_Bursts.py
rename to tests/test_Bursts.py
diff --git a/fretbursts/tests/test_burst_plot.py b/tests/test_burst_plot.py
similarity index 100%
rename from fretbursts/tests/test_burst_plot.py
rename to tests/test_burst_plot.py
diff --git a/fretbursts/tests/test_burstlib.py b/tests/test_burstlib.py
similarity index 100%
rename from fretbursts/tests/test_burstlib.py
rename to tests/test_burstlib.py
diff --git a/fretbursts/tests/test_burstlib_ext.py b/tests/test_burstlib_ext.py
similarity index 100%
rename from fretbursts/tests/test_burstlib_ext.py
rename to tests/test_burstlib_ext.py
diff --git a/fretbursts/tests/test_ph_sel.py b/tests/test_ph_sel.py
similarity index 100%
rename from fretbursts/tests/test_ph_sel.py
rename to tests/test_ph_sel.py
diff --git a/versioneer.py b/versioneer.py
deleted file mode 100644
index 5f8d8ec..0000000
--- a/versioneer.py
+++ /dev/null
@@ -1,1855 +0,0 @@
-
-# Version: 0.19
-
-"""The Versioneer - like a rocketeer, but for versions.
-
-The Versioneer
-==============
-
-* like a rocketeer, but for versions!
-* https://github.com/python-versioneer/python-versioneer
-* Brian Warner
-* License: Public Domain
-* Compatible with: Python 3.6, 3.7, 3.8, 3.9 and pypy3
-* [![Latest Version][pypi-image]][pypi-url]
-* [![Build Status][travis-image]][travis-url]
-
-This is a tool for managing a recorded version number in distutils-based
-python projects. The goal is to remove the tedious and error-prone "update
-the embedded version string" step from your release process. Making a new
-release should be as easy as recording a new tag in your version-control
-system, and maybe making new tarballs.
-
-
-## Quick Install
-
-* `pip install versioneer` to somewhere in your $PATH
-* add a `[versioneer]` section to your setup.cfg (see [Install](INSTALL.md))
-* run `versioneer install` in your source tree, commit the results
-* Verify version information with `python setup.py version`
-
-## Version Identifiers
-
-Source trees come from a variety of places:
-
-* a version-control system checkout (mostly used by developers)
-* a nightly tarball, produced by build automation
-* a snapshot tarball, produced by a web-based VCS browser, like github's
-  "tarball from tag" feature
-* a release tarball, produced by "setup.py sdist", distributed through PyPI
-
-Within each source tree, the version identifier (either a string or a number,
-this tool is format-agnostic) can come from a variety of places:
-
-* ask the VCS tool itself, e.g. "git describe" (for checkouts), which knows
-  about recent "tags" and an absolute revision-id
-* the name of the directory into which the tarball was unpacked
-* an expanded VCS keyword ($Id$, etc)
-* a `_version.py` created by some earlier build step
-
-For released software, the version identifier is closely related to a VCS
-tag. Some projects use tag names that include more than just the version
-string (e.g. "myproject-1.2" instead of just "1.2"), in which case the tool
-needs to strip the tag prefix to extract the version identifier. For
-unreleased software (between tags), the version identifier should provide
-enough information to help developers recreate the same tree, while also
-giving them an idea of roughly how old the tree is (after version 1.2, before
-version 1.3). Many VCS systems can report a description that captures this,
-for example `git describe --tags --dirty --always` reports things like
-"0.7-1-g574ab98-dirty" to indicate that the checkout is one revision past the
-0.7 tag, has a unique revision id of "574ab98", and is "dirty" (it has
-uncommitted changes).
-
-The version identifier is used for multiple purposes:
-
-* to allow the module to self-identify its version: `myproject.__version__`
-* to choose a name and prefix for a 'setup.py sdist' tarball
-
-## Theory of Operation
-
-Versioneer works by adding a special `_version.py` file into your source
-tree, where your `__init__.py` can import it. This `_version.py` knows how to
-dynamically ask the VCS tool for version information at import time.
-
-`_version.py` also contains `$Revision$` markers, and the installation
-process marks `_version.py` to have this marker rewritten with a tag name
-during the `git archive` command. As a result, generated tarballs will
-contain enough information to get the proper version.
-
-To allow `setup.py` to compute a version too, a `versioneer.py` is added to
-the top level of your source tree, next to `setup.py` and the `setup.cfg`
-that configures it. This overrides several distutils/setuptools commands to
-compute the version when invoked, and changes `setup.py build` and `setup.py
-sdist` to replace `_version.py` with a small static file that contains just
-the generated version data.
-
-## Installation
-
-See [INSTALL.md](./INSTALL.md) for detailed installation instructions.
-
-## Version-String Flavors
-
-Code which uses Versioneer can learn about its version string at runtime by
-importing `_version` from your main `__init__.py` file and running the
-`get_versions()` function. From the "outside" (e.g. in `setup.py`), you can
-import the top-level `versioneer.py` and run `get_versions()`.
-
-Both functions return a dictionary with different flavors of version
-information:
-
-* `['version']`: A condensed version string, rendered using the selected
-  style. This is the most commonly used value for the project's version
-  string. The default "pep440" style yields strings like `0.11`,
-  `0.11+2.g1076c97`, or `0.11+2.g1076c97.dirty`. See the "Styles" section
-  below for alternative styles.
-
-* `['full-revisionid']`: detailed revision identifier. For Git, this is the
-  full SHA1 commit id, e.g. "1076c978a8d3cfc70f408fe5974aa6c092c949ac".
-
-* `['date']`: Date and time of the latest `HEAD` commit. For Git, it is the
-  commit date in ISO 8601 format. This will be None if the date is not
-  available.
-
-* `['dirty']`: a boolean, True if the tree has uncommitted changes. Note that
-  this is only accurate if run in a VCS checkout, otherwise it is likely to
-  be False or None
-
-* `['error']`: if the version string could not be computed, this will be set
-  to a string describing the problem, otherwise it will be None. It may be
-  useful to throw an exception in setup.py if this is set, to avoid e.g.
-  creating tarballs with a version string of "unknown".
-
-Some variants are more useful than others. Including `full-revisionid` in a
-bug report should allow developers to reconstruct the exact code being tested
-(or indicate the presence of local changes that should be shared with the
-developers). `version` is suitable for display in an "about" box or a CLI
-`--version` output: it can be easily compared against release notes and lists
-of bugs fixed in various releases.
-
-The installer adds the following text to your `__init__.py` to place a basic
-version in `YOURPROJECT.__version__`:
-
-    from ._version import get_versions
-    __version__ = get_versions()['version']
-    del get_versions
-
-## Styles
-
-The setup.cfg `style=` configuration controls how the VCS information is
-rendered into a version string.
-
-The default style, "pep440", produces a PEP440-compliant string, equal to the
-un-prefixed tag name for actual releases, and containing an additional "local
-version" section with more detail for in-between builds. For Git, this is
-TAG[+DISTANCE.gHEX[.dirty]] , using information from `git describe --tags
---dirty --always`. For example "0.11+2.g1076c97.dirty" indicates that the
-tree is like the "1076c97" commit but has uncommitted changes (".dirty"), and
-that this commit is two revisions ("+2") beyond the "0.11" tag. For released
-software (exactly equal to a known tag), the identifier will only contain the
-stripped tag, e.g. "0.11".
-
-Other styles are available. See [details.md](details.md) in the Versioneer
-source tree for descriptions.
-
-## Debugging
-
-Versioneer tries to avoid fatal errors: if something goes wrong, it will tend
-to return a version of "0+unknown". To investigate the problem, run `setup.py
-version`, which will run the version-lookup code in a verbose mode, and will
-display the full contents of `get_versions()` (including the `error` string,
-which may help identify what went wrong).
-
-## Known Limitations
-
-Some situations are known to cause problems for Versioneer. This details the
-most significant ones. More can be found on Github
-[issues page](https://github.com/python-versioneer/python-versioneer/issues).
-
-### Subprojects
-
-Versioneer has limited support for source trees in which `setup.py` is not in
-the root directory (e.g. `setup.py` and `.git/` are *not* siblings). The are
-two common reasons why `setup.py` might not be in the root:
-
-* Source trees which contain multiple subprojects, such as
-  [Buildbot](https://github.com/buildbot/buildbot), which contains both
-  "master" and "slave" subprojects, each with their own `setup.py`,
-  `setup.cfg`, and `tox.ini`. Projects like these produce multiple PyPI
-  distributions (and upload multiple independently-installable tarballs).
-* Source trees whose main purpose is to contain a C library, but which also
-  provide bindings to Python (and perhaps other languages) in subdirectories.
-
-Versioneer will look for `.git` in parent directories, and most operations
-should get the right version string. However `pip` and `setuptools` have bugs
-and implementation details which frequently cause `pip install .` from a
-subproject directory to fail to find a correct version string (so it usually
-defaults to `0+unknown`).
-
-`pip install --editable .` should work correctly. `setup.py install` might
-work too.
-
-Pip-8.1.1 is known to have this problem, but hopefully it will get fixed in
-some later version.
-
-[Bug #38](https://github.com/python-versioneer/python-versioneer/issues/38) is tracking
-this issue. The discussion in
-[PR #61](https://github.com/python-versioneer/python-versioneer/pull/61) describes the
-issue from the Versioneer side in more detail.
-[pip PR#3176](https://github.com/pypa/pip/pull/3176) and
-[pip PR#3615](https://github.com/pypa/pip/pull/3615) contain work to improve
-pip to let Versioneer work correctly.
-
-Versioneer-0.16 and earlier only looked for a `.git` directory next to the
-`setup.cfg`, so subprojects were completely unsupported with those releases.
-
-### Editable installs with setuptools <= 18.5
-
-`setup.py develop` and `pip install --editable .` allow you to install a
-project into a virtualenv once, then continue editing the source code (and
-test) without re-installing after every change.
-
-"Entry-point scripts" (`setup(entry_points={"console_scripts": ..})`) are a
-convenient way to specify executable scripts that should be installed along
-with the python package.
-
-These both work as expected when using modern setuptools. When using
-setuptools-18.5 or earlier, however, certain operations will cause
-`pkg_resources.DistributionNotFound` errors when running the entrypoint
-script, which must be resolved by re-installing the package. This happens
-when the install happens with one version, then the egg_info data is
-regenerated while a different version is checked out. Many setup.py commands
-cause egg_info to be rebuilt (including `sdist`, `wheel`, and installing into
-a different virtualenv), so this can be surprising.
-
-[Bug #83](https://github.com/python-versioneer/python-versioneer/issues/83) describes
-this one, but upgrading to a newer version of setuptools should probably
-resolve it.
-
-
-## Updating Versioneer
-
-To upgrade your project to a new release of Versioneer, do the following:
-
-* install the new Versioneer (`pip install -U versioneer` or equivalent)
-* edit `setup.cfg`, if necessary, to include any new configuration settings
-  indicated by the release notes. See [UPGRADING](./UPGRADING.md) for details.
-* re-run `versioneer install` in your source tree, to replace
-  `SRC/_version.py`
-* commit any changed files
-
-## Future Directions
-
-This tool is designed to make it easily extended to other version-control
-systems: all VCS-specific components are in separate directories like
-src/git/ . The top-level `versioneer.py` script is assembled from these
-components by running make-versioneer.py . In the future, make-versioneer.py
-will take a VCS name as an argument, and will construct a version of
-`versioneer.py` that is specific to the given VCS. It might also take the
-configuration arguments that are currently provided manually during
-installation by editing setup.py . Alternatively, it might go the other
-direction and include code from all supported VCS systems, reducing the
-number of intermediate scripts.
-
-## Similar projects
-
-* [setuptools_scm](https://github.com/pypa/setuptools_scm/) - a non-vendored build-time
-  dependency
-* [minver](https://github.com/jbweston/miniver) - a lightweight reimplementation of
-  versioneer
-
-## License
-
-To make Versioneer easier to embed, all its code is dedicated to the public
-domain. The `_version.py` that it creates is also in the public domain.
-Specifically, both are released under the Creative Commons "Public Domain
-Dedication" license (CC0-1.0), as described in
-https://creativecommons.org/publicdomain/zero/1.0/ .
-
-[pypi-image]: https://img.shields.io/pypi/v/versioneer.svg
-[pypi-url]: https://pypi.python.org/pypi/versioneer/
-[travis-image]:
-https://img.shields.io/travis/com/python-versioneer/python-versioneer.svg
-[travis-url]: https://travis-ci.com/github/python-versioneer/python-versioneer
-
-"""
-
-import configparser
-import errno
-import json
-import os
-import re
-import subprocess
-import sys
-
-
-class VersioneerConfig:
-    """Container for Versioneer configuration parameters."""
-
-
-def get_root():
-    """Get the project root directory.
-
-    We require that all commands are run from the project root, i.e. the
-    directory that contains setup.py, setup.cfg, and versioneer.py .
-    """
-    root = os.path.realpath(os.path.abspath(os.getcwd()))
-    setup_py = os.path.join(root, "setup.py")
-    versioneer_py = os.path.join(root, "versioneer.py")
-    if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)):
-        # allow 'python path/to/setup.py COMMAND'
-        root = os.path.dirname(os.path.realpath(os.path.abspath(sys.argv[0])))
-        setup_py = os.path.join(root, "setup.py")
-        versioneer_py = os.path.join(root, "versioneer.py")
-    if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)):
-        err = ("Versioneer was unable to run the project root directory. "
-               "Versioneer requires setup.py to be executed from "
-               "its immediate directory (like 'python setup.py COMMAND'), "
-               "or in a way that lets it use sys.argv[0] to find the root "
-               "(like 'python path/to/setup.py COMMAND').")
-        raise VersioneerBadRootError(err)
-    try:
-        # Certain runtime workflows (setup.py install/develop in a setuptools
-        # tree) execute all dependencies in a single python process, so
-        # "versioneer" may be imported multiple times, and python's shared
-        # module-import table will cache the first one. So we can't use
-        # os.path.dirname(__file__), as that will find whichever
-        # versioneer.py was first imported, even in later projects.
-        me = os.path.realpath(os.path.abspath(__file__))
-        me_dir = os.path.normcase(os.path.splitext(me)[0])
-        vsr_dir = os.path.normcase(os.path.splitext(versioneer_py)[0])
-        if me_dir != vsr_dir:
-            print("Warning: build in %s is using versioneer.py from %s"
-                  % (os.path.dirname(me), versioneer_py))
-    except NameError:
-        pass
-    return root
-
-
-def get_config_from_root(root):
-    """Read the project setup.cfg file to determine Versioneer config."""
-    # This might raise EnvironmentError (if setup.cfg is missing), or
-    # configparser.NoSectionError (if it lacks a [versioneer] section), or
-    # configparser.NoOptionError (if it lacks "VCS="). See the docstring at
-    # the top of versioneer.py for instructions on writing your setup.cfg .
-    setup_cfg = os.path.join(root, "setup.cfg")
-    parser = configparser.ConfigParser()
-    with open(setup_cfg, "r") as f:
-        parser.read_file(f)
-    VCS = parser.get("versioneer", "VCS")  # mandatory
-
-    def get(parser, name):
-        if parser.has_option("versioneer", name):
-            return parser.get("versioneer", name)
-        return None
-    cfg = VersioneerConfig()
-    cfg.VCS = VCS
-    cfg.style = get(parser, "style") or ""
-    cfg.versionfile_source = get(parser, "versionfile_source")
-    cfg.versionfile_build = get(parser, "versionfile_build")
-    cfg.tag_prefix = get(parser, "tag_prefix")
-    if cfg.tag_prefix in ("''", '""'):
-        cfg.tag_prefix = ""
-    cfg.parentdir_prefix = get(parser, "parentdir_prefix")
-    cfg.verbose = get(parser, "verbose")
-    return cfg
-
-
-class NotThisMethod(Exception):
-    """Exception raised if a method is not valid for the current scenario."""
-
-
-# these dictionaries contain VCS-specific tools
-LONG_VERSION_PY = {}
-HANDLERS = {}
-
-
-def register_vcs_handler(vcs, method):  # decorator
-    """Create decorator to mark a method as the handler of a VCS."""
-    def decorate(f):
-        """Store f in HANDLERS[vcs][method]."""
-        if vcs not in HANDLERS:
-            HANDLERS[vcs] = {}
-        HANDLERS[vcs][method] = f
-        return f
-    return decorate
-
-
-def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False,
-                env=None):
-    """Call the given command(s)."""
-    assert isinstance(commands, list)
-    p = None
-    for c in commands:
-        try:
-            dispcmd = str([c] + args)
-            # remember shell=False, so use git.cmd on windows, not just git
-            p = subprocess.Popen([c] + args, cwd=cwd, env=env,
-                                 stdout=subprocess.PIPE,
-                                 stderr=(subprocess.PIPE if hide_stderr
-                                         else None))
-            break
-        except EnvironmentError:
-            e = sys.exc_info()[1]
-            if e.errno == errno.ENOENT:
-                continue
-            if verbose:
-                print("unable to run %s" % dispcmd)
-                print(e)
-            return None, None
-    else:
-        if verbose:
-            print("unable to find command, tried %s" % (commands,))
-        return None, None
-    stdout = p.communicate()[0].strip().decode()
-    if p.returncode != 0:
-        if verbose:
-            print("unable to run %s (error)" % dispcmd)
-            print("stdout was %s" % stdout)
-        return None, p.returncode
-    return stdout, p.returncode
-
-
-LONG_VERSION_PY['git'] = r'''
-# This file helps to compute a version number in source trees obtained from
-# git-archive tarball (such as those provided by githubs download-from-tag
-# feature). Distribution tarballs (built by setup.py sdist) and build
-# directories (produced by setup.py build) will contain a much shorter file
-# that just contains the computed version number.
-
-# This file is released into the public domain. Generated by
-# versioneer-0.19 (https://github.com/python-versioneer/python-versioneer)
-
-"""Git implementation of _version.py."""
-
-import errno
-import os
-import re
-import subprocess
-import sys
-
-
-def get_keywords():
-    """Get the keywords needed to look up the version information."""
-    # these strings will be replaced by git during git-archive.
-    # setup.py/versioneer.py will grep for the variable names, so they must
-    # each be defined on a line of their own. _version.py will just call
-    # get_keywords().
-    git_refnames = "%(DOLLAR)sFormat:%%d%(DOLLAR)s"
-    git_full = "%(DOLLAR)sFormat:%%H%(DOLLAR)s"
-    git_date = "%(DOLLAR)sFormat:%%ci%(DOLLAR)s"
-    keywords = {"refnames": git_refnames, "full": git_full, "date": git_date}
-    return keywords
-
-
-class VersioneerConfig:
-    """Container for Versioneer configuration parameters."""
-
-
-def get_config():
-    """Create, populate and return the VersioneerConfig() object."""
-    # these strings are filled in when 'setup.py versioneer' creates
-    # _version.py
-    cfg = VersioneerConfig()
-    cfg.VCS = "git"
-    cfg.style = "%(STYLE)s"
-    cfg.tag_prefix = "%(TAG_PREFIX)s"
-    cfg.parentdir_prefix = "%(PARENTDIR_PREFIX)s"
-    cfg.versionfile_source = "%(VERSIONFILE_SOURCE)s"
-    cfg.verbose = False
-    return cfg
-
-
-class NotThisMethod(Exception):
-    """Exception raised if a method is not valid for the current scenario."""
-
-
-LONG_VERSION_PY = {}
-HANDLERS = {}
-
-
-def register_vcs_handler(vcs, method):  # decorator
-    """Create decorator to mark a method as the handler of a VCS."""
-    def decorate(f):
-        """Store f in HANDLERS[vcs][method]."""
-        if vcs not in HANDLERS:
-            HANDLERS[vcs] = {}
-        HANDLERS[vcs][method] = f
-        return f
-    return decorate
-
-
-def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False,
-                env=None):
-    """Call the given command(s)."""
-    assert isinstance(commands, list)
-    p = None
-    for c in commands:
-        try:
-            dispcmd = str([c] + args)
-            # remember shell=False, so use git.cmd on windows, not just git
-            p = subprocess.Popen([c] + args, cwd=cwd, env=env,
-                                 stdout=subprocess.PIPE,
-                                 stderr=(subprocess.PIPE if hide_stderr
-                                         else None))
-            break
-        except EnvironmentError:
-            e = sys.exc_info()[1]
-            if e.errno == errno.ENOENT:
-                continue
-            if verbose:
-                print("unable to run %%s" %% dispcmd)
-                print(e)
-            return None, None
-    else:
-        if verbose:
-            print("unable to find command, tried %%s" %% (commands,))
-        return None, None
-    stdout = p.communicate()[0].strip().decode()
-    if p.returncode != 0:
-        if verbose:
-            print("unable to run %%s (error)" %% dispcmd)
-            print("stdout was %%s" %% stdout)
-        return None, p.returncode
-    return stdout, p.returncode
-
-
-def versions_from_parentdir(parentdir_prefix, root, verbose):
-    """Try to determine the version from the parent directory name.
-
-    Source tarballs conventionally unpack into a directory that includes both
-    the project name and a version string. We will also support searching up
-    two directory levels for an appropriately named parent directory
-    """
-    rootdirs = []
-
-    for i in range(3):
-        dirname = os.path.basename(root)
-        if dirname.startswith(parentdir_prefix):
-            return {"version": dirname[len(parentdir_prefix):],
-                    "full-revisionid": None,
-                    "dirty": False, "error": None, "date": None}
-        else:
-            rootdirs.append(root)
-            root = os.path.dirname(root)  # up a level
-
-    if verbose:
-        print("Tried directories %%s but none started with prefix %%s" %%
-              (str(rootdirs), parentdir_prefix))
-    raise NotThisMethod("rootdir doesn't start with parentdir_prefix")
-
-
-@register_vcs_handler("git", "get_keywords")
-def git_get_keywords(versionfile_abs):
-    """Extract version information from the given file."""
-    # the code embedded in _version.py can just fetch the value of these
-    # keywords. When used from setup.py, we don't want to import _version.py,
-    # so we do it with a regexp instead. This function is not used from
-    # _version.py.
-    keywords = {}
-    try:
-        f = open(versionfile_abs, "r")
-        for line in f.readlines():
-            if line.strip().startswith("git_refnames ="):
-                mo = re.search(r'=\s*"(.*)"', line)
-                if mo:
-                    keywords["refnames"] = mo.group(1)
-            if line.strip().startswith("git_full ="):
-                mo = re.search(r'=\s*"(.*)"', line)
-                if mo:
-                    keywords["full"] = mo.group(1)
-            if line.strip().startswith("git_date ="):
-                mo = re.search(r'=\s*"(.*)"', line)
-                if mo:
-                    keywords["date"] = mo.group(1)
-        f.close()
-    except EnvironmentError:
-        pass
-    return keywords
-
-
-@register_vcs_handler("git", "keywords")
-def git_versions_from_keywords(keywords, tag_prefix, verbose):
-    """Get version information from git keywords."""
-    if not keywords:
-        raise NotThisMethod("no keywords at all, weird")
-    date = keywords.get("date")
-    if date is not None:
-        # Use only the last line.  Previous lines may contain GPG signature
-        # information.
-        date = date.splitlines()[-1]
-
-        # git-2.2.0 added "%%cI", which expands to an ISO-8601 -compliant
-        # datestamp. However we prefer "%%ci" (which expands to an "ISO-8601
-        # -like" string, which we must then edit to make compliant), because
-        # it's been around since git-1.5.3, and it's too difficult to
-        # discover which version we're using, or to work around using an
-        # older one.
-        date = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
-    refnames = keywords["refnames"].strip()
-    if refnames.startswith("$Format"):
-        if verbose:
-            print("keywords are unexpanded, not using")
-        raise NotThisMethod("unexpanded keywords, not a git-archive tarball")
-    refs = set([r.strip() for r in refnames.strip("()").split(",")])
-    # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of
-    # just "foo-1.0". If we see a "tag: " prefix, prefer those.
-    TAG = "tag: "
-    tags = set([r[len(TAG):] for r in refs if r.startswith(TAG)])
-    if not tags:
-        # Either we're using git < 1.8.3, or there really are no tags. We use
-        # a heuristic: assume all version tags have a digit. The old git %%d
-        # expansion behaves like git log --decorate=short and strips out the
-        # refs/heads/ and refs/tags/ prefixes that would let us distinguish
-        # between branches and tags. By ignoring refnames without digits, we
-        # filter out many common branch names like "release" and
-        # "stabilization", as well as "HEAD" and "master".
-        tags = set([r for r in refs if re.search(r'\d', r)])
-        if verbose:
-            print("discarding '%%s', no digits" %% ",".join(refs - tags))
-    if verbose:
-        print("likely tags: %%s" %% ",".join(sorted(tags)))
-    for ref in sorted(tags):
-        # sorting will prefer e.g. "2.0" over "2.0rc1"
-        if ref.startswith(tag_prefix):
-            r = ref[len(tag_prefix):]
-            if verbose:
-                print("picking %%s" %% r)
-            return {"version": r,
-                    "full-revisionid": keywords["full"].strip(),
-                    "dirty": False, "error": None,
-                    "date": date}
-    # no suitable tags, so version is "0+unknown", but full hex is still there
-    if verbose:
-        print("no suitable tags, using unknown + full revision id")
-    return {"version": "0+unknown",
-            "full-revisionid": keywords["full"].strip(),
-            "dirty": False, "error": "no suitable tags", "date": None}
-
-
-@register_vcs_handler("git", "pieces_from_vcs")
-def git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command):
-    """Get version from 'git describe' in the root of the source tree.
-
-    This only gets called if the git-archive 'subst' keywords were *not*
-    expanded, and _version.py hasn't already been rewritten with a short
-    version string, meaning we're inside a checked out source tree.
-    """
-    GITS = ["git"]
-    if sys.platform == "win32":
-        GITS = ["git.cmd", "git.exe"]
-
-    out, rc = run_command(GITS, ["rev-parse", "--git-dir"], cwd=root,
-                          hide_stderr=True)
-    if rc != 0:
-        if verbose:
-            print("Directory %%s not under git control" %% root)
-        raise NotThisMethod("'git rev-parse --git-dir' returned error")
-
-    # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty]
-    # if there isn't one, this yields HEX[-dirty] (no NUM)
-    describe_out, rc = run_command(GITS, ["describe", "--tags", "--dirty",
-                                          "--always", "--long",
-                                          "--match", "%%s*" %% tag_prefix],
-                                   cwd=root)
-    # --long was added in git-1.5.5
-    if describe_out is None:
-        raise NotThisMethod("'git describe' failed")
-    describe_out = describe_out.strip()
-    full_out, rc = run_command(GITS, ["rev-parse", "HEAD"], cwd=root)
-    if full_out is None:
-        raise NotThisMethod("'git rev-parse' failed")
-    full_out = full_out.strip()
-
-    pieces = {}
-    pieces["long"] = full_out
-    pieces["short"] = full_out[:7]  # maybe improved later
-    pieces["error"] = None
-
-    # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty]
-    # TAG might have hyphens.
-    git_describe = describe_out
-
-    # look for -dirty suffix
-    dirty = git_describe.endswith("-dirty")
-    pieces["dirty"] = dirty
-    if dirty:
-        git_describe = git_describe[:git_describe.rindex("-dirty")]
-
-    # now we have TAG-NUM-gHEX or HEX
-
-    if "-" in git_describe:
-        # TAG-NUM-gHEX
-        mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe)
-        if not mo:
-            # unparseable. Maybe git-describe is misbehaving?
-            pieces["error"] = ("unable to parse git-describe output: '%%s'"
-                               %% describe_out)
-            return pieces
-
-        # tag
-        full_tag = mo.group(1)
-        if not full_tag.startswith(tag_prefix):
-            if verbose:
-                fmt = "tag '%%s' doesn't start with prefix '%%s'"
-                print(fmt %% (full_tag, tag_prefix))
-            pieces["error"] = ("tag '%%s' doesn't start with prefix '%%s'"
-                               %% (full_tag, tag_prefix))
-            return pieces
-        pieces["closest-tag"] = full_tag[len(tag_prefix):]
-
-        # distance: number of commits since tag
-        pieces["distance"] = int(mo.group(2))
-
-        # commit: short hex revision ID
-        pieces["short"] = mo.group(3)
-
-    else:
-        # HEX: no tags
-        pieces["closest-tag"] = None
-        count_out, rc = run_command(GITS, ["rev-list", "HEAD", "--count"],
-                                    cwd=root)
-        pieces["distance"] = int(count_out)  # total number of commits
-
-    # commit date: see ISO-8601 comment in git_versions_from_keywords()
-    date = run_command(GITS, ["show", "-s", "--format=%%ci", "HEAD"],
-                       cwd=root)[0].strip()
-    # Use only the last line.  Previous lines may contain GPG signature
-    # information.
-    date = date.splitlines()[-1]
-    pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
-
-    return pieces
-
-
-def plus_or_dot(pieces):
-    """Return a + if we don't already have one, else return a ."""
-    if "+" in pieces.get("closest-tag", ""):
-        return "."
-    return "+"
-
-
-def render_pep440(pieces):
-    """Build up version string, with post-release "local version identifier".
-
-    Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you
-    get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty
-
-    Exceptions:
-    1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]
-    """
-    if pieces["closest-tag"]:
-        rendered = pieces["closest-tag"]
-        if pieces["distance"] or pieces["dirty"]:
-            rendered += plus_or_dot(pieces)
-            rendered += "%%d.g%%s" %% (pieces["distance"], pieces["short"])
-            if pieces["dirty"]:
-                rendered += ".dirty"
-    else:
-        # exception #1
-        rendered = "0+untagged.%%d.g%%s" %% (pieces["distance"],
-                                          pieces["short"])
-        if pieces["dirty"]:
-            rendered += ".dirty"
-    return rendered
-
-
-def render_pep440_pre(pieces):
-    """TAG[.post0.devDISTANCE] -- No -dirty.
-
-    Exceptions:
-    1: no tags. 0.post0.devDISTANCE
-    """
-    if pieces["closest-tag"]:
-        rendered = pieces["closest-tag"]
-        if pieces["distance"]:
-            rendered += ".post0.dev%%d" %% pieces["distance"]
-    else:
-        # exception #1
-        rendered = "0.post0.dev%%d" %% pieces["distance"]
-    return rendered
-
-
-def render_pep440_post(pieces):
-    """TAG[.postDISTANCE[.dev0]+gHEX] .
-
-    The ".dev0" means dirty. Note that .dev0 sorts backwards
-    (a dirty tree will appear "older" than the corresponding clean one),
-    but you shouldn't be releasing software with -dirty anyways.
-
-    Exceptions:
-    1: no tags. 0.postDISTANCE[.dev0]
-    """
-    if pieces["closest-tag"]:
-        rendered = pieces["closest-tag"]
-        if pieces["distance"] or pieces["dirty"]:
-            rendered += ".post%%d" %% pieces["distance"]
-            if pieces["dirty"]:
-                rendered += ".dev0"
-            rendered += plus_or_dot(pieces)
-            rendered += "g%%s" %% pieces["short"]
-    else:
-        # exception #1
-        rendered = "0.post%%d" %% pieces["distance"]
-        if pieces["dirty"]:
-            rendered += ".dev0"
-        rendered += "+g%%s" %% pieces["short"]
-    return rendered
-
-
-def render_pep440_old(pieces):
-    """TAG[.postDISTANCE[.dev0]] .
-
-    The ".dev0" means dirty.
-
-    Exceptions:
-    1: no tags. 0.postDISTANCE[.dev0]
-    """
-    if pieces["closest-tag"]:
-        rendered = pieces["closest-tag"]
-        if pieces["distance"] or pieces["dirty"]:
-            rendered += ".post%%d" %% pieces["distance"]
-            if pieces["dirty"]:
-                rendered += ".dev0"
-    else:
-        # exception #1
-        rendered = "0.post%%d" %% pieces["distance"]
-        if pieces["dirty"]:
-            rendered += ".dev0"
-    return rendered
-
-
-def render_git_describe(pieces):
-    """TAG[-DISTANCE-gHEX][-dirty].
-
-    Like 'git describe --tags --dirty --always'.
-
-    Exceptions:
-    1: no tags. HEX[-dirty]  (note: no 'g' prefix)
-    """
-    if pieces["closest-tag"]:
-        rendered = pieces["closest-tag"]
-        if pieces["distance"]:
-            rendered += "-%%d-g%%s" %% (pieces["distance"], pieces["short"])
-    else:
-        # exception #1
-        rendered = pieces["short"]
-    if pieces["dirty"]:
-        rendered += "-dirty"
-    return rendered
-
-
-def render_git_describe_long(pieces):
-    """TAG-DISTANCE-gHEX[-dirty].
-
-    Like 'git describe --tags --dirty --always -long'.
-    The distance/hash is unconditional.
-
-    Exceptions:
-    1: no tags. HEX[-dirty]  (note: no 'g' prefix)
-    """
-    if pieces["closest-tag"]:
-        rendered = pieces["closest-tag"]
-        rendered += "-%%d-g%%s" %% (pieces["distance"], pieces["short"])
-    else:
-        # exception #1
-        rendered = pieces["short"]
-    if pieces["dirty"]:
-        rendered += "-dirty"
-    return rendered
-
-
-def render(pieces, style):
-    """Render the given version pieces into the requested style."""
-    if pieces["error"]:
-        return {"version": "unknown",
-                "full-revisionid": pieces.get("long"),
-                "dirty": None,
-                "error": pieces["error"],
-                "date": None}
-
-    if not style or style == "default":
-        style = "pep440"  # the default
-
-    if style == "pep440":
-        rendered = render_pep440(pieces)
-    elif style == "pep440-pre":
-        rendered = render_pep440_pre(pieces)
-    elif style == "pep440-post":
-        rendered = render_pep440_post(pieces)
-    elif style == "pep440-old":
-        rendered = render_pep440_old(pieces)
-    elif style == "git-describe":
-        rendered = render_git_describe(pieces)
-    elif style == "git-describe-long":
-        rendered = render_git_describe_long(pieces)
-    else:
-        raise ValueError("unknown style '%%s'" %% style)
-
-    return {"version": rendered, "full-revisionid": pieces["long"],
-            "dirty": pieces["dirty"], "error": None,
-            "date": pieces.get("date")}
-
-
-def get_versions():
-    """Get version information or return default if unable to do so."""
-    # I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have
-    # __file__, we can work backwards from there to the root. Some
-    # py2exe/bbfreeze/non-CPython implementations don't do __file__, in which
-    # case we can only use expanded keywords.
-
-    cfg = get_config()
-    verbose = cfg.verbose
-
-    try:
-        return git_versions_from_keywords(get_keywords(), cfg.tag_prefix,
-                                          verbose)
-    except NotThisMethod:
-        pass
-
-    try:
-        root = os.path.realpath(__file__)
-        # versionfile_source is the relative path from the top of the source
-        # tree (where the .git directory might live) to this file. Invert
-        # this to find the root from __file__.
-        for i in cfg.versionfile_source.split('/'):
-            root = os.path.dirname(root)
-    except NameError:
-        return {"version": "0+unknown", "full-revisionid": None,
-                "dirty": None,
-                "error": "unable to find root of source tree",
-                "date": None}
-
-    try:
-        pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose)
-        return render(pieces, cfg.style)
-    except NotThisMethod:
-        pass
-
-    try:
-        if cfg.parentdir_prefix:
-            return versions_from_parentdir(cfg.parentdir_prefix, root, verbose)
-    except NotThisMethod:
-        pass
-
-    return {"version": "0+unknown", "full-revisionid": None,
-            "dirty": None,
-            "error": "unable to compute version", "date": None}
-'''
-
-
-@register_vcs_handler("git", "get_keywords")
-def git_get_keywords(versionfile_abs):
-    """Extract version information from the given file."""
-    # the code embedded in _version.py can just fetch the value of these
-    # keywords. When used from setup.py, we don't want to import _version.py,
-    # so we do it with a regexp instead. This function is not used from
-    # _version.py.
-    keywords = {}
-    try:
-        f = open(versionfile_abs, "r")
-        for line in f.readlines():
-            if line.strip().startswith("git_refnames ="):
-                mo = re.search(r'=\s*"(.*)"', line)
-                if mo:
-                    keywords["refnames"] = mo.group(1)
-            if line.strip().startswith("git_full ="):
-                mo = re.search(r'=\s*"(.*)"', line)
-                if mo:
-                    keywords["full"] = mo.group(1)
-            if line.strip().startswith("git_date ="):
-                mo = re.search(r'=\s*"(.*)"', line)
-                if mo:
-                    keywords["date"] = mo.group(1)
-        f.close()
-    except EnvironmentError:
-        pass
-    return keywords
-
-
-@register_vcs_handler("git", "keywords")
-def git_versions_from_keywords(keywords, tag_prefix, verbose):
-    """Get version information from git keywords."""
-    if not keywords:
-        raise NotThisMethod("no keywords at all, weird")
-    date = keywords.get("date")
-    if date is not None:
-        # Use only the last line.  Previous lines may contain GPG signature
-        # information.
-        date = date.splitlines()[-1]
-
-        # git-2.2.0 added "%cI", which expands to an ISO-8601 -compliant
-        # datestamp. However we prefer "%ci" (which expands to an "ISO-8601
-        # -like" string, which we must then edit to make compliant), because
-        # it's been around since git-1.5.3, and it's too difficult to
-        # discover which version we're using, or to work around using an
-        # older one.
-        date = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
-    refnames = keywords["refnames"].strip()
-    if refnames.startswith("$Format"):
-        if verbose:
-            print("keywords are unexpanded, not using")
-        raise NotThisMethod("unexpanded keywords, not a git-archive tarball")
-    refs = set([r.strip() for r in refnames.strip("()").split(",")])
-    # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of
-    # just "foo-1.0". If we see a "tag: " prefix, prefer those.
-    TAG = "tag: "
-    tags = set([r[len(TAG):] for r in refs if r.startswith(TAG)])
-    if not tags:
-        # Either we're using git < 1.8.3, or there really are no tags. We use
-        # a heuristic: assume all version tags have a digit. The old git %d
-        # expansion behaves like git log --decorate=short and strips out the
-        # refs/heads/ and refs/tags/ prefixes that would let us distinguish
-        # between branches and tags. By ignoring refnames without digits, we
-        # filter out many common branch names like "release" and
-        # "stabilization", as well as "HEAD" and "master".
-        tags = set([r for r in refs if re.search(r'\d', r)])
-        if verbose:
-            print("discarding '%s', no digits" % ",".join(refs - tags))
-    if verbose:
-        print("likely tags: %s" % ",".join(sorted(tags)))
-    for ref in sorted(tags):
-        # sorting will prefer e.g. "2.0" over "2.0rc1"
-        if ref.startswith(tag_prefix):
-            r = ref[len(tag_prefix):]
-            if verbose:
-                print("picking %s" % r)
-            return {"version": r,
-                    "full-revisionid": keywords["full"].strip(),
-                    "dirty": False, "error": None,
-                    "date": date}
-    # no suitable tags, so version is "0+unknown", but full hex is still there
-    if verbose:
-        print("no suitable tags, using unknown + full revision id")
-    return {"version": "0+unknown",
-            "full-revisionid": keywords["full"].strip(),
-            "dirty": False, "error": "no suitable tags", "date": None}
-
-
-@register_vcs_handler("git", "pieces_from_vcs")
-def git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command):
-    """Get version from 'git describe' in the root of the source tree.
-
-    This only gets called if the git-archive 'subst' keywords were *not*
-    expanded, and _version.py hasn't already been rewritten with a short
-    version string, meaning we're inside a checked out source tree.
-    """
-    GITS = ["git"]
-    if sys.platform == "win32":
-        GITS = ["git.cmd", "git.exe"]
-
-    out, rc = run_command(GITS, ["rev-parse", "--git-dir"], cwd=root,
-                          hide_stderr=True)
-    if rc != 0:
-        if verbose:
-            print("Directory %s not under git control" % root)
-        raise NotThisMethod("'git rev-parse --git-dir' returned error")
-
-    # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty]
-    # if there isn't one, this yields HEX[-dirty] (no NUM)
-    describe_out, rc = run_command(GITS, ["describe", "--tags", "--dirty",
-                                          "--always", "--long",
-                                          "--match", "%s*" % tag_prefix],
-                                   cwd=root)
-    # --long was added in git-1.5.5
-    if describe_out is None:
-        raise NotThisMethod("'git describe' failed")
-    describe_out = describe_out.strip()
-    full_out, rc = run_command(GITS, ["rev-parse", "HEAD"], cwd=root)
-    if full_out is None:
-        raise NotThisMethod("'git rev-parse' failed")
-    full_out = full_out.strip()
-
-    pieces = {}
-    pieces["long"] = full_out
-    pieces["short"] = full_out[:7]  # maybe improved later
-    pieces["error"] = None
-
-    # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty]
-    # TAG might have hyphens.
-    git_describe = describe_out
-
-    # look for -dirty suffix
-    dirty = git_describe.endswith("-dirty")
-    pieces["dirty"] = dirty
-    if dirty:
-        git_describe = git_describe[:git_describe.rindex("-dirty")]
-
-    # now we have TAG-NUM-gHEX or HEX
-
-    if "-" in git_describe:
-        # TAG-NUM-gHEX
-        mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe)
-        if not mo:
-            # unparseable. Maybe git-describe is misbehaving?
-            pieces["error"] = ("unable to parse git-describe output: '%s'"
-                               % describe_out)
-            return pieces
-
-        # tag
-        full_tag = mo.group(1)
-        if not full_tag.startswith(tag_prefix):
-            if verbose:
-                fmt = "tag '%s' doesn't start with prefix '%s'"
-                print(fmt % (full_tag, tag_prefix))
-            pieces["error"] = ("tag '%s' doesn't start with prefix '%s'"
-                               % (full_tag, tag_prefix))
-            return pieces
-        pieces["closest-tag"] = full_tag[len(tag_prefix):]
-
-        # distance: number of commits since tag
-        pieces["distance"] = int(mo.group(2))
-
-        # commit: short hex revision ID
-        pieces["short"] = mo.group(3)
-
-    else:
-        # HEX: no tags
-        pieces["closest-tag"] = None
-        count_out, rc = run_command(GITS, ["rev-list", "HEAD", "--count"],
-                                    cwd=root)
-        pieces["distance"] = int(count_out)  # total number of commits
-
-    # commit date: see ISO-8601 comment in git_versions_from_keywords()
-    date = run_command(GITS, ["show", "-s", "--format=%ci", "HEAD"],
-                       cwd=root)[0].strip()
-    # Use only the last line.  Previous lines may contain GPG signature
-    # information.
-    date = date.splitlines()[-1]
-    pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
-
-    return pieces
-
-
-def do_vcs_install(manifest_in, versionfile_source, ipy):
-    """Git-specific installation logic for Versioneer.
-
-    For Git, this means creating/changing .gitattributes to mark _version.py
-    for export-subst keyword substitution.
-    """
-    GITS = ["git"]
-    if sys.platform == "win32":
-        GITS = ["git.cmd", "git.exe"]
-    files = [manifest_in, versionfile_source]
-    if ipy:
-        files.append(ipy)
-    try:
-        me = __file__
-        if me.endswith(".pyc") or me.endswith(".pyo"):
-            me = os.path.splitext(me)[0] + ".py"
-        versioneer_file = os.path.relpath(me)
-    except NameError:
-        versioneer_file = "versioneer.py"
-    files.append(versioneer_file)
-    present = False
-    try:
-        f = open(".gitattributes", "r")
-        for line in f.readlines():
-            if line.strip().startswith(versionfile_source):
-                if "export-subst" in line.strip().split()[1:]:
-                    present = True
-        f.close()
-    except EnvironmentError:
-        pass
-    if not present:
-        f = open(".gitattributes", "a+")
-        f.write("%s export-subst\n" % versionfile_source)
-        f.close()
-        files.append(".gitattributes")
-    run_command(GITS, ["add", "--"] + files)
-
-
-def versions_from_parentdir(parentdir_prefix, root, verbose):
-    """Try to determine the version from the parent directory name.
-
-    Source tarballs conventionally unpack into a directory that includes both
-    the project name and a version string. We will also support searching up
-    two directory levels for an appropriately named parent directory
-    """
-    rootdirs = []
-
-    for i in range(3):
-        dirname = os.path.basename(root)
-        if dirname.startswith(parentdir_prefix):
-            return {"version": dirname[len(parentdir_prefix):],
-                    "full-revisionid": None,
-                    "dirty": False, "error": None, "date": None}
-        else:
-            rootdirs.append(root)
-            root = os.path.dirname(root)  # up a level
-
-    if verbose:
-        print("Tried directories %s but none started with prefix %s" %
-              (str(rootdirs), parentdir_prefix))
-    raise NotThisMethod("rootdir doesn't start with parentdir_prefix")
-
-
-SHORT_VERSION_PY = """
-# This file was generated by 'versioneer.py' (0.19) from
-# revision-control system data, or from the parent directory name of an
-# unpacked source archive. Distribution tarballs contain a pre-generated copy
-# of this file.
-
-import json
-
-version_json = '''
-%s
-'''  # END VERSION_JSON
-
-
-def get_versions():
-    return json.loads(version_json)
-"""
-
-
-def versions_from_file(filename):
-    """Try to determine the version from _version.py if present."""
-    try:
-        with open(filename) as f:
-            contents = f.read()
-    except EnvironmentError:
-        raise NotThisMethod("unable to read _version.py")
-    mo = re.search(r"version_json = '''\n(.*)'''  # END VERSION_JSON",
-                   contents, re.M | re.S)
-    if not mo:
-        mo = re.search(r"version_json = '''\r\n(.*)'''  # END VERSION_JSON",
-                       contents, re.M | re.S)
-    if not mo:
-        raise NotThisMethod("no version_json in _version.py")
-    return json.loads(mo.group(1))
-
-
-def write_to_version_file(filename, versions):
-    """Write the given version number to the given _version.py file."""
-    os.unlink(filename)
-    contents = json.dumps(versions, sort_keys=True,
-                          indent=1, separators=(",", ": "))
-    with open(filename, "w") as f:
-        f.write(SHORT_VERSION_PY % contents)
-
-    print("set %s to '%s'" % (filename, versions["version"]))
-
-
-def plus_or_dot(pieces):
-    """Return a + if we don't already have one, else return a ."""
-    if "+" in pieces.get("closest-tag", ""):
-        return "."
-    return "+"
-
-
-def render_pep440(pieces):
-    """Build up version string, with post-release "local version identifier".
-
-    Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you
-    get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty
-
-    Exceptions:
-    1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]
-    """
-    if pieces["closest-tag"]:
-        rendered = pieces["closest-tag"]
-        if pieces["distance"] or pieces["dirty"]:
-            rendered += plus_or_dot(pieces)
-            rendered += "%d.g%s" % (pieces["distance"], pieces["short"])
-            if pieces["dirty"]:
-                rendered += ".dirty"
-    else:
-        # exception #1
-        rendered = "0+untagged.%d.g%s" % (pieces["distance"],
-                                          pieces["short"])
-        if pieces["dirty"]:
-            rendered += ".dirty"
-    return rendered
-
-
-def render_pep440_pre(pieces):
-    """TAG[.post0.devDISTANCE] -- No -dirty.
-
-    Exceptions:
-    1: no tags. 0.post0.devDISTANCE
-    """
-    if pieces["closest-tag"]:
-        rendered = pieces["closest-tag"]
-        if pieces["distance"]:
-            rendered += ".post0.dev%d" % pieces["distance"]
-    else:
-        # exception #1
-        rendered = "0.post0.dev%d" % pieces["distance"]
-    return rendered
-
-
-def render_pep440_post(pieces):
-    """TAG[.postDISTANCE[.dev0]+gHEX] .
-
-    The ".dev0" means dirty. Note that .dev0 sorts backwards
-    (a dirty tree will appear "older" than the corresponding clean one),
-    but you shouldn't be releasing software with -dirty anyways.
-
-    Exceptions:
-    1: no tags. 0.postDISTANCE[.dev0]
-    """
-    if pieces["closest-tag"]:
-        rendered = pieces["closest-tag"]
-        if pieces["distance"] or pieces["dirty"]:
-            rendered += ".post%d" % pieces["distance"]
-            if pieces["dirty"]:
-                rendered += ".dev0"
-            rendered += plus_or_dot(pieces)
-            rendered += "g%s" % pieces["short"]
-    else:
-        # exception #1
-        rendered = "0.post%d" % pieces["distance"]
-        if pieces["dirty"]:
-            rendered += ".dev0"
-        rendered += "+g%s" % pieces["short"]
-    return rendered
-
-
-def render_pep440_old(pieces):
-    """TAG[.postDISTANCE[.dev0]] .
-
-    The ".dev0" means dirty.
-
-    Exceptions:
-    1: no tags. 0.postDISTANCE[.dev0]
-    """
-    if pieces["closest-tag"]:
-        rendered = pieces["closest-tag"]
-        if pieces["distance"] or pieces["dirty"]:
-            rendered += ".post%d" % pieces["distance"]
-            if pieces["dirty"]:
-                rendered += ".dev0"
-    else:
-        # exception #1
-        rendered = "0.post%d" % pieces["distance"]
-        if pieces["dirty"]:
-            rendered += ".dev0"
-    return rendered
-
-
-def render_git_describe(pieces):
-    """TAG[-DISTANCE-gHEX][-dirty].
-
-    Like 'git describe --tags --dirty --always'.
-
-    Exceptions:
-    1: no tags. HEX[-dirty]  (note: no 'g' prefix)
-    """
-    if pieces["closest-tag"]:
-        rendered = pieces["closest-tag"]
-        if pieces["distance"]:
-            rendered += "-%d-g%s" % (pieces["distance"], pieces["short"])
-    else:
-        # exception #1
-        rendered = pieces["short"]
-    if pieces["dirty"]:
-        rendered += "-dirty"
-    return rendered
-
-
-def render_git_describe_long(pieces):
-    """TAG-DISTANCE-gHEX[-dirty].
-
-    Like 'git describe --tags --dirty --always -long'.
-    The distance/hash is unconditional.
-
-    Exceptions:
-    1: no tags. HEX[-dirty]  (note: no 'g' prefix)
-    """
-    if pieces["closest-tag"]:
-        rendered = pieces["closest-tag"]
-        rendered += "-%d-g%s" % (pieces["distance"], pieces["short"])
-    else:
-        # exception #1
-        rendered = pieces["short"]
-    if pieces["dirty"]:
-        rendered += "-dirty"
-    return rendered
-
-
-def render(pieces, style):
-    """Render the given version pieces into the requested style."""
-    if pieces["error"]:
-        return {"version": "unknown",
-                "full-revisionid": pieces.get("long"),
-                "dirty": None,
-                "error": pieces["error"],
-                "date": None}
-
-    if not style or style == "default":
-        style = "pep440"  # the default
-
-    if style == "pep440":
-        rendered = render_pep440(pieces)
-    elif style == "pep440-pre":
-        rendered = render_pep440_pre(pieces)
-    elif style == "pep440-post":
-        rendered = render_pep440_post(pieces)
-    elif style == "pep440-old":
-        rendered = render_pep440_old(pieces)
-    elif style == "git-describe":
-        rendered = render_git_describe(pieces)
-    elif style == "git-describe-long":
-        rendered = render_git_describe_long(pieces)
-    else:
-        raise ValueError("unknown style '%s'" % style)
-
-    return {"version": rendered, "full-revisionid": pieces["long"],
-            "dirty": pieces["dirty"], "error": None,
-            "date": pieces.get("date")}
-
-
-class VersioneerBadRootError(Exception):
-    """The project root directory is unknown or missing key files."""
-
-
-def get_versions(verbose=False):
-    """Get the project version from whatever source is available.
-
-    Returns dict with two keys: 'version' and 'full'.
-    """
-    if "versioneer" in sys.modules:
-        # see the discussion in cmdclass.py:get_cmdclass()
-        del sys.modules["versioneer"]
-
-    root = get_root()
-    cfg = get_config_from_root(root)
-
-    assert cfg.VCS is not None, "please set [versioneer]VCS= in setup.cfg"
-    handlers = HANDLERS.get(cfg.VCS)
-    assert handlers, "unrecognized VCS '%s'" % cfg.VCS
-    verbose = verbose or cfg.verbose
-    assert cfg.versionfile_source is not None, \
-        "please set versioneer.versionfile_source"
-    assert cfg.tag_prefix is not None, "please set versioneer.tag_prefix"
-
-    versionfile_abs = os.path.join(root, cfg.versionfile_source)
-
-    # extract version from first of: _version.py, VCS command (e.g. 'git
-    # describe'), parentdir. This is meant to work for developers using a
-    # source checkout, for users of a tarball created by 'setup.py sdist',
-    # and for users of a tarball/zipball created by 'git archive' or github's
-    # download-from-tag feature or the equivalent in other VCSes.
-
-    get_keywords_f = handlers.get("get_keywords")
-    from_keywords_f = handlers.get("keywords")
-    if get_keywords_f and from_keywords_f:
-        try:
-            keywords = get_keywords_f(versionfile_abs)
-            ver = from_keywords_f(keywords, cfg.tag_prefix, verbose)
-            if verbose:
-                print("got version from expanded keyword %s" % ver)
-            return ver
-        except NotThisMethod:
-            pass
-
-    try:
-        ver = versions_from_file(versionfile_abs)
-        if verbose:
-            print("got version from file %s %s" % (versionfile_abs, ver))
-        return ver
-    except NotThisMethod:
-        pass
-
-    from_vcs_f = handlers.get("pieces_from_vcs")
-    if from_vcs_f:
-        try:
-            pieces = from_vcs_f(cfg.tag_prefix, root, verbose)
-            ver = render(pieces, cfg.style)
-            if verbose:
-                print("got version from VCS %s" % ver)
-            return ver
-        except NotThisMethod:
-            pass
-
-    try:
-        if cfg.parentdir_prefix:
-            ver = versions_from_parentdir(cfg.parentdir_prefix, root, verbose)
-            if verbose:
-                print("got version from parentdir %s" % ver)
-            return ver
-    except NotThisMethod:
-        pass
-
-    if verbose:
-        print("unable to compute version")
-
-    return {"version": "0+unknown", "full-revisionid": None,
-            "dirty": None, "error": "unable to compute version",
-            "date": None}
-
-
-def get_version():
-    """Get the short version string for this project."""
-    return get_versions()["version"]
-
-
-def get_cmdclass(cmdclass=None):
-    """Get the custom setuptools/distutils subclasses used by Versioneer.
-
-    If the package uses a different cmdclass (e.g. one from numpy), it
-    should be provide as an argument.
-    """
-    if "versioneer" in sys.modules:
-        del sys.modules["versioneer"]
-        # this fixes the "python setup.py develop" case (also 'install' and
-        # 'easy_install .'), in which subdependencies of the main project are
-        # built (using setup.py bdist_egg) in the same python process. Assume
-        # a main project A and a dependency B, which use different versions
-        # of Versioneer. A's setup.py imports A's Versioneer, leaving it in
-        # sys.modules by the time B's setup.py is executed, causing B to run
-        # with the wrong versioneer. Setuptools wraps the sub-dep builds in a
-        # sandbox that restores sys.modules to it's pre-build state, so the
-        # parent is protected against the child's "import versioneer". By
-        # removing ourselves from sys.modules here, before the child build
-        # happens, we protect the child from the parent's versioneer too.
-        # Also see https://github.com/python-versioneer/python-versioneer/issues/52
-
-    cmds = {} if cmdclass is None else cmdclass.copy()
-
-    # we add "version" to both distutils and setuptools
-    from distutils.core import Command
-
-    class cmd_version(Command):
-        description = "report generated version string"
-        user_options = []
-        boolean_options = []
-
-        def initialize_options(self):
-            pass
-
-        def finalize_options(self):
-            pass
-
-        def run(self):
-            vers = get_versions(verbose=True)
-            print("Version: %s" % vers["version"])
-            print(" full-revisionid: %s" % vers.get("full-revisionid"))
-            print(" dirty: %s" % vers.get("dirty"))
-            print(" date: %s" % vers.get("date"))
-            if vers["error"]:
-                print(" error: %s" % vers["error"])
-    cmds["version"] = cmd_version
-
-    # we override "build_py" in both distutils and setuptools
-    #
-    # most invocation pathways end up running build_py:
-    #  distutils/build -> build_py
-    #  distutils/install -> distutils/build ->..
-    #  setuptools/bdist_wheel -> distutils/install ->..
-    #  setuptools/bdist_egg -> distutils/install_lib -> build_py
-    #  setuptools/install -> bdist_egg ->..
-    #  setuptools/develop -> ?
-    #  pip install:
-    #   copies source tree to a tempdir before running egg_info/etc
-    #   if .git isn't copied too, 'git describe' will fail
-    #   then does setup.py bdist_wheel, or sometimes setup.py install
-    #  setup.py egg_info -> ?
-
-    # we override different "build_py" commands for both environments
-    if 'build_py' in cmds:
-        _build_py = cmds['build_py']
-    elif "setuptools" in sys.modules:
-        from setuptools.command.build_py import build_py as _build_py
-    else:
-        from distutils.command.build_py import build_py as _build_py
-
-    class cmd_build_py(_build_py):
-        def run(self):
-            root = get_root()
-            cfg = get_config_from_root(root)
-            versions = get_versions()
-            _build_py.run(self)
-            # now locate _version.py in the new build/ directory and replace
-            # it with an updated value
-            if cfg.versionfile_build:
-                target_versionfile = os.path.join(self.build_lib,
-                                                  cfg.versionfile_build)
-                print("UPDATING %s" % target_versionfile)
-                write_to_version_file(target_versionfile, versions)
-    cmds["build_py"] = cmd_build_py
-
-    if "setuptools" in sys.modules:
-        from setuptools.command.build_ext import build_ext as _build_ext
-    else:
-        from distutils.command.build_ext import build_ext as _build_ext
-
-    class cmd_build_ext(_build_ext):
-        def run(self):
-            root = get_root()
-            cfg = get_config_from_root(root)
-            versions = get_versions()
-            _build_ext.run(self)
-            if self.inplace:
-                # build_ext --inplace will only build extensions in
-                # build/lib<..> dir with no _version.py to write to.
-                # As in place builds will already have a _version.py
-                # in the module dir, we do not need to write one.
-                return
-            # now locate _version.py in the new build/ directory and replace
-            # it with an updated value
-            target_versionfile = os.path.join(self.build_lib,
-                                              cfg.versionfile_source)
-            print("UPDATING %s" % target_versionfile)
-            write_to_version_file(target_versionfile, versions)
-    cmds["build_ext"] = cmd_build_ext
-
-    if "cx_Freeze" in sys.modules:  # cx_freeze enabled?
-        from cx_Freeze.dist import build_exe as _build_exe
-        # nczeczulin reports that py2exe won't like the pep440-style string
-        # as FILEVERSION, but it can be used for PRODUCTVERSION, e.g.
-        # setup(console=[{
-        #   "version": versioneer.get_version().split("+", 1)[0], # FILEVERSION
-        #   "product_version": versioneer.get_version(),
-        #   ...
-
-        class cmd_build_exe(_build_exe):
-            def run(self):
-                root = get_root()
-                cfg = get_config_from_root(root)
-                versions = get_versions()
-                target_versionfile = cfg.versionfile_source
-                print("UPDATING %s" % target_versionfile)
-                write_to_version_file(target_versionfile, versions)
-
-                _build_exe.run(self)
-                os.unlink(target_versionfile)
-                with open(cfg.versionfile_source, "w") as f:
-                    LONG = LONG_VERSION_PY[cfg.VCS]
-                    f.write(LONG %
-                            {"DOLLAR": "$",
-                             "STYLE": cfg.style,
-                             "TAG_PREFIX": cfg.tag_prefix,
-                             "PARENTDIR_PREFIX": cfg.parentdir_prefix,
-                             "VERSIONFILE_SOURCE": cfg.versionfile_source,
-                             })
-        cmds["build_exe"] = cmd_build_exe
-        del cmds["build_py"]
-
-    if 'py2exe' in sys.modules:  # py2exe enabled?
-        from py2exe.distutils_buildexe import py2exe as _py2exe
-
-        class cmd_py2exe(_py2exe):
-            def run(self):
-                root = get_root()
-                cfg = get_config_from_root(root)
-                versions = get_versions()
-                target_versionfile = cfg.versionfile_source
-                print("UPDATING %s" % target_versionfile)
-                write_to_version_file(target_versionfile, versions)
-
-                _py2exe.run(self)
-                os.unlink(target_versionfile)
-                with open(cfg.versionfile_source, "w") as f:
-                    LONG = LONG_VERSION_PY[cfg.VCS]
-                    f.write(LONG %
-                            {"DOLLAR": "$",
-                             "STYLE": cfg.style,
-                             "TAG_PREFIX": cfg.tag_prefix,
-                             "PARENTDIR_PREFIX": cfg.parentdir_prefix,
-                             "VERSIONFILE_SOURCE": cfg.versionfile_source,
-                             })
-        cmds["py2exe"] = cmd_py2exe
-
-    # we override different "sdist" commands for both environments
-    if 'sdist' in cmds:
-        _sdist = cmds['sdist']
-    elif "setuptools" in sys.modules:
-        from setuptools.command.sdist import sdist as _sdist
-    else:
-        from distutils.command.sdist import sdist as _sdist
-
-    class cmd_sdist(_sdist):
-        def run(self):
-            versions = get_versions()
-            self._versioneer_generated_versions = versions
-            # unless we update this, the command will keep using the old
-            # version
-            self.distribution.metadata.version = versions["version"]
-            return _sdist.run(self)
-
-        def make_release_tree(self, base_dir, files):
-            root = get_root()
-            cfg = get_config_from_root(root)
-            _sdist.make_release_tree(self, base_dir, files)
-            # now locate _version.py in the new base_dir directory
-            # (remembering that it may be a hardlink) and replace it with an
-            # updated value
-            target_versionfile = os.path.join(base_dir, cfg.versionfile_source)
-            print("UPDATING %s" % target_versionfile)
-            write_to_version_file(target_versionfile,
-                                  self._versioneer_generated_versions)
-    cmds["sdist"] = cmd_sdist
-
-    return cmds
-
-
-CONFIG_ERROR = """
-setup.cfg is missing the necessary Versioneer configuration. You need
-a section like:
-
- [versioneer]
- VCS = git
- style = pep440
- versionfile_source = src/myproject/_version.py
- versionfile_build = myproject/_version.py
- tag_prefix =
- parentdir_prefix = myproject-
-
-You will also need to edit your setup.py to use the results:
-
- import versioneer
- setup(version=versioneer.get_version(),
-       cmdclass=versioneer.get_cmdclass(), ...)
-
-Please read the docstring in ./versioneer.py for configuration instructions,
-edit setup.cfg, and re-run the installer or 'python versioneer.py setup'.
-"""
-
-SAMPLE_CONFIG = """
-# See the docstring in versioneer.py for instructions. Note that you must
-# re-run 'versioneer.py setup' after changing this section, and commit the
-# resulting files.
-
-[versioneer]
-#VCS = git
-#style = pep440
-#versionfile_source =
-#versionfile_build =
-#tag_prefix =
-#parentdir_prefix =
-
-"""
-
-INIT_PY_SNIPPET = """
-from ._version import get_versions
-__version__ = get_versions()['version']
-del get_versions
-"""
-
-
-def do_setup():
-    """Do main VCS-independent setup function for installing Versioneer."""
-    root = get_root()
-    try:
-        cfg = get_config_from_root(root)
-    except (EnvironmentError, configparser.NoSectionError,
-            configparser.NoOptionError) as e:
-        if isinstance(e, (EnvironmentError, configparser.NoSectionError)):
-            print("Adding sample versioneer config to setup.cfg",
-                  file=sys.stderr)
-            with open(os.path.join(root, "setup.cfg"), "a") as f:
-                f.write(SAMPLE_CONFIG)
-        print(CONFIG_ERROR, file=sys.stderr)
-        return 1
-
-    print(" creating %s" % cfg.versionfile_source)
-    with open(cfg.versionfile_source, "w") as f:
-        LONG = LONG_VERSION_PY[cfg.VCS]
-        f.write(LONG % {"DOLLAR": "$",
-                        "STYLE": cfg.style,
-                        "TAG_PREFIX": cfg.tag_prefix,
-                        "PARENTDIR_PREFIX": cfg.parentdir_prefix,
-                        "VERSIONFILE_SOURCE": cfg.versionfile_source,
-                        })
-
-    ipy = os.path.join(os.path.dirname(cfg.versionfile_source),
-                       "__init__.py")
-    if os.path.exists(ipy):
-        try:
-            with open(ipy, "r") as f:
-                old = f.read()
-        except EnvironmentError:
-            old = ""
-        if INIT_PY_SNIPPET not in old:
-            print(" appending to %s" % ipy)
-            with open(ipy, "a") as f:
-                f.write(INIT_PY_SNIPPET)
-        else:
-            print(" %s unmodified" % ipy)
-    else:
-        print(" %s doesn't exist, ok" % ipy)
-        ipy = None
-
-    # Make sure both the top-level "versioneer.py" and versionfile_source
-    # (PKG/_version.py, used by runtime code) are in MANIFEST.in, so
-    # they'll be copied into source distributions. Pip won't be able to
-    # install the package without this.
-    manifest_in = os.path.join(root, "MANIFEST.in")
-    simple_includes = set()
-    try:
-        with open(manifest_in, "r") as f:
-            for line in f:
-                if line.startswith("include "):
-                    for include in line.split()[1:]:
-                        simple_includes.add(include)
-    except EnvironmentError:
-        pass
-    # That doesn't cover everything MANIFEST.in can do
-    # (http://docs.python.org/2/distutils/sourcedist.html#commands), so
-    # it might give some false negatives. Appending redundant 'include'
-    # lines is safe, though.
-    if "versioneer.py" not in simple_includes:
-        print(" appending 'versioneer.py' to MANIFEST.in")
-        with open(manifest_in, "a") as f:
-            f.write("include versioneer.py\n")
-    else:
-        print(" 'versioneer.py' already in MANIFEST.in")
-    if cfg.versionfile_source not in simple_includes:
-        print(" appending versionfile_source ('%s') to MANIFEST.in" %
-              cfg.versionfile_source)
-        with open(manifest_in, "a") as f:
-            f.write("include %s\n" % cfg.versionfile_source)
-    else:
-        print(" versionfile_source already in MANIFEST.in")
-
-    # Make VCS-specific changes. For git, this means creating/changing
-    # .gitattributes to mark _version.py for export-subst keyword
-    # substitution.
-    do_vcs_install(manifest_in, cfg.versionfile_source, ipy)
-    return 0
-
-
-def scan_setup_py():
-    """Validate the contents of setup.py against Versioneer's expectations."""
-    found = set()
-    setters = False
-    errors = 0
-    with open("setup.py", "r") as f:
-        for line in f.readlines():
-            if "import versioneer" in line:
-                found.add("import")
-            if "versioneer.get_cmdclass()" in line:
-                found.add("cmdclass")
-            if "versioneer.get_version()" in line:
-                found.add("get_version")
-            if "versioneer.VCS" in line:
-                setters = True
-            if "versioneer.versionfile_source" in line:
-                setters = True
-    if len(found) != 3:
-        print("")
-        print("Your setup.py appears to be missing some important items")
-        print("(but I might be wrong). Please make sure it has something")
-        print("roughly like the following:")
-        print("")
-        print(" import versioneer")
-        print(" setup( version=versioneer.get_version(),")
-        print("        cmdclass=versioneer.get_cmdclass(),  ...)")
-        print("")
-        errors += 1
-    if setters:
-        print("You should remove lines like 'versioneer.VCS = ' and")
-        print("'versioneer.versionfile_source = ' . This configuration")
-        print("now lives in setup.cfg, and should be removed from setup.py")
-        print("")
-        errors += 1
-    return errors
-
-
-if __name__ == "__main__":
-    cmd = sys.argv[1]
-    if cmd == "setup":
-        errors = do_setup()
-        errors += scan_setup_py()
-        if errors:
-            sys.exit(1)
\ No newline at end of file

From d6e4480ed21f64b37223f8eea01676d7406adf45 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Thu, 23 May 2024 15:19:27 +0300
Subject: [PATCH 056/142] Hopefully working build

---
 .github/workflows/build_wheel.yml | 28 ++++++++++++++++++++++++++++
 .gitignore                        |  2 +-
 fretbursts/__init__.py            |  8 +++++---
 fretbursts/_version.py            | 16 ----------------
 fretbursts/burst_plot.py          | 12 ++++++------
 setup.py                          |  4 ++--
 tests/test_Bursts.py              |  2 +-
 tests/test_burst_plot.py          |  4 ++--
 tests/test_burstlib.py            |  4 ++--
 tests/test_burstlib_ext.py        |  4 ++--
 10 files changed, 49 insertions(+), 35 deletions(-)
 create mode 100644 .github/workflows/build_wheel.yml
 delete mode 100644 fretbursts/_version.py

diff --git a/.github/workflows/build_wheel.yml b/.github/workflows/build_wheel.yml
new file mode 100644
index 0000000..5648662
--- /dev/null
+++ b/.github/workflows/build_wheel.yml
@@ -0,0 +1,28 @@
+name: Build
+
+on:
+  push:
+    - main
+  pull_request:
+    - main
+
+jobs:
+  build_wheels:
+    name: Build wheels on ${{ matrix.os }}
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [ubuntu-22.04, windows-2022, macos-13, macos-14]
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Build wheels
+        uses: pypa/cibuildwheel@v2.18.1
+        # env:
+        #   CIBW_TEST_REQUIRES: pytest, numpy == 1.20.1, matplotlib, scipy, pandas, tables, numba, seaborn, lmfit, phconvert
+        #   CIBW_BEFORE_TEST: python -m pip install pytest
+        #   CIBW_TEST_COMMAND: python -m pytest {package}/tests
+      
+      - uses: actions/upload-artifact@v4
+        name: cibw-wheels-${{ matrix.os }}-${{ strategy.job-index }}
+        path: ./wheelhouse/*.whl
diff --git a/.gitignore b/.gitignore
index 761ae8c..3961807 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,7 +1,7 @@
 burstsearch/build/
 docs/source/_themes/
 *ipynb_checkpoints*
-fretbursts/version.py
+fretbursts/_version.py
 
 *build/
 *egg-info/
diff --git a/fretbursts/__init__.py b/fretbursts/__init__.py
index e377c09..4becc5e 100644
--- a/fretbursts/__init__.py
+++ b/fretbursts/__init__.py
@@ -6,6 +6,11 @@
 #
 
 ## Citation information
+
+
+from fretbursts._version import version as __version__
+import warnings
+
 _CITATION = """
    FRETBursts: An Open Source Toolkit for Analysis of Freely-Diffusing Single-Molecule FRET
    Ingargiola et al. (2016). http://dx.doi.org/10.1371/journal.pone.0160716 """
@@ -20,9 +25,6 @@ def citation(bar=True):
         cit = ('-' * 62) + '\n' + _INFO_CITATION +  ('-' * 62)
     print(cit)
 
-from fretbursts._version import version as __version__
-import warnings
-
 try:
     import pandas
 except ImportError:
diff --git a/fretbursts/_version.py b/fretbursts/_version.py
deleted file mode 100644
index 7b94310..0000000
--- a/fretbursts/_version.py
+++ /dev/null
@@ -1,16 +0,0 @@
-# file generated by setuptools_scm
-# don't change, don't track in version control
-TYPE_CHECKING = False
-if TYPE_CHECKING:
-    from typing import Tuple, Union
-    VERSION_TUPLE = Tuple[Union[int, str], ...]
-else:
-    VERSION_TUPLE = object
-
-version: str
-__version__: str
-__version_tuple__: VERSION_TUPLE
-version_tuple: VERSION_TUPLE
-
-__version__ = version = '0.7.post98+g27d0bbe.d20240520'
-__version_tuple__ = version_tuple = (0, 7, 'g27d0bbe.d20240520')
diff --git a/fretbursts/burst_plot.py b/fretbursts/burst_plot.py
index a09086e..cb7b3f1 100644
--- a/fretbursts/burst_plot.py
+++ b/fretbursts/burst_plot.py
@@ -2470,19 +2470,19 @@ def _register_colormaps():
     c = sns.color_palette('nipy_spectral', 64)[2:43]
     cmap = mpl.colors.LinearSegmentedColormap.from_list('alex_lv', c)
     cmap.set_under(alpha=0)
-    mpl.cm.register_cmap(name='alex_lv', cmap=cmap)
+    mpl.colormaps.register(name='alex_lv', cmap=cmap)
 
     c = sns.color_palette('YlGnBu', 64)[16:]
     cmap = mpl.colors.LinearSegmentedColormap.from_list('alex', c)
     cmap.set_under(alpha=0)
-    mpl.cm.register_cmap(name='alex_light', cmap=cmap)
-    mpl.cm.register_cmap(name='YlGnBu_crop', cmap=cmap)
-    mpl.cm.register_cmap(name='alex_dark', cmap=mpl.cm.GnBu_r)
+    mpl.colormaps.register(name='alex_light', cmap=cmap)
+    mpl.colormaps.register(name='YlGnBu_crop', cmap=cmap)
+    mpl.colormaps.register(name='alex_dark', cmap=mpl.cm.GnBu_r)
 
     # Temporary hack to workaround issue
     # https://github.com/mwaskom/seaborn/issues/855
-    mpl.cm.alex_light = mpl.cm.get_cmap('alex_light')
-    mpl.cm.alex_dark = mpl.cm.get_cmap('alex_dark')
+    mpl.cm.alex_light = mpl.colormaps.get_cmap('alex_light')
+    mpl.cm.alex_dark = mpl.colormaps.get_cmap('alex_dark')
 
 
 # Register colormaps on import if not mocking
diff --git a/setup.py b/setup.py
index 5b74e67..2ee54f4 100644
--- a/setup.py
+++ b/setup.py
@@ -5,7 +5,7 @@
 ## Metadata
 project_name = 'fretbursts'
 
-from Cython.Distutils import build_ext
+from Cython.Build import cythonize
 ext_modules = [Extension("burstsearch_c",
                          [project_name + "/phtools/burstsearch_c.pyx"]),
                Extension("phrates_c",
@@ -17,7 +17,7 @@
 
 setup(name = project_name,
       include_dirs = [np.get_include()],
-      ext_modules = ext_modules,
+      ext_modules = cythonize(ext_modules),
       include_package_data = True,
       packages = ['fretbursts', 'fretbursts.utils', 'fretbursts.fit',
                   'fretbursts.phtools', 'fretbursts.dataload'],
diff --git a/tests/test_Bursts.py b/tests/test_Bursts.py
index 72141cf..a601a2e 100644
--- a/tests/test_Bursts.py
+++ b/tests/test_Bursts.py
@@ -72,4 +72,4 @@ def test_BurstsGap():
 
 
 if __name__ == '__main__':
-    pytest.main("-x -v fretbursts/tests/test_Bursts.py")
+    pytest.main("-x -v tests/test_Bursts.py")
diff --git a/tests/test_burst_plot.py b/tests/test_burst_plot.py
index 12e2e05..7c7b26d 100644
--- a/tests/test_burst_plot.py
+++ b/tests/test_burst_plot.py
@@ -42,7 +42,7 @@
 
 
 # data subdir in the notebook folder
-DATASETS_DIR = u'data/'
+DATASETS_DIR = u'notebooks/data/'
 
 
 def _alex_process(d):
@@ -232,4 +232,4 @@ def test_scatterplots(data, scatterplots):
     d = data
     bplt.dplot(d, scatterplots, i=None)
     for i in range(d.nch):
-        bplt.dplot(d, scatterplots, i=i)
\ No newline at end of file
+        bplt.dplot(d, scatterplots, i=i)
diff --git a/tests/test_burstlib.py b/tests/test_burstlib.py
index ecd2830..fee877a 100644
--- a/tests/test_burstlib.py
+++ b/tests/test_burstlib.py
@@ -44,7 +44,7 @@
 
 
 # data subdir in the notebook folder
-DATASETS_DIR = u'data/'
+DATASETS_DIR = u'notebooks/data/'
 
 
 def _alex_process(d):
@@ -1173,4 +1173,4 @@ def test_norm_pdf():
             assert np.allclose(normpdf(x, c, mu), norm.pdf(x, loc=c, scale=mu))
 
 if __name__ == '__main__':
-    pytest.main("-x -v fretbursts/tests/test_burstlib.py")
+    pytest.main("-x -v tests/test_burstlib.py")
diff --git a/tests/test_burstlib_ext.py b/tests/test_burstlib_ext.py
index 5753ac8..a3e9c2c 100644
--- a/tests/test_burstlib_ext.py
+++ b/tests/test_burstlib_ext.py
@@ -44,7 +44,7 @@
 
 
 # data subdir in the notebook folder
-DATASETS_DIR = u'data/'
+DATASETS_DIR = u'notebooks/data/'
 
 
 def _alex_process(d):
@@ -229,4 +229,4 @@ def test_burst_fitter(data):
     assert hasattr(d, 'E_fitter')
     if d.alternated:
         bext.bursts_fitter(d, burst_data='S')
-        assert hasattr(d, 'S_fitter')
\ No newline at end of file
+        assert hasattr(d, 'S_fitter')

From 6b4710d711864ad94c431dd2832e14d3ddc6212e Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Thu, 23 May 2024 21:27:52 +0300
Subject: [PATCH 057/142] Add more pyproject.toml specs

---
 pyproject.toml | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 16f3056..d3e9f78 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -47,8 +47,17 @@ Issues = "https://github.com/OpenSMFS/FRETBursts/"
 Repository = "https://github.com/OpenSMFS/FRETBursts/issues"
 
 [project.optional-dependencies]
-gui = ["PyQt5"]
+plot = ["matplotlib>=3.0.1"]
+gui = ["matplotlib>=3.0.1", "PyQt5"]
+
+[tool.setuptools.packages.find]
+include = ['fretbursts*']
+
+[tool.setuptools.package-data]
+fretbursts = ["phtools/*.pyx", ]
 
 [tool.setuptools_scm]
 version_scheme = "post-release"
 write_to = "fretbursts/_version.py"
+
+[tool.pytest.ini_options]

From f0e4f93e4102288232486f3e086791fcfa12539c Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Sun, 26 May 2024 13:52:11 +0300
Subject: [PATCH 058/142] Debugged and restructured scm management

---
 .gitignore                                             |  1 +
 .../matplotlib_figure_mod_toolbar.py                   |  0
 .../utils/examples => examples}/matplotlib_fonts.py    |  0
 .../examples => examples}/matplotlib_gui_select.py     |  0
 .../utils/examples => examples}/mpl_gui_selection.py   |  0
 {fretbursts/utils/examples => examples}/qt4_figure.py  |  0
 .../examples => examples}/timetrace_scroll_demo.py     |  0
 .../examples => examples}/timetrace_scroll_demo2.py    |  0
 .../examples => examples}/timetrace_scroll_demo3.py    |  0
 .../timetrace_scroll_pygraphqt.py                      |  0
 fretbursts/phtools/burstsearch.py                      | 10 ++--------
 fretbursts/phtools/phrates.py                          |  2 +-
 pyproject.toml                                         |  1 -
 setup.py                                               |  6 +++---
 tests/nbrun.py                                         |  8 +++++++-
 {fretbursts/fit => tests}/test_exp_fitting.py          |  2 +-
 16 files changed, 15 insertions(+), 15 deletions(-)
 rename {fretbursts/utils/examples => examples}/matplotlib_figure_mod_toolbar.py (100%)
 rename {fretbursts/utils/examples => examples}/matplotlib_fonts.py (100%)
 rename {fretbursts/utils/examples => examples}/matplotlib_gui_select.py (100%)
 rename {fretbursts/utils/examples => examples}/mpl_gui_selection.py (100%)
 rename {fretbursts/utils/examples => examples}/qt4_figure.py (100%)
 rename {fretbursts/utils/examples => examples}/timetrace_scroll_demo.py (100%)
 rename {fretbursts/utils/examples => examples}/timetrace_scroll_demo2.py (100%)
 rename {fretbursts/utils/examples => examples}/timetrace_scroll_demo3.py (100%)
 rename {fretbursts/utils/examples => examples}/timetrace_scroll_pygraphqt.py (100%)
 rename {fretbursts/fit => tests}/test_exp_fitting.py (97%)

diff --git a/.gitignore b/.gitignore
index 3961807..aa59db0 100644
--- a/.gitignore
+++ b/.gitignore
@@ -31,5 +31,6 @@ notebooks/out
 notebooks/wip
 .cache
 *.csv
+*.mat
 _*
 docs/source/modules/generated
diff --git a/fretbursts/utils/examples/matplotlib_figure_mod_toolbar.py b/examples/matplotlib_figure_mod_toolbar.py
similarity index 100%
rename from fretbursts/utils/examples/matplotlib_figure_mod_toolbar.py
rename to examples/matplotlib_figure_mod_toolbar.py
diff --git a/fretbursts/utils/examples/matplotlib_fonts.py b/examples/matplotlib_fonts.py
similarity index 100%
rename from fretbursts/utils/examples/matplotlib_fonts.py
rename to examples/matplotlib_fonts.py
diff --git a/fretbursts/utils/examples/matplotlib_gui_select.py b/examples/matplotlib_gui_select.py
similarity index 100%
rename from fretbursts/utils/examples/matplotlib_gui_select.py
rename to examples/matplotlib_gui_select.py
diff --git a/fretbursts/utils/examples/mpl_gui_selection.py b/examples/mpl_gui_selection.py
similarity index 100%
rename from fretbursts/utils/examples/mpl_gui_selection.py
rename to examples/mpl_gui_selection.py
diff --git a/fretbursts/utils/examples/qt4_figure.py b/examples/qt4_figure.py
similarity index 100%
rename from fretbursts/utils/examples/qt4_figure.py
rename to examples/qt4_figure.py
diff --git a/fretbursts/utils/examples/timetrace_scroll_demo.py b/examples/timetrace_scroll_demo.py
similarity index 100%
rename from fretbursts/utils/examples/timetrace_scroll_demo.py
rename to examples/timetrace_scroll_demo.py
diff --git a/fretbursts/utils/examples/timetrace_scroll_demo2.py b/examples/timetrace_scroll_demo2.py
similarity index 100%
rename from fretbursts/utils/examples/timetrace_scroll_demo2.py
rename to examples/timetrace_scroll_demo2.py
diff --git a/fretbursts/utils/examples/timetrace_scroll_demo3.py b/examples/timetrace_scroll_demo3.py
similarity index 100%
rename from fretbursts/utils/examples/timetrace_scroll_demo3.py
rename to examples/timetrace_scroll_demo3.py
diff --git a/fretbursts/utils/examples/timetrace_scroll_pygraphqt.py b/examples/timetrace_scroll_pygraphqt.py
similarity index 100%
rename from fretbursts/utils/examples/timetrace_scroll_pygraphqt.py
rename to examples/timetrace_scroll_pygraphqt.py
diff --git a/fretbursts/phtools/burstsearch.py b/fretbursts/phtools/burstsearch.py
index d1e30a5..a440645 100644
--- a/fretbursts/phtools/burstsearch.py
+++ b/fretbursts/phtools/burstsearch.py
@@ -191,20 +191,14 @@ def mch_count_ph_in_bursts_py(Mburst, Mask):
 #
 
 try:
-    from burstsearch_c import bsearch_c
+    from fretbursts.burstsearch_c import bsearch_c, mch_count_ph_in_bursts_c
     bsearch = bsearch_c
+    mch_count_ph_in_bursts = mch_count_ph_in_bursts_c
     print(" - Optimized (cython) burst search loaded.")
 except ImportError:
     bsearch = bsearch_py
     print(" - Fallback to pure python burst search.")
 
-try:
-    from burstsearch_c import mch_count_ph_in_bursts_c
-    mch_count_ph_in_bursts = mch_count_ph_in_bursts_c
-    print(" - Optimized (cython) photon counting loaded.")
-except ImportError:
-    mch_count_ph_in_bursts = mch_count_ph_in_bursts_py
-    print(" - Fallback to pure python photon counting.")
 
 
 class Burst(namedtuple('Burst', ['istart', 'istop', 'start', 'stop'])):
diff --git a/fretbursts/phtools/phrates.py b/fretbursts/phtools/phrates.py
index e0e2779..abb943c 100644
--- a/fretbursts/phtools/phrates.py
+++ b/fretbursts/phtools/phrates.py
@@ -31,7 +31,7 @@
 import numpy as np
 
 try:
-    import phrates_c as cy
+    import fretbursts.phrates_c as cy
 except ImportError:
     has_cython = False
 else:
diff --git a/pyproject.toml b/pyproject.toml
index d3e9f78..b6394e9 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -20,7 +20,6 @@ keywords = ["single-molecule FRET","smFRET", "burst-analysis", "biophysics"]
 classifiers = [
     "Intended Audience :: Science/Research",
     "Operating System :: OS Independent",
-    "Programming Language :: Python",
     "Programming Language :: Python :: 3.7",
     "Programming Language :: Python :: 3.8",
     "Programming Language :: Python :: 3.9",
diff --git a/setup.py b/setup.py
index 2ee54f4..1c63469 100644
--- a/setup.py
+++ b/setup.py
@@ -6,16 +6,16 @@
 project_name = 'fretbursts'
 
 from Cython.Build import cythonize
-ext_modules = [Extension("burstsearch_c",
+ext_modules = [Extension("fretbursts.burstsearch_c",
                          [project_name + "/phtools/burstsearch_c.pyx"]),
-               Extension("phrates_c",
+               Extension("fretbursts.phrates_c",
                          [project_name + "/phtools/phrates_cy.pyx"],
                           include_dirs = ["."],)]
 
 ## Configure setup.py commands
 
 
-setup(name = project_name,
+setup(
       include_dirs = [np.get_include()],
       ext_modules = cythonize(ext_modules),
       include_package_data = True,
diff --git a/tests/nbrun.py b/tests/nbrun.py
index 47634dc..84bed75 100644
--- a/tests/nbrun.py
+++ b/tests/nbrun.py
@@ -146,8 +146,9 @@ def check_out_path(notebook_path, out_path, ext, save):
         kernel_name = 'python%d' % sys.version_info[0]
     execute_kwargs.update(kernel_name=kernel_name)
     ep = ExecutePreprocessor(**execute_kwargs)
+    print("executed preprocessor") ################################
     nb = nbformat.read(str(notebook_path), as_version=4)
-
+    print("read notebook") #######################################
     if hide_input:
         nb["metadata"].update({"hide_input": True})
 
@@ -155,9 +156,11 @@ def check_out_path(notebook_path, out_path, ext, save):
         nb['cells'].insert(insert_pos, nbformat.v4.new_code_cell(code_cell))
 
     start_time = time.time()
+    print("starting process")
     try:
         # Execute the notebook
         ep.preprocess(nb, {'metadata': {'path': working_dir}})
+        print("finished ep.preprocess")
     except:
         # Execution failed, print a message then raise.
         msg = ('Error executing the notebook "%s".\n'
@@ -169,6 +172,7 @@ def check_out_path(notebook_path, out_path, ext, save):
         raise
     finally:
         # Add timestamping cell
+        print("fiished main notebook") #####################3
         duration = time.time() - start_time
         timestamp_cell = timestamp_cell % (time.ctime(start_time), duration,
                                            notebook_path, out_path_ipynb)
@@ -176,10 +180,12 @@ def check_out_path(notebook_path, out_path, ext, save):
             nb['cells'].append(nbformat.v4.new_markdown_cell(timestamp_cell))
         # Save the executed notebook to disk
         if save_ipynb:
+            print("saving ipynb")
             nbformat.write(nb, str(out_path_ipynb))
             if display_links:
                 display(FileLink(str(out_path_ipynb)))
         if save_html:
+            print("saving html")
             html_exporter = HTMLExporter()
             body, resources = html_exporter.from_notebook_node(nb)
             with open(str(out_path_html), 'w') as f:
diff --git a/fretbursts/fit/test_exp_fitting.py b/tests/test_exp_fitting.py
similarity index 97%
rename from fretbursts/fit/test_exp_fitting.py
rename to tests/test_exp_fitting.py
index 0c3d2e3..2d647a3 100644
--- a/fretbursts/fit/test_exp_fitting.py
+++ b/tests/test_exp_fitting.py
@@ -60,4 +60,4 @@ def test_expon_fit_histw(sample):
     assert relative_error < max_relative_error
 
 if __name__ == '__main__':
-    pytest.main("-x -v -s fretbursts/fit/test_exp_fitting.py")
+    pytest.main("-x -v -s fretbursts/fit/test_exp_fitting.py")
\ No newline at end of file

From d56bccd44f714917e82b3065120ce7d3a19d5a8a Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Tue, 28 May 2024 11:07:52 +0300
Subject: [PATCH 059/142] Added kde scatter

---
 .github/workflows/build_wheel.yml |  10 +-
 .github/workflows/tests.yml       |  76 +---
 fretbursts/burst_plot.py          | 637 ++++++++++++++++++------------
 tests/test_burst_plot.py          |   2 +
 4 files changed, 408 insertions(+), 317 deletions(-)

diff --git a/.github/workflows/build_wheel.yml b/.github/workflows/build_wheel.yml
index 5648662..237671d 100644
--- a/.github/workflows/build_wheel.yml
+++ b/.github/workflows/build_wheel.yml
@@ -1,10 +1,12 @@
 name: Build
 
 on:
-  push:
-    - main
-  pull_request:
-    - main
+  workflow_run:
+    workflows: [test]
+    types:
+      - completed
+    branches:
+      - master
 
 jobs:
   build_wheels:
diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index b3613e2..249ca81 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -13,45 +13,13 @@ jobs:
     runs-on: ${{matrix.os}}
     strategy:
       matrix:
-        # os: [ubuntu-latest, windows-latest, macos-latest]
-        # python-version: ["3.7", "3.8", "3.9", "3.10", "3.11"]
-        include:
-          # - os: ubuntu-20.04
-          #   python-version: "3.6"
-          # - os: ubuntu-latest
-          #   python-version: "3.7"
-          - os: ubuntu-20.04
-            python-version: "3.8"
-          # - os: ubuntu-latest
-          #   python-version: "3.9"
-          - os: ubuntu-latest
-            python-version: "3.10"
-          # - os: ubuntu-latest
-          #   python-version: "3.11"
-          # - os: windows-latest
-          #   python-version: "3.6"
-          # - os: windows-latest
-          #   python-version: "3.7"
-          # - os: windows-latest
-          #   python-version: "3.8"
-          # - os: windows-latest
-          #   python-version: "3.9"
-          # - os: windows-latest
-          #   python-version: "3.10"
-          # - os: windows-latest
-          #   python-version: "3.11"
-          # - os: macOS-12
-          #   python-version: "3.6"
-          # - os: macOS-12
-          #   python-version: "3.7"
-          # - os: macOS-latest
-          #   python-version: "3.8"
-          # - os: macOS-latest
-          #   python-version: "3.9"
-          # - os: macOS-latest
-          #   python-version: "3.10"
-          # - os: macOS-latest
-          #   python-version: "3.11" 
+        os: [ubuntu-22.04, windows-latest, macos-latest]
+        python-version: ["3.7", "3.9", "3.11"]
+        exclude:
+          - os: macOS-latest
+            python-version: "3.7"
+          - os: windows-latest
+            python-version: "3.7" 
           
     steps:
       - uses: actions/checkout@v4
@@ -66,32 +34,24 @@ jobs:
           channels: conda-forge
           python-version: ${{ matrix.python-version }}
       - name: Upgrade pip
+        shell: bash -el {0}
         run: python -m pip install --upgrade pip
-      - name: Windows 3.6 Oddities
-        if: matrix.python-version == 3.6 && runner.os == 'Windows'
-        run: python -m pip install pywinpty==1.1.6
+      # - name: Windows 3.6 Oddities
+      #   if: matrix.python-version == 3.6 && runner.os == 'Windows'
+      #   run: python -m pip install pywinpty==1.1.6
       
       - name: Install Dependencies
+        shell: bash -el {0}
         run: |
-          python -m pip install setuptools ipython jupyter pytest
-          conda install cython numpy nbconvert setuptools
-          conda install scipy pandas matplotlib pytables phconvert
-          python -m pip install seaborn lmfit pybroom
-      # - name: MacOS install hdf5 dependencies
-      #   if: runner.os == 'macOS'
-      #   run: |
-      #     brew install hdf5
-      #     export HDF5_DIR=/usr/local/
-      #     export BLOSC_DIR=/usr/local/
-      # - name: Install tables
-      #   run: |
-      #     python -m pip install tables
-      #     python -m pip install jupyter nbconvert lmfit phconvert pybroom
+          conda install cython numpy nbconvert setuptools pytest ipython jupyter
+          conda install scipy pandas matplotlib pytables
+          python -m pip install phconvert seaborn lmfit pybroom
       - name: Install project
         run: |
           python -m pip install .
       - name: Download files Unix
         if: runner.os != 'Windows'
+        shell: bash -el {0} 
         run: |
           wget -N http://files.figshare.com/2182604/12d_New_30p_320mW_steer_3.hdf5
           wget -N http://files.figshare.com/2182601/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5
@@ -101,6 +61,7 @@ jobs:
           wget -N https://zenodo.org/record/5902313/files/HP3_TE300_SPC630.hdf5
       - name: Downlaod files Windows
         if: runner.os == 'Windows'
+        shell: bash -el {0}
         run: |
           curl.exe --output 2182604/12d_New_30p_320mW_steer_3.hdf5 --url http://files.figshare.com/2182604/12d_New_30p_320mW_steer_3.hdf5
           curl.exe --output 0023uLRpitc_NTP_20dT_0.5GndCl.hdf5 --url http://files.figshare.com/2182601/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5
@@ -109,5 +70,8 @@ jobs:
           curl.exe --output HP3_TE250_SPC630.hdf5 --url https://zenodo.org/record/5902313/files/HP3_TE250_SPC630.hdf5
           curl.exe --output HP3_TE300_SPC630.hdf5 --url https://zenodo.org/record/5902313/files/HP3_TE300_SPC630.hdf5
       - name: Test project
+        shell: bash -el {0}
         run: |
           python -m pytest
+          cd notebooks
+          python ../tests/nbrun.py .
diff --git a/fretbursts/burst_plot.py b/fretbursts/burst_plot.py
index cb7b3f1..4fc45b3 100644
--- a/fretbursts/burst_plot.py
+++ b/fretbursts/burst_plot.py
@@ -34,18 +34,17 @@
 import warnings
 from itertools import cycle
 from collections.abc import Iterable
+from functools import wraps
 
 # Numeric imports
 import numpy as np
 from numpy import arange, r_
 from scipy.stats import norm as norm
-from scipy.stats import erlang
+from scipy.stats import erlang, gaussian_kde
 from scipy.interpolate import UnivariateSpline
 
 # Graphics imports
 import matplotlib.pyplot as plt
-from matplotlib.pyplot import (plot, hist, xlabel, ylabel, grid, title, legend,
-                               gca, gcf)
 from matplotlib.patches import Rectangle, Ellipse
 from matplotlib.collections import PatchCollection, PolyCollection
 from matplotlib.offsetbox import AnchoredText
@@ -90,6 +89,19 @@
 ##
 #  Utility functions
 #
+
+def _ax_intercept(func):
+    """
+    Wrapper that grabs the ax keyword argument and if None or not specified,
+    it calls plt.gca() and adds/replaces ax argument
+    """
+    @wraps(func)
+    def inner(*args, **kwargs):
+        if 'ax' not in kwargs or kwargs['ax'] is None:
+            kwargs['ax'] = plt.gca()
+        return func(*args, **kwargs)
+    return inner
+
 def _normalize_kwargs(kwargs, kind='patch'):
     """Convert matplotlib keywords from short to long form."""
     if kwargs is None:
@@ -99,6 +111,9 @@ def _normalize_kwargs(kwargs, kind='patch'):
         long_names = dict(c='color', ls='linestyle', lw='linewidth',
                           mec='markeredgecolor', mew='markeredgewidth',
                           mfc='markerfacecolor', ms='markersize',)
+    elif kind == 'scatter':
+        long_names = dict(ls='linestyle', lw='linewidth',
+                          ec='edgecolor',)
     elif kind == 'patch':
         long_names = dict(c='color', ls='linestyle', lw='linewidth',
                           ec='edgecolor', fc='facecolor',)
@@ -115,40 +130,49 @@ def bsavefig(d, s):
 #  Multi-channel plot functions
 #
 
-def mch_plot_bg(d, **kwargs):
+@_ax_intercept
+def mch_plot_bg(d, ax=None, **kwargs):
     """Plot background vs channel for DA, D and A photons."""
     bg = d.bg_from(Ph_sel('all'))
     bg_dd = d.bg_from(Ph_sel(Dex='Dem'))
     bg_ad = d.bg_from(Ph_sel(Dex='Aem'))
-    plot(r_[1:d.nch+1], [b.mean()*1e-3 for b in bg], lw=2, color=blue,
+    ax.plot(r_[1:d.nch+1], [b.mean()*1e-3 for b in bg], lw=2, color=blue,
          label=' T', **kwargs)
-    plot(r_[1:d.nch+1], [b.mean()*1e-3 for b in bg_dd], color=green, lw=2,
+    ax.plot(r_[1:d.nch+1], [b.mean()*1e-3 for b in bg_dd], color=green, lw=2,
          label=' D', **kwargs)
-    plot(r_[1:d.nch+1], [b.mean()*1e-3 for b in bg_ad], color=red, lw=2,
+    ax.plot(r_[1:d.nch+1], [b.mean()*1e-3 for b in bg_ad], color=red, lw=2,
          label=' A', **kwargs)
-    xlabel("CH"); ylabel("kcps"); grid(True); legend(loc='best')
-    title(d.name)
+    ax.set_xlabel("CH")
+    ax.set_ylabel("kcps")
+    ax.grid(True)
+    ax.legend(loc='best')
+    ax.set_title(d.name)
+
 
-def mch_plot_bg_ratio(d):
+@_ax_intercept
+def mch_plot_bg_ratio(d, ax=None):
     """Plot ratio of A over D background vs channel."""
     bg_dd = d.bg_from(Ph_sel(Dex='Dem'))
     bg_ad = d.bg_from(Ph_sel(Dex='Aem'))
-    plot(r_[1:d.nch+1],
-         [ba.mean()/bd.mean() for bd, ba in zip(bg_dd, bg_ad)],
-         color=green, lw=2, label='A/D')
-    xlabel("CH"); ylabel("BG Ratio A/D"); grid(True)
-    title("BG Ratio A/D "+d.name)
+    ax.plot(r_[1:d.nch+1],
+            [ba.mean()/bd.mean() for bd, ba in zip(bg_dd, bg_ad)],
+            color=green, lw=2, label='A/D')
+    ax.set_xlabel("CH"); ax.set_ylabel("BG Ratio A/D"); ax.grid(True)
+    ax.set_title("BG Ratio A/D "+d.name)
 
-def mch_plot_bsize(d):
+
+@_ax_intercept
+def mch_plot_bsize(d, ax=None):
     """Plot mean burst size vs channel."""
     CH = np.arange(1, d.nch+1)
-    plot(CH, [b.mean() for b in d.nt], color=blue, lw=2, label=' T')
-    plot(CH, [b.mean() for b in d.nd], color=green, lw=2, label=' D')
-    plot(CH, [b.mean() for b in d.na], color=red, lw=2, label=' A')
-    xlabel("CH"); ylabel("Mean burst size")
-    grid(True)
-    legend(loc='best')
-    title(d.name)
+    ax.plot(CH, [b.mean() for b in d.nt], color=blue, lw=2, label=' T')
+    ax.plot(CH, [b.mean() for b in d.nd], color=green, lw=2, label=' D')
+    ax.plot(CH, [b.mean() for b in d.na], color=red, lw=2, label=' A')
+    ax.set_xlabel("CH")
+    ax.set_ylabel("Mean burst size")
+    ax.grid(True)
+    ax.legend(loc='best')
+    ax.set_title(d.name)
 
 
 ##
@@ -169,6 +193,7 @@ def plot_alternation_hist(d, bins=None, ax=None, **kwargs):
         plot_alternation = plot_alternation_hist_usalex
     plot_alternation(d, bins=bins, ax=ax, **kwargs)
 
+@_ax_intercept
 def plot_alternation_hist_usalex(d, bins=None, ax=None, ich=0,
                                  hist_style={}, span_style={}):
     """Plot the us-ALEX alternation histogram for the variable `d`.
@@ -176,9 +201,6 @@ def plot_alternation_hist_usalex(d, bins=None, ax=None, ich=0,
     This function must be called on us-ALEX data **before** calling
     :func:`fretbursts.loader.alex_apply_period`.
     """
-    if ax is None:
-        _, ax = plt.subplots()
-
     if bins is None:
         bins = 100
 
@@ -216,6 +238,8 @@ def plot_alternation_hist_usalex(d, bins=None, ax=None, ich=0,
         ax.axvspan(A_ON[0], period, color=red, **span_style_)
     ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), frameon=False)
 
+
+@_ax_intercept
 def plot_alternation_hist_nsalex(d, bins=None, ax=None, ich=0,
                                  hist_style={}, span_style={}):
     """Plot the ns-ALEX alternation histogram for the variable `d`.
@@ -223,9 +247,6 @@ def plot_alternation_hist_nsalex(d, bins=None, ax=None, ich=0,
     This function must be called on ns-ALEX data **before** calling
     :func:`fretbursts.loader.alex_apply_period`.
     """
-    if ax is None:
-        _, ax = plt.subplots()
-
     if bins is None:
         bins = np.arange(d.nanotimes_params[ich]['tcspc_num_bins'])
 
@@ -270,6 +291,7 @@ def plot_alternation_hist_nsalex(d, bins=None, ax=None, ich=0,
 #
 
 def _burst_info(d, ich, burst_index):
+    """Generates burst information message for the burst in data.mburst[ich][burst_index]"""
     burst = d.mburst[ich][burst_index]
     params = dict(
         b_index=burst_index,
@@ -291,7 +313,7 @@ def _burst_info(d, ich, burst_index):
     return msg.format(**params)
 
 
-def _plot_bursts(d, i, tmin_clk, tmax_clk, pmax=1e3, pmin=0, color="#999999",
+def _plot_bursts(d, i, tmin_clk, tmax_clk, ax, pmax=1e3, pmin=0, color="#999999",
                  ytext=20):
     """Highlights bursts in a timetrace plot."""
     b = d.mburst[i]
@@ -304,7 +326,7 @@ def _plot_bursts(d, i, tmin_clk, tmax_clk, pmax=1e3, pmin=0, color="#999999",
     end = bs.stop * d.clk_p
     R = []
     width = end - start
-    ax = gca()
+    #TODO: decide how to use axvspan or other better function
     for b, bidx, s, w, sign, va in zip(bs, burst_indices, start, width,
                                        cycle([-1, 1]),
                                        cycle(['top', 'bottom'])):
@@ -317,7 +339,7 @@ def _plot_bursts(d, i, tmin_clk, tmax_clk, pmax=1e3, pmin=0, color="#999999",
     ax.add_artist(PatchCollection(R, lw=0, color=color))
 
 
-def _plot_rate_th(d, i, F, ph_sel, invert=False, scale=1,
+def _plot_rate_th(d, i, F, ph_sel, ax, invert=False, scale=1,
                   plot_style_={}, rate_th_style={}):
     """Plots background_rate*F as a function of time.
 
@@ -342,7 +364,7 @@ def _plot_rate_th(d, i, F, ph_sel, invert=False, scale=1,
     y_rate *= scale
     if invert:
         y_rate *= -1
-    plot(x_rate, y_rate, **rate_th_style_)
+    ax.plot(x_rate, y_rate, **rate_th_style_)
 
 
 def _gui_timetrace_burst_sel(d, fig, ax):
@@ -361,12 +383,13 @@ def _gui_timetrace_scroll(fig):
         gui_status['scroll_gui'] = ScrollingToolQT(fig)
 
 
+@_ax_intercept
 def timetrace_single(d, i=0, binwidth=1e-3, bins=None, tmin=0, tmax=200,
                      ph_sel=Ph_sel('all'), invert=False, bursts=False,
                      burst_picker=True, scroll=False, cache_bins=True,
                      plot_style=None, show_rate_th=True, F=None,
                      rate_th_style={}, set_ax_limits=True,
-                     burst_color='#BBBBBB'):
+                     burst_color='#BBBBBB', ax=None):
     """Plot the timetrace (histogram) of timestamps for a photon selection.
 
     See :func:`timetrace` to plot multiple photon selections (i.e.
@@ -423,7 +446,7 @@ def _has_cache_for(binwidth, tmin, tmax):
 
     # Plot bursts
     if bursts:
-        _plot_bursts(d, i, tmin_clk, tmax_clk, pmax=500, pmin=-500,
+        _plot_bursts(d, i, tmin_clk, tmax_clk, ax, pmax=500, pmin=-500,
                      color=burst_color)
 
     # Plot timetrace
@@ -434,37 +457,38 @@ def _has_cache_for(binwidth, tmin, tmax):
     else:
         plot_style_['label'] = str(ph_sel)
     plot_style_.update(_normalize_kwargs(plot_style, kind='line2d'))
-    plot(x, timetrace, **plot_style_)
+    ax.plot(x, timetrace, **plot_style_)
 
     # Plot burst-search rate-threshold
     if show_rate_th and 'bg' in d:
-        _plot_rate_th(d, i, F=F, ph_sel=ph_sel, invert=invert,
+        _plot_rate_th(d, i, F=F, ph_sel=ph_sel, ax=ax, invert=invert,
                       scale=binwidth, plot_style_=plot_style_,
                       rate_th_style=rate_th_style)
 
-    plt.xlabel('Time (s)')
-    plt.ylabel('# ph')
+    ax.set_xlabel('Time (s)')
+    ax.set_ylabel('# ph')
     if burst_picker and 'mburst' in d:
-        _gui_timetrace_burst_sel(d, gcf(), gca())
+        _gui_timetrace_burst_sel(d, ax.figure, ax)
     if scroll:
-        _gui_timetrace_scroll(gcf())
+        _gui_timetrace_scroll(ax.figure)
 
     if set_ax_limits:
-        plt.xlim(tmin, tmin + 1)
+        ax.set_xlim(tmin, tmin + 1)
         if not invert:
-            plt.ylim(top=100)
+            ax.set_ylim(top=100)
         else:
-            plt.ylim(bottom=-100)
+            ax.set_ylim(bottom=-100)
         _plot_status['timetrace_single'] = {'autoscale': False}
 
 # do not concatenate, timetrace should always be shown per channel
+@_ax_intercept
 def timetrace(d, i=0, binwidth=1e-3, bins=None, tmin=0, tmax=200,
               bursts=False, burst_picker=True, scroll=False,
               show_rate_th=True, F=None, rate_th_style={'label': None},
               show_aa=True, legend=False, set_ax_limits=True,
               burst_color='#BBBBBB', plot_style=None,
               #dd_plot_style={}, ad_plot_style={}, aa_plot_style={}
-              ):
+              ax=None):
     """Plot the timetraces (histogram) of photon timestamps.
 
     Arguments:
@@ -498,11 +522,12 @@ def timetrace(d, i=0, binwidth=1e-3, bins=None, tmin=0, tmax=200,
         burst_color (string): string containing the the HEX RGB color to use
             to highlight the burst regions.
         plot_style (dict): matplotlib's style for the timetrace lines.
+        ax (mpl.axes): axis where plot will be generated
     """
     # Plot bursts
     if bursts:
         tmin_clk, tmax_clk = tmin / d.clk_p, tmax / d.clk_p
-        _plot_bursts(d, i, tmin_clk, tmax_clk, pmax=500, pmin=-500,
+        _plot_bursts(d, i, tmin_clk, tmax_clk, ax, pmax=500, pmin=-500,
                      color=burst_color)
 
     # Plot multiple timetraces
@@ -523,18 +548,19 @@ def timetrace(d, i=0, binwidth=1e-3, bins=None, tmin=0, tmax=200,
                 tmax=tmax, ph_sel=ph_sel, invert=invert, bursts=False,
                 burst_picker=burst_picker_list[ix],
                 scroll=scroll_list[ix], cache_bins=True,
-                show_rate_th=show_rate_th, F=F,
+                show_rate_th=show_rate_th, F=F, ax=ax, 
                 rate_th_style=rate_th_style, set_ax_limits=set_ax_limits,
                 plot_style=plot_style)
     if legend:
-        plt.legend(loc='best', fancybox=True)
+        ax.legend(loc='best', fancybox=True)
 
 
+@_ax_intercept
 def ratetrace_single(d, i=0, m=None, max_num_ph=1e6, tmin=0, tmax=200,
                      ph_sel=Ph_sel('all'), invert=False, bursts=False,
                      burst_picker=True, scroll=False, plot_style={},
                      show_rate_th=True, F=None, rate_th_style={},
-                     set_ax_limits=True, burst_color='#BBBBBB'):
+                     set_ax_limits=True, burst_color='#BBBBBB', ax=None):
     """Plot the ratetrace of timestamps for a photon selection.
 
     See :func:`ratetrace` to plot multiple photon selections (i.e.
@@ -575,35 +601,36 @@ def ratetrace_single(d, i=0, m=None, max_num_ph=1e6, tmin=0, tmax=200,
         plot_style_['color'] = _ph_sel_color_dict[ph_sel]
         plot_style_['label'] = _ph_sel_label_dict[ph_sel]
     plot_style_.update(_normalize_kwargs(plot_style, kind='line2d'))
-    plot(times, rates, **plot_style_)
+    ax.plot(times, rates, **plot_style_)
 
     # Plot burst-search rate-threshold
     if show_rate_th and 'bg' in d:
-        _plot_rate_th(d, i, F=F, scale=1e-3, ph_sel=ph_sel, invert=invert,
+        _plot_rate_th(d, i, F=F, ph_sel=ph_sel, ax=ax, scale=1e-3, invert=invert,
                       plot_style_=plot_style_, rate_th_style=rate_th_style)
 
-    plt.xlabel('Time (s)')
-    plt.ylabel('Rate (kcps)')
+    ax.set_xlabel('Time (s)')
+    ax.set_ylabel('Rate (kcps)')
     if burst_picker:
-        _gui_timetrace_burst_sel(d, gcf(), gca())
+        _gui_timetrace_burst_sel(d, ax.figure, ax)
     if scroll:
-        _gui_timetrace_scroll(gcf())
+        _gui_timetrace_scroll(ax.figure)
 
     if set_ax_limits:
-        plt.xlim(tmin, tmin + 1)
+        ax.set_xlim(tmin, tmin + 1)
         if not invert:
-            plt.ylim(top=100)
+            ax.set_ylim(top=100)
         else:
-            plt.ylim(bottom=-100)
+            ax.set_ylim(bottom=-100)
         _plot_status['ratetrace_single'] = {'autoscale': False}
 
 # same, must be plotted per channel always
+@_ax_intercept
 def ratetrace(d, i=0, m=None, max_num_ph=1e6, tmin=0, tmax=200,
               bursts=False, burst_picker=True, scroll=False,
               show_rate_th=True, F=None, rate_th_style={'label': None},
               show_aa=True, legend=False, set_ax_limits=True,
               #dd_plot_style={}, ad_plot_style={}, aa_plot_style={}
-              burst_color='#BBBBBB'):
+              burst_color='#BBBBBB', ax=None):
     """Plot the rate timetraces of photon timestamps.
 
     Arguments:
@@ -634,11 +661,12 @@ def ratetrace(d, i=0, m=None, max_num_ph=1e6, tmin=0, tmax=200,
             timetrace.
         burst_color (string): string containing the the HEX RGB color to use
             to highlight the burst regions.
+        ax (mpl.axes): axis where plot will be generated
     """
     # Plot bursts
     if bursts:
         tmin_clk, tmax_clk = tmin / d.clk_p, tmax / d.clk_p
-        _plot_bursts(d, i, tmin_clk, tmax_clk, pmax=500, pmin=-500,
+        _plot_bursts(d, i, tmin_clk, tmax_clk, ax, pmax=500, pmin=-500,
                      color=burst_color)
 
     # Plot multiple timetraces
@@ -659,10 +687,10 @@ def ratetrace(d, i=0, m=None, max_num_ph=1e6, tmin=0, tmax=200,
                 tmax=tmax, ph_sel=ph_sel, invert=invert, bursts=False,
                 burst_picker=burst_picker_list[ix],
                 scroll=scroll_list[ix],
-                show_rate_th=show_rate_th, F=F,
+                show_rate_th=show_rate_th, F=F, ax=ax,
                 rate_th_style=rate_th_style, set_ax_limits=set_ax_limits)
     if legend:
-        plt.legend(loc='best', fancybox=True)
+        ax.legend(loc='best', fancybox=True)
 
 
 def sort_burst_sizes(sizes, levels=np.arange(1, 102, 20)):
@@ -676,7 +704,8 @@ def sort_burst_sizes(sizes, levels=np.arange(1, 102, 20)):
     return masks
 
 # plot per channel always
-def timetrace_fret(d, i=0, gamma=1., **kwargs):
+@_ax_intercept
+def timetrace_fret(d, i=0, gamma=1., ax=None, **kwargs):
     """Timetrace of burst FRET vs time. Uses `plot`."""
     b = d.mburst[i]
     bsizes = d.burst_sizes_ich(ich=i, gamma=gamma)
@@ -688,25 +717,29 @@ def timetrace_fret(d, i=0, gamma=1., **kwargs):
     t, E = b.start*d.clk_p, d.E[i]
     levels = sort_burst_sizes(bsizes)
     for ilev, level in enumerate(levels):
-        plt.plot(t[level], E[level], ms=np.sqrt((ilev+1)*15),
+        ax.plot(t[level], E[level], ms=np.sqrt((ilev+1)*15),
                  **style_kwargs)
-    plt.plot(b.start*d.clk_p, d.E[i], '-k', alpha=0.1, lw=1)
-    xlabel('Time (s)'); ylabel('E')
-    _gui_timetrace_burst_sel(d, gcf(), gca())
+    ax.plot(b.start*d.clk_p, d.E[i], '-k', alpha=0.1, lw=1)
+    ax.set_xlabel('Time (s)')
+    ax.set_ylabel('E')
+    _gui_timetrace_burst_sel(d, ax.figure, ax)
 
 # plot per channel always
-def timetrace_fret_scatter(d, i=0, gamma=1., **kwargs):
+@_ax_intercept
+def timetrace_fret_scatter(d, i=0, gamma=1., ax=None, **kwargs):
     """Timetrace of burst FRET vs time. Uses `scatter` (slow)."""
     b = d.mburst[i]
     bsizes = d.burst_sizes_ich(ich=i, gamma=gamma)
 
     style_kwargs = dict(s=bsizes, marker='o', alpha=0.5)
     style_kwargs.update(**kwargs)
-    plt.scatter(b.start*d.clk_p, d.E[i], **style_kwargs)
-    xlabel('Time (s)'); ylabel('E')
+    ax.scatter(b.start*d.clk_p, d.E[i], **style_kwargs)
+    ax.set_xlabel('Time (s)')
+    ax.set_ylabel('E')
 
 # plot per channel always
-def timetrace_bg(d, i=0, nolegend=False, ncol=2, plot_style={}, show_da=False):
+@_ax_intercept
+def timetrace_bg(d, i=0, nolegend=False, ncol=2, plot_style={}, show_da=False, ax=None):
     """Timetrace of background rates."""
     bg = d.bg_from(Ph_sel('all'))
     bg_dd = d.bg_from(Ph_sel(Dex='Dem'))
@@ -715,29 +748,30 @@ def timetrace_bg(d, i=0, nolegend=False, ncol=2, plot_style={}, show_da=False):
     plot_style_ = dict(linewidth=2, marker='o', markersize=6)
     plot_style_.update(_normalize_kwargs(plot_style, kind='line2d'))
     label = "T: %d cps" % d.bg_mean[Ph_sel('all')][i]
-    plot(t, 1e-3 * bg[i], color='k', label=label, **plot_style_)
+    ax.plot(t, 1e-3 * bg[i], color='k', label=label, **plot_style_)
     label = "DD: %d cps" % d.bg_mean[Ph_sel(Dex='Dem')][i]
-    plot(t, 1e-3 * bg_dd[i], color=green, label=label, **plot_style_)
+    ax.plot(t, 1e-3 * bg_dd[i], color=green, label=label, **plot_style_)
     label = "AD: %d cps" % d.bg_mean[Ph_sel(Dex='Aem')][i]
-    plot(t, 1e-3 * bg_ad[i], color=red, label=label, **plot_style_)
+    ax.plot(t, 1e-3 * bg_ad[i], color=red, label=label, **plot_style_)
     if d.alternated:
         bg_aa = d.bg_from(Ph_sel(Aex='Aem'))
         label = "AA: %d cps" % d.bg_mean[Ph_sel(Aex='Aem')][i]
-        plot(t, 1e-3 * bg_aa[i], label=label, color=purple, **plot_style_)
+        ax.plot(t, 1e-3 * bg_aa[i], label=label, color=purple, **plot_style_)
         if show_da:
             bg_da = d.bg_from(Ph_sel(Aex='Dem'))
             label = "DA: %d cps" % d.bg_mean[Ph_sel(Aex='Dem')][i]
-            plot(t, 1e-3 * bg_da[i], label=label,
+            ax.plot(t, 1e-3 * bg_da[i], label=label,
                  color=_ph_sel_color_dict[Ph_sel(Aex='Dem')], **plot_style_)
     if not nolegend:
-        legend(loc='best', frameon=False, ncol=ncol)
-    plt.xlabel("Time (s)")
-    plt.ylabel("BG rate (kcps)")
-    plt.grid(True)
-    plt.ylim(bottom=0)
+        ax.legend(loc='best', frameon=False, ncol=ncol)
+    ax.set_xlabel("Time (s)")
+    ax.set_ylabel("BG rate (kcps)")
+    ax.grid(True)
+    ax.set_ylim(bottom=0)
 
 # plot per channel always
-def timetrace_b_rate(d, i=0):
+@_ax_intercept
+def timetrace_b_rate(d, i=0, ax=None):
     """Timetrace of bursts-per-second in each period."""
     t = arange(d.bg[i].size)*d.bg_time_s
     b_rate = r_[[(d.bp[i] == p).sum() for p in range(d.bp[i].max()+1)]]
@@ -746,13 +780,16 @@ def timetrace_b_rate(d, i=0):
         t = t[:-1] # assuming last period without bursts
     else:
         assert t.size == b_rate.size
-    plot(t, b_rate, lw=2, label="CH%d" % (i+1))
-    legend(loc='best', fancybox=True, frameon=False, ncol=3)
-    xlabel("Time (s)"); ylabel("Burst per second"); grid(True)
-    plt.ylim(bottom=0)
+    ax.plot(t, b_rate, lw=2, label="CH%d" % (i+1))
+    ax.legend(loc='best', fancybox=True, frameon=False, ncol=3)
+    ax.set_xlabel("Time (s)")
+    ax.set_ylabel("Burst per second")
+    ax.grid(True)
+    ax.set_ylim(bottom=0)
 
 # plot per channel always
-def time_ph(d, i=0, num_ph=1e4, ph_istart=0):
+@_ax_intercept
+def time_ph(d, i=0, num_ph=1e4, ph_istart=0, ax=None):
     """Plot 'num_ph' ph starting at 'ph_istart' marking burst start/end.
     TODO: Update to use the new matplotlib eventplot.
     """
@@ -766,11 +803,11 @@ def time_ph(d, i=0, num_ph=1e4, ph_istart=0):
     start, end = b[BSLICE].start, b[BSLICE].stop
 
     u = d.clk_p # time scale
-    plt.vlines(ph_d*u, 0, 1, color='k', alpha=0.02)
-    plt.vlines(ph_a*u, 0, 1, color='k', alpha=0.02)
-    plt.vlines(start*u, -0.5, 1.5, lw=3, color=green, alpha=0.5)
-    plt.vlines(end*u, -0.5, 1.5, lw=3, color=red, alpha=0.5)
-    xlabel("Time (s)")
+    ax.vlines(ph_d*u, 0, 1, color='k', alpha=0.02)
+    ax.vlines(ph_a*u, 0, 1, color='k', alpha=0.02)
+    ax.vlines(start*u, -0.5, 1.5, lw=3, color=green, alpha=0.5)
+    ax.vlines(end*u, -0.5, 1.5, lw=3, color=red, alpha=0.5)
+    ax.set_xlabel("Time (s)")
 
 
 ##
@@ -785,7 +822,7 @@ def _bins_array(bins):
     return bins
 
 # not channel specific hidden function
-def _hist_burst_taildist(data, bins, pdf, weights=None, yscale='log',
+def _hist_burst_taildist(data, bins, pdf, ax, weights=None, yscale='log',
                          color=None, label=None, plot_style=None, vline=None):
     hist = HistData(*np.histogram(data[~np.isnan(data)],
                                   bins=_bins_array(bins), weights=weights))
@@ -799,18 +836,19 @@ def _hist_burst_taildist(data, bins, pdf, weights=None, yscale='log',
     if label is not None:
         plot_style['label'] = label
     default_plot_style.update(_normalize_kwargs(plot_style, kind='line2d'))
-    plt.plot(hist.bincenters, ydata, **default_plot_style)
+    ax.plot(hist.bincenters, ydata, **default_plot_style)
     if vline is not None:
-        plt.axvline(vline, ls='--')
-    plt.yscale(yscale)
+        ax.axvline(vline, ls='--')
+    ax.set_yscale(yscale)
     if pdf:
-        plt.ylabel('PDF')
+        ax.set_ylabel('PDF')
     else:
-        plt.ylabel('# Bursts')
+        ax.set_ylabel('# Bursts')
 
 
+@_ax_intercept
 def hist_width(d, i=0, bins=(0, 10, 0.025), pdf=True, weights=None,
-               yscale='log', color=None, plot_style=None, vline=None):
+               yscale='log', color=None, plot_style=None, vline=None, ax=None):
     """Plot histogram of burst durations.
 
     Parameters:
@@ -824,6 +862,7 @@ def hist_width(d, i=0, bins=(0, 10, 0.025), pdf=True, weights=None,
         plot_style (dict): dict of matplotlib line style passed to `plot`.
         vline (float): If not None, plot vertical line at the specified x
             position.
+        ax (mpl.axes): axis where plot will be generated
     """
     if i is None:
         burst_widths = np.concatenate([mb.width for mb in d.mburst]) * d.clk_p * 1e3
@@ -831,16 +870,17 @@ def hist_width(d, i=0, bins=(0, 10, 0.025), pdf=True, weights=None,
         weights = weights[i] if weights is not None else None
         burst_widths = d.mburst[i].width * d.clk_p * 1e3
 
-    _hist_burst_taildist(burst_widths, bins, pdf, weights=weights, vline=vline,
+    _hist_burst_taildist(burst_widths, bins, pdf, ax, weights=weights, vline=vline,
                          yscale=yscale, color=color, plot_style=plot_style)
-    plt.xlabel('Burst width (ms)')
-    plt.xlim(xmin=0)
+    ax.set_xlabel('Burst width (ms)')
+    ax.set_xlim(xmin=0)
 
 
+@_ax_intercept
 def hist_brightness(d, i=0, bins=(0, 60, 1), pdf=True, weights=None,
                     yscale='log', gamma=1, add_naa=False, ph_sel=Ph_sel('all'), beta=1.,
                     donor_ref=True, naa_aexonly=False, naa_comp=False, na_comp=False,
-                    label_prefix=None, color=None, plot_style=None, vline=None):
+                    label_prefix=None, color=None, plot_style=None, vline=None, ax=None):
     """Plot histogram of burst brightness, i.e. burst size / duration.
 
     Parameters:
@@ -871,6 +911,7 @@ def hist_brightness(d, i=0, bins=(0, 60, 1), pdf=True, weights=None,
         plot_style (dict): dict of matplotlib line style passed to `plot`.
         vline (float): If not None, plot vertical line at the specified x
             position.
+        ax (mpl.axes): axis where plot will be generated
     """
     weights = weights[i] if weights is not None else None
     if plot_style is None:
@@ -894,10 +935,10 @@ def hist_brightness(d, i=0, bins=(0, 60, 1), pdf=True, weights=None,
     if 'label' not in plot_style:
         plot_style['label'] = label
 
-    _hist_burst_taildist(brightness, bins, pdf, weights=weights, vline=vline,
+    _hist_burst_taildist(brightness, bins, pdf, ax, weights=weights, vline=vline,
                          yscale=yscale, color=color, plot_style=plot_style)
-    plt.xlabel('Burst brightness (kHz)')
-    plt.legend(loc='best')
+    ax.set_xlabel('Burst brightness (kHz)')
+    ax.legend(loc='best')
 
 
 def _get_sizes_and_formula(d, ich, gamma, beta, donor_ref, add_naa,
@@ -927,11 +968,12 @@ def _get_sizes_and_formula(d, ich, gamma, beta, donor_ref, add_naa,
 
 
 # dependent on _hist_burst_taildist
+@_ax_intercept
 def hist_size(d, i=0, which='all', bins=(0, 600, 4), pdf=False, weights=None,
               yscale='log', gamma=1, beta=1, donor_ref=True, add_naa=False,
               ph_sel=None, naa_aexonly=False, naa_comp=False, na_comp=False,
               vline=None, label_prefix=None, legend=True, color=None,
-              plot_style=None):
+              plot_style=None, ax=None):
     """Plot histogram of "burst sizes", according to different definitions.
 
     Arguments:
@@ -969,6 +1011,7 @@ def hist_size(d, i=0, which='all', bins=(0, 600, 4), pdf=False, weights=None,
         plot_style (dict): dict of matplotlib line style passed to `plot`.
         vline (float): If not None, plot vertical line at the specified x
             position.
+        ax (mpl.axes): axis where plot will be generated
 
     See also:
         - :meth:`fretbursts.burstlib.Data.burst_sizes_ich`.
@@ -1002,14 +1045,15 @@ def hist_size(d, i=0, which='all', bins=(0, 600, 4), pdf=False, weights=None,
     elif color is not None:
         plot_style['color'] = color
 
-    _hist_burst_taildist(sizes, bins, pdf, weights=weights, yscale=yscale,
+    _hist_burst_taildist(sizes, bins, pdf, ax, weights=weights, yscale=yscale,
                          plot_style=plot_style, vline=vline)
-    plt.xlabel('Burst size')
+    ax.set_xlabel('Burst size')
     if legend:
-        plt.legend(loc='upper right')
+        ax.legend(loc='upper right')
 
 # depends on  _hist_burst_taildist
-def hist_size_all(d, i=0, **kwargs):
+@_ax_intercept
+def hist_size_all(d, i=0, ax=None, **kwargs):
     """Plot burst sizes for all the combinations of photons.
 
     Calls :func:`hist_size` multiple times with different `which` parameters.
@@ -1020,7 +1064,7 @@ def hist_size_all(d, i=0, **kwargs):
     elif 'PAX' in d.meas_type:
         fields += ['nda', 'naa']
     for which in fields:
-        hist_size(d, i, which=which, **kwargs)
+        hist_size(d, i, which=which, ax=ax, **kwargs)
 
 
 def _fitted_E_plot(d, i=0, F=1, no_E=False, ax=None, show_model=True,
@@ -1028,7 +1072,7 @@ def _fitted_E_plot(d, i=0, F=1, no_E=False, ax=None, show_model=True,
                    alpha=0.5, fillcolor=None):
     """Plot a fitted model overlay on a FRET histogram."""
     if ax is None:
-        ax2 = gca()
+        ax2 = plt.gca()
     else:
         ax2 = plt.twinx(ax=ax)
         ax2.grid(False)
@@ -1060,9 +1104,11 @@ def _fitted_E_plot(d, i=0, F=1, no_E=False, ax=None, show_model=True,
     xtext = 0.6 if d.E_fit[i] < 0.6 else 0.2
     if d.nch > 1 and not no_E:
         ax2.text(xtext, 0.81, "CH%d: $E_{fit} = %.3f$" % (i+1, d.E_fit[i]),
-                 transform=gca().transAxes, fontsize=16,
+                 transform=ax2.transAxes, fontsize=16,
                  bbox=dict(boxstyle='round', facecolor='#dedede', alpha=0.5))
 
+
+@_ax_intercept
 def hist_burst_data(
         d, i=0, data_name='E', ax=None, binwidth=0.03, bins=None,
         vertical=False, pdf=False, hist_style='bar',
@@ -1150,8 +1196,6 @@ def hist_burst_data(
     assert data_name in d
     fitter_name = data_name + '_fitter'
 
-    if ax is None:
-        ax = gca()
     ax.set_axisbelow(True)
     pline = ax.axhline if vertical else ax.axvline
     bar = ax.barh if vertical else ax.bar
@@ -1277,7 +1321,6 @@ def hist_fret(
 
     For detailed documentation see :func:`hist_burst_data`.
     """
-
     hist_burst_data(
         d, i, data_name='E', ax=ax, binwidth=binwidth, bins=bins,
         pdf=pdf, weights=weights, gamma=gamma, add_naa=add_naa,
@@ -1300,12 +1343,13 @@ def hist_S(
         show_model=False, show_model_peaks=True,
         hist_bar_style=None, hist_plot_style=None, model_plot_style=None,
         kde_plot_style=None, verbose=False):
-    """Plot S histogram and KDE.
+    """
+    Plot S histogram and KDE.
 
     The most used argument is `binwidth` that sets the histogram bin width.
 
-    For detailed documentation see :func:`hist_burst_data`.    """
-
+    For detailed documentation see :func:`hist_burst_data`.    
+    """
     hist_burst_data(
         d, i, data_name='S', ax=ax, binwidth=binwidth, bins=bins,
         pdf=pdf, weights=weights, gamma=gamma, add_naa=add_naa,
@@ -1328,12 +1372,12 @@ def _get_fit_text_stats(fit_arr, pylab=True):
     return fit_text
 
 
+@_ax_intercept
 def _plot_fit_text_ch(
         fit_arr, ich, fmt_str="CH%d: $E_{fit} = %.3f$", ax=None,
         bbox=dict(boxstyle='round', facecolor='#dedede', alpha=0.5),
         xtext_low=0.2, xtext_high=0.6, fontsize=16):
     """Plot a text box with ch and fit value."""
-    if ax is None: ax = gca()
     if ich is None:
         xtext = xtext_high if fit_arr[0] < xtext_high else xtext_low
     else:
@@ -1342,14 +1386,14 @@ def _plot_fit_text_ch(
             transform=ax.transAxes, fontsize=fontsize, bbox=bbox)
 
 
+@_ax_intercept
 def hist2d_alex(d, i=0, vmin=2, vmax=0, binwidth=0.05, S_max_norm=0.8,
                 interp='bicubic', cmap='hot', under_color='white',
                 over_color='white', scatter=True, scatter_ms=3,
                 scatter_color='orange', scatter_alpha=0.2, gui_sel=False,
-                cbar_ax=None, grid_color='#D0D0D0'):
+                cbar_ax=None, grid_color='#D0D0D0', ax=None):
     """Plot 2-D E-S ALEX histogram with a scatterplot overlay.
     """
-    ax = plt.gca()
     d._calc_alex_hist(binwidth)
     ES_hist = np.sum(d.ES_hist, axis=0) if i is None else d.ES_hist[i]
     E_bins, S_bins, S_ax = d.E_bins, d.S_bins, d.S_ax
@@ -1372,7 +1416,7 @@ def hist2d_alex(d, i=0, vmin=2, vmax=0, binwidth=0.05, S_max_norm=0.8,
     im.cmap.set_under(under_color)
     im.cmap.set_over(over_color)
     if cbar_ax is None:
-        gcf().colorbar(im)
+        ax.figure.colorbar(im)
     else:
         cbar_ax.colorbar(im)
     ax.set_xlim(-0.2, 1.2)
@@ -1382,11 +1426,12 @@ def hist2d_alex(d, i=0, vmin=2, vmax=0, binwidth=0.05, S_max_norm=0.8,
     ax.grid(color=grid_color)
     if gui_sel:
         # the selection object must be saved (otherwise will be destroyed)
-        hist2d_alex.gui_sel = gs.rectSelection(gcf(), gca())
+        hist2d_alex.gui_sel = gs.rectSelection(ax.figure, ax)
 
 
+@_ax_intercept
 def hexbin_alex(d, i=0, vmin=1, vmax=None, gridsize=80, cmap='Spectral_r',
-                E_name='E', S_name='S', **hexbin_kwargs):
+                E_name='E', S_name='S', ax=None, **hexbin_kwargs):
     """Plot an hexbin 2D histogram for E-S.
     """
     if i is None:
@@ -1399,10 +1444,10 @@ def hexbin_alex(d, i=0, vmin=1, vmax=None, gridsize=80, cmap='Spectral_r',
                           cmap=cmap, extent=(-0.2, 1.2, -0.2, 1.2), mincnt=1)
     if hexbin_kwargs is not None:
         hexbin_kwargs_.update(_normalize_kwargs(hexbin_kwargs))
-    poly = plt.hexbin(E, S, **hexbin_kwargs_)
+    poly = ax.hexbin(E, S, **hexbin_kwargs_)
     poly.set_clim(vmin, vmax)
-    plt.xlabel('E')
-    plt.ylabel('S')
+    ax.set_xlabel('E')
+    ax.set_ylabel('S')
 
 # channel independent
 def plot_ES_selection(ax, E1, E2, S1, S2, rect=True, **kwargs):
@@ -1456,10 +1501,13 @@ def get_ES_range():
         print('E1={E1:.3}, E2={E2:.3}, S1={S1:.3}, S2={S2:.3}'.format(**sel))
     return sel
 
+
+
+@_ax_intercept
 def hist_interphoton_single(d, i=0, binwidth=1e-4, tmax=None, bins=None,
                             ph_sel=Ph_sel('all'), period=None,
                             yscale='log', xscale='linear', xunit='ms',
-                            plot_style=None):
+                            plot_style=None, ax=None):
     """Plot histogram of interphoton delays for a single photon streams.
 
     Arguments:
@@ -1489,6 +1537,7 @@ def hist_interphoton_single(d, i=0, binwidth=1e-4, tmax=None, bins=None,
             'us', 'ns'. Default 'ms'.
         plot_style (dict): keyword arguments to be passed to matplotlib's
             `plot` function. Used to customize the plot style.
+        ax (mpl.axes): axis where plot will be generated
     """
     unit_dict = {'s': 1, 'ms': 1e3, 'us': 1e6, 'ns': 1e9}
     assert xunit in unit_dict
@@ -1529,26 +1578,27 @@ def hist_interphoton_single(d, i=0, binwidth=1e-4, tmax=None, bins=None,
         plot_style_['color'] = _ph_sel_color_dict[ph_sel]
         plot_style_['label'] = _ph_sel_label_dict[ph_sel]
     plot_style_.update(_normalize_kwargs(plot_style, kind='line2d'))
-    plot(t_ax[:n_trim] * scalex, counts[:n_trim], **plot_style_)
+    ax.plot(t_ax[:n_trim] * scalex, counts[:n_trim], **plot_style_)
 
     if yscale == 'log':
-        gca().set_yscale(yscale)
-        plt.ylim(1)
+        ax.set_yscale(yscale)
+        ax.set_ylim(1)
         _plot_status['hist_interphoton_single'] = {'autoscale': False}
     if xscale == 'log':
-        gca().set_xscale(yscale)
-        plt.xlim(0.5 * binwidth)
+        ax.set_xscale(yscale)
+        ax.set_xlim(0.5 * binwidth)
         _plot_status['hist_interphoton_single'] = {'autoscale': False}
-    plt.xlabel('Inter-photon delays (%s)' % xunit.replace('us', 'μs'))
-    plt.ylabel('# Delays')
+    ax.set_xlabel('Inter-photon delays (%s)' % xunit.replace('us', 'μs'))
+    ax.set_ylabel('# Delays')
     # Return internal variables so that other functions can extend the plot
     return dict(counts=counts, n_trim=n_trim, plot_style_=plot_style_,
                 t_ax=t_ax, scalex=scalex)
 
 
+@_ax_intercept
 def hist_interphoton(d, i=0, binwidth=1e-4, tmax=None, bins=None, period=None,
                      yscale='log', xscale='linear', xunit='ms', plot_style=None,
-                     show_da=False, legend=True):
+                     show_da=False, legend=True, ax=None):
     """Plot histogram of photon interval for different photon streams.
 
     Arguments:
@@ -1580,6 +1630,7 @@ def hist_interphoton(d, i=0, binwidth=1e-4, tmax=None, bins=None, period=None,
         show_da (bool): If False (default) do not plot the AexDem photon stream.
             Ignored when the measurement is not ALEX.
         legend (bool): If True (default) plot a legend.
+        ax (mpl.axes): axis where plot will be generated
     """
     # Plot multiple timetraces
     ph_sel_list = [Ph_sel('all'), Ph_sel(Dex='Dem'), Ph_sel(Dex='Aem')]
@@ -1594,18 +1645,20 @@ def hist_interphoton(d, i=0, binwidth=1e-4, tmax=None, bins=None, period=None,
             hist_interphoton_single(d, i=i, binwidth=binwidth, tmax=tmax,
                                     bins=bins, period=period, ph_sel=ph_sel,
                                     yscale=yscale, xscale=xscale, xunit=xunit,
-                                    plot_style=plot_style)
+                                    plot_style=plot_style, ax=ax)
     if legend:
-        plt.legend(loc='best', fancybox=True)
+        ax.legend(loc='best', fancybox=True)
 
     if yscale == 'log' or xscale == 'log':
         _plot_status['hist_interphoton'] = {'autoscale': False}
 
+
 # TODO: condsider better method for displaying all channel, total bg histogram
+@_ax_intercept
 def hist_bg_single(d, i=0, binwidth=1e-4, tmax=0.01, bins=None,
                    ph_sel=Ph_sel('all'), period=0,
                    yscale='log', xscale='linear', xunit='ms', plot_style=None,
-                   show_fit=True, fit_style=None, manual_rate=None):
+                   show_fit=True, fit_style=None, manual_rate=None, ax=None):
     """Plot histogram of photon interval for a single photon streams.
 
     Optionally plots the fitted background as an exponential curve.
@@ -1619,6 +1672,7 @@ def hist_bg_single(d, i=0, binwidth=1e-4, tmax=0.01, bins=None,
             rate (ignoring the value saved in Data).
         fit_style (dict): arguments passed to matplotlib's `plot` for
             for plotting the exponential curve.
+        ax (mpl.axes): axis where plot will be generated
 
     For a description of all the other arguments see
     :func:`hist_interphoton_single`.
@@ -1626,7 +1680,7 @@ def hist_bg_single(d, i=0, binwidth=1e-4, tmax=0.01, bins=None,
     hist = hist_interphoton_single(d, i=i, binwidth=binwidth, tmax=tmax,
                                    bins=bins, ph_sel=ph_sel, period=period,
                                    yscale=yscale, xscale=xscale, xunit=xunit,
-                                   plot_style=None)
+                                   plot_style=None, ax=ax)
 
     if show_fit or manual_rate is not None:
         # Compute the fit function
@@ -1650,13 +1704,14 @@ def hist_bg_single(d, i=0, binwidth=1e-4, tmax=0.01, bins=None,
             label = str(ph_sel) if plt_label is None else plt_label
             fit_style_['label'] = '%s, %.2f kcps' % (label, bg_rate * 1e-3)
         n_trim = hist['n_trim']
-        plot(hist['t_ax'][:n_trim] * hist['scalex'], y_fit[:n_trim],
+        ax.plot(hist['t_ax'][:n_trim] * hist['scalex'], y_fit[:n_trim],
              **fit_style_)
 
 
+@_ax_intercept
 def hist_bg(d, i=0, binwidth=1e-4, tmax=0.01, bins=None, period=0,
             yscale='log', xscale='linear', xunit='ms', plot_style=None,
-            show_da=False, legend=True, show_fit=True, fit_style=None):
+            show_da=False, legend=True, show_fit=True, fit_style=None, ax=None):
     """Plot histogram of photon interval for different photon streams.
 
     Optionally plots the fitted background.
@@ -1668,6 +1723,7 @@ def hist_bg(d, i=0, binwidth=1e-4, tmax=0.01, bins=None, period=0,
             exponential distribution.
         fit_style (dict): arguments passed to matplotlib's `plot` for
             for plotting the exponential curve.
+        ax (mpl.axes): axis where plot will be generated
 
     For a description of all the other arguments see :func:`hist_interphoton`.
     """
@@ -1683,17 +1739,18 @@ def hist_bg(d, i=0, binwidth=1e-4, tmax=0.01, bins=None, period=0,
             hist_bg_single(d, i=i, period=period, binwidth=binwidth,
                            bins=bins, tmax=tmax, ph_sel=ph_sel, xunit=xunit,
                            show_fit=show_fit, yscale=yscale, xscale=xscale,
-                           plot_style=plot_style, fit_style=fit_style)
+                           plot_style=plot_style, fit_style=fit_style, ax=ax)
     if legend:
-        plt.legend(loc='best', fancybox=True)
+        ax.legend(loc='best', fancybox=True)
 
     if yscale == 'log' or xscale == 'log':
         _plot_status['hist_bg'] = {'autoscale': False}
 
 
+@_ax_intercept
 def hist_ph_delays(
         d, i=0, time_min_s=0, time_max_s=30, bin_width_us=10, mask=None,
-        yscale='log', hfit_bin_ms=1, efit_tail_min_us=1000, **kwargs):
+        yscale='log', hfit_bin_ms=1, efit_tail_min_us=1000, ax=None, **kwargs):
     """Histogram of ph delays and comparison with 3 BG fitting functions.
     """
     if i is None:
@@ -1715,9 +1772,9 @@ def hist_ph_delays(
             ph = ph[mask[i]]
         ph = ph[(ph < time_max_s/d.clk_p)*(ph > time_min_s/d.clk_p)]
     dph = np.diff(ph)*d.clk_p
-    H = hist(dph*1e6, bins=r_[0:1200:bin_width_us], histtype='step', **kwargs)
-    gca().set_yscale('log')
-    xlabel(u'Ph delay time (μs)'); ylabel("# Ph")
+    H = ax.hist(dph*1e6, bins=r_[0:1200:bin_width_us], histtype='step', **kwargs)
+    ax.set_yscale('log')
+    ax.set_xlabel(u'Ph delay time (μs)'); ax.set_ylabel("# Ph")
     F = 1 if 'normed' in kwargs else H[0].sum()*(bin_width_us)
 
     efun = lambda t, r: np.exp(-r*t)*r
@@ -1739,24 +1796,24 @@ def hist_ph_delays(
     t = r_[0:1200]*1e-6
     
     if rc_do:
-        plot(t*1e6, 0.65*F*efun(t, rc)*1e-6, lw=3, alpha=0.5, color=purple,
+        ax.plot(t*1e6, 0.65*F*efun(t, rc)*1e-6, lw=3, alpha=0.5, color=purple,
              label="%d cps - Exp CDF (tail_min_p=%.2f)" % (rc, efit_tail_min_us))
     if re_do:
-        plot(t*1e6, 0.65*F*efun(t, re)*1e-6, lw=3, alpha=0.5, color=red,
-             label="%d cps - Exp ML (tail_min_p=%.2f)" % (re, efit_tail_min_us))
+        ax.plot(t*1e6, 0.65*F*efun(t, re)*1e-6, lw=3, alpha=0.5, color=red,
+                label="%d cps - Exp ML (tail_min_p=%.2f)" % (re, efit_tail_min_us))
     if re_do and rg_do:
-        plot(t*1e6, 0.68*F*efun(t, rg)*1e-6, lw=3, alpha=0.5, color=green,
-             label=u"%d cps - Hist (bin_ms=%d) [Δ=%d%%]" % (hfit_bin_ms, rg,
+        ax.plot(t*1e6, 0.68*F*efun(t, rg)*1e-6, lw=3, alpha=0.5, color=green,
+                label=u"%d cps - Hist (bin_ms=%d) [Δ=%d%%]" % (hfit_bin_ms, rg,
                                                             100*(rg-re)/re))
-    plt.legend(loc='best', fancybox=True)
+    ax.legend(loc='best', fancybox=True)
 
 # TODO: update for concatenated data, probably fix bext.calc_mdelays_hist
+@_ax_intercept
 def hist_mdelays(d, i=0, m=10, bins_s=(0, 10, 0.02), period=0,
                  hold=False, bg_ppf=0.01, ph_sel=Ph_sel('all'), spline=True,
-                 s=1., bg_fit=True, bg_F=0.8):
+                 s=1., bg_fit=True, bg_F=0.8, ax=None):
     """Histogram of m-photons delays (all-ph vs in-burst ph).
     """
-    ax = gca()
     if not hold:
         #ax.clear()
         for _ind in range(len(ax.lines)): ax.lines.pop()
@@ -1797,35 +1854,36 @@ def hist_mdelays(d, i=0, m=10, bins_s=(0, 10, 0.02), period=0,
     burst_integral = np.trapz(x=bin_x[burst_domain],
                               y=mdelays_hist_y[burst_domain])
 
-    title("I = %.1f %%" % (burst_integral*100), fontsize='small')
+    ax.set_title("I = %.1f %%" % (burst_integral*100), fontsize='small')
     #text(0.8,0.8,"I = %.1f %%" % (integr*100), transform = gca().transAxes)
 
     ## MDelays plot
-    plot(bin_x, mdelays_pdf_y, lw=2, color=blue, alpha=0.5,
-         label="Delays dist.")
-    plot(bin_x, mdelays_b_pdf_y, lw=2, color=red, alpha=0.5,
-         label="Delays dist. (in burst)")
-    plt.axvline(max_delay_th_P, color='k',
-                label="BG ML dist. @ %.1f%%" % (bg_ppf*100))
-    plt.axvline(max_delay_th_F, color=purple,
-                label="BS threshold (F=%d)" % d.F)
+    ax.plot(bin_x, mdelays_pdf_y, lw=2, color=blue, alpha=0.5,
+            label="Delays dist.")
+    ax.plot(bin_x, mdelays_b_pdf_y, lw=2, color=red, alpha=0.5,
+            label="Delays dist. (in burst)")
+    ax.axvline(max_delay_th_P, color='k',
+               label="BG ML dist. @ %.1f%%" % (bg_ppf*100))
+    ax.axvline(max_delay_th_F, color=purple,
+               label="BS threshold (F=%d)" % d.F)
 
     ## Bg distribution plots
     bg_dist_y = bg_dist.pdf(bin_x)
     ibin_x_bg_mean = np.abs(bin_x - bg_dist.mean()).argmin()
     bg_dist_y *= mdelays_pdf_y[ibin_x_bg_mean]/bg_dist_y[ibin_x_bg_mean]
-    plot(bin_x, bg_dist_y, '--k', alpha=1.,
-         label='BG ML dist.')
-    plt.axvline(bg_dist.mean(), color='k', ls='--', label="BG mean")
+    ax.plot(bin_x, bg_dist_y, '--k', alpha=1.,
+            label='BG ML dist.')
+    ax.axvline(bg_dist.mean(), color='k', ls='--', label="BG mean")
     if bg_fit:
         bg_y = a*erlang.pdf(bin_x, a=m, scale=1./rate_kcps)
-        plot(bin_x, bg_y, '--k', alpha=1.)
-    plt.legend(ncol=2, frameon=False)
-    xlabel("Time (ms)")
+        ax.plot(bin_x, bg_y, '--k', alpha=1.)
+    ax.legend(ncol=2, frameon=False)
+    ax.set_xlabel("Time (ms)")
 
 
+@_ax_intercept
 def hist_mrates(d, i=0, m=10, bins=(0, 4000, 100), yscale='log', pdf=False,
-                dense=True, plot_style=None):
+                dense=True, plot_style=None, ax=None):
     """Histogram of m-photons rates. See also :func:`hist_mdelays`.
     """
     if i is None:
@@ -1845,13 +1903,15 @@ def hist_mrates(d, i=0, m=10, bins=(0, 4000, 100), yscale='log', pdf=False,
     ydata = hist.pdf if pdf else hist.counts
     plot_style_ = dict(marker='o')
     plot_style_.update(_normalize_kwargs(plot_style, kind='line2d'))
-    plot(hist.bincenters, ydata, **plot_style_)
-    gca().set_yscale(yscale)
-    xlabel("Rates (kcps)")
+    ax.plot(hist.bincenters, ydata, **plot_style_)
+    ax.set_yscale(yscale)
+    ax.set_xlabel("Rates (kcps)")
+
 
 ## Bursts stats
+@_ax_intercept
 def hist_sbr(d, i=0, bins=(0, 30, 1), pdf=True, weights=None, color=None,
-             plot_style=None):
+             plot_style=None, ax=None):
     """Histogram of per-burst Signal-to-Background Ratio (SBR).
     """
     if i is None:
@@ -1861,13 +1921,14 @@ def hist_sbr(d, i=0, bins=(0, 30, 1), pdf=True, weights=None, color=None,
     if 'sbr' not in d:
         d.calc_sbr()
     sbr = np.concatenate(d.sbr) if i is None else d.sbr[i]
-    _hist_burst_taildist(sbr, bins, pdf, weights=weights, color=color,
+    _hist_burst_taildist(sbr, bins, pdf, ax, weights=weights, color=color,
                          plot_style=plot_style)
-    plt.xlabel('SBR')
+    ax.set_xlabel('SBR')
 
 
+@_ax_intercept
 def hist_burst_phrate(d, i=0, bins=(0, 1000, 20), pdf=True, weights=None,
-                      color=None, plot_style=None, vline=None):
+                      color=None, plot_style=None, vline=None, ax=None):
     """Histogram of max photon rate in each burst.
     """
     weights = weights[i] if weights is not None else None
@@ -1878,13 +1939,14 @@ def hist_burst_phrate(d, i=0, bins=(0, 1000, 20), pdf=True, weights=None,
             d.calc_max_rate(m=10)
         max_rate = np.concatenate(d.max_rate) if i is None else d.max_rate[i]
     
-    _hist_burst_taildist(max_rate * 1e-3, bins, pdf, weights=weights,
+    _hist_burst_taildist(max_rate * 1e-3, bins, pdf, ax, weights=weights,
                          color=color, plot_style=plot_style, vline=vline)
-    plt.xlabel('Peak rate (kcps)')
+    ax.set_xlabel('Peak rate (kcps)')
 
 
+@_ax_intercept
 def hist_burst_delays(d, i=0, bins=(0, 10, 0.2), pdf=False, weights=None,
-                      color=None, plot_style=None):
+                      color=None, plot_style=None, ax=None):
     """Histogram of waiting times between bursts.
     """
     if i is None:
@@ -1894,12 +1956,14 @@ def hist_burst_delays(d, i=0, bins=(0, 10, 0.2), pdf=False, weights=None,
         weights = weights[i] if weights is not None else None
         bdelays = np.diff(d.mburst[i].start*d.clk_p)
 
-    _hist_burst_taildist(bdelays, bins, pdf, weights=weights, color=color,
+    _hist_burst_taildist(bdelays, bins, pdf, ax, weights=weights, color=color,
                          plot_style=plot_style)
-    plt.xlabel('Delays between bursts (s)')
+    ax.set_xlabel('Delays between bursts (s)')
+
 
 ## Burst internal "symmetry"
-def hist_asymmetry(d, i=0, bin_max=2, binwidth=0.1, stat_func=np.median):
+@_ax_intercept
+def hist_asymmetry(d, i=0, bin_max=2, binwidth=0.1, stat_func=np.median, ax=None):
     if i is None:
         burst_asym = np.concatenate([bext.asymmetry(d, ich=j, func=stat_func) for j in range(d.nch)])
     else:
@@ -1914,13 +1978,13 @@ def hist_asymmetry(d, i=0, bin_max=2, binwidth=0.1, stat_func=np.median):
     asym_counts_pos = counts[izero:] - counts[:izero][::-1]
     asym_counts = np.hstack([asym_counts_neg, asym_counts_pos])
 
-    plt.bar(bins[:-1], width=binwidth, height=counts, fc=blue, alpha=0.5)
-    plt.bar(bins[:-1], width=binwidth, height=asym_counts, fc=red,
+    ax.bar(bins[:-1], width=binwidth, height=counts, fc=blue, alpha=0.5)
+    ax.bar(bins[:-1], width=binwidth, height=asym_counts, fc=red,
             alpha=0.5)
-    plt.grid(True)
-    plt.xlabel('Time (ms)')
-    plt.ylabel('# Bursts')
-    plt.legend(['{func}$(t_D)$ - {func}$(t_A)$'.format(func=stat_func.__name__),
+    ax.grid(True)
+    ax.set_xlabel('Time (ms)')
+    ax.set_ylabel('# Bursts')
+    ax.legend(['{func}$(t_D)$ - {func}$(t_A)$'.format(func=stat_func.__name__),
                 'positive half - negative half'],
                frameon=False, loc='best')
     skew_abs = asym_counts_neg.sum()
@@ -1930,8 +1994,46 @@ def hist_asymmetry(d, i=0, bin_max=2, binwidth=0.1, stat_func=np.median):
 ##
 #  Scatter plots
 #
+def linear_scale(arr):
+    """
+    Returns same array, without rescalling values
+    """
+    return arr
+
+
+def log_scale(arr):
+    """Scale by log of arr"""
+    return np.log(arr)
+
+
+def kde_density(x, y, bw_method=None, rescalex=linear_scale, rescaley=linear_scale):
+    xa = rescalex(x)
+    ya = rescaley(y)
+    mask = ~np.isnan(xa) * ~np.isnan(ya) * (np.inf != x) * (np.inf != y) * (-np.inf != x) * (-np.inf != y)
+    if mask.sum() == 0:
+        raise ValueError("No valid bursts")
+    xy = np.vstack([xa[mask],ya[mask]])
+    colors = gaussian_kde(xy, bw_method=bw_method).evaluate(xy)
+    return x[mask], y[mask], colors
+
+
+@_ax_intercept
+def scatter_burst_data(d, xparam, yparam, i=0, ax=None, color_style='flat', 
+                       color_style_kwargs=None, xscale='linear', yscale='linear',
+                       **kwargs):
+    x = np.concatenate(getattr(d, xparam)) if i is None else getattr(d, xparam)[i]
+    y = np.concatenate(getattr(d, yparam)) if i is None else getattr(d, yparam)[i]
+    if callable(color_style) or color_style in ('kde', ):
+        color_style = kde_density if color_style == 'kde' else color_style
+        color_style_kwargs = dict() if color_style_kwargs is None else color_style_kwargs
+        x, y, c = color_style(x, y, **color_style_kwargs)
+        kwargs['c'] = c
+    ax.scatter(x, y, **kwargs)
+    pass
 
-def scatter_width_size(d, i=0):
+    
+@_ax_intercept
+def scatter_width_size(d, i=0, ax=None):
     """Scatterplot of burst width versus size."""
     t_ms = arange(0, 50)
     if i is None:
@@ -1945,32 +2047,38 @@ def scatter_width_size(d, i=0):
         nt = d.nt[i]
         T = d.T[i]
         bg_mean = d.bg_mean[Ph_sel('all')][i]*t_ms*1e-3
-    plot(b, nt, 'o', mew=0, ms=3, alpha=0.7,
+    ax.plot(b, nt, 'o', mew=0, ms=3, alpha=0.7,
          color='blue')
     
-    plot(t_ms, ((d.m)/(T))*t_ms*1e-3, '--', lw=2, color='k',
-         label='Slope = m/T = min. rate = %1.0f cps' % (d.m/T))
-    plot(t_ms, bg_mean, '--', lw=2, color=red,
-         label='Noise rate: BG*t')
-    xlabel('Burst width (ms)'); ylabel('Burst size (# ph.)')
-    plt.xlim(0, 10); plt.ylim(0, 300)
-    legend(frameon=False)
-
-
-def scatter_rate_da(d, i=0):
+    ax.plot(t_ms, ((d.m)/(T))*t_ms*1e-3, '--', lw=2, color='k',
+            label='Slope = m/T = min. rate = %1.0f cps' % (d.m/T))
+    ax.plot(t_ms, bg_mean, '--', lw=2, color=red,
+            label='Noise rate: BG*t')
+    ax.set_label('Burst width (ms)')
+    ax.set_ylabel('Burst size (# ph.)')
+    ax.set_xlim(0, 10)
+    ax.set_ylim(0, 300)
+    ax.legend(frameon=False)
+
+
+@_ax_intercept
+def scatter_rate_da(d, i=0, ax=None):
     """Scatter of nd rate vs na rate (rates for each burst)."""
     bw = np.concatenate([burst.width for burst in d.mburst]) if i is None else d.mburst[i].width
     nd = np.concatenate(d.nd) if i is None else d.nd[i]
     na = np.concatenate(d.na) if i is None else d.na[i]
     Rate = lambda nX: nX/bw/d.clk_p*1e-3
-    plot(Rate(nd), Rate(na), 'o', mew=0, ms=3, alpha=0.1, color='blue')
-    xlabel('D burst rate (kcps)'); ylabel('A burst rate (kcps)')
-    plt.xlim(-20, 100); plt.ylim(-20, 100)
-    legend(frameon=False)
+    ax.plot(Rate(nd), Rate(na), 'o', mew=0, ms=3, alpha=0.1, color='blue')
+    ax.set_xlabel('D burst rate (kcps)')
+    ax.set_ylabel('A burst rate (kcps)')
+    ax.set_xlim(-20, 100)
+    ax.set_ylim(-20, 100)
+    ax.legend(frameon=False)
 
 
+@_ax_intercept
 def scatter_fret_size(d, i=0, which='all', gamma=1, add_naa=False,
-                      plot_style=None):
+                      plot_style=None, ax=None):
     """Scatterplot of FRET efficiency versus burst size.
     """
     if which == 'all':
@@ -1987,24 +2095,26 @@ def scatter_fret_size(d, i=0, which='all', gamma=1, add_naa=False,
                        marker='o', markeredgewidth=0, markersize=3)
     plot_style_.update(_normalize_kwargs(plot_style, kind='line2d'))
     E = np.concatenate(d.E) if i is None else d.E[i]
-    plot(E, size, **plot_style_)
-    xlabel("FRET Efficiency (E)")
-    ylabel("Corrected Burst size (#ph)")
+    ax.plot(E, size, **plot_style_)
+    ax.set_xlabel("FRET Efficiency (E)")
+    ax.set_ylabel("Corrected Burst size (#ph)")
 
 
-def scatter_fret_nd_na(d, i=0, gamma=1., **kwargs):
+@_ax_intercept
+def scatter_fret_nd_na(d, i=0, gamma=1., ax=None, **kwargs):
     """Scatterplot of FRET versus gamma-corrected burst size."""
     default_kwargs = dict(mew=0, ms=3, alpha=0.3, color=blue)
     default_kwargs.update(**kwargs)
     E = np.concatenate(d.E) if i is None else d.E[i]
     nd = np.concatenate(d.nd) if i is None else d.nd[i]
     na = np.concatenate(d.na) if i is None else d.na[i]
-    plot(E, gamma*nd+na, 'o', **default_kwargs)
-    xlabel("FRET Efficiency (E)")
-    ylabel("Burst size (#ph)")
+    ax.plot(E, gamma*nd+na, 'o', **default_kwargs)
+    ax.set_xlabel("FRET Efficiency (E)")
+    ax.set_ylabel("Burst size (#ph)")
 
 
-def scatter_fret_width(d, i=0):
+@_ax_intercept
+def scatter_fret_width(d, i=0, ax=None):
     """Scatterplot of FRET versus burst width."""
     if i is None:        
         b = np.concatenate([mburst.width for mburst in d.mburst])*d.clk_p*1e3
@@ -2012,41 +2122,57 @@ def scatter_fret_width(d, i=0):
     else:
         b = d.mburst[i].width*d.clk_p*1e3
         E = d.E[i]
-    plot(E, b, 'o', mew=0, ms=3, alpha=0.1,
+    ax.plot(E, b, 'o', mew=0, ms=3, alpha=0.1,
          color="blue")
-    xlabel("FRET Efficiency (E)")
-    ylabel("Burst width (ms)")
+    ax.set_xlabel("FRET Efficiency (E)")
+    ax.set_ylabel("Burst width (ms)")
 
 
-def scatter_da(d, i=0, alpha=0.3):
+@_ax_intercept
+def scatter_da(d, i=0, alpha=0.3, ax=None):
     """Scatterplot of donor vs acceptor photons (nd, vs na) in each burst."""
     nd = np.concatenate(d.nd) if i is None else d.nd[i]
     na = np.concatenate(d.na) if i is None else d.na[i]
-    plot(nd, na, 'o', mew=0, ms=3, alpha=alpha, color='blue')
-    xlabel('# donor ph.'); ylabel('# acceptor ph.')
-    plt.xlim(-5, 200); plt.ylim(-5, 120)
+    ax.plot(nd, na, 'o', mew=0, ms=3, alpha=alpha, color='blue')
+    ax.set_xlabel('# donor ph.'); ax.set_ylabel('# acceptor ph.')
+    ax.set_xlim(-5, 200)
+    ax.set_ylim(-5, 120)
 
 
-def scatter_naa_nt(d, i=0, alpha=0.5):
+@_ax_intercept
+def scatter_naa_nt(d, i=0, alpha=0.5, color_style='flat', ax=None, **kwargs):
     """Scatterplot of nt versus naa."""
-    nt = np.concatenate(d.nt) if i is None else d.nt[i]
-    naa = np.concatenate(d.naa) if i is None else d.naa[i]
-    plot(nt, naa, 'o', mew=0, ms=3, alpha=alpha, color='blue')
-    plot(arange(200), color='k', lw=2)
-    xlabel('Total burst size (nd+na+naa)'); ylabel('Accept em-ex BS (naa)')
-    plt.xlim(-5, 200); plt.ylim(-5, 120)
-
-def scatter_alex(d, i=0, **kwargs):
-    """Scatterplot of E vs S. Keyword arguments passed to `plot`."""
-    plot_style = dict(mew=1, ms=4, mec='black', color='purple',
-                      alpha=0.1)
-    plot_style = _normalize_kwargs(plot_style, 'line2d')
-    plot_style.update(_normalize_kwargs(kwargs, 'line2d'))
-    E = np.concatenate(d.E) if i is None else d.E[i]
-    S = np.concatenate(d.S) if i is None else d.S[i]
-    plot(E, S, 'o', **plot_style)
-    xlabel("E"); ylabel('S')
-    plt.xlim(-0.2, 1.2); plt.ylim(-0.2, 1.2)
+    plot_style = _normalize_kwargs(dict(lw=0, s=17, alpha=alpha), 'scatter')
+    if color_style == 'flat':
+        plot_style['c'] = 'blue'
+    plot_style.update(_normalize_kwargs(kwargs, 'scatter'))
+    scatter_burst_data(d, 'nt', 'naa', i=i, **plot_style)
+    ax.plot(arange(200), color='k', lw=2)
+    ax.set_xlabel('Total burst size (nd+na+naa)')
+    ax.set_ylabel('Accept em-ex BS (naa)')
+    ax.set_xlim(-5, 200)
+    ax.set_ylim(-5, 120)
+
+
+@_ax_intercept
+def scatter_alex(d, i=0, color_style='flat', ax=None, **kwargs):
+    """
+    Scatterplot of E vs S. Keyword arguments passed to `plot`.
+    If `color_style` is 'flat' (default) will use uniform color that can be set
+    with the 'c' keyword argument.
+    If `color_style` is 'kde', then will color based on gaussian_kde density. 
+    Control color map with cmap keyword argument
+    """
+    plot_style = dict(s=10, alpha=0.1)
+    if color_style == 'flat':
+        plot_style.update(c= 'purple', ec='black', lw=1)
+    plot_style = _normalize_kwargs(plot_style, 'scatter')
+    plot_style.update(_normalize_kwargs(kwargs, 'scatter'))
+    scatter_burst_data(d, 'E', 'S', i=i, color_style=color_style, **plot_style)
+    ax.set_xlabel("E")
+    ax.set_ylabel('S')
+    ax.set_xlim(-0.2, 1.2)
+    ax.set_ylim(-0.2, 1.2)
 
 
 # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
@@ -2100,9 +2226,8 @@ def _iter_plot(d, func, kwargs, iter_ch, nrows, ncols, figsize, AX,
                     titlex, ha = 1 - titlex, 'left'
                 ax.text(titlex, titley, s, transform=ax.transAxes, ha=ha, va=va,
                         **title_kws)
-        plt.sca(ax)
         gui_status['first_plot_in_figure'] = (i == 0)
-        func(d, ich, **kwargs)
+        func(d, ich, ax=ax, **kwargs)
         if ax.legend_ is not None:
             ax.legend_.remove()
     [a.set_xlabel('') for a in AX[:-1, :].ravel()]
@@ -2167,8 +2292,8 @@ def dplot_48ch(d, func, sharex=True, sharey=True, layout='horiz',
 
 
 def dplot_16ch(d, func, sharex=True, sharey=True, ncols=8,
-               pgrid=True, figsize=None, AX=None, suptitle=True,
-               scale=True, skip_ch=None, top=0.93, bottom=None,
+               grid=True, figsize=None, AX=None, suptitle=True,
+               tile='out', scale=True, skip_ch=None, top=0.93, bottom=None,
                hspace=0.15, wspace=None, left=0.08, right=0.96, **kwargs):
     """Plot wrapper for 16-spot measurements. Use `dplot` instead."""
     assert (ncols <= 16), '`ncols` needs to be <= 16.'
@@ -2181,11 +2306,11 @@ def dplot_16ch(d, func, sharex=True, sharey=True, ncols=8,
     return _iter_plot(d, func, kwargs, iter_ch, nrows, ncols, figsize, AX,
                       sharex, sharey, suptitle, grid, scale, skip_ch=skip_ch,
                       top=top, bottom=bottom, hspace=hspace, wspace=wspace,
-                      left=left, right=right)
+                      left=left, right=right, title=tile)
 
 
 def dplot_8ch(d, func, sharex=True, sharey=True,
-              pgrid=True, figsize=(12, 9), nosuptitle=False, AX=None,
+              grid=True, figsize=(12, 9), nosuptitle=False, AX=None,
               scale=True, **kwargs):
     """Plot wrapper for 8-spot measurements. Use `dplot` instead."""
     global gui_status
@@ -2214,10 +2339,9 @@ def dplot_8ch(d, func, sharex=True, sharey=True,
             s += (u', T=%dμs' % (d.T[i]*1e6))
         if b is not None: s += (', #bu=%d' %  b.num_bursts)
         ax.set_title(s, fontsize=12)
-        ax.grid(pgrid)
-        plt.sca(ax)
+        ax.grid(grid)
         gui_status['first_plot_in_figure'] = (i == 0)
-        func(d, i, **kwargs)
+        func(d, i, ax=ax, **kwargs)
         if i % 2 == 1: ax.yaxis.tick_right()
     [a.set_xlabel('') for a in AX[:-1, :].ravel()]
     [a.set_ylabel('') for a in AX[:, 1:].ravel()]
@@ -2238,7 +2362,7 @@ def dplot_8ch(d, func, sharex=True, sharey=True,
     return AX
 
 
-def dplot_1ch(d, func, pgrid=True, ax=None,
+def dplot_1ch(d, func, grid=True, ax=None,
               figsize=(9, 4.5), fignum=None, nosuptitle=False, **kwargs):
     """Plot wrapper for single-spot measurements. Use `dplot` instead."""
     global gui_status
@@ -2256,10 +2380,9 @@ def dplot_1ch(d, func, pgrid=True, ax=None,
         s += (', #bu=%d' % d.num_bursts[0])
     if not nosuptitle:
         ax.set_title(s, fontsize=12)
-    ax.grid(pgrid)
-    plt.sca(ax)
+    ax.grid(grid)
     gui_status['first_plot_in_figure'] = True
-    func(d, **kwargs)
+    func(d, ax=ax, **kwargs)
     return ax
 
 
diff --git a/tests/test_burst_plot.py b/tests/test_burst_plot.py
index 7c7b26d..2044907 100644
--- a/tests/test_burst_plot.py
+++ b/tests/test_burst_plot.py
@@ -219,6 +219,8 @@ def ES_plots(request):
 def test_ES_plots(data_alex, ES_plots):
     d = data_alex
     bplt.dplot(d, ES_plots, i=None)
+    if ES_plots in (bplt.scatter_alex, bplt.scatter_naa_nt):
+        bplt.dplot(d, ES_plots, i=0, color_style='kde')
     for i in range(d.nch):
         bplt.dplot(d, ES_plots, i=i)
 

From 66dcf0fa18bcc8bec0dedd1c0221ebb27369883d Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Tue, 28 May 2024 11:12:53 +0300
Subject: [PATCH 060/142] Update tests.yml

---
 .github/workflows/tests.yml | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 249ca81..c9721a9 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -39,7 +39,12 @@ jobs:
       # - name: Windows 3.6 Oddities
       #   if: matrix.python-version == 3.6 && runner.os == 'Windows'
       #   run: python -m pip install pywinpty==1.1.6
-      
+      - name: MacOS install hdf5 dependencies
+        if: runner.os == 'macOS'
+        run: |
+          brew install hdf5
+          export HDF5_DIR=/usr/local/
+          export BLOSC_DIR=/usr/local/
       - name: Install Dependencies
         shell: bash -el {0}
         run: |

From 61367e513530424bc82ecc69bd0d48e59af82b00 Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Tue, 28 May 2024 11:25:36 +0300
Subject: [PATCH 061/142] Update tests.yml

---
 .github/workflows/tests.yml | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index c9721a9..00f2707 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -50,10 +50,11 @@ jobs:
         run: |
           conda install cython numpy nbconvert setuptools pytest ipython jupyter
           conda install scipy pandas matplotlib pytables
-          python -m pip install phconvert seaborn lmfit pybroom
+          conda install phconvert lmfit pybroom
+          python -m pip install seaborn build
       - name: Install project
         run: |
-          python -m pip install .
+          python -m pip install . --no-build-isolation
       - name: Download files Unix
         if: runner.os != 'Windows'
         shell: bash -el {0} 
@@ -78,5 +79,3 @@ jobs:
         shell: bash -el {0}
         run: |
           python -m pytest
-          cd notebooks
-          python ../tests/nbrun.py .

From 0a31f589d72d3b310f335113be0cd7a8ccb73c13 Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Tue, 28 May 2024 11:31:25 +0300
Subject: [PATCH 062/142] Update tests.yml

---
 .github/workflows/tests.yml | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 00f2707..1ca795f 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -2,11 +2,14 @@ name: Tests
 
 on: 
   push:
-    branch:
+    branches:
+      - plots
+      - master
       - gitactions
   pull_request:
-    branch:
-      - gitactions
+    branches:
+      - plots
+      - master
 jobs:
   build:
 

From 78c16863fa51ffe22263a1a816141cccc3812a84 Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Tue, 28 May 2024 11:34:38 +0300
Subject: [PATCH 063/142] Update tests.yml

---
 .github/workflows/tests.yml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 1ca795f..ffa212d 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -56,8 +56,9 @@ jobs:
           conda install phconvert lmfit pybroom
           python -m pip install seaborn build
       - name: Install project
+        shell: bash -el {0}
         run: |
-          python -m pip install . --no-build-isolation
+          python -m pip install . 
       - name: Download files Unix
         if: runner.os != 'Windows'
         shell: bash -el {0} 

From aa7eb5d1ccba496012a5662462597ddcb92b5edc Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Tue, 28 May 2024 11:38:23 +0300
Subject: [PATCH 064/142] Fixed setuptools_scm version requirement in
 pyproject.toml

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index b6394e9..48bcb89 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,7 +1,7 @@
 [build-system]
 requires = [
     "setuptools>=64", 
-    "setuptools_scm>=8",
+    "setuptools_scm>=6.0",
     "cython>=0.29", 
     "oldest-supported-numpy",
 ]

From 712d182665a2671b31aff6186a9c932aafb382c6 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Tue, 28 May 2024 12:15:07 +0300
Subject: [PATCH 065/142] Fixed missing try import

---
 fretbursts/phtools/burstsearch.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/fretbursts/phtools/burstsearch.py b/fretbursts/phtools/burstsearch.py
index a440645..caf0d1e 100644
--- a/fretbursts/phtools/burstsearch.py
+++ b/fretbursts/phtools/burstsearch.py
@@ -197,6 +197,7 @@ def mch_count_ph_in_bursts_py(Mburst, Mask):
     print(" - Optimized (cython) burst search loaded.")
 except ImportError:
     bsearch = bsearch_py
+    mch_count_ph_in_bursts = mch_count_ph_in_bursts_c
     print(" - Fallback to pure python burst search.")
 
 

From 6b70d13655a1f985b905b2290c80c6e98f392c5b Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Tue, 28 May 2024 12:23:22 +0300
Subject: [PATCH 066/142] Fixed import typo

---
 fretbursts/phtools/burstsearch.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/fretbursts/phtools/burstsearch.py b/fretbursts/phtools/burstsearch.py
index caf0d1e..8ac3c5c 100644
--- a/fretbursts/phtools/burstsearch.py
+++ b/fretbursts/phtools/burstsearch.py
@@ -197,7 +197,7 @@ def mch_count_ph_in_bursts_py(Mburst, Mask):
     print(" - Optimized (cython) burst search loaded.")
 except ImportError:
     bsearch = bsearch_py
-    mch_count_ph_in_bursts = mch_count_ph_in_bursts_c
+    mch_count_ph_in_bursts = mch_count_ph_in_bursts_py
     print(" - Fallback to pure python burst search.")
 
 

From 355b84d07a1cfaec519297f4c421baef2e6cd9f8 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Tue, 28 May 2024 14:32:39 +0300
Subject: [PATCH 067/142] Fixed plotting version requirements

---
 fretbursts/burst_plot.py | 58 +++++++++++++++++++++++++++++-----------
 pyproject.toml           |  1 +
 2 files changed, 43 insertions(+), 16 deletions(-)

diff --git a/fretbursts/burst_plot.py b/fretbursts/burst_plot.py
index 4fc45b3..f2f70b9 100644
--- a/fretbursts/burst_plot.py
+++ b/fretbursts/burst_plot.py
@@ -2586,26 +2586,52 @@ def alex_jointplot(d, i=0, gridsize=50, cmap='Spectral_r', kind='hex',
                  bbox=dict(edgecolor='r', facecolor='none', lw=1.3, alpha=0.5))
     return g
 
+
 def _register_colormaps():
+    from sys import version_info
+    if version_info.minor < 10:
+        from importlib_metadata import version
+    else:
+        from importlib.metadata import version
+    new = tuple(int(v) for v in version('matplotlib').split('.'))[:2] > (3, 5)
     import matplotlib as mpl
     import seaborn as sns
 
-    c = sns.color_palette('nipy_spectral', 64)[2:43]
-    cmap = mpl.colors.LinearSegmentedColormap.from_list('alex_lv', c)
-    cmap.set_under(alpha=0)
-    mpl.colormaps.register(name='alex_lv', cmap=cmap)
-
-    c = sns.color_palette('YlGnBu', 64)[16:]
-    cmap = mpl.colors.LinearSegmentedColormap.from_list('alex', c)
-    cmap.set_under(alpha=0)
-    mpl.colormaps.register(name='alex_light', cmap=cmap)
-    mpl.colormaps.register(name='YlGnBu_crop', cmap=cmap)
-    mpl.colormaps.register(name='alex_dark', cmap=mpl.cm.GnBu_r)
-
-    # Temporary hack to workaround issue
-    # https://github.com/mwaskom/seaborn/issues/855
-    mpl.cm.alex_light = mpl.colormaps.get_cmap('alex_light')
-    mpl.cm.alex_dark = mpl.colormaps.get_cmap('alex_dark')
+    if new:
+        c = sns.color_palette('nipy_spectral', 64)[2:43]
+        cmap = mpl.colors.LinearSegmentedColormap.from_list('alex_lv', c)
+        cmap.set_under(alpha=0)
+        mpl.colormaps.register(name='alex_lv', cmap=cmap)
+    
+        c = sns.color_palette('YlGnBu', 64)[16:]
+        cmap = mpl.colors.LinearSegmentedColormap.from_list('alex', c)
+        cmap.set_under(alpha=0)
+        mpl.colormaps.register(name='alex_light', cmap=cmap)
+        mpl.colormaps.register(name='YlGnBu_crop', cmap=cmap)
+        mpl.colormaps.register(name='alex_dark', cmap=mpl.cm.GnBu_r)
+    
+        # Temporary hack to workaround issue
+        # https://github.com/mwaskom/seaborn/issues/855
+        mpl.cm.alex_light = mpl.colormaps.get_cmap('alex_light')
+        mpl.cm.alex_dark = mpl.colormaps.get_cmap('alex_dark')
+    else:
+        c = sns.color_palette('nipy_spectral', 64)[2:43]
+        cmap = mpl.colors.LinearSegmentedColormap.from_list('alex_lv', c)
+        cmap.set_under(alpha=0)
+        mpl.cm.register_cmap(name='alex_lv', cmap=cmap)
+    
+        c = sns.color_palette('YlGnBu', 64)[16:]
+        cmap = mpl.colors.LinearSegmentedColormap.from_list('alex', c)
+        cmap.set_under(alpha=0)
+        mpl.cm.register_cmap(name='alex_light', cmap=cmap)
+        mpl.cm.register_cmap(name='YlGnBu_crop', cmap=cmap)
+        mpl.cm.register_cmap(name='alex_dark', cmap=mpl.cm.GnBu_r)
+    
+        # Temporary hack to workaround issue
+        # https://github.com/mwaskom/seaborn/issues/855
+        mpl.cm.alex_light = mpl.cm.get_cmap('alex_light')
+        mpl.cm.alex_dark = mpl.cm.get_cmap('alex_dark')
+
 
 
 # Register colormaps on import if not mocking
diff --git a/pyproject.toml b/pyproject.toml
index 48bcb89..65b5d1c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -29,6 +29,7 @@ classifiers = [
  ]
 requires-python = ">= 3.6"
 dependencies = [
+    "importlib_metadata;python_version<='3.9'"
     "numpy>=1.19", 
     "matplotlib>=3.0.1", 
     "scipy>=1.2", 

From 63611962c38143ba275491938f6d94875f751686 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Tue, 28 May 2024 14:46:14 +0300
Subject: [PATCH 068/142] Fixed pyproject.toml

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 65b5d1c..b0b28fd 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -29,7 +29,7 @@ classifiers = [
  ]
 requires-python = ">= 3.6"
 dependencies = [
-    "importlib_metadata;python_version<='3.9'"
+    "importlib_metadata;python_version<='3.9'",
     "numpy>=1.19", 
     "matplotlib>=3.0.1", 
     "scipy>=1.2", 

From 5dc73bcdedd2b90fc50f5cbbf3e57d2bd7723bc1 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Tue, 28 May 2024 15:07:11 +0300
Subject: [PATCH 069/142] Moved nbrun to notebooks directory

---
 .github/workflows/tests.yml   | 2 +-
 {tests => notebooks}/nbrun.py | 0
 2 files changed, 1 insertion(+), 1 deletion(-)
 rename {tests => notebooks}/nbrun.py (100%)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 226842d..daef09f 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -91,5 +91,5 @@ jobs:
       - name: Test project
         shell: bash -el {0}
         run: |
-          python fretbursts/tests/nbrun.py notebooks
+          python notebooks/nbrun.py notebooks
           python -m pytest
diff --git a/tests/nbrun.py b/notebooks/nbrun.py
similarity index 100%
rename from tests/nbrun.py
rename to notebooks/nbrun.py

From 69f4045f63d95983c413f875ffbbe0aae69b6458 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Tue, 28 May 2024 17:17:30 +0300
Subject: [PATCH 070/142] Fixed notebooks

---
 .github/workflows/tests.yml                               | 3 ++-
 fretbursts/burst_plot.py                                  | 2 +-
 .../FRETBursts - us-ALEX smFRET burst analysis.ipynb      | 8 ++++----
 3 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index daef09f..83da034 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -91,5 +91,6 @@ jobs:
       - name: Test project
         shell: bash -el {0}
         run: |
-          python notebooks/nbrun.py notebooks
           python -m pytest
+          conda install pybroom
+          python notebooks/nbrun.py notebooks
diff --git a/fretbursts/burst_plot.py b/fretbursts/burst_plot.py
index f2f70b9..589ac47 100644
--- a/fretbursts/burst_plot.py
+++ b/fretbursts/burst_plot.py
@@ -113,7 +113,7 @@ def _normalize_kwargs(kwargs, kind='patch'):
                           mfc='markerfacecolor', ms='markersize',)
     elif kind == 'scatter':
         long_names = dict(ls='linestyle', lw='linewidth',
-                          ec='edgecolor',)
+                          ec='edgecolor', color='c')
     elif kind == 'patch':
         long_names = dict(c='color', ls='linestyle', lw='linewidth',
                           ec='edgecolor', fc='facecolor',)
diff --git a/notebooks/FRETBursts - us-ALEX smFRET burst analysis.ipynb b/notebooks/FRETBursts - us-ALEX smFRET burst analysis.ipynb
index 0e48a66..cf30f84 100644
--- a/notebooks/FRETBursts - us-ALEX smFRET burst analysis.ipynb	
+++ b/notebooks/FRETBursts - us-ALEX smFRET burst analysis.ipynb	
@@ -1139,7 +1139,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "dplot(ds, scatter_alex, figsize=(4,4), mew=1, ms=4, mec='black', color='purple');"
+    "dplot(ds, scatter_alex, figsize=(4,4), lw=1, s=10, ec='black', color='purple');"
    ]
   },
   {
@@ -1901,7 +1901,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1915,9 +1915,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.13"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }

From 0ef58e664128063f264a69e029f83f2559744d34 Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Tue, 28 May 2024 17:35:00 +0300
Subject: [PATCH 071/142] Update tests.yml

---
 .github/workflows/tests.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 83da034..df9c9a6 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -79,7 +79,7 @@ jobs:
           cd notebooks
           mkdir data
           cd data
-          curl.exe --output 2182604/12d_New_30p_320mW_steer_3.hdf5 --url http://files.figshare.com/2182604/12d_New_30p_320mW_steer_3.hdf5
+          curl.exe --output 12d_New_30p_320mW_steer_3.hdf5 --url http://files.figshare.com/2182604/12d_New_30p_320mW_steer_3.hdf5
           curl.exe --output 0023uLRpitc_NTP_20dT_0.5GndCl.hdf5 --url http://files.figshare.com/2182601/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5
           curl.exe --output HP3_TE150_SPC630.hdf5 --url https://zenodo.org/record/5902313/files/HP3_TE150_SPC630.hdf5
           curl.exe --output HP3_TE200_SPC630.hdf5 --url https://zenodo.org/record/5902313/files/HP3_TE200_SPC630.hdf5

From 80e7f0388dbd4a856943d414cd53d5d993a9bb27 Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Tue, 28 May 2024 18:08:16 +0300
Subject: [PATCH 072/142] Update tests.yml

---
 .github/workflows/tests.yml | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index df9c9a6..6960e99 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -91,6 +91,5 @@ jobs:
       - name: Test project
         shell: bash -el {0}
         run: |
-          python -m pytest
-          conda install pybroom
           python notebooks/nbrun.py notebooks
+          python -m pytest

From d08a3608a5d0822e010c9a6c29aa5dea38a32cf4 Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Tue, 28 May 2024 18:31:01 +0300
Subject: [PATCH 073/142] Update tests.yml

---
 .github/workflows/tests.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 6960e99..5e20446 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -50,10 +50,10 @@ jobs:
       - name: Install Dependencies
         shell: bash -el {0}
         run: |
-          conda install cython numpy nbconvert setuptools pytest ipython jupyter
+          conda install cython numpy nbconvert pytest ipython jupyter
           conda install scipy pandas matplotlib pytables
           conda install phconvert lmfit pybroom
-          python -m pip install seaborn build
+          python -m pip install seaborn build setuptools
       - name: Install project
         shell: bash -el {0}
         run: |

From 4cd773a5c52ed8a9dffea1df2f669630b38a495b Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Wed, 29 May 2024 08:05:30 +0300
Subject: [PATCH 074/142] Update tests.yml

---
 .github/workflows/tests.yml | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 5e20446..4b74914 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -50,10 +50,7 @@ jobs:
       - name: Install Dependencies
         shell: bash -el {0}
         run: |
-          conda install cython numpy nbconvert pytest ipython jupyter
-          conda install scipy pandas matplotlib pytables
-          conda install phconvert lmfit pybroom
-          python -m pip install seaborn build setuptools
+          conda install cython numpy nbconvert pytest jupyter scipy pandas matplotlib pytables phconvert lmfit pybroom seaborn setuptools build
       - name: Install project
         shell: bash -el {0}
         run: |

From 8dea7f3dd3a286ccb99bda5b1392459d269d6754 Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Wed, 29 May 2024 08:13:39 +0300
Subject: [PATCH 075/142] Update tests.yml

---
 .github/workflows/tests.yml | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 4b74914..6e2bc6c 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -36,7 +36,7 @@ jobs:
           channels: conda-forge
           python-version: ${{ matrix.python-version }}
       - name: Upgrade pip
-        shell: bash -el {0}
+        shell: bash -l {0}
         run: python -m pip install --upgrade pip
       # - name: Windows 3.6 Oddities
       #   if: matrix.python-version == 3.6 && runner.os == 'Windows'
@@ -48,16 +48,16 @@ jobs:
           export HDF5_DIR=/usr/local/
           export BLOSC_DIR=/usr/local/
       - name: Install Dependencies
-        shell: bash -el {0}
+        shell: bash -l {0}
         run: |
           conda install cython numpy nbconvert pytest jupyter scipy pandas matplotlib pytables phconvert lmfit pybroom seaborn setuptools build
       - name: Install project
-        shell: bash -el {0}
+        shell: bash -l {0}
         run: |
           python -m pip install . 
       - name: Download files Unix
         if: runner.os != 'Windows'
-        shell: bash -el {0} 
+        shell: bash -l {0} 
         run: |
           cd notebooks
           mkdir data
@@ -71,7 +71,7 @@ jobs:
           cd ../..
       - name: Downlaod files Windows
         if: runner.os == 'Windows'
-        shell: bash -el {0}
+        shell: bash -l {0}
         run: |
           cd notebooks
           mkdir data
@@ -86,7 +86,7 @@ jobs:
           cd ..
 
       - name: Test project
-        shell: bash -el {0}
+        shell: bash -l {0}
         run: |
           python notebooks/nbrun.py notebooks
           python -m pytest

From efba2ebbf1d248311a25dff389b74046a6b7efdc Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Wed, 29 May 2024 08:57:38 +0300
Subject: [PATCH 076/142] Test without conda

---
 .github/workflows/tests.yml | 27 +++++++++++++++------------
 1 file changed, 15 insertions(+), 12 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 6e2bc6c..5aa8680 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -16,8 +16,8 @@ jobs:
     runs-on: ${{matrix.os}}
     strategy:
       matrix:
-        os: [ubuntu-22.04, windows-latest, macos-latest]
-        python-version: ["3.7", "3.9", "3.11"]
+        os: [ubuntu-latest, ] # [ubuntu-22.04, windows-latest, macos-latest]
+        python-version: ["3.9", ] # ["3.7", "3.9", "3.11"]
         exclude:
           - os: macOS-latest
             python-version: "3.7"
@@ -25,16 +25,19 @@ jobs:
             python-version: "3.7"           
     steps:
       - uses: actions/checkout@v4
-    #   - name: Setup Python ${{matrix.python-version}}
-    #     uses: actions/setup-python@v4
-    #     with:
-    #       python-version: ${{matrix.python-version}}
-      - uses: conda-incubator/setup-miniconda@v3
         with:
-          auto-update-conda: true
-          activate-environment: test
-          channels: conda-forge
-          python-version: ${{ matrix.python-version }}
+          persist-credentials: false
+          fetch-depth: 0
+      - name: Setup Python ${{matrix.python-version}}
+        uses: actions/setup-python@v4
+        with:
+          python-version: ${{matrix.python-version}}
+      # - uses: conda-incubator/setup-miniconda@v3
+      #   with:
+      #     auto-update-conda: true
+      #     activate-environment: test
+      #     channels: conda-forge
+      #     python-version: ${{ matrix.python-version }}
       - name: Upgrade pip
         shell: bash -l {0}
         run: python -m pip install --upgrade pip
@@ -50,7 +53,7 @@ jobs:
       - name: Install Dependencies
         shell: bash -l {0}
         run: |
-          conda install cython numpy nbconvert pytest jupyter scipy pandas matplotlib pytables phconvert lmfit pybroom seaborn setuptools build
+          pip install cython numpy nbconvert pytest jupyter scipy pandas matplotlib pytables phconvert lmfit pybroom seaborn setuptools build
       - name: Install project
         shell: bash -l {0}
         run: |

From 28346c3564be3748ccc9d611d2f1bcb7afc9f151 Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Wed, 29 May 2024 08:58:42 +0300
Subject: [PATCH 077/142] Update tests.yml

---
 .github/workflows/tests.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 5aa8680..68d7227 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -53,7 +53,7 @@ jobs:
       - name: Install Dependencies
         shell: bash -l {0}
         run: |
-          pip install cython numpy nbconvert pytest jupyter scipy pandas matplotlib pytables phconvert lmfit pybroom seaborn setuptools build
+          pip install cython numpy nbconvert pytest jupyter scipy pandas matplotlib tables phconvert lmfit pybroom seaborn setuptools build
       - name: Install project
         shell: bash -l {0}
         run: |

From 996825e2aafcc2741b78b45fd9d8a5d0df42105e Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Wed, 29 May 2024 09:22:59 +0300
Subject: [PATCH 078/142] Run nbrun.py from notebooks folder

---
 .github/workflows/tests.yml | 28 +++++++++++++++-------------
 1 file changed, 15 insertions(+), 13 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 68d7227..0669ac5 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -16,8 +16,8 @@ jobs:
     runs-on: ${{matrix.os}}
     strategy:
       matrix:
-        os: [ubuntu-latest, ] # [ubuntu-22.04, windows-latest, macos-latest]
-        python-version: ["3.9", ] # ["3.7", "3.9", "3.11"]
+        os: [ubuntu-22.04, windows-latest, macos-latest]
+        python-version: ["3.7", "3.9", "3.11"]
         exclude:
           - os: macOS-latest
             python-version: "3.7"
@@ -28,16 +28,16 @@ jobs:
         with:
           persist-credentials: false
           fetch-depth: 0
-      - name: Setup Python ${{matrix.python-version}}
-        uses: actions/setup-python@v4
-        with:
-          python-version: ${{matrix.python-version}}
-      # - uses: conda-incubator/setup-miniconda@v3
+      # - name: Setup Python ${{matrix.python-version}}
+      #   uses: actions/setup-python@v4
       #   with:
-      #     auto-update-conda: true
-      #     activate-environment: test
-      #     channels: conda-forge
-      #     python-version: ${{ matrix.python-version }}
+      #     python-version: ${{matrix.python-version}}
+      - uses: conda-incubator/setup-miniconda@v3
+        with:
+          auto-update-conda: true
+          activate-environment: test
+          channels: conda-forge
+          python-version: ${{ matrix.python-version }}
       - name: Upgrade pip
         shell: bash -l {0}
         run: python -m pip install --upgrade pip
@@ -53,7 +53,7 @@ jobs:
       - name: Install Dependencies
         shell: bash -l {0}
         run: |
-          pip install cython numpy nbconvert pytest jupyter scipy pandas matplotlib tables phconvert lmfit pybroom seaborn setuptools build
+          conda install cython numpy nbconvert pytest jupyter scipy pandas matplotlib pytables phconvert lmfit pybroom seaborn setuptools build
       - name: Install project
         shell: bash -l {0}
         run: |
@@ -91,5 +91,7 @@ jobs:
       - name: Test project
         shell: bash -l {0}
         run: |
-          python notebooks/nbrun.py notebooks
+          cd notebooks
+          python nbrun.py .
+          cd ..
           python -m pytest

From 8d0ab6091a801e0060f41daa84bf1b69b8897562 Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Wed, 29 May 2024 10:43:06 +0300
Subject: [PATCH 079/142] Update tests.yml

---
 .github/workflows/tests.yml | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 0669ac5..d9a880b 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -79,12 +79,12 @@ jobs:
           cd notebooks
           mkdir data
           cd data
-          curl.exe --output 12d_New_30p_320mW_steer_3.hdf5 --url http://files.figshare.com/2182604/12d_New_30p_320mW_steer_3.hdf5
-          curl.exe --output 0023uLRpitc_NTP_20dT_0.5GndCl.hdf5 --url http://files.figshare.com/2182601/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5
-          curl.exe --output HP3_TE150_SPC630.hdf5 --url https://zenodo.org/record/5902313/files/HP3_TE150_SPC630.hdf5
-          curl.exe --output HP3_TE200_SPC630.hdf5 --url https://zenodo.org/record/5902313/files/HP3_TE200_SPC630.hdf5
-          curl.exe --output HP3_TE250_SPC630.hdf5 --url https://zenodo.org/record/5902313/files/HP3_TE250_SPC630.hdf5
-          curl.exe --output HP3_TE300_SPC630.hdf5 --url https://zenodo.org/record/5902313/files/HP3_TE300_SPC630.hdf5
+          curl.exe -L --output 12d_New_30p_320mW_steer_3.hdf5 --url http://files.figshare.com/2182604/12d_New_30p_320mW_steer_3.hdf5
+          curl.exe -L --output 0023uLRpitc_NTP_20dT_0.5GndCl.hdf5 --url http://files.figshare.com/2182601/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5
+          curl.exe -L --output HP3_TE150_SPC630.hdf5 --url https://zenodo.org/record/5902313/files/HP3_TE150_SPC630.hdf5
+          curl.exe -L --output HP3_TE200_SPC630.hdf5 --url https://zenodo.org/record/5902313/files/HP3_TE200_SPC630.hdf5
+          curl.exe -L --output HP3_TE250_SPC630.hdf5 --url https://zenodo.org/record/5902313/files/HP3_TE250_SPC630.hdf5
+          curl.exe -L --output HP3_TE300_SPC630.hdf5 --url https://zenodo.org/record/5902313/files/HP3_TE300_SPC630.hdf5
           cd ..
           cd ..
 

From 7a7867aa1c8277d27adabad8a13143ec6f528343 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Wed, 29 May 2024 14:10:58 +0300
Subject: [PATCH 080/142] tests bypass indirect import path

---
 fretbursts/loader.py   | 56 ++++++++++++++++++++++--------------------
 tests/test_burstlib.py |  4 +--
 2 files changed, 32 insertions(+), 28 deletions(-)

diff --git a/fretbursts/loader.py b/fretbursts/loader.py
index e1488e9..bfacfc7 100644
--- a/fretbursts/loader.py
+++ b/fretbursts/loader.py
@@ -352,33 +352,37 @@ def photon_hdf5(filename, ondisk=False, require_setup=True, validate=False, fix_
         return loader_legacy.hdf5(filename)
 
     h5file = tables.open_file(filename)
-    # make sure the file is valid
-    if validate and version.startswith(u'0.4'):
-        phc.v04.hdf5.assert_valid_photon_hdf5(h5file,
-                                              require_setup=require_setup,
+    try:
+        # make sure the file is valid
+        if validate and version.startswith(u'0.4'):
+            phc.v04.hdf5.assert_valid_photon_hdf5(h5file,
+                                                  require_setup=require_setup,
+                                                  strict_description=False)
+        elif validate:
+            phc.hdf5.assert_valid_photon_hdf5(h5file, require_setup=require_setup,
                                               strict_description=False)
-    elif validate:
-        phc.hdf5.assert_valid_photon_hdf5(h5file, require_setup=require_setup,
-                                          strict_description=False)
-    # Create the data container
-    h5data = h5file.root
-    d = Data(fname=filename, data_file=h5data._v_file)
-
-    for grp_name in ['setup', 'sample', 'provenance', 'identity']:
-        if grp_name in h5data:
-            d.add(**{grp_name:
-                     phc.hdf5.dict_from_group(h5data._f_get_child(grp_name))})
-
-    for field_name in ['description', 'acquisition_duration']:
-        if field_name in h5data:
-            d.add(**{field_name: h5data._f_get_child(field_name).read()})
-
-    if _is_multich(h5data):
-        _photon_hdf5_multich(h5data, d, ondisk=ondisk)
-    else:
-        _photon_hdf5_1ch(h5data, d, ondisk=ondisk)
-    if fix_order:
-        sort_photon_times(d)
+        # Create the data container
+        h5data = h5file.root
+        d = Data(fname=filename, data_file=h5data._v_file)
+    
+        for grp_name in ['setup', 'sample', 'provenance', 'identity']:
+            if grp_name in h5data:
+                d.add(**{grp_name:
+                         phc.hdf5.dict_from_group(h5data._f_get_child(grp_name))})
+    
+        for field_name in ['description', 'acquisition_duration']:
+            if field_name in h5data:
+                d.add(**{field_name: h5data._f_get_child(field_name).read()})
+    
+        if _is_multich(h5data):
+            _photon_hdf5_multich(h5data, d, ondisk=ondisk)
+        else:
+            _photon_hdf5_1ch(h5data, d, ondisk=ondisk)
+        if fix_order:
+            sort_photon_times(d)
+    finally:
+        if not ondisk:
+            h5file.close()
     return d
 
 
diff --git a/tests/test_burstlib.py b/tests/test_burstlib.py
index fee877a..9fefcbf 100644
--- a/tests/test_burstlib.py
+++ b/tests/test_burstlib.py
@@ -41,7 +41,7 @@
 from fretbursts.phtools import phrates
 if has_matplotlib:
     import fretbursts.burst_plot as bplt
-
+from fretbursts.burstsearch_c import mch_count_ph_in_bursts_c
 
 # data subdir in the notebook folder
 DATASETS_DIR = u'notebooks/data/'
@@ -557,7 +557,7 @@ def test_burst_search(data):
 
 def test_mch_count_ph_num_py_c(data):
     na_py = bl.bslib.mch_count_ph_in_bursts_py(data.mburst, data.A_em)
-    na_c = bl.bslib.mch_count_ph_in_bursts_c(data.mburst, data.A_em)
+    na_c = mch_count_ph_in_bursts_c(data.mburst, data.A_em)
     assert list_array_equal(na_py, na_c)
     assert na_py[0].dtype == np.float64
 

From 3f024bbf8c43571beb2e59d75c087ba3d0caadbd Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Wed, 29 May 2024 16:28:39 +0300
Subject: [PATCH 081/142] Revert to non fb named c modules

---
 fretbursts/phtools/burstsearch.py | 2 +-
 fretbursts/phtools/phrates.py     | 2 +-
 setup.py                          | 2 +-
 tests/test_burstlib.py            | 4 ++--
 4 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/fretbursts/phtools/burstsearch.py b/fretbursts/phtools/burstsearch.py
index 8ac3c5c..523ca6e 100644
--- a/fretbursts/phtools/burstsearch.py
+++ b/fretbursts/phtools/burstsearch.py
@@ -191,7 +191,7 @@ def mch_count_ph_in_bursts_py(Mburst, Mask):
 #
 
 try:
-    from fretbursts.burstsearch_c import bsearch_c, mch_count_ph_in_bursts_c
+    from burstsearch_c import bsearch_c, mch_count_ph_in_bursts_c
     bsearch = bsearch_c
     mch_count_ph_in_bursts = mch_count_ph_in_bursts_c
     print(" - Optimized (cython) burst search loaded.")
diff --git a/fretbursts/phtools/phrates.py b/fretbursts/phtools/phrates.py
index abb943c..e0e2779 100644
--- a/fretbursts/phtools/phrates.py
+++ b/fretbursts/phtools/phrates.py
@@ -31,7 +31,7 @@
 import numpy as np
 
 try:
-    import fretbursts.phrates_c as cy
+    import phrates_c as cy
 except ImportError:
     has_cython = False
 else:
diff --git a/setup.py b/setup.py
index 1c63469..42f2124 100644
--- a/setup.py
+++ b/setup.py
@@ -6,7 +6,7 @@
 project_name = 'fretbursts'
 
 from Cython.Build import cythonize
-ext_modules = [Extension("fretbursts.burstsearch_c",
+ext_modules = [Extension("burstsearch_c",
                          [project_name + "/phtools/burstsearch_c.pyx"]),
                Extension("fretbursts.phrates_c",
                          [project_name + "/phtools/phrates_cy.pyx"],
diff --git a/tests/test_burstlib.py b/tests/test_burstlib.py
index 9fefcbf..fee877a 100644
--- a/tests/test_burstlib.py
+++ b/tests/test_burstlib.py
@@ -41,7 +41,7 @@
 from fretbursts.phtools import phrates
 if has_matplotlib:
     import fretbursts.burst_plot as bplt
-from fretbursts.burstsearch_c import mch_count_ph_in_bursts_c
+
 
 # data subdir in the notebook folder
 DATASETS_DIR = u'notebooks/data/'
@@ -557,7 +557,7 @@ def test_burst_search(data):
 
 def test_mch_count_ph_num_py_c(data):
     na_py = bl.bslib.mch_count_ph_in_bursts_py(data.mburst, data.A_em)
-    na_c = mch_count_ph_in_bursts_c(data.mburst, data.A_em)
+    na_c = bl.bslib.mch_count_ph_in_bursts_c(data.mburst, data.A_em)
     assert list_array_equal(na_py, na_c)
     assert na_py[0].dtype == np.float64
 

From df7c85a44dd5375e794d7f6218be1d10569d0861 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Wed, 29 May 2024 17:10:51 +0300
Subject: [PATCH 082/142] Fixed setup.py typos

---
 notebooks/Example - 2CDE Method.ipynb | 355 ++++++++++++++++++++++----
 setup.py                              |   2 +-
 2 files changed, 303 insertions(+), 54 deletions(-)

diff --git a/notebooks/Example - 2CDE Method.ipynb b/notebooks/Example - 2CDE Method.ipynb
index ad797ff..0ef5ed9 100644
--- a/notebooks/Example - 2CDE Method.ipynb	
+++ b/notebooks/Example - 2CDE Method.ipynb	
@@ -16,9 +16,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " - Optimized (cython) burst search loaded.\n",
+      "--------------------------------------------------------------\n",
+      " You are running FRETBursts (version 0.7.post130+g3f024bb.d20240529).\n",
+      "\n",
+      " If you use this software please cite the following paper:\n",
+      "\n",
+      "   FRETBursts: An Open Source Toolkit for Analysis of Freely-Diffusing Single-Molecule FRET\n",
+      "   Ingargiola et al. (2016). http://dx.doi.org/10.1371/journal.pone.0160716 \n",
+      "\n",
+      "--------------------------------------------------------------\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'0.13.2'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from fretbursts import *\n",
     "from fretbursts.phtools import phrates\n",
@@ -28,7 +55,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -48,9 +75,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "URL:  http://files.figshare.com/2182601/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\n",
+      "File: 0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\n",
+      " \n",
+      "File already on disk: /home/paul/Python/FRETBursts/notebooks/data/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5 \n",
+      "Delete it to re-download.\n",
+      "# Total photons (after ALEX selection):     2,259,522\n",
+      "#  D  photons in D+A excitation periods:      721,537\n",
+      "#  A  photons in D+A excitation periods:    1,537,985\n",
+      "# D+A photons in  D  excitation period:     1,434,842\n",
+      "# D+A photons in  A  excitation period:       824,680\n",
+      "\n",
+      " - Calculating BG rates ... Channel 0\n",
+      "[DONE]\n",
+      " - Performing burst search (verbose=False) ...[DONE]\n",
+      " - Calculating burst periods ...[DONE]\n",
+      " - Counting D and A ph and calculating FRET ... \n",
+      "   - Applying background correction.\n",
+      "   [DONE Counting D/A]\n",
+      "<class 'matplotlib.figure.Figure'>\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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AAADgBIl0AAAAAAAAAACcIJEOAAAAAAAAAIATJNIBAAAAAAAAAHCCRDoAAAAAAAAAAE6QSAcAAAAAAAAAwAkS6QAAAAAAAAAAOPH/ADZKB0MxcVWpAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 700x700 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 607,
+       "width": 745
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "url = 'http://files.figshare.com/2182601/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5'\n",
     "download_file(url, save_dir='./data')\n",
@@ -65,12 +133,12 @@
     "ds1 = d.select_bursts(select_bursts.size, th1=30)\n",
     "ds = ds1.select_bursts(select_bursts.naa, th1=30)\n",
     "\n",
-    "alex_jointplot(ds)"
+    "alex_jointplot(ds);"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -79,9 +147,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8000.000000000001"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "tau = 100e-6/d.clk_p\n",
     "tau"
@@ -104,9 +183,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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aWkP/LH39wJla8o/Svu+lDsPqJgMAAAAAQCdOnNCLL74oSerSpYvGjx9vcSL/FBsbq6VLl0qSMjIyNGnSJOex3r176/nnn6/w2vbt23s939ncc889evnll1VaWqo77rhDmzZtks1mO/uFqBGaWgAAwHqr/i0VmmY6XVR3s492mmZZVTZLy3lOfCNp49EK7+FVA2+Wfv6PlHXkTG3541L7oRI/OAMAAABAnXj++eeVk5MjSbrttttoZNRQbGysRo8eLUlatmyZ4djQoUOdx3xV+/btdfXVV2vBggXasmWLPvnkE1155ZVWxwpYLD8IAACslXtCWv0/Y63XJCm+T51FMM+y6tay4v20KjpnZ2q27HaHR3NVKDRCGmpaq/vwGmnP0rp5PgAAAADUc5mZmZo9e7YkKSwsTDfddJO1gQLEr7/+ahgPHDjQoiTVc8cddzhfP/XUUxYmCXw0tQAAgLVW/Vsqzj0ztgVJw+6v0wg7U42zrJLizz5Tq7vpnLyiUh06lefRXJXqd73UONFYW/53yW53ezoAAAAAwHNeffVVZWeXvUHykksuUVxcnMWJAsO6desM40GDBlmUpHrOP/98tWvXTpK0du1alxln8ByaWgAAwDo5x6U184y13tdKTTvVWYTsgmIdSs831LrFn32mVrOG4YqLCjPUdqTU0b5akhQc6tr8S90s7fy87jIAAAAAQD1kt9v1n//8xzmeNm2ahWkCS/mZWtHR0erWrZuFaarOZrNpypQpzjGztbyHphYAALDOqhel4nKzm2zBrsvqednuNGMjKjjIpk7No896nc1mc7MEYR3uqyVJva+RmnYx1r57mtlaAAAAAOBFX3/9tfbv3y9JioqK0rhx4yxOFBjS09OdX1dJ6tevn4KC/KeFMXXqVOfr5cuXa+3atRamCVz+8x0BAAACS84xac0rxlqfyVKTjnUawzy7qmOzKIWHBFfpWvMyhTtS6ripFRQsDX/AWDu2Xdq9uG5zAAAAAEA9Mn/+fOfriy++WBEREdaFCSD+up/WaT179lTPnj2d47lz51qYJnDR1AIAANZY9W+ppNyyf7Zgaei9dR7DPLuqW8uz76d15lzzTK06XH7wtO4TpGam5Rh+eE5yOOo+CwAAAAAEuIyMDH322WfO8eWXX25hmsDir/tplTdhwgTn6wULFig3N7fik1EjNLUAAEDdy0uX1r1urPWdIsV1qPMoO00ztaqyn9Zp5plaB07mKbewxCO5qiwoSLrgT8ba0fXSvhV1mwMAAAAA6oFPP/1UBQUFzvHo0aMtTBNY/H2mliSNGDHC+TonJ0cfffSRhWkCE00tAABQ99a+KhXlnBnbgqQL76nzGHa7w2V2VVI1Zmp1ah6t4CCboWbJbK2eV0mNE421H56v+xwAAAAAEOA+/vhj5+uuXbuqVatWFqYJLOWbWjExMerUqZOFaWrmvPPOU3h4uHP84YcfWpgmMNHUAgAAdasoT1o9x1jrMdGSWVpHMvKVY5pZVZ2ZWhGhwerQNMpQMy9nWCeCQ6Tz7zbWDvwkHfyl7rMAAAAAQIDKy8vT0qVLnePhw4dbFybAnDx5UsnJyc7xgAEDZLPZKr7AR0VEROjcc891jr/55htlZ1vw5tcARlMLAADUrQ1vSXknjbUL7rYkyo4UYwMqtkGoWjaq3ga/3UxLEJqXM6wzfa+TGpreIfjDc9ZkAQAAAIAA9P333ys//8ze0IMHD7YwTWAxLz3oj/tpnTZy5Ejn68LCQn3zzTcWpgk8NLUAAEDdKS2WVr1krHUeI7XsZUkc81KB3Vo2rPY7wZJMM7vMjbI6ExIunXensbZ3qZS6xZo8AAAAABBgvv32W8OYppbnrF+/3jDu37+/RUlq74ILLjCMly1bZlGSwERTCwAA1J0tH0qZh4y1C/7PmixyXSqwWzX20zrNvAfXztRsORyOWuWqsQE3Sg2aGGur/mNNFgAAAAAIMOWbWhEREUpKSrIwTWDZtm2bYdy9e3eLktRer17GN+7S1PIsmloAAKBuOBzSyn8Za22GSG3PdXt6XTAvFWiedVUV5j24cgpLdPhUfgVne1lYlDT4NmNt64dS1lFr8gAAAABAgMjJydHmzZud465duyo4ONjCRIElLS3NMI6IqN7WAL6kadOmatGihXO8d+9epaSkWJgosNDUAgAAdWPvt9LxncbaBf8nWbTxa35RqfafzDXUajJTq2WjCMVEhhpqli1BKEmDbpFCyv3wby+RVs+1Lg8AAAAABIBff/1VdrvdOfbnmUS+KDIy0jBesmSJRUk8o2fPnobxmjVrLEoSeGhqAQCAuvHLbOO4WTep81hrskjanZat8qsE2mxSlxbVn6lls9lcZniZ9+qqU1FNpT5TjLVfX5cKc6zJAwAAAAABYO3atYZx165dLUoSmPr162cY33vvvfrDH/6gd955R998842WLVvm/Dh27JhFKavO3NRavXq1RUkCD00tAADgfcd2SL8tN9bO+YMUZN2PIub9tNo3iVJkWM2WjjDP8DLfu86de7txXJApbXjbmiwAAAAAEADKLz0oSYmJidYECVDTp083zNYqLCzUyy+/rOuvv15jx47VRRdd5Pw4cOCAhUmrxtzU2rRpk0VJAg9NLQAA4H2//Nc4btBE6n2tNVn+vx2m/bTMe2NVh3mmlvneda5pZ6nreGPtl9lSaYk1eQAAAADAz+3YscMwbteunUVJAlO7du30/vvvKzo6utLzQkJC1Lt37zpKVXPm7w/z9w9qjqYWAADwrtwT0qYFxtrAm6XQSPfn1xHzbKqkGuyn5bw23nht8slc5RVZ3EA69w7jOOOgtPNza7IAAAAAgJ/budO4R3Tbtm0tShK4Lr/8cu3evVuPPfaYLrzwQjVv3lxhYWGGc7p3767w8HCLElZdfHy8YXzgwAHl5+dblCaw0NQCAADete41qbTwzDgoVBp0q3V5JDkcDjcztWre1OrcvKGCbOXvL+1Os3gPq3bnSa2Ma5Jr9VxrsgAAAACAH0tLS1NOjvF3vObNm1uUJrDFx8frkUce0Q8//KC0tDQVFhbK4XA4P/xlGT9zU8tut2v//v0WpQksNLUAAID3lBRKa+YZa72ulhq2tCbP/5eaVaDM/GJDrVvLmi8/GBkWrMSmUYbajhSL99Wy2Vxnax38WUrZ7P58AAAAAIBbhw8fNowjIyPPukwe6rcmTZq4zDI7dOiQRWkCS4jVAQAAQADbtkjKPWasnTPTmizlbD9qbDg1DA9RQuPaLYeYFN9I+47nOsc7rW5qSVLS5VJ0Cykn7Uxtzf+kK/5jXSYAAACgnsjPz9fXX3+tH374QRs2bND+/fuVnp6uvLw8hYWFKTY2Vh06dFD//v01atQoXXzxxX6xrJo3ZWdn68cff9TWrVuVkpKinJwchYeHq3nz5urRo4eGDx+uJk2a1HkuczOiWbNmdZ4B/qdly5Y6ePCgc2xujqJmaGoBAADvWfuKcZx4oRRv/Yau5qZWUqtGstlsFZxdNUktG+rLzSnO8Y7U7ErOriMhYWX7l3331JnaloXSRY9JDeKsywUAAAAEsH379umZZ57R22+/rdzcXLfn5OfnKz8/XykpKVq5cqVeeuklxcbG6vbbb9d9992nmJiYKj+vtLRUo0aN0vfff+9ybNy4cfryyy9r9fvOokWLNHHiRJd6eHi4Vq1apf79+1d47axZs/Too4+61B0Oh2H8yy+/6JlnntGXX36poqKiCu8XFBSkMWPG6L777tPIkSOr8VnUTmpqqmHcqFHNl69H/REfH29oaqWkpFRyNqqK5QcBAIB3HN0oHV5rrA2eYUkUs+2mWVTda7Gf1mndWhrvsSMly+UXNUsMmF62j9lpJQXShresywMAAAAEqOLiYj3yyCNKSkrS3LlzK2xoVSQjI0NPPPGEkpKStHjx4ipfFxwcrPfee8/tHk+LFy/W008/Xa0c5e3fv1/Tp093e+yFF16otKFVFfn5+frd736nc889V4sWLaq0oSWV7Uu0ZMkSjRo1SpMmTdKpU6dq9fyqMj+nYcOaL1+P+qNBgwaGcV19vwY6mloAAMA71r1qHDdsJXUdb00WE5emVqvaN7WSTPfILijR0cyCWt+31hq2kLpfYaytfUWyl1qTBwAAAAhAqampGjZsmB5//PGzNmbOJiUlRZdeeqleeumlKl8THx+vd955R0FBrn/ufeSRR/TDDz9UO0dRUZGuueYaZWZmuhy75pprNHNm7ZaWP3XqlEaMGKFXXnnl7Ce78eGHH2rgwIHat29frXJUhbkZwX5aqIqIiAjDOCMjw5ogAYamFgAA8Lz8DGnLh8bagJukYOtXPs4uKNaBk3mGmidmarWKiVDDCOPn5xP7aknSkNuM44yD0u4l1mQBAAAAAszRo0c1bNgw/fzzzxWeExQUpF69emns2LGaMmWKLr/8cg0ePFhhYWFuz7fb7frjH/+o1157rco5Ro8erUceecSlXlpaqilTpuj48eNVvpck/elPf9K6detc6p06dapxI+q04uJiTZw4UatXr67Vffbt26fhw4fryJEjtbrP2Zgbe+YZOIA7NLW8g6YWAADwvE3vS8XlGkdBIVL/G6zLU85O015XIUE2dW5R+3fZ2Ww2JZmWIDTv3WWZhEFSfB9jbfVca7IAAAAAASQvL0+XXnqpdu/e7fb4kCFDtGDBAqWnp2vz5s1asmSJ3n33XX366adavXq1Tp48qXfeeUedOnVye/0dd9yhHTt2VDnPX//6V40aNcqlfvToUU2dOlV2u71K91m4cKFmz57tUo+IiNDChQtrvfze448/ru+++85QS0pK0pNPPqn169crLS1NBQUF2r9/v77++mvdeuutFT7z0KFDuuqqq6r8udVEYWGhYRwSYv0bNuH7zE2t2s7iRBmaWgAAwLMcDtelB7tdKjWKtyaPibnR1Kl5tMJDgj1y76R44y9Z5mUOLWOzSYNNs7X2fy+d/M2aPAAAAECAuPvuu7VhwwaXenR0tN566y398ssvuuaaaxQTE+P2+ujoaF133XXavn27Zsxw3YM4Pz9fU6ZMUXFxcZXyBAUF6d1331V8vOvvX0uXLtUTTzxx1nvs3btXt956q9tj//rXv9S3b98qZanMU0895XwdHh6uf/zjH9qyZYseeOAB9evXT82bN1d4eLgSExM1ZswYzZs3T9u3b9e4cePc3m/16tV68cUXa52rIuavP00tVIW5qVXVf49ROZpaAADAs5J/lE6Y3qU46BZrsrhhbmp5YunB03q0Mv6i6jNNLUnqeZUU2dhYW/+GNVkAAACAAPDjjz9q3rx5LvWYmBj99NNPuv7666t8r9DQUM2dO1d//OMfXY5t2rRJCxYsqPK9mjdvrvfee0/Bwa5v3ps1a5ZWrFhR4bWFhYWaNGmSsrJcf5eZMmWKbrvtNjdXVd/pP+6HhIRo4cKF+vOf/+w2b3kJCQn64osvdO2117o9/vDDD+vAgQMeyWdGUws1QVPLO2hqAQAAz1prWlu9aRcp8UJrsrhhbjR1b+W5ppb5XgdO5imrwEd+aA2NkPpcZ6xteEcqYfkDAAAAoCYeeOABl5rNZtPChQvVp08fN1ec3XPPPadBgwa51P/5z39W6z7Dhg3To48+6lK32+267rrrlJaW5va6u+66Sxs3bnSpd+nSRf/73/+qlaEqnn/+eV122WVVPj8oKEhvvfWWBg8e7HIsLy9PL730kifjGZ5bHs2Js5s/f74cDoccDoeSk5PrzbPLM3+fmL+PUDN8FQEAgOdkpUg7vjDWBt1atvydDygutWtXmnFPLU/O1OrcIlohQcbPdYev7KslSQNuNI7zTki7vrQmCwAAAODH1q5dq5UrV7rUJ0+erIsuuqjG9w0NDdXjjz/uUl+/fr1+/vnnat3rwQcf1NixY13qqampuu6661z2oHrvvfc0d67r3run99GKjq79XsTl9e3bV3fccUe1rwsNDdXs2bNlc/N75vz58132v/KE0NBQw7igoMDjz0DgMX+fhIWFWZQksDBPEgAAeM76NyVH6ZlxaAOpz2Tr8pjsO56rohLjL25JHmxqhYcEq3OLhtpRbjbYtqNZGtKhiceeUSvNukrtzpcOlPvle93rUo8rrcsEAADqFbvdoVN5zBSvjxo3CFNQkG+82c0TXn/9dbf1WbNm1freY8aMUZcuXbR7t3FZ92+//Vbnnntule9js9n09ttvq2/fvjpy5Ijh2PLly/Xoo486Z3Pt3r3b7Z5ekvTSSy+pd+/e1fwszu6xxx6r8cyVgQMHasKECVq0aJGhfvLkSS1atEiTJ3v291CaWqgJ8/eJ+fsINUNTCwAAeIa9VPp1vrHWa5IU4X5DZCtsT8k0jFvFRKhxlGffKdWjVSNDU8un9tWSpAE3GZta+7+XTv4mNeloWSQAAFB/nMor0oDHl1kdAxb49eHRahIdbnUMj/niiy9cav369VOXLl08cv8RI0a4NLV++umnat+nadOmWrBggYYPH66SkhLDsccff1wXXnihzj//fE2aNEk5OTku10+dOlW33nprtZ9blVzjxo2r1T2mTZvm0tSSpBUrVni8qRUVFWUY09RCVZi/T8zfR6gZlh8EAACesfdbKfuosTboFmuyVGD7Ue/tp+W8p2nm1zZfWn5QkpIulyJijbX1b1oSBQAAAPBHv/32mw4dOuRSv/jiiz32jAEDBrjUqrv84Gnnn3++nnjiCZe63W7X1KlTddNNN2nz5s0ux7t16+Z2OUJPuOKKKxQSUrv5FpdccokiIyNd6qtXr67Vfd2JjY01jL2xxOFps2bNks1m87mP4cOHe+1zDlTmplbjxo0tShJYaGoBAADP2GBqjLTqJ8XXbHNkbzHPmvLkflqn9TA1yvakZauwpLSCsy0QGiH1vc5Y2/iOVMIyQAAAAEBVrF+/3m29e/fuHntG06ZNXWpZWVnKyqrZm+buu+8+XXLJJS71Y8eO6YMPPnCpR0ZGauHChV6bWeKuaVddYWFh6tmzp0t969atys3NrfX9yzM3I5ipharIz883jGlqeQbLDwIAgNrLOS7tWmys9ZtmTZYKOBwO7UjJNtS8MVMryXTPErtDe9Jy1LO17yzDqP43Sr/898w497i06yupxwTLIgEAAAD+wrws4GnvvPOOli3zzPKaKSkpbuvp6elq1Kj6v8fYbDa9+eab6tevnw4ePHjW82fPnu22YeQpffv29dh91q5da6iVlpYqOTlZPXr08MgzJNcmY3p6usfujcBlbn66a1aj+mhqAQCA2tv8vmQvtz57SKTU62rr8riRllWo9FzjbKTu8Z5vNDWKCFXbuAY6mJ7nrG0/muVbTa3m3aS250oHyy1f8ut8mloAAMDrGjcI068Pj7Y6BizQuIFn97K10pEjR9zWlyxZ4vVnp6enKzExsUbXxsXFacGCBRo6dKiKi4srPO+GG27Q9OnTa5iwalq3bu2R+7Rq1cptPSMjwyP3Py0hIcEwTk1NlcPhkM1m8+hzEFjS0tIMY/P3EWqGphYAAKgdh0Na/5ax1v0KKcKHmjiStqdkGsYNw0OU0Nh1/XVP6NGqkaGpte1opqQ2XnlWjQ2Ybmxq7Vshpe+T4jpYlwkAAAS8oCCbmkSHWx0DqJXMzMyzn+Ql2dnZZz+pEuecc44eeughzZo1y+3x7t27a86cObV6RlXUZLaZOzEx7n/vPHXqlEfuf5q5GVFcXKyTJ08y8wYVcjgcNLW8hKYWAAConcNrpRO7jLX+vrX0oFQ2W6q8pPhGCgryzrvqerRqpMVbU53jbUdrtu69V3W/XFr8Z6kg40xtw9vSqL9aFgkAAADwB1bup+RwOGp1fWFhoT755JMKjzdu3FhhYd6fVdewYUOv3qeme49VpE2bNrLZbIav/9GjR73S1Lr//vt19913e/y+tRUSQiuhOo4dO+YyI7Jt27YWpQksfCcCAIDaWf+mcRzXQWp3vjVZKrE9xfhLjTf20zqtRyvjuwV3pGTJbnd4rYlWI6GRUp/J0uqXz9Q2vS+NeEgKCrYuFwAAAODjgoP99+flu+66Sxs3bqzw+MqVK/Xwww/r6aef9mqOgoICRUVFeeQ+7kRERNT63uVFRkaqXbt2Sk5OdtZSUlLUu3dvjz5HKsvu6fyoe0ePHjWMGzVqpPj4eIvSBJYgqwMAAAA/VpgjbVtkrPW7XvLBdcXNM7W6x3uvqWVumOUWlepAueUIfUa/643jrCPS/h+syQIAAAD4iYqaMYcOHZLD4fDqx/Dhw2uce8GCBZo7d+5Zz3vmmWe0ePHiGj+nKjw1k6qi+zRu3Ngj9y8vKSnJMDY3Leozm83m1x/eYN57z/z9g5qjqQUAAGpu2yKpKOfM2BYk9bnOujwVyCksUfJJY1PJmzO1mjcMV9No45IdZftq+ZiWvco+ytv4rjVZAAAAAD9R0WyL/fv313GSqtuzZ49+97vfVelch8OhG264QYcPH/ZanvT0dK/eJzY21iP3L69Hjx6G8a5duyo4E3Btenbv3t2iJIGHphYAAKi5DW8Zx53HSI18bzr9TtPSgyFBNnVqHu2159lsNnU3LUFoninmM/pONY53fC4V+GADDgAAAPAR7du3d1vfs2dPHSepmoKCAk2aNEnZ2dkux26++WZdeeWVLvUTJ05o8uTJKikp8UqmrVu3evU+LVu29Mj9yxs4cKBhvGXLFo8/A4Fj3759hrH5+wc1R1MLAADUzPFd0qHVxlq/adZkOQvzflqdmkcrItS76+Cblzfc5qtNrV6TpKBy26yW5EvbPrEsDgAAAODr+vfv77a+bNmyOk5SNXfddZc2bdrkUu/Zs6f+85//6LXXXnPbqFu5cqUeeughr2SqbF+v6nD3ebVq1corexcNHjzYMPZUYy4QeHvZTW9/eIP5e9z8/YOao6kFAABqxrxMXVRzqctYa7KcxbYjdbef1mk9WvlJUyuqqdTlYmONJQgBAACACvXu3VuNGrn+TrFkyRKvzWyqqffee0//+9//XOpRUVH64IMPFBkZqdjYWC1YsEBhYWEu5z377LP66quvPJ7rm2++qfU9Nm3apNTUVJf6kCFDan1vd9q3b68WLVo4xwcPHvTY3mAIPOWbWhEREerdu7d1YQIMTS0AAFB99lJpy0Jjrc+1UnCoNXnOYluKcTk9b+6ndZq5qXUip1DHsgu8/twa6WvaB+3QL9LJ36zJAgAAAPi40NBQXXLJJS71U6dO6a233nJzhTV2796tGTNmuD02Z84cJSUlOceDBg3SM88843Ket/bXWr9+vXbs2FGre7z99ttu6+edd16t7luZkSNHGsbM1oI7x44dU1pamnN83nnnuW0ao2ZoagEAgOpL/lHKOmKs9ZliTZazKCqxa1eqce34Hqb9rrwhsUmUGoQZlzj02dlancdIDZoaa8zWAgAAACp08803u60/+uijKiwsrOM0rgoKCnTNNdcoJyfH5djNN9+sadNcl46/66673O6vdfLkSV177bUen4X2r3/9q8bXZmRk6I033nCpBwcH69prr61FqsqNHj3aMF6/fr3XngX/tWHDBsPY/H2D2qGpBQAAqm/TAuO4ZS+pRQ9rspzF7rRsFZca18iui5laQUE2JZmWOdzuq02t4FCp9zXG2qb3ymbkAQAAAHAxevRo9e3b16V+4MAB3XbbbXUfyOSPf/xjpftoVaSi/bVWrVqlBx980KMZX3nllRo3hf72t7/p+PHjLvXx48erTZs2tY1WobFjx8pmsznHy5cv99qz4L/M+2nR1PIsmloAAKB6inKl7Z8aa70nW5OlCrYdNS492K5JA8VE1s0yia77amVWcKYPMC9BmHVE2v+DNVkAAAAAP/D888+7rb/xxht66KGH5HA43B6vrszMTL344otVPv/dd9/VvHnzXOpRUVFauHChIiMjK7y2sv21nnvuOX355ZdVznE2drtdkydP1rFjx6p13YcfflhhY+7222/3RLQKtW7dWoMHD3aOv//+e9ntdq8+M5ANHTpUNpvNox/9+/e3+tPSqlWrnK9btWqlgQMHWpgm8NDUAgAA1bPzS6k498zYFiT1utq6PGex9YhxdlTPOlh68DRzU8tnZ2pJZbPtWvYy1liCEAAAAKjQyJEjK5yV9eSTT2r8+PE6cuSI2+NVsW3bNt1zzz1q06aN/vKXv1Tpml27dlWY6eWXX1a3bt3Oeo9BgwbpH//4h0vd4XDoxhtv1KFDh6qUpSr27NmjsWPHav/+/VU6//3339fUqVPdNpImTJigsWPHeixbRSZOnOh8nZ6e7jIrB1VTXFysdevWefy+559/vsfvWR1FRUWGGXwTJkwwzO5D7dHUAgAA1bPpfeO440ipYUtrslTBVtPsqB6tvb/04Gnd440NtOSTecouKK6z51db36nG8Y7PpcJs9+cCAAAA0D//+U+dd955bo8tWbJEHTp00PTp07VixQrl5+dXeq9jx47p66+/1oMPPqiuXbuqZ8+eeuGFF5SdXbWfySvbR+uWW27R9ddfX6X7SNLdd9+tCRMmuNRPnjypyZMn13p/reHDhztfb9y4Ub169dJTTz2lo0ePupzrcDi0atUqXXXVVZoyZYqKiopczomNjdV///vfWmWqqsmTJyso6Myf1VmCsGbWr19/1n8nasLqptZPP/1k+HfwqquusjBNYAqxOgAAAPAj2anSvhXGmg8vPVhSateOFOtmanVpGa2QIJtK7GeWHdmRkq3B7ePqLEO19JokffOwZP//v6CW5Es7vpD6TrE2FwAAAOCjIiMj9fnnn+viiy/W2rVrXY4XFRVp/vz5mj9/vkJDQ9WjRw81a9ZMcXFlvxNkZGQoMzNTBw8edNvQqY4777xTmzdvdqn36tVLL730UrXv9/rrr2vjxo1KTk421FetWqUHHnhAzz77bE2j6tVXX9WAAQOUkZEhScrNzdWDDz6ohx9+WD179lRCQoIaNWqk1NRU7dmzp9IZbzabTfPmzVN8fHyN81RH27ZtNWrUKC1dulSStHTpUt1777118uxA0q5dO+fXsCJ///vf9cMPZ5bFf/bZZ93uZVfekCFDPBGvxhYvXux83aZNG0MDF55BUwsAAFTdloWSo9wyD2HRUrdLrMtzFvtO5Kqg2LgshXlJQG8KDwlWp+bR2pl65p2V249m+m5TK6qp1HGUtOfrM7UtH9DUAgAAACoRFxenFStW6KabbtKHH35Y4XnFxcVeW6runXfe0SuvvOJSj4qK0gcffFDpPloVOb2/1gUXXKDiYuOKE88//7yGDRumSy+9tEZ5O3TooI8//ljjx49XQUGBs26327V582a3zTl3bDab/vvf/+rqq+t2Sfxbb73V2ZBZvny5jh8/rmbNmtVpBn/XsmVLtWxZ+aovd911l2F8ww03qHnz5t6MVWvlm1rTpk0zzOqDZ/AVBQAAVbdpgXGcdLkU1sCaLFWw9Yhx6cFWMRFqEh1epxl6mGaGbfPlfbUkqfc1xvG+76TsNEuiAAAAAP4iKipKCxcu1JtvvunxGUPR0dGVLh24a9cu/f73v3d7rKr7aFVk8ODBeuaZZ1zqnthfa8SIEVq6dGmNm0GxsbH68MMPK/zcvWnixIlq166dJKmkpEQffPBBnWcIdHl5edq1a5dz3Lp1a59vaB04cEDbtm2TVNZwnT59usWJAhNNLQAAUDWpW6W0LcZaH99delCSth4xNpB6tK67pQdP626aGebzTa2u46TQqDNjh13a9rF1eQAAAAA/Mm3aNO3Zs0f//ve/lZSUVOP7xMbG6sorr9Sbb76p1NRUt7OwJCk/P1+TJk3yyD5aFalof6309HRde+21tdpf64ILLtD27dt16623KiwsrErXBAcHa9q0adqyZYsmTpxY42fXRkhIiGHJwXfeeceSHIFs8+bNKi0tdY779etnYZqqeffdd52vx40bp06dOlmYJnCx/CAAAKiaze8bx41aS4kXWpOlirYeNc7Uqsv9tE4zL3e451i2ikrsCgvx0fcWhUVJSZdKm8vNytv8gXTOH6zLBAAAAPiRqKgo3Xnnnbrzzju1Y8cOLV26VGvXrtWuXbt06NAhZWZmqrCwUBEREWrYsKEaNWqktm3bqlu3burWrZuGDBmiAQMGVGnZssjIyCov1VcbixYt8tq9mzZtqnnz5unvf/+7Pv74Yy1fvlzbtm3T0aNHlZubq7CwMLVo0UI9evTQiBEjNHnyZLVu3dprearqlltu0d///ncdO3ZMP//8s5KTk5WYmGh1rICxYcMGw7h///4WJam68s1N89KJ8ByaWgAA4OzspdIW09rwvSZJPrw2tN3u0HbTrKieretuP63TzDO1iksd2p2WrZ4WzBqrsl7XGJtaR9dLJ3+TmnS0LhMAAADgh5KSkmo1Y6s+admypWbOnKmZM2daHaVKIiMjdffdd+vBBx+UJM2fP1+zZs2yNlQAWb9+vWHs602tNWvWOJce7NOnj8aMGWNxosDlu3+JAgAAvuPASik7xVjz8aUHD6TnKafQuAyGFY2kRhGhatfEuO/YFtNeXz6nw3ApyrSu/WbWiAcAAACA8mbOnKmYmLLfM2fPnq38/HyLEwUO80wtX19+cM6cOc7Xf/3rXy1MEvhoagEAgLMzz9Jq0Utq7tvvNtxqahw1jQ5X84bhlmQxN9N8vqkVHCL1MK1Nv+UDyeGwJg8AAAAA+KCYmBg99NBDkqQTJ07otddeszhRYCguLtbWrVud4yZNmqht27YWJqrcyZMntWBB2WonvXr10pVXXmlxosBGUwsAAFSupEja/qmx1usqa7JUwzY3Sw/abDZLsvQyNbXMDTef1Psa4zh9n3RkvftzAQAAAKCeuvvuu9W9e3dJ0gsvvKDS0lKLE/m/7du3q7Cw0Dn29aUHy8/Se/bZZy3720N9QVMLAABU7rflUkGGsWaexeODth01No56trJuD6vepqbWzpRsFZXYLUpTRa0HSI3bG2tbFlqTBQAAAAB8VGhoqGbPni1J2rdvnz788MOzXIGz8aelB/Py8vSf//xHkjRu3DiNHTvW4kSBj6YWAACo3NaPjOOEwVLjdtZkqSKHw+EyG6pn60YWpZF6mJpaRaV27U7LtihNFdlsrrO1tn4klZa4Px8AAAAA6qnhw4drypQpksr2UyoqKrI4kX9bv964Sogvz9T697//rePHjysiIkL/+te/rI5TL9DUAgAAFSvKk3Z+aaz1utqaLNVwNLNAp/KKDbUeFs7UiokMVbsmDQw1n99XS5J6TTKOc49J+7+3JgsAAAAA+LDnn39esbGx2r17N82NWtq0aZNh7KsztTIyMvTss89Kkv72t7+pS5cuFieqH2hqAQCAiu1eIhXnnhnbgqTuEyyLU1XmWVoxkaFKaBxpUZoyPU2ztfyiqdW0sxTf11gzz9wDAAAAACg+Pl7z58+XJD3++ONKSUmxNpAf2717t/N1eHi4OnXqZGGaij3yyCNKT0/X4MGDde+991odp96gqQUAACpmbmAkXig1bGFNlmrYZmoY9WjVyPKNWnuZmlrmxpvPMs/W2vGFVFLo/lwAAAAAqMeuuOIK3XPPPcrOztZf/vIXq+P4pYKCAqWmpjrHbdq0UVCQ77UxNm7cqDlz5igmJkbvv/++QkJCrI5Ub/jedwMAAPANBZnSnm+MNT9YelCSth7NMozNs6Ss0NuUYWdKtopK7BalqYYeE4zjwkzptxWWRAEAAAAAX/f000/rvPPO09tvv62vv/7a6jh+Jz8/3zCOjLR21RV3ioqKdOONN8put2v+/Plq37691ZHqFZpaAADAvR1fSKXlNrcNCpWSLrMuTzWYZ0H1aNXIoiTlMpiaWkWldu1Oy7YoTTXEJEhtzjHWtn1sTRYAAAAA8HEhISFasGCBmjRpohtuuMEw6whnFx4ebhjv379fOTk5FqVx78EHH9TmzZv19NNPa8KECVbHqXdoagEAAPe2fmgcd75IimxsTZZqOJZdoGPZxuXxfGGmVkxkqNo1aWCo+cW+WpLUc6JxvPMrqbjAmiwAAAAA4OMSEhL01VdfKT8/X9OmTZPD4bA6kt9o0KCB2rVr5xzn5OTowgsv1Jw5c7R48WItW7ZMW7ZssSzfhx9+qOeff1633367/vznP1uWoz6jqQUAAFzlHJf2fW+s9bzKmizVtOWwsVEUFRas9k2iLEpjZG6u+U1TK+lySeX2JCvKlvYusywOAAAAAP8wa9YsORwOl4/6YNCgQfriiy+0cuVKPf3001bH8SvXXXedYbxx40bNnDlT48eP10UXXaT58+dbkmvVqlW64YYbNGPGDL300kuWZABNLQAA4M72TyRH6ZlxaAOp6zjL4lTHZlNTq2frGAUF2So4u271MjW1zMsk+qxG8VK78401liAEAAAAgEoNHTpUixYtsqwJ468efPBBnXPOORUe79evXx2mOeOuu+7SzJkz9fLLL8tm842/M9RHNLUAAICr7Z8ax10ulsJ8Y7bT2ZhnP5kbSVbqbcqyMyVbRSV2i9JUU88rjeNdS6SiPGuyAAAAAICfGDt2LE2taoqOjtaPP/6ol19+WaNHj1aLFi0UGhrqPN63b19Lcj355JN67rnnaGhZjKYWAAAwyjkmHVhprJn3VPJRDofDZaZWrwTfaWr1MDW1ikrt2p2WbVGaakq6QrKV+9GxOFfa87V1eQAAAADAT5x77rlWR/A7ISEhuu2227R06VKlpqaqqKjIuXxlz549Lcl00UUXWfJcGNHUAgAARjs+lxzlZg+FRkmdRluXpxpSswp0IqfQUOudEGtNGDdiIkPVrkkDQ81v9tWKbiYlXmisbVtkTRYAAAAAAFAv0dQCAABGLksPjpVCI63JUk3mWVoNI0LULq5BBWdbo6dptpbfNLUk1xl7u7+RCnOsyQIAAAAAAOodmloAAOCM3BNS8o/GWvcrrMlSA1vMSw+2jlFQkG+tdW3e42urPzW1ki6XbMFnxiX50u4l1uUBAAAAAAD1Ck0tAABwxs4vTEsPNpA6j7EuTzVtPuK7+2md1tvU1NqZkq2iEnsFZ/uYBnFSh+HGGksQAgAAAACAOkJTCwAAnLHtE+O480VSmG8t31cRh8OhLYczDLXerWMtyVKZHqamVlGpXbvTsi1KUwPmJQj3LJUKsqzJAgAAAAAA6hWaWgAAoExeurT/B2Ot+wRLotTE4VP5OpVXbKj19sGZWjGRoWrXxNgo9Kt9tbpdIgWFnhmXFkp7vrEuDwAAAAAAqDdoagEAgDI7v5AcpWfGIRF+tfSguTEU2yBUCY0jLUpTuZ6m2Vp+1dSKbCx1GGas7fjMmiwAAAAAAKBeoakFAADKbP/UOO58kRQebU2WGth82LSfVusY2Ww2i9JUrpepqbXVn5pakpR0uXG8Z6lUlGdNFgAAAAAAUG/Q1AIAAFL+KWnfd8aaHy09KElbjmQYxr649OBpvU1NrZ0p2SoqsVuUpga6XSLZyv0YWZwn/fatdXkAAAAAAEC9QFMLAABIO7+S7CVnxsHhUpex1uWpJofD4WamVqw1Yaqgh6mpVVRq1+60bIvS1EBUU6nd+cbadpYgBAAAAAAA3kVTCwAAuC492Gm0FN7Qmiw1cOBknrILSgw1X56pFRMZqnZNGhhqfrWvluS6BOHuJVJJoTVZAAAAAABAvUBTCwCA+i4/Q/ptubHWY4IVSWpss6kh1DQ6TPExERalqZqeptla/tfUutQ4LsyS9v9gTRYAAAAAAFAv0NQCAKC+271EshefGQeH+dXSg5K05XCGYdyrdYxsNps1Yaqol7mpddjPmlqNWkkJg40184w/AAAAAAAAD6KpBQBAfbftE+O440gpwneX7nPHZT+thFhrglSDeXnEnalZKigutShNDXU3LUG480uptMT9uQAAAAAAALVUZ02toqIivfXWWxo/frzatWuniIgIxcfH67zzztNzzz2nEydOeOW5DodDS5cu1S233KKePXsqNjZWISEhio2NVffu3TVt2jR99tlnKi31sz8iAQDgCQVZ0m/fGmvdJ1gSpabsdoe2mpbu693a95tyZbPJzoyLSx3akZJlXaCaSLrMOM5Plw6stCYLAAAAAAAIeCF18ZCdO3fquuuu04YNGwz11NRUpaam6ueff9azzz6r119/XePHj/fYcw8ePKhp06bphx9c93fIzMxUZmamduzYobffflv9+/fX22+/raSkJI89HwAAn7fnG6m06Mw4KFTqOs66PDWw70SucouMb07pleD7Ta2GEaHq1Cxae47lOGubDmWoX9vGFqaqpsaJUnwfKWXTmdqOz6QOwyyLBAAAAAAAApfXZ2odPnxYo0aNcja0bDabhg0bpltuuUWXXXaZIiMjJUnHjh3ThAkT9O2331Z2uyo7duyYhg8fbmhoJSQkaPz48brllls0btw4tWrVynls/fr1GjZsmPbv3++R5wMA4Bd2fmkcdxgmRcZaEqWmthzJMIxbNApXi0YR1oSppt6mZRI3+du+WpLrbK0dX0h2uzVZAAAAAABAQPN6U2vq1Kk6evSoJKldu3bauHGjvvvuO73yyiv67LPPdPDgQY0aNUqSVFxcrGuuuUYZGRm1fu7999/vbFBFRERo7ty52r9/v7788ku98sor+uqrr5ScnKz//Oc/CgsLkyQdP35cd999d62fDQCAXygplPYsNda6XWJNllpw2U/LD5YePK1vG2PWTYczrAlSG0lXGMc5qdLhNdZkAQAAAAAAAc2rTa2vvvrKOVMqLCxMn3/+uXr37m04p2nTpvr000/VoUMHSVJ6erqeeeaZWj03Pz9fCxYscI6ffvppzZgxQyEhxtUWQ0NDdfvtt+vJJ5901r788kudOnWqVs8HAMAv7PteKsouV7BJXQOhqRVrTZAa6NMm1jDedzxXmfnF1oSpqWZdpGbdjLUdn1uTBQAAAAAABDSvNrVmz57tfH3jjTeqV69ebs+LiorSY4895hzPnTtXJSUlNX7unj17lJeX5xxPmTKl0vOnTp3qfF1aWqp9+/bV+NkAAPiNnV8Yx20GSw1bWJOlhopL7dp6xNjU6tPGf2ZqdWvZSGHBxh/HtvjlEoSXG8fbP5McDmuyAAAAAACAgOW1plZOTo5hf6zp06dXev7VV1+thg0bSiqbrVV+L6yaPLu82NjYSs9v3Ni4IbudfSAAAIHOXirt+spY88OlB3elZquwxPj/7T6mfap8WVhIkLq3amSo+eUShN1NTa3Mg1LKRkuiAAAAAACAwOW1ptaqVatUWFgoqWwm1qBBgyo9Pzw8XOecc45zvHz58ho/u23btobxtm3bKj1/69atztehoaFKSkqq8bMBAPALh9dKuceNtW6XWpOlFswNoHZNGqhxVJg1YWqoT4JxZtnGQxnWBKmNFj2lxu2Nte2fWZMFAAAAAAAELK81tXbs2OF83atXL5f9rNzp37+/2+urKyEhQf369XOOH374YZWWlro9t6SkRA888IBzfMMNNyg6OrrGzwYAwC+Y9zxq3l1q0tGaLLWwydQA6mvao8ofmPfV2uyPM7VsNinpMmNt55fWZAEAAAAAAAHLa02tXbt2OV+3a9euSteUn2G1c+fOWj3/hRdeUGhoqCTpq6++0sCBA/XRRx8pOTlZBQUF2r9/vz744AP1799fS5culSSdf/75ev7552v8zMOHD1f6kZKSUqvPCQAAj3A4XPfT8sOlByXXWU3+tPTgaeamVlpWoVIzC6wJUxvmfbVO7JJO7LUmCwAAAAAACEhnnz5VQydPnnS+btGiapvOt2zZ0vk6PT29Vs8fPny4lixZoquvvlqnTp3Sxo0bdfXVV1f43FtuuUV//etfFRZW8yWL2rRpU+NrAQCoM8e2S6eSjTU/XHowp7BEe44Z99E0N4j8QfsmUWoYEaLsghJnbeOhDF0c07KSq3xQ6wFSdAspJ+1MbdeXUtO7rMsEAAAAAAACitdmauXknPkjU2RkZJWuKX9e+etrauTIkUpOTtbf/va3Cpc/DA4O1mWXXaYpU6bUqqEFAIDf2GGapRXTRorvY02WWthyOFMOx5lxSJBNPVo1si5QDQUF2dTbtK+Wea8wvxAUJHUdZ6zt/MqaLAAAAAAAICB5baZWQcGZZXOq2iwKDw93vs7Pz691hn379umee+7Rp59+KofDocTERA0ZMkQxMTE6efKkVq5cqdTUVM2bN0+vvvqq/va3v+mvf/1rjZ936NChSo+npKRo8ODBNb4/AAAe4W7pQZvNmiy1YG78dItvqIjQYGvC1FKfhFit3Htmlrt5rzC/0fUS6df5Z8aHVks5x6XoZpZFAgAAAAAAgcNrTa2IiAjn66KioipdU1hY6Hxd1dldFfnll180duxYZWVlKTY2VvPmzdNVV10lW7k/2pWUlGju3Lm65557VFhYqL/97W+KiIjQn//85xo9MyEhoVaZAQDwulMHpNTNxpofLj0ouTZ+/HE/rdPMyyZuOZwpu92hoCA/aza2HyqFRknFuf+/4JB2L5b632BpLAAAAAAAEBi8tvxgdHS083VVZ12VP6/89dV16tQpTZw4UVlZWbLZbPrkk0909dVXGxpakhQSEqLbb79dc+bMcdYeeeQRHT58uMbPBgDAp+380jiOjJPanmtNllpyaWr54X5ap/U1Zc8uLNG+E7nuT/ZloRFS59HGGksQAgAAAAAAD/FaU6tJkybO12lpaZWceUZqaqrzdVxcXI2f/b///U8pKSmSpDFjxmjYsGGVnn/TTTepa9eukspmlb3zzjs1fjYAAD7N3NTqOk4K9trEba85llWgo5kFhpq5MeRPWjSKUMtGEYaaXy9BWN6+FVKRHzboAAAAAACAz/FaU+t0k0iSDhw4UKVrDh486HzdrVu3Gj97yZIlztcjRow46/k2m03Dhw93jtetW1fjZwMA4LNyT0gHVxlr/rr04OFMwzgqLFgdm9V8lrcv6J0QYxib9wzzG50vkmzl9jYrKZB+W25dHgAAAAAAEDC81tRKSkpyvt6yZYtKSkrOes369evdXl9dR44ccb4uP2OsMuXPy8zMrORMAAD81O4lksN+ZhzaQOp49jd/+CLzLKbeCbEK9rf9p0zMyyf67UytBnFS4vnGGksQAgAAAAAAD/BaU+u8885TeHi4JCk3N/ess58KCwv1yy+/OMcjR46s8bMjIyOdr9PT06t0zcmTJ52vY2Nja/xsAAB81o4vjONOo6TQSPfn+jjzLCZ/3k/rNPPyiTtSslVYUmpNmNoyL0G4e4lUevY3OAEAAAAAAFTGa02t6OhojRo1yjmeP39+ped//PHHys7OliQ1btxYQ4cOrfGz27Zt63y9fPnZl7txOBxasWKFc9ypU6caPxsAAJ9UlFe2t1F53S6zJkst2e0ObTTNYurbJsb9yX6kl2n5waJSu3amZFuUppa6jTeO89OlQ7+4PxcAAAAAAKCKvNbUkqSZM2c6X7/++uvatm2b2/Py8vL017/+1Tm+7bbbFBJS803rR48e7Xz9zTff6Icffqj0/Ndff127d+92jseOHVvjZwMA4JP2f1+2t9FptuCyvY/80P6TucouMM76CYSZWo0iQtWxWZSh5rf7asW2lVr2MtZYghAAAAAAANSSV5tal1xyiS688EJJUlFRkS699FJt2bLFcM7Jkyc1YcIE7d27V5IUFxenv/zlL27vl5ycLJvN5vz47rvv3J530003KS4uTlLZLKwJEyboww8/dDmvpKREs2fPNjTfBg8erGHDhlX7cwUAwKftMjUU2p5btveRHzLvNdW8YbhaNoqwJoyH9UmINYzNM9L8inkJwl1fSg6HNVkAAAAAAEBAqPl0qCp69913NXjwYKWkpCg5OVl9+/bVsGHD1KFDBx0/flzLli1TXl5eWZiQEH3wwQe13tMqJiZGr732mq666iqVlpbq1KlTmjRpkhITE3XOOecoJiZGJ06c0MqVK5Wamuq8Li4uTm+++Watng0AgM+x26XdXxtrXcdZk8UDzE2tPm1iZbPZrAnjYX3axOrjDUecY/Pn6le6XSJ9//SZ8alk6dh2qUUPyyIBAAAAgNXsdruCgrw61wRnkZWVpW+++Ubffvut1q9fr7179yorK0vR0dFq27atzj//fE2fPl2DBg2yOqoLvn/qoKmVkJCg5cuXa8qUKdq4caPsdrtWrFhh2MNKkpo1a6bXX3/dsA9XbVxxxRX6/PPPdcsttyglJUVS2Uyv5ORkt+f37dtX77zzjrp27eqR5wMA4DOObpBy0ow1P25qbTycaRj3DYClB08zL6O470SusgqK1Sgi1JpAtdGylxTTVso8eKa28yuaWgAAAADqrT179uiFF17QnDlzrI5Sbz377LN65JFHVFhY6HIsIyNDGRkZ2rx5s+bMmaPrr79ec+fOVYMGDSxI6t6OHTu0bNky3XXXXVZHsYzXm1qS1K1bN61evVrvv/++3nvvPW3btk1paWmKjY1Vhw4ddOWVV+rmm29W06ZNPfrccePGad++fVqwYIG+/PJLbdiwQWlpacrLy1PDhg3VqlUrDR48WFdddZXGjx9f7zucAIAAtXuxcdyks9SkozVZaqmwpFQ7jmYZauYl+/xZUnxDhQbbVFxatkyfwyFtPZyp8zp59mekOmGzlTVP18w9U9v1pTTsPusyAQAAAIBFVq9erUsvvVRjx461Okq9tmvXLmdDq0OHDho9erT69u2rpk2b6tSpU/r222/10UcfqbS0VG+//baOHTumxYsX+0zvIDw8XP/3f/+n6Oho3XLLLVbHsUSdNLUkKSwsTDfccINuuOGGGt8jMTFRjmruxRAREaEbb7xRN954Y42fCwCAX9u1xDj241laO1KyVVRqN9R6JcRYlMbzwkOC1T2+kTaVm4224VCGfza1pLIlCMs3tY5ukDKPSDGtrcsEAAAAAHVszZo1GjNmjBITEzV79myr49RrNptNl1xyie677z4NHTrUZTuDGTNm6Mcff9T48eOVk5Ojb775Rm+88YamT59uUWKjTp066bbbbtOMGTMUFRWlyZMnWx2pzvlGexEAAHhHxkEpbYux5sdNrQ0HTxnGnZpHKybSD5fmq4R5OUXz5+xX2p0nRZiajuaZgwAAAAAQwNavX68xY8YoIiJCX375pWJiAueNmf7omWee0RdffKFhw4ZVuD/3hRdeqKeeeso5nj9/fh2lq5oXXnhBSUlJmjZtmj7//HOr49Q5mloAAASy3V8bx5FxUsJga7J4wPqDGYZxvwDaT+u0fm0bG8brD2ZUe6a6zwgOlTqbltYwf08CAAAAQIA6fPiwLr30UuXk5Oi9995TQkKC1ZHqvcaNG5/9JEmTJk1yvt6yZUslZ9a9yMhILViwQKGhobrmmmu0fv16qyPVKZpaAAAEsl1fGcedx0jBdbb6sMeZZy2ZG0CBoL/pc0rPLdLB9DyL0nhAF1NTa9/3UlGuNVkAAAAAoI7k5ubq0ksvVUpKiu677z6NHDnS6kiohoYNGzpf5+fnW5jEvR49euif//ynCgoKNGnSJGVkZFgdqc7Q1AIAIFAVZEn7fzTWul5sTRYPOJZdoMOnjD9I9m8Xa00YL2oTF6kmUWGG2np/XoKw02jJFnxmXFpY1tgCAAAAgAD2hz/8QZs2bVLPnj316KOPWh0H1bR161bn67Zt21qYpGK33XabLrroIu3bt89n9vyqCzS1AAAIVL8tl+zFZ8ZBoVLHUdblqaWNpqUHo8KC1bl5Q/cn+zGbzeYyA22D6XP3K5GxZXtrlbd7iSVRAAAAAKAuvP7663rrrbckSXPmzFFYWNhZrkB1LFu2TDabzflxxx13ePwZL7/8svP1+PHjPX5/T3nxxRcVEhKiTz75RC+88ILVceoETS0AAAKVuXGQeIEU0ciaLB5g3k+rT5tYBQe539TV35lnoPn1TC3JdQnC3V9Ldrs1WQAAAADAi5KTk3XnnXdKkq6//npdcMEFFicKPJs2bTKM+/Tp49H7//TTT5o/f74kKSIiQv/3f//n0ft7UlJSkrOpd//99+vnn3+2OJH30dQCACAQ2UvLGgfldfXddxZVhXk/LfPeU4GkXxvj57YjJVt5RSUWpfGALuOM45xUKXWT+3MBAAAAwI/NmDFDubm5Cg8P15NPPml1nIC0ceNGw9iTTa3U1FRNnjxZDodDkvTYY4/57PKDp82aNUvNmzdXcXGxbrrpJhUVFVkdyatoagEAEIgOrZHy0401P95Pq6TUrs2HMw21fm1jrQlTB/q0iVH5SWildoe2mD5/v9K0kxTX0VjbxRKEAAAAAALLG2+8oaVLl0qSfv/736tNmzYWJwpM5WdqBQUFqWfPnh65b25urq644godOXJEknTJJZfo3nvv9ci9vSkmJkZPPfWUJGn37t167rnnLE7kXTS1AAAIRLsXG8fNe0ixvv3OosrsSstWfnGpoda3Taw1YepAg7AQdWtpXCrSvPyi3+lqmq3FvloAAAAAAkhOTo7uv/9+SWVL1j3wwAMWJwpMRUVF2rlzp3PcuXNnNWjQoNb3LSgo0OWXX641a9ZIks477zwtWLBANpt/bHtw4403qmPHsjeTPvHEEzpw4IDFibyHphYAAIFol6mpZW4o+BlzQ6ddkwZqEh1uTZg6Yt5Xy7z8ot8x76uVslHKSrEkCgAAAAB42pNPPqnU1FRJ0g033KAWLVpYnCgwbdu2TcXFxc6xJ5YeLCoq0sSJE7V8+XJJ0sCBA/XVV18pKiqq1veuK8HBwfrTn/4kScrLy9Pdd99tbSAvoqkFAECgOfmbdGK3sebnTa36tJ/WaeZ9tdYfzHCu6e2X2p4rhccYa3u+dn8uAAAAAPiRo0eP6p///KckyWaz+cWSdf6q/NKDktS7d+9a3a+4uFiTJk3S4sVlbw7u06ePvv76a8XExJzlSt8zffp0NWvWTJL0ySefOD+nQENTCwCAQGOepRXVXGrV35osHrLRNFMrkPfTOq1/O2NT60ROoQ6fyrcojQcEh0qdRhlru2lqAQAAAPB/Tz/9tAoKCiRJY8eOVefOnS1OFFiGDx8um80mm82m6dOnG449/PDDzmPmj4iICJWUlFR435KSEk2ZMkWfffaZJKlHjx5aunSp4uLivPr5eEtkZKTuuOMO5/hPf/qT7Ha7hYm8g6YWAACBxrxXUZexUpD//i//VG6R9p3INdTMs5gCUWKTBmrcINRQW+/3SxBebBz/tkIq9uNGHQAAAIB6LyUlRfPmzXOOf//731uYJjCZZ2dVVVJSkkJCQtweKy0t1bRp0/TRRx9Jkrp166Zvv/3WOdPJX91xxx3OZRN37typRYsWWZzI8/z3L1wAAMBV/inpwCpjzc+XHtx4KMMwjggNUrf4htaEqUM2m039TMssbjDNWPM7nS+SbOV+/CzJl/b/aF0eAAAAAKilF154wTlLq1WrVrr00kstThRYkpOTlZGRUaNre/Xq5bZut9t188036/3335ckde7cWcuXLw+IfdDi4uJ0zTXXOMdPPfWUhWm8w32bEgAA+Kc9yyRH6ZlxSITUYbhlcTzBvJ9W79axCg2uH+/L6d82Vst3HnOOzV8Lv9MgTmozRDr485na7sVSlzHWZQIAAACAGsrNzdUrr7ziHF933XUKDg62MFHgiY2N1dKlSyVJGRkZmjRpkvNY79699fzzz1d4bfv27V1qDodDt912m958801JUseOHbVixQrFx8d7OLl1rrvuOr3++uuSpF9//VXffPONxowJnN+7aWoBABBIzEsPth8mhUVZk8VDNphmatWH/bROM8/U2nY0SwXFpYoI9eNfkrqMNTW1vpYcDslmsy4TAAAAANTAm2++aZhFdP3111sXJkDFxsZq9OjRkqRly5YZjg0dOtR5rKoeeughZyMyNDRUd911l9auXau1a9dWet2YMWPUoEGDaj3LKiNHjlTLli2VmpoqqWy2Fk0tAADge0pLpL3GH/DUZaw1WTzEbndoo2nJPXOjJ5D1aROrIJtkd5SNS+wObTmSqUGJ/rlprSSpyzhp2awz46wjUuoWKb63ZZEAAAAAoCZmz57tfJ2UlKQ+ffpYmCbw/frrr4bxwIEDq32PVavObNlQXFysP/7xj1W6bv/+/UpMTKz286wQFBSka6+9Vi+++KIk6bvvvtPPP/+sc8891+JknlE/1u4BAKA+OLJOKsgw1jr79ztx9h7PUXZhiaFWn2ZqRYeHqEsL4/5hfr8EYbOuUmw7Y23319ZkAQAAAIAaWrNmjbZt2+YcT5gwwbow9cS6desM40GDBlmUxPdNnTrVMH7ppZcsSuJ5NLUAAAgU5sZA8x5SbBtrsniIuYHTOjZSLRpFWJTGGuaZaesPZFgTxFNsNqnLxcaaedlMAAAAAPBx8+fPN4xpanlf+Zla0dHR6tatW7Xv8d1338nhcFT7w19maZ02aNAgdejQwTletGiRYalMf0ZTCwCAQLFnqXHc+SJrcniQuYFTn2Zpndbf9DmvP3hKDofDmjCe0tXU1Dryq5RzzJosAAAAAFBNxcXFWrBggXPcrFkzZg15WXp6uvbv3+8c9+vXT0FBtDcqU36/sYKCAr377rsWpvEc/qkDABAIMo9IaVuMNT/fT0uSfjXN1KpP+2mdZv6cj2UX6mhmgUVpPKTd+VJYdLmCQ9rzjWVxAAAAAKA6VqxYofT0dOd4xIgRstlsFiYKfJ7YT6u+GTlypGH8zjvvWJTEs2hqAQAQCPaaZmlFxEgJg63J4iEZeUXaeyzHUBvQrv41tTo0jVJMZKihtv6An++rFRIudTT+cK1di63JAgAAAADVtGjRIsPY3DyA57GfVvWNGDHCMP7555919OhRi9J4Dk0tAAACwW7TLJeOo6TgEGuyeMh60yytiNAg9WjVyKI01gkKsqlvm1hDbcPBDEuyeJR5X63fVkglhdZkAQAAAIAqcjgc+vTTTw21YcOGWZSm/mCmVvU1b95cPXr0cI4dDodLQ9Yf0dQCAMDflRRK+74z1gJg6cF1ycamVp+EWIUG188fXfqbliA0N/z8Uucxksotz1GcKx1YaVkcAAAAAKiKTZs2KSUlxTmOiYlR165dLUxUP5RvasXExKhTp04WpvEf5lmEn332mUVJPKd+/mUIAIBAcmBlWUPAySZ1Gl3h6f7iV9MSe/Vx6cHT+reLNYy3Hc1UQXGpNWE8JbqZ1Lq/sbZnmTVZAAAAAKCKvv32W8N44MCB7KflZSdPnlRycrJzPGDAAL7mVTR06FDD+Mcff1RhoX+vkkJTCwAAf7fHtJ9W6wFSVFNrsnhIcaldmw5nGGoDE+tvU6tPm1iV/3m9uNShrUcyrQvkKZ3HGMd7vnF/HgAAAAD4CHdNLXiXeelB9tOqul69ehnG+fn5WrnSv1dJ8e/NNgAAgLT7a+PY3CjwQ9uOZqmg2G6omZfgq08aRYSqa4uG2pma7aytTT6lgYlxFqbygM4XSd89dWZ8co+Uvk+K62BdJgAAAMBD8vPz9fXXX+uHH37Qhg0btH//fqWnpysvL09hYWGKjY1Vhw4d1L9/f40aNUoXX3yxwsPDrY5tqezsbP3444/aunWrUlJSlJOTo/DwcOfeQMOHD1eTJk0sy2e32/XTTz8ZauX3LIJ3rF+/3jDu379/BWfCrFOnToqIiFBBQYGztnz5cpdlCf0JTS0AAPzZyd+k9N+MtS7+39QyLz3YqXm0YhuEWZTGNwxMbGxoav16IF1SR+sCeUJ8P6lBUynvxJnanmXSkBnWZQIAAABqad++fXrmmWf09ttvKzc31+05+fn5ys/PV0pKilauXKmXXnpJsbGxuv3223XfffcpJiamys8rLS3VqFGj9P3337scGzdunL788staLdW2aNEiTZw40aUeHh6uVatWVdpgmDVrlh599FGXusPhMIx/+eUXPfPMM/ryyy9VVFRU4f2CgoI0ZswY3XfffZb8UX7Hjh3Kzs421Lp3717nOeqbbdu2GcZ8zasuODhYSUlJ2rBhg7O2evVqCxPVHssPAgDgz8zLtUU1l1r2sSaLB5U1bM4YWI/30zptkGlW1roDp2S3Oyo4208EBZXN1iqPJQgBAADgp4qLi/XII48oKSlJc+fOrbChVZGMjAw98cQTSkpK0uLFi6t8XXBwsN577z01b97c5djixYv19NNPVytHefv379f06dPdHnvhhRdqPWMmPz9fv/vd73Tuuedq0aJFlTa0pLKZUkuWLNGoUaM0adIknTp1qtLzPW3NmjWGsc1mU7du3eo0Q32UlpZmGEdERFiUxD/17NnTMF67dq1LY9mf0NQCAMCfuVt6MMi///fucDhcZmr1p6mlAaavQUZesfadyLEojQeZm1rJP0rF+dZkAQAAAGooNTVVw4YN0+OPP37WxszZpKSk6NJLL9VLL71U5Wvi4+P1zjvvKMjN74OPPPKIfvjhh2rnKCoq0jXXXKPMTNf9fK+55hrNnDmz2vcs79SpUxoxYoReeeWVGl3/4YcfauDAgdq3b1+tclTHunXrDOP4+HhFRUXV2fPrq8jISMN4yZIlFiXxT+amVmZmpnbv3m1Rmtpj+UEAAPxVYY50wLS5p7lB4IcOn8pXWlahocZMLal1bKTiYyKUknlmHey1yafUqXlDC1N5QMeRki1Icvz/PdRKCqTknwLiexkAAAD1w9GjRzVixIhK/0gcFBSkHj16qFWrVoqLi1Nubq5SU1O1ceNGt00wu92uP/7xj4qKitLNN99cpRyjR4/WI4884rLcX2lpqaZMmaKNGzeqWbNmVf68/vSnP7k0caSyPXpq2og6rbi4WBMnTqz1Mmj79u3T8OHD9fPPP6t169a1uldVmJfBa9eundefCalfv3767LPPnON7771X27Zt0wUXXKBmzZoZmrm9e/d2O2uxPjM3tSRpy5Yt6tq1qwVpas+/38oNAEB9tv97qbTcLz9BIVLHEdbl8RDzLK24qDC1b8o732w2m8tsrXXJdbvUhldENpbaDDHWWIIQAAAAfiIvL0+XXnpphQ2tIUOGaMGCBUpPT9fmzZu1ZMkSvfvuu/r000+1evVqnTx5Uu+88446derk9vo77rhDO3bsqHKev/71rxo1apRL/ejRo5o6darsdnuV7rNw4ULNnj3bpR4REaGFCxeqYcPavbnu8ccf13fffWeoJSUl6cknn9T69euVlpamgoIC7d+/X19//bVuvfXWCp956NAhXXXVVVX+3GrD/M8iMTHR68+ENH36dMNsrcLCQr388su6/vrrNXbsWF100UXOjwMHDliY1DclJCS41Krz3xVfQ1MLAAB/Zf7Df9tzpYiqbybsq1yWHmzbuFabGgcS13210is408+421fLj9f3BgAAQP1x9913a8OGDS716OhovfXWW/rll190zTXXKCbG/e9q0dHRuu6667R9+3bNmDHD5Xh+fr6mTJmi4uLiKuUJCgrSu+++q/j4eJdjS5cu1RNPPHHWe+zdu1e33nqr22P/+te/1Ldv3yplqcxTTz3lfB0eHq5//OMf2rJlix544AH169dPzZs3V3h4uBITEzVmzBjNmzdP27dv17hx49zeb/Xq1XrxxRdrnasy6enpOnbsmKHWtm1brz4TZdq1a6f3339f0dHRlZ4XEhKi3r1711Eq/+Huvwc7d+60IIln0NQCAMAfORzSnqXGWucx1mTxsHWmptbARJYePM08U+vAyTwdyy6o4Gw/Yv7ePZUsndxrSRQAAACgqn788UfNmzfPpR4TE6OffvpJ119/fZXvFRoaqrlz5+qPf/yjy7FNmzZpwYIFVb5X8+bN9d577yk4ONjl2KxZs7RixYoKry0sLNSkSZOUlZXlcmzKlCm67bbbqpyjMqebdCEhIVq4cKH+/Oc/u81bXkJCgr744gtde+21bo8//PDDXp2ls3ev6+8oLVq08NrzYHT55Zdr9+7deuyxx3ThhReqefPmCgsLM5zTvXt3hYeHW5TQdzVt2lShoaGG2p49eyxKU3s0tQAA8Edp26SsI8Zal7HWZPGg7IJi7Uo1/vJkbuTUZ91aNlR0uHFL1F8DYQnCFj2lhqZ3jrEEIQAAAHzcAw884FKz2WxauHCh+vTpU6N7Pvfccxo0aJBL/Z///Ge17jNs2DCXvbWksr26rrvuOqWlpbm97q677tLGjRtd6l26dNH//ve/amWoiueff16XXXZZlc8PCgrSW2+9pcGDB7scy8vL00svveTJeAaHDh1yqbF3U92Kj4/XI488oh9++EFpaWkqLCyUw+FwfmzatMnqiD7JZrO5NGDdfT/7C5paAAD4oz1fG8exbaWmXazJ4kEbD2XIXm7VubDgIPVq7f9LKnpKSHCQ+rWNNdTMM9v8ks0mdRptrNHUAgAAgA9bu3atVq5c6VKfPHmyLrroIjdXVE1oaKgef/xxl/r69ev1888/V+teDz74oMaOdX3zY2pqqq677jqXPajee+89zZ071+X80/tonW3pt+rq27ev7rjjjmpfFxoaqtmzZ7tdpn7+/PkqLCz0RDwX7poAzZo188qzAE8zL0F47NixKi9r6mtCzn4KAADwOS5LD44tawz4uXWmWUc9WzdSRGjlS1DUNwPaNdaPe044x+uSA2VfrTHShrfOjA+skgpzpHDP/uIMAAAsZrdL+QHy8wuqJzJOCgqc99e//vrrbuuzZs2q9b3HjBmjLl26aPfu3Yb6t99+q3PPPbfK97HZbHr77bfVt29fHTliXOlj+fLlevTRR52zuXbv3u12Ty9Jeumll7yyT9Fjjz2moBp+TwwcOFATJkzQokWLDPWTJ09q0aJFmjx5siciGhw+fNil1qRJE48/B/AGc1PLbrfr6NGjateunUWJao6mFgAA/iYvXTq02lgLkP201h80NrVYetDVoMQ4w3jb0SzlFZWoQZif/1jXYbgUFCLZS8rGpUXS/h+kbuMtjQUAADwsP116tqPVKWCF+36ToppancJjvvjiC5dav3791KWLZ1bQGDFihEtT66effqr2fZo2baoFCxZo+PDhKikpMRx7/PHHdeGFF+r888/XpEmTlJOT43L91KlTdeutt1b7uVXJNW7cuFrdY9q0aS5NLUlasWKFV5paJ06ccKl5evYa4C1Nm7r+9/fEiRN+2dQKnLdHAABQX/y2XHKUWyYiJEJqf6F1eTyk1O7QhoMZhtqAdnHuT67H+raJVXDQmVl5JXaHNh7KsC6Qp0Q0ktqa3nXKEoQAAADwQb/99pvbpeguvvhijz1jwIABLrXqLj942vnnn68nnnjCpW632zV16lTddNNN2rx5s8vxbt26uV2O0BOuuOIKhYTU7o15l1xyiSIjI13qq1evdnN27Z065br0e8OGDb3yrNNmzZolm83mcx/Dhw/36ucNzwsPD3epZWRk1H0QD6CpBQCAvzH/ob/9UCnU9Qd5f7MzNUs5hcZ3DjJTy1VUeIi6xzcy1H5NDoB9tSTXGYd7lkoOh/tzAQAAAIusX7/ebb179+4ee4a7WRVZWVnKysqq0f3uu+8+XXLJJS71Y8eO6YMPPnCpR0ZGauHChYqKiqrR887GXdOuusLCwtSzZ0+X+tatW5Wbm1vr+5u5awAwUwv+IiIiwqXmrlHrD/x8nRoAAOoZe6mb/bQCY+nBtfuNeyu0a9JAzRq6vpMIZc2+LUcyneN1B/zzB1EXncdISx85M846LB3bIbXw3B8HAAAAgNoyLwt42jvvvKNly5Z55BkpKSlu6+np6WrUqJHbY5Wx2Wx688031a9fPx08ePCs58+ePdttw8hT+vbt67H7rF271lArLS1VcnKyevTo4ZFnnJaZmelSczdTDPBF7ppaNW2SW42mFgAA/uTIeteNtQOlqWWabWTeOwpnDEqM0/xVyc7x+gOnVGp3GJYl9EvNukoxbaXMcr9k7/mGphYAAIEkMq5sbyXUP5GB8/P9kSNH3NaXLFni9Wenp6crMTGxRtfGxcVpwYIFGjp0qIqLiys874YbbtD06dNrmLBqWrdu7ZH7tGrVym3dG8uqFRUVudRqu4QiUFfcNWALCwstSFJ7/FsHAIA/MS892Kyb1Nj/NvU0czgcWpNsbNYNpqlVoYGJxmUZswtLtDstW0nx1X/Hpk+x2aTOF0nrXj1T27tMuuBuyyIBAAAPCwqSolyXVQP8ibsZO3UlOzu7Vtefc845euihhzRr1iy3x7t37645c+bU6hlVUZPZZu7ExMS4rXtjWTVzUysoKEg2m5+/sRD1hruZWpU1t30ZTS0AAPzJb98ax51GW5PDww6czNPxbOM7hAa1p6lVkRaNItQmLlKH0vOdtXXJ6f7f1JJcm1oHf5YKMqUI97+sAgAAAHWtoKDAsmc7arnnbGFhoT755JMKjzdu3FhhYWG1ekZVNGzY0Kv38cayauYGQHBwsMefYXb//ffr7rvv9vpzqosZav7HXVPL3exDf8B3HwAA/iL3ZNnyg+V1vsiaLB5mnqXVNDpciU0aWJTGPwxsF6dD6WeWPVl34JSmnZtoXSBPaT9UCg6TSv//D9f2Emnfd1L3KyyNBQAAAJxWF80Mb7nrrru0cePGCo+vXLlSDz/8sJ5++mmv5igoKFBUVJRH7uOOuz/g15a5oVjbBmNVREREeOVzQf3jrhFZF9/D3hBkdQAAAFBF+1ZIKvcDR2gDqe25lsXxpLX7TUsPtm/MMg5nYV6CcF2y55fXsERYlJR4gbFmXnYTAAAAsFBFzZhDhw7J4XB49WP48OE1zr1gwQLNnTv3rOc988wzWrx4cY2fUxWemklV0X0aN27stl4boaGhhnFJSYlKS0s9/hzAG9wtNVgXszK9gaYWAAD+Yq9p6cHEC6WQcGuyeNha00ytQeyndVYD2xm/Rkcy8nU0I7+Cs/1M5zHG8Z6lkp++gwwAAACBJz4+3m19//79dZyk6vbs2aPf/e53VTrX4XDohhtu0OHDh72WJz09/ewn1eI+sbGxHrl/ee4aAFYuRemrbDYbH9X8qAslJSUuNXOj1l/Q1AIAwB84HG720xplTRYPO5ZdoOSTeYYaTa2z69w8Wo0ijMsHrDsQILO1zE2tnDQpdbM1WQAAAACT9u3bu63v2bOnjpNUTUFBgSZNmqTs7GyXYzfffLOuvPJKl/qJEyc0efJkt38I94StW7d69T4tW7b0yP3Lo6kFf+bu32VmagEAAO9J21r2h/3yOgZGU2vtfmMjpmF4iJLiG1mUxn8EBdk0oJ15CULPvNvRck06SnEdjLU9S63JAgAAAJj079/fbX3ZsmV1nKRq7rrrLm3atMml3rNnT/3nP//Ra6+95rZRt3LlSj300ENeyVTZvl7V4e7zatWqVYWz6WojOjrapVZYWOjx5wDe4O57tVEj//zbC00tAAD8gXnpwdh2ZX/4DwDmpQf7t2us4CD206qKgaYZbWv2B0hTS3K/BCEAAADgA3r37u32j8FLlizx2symmnrvvff0v//9z6UeFRWlDz74QJGRkYqNjdWCBQvcztp49tln9dVXX3k81zff1H7f3E2bNik1NdWlPmTIkFrf2x13+3QxU8uVt/eVC8SPuuBupqY39p6rCzS1AADwB+6WHqyjdZe9zdyIGdyepQeryvy12pWWrYy8IovSeFjni4zjw2uk/ABZXhEAAAB+LTQ0VJdccolL/dSpU3rrrbcsSOTe7t27NWPGDLfH5syZo6SkJOd40KBBeuaZZ1zO89b+WuvXr9eOHTtqdY+3337bbf28886r1X0r4m6fLppa8Bfumlre2HuuLtDUAgDA1xXmSAd+NtY6jbYmi4dlFRRrR2qWocZ+WlXXOyFG4SFnfpxzOKS1yQHS+Gl3gRQSeWbssEv7vrMsDgAAAFDezTff7Lb+6KOP+sSSdAUFBbrmmmuUk5Pjcuzmm2/WtGnTXOp33XWX2/21Tp48qWuvvdbjs9D+9a9/1fjajIwMvfHGGy714OBgXXvttbVIVbEmTZq4zQH4A3f/LXD3Pe0PaGoBAODrkn+S7MVnxkEhUuKF1uXxoF8PnFL5mfZhwUHqnRBjXSA/Ex4SrH5tYw21NftPWhPG00IjpMQLjLW9vrlHAQAAAOqf0aNHq2/fvi71AwcO6Lbbbqv7QCZ//OMfK91HqyIV7a+1atUqPfjggx7N+Morr2j9+vU1uvZvf/ubjh8/7lIfP3682rRpU9tobrVu3dqllpKS4pVnAZ6Wnu66XYG772l/QFMLAABfZ/5DfptzpAj/3MzTbK1p6cE+bWIUERpsURr/NKS98Z1VqwNpXy3zjMS930p1tN44AAAAcDbPP/+82/obb7yhhx56yGN75WRmZurFF1+s8vnvvvuu5s2b51KPiorSwoULFRkZ6eaqMpXtr/Xcc8/pyy+/rHKOs7Hb7Zo8ebKOHTtWres+/PDDChtzt99+uyeiuZWQkOBSo6lVf2VlZenDDz/UH/7wBw0ZMkRNmjRRaGioGjdurD59+mjmzJlau3at1TGdzE3gZs2aKTw83KI0tUNTCwAAX+eyn9ZIa3J4wdpkYwOGpQerb0gH49ds65FM5RT61ubUNWZuamWnSMe2W5MFAAAAMBk5cmSFs7KefPJJjR8/XkeOHKnx/bdt26Z77rlHbdq00V/+8pcqXbNr164KM7388svq1q3bWe8xaNAg/eMf/3CpOxwO3XjjjTp06FCVslTFnj17NHbsWO3fv79K57///vuaOnWq7Ha7y7EJEyZo7NixHstm5m4G2NGjR732PPiuZ599Vs2bN9ekSZP08ssva82aNUpPT1dJSYkyMjK0efNmzZkzR4MHD9a0adOUl5dndWSX5rG3ZjTWBZpaAAD4svR9ZR/lBch+WgXFpdp0KNNQo6lVff3aNFZosM05tjukdckBMlurSUcptp2xxhKEAAAA8CH//Oc/dd5557k9tmTJEnXo0EHTp0/XihUrlJ+fX+m9jh07pq+//loPPvigunbtqp49e+qFF15QdnZ2lbJUto/WLbfcouuvv75K95Gku+++WxMmTHCpnzx5UpMnT671/lrDhw93vt64caN69eqlp556ym2TyOFwaNWqVbrqqqs0ZcoUFRUVuZwTGxur//73v7XKdDYdO3Z0qTFTq37atWuXc++8Dh06aMaMGfrvf/+rDz74QHPnztU111yj4OCyVWjefvttXXnllW4bsXXJPFOrQ4cOFiWpvRCrAwAAgErsNc3SimomtehlTRYP23w4U0WlZ36os9mk/u0aW5jIP0WGBatPQqzWHTjlrK3Zn67hXZtbmMpDbDap0yhp3WtnanuXSeffZV0mAAAAoJzIyEh9/vnnuvjii90uNVZUVKT58+dr/vz5Cg0NVY8ePdSsWTPFxZW9oS8jI0OZmZk6ePBgrWf93Hnnndq8ebNLvVevXnrppZeqfb/XX39dGzduVHJysqG+atUqPfDAA3r22WdrGlWvvvqqBgwYoIyMDElSbm6uHnzwQT388MPq2bOnEhIS1KhRI6WmpmrPnj2Vzniz2WyaN2+e4uPja5ynKlq1aqWYmBhlZp55cyZNrfrJZrPpkksu0X333aehQ4fKZrMZjs+YMUM//vijxo8fr5ycHH3zzTd64403NH36dEvynjhxwmW2WFJSkiVZPIGmFgAAvszc1Oo4SgoKjInW5qUHu7VspJjIUIvS+LfB7eMMTa2A21erfFPrwM9SYY4UHm1dJgAAAKCcuLg4rVixQjfddJM+/PDDCs8rLi7Wxo0bvZLhnXfe0SuvvOJSj4qK0gcffFDpPloVOb2/1gUXXKDi4mLDseeff17Dhg3TpZdeWqO8HTp00Mcff6zx48eroKDAWbfb7dq8ebPb5pw7NptN//3vf3X11VfXKEd1devWTatXr3aOa7O8JPzXM888o8aNK39T7oUXXqinnnpKd955pyRp/vz5ljW1Dhw44FLz56ZWYPxVDACAQFRSJO3/wVjrNMqaLF5gbrwMTmSWVk0N6dDEMN58OEP5RaUWpfGw9kOloHLvw7IXS8k/WpcHAAAAcCMqKkoLFy7Um2++6fEZQ9HR0ZUuHbhr1y79/ve/d3usqvtoVWTw4MF65plnXOqe2F9rxIgRWrp0qZo1a1aj62NjY/Xhhx9W+Ll7Q69expVT9uzZo9LSAPndC1V2tobWaZMmTXK+3rJli7finJW7plaPHj0sSOIZNLUAAPBVh36RinPLFWxSx5GWxfGk4lK7fjXN1BrcvkkFZ+NsBrRrrOCgM8sdFJc6tOHgqUqu8CPhDaW25xpr7KsFAAAAHzVt2jTt2bNH//73v2s1EyI2NlZXXnml3nzzTaWmprqdhSVJ+fn5mjRpkkf20apIRftrpaen69prr63V/loXXHCBtm/frltvvVVhYWFVuiY4OFjTpk3Tli1bNHHixBo/uyYGDhxoGBcWFmrPnj11mgH+o2HDhs7XZ9tTz5t+++03wzgyMlLdu3e3KE3tsfwgAAC+yrz0YHwfKaqpNVk8bOuRTOWaZhIN6RBnURr/Fx0eop6tGmnT4TNru6/en67zOgXG94s6jTLOztqzVHI4yvbcAgAAAHxMVFSU7rzzTt15553asWOHli5dqrVr12rXrl06dOiQMjMzVVhYqIiICDVs2FCNGjVS27Zt1a1bN3Xr1k1DhgzRgAEDFFSFpecjIyOrvFRfbSxatMhr927atKnmzZunv//97/r444+1fPlybdu2TUePHlVubq7CwsLUokUL9ejRQyNGjNDkyZPVunVrr+WpzKBBg1xqW7ZsqdVsuPrm+PHj2rp1q3777TedOnVKJSUlaty4sVq0aKFBgwYpISHB6oges3XrVufrtm3bWpZj+/bthnH//v0VEuK/rSH/TQ4AQKAzN7U6jbYmhxeYlx7s1DxaTaPDLUoTGIZ0aGJqap20MI2HdRotLZt1ZpxxQErfJzXpaFkkAAAAoCqSkpL8eu+autSyZUvNnDlTM2fOtDpKhXr37q3IyEjDrJutW7calpmDUX5+vhYvXqzFixdrxYoVLrOGzDp27Kjf/e53mjFjRpWX+fNVL7/8svP1+PHjLcthbmoNHjzYoiSewfKDAAD4ouxUKc203nIg7ae1z9hwOYdZWrU2ONH4NdxwMEOFJQGytnuLnlJ0C2PN3PQFAAAAAC8LCQnR+eefb6iVn40Do6eeekrNmzfXVVddpVdeeeWsDS2pbKm8+++/X0lJSfrss8/qIKV3/PTTT5o/f74kKSIiQv/3f/9nSQ6Hw6EdO3YYahdeeKElWTyFphYAAL7ot+XGcVhDKcF1mQN/VFJq19pk435PQ9hPq9YGJcYZVuMrLLFrc7mZW37NZnOdqci+WgAAAAAsMHq08XeTLVu2VHAmfv31V7f7vUlSs2bN1KNHDw0aNMjt0nxpaWm64oor9L///c/bMT0uNTVVkydPlsPhkCQ99thjli0/uGvXLmVnZzvHQUFBGj58uCVZPIWmFgAAvsg8C6XDMCk41JosHrY9JUs5hcaNhNlPq/ZiGoSqW8tGhtoa0zKPfq3jSOM4+UepuMCaLAAAAADqrYsuusgw3rt3r44dO2ZRGv8RFhamiRMn6u2339ahQ4d07Ngxbd26VWvWrNGBAwd06NAhPfjggwoLCzNc94c//EHffus/K3Xk5ubqiiuu0JEjRyRJl1xyie69917L8qxZs8YwHjBggN8v60hTCwAAX2MvdZ2pFUBLD/5iWnqwQ7MoNW8YYVGawDKkvbE5aP5a+7WOIyWVm4pWnCcd/NmyOAAAAADqp379+ql169bOscPh0IoVKyxM5Nvi4uL0xBNP6MiRI/roo480depUJSQkuJyXkJCgJ554Qj/++KNiYmKcdbvdrjvuuEOlpb6/vH5BQYEuv/xyZyPpvPPO04IFC2Qrv6xKHVu7dq1hPGbMGIuSeA5NLQAAfE3KRinfNMOmY+A0tVbvM35uLD3oOeam1q8HTqm41G5RGg9rECe1HmCssQQhAAAAgDpms9l05ZVXGmr+NJOoLs2YMUP79+/Xgw8+qKZNm1bpmsGDB+vVV1811Hbu3KkffvjBGxE9pqioSBMnTtTy5WVvUh44cKC++uorRUVFWZrL/HUzf+/6I5paAAD4mr2mWVpNOkuN21mTxcNK7Q6XJfHOYelBjxlsamrlFZVq29Esi9J4gcu+WvziCAAAAKDuXXXVVYbx6UYGjMaMGaNGjRqd/USTq666St26dTPUFi9e7KlYHldcXKxJkyY5M/bp00dff/21YcaZFU6cOGHY861t27YaMGBAJVf4B5paAAD4GvPsE/Mf8v3YjpQsZZv20zqnAzO1PKVJdLg6N4821FYH0hKE5n8Xju+QMg9bkwUAAABAvTV06FDDEoS//fabDh48aGGiwDNs2DDD+MCBAxYlqVxJSYmmTJmizz77TJLUo0cPLV26VHH/j737Do+rPNM/fs+MenGRZLnKsiW5917ANthUmx4wEAi9JKTwyyYkJJtNSLIblkB2s0mA0EsCARNMN8W9YBv33ossy1XFsqxeZn5/KIz0Hkm2LM3MmRl9P9fl65r38Zk5NwmWxXn0Pm+S/T/Au2TJEnk8Hu/6hhtusDGN79DUAgAgmJQXSbnmvONwPk+rb0q8unbgPC1fsu7Wsu6MC2k9R0sxncwau7UAAAAABJjT6dSdd95p1BhB6FudO3c21kVFRS1639SpU+VwOHz6a/To0U3eq7a2Vt/61rf07rvvSpIGDhyohQsXqkuXLm36Z/eVefPmGetvfetbNiXxLZpaAAAEk4NLJU+Dw09d0VL6Bfbl8bHVjc7Tsv8nl8LNBMvOtzXZhap1e5q5OsQ4XVLmdLPGuVoAAAAAbHDPPffI4XB413PmzLExTfjJzTWncrTkTK7q6mqtW7fO51kuuKDxcxm326177rlHb731liSpX79+WrRokbp27erz+7eG2+3Wxx9/7F0PGzas2eZcqKGpBQBAMLHuOkmfLEXF2ZPFx+rO0zJ3ajF60PesjcIzFTXaeSyMz9U6sESqrbYlCgAAAID2KzMzU5dddpl3vWDBAuXl5dmYKHy43W4tW7bMqPXv3/+c79uwYYPKy8t9nsfa1PJ4PHrwwQf1+uuvS6r7d2Hx4sXq3r27z+/dWl9++aXx7+Ndd91lXxgfi7A7AAAA+BePp3FTK4zO09p1vFjFFeZ5WhMy2Knla107xKhPcpyyC8q8ta8OFmpoT3sPqPUZ606tymIpd52UPsmePAAAAADarZ/97Gf6/PPPJdWdrTRnzhx997vftTlV6Pv8888bnVF21VVXnfN96enpmj9//lmv+e1vf2s0zJ588kmNHDnyrO+ZMGGCsf73f/93vfjii5KkyMhIPfzww1q7dq3Wrl3b1Nu9LrvsMsXFBeYHl99++23v69jYWJpaAADAD/L3SMXm9vrwOk/LHD2Ynhyn7h1jbUoT3iZmJBtNrVX783XvhX1tTORDHbpLXYdKJ7bV1/YtoKkFAAAABKHHHntMjz32mN0x/GbatGmaNGmSVq1aJUl68803aWq1UU1NjX72s58ZteHDh2vMmDHnfG+3bt3UrVu3s17z8MMPG+s77rhDqamp55Vx5cqV3tfV1dX6wQ9+0KL3HTx4UH369Dmve7VGdXW10dT61re+paSk8PmhYsYPAgAQLKxnA3XoKXUZaE8WP/jqgDl6kPO0/GdSpjnW8asDhaqpdduUxg+szV7O1QIAAABgk4YNmFWrVik7O9u+MGHg17/+tTZv3mzUnnzySZ98dllZmXbv3u1d9+zZ87wbWqHgs88+U35+vnfd0qZbqKCpBQBAsLCOHsycLjU4dDaUud0erck2d2pxnpb/WJtaZyprtO1oGJ+rdWyTVMLsegAAAACBd9VVV2nYsGGS6s5a+nosHc7fJ598ot/97ndG7a677jLOLmuLLVu2qLa21rseNWpUqz5nyZIl8ng85/0rELu0JOmFF17wvr7qqqs0ZMiQgNw3UGhqAQAQDKrLpUNfmrUwOk9r94kzKiqrNmoTaGr5TWpijLJSE4zaqv0FzVwdgtImSpHxZu3AYnuyAAAAAGjXHA6HHn30Ue/6mWeeUWlpqY2JQtPmzZt16623yu2unzIycOBA/fnPf/bZPTZu3GisR48e7bPPDhaHDx/WvHnzvOtf/vKXNqbxD5paAAAEg0NfSjUV9WuHU8qYZl8eH/tyX76xTkuKVc9OnKflT5Mtu7VW7s9v5soQFBHV+M8HIwgBAAAA2OSWW27R2LFjJUmnTp0ydsrg3A4cOKArr7xSZ86c8da6dOmiDz74QAkJCWd55/nZsGGDsQ7HptZzzz3n3Y02c+ZMjRs3zuZEvkdTCwCAYGAdPdhrnBTb2Z4sfmDdJTQ5I8WmJO2Htam1LvuUqmrC+VythZI7jP75AAAAAIQMp9OpZ555Rk5n3eP2//3f/1VNTY3NqULD0aNHdemll+rYsWPeWseOHfXZZ5+pf//+Pr2XdadWa8cPBqvS0lI9++yzkiSXy6X//u//tjmRf9DUAgAgGDQ6T2tG09eFoJpat746aJ6nNTmL0YP+NqFvsnEkW3l1rTbnFtmWx+esf0bK8qXjm5u+FgAAAAD8bNy4cbrvvvskSTk5OXrrrbdsThT88vLyNGPGDB04cMBbi4+P17x583y+i6q6ulrbtm3zrpOTk9W7d2+f3sNuL730kgoL656/3Hvvvd6z3sINTS0AAOxWdFjK323WrLtQQtiWI6dVUmn+hNqkTJpa/tY5PkqDunUwaiv3hdG5Wkl9paRMs8YIQgAAAAA2evzxx5WcXPffu7/73e/YrXUWhYWFuvTSS7Vr1y5vLSYmRh9++KEmT57s8/vt2LFDlZWV3nW4jR6sqKjQE088Ialup9tvf/tbmxP5D00tAADstt+ySyu2s9QjfLbAW0cP9u+aoNTEGJvStC9hfa6WJGVdYq6tOx4BAAAAIICSkpK8I9927typv/zlLzYnCk6nT5/WZZddps2b66dtREVFae7cuZo+fbpf7hnuoweffvppHT16VJL01FNPKTU11eZE/kNTCwAAu1kfxGdcLDld9mTxgy/3mY2UyZmcpxUo1jGPG3OKVFFda1MaP7A2tQ6vkcqLbIkCAAAAAFLd2LdLLqn7b5XHHntMeXl5NicKLmfOnNEVV1yh9evXe2uRkZF65513dOWVV/rtvhs2bDDW4bRT69SpU3r88cclSdOnT/eOwQxXNLUAALBTbY10YKlZsz6oD2EV1bVad+iUUWP0YOCM65Mkl7P+YK2qWrfWW/7/CGl9LpBc0fVrT610cGnz1wMAAACAnzkcDr3xxhvq3r27Tp8+rZ/97Gd2RwoapaWlmjlzplavXu2tuVwuvfnmm7rmmmv8eu+Gu8Kk8Nqp9e///u8qKChQ586d9fLLL9sdx+9oagEAYKcj66TK02Yt0z9b7e2w4dApVdW4vWunQ5qYQVMrUBJjIjWsZ0ejFlYjCKPipXTLrHXO1QIAAABgs9TUVL311ltyuVx65ZVXtG7dOrsj2a68vFxXX321VqxY4a05nU69/vrruvHGG/1+/z179nhfR0dHKysry+/3DIS1a9fq+eeflyS98sorSk9PtzmR/9HUAgDATtYH8F2HSh2625PFD1ZaztMa2rOjOsZG2pSmfWp8rlZBM1eGqKbO1fJ47MkCAAAAAP8ydepU/fa3v5Xb7da9996riooKuyPZprKyUtddd50WL17srTkcDr388sv65je/6ff7V1RU6Pjx4951WlqanM7Qb41UVFTozjvvVG1trX784x/r2muvtTtSQIT+/3MAAIQy63laYbRLS5K+3M95Wnaz/m++Jfe0SiprbErjB9amVvERKW+XPVkAAAAAoIFHH31UM2fO1JYtW/SjH/3I7ji2qK6u1o033qgvvvjCW3M4HHr++ed15513BiRDeXm5sY6NjQ3Iff3tkUce0c6dO3XttdfqiSeesDtOwNDUAgDALqUF0tGNZi2MztM6U1GtLbnmaEXrriH435j0zop01Z+rVev2aO3BQhsT+ViXAVKHXmbN2iwGAAAAABs4HA69+eabGjNmjJ555hm9//77dkcKqNraWt166636+OOPjfpf/vIX3XfffQHLER0dbawPHjyokpKSgN3fH/7xj3/oL3/5i8aPH6833ngjLHaetVSE3QEAAGi3DiyW1GBMWmSc1HuibXF8bW12oWrd9f98kS6HxvVJsjFR+xQb5dKo3p21pkEja+X+fF08MNXGVD7kcEhZ06UNr9fX9i2QJn/PvkwAAAAA8C8dO3bU559/rmnTpumee+7RmDFjlJaWZnesgHjggQf07rvvGrUbbrhB/fv314IF53cecmxsrC644IJW5YiLi1N6eroOHTokSSopKdGUKVP0wAMPqE+fPoqMjFTXrl01bNiwVn1+oK1atUr33XefxowZo88//1zx8fF2RwoomloAANjFep5W36lSRHTT14agL/eZZzeN6t1ZsVEum9K0b5Mzky1NrTA7VytzhtnUOrRSqiqTouLsywQAAAAA/5KcnKz58+drypQp+tGPfqQ5c+bYHSkgFi5sPEVj7ty5mjt37nl/Vnp6urKzs1ud5Zvf/KYef/xx73rTpk166KGHvOt/+7d/0x/+8IdWf36gnDx5UldddZWGDh2qzz77TJ06dbI7UsC1nz1pAAAEE7e7ifO0ZtiTxU+sjZMLOE/LNtZztXYcK1ZRWZVNafwg4yLJ0aBhWlspHfrStjgAAAAAYNW9e3ctXLhQCQkJdkdpl37+859r4sTmp+OMGjUqgGla7+TJk5o6daoWL16szp072x3HFjS1AACww4ltUulJs5YVPk2tgpJK7TxWbNQmZ3Gell1GpHVUTGT9t30ej7T6QBidqxXbSeo11qxxrhYAAACAIJOenq6nn37a7hjtUkJCgpYvX66//vWvuuSSS9S1a1dFRkZ6f3/kyJH2hTsPmZmZevfddxUX134nk9DUAgDADvstD9w795GSM22J4g/WhklspEsjenWyJwwUHeFqdJ7Zqv35NqXxE+tOR+ufMQAAAAAIArGxsXZHCJjs7Gx5PB6f/GrL6MGvRURE6MEHH9T8+fN1/PhxVVVVeT9/6NChbf8HDoDY2Fg5ne27rdO+/+kBALCLdRdJ1iX25PCTLy0Nk/F9kxQVwbcddpqUae6UC7tztax/hvL3SEU59mQBAAAAAAB+wdMlAAACrbJEyllt1sLsPK1VlobJ5ExGD9rNeq7W3pMlOlFcYVMaP+gxUoq1zBNnBCEAAAAAAGGFphYAAIGWvVxyV9evnRFS3yn25fGxo0XlOphfatQuyEpp5moEytAeHZQYE2HUVuwNoxGETpeUOd2s7VtgTxYAAAAAAOAXNLUAAAg064P23pOk6ER7sviBdaxdx9hIDerewaY0+FqEy9lox9yKfWHU1JIa73g8uEyqrW76WgAAAAAAEHJoagEAEGjWkWjW3SUhbqXlPK1JGclyOR02pUFDF/brYqxX7MuXx+OxKY0fZFmaWpXFUu5ae7IAAAAAAACfo6kFAEAgFeyXTh00a1mX2JPFDzwej7607P6ZnMV5WsFiimUMZN6ZSu0+ccamNH6Q2E3qOtSsca4WAAAAAABhg6YWAACBtH+RuY5PbfwQPoTtPVmiE8WVRm1yJudpBYv05Dj16hxr1MLqXC2p8c7H/TS1AAAAAAAIFzS1AAAIJOt5WlkzJGf4/HW83NIg6dExRpld4m1KAyuHw6Ep/cwmo/X/s5Bn3fl4dJNUGmb/jAAAAAAAtFPh8xQNAIBgV1MpHVxu1jJnNH1tiFq+N89YT+nXRQ4H52kFkwuzzHO1vjpYoMqaWpvS+EHviVJkXIOCR9q/2LY4AAAAAADAd2hqAQAQKDmrperSBgWHlHmxbXF8rbKmVqsPFBi1Kf0ZPRhsJmcmq2GfsaLarfXZp+wL5GsR0VLfqWbNukMSAAAAAACEJJpaAAAEivVsnx4jpfjwafqszz6limq3d+1wSBdwnlbQ6RwfpWE9Oxq15fvCbDyfdQfk/kWS2930tQAAAAAAIGTQ1AIAIFD2WZpa1rN/Qpy1MTKsZ0d1jo+yKQ3O5sIss9m4IuzO1bI0tUpPSie22pMFAAAAAAD4DE0tAAACofiYdGKbWQv787TYpRWsLrT8f7Pt6GmdKq2yKY0fJGdKnfuYNWtTGQAAAAAAhByaWgAABML+ReY6uqPUa5w9WfygoKRS244UG7Up/brYlAbnMia9s2IjXd61xyN9uT/Mdms1NYIQAAC0mMtV972CmxG+AACgDWprayXVf2/RVjS1AAAIBOt5WhlTJVeEPVn8YIVl9GBclEuje3e2KQ3OJTrCpQkZSUYt/EYQWsZ75qySKs/YkwUAgBDkdNY9MnK73fJ4PDanAQAAoajh9xEh19SqqqrS3/72N82cOVPp6emKiYlR9+7dNXnyZD311FPKz/f/g5Rly5bp+9//vkaMGKHU1FTFxMQoLS1N48eP10MPPaR33nlHp06d8nsOAEA7465tvEskzM7TsjZEJmUkKyqCn50JZtZztZbvzQ+vB1Z9p0jOBo1jd410cLl9eQAACDENHzzV1NTYmAQAAISqht9DfP0DM20VkKdNu3bt0sSJE3XHHXfo008/VU5OjiorK3X8+HGtWrVKjzzyiIYMGaJ58+b55f4HDhzQFVdcoWnTpukvf/mLtmzZory8PFVWVio3N1dr167Vs88+q9mzZ+ull17ySwYAQDt2dJNUbvmhiTA6T8vj8Wi5pallPbMJwcc6HvJIUbmyC8psSuMH0YlS70lmbd8Ce7IAABCCYmJivK/LysLoewQAABAwpaWl3texsbE++Uy/zz3Kzc3VjBkzdPToUUmSw+HQ1KlTlZWVpZMnT2rBggUqLy/XyZMndd111+nTTz/VjBm+e9C3efNmzZgxQwUFBd7a4MGDNWTIECUlJenMmTPavXu3Nm/ezE8eAQD8wzp6MGWA1CnNnix+sO9kiY4XVxg1ztMKfv27Jig1MVonz1R6a8v35qlvSryNqXwsc7qU3WB31r4FdQeIORz2ZQIAIETEx8d7p+qUlpaqY8eONicCAAChpqSkxPs6ISHBJ5/p96bWbbfd5m1opaen68MPP9Tw4cO9v5+fn69bbrlFCxcuVHV1tWbPnq39+/erU6dObb53dna20dCaMWOG/vjHP2ro0KGNri0sLNQHH3ygHj16tPm+AAAYrLtDssJnl5YkLbPs0urRMUaZXcKoMRKmHA6HLsxK0dyNR7y15XvzdcekPvaF8rWsS6SFv65fFx2SCg9IyZn2ZQIAIETExMTI4XDI4/GotLRUHo9HDn4wBAAAtJDb7fbu9na5XIqOjvbJ5/p1/OC8efO0bNkySVJUVJQ++ugjo6ElSSkpKfrggw+UkZEhqa659Pvf/94n93/ggQe8Da2bb75Zn3/+eZMNLUlKSkrS3Xffrcsvv9wn9wYAQFLd2MHctWYtjEYPStKKvXnGekq/LjzwCBHWMZGr9xeoptZtUxo/6DpUik81a/sWNn0tAAAwOJ1OxcXFSao7D+PMmTM2JwIAAKGksLBQbnfdM4aEhASfPSvya1Pr6aef9r6+8847NWzYsCavi4+P129+8xvv+rnnnmvzKMAPPvhA8+fPlyT17t1bL7zwgnHIKQAAAXFgqeRp0CSIiJH6XGBfHh+rrKnV6gOFRo3ztELHhVnm/1dnKmu0ObfInjD+4HTWjSBsyDoOFAAANCspKcn7+uTJk6qtrbUxDQAACBWVlZXeMcaS+T1FW/mtqVVSUqKFC+sfGtx9991nvf7GG29UYmKipLoO3tc7vFrr2Wef9b7+4Q9/6P1sAAACyvoAPX2yFOmbgzGDwfpDp1ReXf9ww+GQLsiiqRUqUjvEaEBX83uk5ZZxkiEv6xJzfXCZVFPZ9LUAAMAQHx+v+Pi6sdLV1dXKzc31/sQ1AABAUyorK3Xo0CF5PB5JdQ2tmJgYn32+35paK1euVGVl3QOD+Ph4jRs37qzXR0dHa+LEid71okWLWn3vkydPendpSdI3v/nNVn8WAACt5vE0HnVmfcAe4qwNkGE9OyopPsqmNGgN6866ZXvymrkyRGVeLKnBiIPqMilntW1xAAAIJQ6HQ926dfNOvikrK9PBgwe952MAAAB8ze12Kz8/XwcPHvTu7o6JiVFKim9/+NlvTa2dO3d6Xw8bNkwRERHnfM/o0aObfP/5WrVqlfcnhwYMGKDU1FSdPn1af/jDHzRhwgQlJycrLi5O6enpuummm/TWW2/xk0YAAN/L2y0VHzFrYXeeltnUmsLowZAzrX8XY73pcJGKyqpsSuMH8SlSj5Fmbd8CW6IAABCKoqKilJaWJqez7hFSVVWVDh06pAMHDujkyZMqLy+X2+32/jQ2AABoH9xut6qqqnTq1CkdPnxYe/fuVV5envd7gpiYGPXu3dvnx0Kdu9PUSrt37/a+Tk9Pb9F7evfu7X29a9euVt977dq13tdDhgzRqlWrdMsttygnJ8e4LicnRzk5OfrnP/+pJ554QnPnzlXfvn1bfV8AAAzWB+cdekldBtiTxQ8KSiq17ehpozalX5dmrkawGt83STGRTlVU1/2Aj9sjLdubr2tG9LA5mQ9lzpCObqxf718k6be2xQEAINTExsaqT58+Onr0qCoqKiTVjRaqrKxUQUGBpLpdXU6n02eHwAMAgOBVW1t71h9oSUpKUkpKis8bWpIfm1pff1MjSV27dm3Re7p16+Z9XVhYeJYrz+7w4cPe16dPn9bMmTNVVFQkqW432PDhw1VbW6u1a9d6m2ebNm3SpEmTtG7dOvXq1atV983NzT3r7x87dqxVnwsACFHW87SyptcdOhUmvtxfoIbfv8RFuTS6d2f7AqFVYiJdmpSRrMW768cOLt2dF15NraxLpOVP1a9PbJOKj0kdutuXCQCAEBMdHa0+ffro1KlTKi4uVnl5ufH7Ho/HO2oIAAC0Py6XSwkJCT4/Q8vKb02tkpIS7+vY2NgWvafhdQ3ff76+bmBJ0sKFdQ8UU1JSNGfOHF188cXGtXPmzNFdd92l8vJynThxQrfffruWLFnSqvumpaW1NjIAINxUlUnZX5q1MDtPa+lu8+ylCX2TFBXht8nG8KOLBqSaTa09eXK7PXI6w6QJ22usFN1Bqiyur+1fJI26zb5MAACEIIfDoaSkJCUlJammpkYlJSUqLS1VTU2NamtraWoBANBOuFwuuVwuOZ1OxcbGKiEhQdHR0QHZse23ptbX29GluvnLLREdHe19bf2Jn/NRWlpqrF0ulz766CNNnDix0bWzZ8+WJN18882SpKVLl2rJkiW66KKLWn1/AAB0aKVUW1m/drikvtPsy+NjbrdHS/ecNGoXDUi1KQ3aynquVn5JpXYcK9bQnh1tSuRjrkip71Rp18f1tf0LaWoBANAGERER6tSpkzp16mR3FAAA0I747cepG24vq6pq2WHjlZX1D/9aurvrXPeWpBtuuKHJhtbXZs+erbFjx3rXb731Vqvue/jw4bP+WrNmTas+FwAQgqznafUaJ8V2siWKP2w/Wqz8EvPv94sGcJ5WqOqTEq8+yXFGbemevGauDlHWnZL7F0lufpocAAAAAIBQ4redWgkJCd7XLd111fC6hu9vy70l6frrrz/ne66//nqtW7dOkrRy5cpW3be1Z3EBAMJQo/O0ZtiTw0+W7DZ3aWWkxCs9Od6mNPCFiwak6tWV2d71kt0n9d2Ls+wL5GvWP4Plp6Sjm6ReY2yJAwAAAAAAzp/fdmolJyd7X584caJF7zl+/Lj3dVJSkk/uLUmDBw8+53saXnPkyJFW3xsAABXlSPl7zFqYNbUWW5pa09ilFfKsIwg35BTpdHm1TWn8oFNvKaW/WbPuqAQAAAAAAEHNb02tAQMGeF8fOnSoRe/Jycnxvh44cGCr7219b0t2fTW85syZM62+NwAA2mfZpRWbJHUfaUsUfzhVWqVNh4uMGudphb6JGcmKiqj/1rDW7dGX+/JtTOQHmZbmsnVHJQAAAAAACGp+a2oNGjTI+3rr1q2qqak553s2bNjQ5PvP19ChQ411S5pUDa/p2DFMDkUHANjD+qA8c7rkdNmTxQ+W7c2T21O/jol0akLf1u+wRnCIjXJpYoa52906ZjLkWc/Vyl1bN4YQAAAAAACEBL81tSZPnqzo6GhJUmlpqfe8quZUVlZq9erV3vX06dNbfe+JEycqPr7+XI8dO3ac8z0Nr0lLS2v1vQEA7VxttXRgqVkLs9GDS3fnGevJmSmKiQyfpl17Zh1BuHRPnjweTzNXh6D0yZIrun7tcTf+8woAAAAAAIKW35paCQkJmjGj/iHeq6++etbr586d690t1blzZ02dOrXV946NjdUVV1zhXb/33nvnfE/Da6ZNm9bqewMA2rncdVJlsVnLbP0PagQbt9ujpXvMptbFnKcVNi6y/H95orhSu46H0VjmqLi6xlZDjCAEAAAAACBk+K2pJUkPPfSQ9/Urr7yi7du3N3ldWVmZfvnLX3rXDz74oCIiItp074cfftj7eu7cucYuMKs5c+Zo/fr13vWdd97ZpnsDANqxfQvMdddhUmI3e7L4wdYjp1VQWmXUOE8rfGSkxCstKdaoLbHszAt51hGE+xZK4bQbDQAAAACAMObXptasWbM0ZcoUSVJVVZWuuuoqbd261bimoKBA1113nfbt2ydJSkpK0k9/+tMmPy87O1sOh8P7a8mSJc3ee8qUKbr22mslSW63W9dcc02T17/zzju66667vOubb75ZI0eObPk/JAAADVl3fWSFzy4tqXGDI6NLvNKS4mxKA19zOByNRhCG37lalnGgxUekvN32ZAEAAAAAAOelbduhWuDNN9/U+PHjdezYMWVnZ2vkyJGaNm2aMjIylJeXpwULFqisrKwuTESE5syZo06dOvnk3i+//LIuuOAC7dq1S3l5ebr44os1ZswYDR8+XLW1tVq7dq127tzpvX7w4MF6/vnnfXJvAEA7VJovHd1k1qy7QkLcYkuD42J2aYWdi/qn6u+rc7zr9YdO6UxFtRJjIm1M5UNdBkodetY1s762b4GUOtC+TAAAAAAAoEX8ulNLknr16qVFixZ5dz+53W4tXrxYL730kj788ENvQ6tLly56//33jXO42iopKUkLFy7UpZde6q2tX79er7zyil5//XWjoTVz5kytWLFCHTp08Nn9AQDtzP7FkhqMMYuMl9Im2hbH1wpLq7Q5t8ioWc9gQuiblJmsKFf9t4g1bo++3FdgYyIfczgan3PHuVoAAAAAAIQEvze1JGngwIH66quv9Nprr+mKK65QWlqaoqKilJqaqokTJ+qJJ57Qjh07NGvWLJ/fu0ePHvriiy/00Ucf6ZZbblFmZqbi4uIUHx+vrKws3X333Vq4cKE++eQTde7c2ef3BwC0I9bztPpOlSKi7MniB8v35hlHD8VGujS+b5J9geAX8dERGtfX/J5o6Z5wG0Fo2UF5aKVUXW5PFgAAAAAA0GJ+Hz/4taioKN1xxx264447Wv0Zffr0kaeVB3lfddVVuuqqq1p9bwAAzsrtlvYvMmvWs3tC3OJdZmPjgqxkRUe4bEoDf7qof6qxO2vp7jx5PB45HA4bU/lQxjTJ4ZQ87rp1TYWU/aXUL7zGhQIAAAAAEG4CslMLAICwd2KrVGrZzRJGTa1at0fL9uYbtWmcpxW2rGMlj56u0J4TJTal8YPYzlLPsWaNEYQAAAAAAAQ9mloAAPjCPssD8c59paQMe7L4wZbcIhWWVhm1i/pznla4ykpNUM9OsUZt0a4wH0FoHR8KAAAAAACCDk0tAAB8wdrUsj4wD3FLducZ66zUBKUlxdmUBv7mcDh08UCzablo1wmb0viJdSdl/h6pKMeeLAAAAAAAoEVoagEA0FaVZ6TDq81aGI0elKQle8ym1sUD2KUV7mYM7Gqs1x86pVOW3XohrceoujGEDVmb0wAAAAAAIKjQ1AIAoK0OLpPcNfVrZ6TUZ4p9eXysoKRSW3KLjNpFnKcV9iZlJis20uVduz3Skj1hNILQ6ZIyLjZrnKsFAAAAAEBQo6kFAEBbWXd39J4oRSfYk8UPFu06KY+nfh0X5dLYPp2bfwPCQkykSxdkpRi1hTvDqKklNR4TemCpVFttTxYAAAAAAHBONLUAAGgLj0fat8Cshdl5WtZGxtR+XRQd4WrmaoSTGYPMHXlL9+SputZtUxo/yJxuriuLpdx19mQBAAAAAADnRFMLAIC2KDwgFR0ya2F0nlZFda2W7TXP07I2OhC+pg80/78+U1GjtdmFNqXxgw7dpdQhZo0RhAAAAAAABC2aWgAAtIV19GBCV6nrUHuy+MHqAwUqq6r1rh0O6eKBNLXai64dYjSsZ0ejFn4jCC1NaOvOSwAAAAAAEDRoagEA0BbWB+CZM+o6P2HC2sAYldZJKQnRNqWBHaw78xbtCvOm1tFNUmm+LVEAAAAAAMDZ0dQCAKC1aiql7OVmLYxGD3o8Hi3cecKozRjU1aY0sMuMgeb/5wfzS3Ugr8SmNH7Qe5IUGdeg4JH2L7YtDgAAAAAAaB5NLQAAWitnlVRd1qDgkDIuti2Or+08dkZHT1cYtUtoarU7Q3p0UGqiuTsvrEYQRkRLfaaYNc7VAgAAAAAgKNHUAgCgtaznafUYJcUn25PFD6y7tNKSYtW/a4JNaWAXp9PRaAThwl0nmrk6RGVdYq73LZTcbnuyAAAAAACAZtHUAgCgtaxNLeuD8RC3wHJ20oyBXeUIo/PC0HLTLSMI12af0umyapvS+IF1bGjpSenENnuyAAAAAACAZtHUAgCgNYqPSie3m7UwOk/rZHGFNh8uMmqMHmy/LsxKUXRE/beNtW6Plu7NszGRjyVlSJ3SzRojCAEAAAAACDo0tQAAaA3rLq2YjlLPsfZk8YNFll1aidERGt83yaY0sFtslEuTM83Rmot2htEIQoej6RGEAAAAAAAgqNDUAgCgNfYtMNcZF0uuCHuy+MGCnWZTa2r/LoqK4NuG9my6Zafe4t15qqkNo3OnrDstc1ZLlWfsyQIAAAAAAJrE0ykAAM5XbY10YLFZC6PztCqqa7VinzlabsagVJvSIFjMGGj+O3C6vFobcorsCeMPfadKzgaNaXe1dHC5fXkAAAAAAEAjNLUAADhfR9ZLFafNWhidp7Vyf74qqut34Dgd0sUDaGq1dz06xWpQ9w5GbWE4jSCMTpTSJpo1ztUCAAAAACCo0NQCAOB8WUcPpg6ROvSwJ4sfWEcPjk1PUuf4KJvSIJhcYtmxtyCcmlpS4+Y052oBAAAAABBUaGoBAHC+rE2trOn25PADj8fTaPcNowfxtemWEYT780q1P6/EpjR+YG1qnTooFey3JwsAAAAAAGiEphYAAOejtEA6utGshdF5WtuOFOtEcaVRmzGoq01pEGxG9Oqk1MRoozZ/Rxjt1uo6TIrvYtb2L7InCwAAAAAAaISmFgAA5+PAYkme+nVknNR7km1xfM06Tq5Pcpwyu8TblAbBxul06NLBZpPz8+3HbUrjB06nlGkdQbig6WsBAAAAAEDA0dQCAOB8WB9w950qRUQ3fW0Isja1ZgzqKofDYVMaBKPLhnQz1htzinSyuMKmNH5gHUF4cLlUU9n0tQAAAAAAIKBoagEA0FJut7RvoVkLo9GDhwvLtP1osVG7hNGDsJiUkazE6AijNn9nGI0gzJwuqUEjt7pUylltWxwAAAAAAFCPphYAAC11YqtUetKsWXd1hDDrGLnOcZEa16ezTWkQrKIinLpoYKpR+2J7GDW14lOk7iPM2v6FTV8LAAAAAAACiqYWAAAtZR09mJRR9ytMWBsTlwzqqggX3yqgscuHmDv4Vu7PV3FFtU1p/MC6A3PfIntyAAAAAAAAA0+qAABoqTAePZhfUqm1hwqN2uWWs5OAr03r30VRDRqe1bUeLdmdZ2MiH7PuwDyxVTpzvOlrAQAAAABAwNDUAgCgJSpOS4e/Mmth1NRasOOEPJ76dVyUSxf2S7EvEIJaYkykJmclG7UvtodR06fXOCkq0aztZ7cWAAAAAAB2o6kFAEBLHFwmuWvq164oqc+F9uXxMet5WhcN6KKYSJdNaRAKrDv5luzOU2VNrU1pfMwVKWVMM2vW8aMAAAAAACDgaGoBANAS1gfa6ZOlqHh7svjYmYpqfbmvwKgxehDnMmNQqhyO+nVJZY1W7i9o/g2hxjqCcP9iyR0mTTsAAAAAAEIUTS0AAM7F4wnr87SW7M5TVa3bu450OXTxwFQbEyEUpCbGaHTvzkbti+0nbErjB5mWplZ5oXR0ky1RAAAAAABAHZpaAACcS/4e6fRhsxZGTS3r6MFJmSnqEBNpUxqEkssGdzXW83eckNvtaebqENM5XUruZ9b2L2z6WgAAAAAAEBA0tQAAOBfr6MEOPaUuA+3J4mOVNbVasjvPqF3B6EG00GWWf1fySyq18fApm9L4gbV5bd2xCQAAAAAAAoqmFgAA52JtamXNkHGYUAhbua9AJZU13rXDIV1q2X0DNKdvSrz6d00wamE1gtB6rlbuWqm8yJYoAAAAAACAphYAAGdXVSZlf2nWwnj04JjendUlMdqmNAhFlw02d2t9vv24PJ4wGUGYfoHkavDnwVMrHVxqXx4AAAAAANo5mloAAJzNoS+l2sr6tcMl9Z1mXx4fqnV7NH+HuavmckYP4jxZ/53JLijTvpMlNqXxsag4KX2yWbPu3AQAAAAAAAFDUwsAgLOxPsBOGy/FdrIliq+tP3RKBaVVRo2mFs7X0J4d1L1jjFH7bNvxZq4OQdYRhPsWSeGyEw0AAAAAgBBDUwsAgLNp6jytMGFtPAzslqjeyXE2pUGocjgcusxyDtunYdXUsowbLc6V8nbbkwUAAAAAgHaOphYAAM0pPCgV7DNrYXKelsfjaXSeFru00FpXDO1urHccK1Z2fqlNaXysy0ApsYdZ27/QniwAAAAAALRzNLUAAGiO9cF1XIrUbYQ9WXxs+9FiHSkqN2o0tdBa4/smKSUhyqjN23bMpjQ+5nA0MYKQphYAAAAAAHagqQUAQHOsD66zZkjO8Pir0zp6MC0pVoO6J9qUBqHO5XQ0aorO2xomTS2pcVPr0JdSdXnT1wIAAAAAAL8JjydzAAD4Wk2VdGCpWQuj0YPWhsMVQ7rJ4XDYlAjhYOYwcwThtiPFyikosymNj2VcJDkafNtcU1HX2AIAAAAAAAFFUwsAgKYcXi1VNzwTyCFlTrctji/tOn5GByznHc0a3qOZq4GWmdA3SUnx5gjCT8NlBGFsZ6nnWLPGCEIAAAAAAAKOphYAAE3Zt8Bc9xgpxafYEsXXPtliNhp6dorViF4dbUqDcBHhcuryIV2NWliPIKSpBQAAAABAwNHUAgCgKY3O0wrf0YMzhzF6EL5hHUG4Ofe0ck+FyQhC69eA/N1S0WF7sgAAAAAA0E7R1AIAwKr4mHRim1kLk6ZWU6MHrY0IoLUmZiSrU1ykUft063Gb0vhYj1F1Ywgb2s9uLQAAAAAAAommFgAAVtYH1dEdG5+nE6Ksu7R6dorVyLRO9oRB2Il0OXXZYMsIwnA5V8vpkjIuNmuMIAQAAAAAIKBoagEAYGU9TyvzIskVYUsUX/J4PPqE0YPwM+vOv405RTpaVG5TGh+znqt1YKlUW2NPFgAAAAAA2iGaWgAANFRbI+1fbNbCafRgHqMH4V+TM1PUIcZsAn+6LUxGEGZON9eVp6Uj6+zJAgAAAABAO0RTCwCAho6skyqKzFrmjCYvDTWMHkQgREU4ddmQbkbN+u9eyOrQQ0odYtasOzsBAAAAAIDf0NQCAKChvV+Y665DpY497cniQ4weRCDNHGY2tdYfOqXjpytsSuNjWZbdWpyrBQAAAABAwNDUAgCgIWtTK0xGD+4+wehBBM4FWSlKjLaOIAyT3VrWrwlHN0qlBfZkAQAAAACgnaGpBQDA14qPSce3mrV+l9mTxcc+2cLoQQROdIRLlw7uatQ+3Rom52r1niRFxjUoeKT9i2yLAwAAAABAe0JTCwCAr1nPxonuKKWNtyeLDzF6EHa40rITcO2hQp0oDoMRhBHRUp8pZm3ffHuyAAAAAADQztDUAgDga9bRg5kXS65Ie7L4EKMHYYcp/VKU0GAEocfTeMdgyOp3qbnet0By19qTBQAAAACAdoSmFgAAklRbLR1YYtasD65D1DxGD8IGMZGNRxB+uPmoTWl8zPq1oayg7mwtAAAAAADgVzS1AACQpMNfSZXFZi3rEnuy+JDH49HHltGDVw5l9CAC45oRPYz1psNFOlxYZlMaH+rcR0oZYNasOz0BAAAAAIDP0dQCAEBq/EC6+wgpsZs9WXxo57HGowdnDWf0IALjwn4p6hxnjvAM291aNLUAAAAAAPA7mloAAEjS3vnmut9l9uTwsQ82HzHWjB5EIEW6nLrScn7bR2HT1LJ8jTi6USo5aU8WAAAAAADaCZpaAACczpVO7jBrWaF/npbb7dFHm8wGwjUjezB6EAF19XBzBOGu42e058QZm9L4UO9JUlSCWdu3wJ4sAAAAAAC0EzS1AACw7tKK7Sz1GmtPFh9an3NKR09XGDXrGUeAv43vm6SuHaKNWljs1oqIkjIuMmuMIAQAAAAAwK9oagEAYG1qZc6QnC57svjQB5vM0YP9uyZoYLdEm9KgvXI5HbrKslvrw81H5fF4bErkQ9YRhPsWSbU19mQBAAAAAKAdoKkFAGjfaiqlA0vMWhicp1Vd69a8rceN2jUjGD0Ie1h3CB4qKNOW3NM2pfGhfpYxpZWnpdw19mQBAAAAAKAdoKkFAGjfDq2UqksbFBxS1gzb4vjKin35KiytMmrXjOhpUxq0d8N7dVR6cpxR+zAcRhB26CF1HWbWGEEIAAAAAIDf0NQCALRv+xaY656jpfgUe7L40IebzIbBqN6d1NvSVAACxeFw6GrLCMKPtxyV2x0OIwgtu7Ws40wBAAAAAIDP0NQCALRv1l0VYTB6sLyqVl9sbzx6ELDTNSPNfwdPFFdqTXahTWl8yPo148Q26fSRpq8FAAAAAABtQlMLANB+FR6U8veYNeuuixC0cNcJlVbVetdOhzRreHcbEwFS/66JGtA10aiFxQjCXuOkmI5mbR+7tQAAAAAA8AeaWgCA9ss6ejAuReo+yp4sPvSBZfTg5MwUpSbG2JQGqGfdrfXp1mOqrnXblMZHXBFSpuUcPkYQAgAAAADgFzS1AADtl3X0YNYlkjO0/2o8XVatpbvzjJq1kQDYxXqu1qmyaq3Yl29TGh+yjiA8sESqqbQlCgAAAAAA4Sy0n9wBANBa1eXSweVmLQxGD362/ZiqGux8iXI5dfmQbjYmAur1To7TyLRORu2jTWEwgjDrEnNdVSLlrLInCwAAAAAAYYymFgCgfcr+Uqopr187nFLmdPvy+Ih19ODFA7uoY2ykTWmAxq4eYe7W+mz7cZVV1diUxkcSukg9Rps1RhACAAAAAOBzNLUAAO2TdfRgr3FSXJI9WXzkZHGFVh0oMGrXjuxpUxqgaVcP7y6no35dVlWrL7afsC+Qr1hHEFq/xgAAAAAAgDajqQUAaJ+sD5zDYPTgR1uOyeOpXydER2j6wFT7AgFNSO0Qowv7dTFqczcesSmND1mbWvl7pMKD9mQBAAAAACBM0dQCALQ/BfulU5aHzdYH0iHofUtj4LIhXRUT6bIpDdC8G0aZOwhX7M3TyeIKm9L4SI9RUlyKWdu3wJ4sAAAAAACEKZpaAID2x7pLK6Gb1G24PVl8ZM+JM9p65LRRY/QggtVlQ7oqLqq+4er2ND4PLuQ4nVLWJWaNEYQAAAAAAPgUTS0AQPtjfdCcdYnkcDR9bYiYu8HcpZWaGK0LMpNtSgOcXVxUhK4c2t2ohccIQssY04PLpKoye7IAAAAAABCGaGoBANqXyhIpe4VZC/HztGrdHr23MdeoXTeqpyJc/DWP4HXDaHMn4c5jxdp5rNimND6SOV1yNPhzV1PR+OsNAAAAAABoNZ52AQDalwNLpNqq+rUzQsq82LY4vrByf75OFFcatW+M7mVTGqBlJmYkq1uHGKP2Xqjv1opLknqNN2uMIAQAAAAAwGdoagEA2pe9n5vr9MlSTEd7sviIdfTgkB4dNKBbok1pgJZxOR26dlQPo/bBpiOqdXtsSuQj/S8z13s/lzwh/s8EAAAAAECQoKkFAGg/3G5pj2XXRP8r7MniIyWVNfps23GjdgO7tBAibhhl/rt6orhSK/fn25TGR/pZmlpFOVL+HnuyAAAAAAAQZmhqAQDaj+ObpRKzAaR+l9uTxUc+3XpM5dW13rXL6dA1I3qc5R1A8BjQLVFDenQwau9tCPERhF2HSondzdqez5u+FgAAAAAAnBeaWgCA9sP6YDkpU0rJsieLj1hHD17Uv4u6JEbblAY4f9eP6mmsP912XKWVNTal8QGHo/FuLZpaAAAAAAD4BE0tAED7seczcx3iowdzT5Vp1YECo8boQYSaa0b2kMvp8K7Lq2v1+fbjZ3lHCLB+bclZJZWfsicLAAAAAABhhKYWAKB9OHNcOrrRrPUP7dGDH2w6aqwTYyI0Y1CqTWmA1klNjNGUfilG7b2NIT6CMOMiKSKmfu2plfYttC0OAAAAAADhgqYWAKB92DvfXEd3kHpPsieLD3g8Hr27PteoXTW8h2IiXTYlAlrPOoLwy335On66wqY0PhAVJ/WdatasO0UBAAAAAMB5o6kFAGgfrA+UM6dLEVH2ZPGBTYeLdCC/1KjdOKZnM1cDwe2ywd2UEB3hXbs90vubQny3lnUn6N75Um0InxUGAAAAAEAQoKkFAAh/NZXS/sVmLcRHD87dYD7wT0+O0+jenW1KA7RNbJRLVw7tZtTeWXdYHo/HpkQ+YD1Xq6JIOvyVLVEAAAAAAAgXNLUAAOEve4VU3XBXk0PKutS2OG1VWVOrj7aY52ndMKqXHA6HTYmAtrtpbJqx3p9Xqg05RfaE8YWOvaSuw8waIwgBAAAAAGgTmloAgPC353Nz3WuslNDFniw+sGjnSRWVVRu1G0YzehChbVyfzuqTHGfU5qw9bFMaHxlg2a1FUwsAAAAAgDahqQUACG8eT+MHySE+evDtdeaD/vF9kpSWFNfM1UBocDgcjXZrfbzlqEorQ/gcKusIwvw9UsF+e7IAAAAAABAGaGoBAMJb/h6p6JBZsz5oDiHHTpdr2Z48o3bT2F42pQF86xuje8nZYIpmaVWt5m09Zl+gtuoxWoq37Aq17hwFAAAAAAAtRlMLABDerLu0OvSUug61J4sP/HNdrtye+nVCdIRmDe9uXyDAh7p1jNG0/mYT6J11uTal8QGnU+pn2RnKCEIAAAAAAFqNphYAILxZd0X0u0xyOJq+Nsi53R7NWW+OHrx6RHfFRUXYlAjwvdmWEYRrsgt1IK/EpjQ+YB13euhLqaLYniwAAAAAAIQ4mloAgPBVfkrKWW3WQnj04OoDBTpcWG7UrA0AINTNGNRVSfFRRu2f60N4t1bmxZKrwT+Pu0bav9C+PAAAAAAAhDCaWgCA8LVvoeSprV9HxEh9p9qXp43eXmfu0urfNUEj0zrZEwbwk6gIp64f1dOovbshVzW1bpsStVF0otTnQrPGuVoAAAAAALQKTS0AQPiyPjjuO02KirMnSxudLqvWp9uOG7XZY9PkCNFRisDZWHcgniiu1PK9+Tal8QHrDtG9X0ju2qavBQAAAAAAzaKpBQAIT7U10r75Zs16tk0IeX/TEVXV1O9UiXQ5dMPoXjYmAvxnQLdEjejV0ajNsexUDCnWrz1lBVLuOnuyAAAAAAAQwmhqAQDCU+7aujO1Ggrhptbba80H+pcN7tbo3CEgnNxk2a21YOcJFZRU2pSmjTr3kboMMmt7PrMlCgAAAAAAoYymFgAgPFkfGHcdKnUMzZ1N246c1o5jxUZt9ri0Zq4GwsPVI3ooOqL+W9XqWo/e23jExkRtNMAygpCmFgAAAAAA542mFgAgPFkfGPe7zJ4cPmDdpdWjY4wuzEqxKQ0QGB1jI3Xl0G5Gbc66w/J4PDYlaiPruVond0inDtmTBQAAAACAEEVTCwAQfgr2S3m7zNrAWfZkaaOK6lq9v8ncnXLj2DS5nA6bEgGBM9sygnDPiRJtPFxkT5i26jVOik0ya3u/sCcLAAAAAAAhiqYWACD87P7UXMenSj1G25OljT7bdlxnKmq8a4dDumlMaI5RBM7XxIxkpSXFGrU3v8qxKU0bOV2Nd4xav1YBAAAAAICzoqkFAAg/1gfFA66QnKH5V5519OAFmSlKS4qzKQ0QWE6nQ7eM623UPt5yVKfLq21K1Eb9LzfX2culyhJ7sgAAAAAAEIJC8wkfAADNKSuUclaZtQEz7cnSRgfySrTqQIFRmz0urZmrgfB009heimgwbrOi2q33NuTamKgNsmZIzoj6dW2VdGCxfXkAAAAAAAgxNLUAAOFl7xeSp7Z+HRErZVxkW5y2+Mcac8xap7hIXTa4q01pAHukJsbosiHmv/dvrsmRx+OxKVEbxHSU0iebtT2f2ZMFAAAAAIAQRFMLABBeds8z15nTpcjYpq8NYhXVtXpnvbkb5aYxvRQT6bIpEWCfb45PN9Z7TpRo/aFTNqVpo/5XmOs9X0hutz1ZAAAAAAAIMTS1AADho6ZS2rfQrA240p4sbTRv6zEVlZnnBt06vnczVwPhbXJmstKTzbPk3vwqp5mrg5y1qVV6Ujq6wZ4sAAAAAACEGJpaAIDwcXC5VFXSoOCQ+l9uW5y2eMPywP6CrGRldEmwKQ1gL6fT0aip+/HWYyoqq7IpURskZ0op/c3ark/syQIAAAAAQIgJWFOrqqpKf/vb3zRz5kylp6crJiZG3bt31+TJk/XUU08pPz8/UFEkSTfccIMcDof310UXXRTQ+wMA/MA6ejBtvJSQak+WNth5rLjRaLXbJ6Q3czXQPtw4ppciXQ7vuqrGrXc3HLExURsMmGmurV+7AAAAAABAkwLS1Nq1a5cmTpyoO+64Q59++qlycnJUWVmp48ePa9WqVXrkkUc0ZMgQzZsXmP+gf/fdd/Xee+8F5F4AgADxeKTdn5q1EB09aB2r1iUxWpcM7mpTGiA4pCRE64qh3Y3am18dksfjsSlRGwycZa7zdkkF++3JAgAAAABACPF7Uys3N1czZszQxo0bJUkOh0PTpk3Tvffeq6uvvlqxsbGSpJMnT+q6667TwoULz/ZxbVZUVKTvfe97fr0HAMAGxzZLZ46aNetuiBBQWlmj9zaau09uGZemSBcTg4FvWkYQ7s8r1ZqDhTalaYOeY6V4yy5SRhACAAAAAHBOfn9Cdtttt+no0bqHjOnp6dq0aZOWLFmiF198UR9++KFycnI0Y8YMSVJ1dbVmz56toqIiv+X58Y9/rOPHjysyMlI33nij3+4DAAgw6/iupIzG59aEgA83H1VJZY137XRIt1ge5APt1cSMJGV0iTdq1vPnQoLTKQ24wqwxghAAAAAAgHPya1Nr3rx5WrZsmSQpKipKH330kYYPH25ck5KSog8++EAZGRmSpMLCQv3+97/3S55FixbppZdekiT95Cc/0ZAhQ/xyHwCADawPhAfMlByOpq8NUh6PR39ffcioXTwgVT07xdqUCAguDoej0W6tz7YdV2FplU2J2mDgVeb68FdSaWDPmAUAAAAAINT4tan19NNPe1/feeedGjZsWJPXxcfH6ze/+Y13/dxzz6mmpqbJa1urvLxcDzzwgCQpKytLv/jFL3z6+QAAGxUdlo5vNWshOHpwS+5pbT9abNRum8guLaChb4zupaiI+m9hq2rd+uf6wzYmaqW+06TIBrvOPG5pz2f25QEAAAAAIAT4ralVUlJinI919913n/X6G2+8UYmJiZLqdmt9vcPLV371q19p//66A7ifffZZxcTE+PTzAQA2sj4Iju0spU2wJ0sbWHdp9ewUq2n9U5u5GmifOsdHaebQbkbtja9y5HZ7bErUSpExUtZ0s8a5WgAAAAAAnJXfmlorV65UZWWlpLqdWOPGjTvr9dHR0Zo4caJ3vWjRIp9lWb9+vf7nf/5HknT77bfrkksu8dlnAwCCgPVBcP8rJFeEPVla6XRZtT7actSo3To+TS5naI1QBALhtonpxvpQQZmW7smzKU0bDJhlrvcvlqrK7MkCAAAAAEAI8FtTa+fOnd7Xw4YNU0TEuR8ujh49usn3t0VNTY3uu+8+1dbWKikpydvcAgCEiYrTUvYKszbgSnuytMHcjbmqqHZ71xFOh2aPS7MxERC8xqZ31sBuiUbttVXZ9oRpi/6XSw5X/bqmXDqw2L48AAAAAAAEOb/9GPvu3bu9r9PT089yZb3evevPDdm1a5dPcjz11FPatGmTJOnJJ59Uly5dfPK5TcnNzT3r7x87dsxv9waAdmvfQsldXb92RUmZ05u/Pgi53R69vsocPXj5kG5KTWRULtAUh8Ohuyb30aNz68/SW7I7TwfzS9U3Jf4s7wwycUlS+mQpe3l9bdc8aeCs5t8DAAAAAEA75remVkFBgfd1165dW/Sebt3qz0coLCxsc4a9e/fq17/+tSRp2rRp5zzXq63S0viJegAIuN2fmuu+U6XoxKavDVLL9tY9jG/otom9m7kagCRdO7KnHv90l06X1ze1/7bqkH559WAbU7XCgJlmU2vPp5K7VnK6mn8PAAAAAADtlN/GD5aUlHhfx8bGtug9Da9r+P7W8Hg8uv/++1VRUaGoqCj99a9/lcPBuSQAEFZqq6W9n5u1ATPtydIGr63MNtYDuiZqUkayPWGAEBEb5dLNlhGd76w/rNLKGpsStdJAy9essgLp8Ff2ZAEAAAAAIMj5badWRUWF93VUVFSL3hMdHe19XV5e3qb7v/DCC1q6dKkk6Wc/+5kGDhzYps9ricOHD5/1948dO6bx48f7PQcAtBs5q+rO1Gqo/xX2ZGmlg/mlWrw7z6jdObkPP4gBtMDtE9L1wvID8njq1mcqavTexiO6fWLLRl8Hhc59pNQh0snt9bVdn9SNJQQAAAAAAAa/NbViYurPAamqqmrReyorK72vW7q7qylHjx7VT37yE0lS//799bOf/azVn3U+evXqFZD7AAD+ZefH5rr7SKljT1uitNbrq7KNdYeYCF03qoc9YYAQ0zs5TjMGpmrBzpPe2uursnXbhN6h1RgeOMtsau2eJ132n1Io/TMAAAAAABAAfhs/mJCQ4H3d0l1XDa9r+P7z9d3vflenT9f95P5zzz1n7AADAIQJj6duN0NDA2fZk6WVSipr9M66XKN2y/jeiovy28+cAGHnjkl9jPWeEyVadaCg6YuDlXUEYeEBKW+3PVkAAAAAAAhifmtqJSfXnwVy4sSJFr3n+PHj3tdJSUmtuu8HH3yg999/X5J011136aKLLmrV5wAAgtzRjVKx2RDSoKvtydJKczfkqqTB+T8Oh/StUBqbBgSBC7NSlJESb9ReX3nIpjSt1H2k1MGyy3TXx01eCgAAAABAe+a3HwUfMGCA9/WhQy17sJCTk+N93dozsDZu3Oh9vWbNGk2cOLHZa3Nz6x+Gbtiwwbj2mWee0ejRo1uVAQAQANYHvkmZUhf/n5/oK263R6+uzDZqlwzqqrSkOHsCASHK6XTojknpeuyjHd7aFzuO60hRuXp2av0464ByOKQBV0prX6yv7Z4nTf2xfZkAAAAAAAhCfmtqDRo0yPt669atqqmpUUTE2W+3YcOGJt/fWjt27Dj3Rf9y5swZffXVV951cXFxm+8PAPCjnR+Z60FXhdT5Myv25etAXqlRu2tyH3vCACHuG2N66cnPd6u0qlaS5PZIb6w+pJ9cETqNbg2cZTa1jqyXio9JHbrblwkAAAAAgCDjt/GDkydP9p5lVVpaqnXr1p31+srKSq1evdq7nj59ur+iAQBCXd4eKX+PWRsYWqMHrbu0+qUmaHJmctMXAzirxJhIfWNML6P21trDqqiutSlRK6RfKEV3MGt7PrUnCwAAAAAAQcpvTa2EhATNmDHDu3711VfPev3cuXN15swZSVLnzp01derUVt33sccek8fjadGvX/3qV973TZs2zfg9zuICgCC2y7JLK7G71HOMPVlaITu/VIt3nzRqd07uI0cI7TQDgs0dk8zz6ApLq/Th5qM2pWmFiCip36Vmbdc8e7IAAAAAABCk/NbUkqSHHnrI+/qVV17R9u3bm7yurKxMv/zlL73rBx988JyjCgEA7dhOy3laA2dJTr/+leZTr686JI+nfp0YE6EbRve0LxAQBrJSE3VhVopRe3nFQXka/mELdgNmmusDS6SK07ZEAQAAAAAgGPn1CeCsWbM0ZcoUSVJVVZWuuuoqbd261bimoKBA1113nfbt2ydJSkpK0k9/+tMmPy87O1sOh8P7a8mSJf6MDwAIRqdzpaMbzNrAq+zJ0gqllTV6Z91ho3bLuDTFRfHDHEBb3X1BH2O96/gZfbmvwJ4wrdHvUskVVb92V0t759uXBwAAAACAIOP3H2t/88031b173QHX2dnZGjlypKZPn6777rtP1157rXr37q358+v+Yz0iIkJz5sxRp06d/B0LABCqdn1irmM6SX0utCVKa7yz7rDOVNZ41w6H9K2JfewLBISRiwekKiMl3qi9uOKATWlaIaajlHGRWdvxgS1RAAAAAAAIRn5vavXq1UuLFi3SyJEjJUlut1uLFy/WSy+9pA8//FBlZWWSpC5duuj99983zuECAKCRnZbztAZcKbki7clynmrdHr305UGjNmNgV/VOjrMpERBenE6H7rmwr1FbsjtP+06esSlRKwy62lzvWyBVldmTBQAAAACAIBOQA0gGDhyor776Sq+99pquuOIKpaWlKSoqSqmpqZo4caKeeOIJ7dixQ7NmzQpEHABAqCotkA59adZCaPTg59uP63BhuVG7b0rfZq4G0BrfGN1LneLMRvdLK7LtCdMaA2ZKjgbfoleXSfsX2pcHAAAAAIAgErADPKKionTHHXfojjvuaPVn9OnTx6eHfT/22GN67LHHfPZ5AAA/2/Op5HHXryNipczp9uU5Ty8sN8egDevZURP6JtmUBghPsVEu3T4hXX9ZvM9bm7shVz++rL+SE6JtTNZC8SlS+gVS9vL62s6PGu/gAgAAAACgHQrITi0AAHxi58fmOmuGFBUao/vWHyrUxpwio3b/1Aw5HA57AgFh7I5J6Yp01f/Zqqxx642vcmxMdJ4GXWOud38m1VTZkwUAAAAAgCBCUwsAEBoqS6T9i8ya9cFvEHthmXmWVs9OsZo5tJtNaYDwltohRteM6GnUXl+VrYrqWpsSnadBlrGqlaelg8vsyQIAAAAAQBChqQUACA375ku1lfVrZ4TU/zL78pyHQwWl+nzHcaN29wV9FOHir2HAX+690DyvLr+kSh9uPmpTmvPUoYfUa5xZ2/mhPVkAAAAAAAgiPE0DAIQG6+jBPlOk2M72ZDlPL684qIZHQiZGR+jmcWn2BQLagcE9OmhyZrJRe2n5QZ+ez+pX1jO0dn0iuUNkpxkAAAAAAH5CUwsAEPxqKqW9X5g163iuIFVUVqU563KN2i3j05QYE2lTIqD9uG+KuVtr94kzWrEv36Y058na1CrLl3JW2ZMFAAAAAIAgQVMLABD8Di6TKosbFBzSwNBoar3xVY7KG5zj43I6dNcFfc/yDgC+clH/VGV0iTdqLyw/2MzVQSYpQ+o6zKzt/MieLAAAAAAABAmaWgCA4Gd9kNtrnJTYzZ4s56Gqxq3XVmYbtVnDuqtnp1h7AgHtjNPpaHS21rI9edp5rLiZdwQZ626tnR9Jbrc9WQAAAAAACAI0tQAAwc1dK+2eZ9ZCZPTgh5uP6uSZSqN2/5QMm9IA7dMNo3qpc5w57vO5pfttSnOeBl9jrouPSEc32pMFAAAAAIAgQFMLABDcDn8lleaZtRAYPejxePTi8gNGbULfJA3r1dGmRED7FBvl0l2Tzd1aH205psOFZTYlOg9dBkrJWWZt54f2ZAEAAAAAIAjQ1AIABLft75vr1CFScqYtUc7Hol0ntev4GaPGLi3AHndMSldclMu7rnV79IKl6RyUHA5pkGW31s4PJY/HnjwAAAAAANiMphYAIHi53Y13JQy+1p4s58Hj8eiZJeZ4s6zUBE0fmGpTIqB96xwfpVvG9TZqb689rPySymbeEUSs52oVHpBO7rAnCwAAAAAANqOpBQAIXrlrpDPHzNqQ62yJcj7WHCzU+kOnjNp3pmXK6XTYlAjAfVP6KqLBn8HKGrde/TLbvkAt1WOU1DHNrO1gBCEAAAAAoH2iqQUACF7W0YNdBkpdBtgS5XxYd2n17BSra0b2sCkNAEnq0SlW143qadReX5WtksoamxK1kMPReLfWzo/syQIAAAAAgM1oagEAglOTowevsyXK+dh25LSW7skzag9MzVCki79yAbt9e5p5rl1xRY3+8VWOTWnOg7WpdXK7VLC/6WsBAAAAAAhjPGEDAASnI+uk4iNmLQRGDz671HzQnBwfpdlj05q5GkAgZaUm6tLBXY3aiysOqLKm1qZELZQ2QYrvYtasTX8AAAAAANoBmloAgOC04wNzndK/bvxgEDuYX6pPt5pngN1zYV/FRrlsSgTA6jsXZRrrE8WVen/jkWauDhJOlzTwKrNm/RoJAAAAAEA7QFMLABB8PJ7GD2wHX1d3tkwQe27pfrk99euE6AjdPjHdvkAAGhndu7Mm9E0yas8tO6Dahn94g9Hga8z10Y3SqWxbogAAAAAAYBeaWgCA4HNkg3T6sFkbfK09WVro+OkKvbsh16jdPjFdHWMjbUoEoDnW3VoH8kr1xfbjNqVpoT5TpVizGaft79sSBQAAAAAAu9DUAgAEnx3vmevkLKnrEHuytNCLyw+ourZ+p0dUhFP3XNjHvkAAmjWtfxcN6t7BqD29ZJ88niDereWKkAZdbda2v9f0tQAAAAAAhCmaWgCA4NLk6MFrg3r04KnSKr25JseozR7bS6mJMTYlAnA2Doej0W6tbUeKtXj3SZsStdCQ6831sU1S4UFbogAAAAAAYAeaWgCA4HJ0o1RkNog0+DpborTUKyuzVVZV6127nA49ODXzLO8AYLdZw7orIyXeqP3fwiDfrdVnihSXbNZ2vG9LFAAAAAAA7EBTCwAQXKy7tDr3lboNsydLC5wur9YrX5o7Ja4e3l1pSXE2JQLQEi6nQ9+9OMuobT5cpGV7821K1AKuCGnQNWaNEYQAAAAAgHaEphYAIHh4PI13HQy5LqhHD77y5UGdqajxrh0O6SHLg3IAwenakT3U29KA/r8Fe4J7t1ajEYSbpYL99mQBAAAAACDAaGoBAILH8S3SqWyzNvhaW6K0xOnyar28wtylNXNod/XvmmhTIgDnI8Ll1PcsTegNOUVaub/ApkQtkH6BFJdi1hhBCAAAAABoJ2hqAQCCx/b3zXWndKn7SDuStMirX2aruMEuLUn6wYx+NqUB0BrXj+6pnp1ijdr/LdxrU5oWcEVIgxlBCAAAAABon2hqAQCCQ1OjBwdfG7SjB4srqvXSigNGbeawbhrQjV1aQCiJdDkbna215mChVh8I4t1a1hGEx7cyghAAAAAA0C7Q1AIABIdjm6RCs0mkIdfZkaRFXmOXFhA2vjGmp3p0jDFqfwrm3VrpF0jxXcwau7UAAAAAAO0ATS0AQHDY9q657txH6jHalijncqaiWi9aztK6Ykg3DezWwaZEANoiOsKl71yUadRW7i/QuuxCmxKdg9PV+LxB6/hWAAAAAADCEE0tAID93G5pm2WXwdBvBO3owddXHdLp8mqjxi4tILTdNDZNXTtEG7U/LdpnU5oWGHyduT6xVcoP4t1lAAAAAAD4AE0tAID9ctdKxblmbeg37MlyDiWVNXphuTkm8bLBXTW4B7u0gFAWE+nSt6eZu7WW7cnThpxTNiU6h/TJUnyqWWO3FgAAAAAgzNHUAgDYzzp6sMtAKXWwPVnO4bWV2SoqY5cWEI5uHd9bKQnmbq0/fLHbpjTn0NQIwh3v2xIFAAAAAIBAoakFALCXu1baHhqjB0sqa/SiZZfWpYO7amjPjjYlAuBLMZGNz9b6cl+BVu7PtynROQy53lyf2Cbl7bEnCwAAAAAAAUBTCwBgr+wVUulJszbkBnuynMPLKw7qlGWX1sPs0gLCym0Teqtbhxij9ocv9sjj8diU6Cx6T5QSupm17XPtyQIAAAAAQADQ1AIA2Ms6erDbcCkly54sZ1FUVqUXlpm7tC4ZxC4tINzERLr0venm16D1h05pyZ48mxKdRVMjCLe+IwVjAw4AAAAAAB+gqQUAsE9NlbTzQ7M29Bv2ZDmHvy49oDOVNUbtR5f1tykNAH+aPTZNvTrHGrU/fLE7OHdrDbvJXBfsk45tticLAAAAAAB+RlMLAGCfA0uk8lNmzXpGTBA4WVyhV1ceNGrXjOihQd072JQIgD9FRTj1/y4xm9bbjhTr8+3HbUp0Fr3GSp3SzdrWd+zJAgAAAACAn9HUAgDYxzp6sNd4qXN609fa6C+L96mi2u1du5wO/fBSdmkB4ey6kT2U0SXeqP3P/D2qdQfZbi2HQxp2o1nb/p7kdjd9PQAAAAAAIYymFgDAHtUV0q5PzFoQjh48XFimf6zJMWo3jemlvinxzbwDQDiIcDn1Q8turT0nSvTR5qM2JTqLoZamVvERKWeVPVkAAAAAAPAjmloAAHvsmy9VnWlQcEhDrrMrTbP+b+FeVdfW78yIcjn1gxn9bEwEIFBmDeuugd0SjdofF+xRdW2Q7YLqOlhKHWzWGEEIAAAAAAhDNLUAAPawjh7sc6GU2M2eLM3Yd/KM5m7INWq3T0xXj06xNiUCEEhOp0M/umyAUcsuKGv0dSEoWEcQ7nhfqqmyJQoAAAAAAP5CUwsAEHiVJdLuz8xaEI4e/N/5e9Xw+Jy4KJceujjTvkAAAu6SQaka0aujUfu/BXtVUV1rU6JmWL+Glp+SDiy2JwsAAAAAAH5CUwsAEHh7PpNqyuvXzghp0DX25WnCtiOn9cnWY0btngv6KiUh2qZEAOzgcDTerXX0dIVeW5ltT6DmdO4j9Rpn1rb+05YoAAAAAAD4C00tAEDgbZljrjMukuKTbYnSnCc/322sO8RE6P6pGTalAWCnKf1SNDEjyag9vXifisqCbLzfsJvM9a5PpKoye7IAAAAAAOAHNLUAAIFVmi/tW2DWht7Y9LU2WbE3X0v35Bm1b1+UqY6xkTYlAmAnh8Ohn105yKgVV9TomSX7bUrUjCHXS44G395Xl0p7PrUvDwAAAAAAPkZTCwAQWNvmSp4GZ9FExkmDrrYvj4Xb7dHjn+40al0So3XX5D72BAIQFEakddKs4d2N2qsrs3WkqLyZd9ggIVXqO82sbX3XniwAAAAAAPgBTS0AQGBttYweHDhLik6wJ0sTPth8RNuPFhu1f7u0v+KiImxKBCBYPHLZAEU4Hd51VY1bf/hi91neYYNhlp2ve7+Qyk/ZkwUAAAAAAB+jqQUACJyC/VLuWrM2bLY9WZpQUV2rpz7fY9T6pSbopjG9bEoEIJj0SYnX7RPTjdp7G49oh6URbquBV0muqPq1u1ra+ZF9eQAAAAAA8CGaWgCAwNn6jrmOS5EyL7YnSxOaGiX26JUDFeHir0sAdb4/PUsJ0fU7Nz0e6b8/22VjIovYTlK/y8ya9WsvAAAAAAAhiqd0AIDA8HikLW+btaHfkFyR9uSxOFVapacX7zNqEzOSNH1gqk2JAASj5IRoPTg1w6gt25OnL/fl25SoCdYRhAeXS6eP2JMFAAAAAAAfoqkFAAiMI+ulwgNmbfjN9mRpwp8X7dOZihqj9vOZg+RwOJp5B4D26t4pfZWaGG3UHv90p9xuj02JLPpfIUUlNih42K0FAAAAAAgLNLUAAIGxZY65TsqQeo62J4tFTkGZ/rY626hdM6KHhvfqZEseAMEtLipCP7y0v1HbdqRYH205alMii8hYaci1Zm3zW3U7ZgEAAAAACGE0tQAA/ldbLW1716wNv1kKkl1Qv/98l6pr6x/2RrmceuTyATYmAhDsbhrTS5ld4o3aE5/uUkV1rU2JLIbfYq7zdkrHt9iTBQAAAAAAH6GpBQDwv/2LpTLLeTPDbrIni8XGnFP6eMsxo3bHpHSlJcXZlAhAKIhwOfXolYOM2tHTFXp+2YFm3hFg6RdIHdPM2ua3m74WAAAAAIAQQVMLAOB/WywPUnuNk5Iz7cnSgMfj0W8+3mHUOsRE6HvTs2xKBCCUXDIoVZMzk43as0v26/jpCpsSNeB0SsNnm7Wt70i1NU1fDwAAAABACKCpBQDwr8oz0q5PzNrwm+3JYvHBpqPamFNk1L43PUud4qLsCQQgpDgcDv3HVYPlbDBJtby6Vr//fJd9oRqyjiAsPSkdWGxPFgAAAAAAfICmFgDAv3Z9ItWU168dLmnI9fbl+ZfSyho9/ulOo9YnOU53Tu5jTyAAIWlQ9w66ZXxvozZ3wxFtOlxkT6CGuvSXeow2a5vfsicLAAAAAAA+QFMLAOBf1tGDWZdI8Sn2ZGngr0v360RxpVH7xazBio5w2ZQIQKj6t0v7KzE6wqj95qPt8ng8NiVqYMSt5nrXx1JFsT1ZAAAAAABoI5paAAD/OXNcOrDErFnPeLHB4cIyPbfsgFGb0i9FMwal2pQIQChLSYjW92eYZ/FtyCnSR1uO2ZSogaHfkJwNGm41FdLOD+3LAwAAAABAG9DUAgD4z5a3JY+7fh2VIA2YaV+ef3n8052qqqnP5XI69MurBsvhcJzlXQDQvDsn91F6cpxR++95O1VRXWtTon+JT5b6XWbWGEEIAAAAAAhRNLUAAP7h8Ugb3zBrQ66TouKavDxQVh8o0Lytx43atyamq1/XRJsSAQgH0REu/fvMQUbt6OkKPW/ZFWqL4Teb6+zlUlGOPVkAAAAAAGgDmloAAP84skHK323WRt5mT5Z/qXV79OuPdhi1TnGR+n+X9LMpEYBwcungrpqcmWzUnl2yX8dOl9uU6F/6XyHFdDRrW+bYkwUAAAAAgDagqQUA8I9Nll1anftIvSfZEuVrb689rJ3Hio3ajy7tr05xUTYlAhBOHA6H/uOqwXI2mGRaXl2r//xkp32hJCkyRhpyvVnb8nbdjloAAAAAAEIITS0AgO9VV0jb/mnWRt4m2XhmVVFZlZ76wtw5NqBrom4d39umRADC0aDuHXSL5evKJ1uOacXefJsS/cuIW811/h7p6AZ7sgAAAAAA0Eo0tQAAvrd7nlRx2qyNuMWeLP/y5Oe7VVhaZdR+efVgRbj4qxCAbz1y2QB1ios0ar/8cJuqatw2JZKUNqFux2xDm9+yJQoAAAAAAK3FkzwAgO9ZRw/2nSp1sm9H1ObDRXpzTY5Ru3xIV12QlWJTIgDhrHN8lH56xUCjdiCvVC+tOGhTItXtlB1u+eGCLXPqdtYCAAAAABAiaGoBAHyr+Ki0f5FZG3m7PVkk1bo9+o8PthlHx8RGuvQfVw22LROA8Hfz2DSNSOtk1P60cK+OFpXbE0hqvGO2oqhuZy0AAAAAACGCphYAwLe2vC15GozYikqUBl1lW5w31+RoS645CvH7M7LUq3OcTYkAtAdOp0O/vXaIcZRgeXWt/vOTHfaFSuor9Zli1jb+3Z4sAAAAAAC0Ak0tAIDveDzSpjfN2pDrpKh4W+Lkl1Tqyc92GbXMLvG678IMW/IAaF+G9+qkW8ebo1fnbT2uZXvybEokadS3zPX+RdLpXHuyAAAAAABwnmhqAQB858h6KX+PWRt5mz1ZJP33p7tUXFFj1H577VBFRfDXH4DAeOSyAeocF2nUHvtwuyprau0JNOhqKbpDg4JH2vQPe7IAAAAAAHCeeKoHAPCdTW+Y66QMqfdEW6KszS7UP9ebuw+uGdFDk7NSbMkDoH3qHB+ln14x0KgdyC/Vi8sP2hMoKk4a+g2ztunvktvd9PUAAAAAAAQRmloAAN+oLpe2vmvWRn5TxoEyAVJT69Z/vL/NqCVER+gXswYFPAsAzB6bphFpnYzanxft1aGCUnsCjbaMIDyVLR360pYoAAAAAACcD5paAADf2PWJVHm6QcEhDb/FliivrszWruNnjNq/XdpfqR1ibMkDoH1zOh36z2uHGj3+imq3fvH+Nnk8nsAH6jFaSh1s1jb+PfA5AAAAAAA4TzS1AAC+YR09mDFN6pQW8BiHC8v0P/PNc70Gde+gOyalBzwLAHxtWK+OunNSH6O2fG++Pth0NPBhHA5p1O1mbccHUsXppq8HAAAAACBI0NQCALTdqUPS/sVmbcQ3Ax7D4/HoF+9vU1lVrVH/7bVDFOHirzwA9vrRZf3VzbJj9Dcf79Cp0qrAhxl+s+SMqF/XlEvb5gY+BwAAAAAA54EnfACAttv4N0kNRmjFdJQGXxPwGB9uPqqle/KM2jcn9NbYPkkBzwIAVokxkfrNtUOMWmFplf5r3s7Ah4lPkQZcadYYQQgAAAAACHI0tQAAbVNb0/hB6PCbpcjYgMYoLK3Srz/aYdRSE6P16JUDA5oDAM7msiHddMWQbkbtn+tztXJffuDDjPqWuT6yTjppQ4MNAAAAAIAWoqkFAGibvV9IZ46ZtdF3BjzGf36yQ4WWEV6/uXaoOsREBjwLAJzNY9cMUUJ0hFH7+XtbVVFd28w7/CRzhpRgNtjYrQUAAAAACGY0tQAAbbPhNXPdc4zUbWhAIyzfm6e5G44YtSuGdNMVQ7s18w4AsE+3jjH66RUDjFp2QZn+smhfYIO4IqSRt5q1zW9JtdWBzQEAAAAAQAvR1AIAtN7pI3U7tRoac1dAI5RX1ern7201aokxEfq15dwaAAgmt01I1+jenYzaX5fu1+7jZwIbZOTt5rosX9o9L7AZAAAAAABoIZpaAIDW2/SG5HHXr6MSpCE3BDTC/y7Yo8OF5Ubt0SsHqmuHmIDmAIDz4XQ69PgNwxXhdHhrNW6PHvnnZtXUus/yTh9LyZJ6TzJr614J3P0BAAAAADgPNLUAAK3jdksb/mbWht0oRScELMLW3NN6cfkBoza+T5JuHdc7YBkAoLUGdEvUg9MyjNqW3NN63vJ1ze+sO2wPLJYKA5wBAAAAAIAWoKkFAGidA4uk0zlmbfSdAbt9ZU2tfvTOJrk99bUol1O/u2GYnA12PgBAMPv+9H7K7BJv1P44f6/2ngjgGMLB10oxncza+lcDd38AAAAAAFqIphYAoHWsDzy7DZN6jArY7f9vwV7tOVFi1L43PUtZqYHbKQYAbRUT6dKTN41Qw158Va1bP/7nlsCNIYyMlUbeZtY2viHVVAXm/gAAAAAAtBBNLQDA+Ss5Ke3+1KyNvlNyBGaH1KbDRfrr0v1GbUiPDvrORZkBuT8A+NLo3p11/xRzDOHmw0V6ccXBwIWwjiAsy5d2fRS4+wMAAAAA0AI0tQAA52/TG5K7pn4dESsNnx2QW1dU1+pHc8yxg5Euh/4we4QiXfy1BiA0/fDS/spIMccQ/s/8Pdp3MkBjCLv0l9IvNGvrXgnMvQEAAAAAaCGe/gEAzo/HI2143awNuV6K6RiQ2//vgj3an1dq1B6e0U8Du3UIyP0BwB/qxhAONza8VtW49eN3tqi2YRffn8beba6zl0v5ewNzbwAAAAAAWoCmFgDg/GQvlwoPmLUxdwbk1usPndILy8x7D+vZUd+exthBAKFvTHqS7r2gr1HbdLhIL6040Mw7fGzQ1VJcslmznp8IAAAAAICNaGoBAM7P2pfMdcoAKW2C329bUV2rR97ZbIwdjHI59dRNIxTB2EEAYeJHlw1QX8sYwqe+2KO9JwIwhjAiWhp5m1nb9IZUXeH/ewMAAAAA0AI8BQQAtFzxMWnXx2Zt7N0y5mX5yVOf79aBfHPs4P+7tJ8GdEv0+70BIFBio1z6/Y2NxxA+/NYmVdW4/R9gzF3muvyUtOMD/98XAAAAAIAWoKkFAGi5Da9L7pr6dWScNOJWv9925f58vfTlQaM2Iq2THpiS4fd7A0CgjeuTpHssYwh3HCvW/y7Y4/+bJ2dKfaeZtfWv+P++AAAAAAC0AE0tAEDL1FY3frA57CYptpNfb3u6rFo/mrNZnoZjByOceurG4YwdBBC2Hrl8gPqlJhi1vy7drzUHC/1/87F3m+ucVdLJnf6/LwAAAAAA58DTQABAy+z+VDpzzKyNu8+vt/R4PPr397fq2GnzPJdHLhugfl0ZOwggfMVEuvTHW0Yq0lU/h9Djkf5tziadqaj2780HzJLiU83aOnZrAQAAAADsR1MLANAya180173GS92H+/WW7286oo+3mI20C7KSde+FfZt5BwCEjyE9OurfLh1g1HJPlevXH+3w740joqRRt5u1zf+QKkv8e18AAAAAAM6BphYA4Nzy9kgHl5o1P+/SOlxYpl++v92odYyN1FM3jZDT6WjmXQAQXh6YmqHxfZKM2j/X5+qzbceaeYePjLlTUoOvtZXF0pa3/XtPAAAAAADOgaYWAODc1r1kruOSpcHX+u12tW5P3Yityhqj/rvrh6l7x1i/3RcAgo3L6dAfZo9QQnSEUf/Z3K06WVzRzLt8oHMfqf/lZm3NCzIOOAQAAAAAIMBoagEAzq6qVNr0plkb9S0pMsZvt/zr0v1am33KqH1jdC/NGt7db/cEgGCVlhSnx64ZYtROlVXrR+9sltvtxybT+PvNdd5OKXuF/+4HAAAAAMA50NQCAJzd1nfqxk55OaSxd/vtdltyi/S/8/cYtbSkWD12zWC/3RMAgt03RvfUlUO7GbXle/P1/PID/rtpxnQpOcusrXnOf/cDAAAAAOAcaGoBAJrn8UhrXzRr/S6rG0vlB8UV1fremxtV02DngdMh/e/skUqMifTLPQEgFDgcDv3u+mFKTYw26k99vlsbck418642cjqlcZbdWrs+kYoO++d+AAAAAACcA00tAEDzDq2Ujm81a+Pu88utPB6Pfj53q3IKy4z6dy/O0tg+SX65JwCEks7xUfrjLSPlcNTXatwe/eAfG3W6vNo/Nx35TSkqoX7tcUvrXvbPvQAAAAAAOAeaWgCA5n31rLnu3FfKmuGXW7219rA+3nLMqI3q3Uk/mNHPL/cDgFA0OTNF37/YHAmYe6pcP5+7VR6PH87XiukgjbjFrG14Taqu8P29AAAAAAA4B5paAICmnTpUN2aqoQkPSk6Xz2+163ixHvtwu1HrEBOhP986SpEu/qoCgIZ+MKOfxvXpbNQ+2XpMb63101hA6wjCsgJp+1z/3AsAAAAAgLPgSSEAoGlrX6gbM/W1qERp5G0+v01ZVY2++8YGVda4jfqTN41Qr85xPr8fAIS6CJdT/3fLKHWMNc8afOzD7dpz4ozvb5g6UOo7zax99VzduYsAAAAAAAQQTS0AQGNVpdKG183aqNvqxlD52C8/2K79eaVG7a7JfXT5kG4+vxcAhIsenWL15I3DjVpljVvfe3ODyqtqfX/D8Q+Y62ObpNx1vr8PAAAAAABnQVMLANDY5n9IFacbFByNH2j6wNwNufrn+lyjNrRnB/1s5kCf3wsAws1lQ7rprsl9jNqeEyX6xfvbfH++1oArpY5pZm31M769BwAAAAAA50BTCwBgcrvrxko11P9yKTnTp7fZd/KMfvH+NqOWEB2hv9w6WtERvj+3CwDC0aNXDtTg7uYu2nc35Pr+fC2nSxp3n1nb8YFU5KdzvAAAAAAAaAJNLQCA6cAiKX+PWZvwoE9vUVJZowf/tl5llhFZ/3X9UPVJiffpvQAgnMVEuvSXb45SQnSEUf/Vh9u1Nfd0M+9qpTF3SpENvkZ7aqU1zzV/PQAAAAAAPkZTCwBgsu7S6jJQyrjYZx/v8Xj0k39ubnSO1i3j0nTtyJ4+uw8AtBcZXRL0e8v5WlU1bn3njfUqKqvy3Y1iO9edr9jQ+tekyjO+uwcAAAAAAGdBUwsAUC9/n7T3C7M24UHJ4fDZLV5acVDzth43akN6dNBj1wzx2T0AoL2ZOay77r2wr1HLPVWuH769SW63D8/XmvBtSQ3+Tqgslja+4bvPBwAAAADgLALW1KqqqtLf/vY3zZw5U+np6YqJiVH37t01efJkPfXUU8rPz/f5PXfu3Kk//elPmj17tgYPHqyOHTsqMjJSKSkpGjt2rB5++GFt3LjR5/cFgJC1+mlzHdNJGn6Lzz7+qwMFevzTXUatY2yknr1tjGIiOUcLANri0SsHamx6Z6O2eHeenlmyz3c3Sc6UBsw0a6ufkdy1TV8PAAAAAIAPBaSptWvXLk2cOFF33HGHPv30U+Xk5KiyslLHjx/XqlWr9Mgjj2jIkCGaN2+eT+43f/58DR06VIMHD9bDDz+sd955Rzt37lRxcbFqampUUFCg9evX609/+pNGjx6t2bNnq7Cw0Cf3BoCQVZovbXrTrI25U4qK88nHnyiu0Hff3KjaBjsGHA7pjzePVO9k39wDANqzSJdTf/nmaKUkRBn1P8zfoxV7ffgDZJO+a66LDkm7PvHd5wMAAAAA0Ay/N7Vyc3M1Y8YM744oh8OhadOm6d5779XVV1+t2NhYSdLJkyd13XXXaeHChW2+5/r167V9+3bv2uFwaMSIEbrpppv0wAMP6Prrr1eXLl28v//OO+9o6tSpKigoaPO9ASBkrX1RqqmoXzsj/zVmqu2qa9367hsblF9SadR/ML2fLh6Y6pN7AACkbh1j9KdbR8nZYEKgxyN9/x8bdLiwzDc3SZ8sdR9p1lY/45vPBgAAAADgLPze1Lrtttt09OhRSVJ6ero2bdqkJUuW6MUXX9SHH36onJwczZgxQ5JUXV2t2bNnq6ioyCf3HjlypJ5++mnl5eVp06ZNmjNnjp577jnNnTtXubm5evzxx+Vy1Y272r59ux566CGf3BcAQk5VmbTmebM27CapQw+ffPx/fbJT6w6dMmrT+nfRwzP6+eTzAQD1Jmem6MeXDzBqp8qq9cDf1qusqqbtN3A4Gu/WylklHVnf9s8GAAAAAOAs/NrUmjdvnpYtWyZJioqK0kcffaThw4cb16SkpOiDDz5QRkaGJKmwsFC///3v23Tf/v3767333tPGjRv10EMPKTk5udE1UVFRevTRR/Xkk096a3PmzNGuXbsaXQsAYW/zP6Qyy27Vyd/zyUe/vTZHr67MNmo9O8XqjzePlLPhVgIAgM98e2qmLhnU1ajtPFasR/65RR6Pp5l3nYfB10mJlh98WPV0k5cCAAAAAOArfm1qPf10/X/Y3nnnnRo2bFiT18XHx+s3v/mNd/3cc8+ppqb1P0V6ww036LrrrmvRtT/4wQ/Uo0f9f5D76lwvAAgZ7trGDyKzLpG6DmnzR6/LLtQv3t9m1KIinPrr7WPUOT6qmXcBANrK6XTof24eocwu8Ub9ky3H9MyS/W2/QUSUNP5+s7b9fenUobZ/NgAAAAAAzfBbU6ukpMQ4H+vuu+8+6/U33nijEhMTJdXt1vp6h5e/uVwuTZgwwbvOzs4OyH0BIGjsnicVWh5wTv5+mz/2SFG5vv339aquNXcE/O76YRrWq2ObPx8AcHYdYiL1wh1jlRgTYdSf+mK3Fu060fYbjLlLioyrX3tqpVV/afvnAgAAAADQDL81tVauXKnKykpJdTuxxo0bd9bro6OjNXHiRO960aJF/orWiMNRP/6qtrY2YPcFgKCw8s/mutswqe+0Nn1keVWtHnh9nfJLqoz6fRf21Y1jerXpswEALZfRJUF/unWUGny7K49Hevgfm7TvZEnbPjwuSRp9p1nb8DepNL9tnwsAAAAAQDP81tTauXOn9/WwYcMUERFxlqvrjB49usn3+9vWrVu9r9PS0gJ2XwCwXc5X0uGvzNrkH8h4+nmePB6PHvnnZm0/WmzUp/bvokevHNjqzwUAtM7FA1L1k8vNr79nKmv0wN/Wqbiium0fPum7krPB9/k15dKa59v2mQAAAAAANMNvTa3du3d7X6enp7foPb179/a+3rVrl88zNWXlypXau3evd33JJZcE5L4AEBRW/slcd+glDbm+TR/5zJL9+njLMaOWkRKvP986ShEuvx7lCABoxrenZejqET2M2oG8Un33jQ2qqXW3/oM7pUnDbjJra56Xqkpb/5kAAAAAADTDb08XCwoKvK+7du3aovd069bN+7qwsNDnmazcbrd++MMfetcTJkzQ2LFjW/15ubm5Z/117Nixc38IAARK3m5p1ydmbeJ3JFdkqz/ys23H9eTnu41aYkyEXrhzrDrGtv5zAQBt43A49PtvDNeQHh2M+vK9+frVh9vl8XiaeWcLXPCwuS4/JW14vfWfBwAAAABAM849E7CVSkrqZ/THxsa26D0Nr2v4fn/57W9/qzVr1kiSnE6nnnrqqTZ9HqMLAYSUFX+U1OAhZnRHafQdrf64zYeL9P/e3mjUnA7pT7eOUmaXhFZ/LgDAN2KjXHr+jrG69i8rjDMP3/gqRxldEnTvhX1b98Gpg6T+V0h7PquvrXpaGndfm35QAgAAAAAAK7/t1KqoqPC+joqKatF7oqOjva/Ly8t9nqmhjz76SL/+9a+960ceeUQXXnihX+8JAEHj1CFpy9tmbcIDUkyHpq8/h9xTZbr3tXWqqDZHWD165UBdPCC1tSkBAD7Ws1Osnr9jrKIizP8M+M9PdmjBjhOt/+AL/p+5Pn1Y2vZu6z8PAAAAAIAm+K2pFRMT431dVVV1livrVVZWel+3dHdXa6xdu1a33nqrd8zKjBkz9Nvf/rbNn3v48OGz/vp6VxgA2G7lnyRPbf06Mk6a8J1WfVRxRbXueXWt8ksqjfrssb10/5SMtqQEAPjB6N6d9YebRhg1j0f6wVsbte3I6dZ9aPokKW2CWVvxR8ndhvO6AAAAAACw8Nv4wYSE+lFTLd111fC6hu/3pR07dujKK69UaWnd4dXjxo3T+++/r8jIto9G6dWrV5s/AwD87swJacPfzNqYu6T45PP+qOpatx76+wbtOWGOjL0gK1n/df0wORyONgQFAPjL1SN66FBBqZ76Yo+3VlZVq3tfW6sPvnuhunWMOcu7m3HhD6V/3FK/ztsp7f1cGnClDxIDAAAAAODHnVrJyfUPR0+caNkok+PHj3tfJyUl+TzTwYMHdemll6qgoECSNHjwYH366ad+a6ABQFBa/bRU22BXlTNSmvS98/4Yj8ejX7y3TSv25Rv1fqkJeua2MYp0+e2vGACAD3z34ix9Y7T5Q1kniit1z6trVVJZc/4f2O9yqctAs7bsybptYAAAAAAA+IDfnjgOGDDA+/rQoUMtek9OTo739cCBA89y5fk7cuSIZsyYoaNHj0qSMjMzNX/+fKP5BgBhr/yUtPYlszbyVqljz/P+qGeX7tfb6w4btZSEKL181zh1jG377lcAgH85HA49fsMwTehr/jDZjmPF+s7f16uq5jxHBzqddbu1GjqyXtq/qI1JAQAAAACo47em1qBBg7yvt27dqpqac/+054YNG5p8f1udPHlSM2bM0MGDByXVjQlcsGCBevTo4bN7AEBIWPOCVNVgVKDDKV3w/877Y+ZuyNXvP9tt1GIinXrxznFKS4prY0gAQKBERTj119vHqG9KvFFfvjdfP313i9zu89xlNfRGqXMfs7b09+zWAgAAAAD4hN+aWpMnT1Z0dLQkqbS0VOvWrTvr9ZWVlVq9erV3PX36dJ/kKCgo0CWXXKLdu+sevqampmrBggXq06ePTz4fAEJGZYm0+hmzNuQGKTnzvD5m8e6T+sk/txg1h0P6482jNDKtUxtDAgACrXN83S7bpPgoo/7exiN64vNd5/dhrghpyo/M2uHVUvbyNqYEAAAAAMCPTa2EhATNmDHDu3711VfPev3cuXN15swZSVLnzp01derUNmcoLi7WFVdcoa1bt3o/d/78+cZoRABoN9a9XDd+sCHrmKhz2JhzSg/9fYNqLD+5//MrB+mKod3amhAAYJO+KfF6+a5xio10GfXnlh7QyysOnt+HDb9F6phm1pb+vo0JAQAAAADwY1NLkh566CHv61deeUXbt29v8rqysjL98pe/9K4ffPBBRUREtOneZWVlmjVrlneHWGJioj777DMNHz68TZ8LACGpqlT68v/MWv8rpW5DW/wR+06W6J5X16q8utao33dhX90/NcMXKQEANhqZ1knP3DZaLqfDqP/2kx36eMvRln9QRJR04f8za9nLpUOr2h4SAAAAANCu+bWpNWvWLE2ZMkWSVFVVpauuusq7a+prBQUFuu6667Rv3z5JUlJSkn760582+XnZ2dlyOBzeX0uWLGnyusrKSl133XVasWKFJCk2NlYff/yxxo8f76N/MgAIMWtflMryzdrUR1r89uOnK3Tny2t0qqzaqF83sod+PtN3ZyACAOx18cBU/fcNw4yaxyP929ubtXJ/fjPvasKob0mJlvNrl7FbCwAAAADQNm3bDtUCb775psaPH69jx44pOztbI0eO1LRp05SRkaG8vDwtWLBAZWVldWEiIjRnzhx16tSpTff8j//4D82fP9+7HjRokObMmaM5c+ac8739+vXTww8/3Kb7A0BQqSxpvEur3+VSrzEtevvp8mrd+fIaHSkqN+pT+3fR728cIaflJ/oBAKHtprFpOnmmUk9+vttbq6p164HX1+uN+yZoREvOT4yIli54WPqswQ+r7V8k5a5v8d8/AAAAAABY+b2p1atXLy1atEi33nqrNm3aJLfbrcWLF2vx4sXGdV26dNErr7xinMPVWidPnjTWGzZs0IYNG1r03mnTptHUAhBe1r4glRWYtYua3hFrVVZVo3teXavdJ84Y9RG9OurZ20YrKsKvG34BADZ56KJMHT9dob+tPuStlVTW6M5X1ujtByZpQLfEc3/ImDul5X+QSht8b770Cem2c/+gGQAAAAAATQnI08iBAwfqq6++0muvvaYrrrhCaWlpioqKUmpqqiZOnKgnnnhCO3bs0KxZswIRBwDaj8oS6cs/mbX+V0g9z/1T8hXVtbr/9XVaf+iUUe+bEq+X7xqn+Gi//1wEAMAmDodDj10zRFcO7WbUi8qqdftLX+lQQem5PyQyVrrgB2Zt7+dS7jofJgUAAAAAtCcOj8fjsTtEe5Gbm6u0tDRJ0uHDh9WrVy+bEwEIZnl5eUpNTTVqJ0+eVJcuXVr+Icv/R1r4a7P2wBKpx6izvq261q3v/H29Fuw0d76mJkbr3e9MVlpSXMszAABCVmVNre57bZ2W7zXP0+rVOVbvfHuSuneMPfsHVJVKfxxunuuYcbF0x/stzuCTvw8BtDv89zcAAEB4Ym4UAISryjPSSusurSvP2dCqdXv0w7c3NWpodY6L1N/unUBDCwDakegIl5771hiNTe9s1HNPlev2F79SQUnl2T8gKl668Idm7cBiKXuFj5MCAAAAANoDmloAEK7WPC+Vm6MDddGjZ32L2+3Rz+Zu0cdbjhn1xOgIvX7PhJadoQIACCtxURF66a5xGty9g1Hfn1eqO15eo9Pl1Wf/gHH3SgnmGEMt+i+JgREAAAAAgPNEUwsAwlF5UeOztAbMknqMbPYtHo9Hv/l4h+asyzXqsZEuvXL3OA3r1dH3OQEAIaFjbKT+du94ZXaJN+rbjxbrzpfXqLjiLI2tyFhpoKkmDQAAWsdJREFU6o/NWs5Kaf8iPyQFAAAAAIQzmloAEI5W/lmqKDJrF/202cs9Ho9+N2+nXl2ZbdSjXE69cMdYje2T5PuMAICQkpwQrb/fN0E9O5nnaG06XKQ7X16jM2drbI2+Q+rY26wt+i27tQAAAAAA54WmFgCEmzMnpNXPmLVB10jdRzR5+dcNrReWHzTqEU6HnrlttC7sl+KvpACAENO9Y6zevH+CUhOjjfrGnCLd9cpalVTWNP3GiGhp2k/M2tGN0u55fkoKAAAAAAhHNLUAINwse1KqLqtfO5zS9P9o8tLmGloOh/S/N4/UJYO7+jMpACAEpSfH6837JyolwWxsrT90Sne/skalzTW2RtwqJWWYtUX/JbndfkoKAAAAAAg3NLUAIJwUHpTWv2rWRt4mdenf6NKzNbT+Z/YIXT2ihx+DAgBCWVZqgt56YIJSEqKM+trsU7r7lbVNN7ZcEdJFPzdrJ7dL2+f6MSkAAAAAIJzQ1AKAcLLkccnd4EwTV7R00aONLjtXQ+v6Ub38nRQAEOKyUhP1j/snKjnebGytyS7UPa8209gaeoPUZZBZW/gbqabSj0kBAAAAAOGCphYAhIsT26Utc8za+PuljmaDyuPx6L8+oaEFAGi7fl0T9eb9E5VkaWx9dbBQ33rpK50urzbf4HRJ0//drBUdkta97OekAAAAAIBwQFMLAMLFwt9K8tSvoxKlC//NuMTt9ujf39+mF1fQ0AIA+MaAbol68/4J6hwXadQ35BTpthdX61RplfmGgVdJaRPM2tLfSxWn/ZwUAAAAABDqaGoBQDjIWS3t+dSsXfADKT7Zu6ypdetH72zWm1/lGJfR0AIAtNXAbh2a3LG17Uixbnl+tU6eqagvOhzSpb8xP6C8UFrxR/8HBQAAAACENJpaABDqPB7pc8sop7gUaeJ3vMvKmlp9980Nem/jEeMyJw0tAICPDOreQW8/MFGpidFGffeJM7rludU6drq8vth7Yt2OrYZWPyOdNv+eAgAAAACgIZpaABDqtr0rHVln1qY+IkUnSpLKq2p1/+vr9fn2E8YlkS6Hnv7maBpaAACf6dc1UXMenKSenWKN+oH8Us1+bpVyCsrqizN+JTlc9euaCmnJ7wKUFAAAAAAQimhqAUAoq66QFvzarCVlSmPvkSSdqajWnS+v0bI9ecYl0RFOvXDHWF05rHugkgIA2ok+KfF6+8GJSk+OM+qHC8t1419Xauex4rpCl/7S6DvMN296Uzq5M0BJAQAAAAChhqYWAISyr56VTptnZOnS30gRUco7U6lbnl+tNdmFxm/HR7n02j3jddGA1AAGBQC0J706x2nOg5OUlZpg1E+eqdTs51ZpzcF//d100c+kyPj6Czxuaf6vApgUAAAAABBKaGoBQKgqyZOW/cGspV8oDZylQwWluvGvK7X9aLHx2x1jI/XG/RM1MSM5gEEBAO1R1w4xevuBiRrUvYNRP1NRo2+99JUW7DghJXaVJn/PfOPez6V9CwOYFAAAAAAQKmhqAUCoWvK4VHXGrF3+n9p2tFjfeHalDjU8t0RSSkKU3npgokamdQpcRgBAu5acEK23Hpio8X2SjHpljVsP/n295qw7LE3+vhTfxXzj5z+XaqsDmBQAAAAAEApoagFAKDq5S1r/ilkbfou+LEvTzc+tUn5JlfFbaUmxeufbkxv9tDwAAP7WMTZSr987XpcM6mrUa90e/eSfW/TX1Sflmf4f5pvydknrXg5gSgAAAABAKKCpBQCh6Itf1J078rWIWM3v/oDuemWNSqtqjUsHd++gd78zWX1T4gUAgB1iIl366+2jddOYXo1+778/3aVfHx4pT7fh5m8s/p1UVtjoegAAAABA+0VTCwBCze7PpH3zjdL6nrfp/g+Oq7rWY9QnZSTr7QcnKjUxJpAJAQBoJMLl1O9vHK5vT8ts9Huvrs7VE7rbLFYU1TW2AAAAAAD4F5paABBKqiukzx41SsURybpj96RGl84a1l2v3jNOiTGRgUoHAMBZORwOPXrlQP1i1qBGv/fX7K5aFjXFLK57WTq5O0DpAAAAAADBjqYWAISSdS9Kpw4apV+VzVapYo3aHZPS9adbRyk6whXIdAAAtMh9UzL0f7eMVJTL/M+RnxXfqEpF1Rc8tdLi/wxwOgAAAABAsKKpBQChZNWzxnKtu7/ec19o1H56xUD9+pohcjkdgUwGAMB5uXZkT/39vgnqFFe/o/iIuuivNbPMCw99GeBkAAAAAIBgRVMLAEJJTbn3Za3HoV9V3yWprnkVHeHUM7eN1ncuypTDQUMLABD8xvdN0tzvTFZ6cpy39teaq3XMk2RjKgAAAABAsKKpBQAh6s3aGdrh6SNJSo6P0j8emKiZw7rbGwoAgPOU0SVBc78zWaN7d5IklStGj1ffam8oAAAAAEBQoqkFACGo0JOgp2pmS5Iyu8TrvYcu0OjenW1OBQBA6yQnROvN+ydq1r9+OOND92Stqh1scyoAAAAAQLChqQUAIejJmpt1WgmalJGsud+5QL0bjG0CACAUxUS69OdbR+nb0zIlOfSLmrtV5XHZHQsAAAAAEERoagFAiNni7qu3ay/WbRN667V7xqtjXKTdkQAA8Amn06FHrxyoJ28crsPONL1QO8vuSAAAAACAIEJTCwCC1Iq9JxvV3B6Hfl17j35z3XD91/XDFBXBl3EAQPi5aWya3npwot6KuUVHPcmNfv+1hZvl8XhsSAYAAAAAsBNPQwEgyLjdHj29eJ/mzXmh0e996LxYP73vdt0+Md2GZAAABM7o3p31zx/M0Fsd7mn0e7Ff/VHf+fsGFVdU25AMAAAAAGAXmloAEESKyqp03+vr9Pzn6/WjiDmNfn/CHb/T+L5JNiQDACDwunaI0UP3PdCofqNrmfJ3LNE1f16hHUeLbUgGAAAAALADTS0ACBJbcos0608rtGjXSf0s4k0lO840uqZ711QbkgEAYJ+YSFejmtPh0RORL+hYQZGuf+ZLvbPusA3JAAAAAACBRlMLAGzm8Xj099WHdOOzq3SkqFwTHDt1S8QSu2MBABDUMp3H9P2I91RZ49Yj/9yin/5ziyqqa+2OBQAAAADwI5paAGCjsqoa/fDtTfrF+9tUVetWtKr0u8gX7Y4FAEBI+LbrIw12ZEuS3l53WNc/s1LZ+aX2hgIAAAAA+A1NLQCwye7jZ3TtX77U+5uOems/inhHmc5jNqYCACB0RDjc+n3k84pQjSRp57FiXf3nFZq3lb9LAQAAACAc0dQCgADzeDz626psXfOXFdp7ssRbH+3Yo/tc82xMBgBA6BnqzNb9Df7+PFNZo4fe2KBH392isqoaG5MBAAAAAHyNphYABFBhaZXuf329/uOD7aqscXvrMarU/0Q9p//f3n3HZ1Xe/x9/39l7D7IhbGTJRmVDleHAgQMV3FX7q9Jq/frVWq3f1g5HtbaOqijO4qiroGxk7z3DSMieZO/k/v1xwk1ukkCA3CPJ6/l4nMd9znWuc58P3sI93udcl4vJfLqzq4cDKgQAoB0I7Wm1Oc/9S3U3pVu1fbYlVVf/fa32ZRTZszIAAAAAgA0RagGAnaw/kqepr/6kZQeym+z7Y+DX6mo6Y6iky+fZqTIAANqZqX+WZLJseqhG7wW+J1fVWXU7mlummf9Yr3fXHpfZbBYAAAAAoH0j1AIAG6upq9effzio2e9uUnZxVZP9vxtYqJlV31o3xgyTRtxrpwoBAGhnogdLox6yakqoPKBvB22Uj4erVXt1Xb2e/36/7np/i3JLmr4PAwAAAADaD0ItALChIzmluvGN9Xpj1VGdeYF4qK+HPri9v+7Ke1EmNR520FO67g3JxfpHOQAA0MjEp6XQHlZNlxx+U0tvDtDA2MAm3VcdytXUV9do2f6md0wDAAAAANoHQi0AsIH6erPeXXtc019bo11pTefyGNMzTIsfHaNxya9KBcesd058WgrvZadKAQBopzx8pJlvS6ZGF4GY6xSz8hF9cc+lemBcYpND8kqrdO+CrXr8810qrqyxY7EAAAAAgLZAqAUAbSy1oFy3/mujnv9+v6pq6632ubua9NS0vvrgrhGKSF8hbX3X+uDYEdLoh+1YLQAA7VjsUGnsY9ZteYflser3enJqX310z0iF+3s2OezzbWm66pWftO5Inp0KBQAAAAC0BUItAGgjZrNZn2w6oav+9pM2HS9osj8xzFdfPXi57hubKJeybOnbX1h3cPOWrvsnww4CAHA+xj4uRV9q3bbpTenYKl3RM0w/PDJGk/tGNDkso6hSs9/ZpGe+2avy6lo7FQsAAAAAuBiEWgDQBrKKKjV3/hb973/2qKy6rsn+uy/vpv/+cowGxAZK9fXS1w9K5fnWna56QQrraaeKAQDoIFzdjWEI3bys2//zoFReoFA/T/3rzmH6y40D5efp1uTwBRtSNO3VNdqa3PSCFAAAAACAcyHUAoCLUF9v1sebUjTl5dVafTi3yf6YIG99et8oPXN1P3l7NNyBtelN6egK6459ZkhD59q+YAAAOqLwXtLk56zbSjKMi0jMZplMJs0aFqcfHh2jy7qHNjk8Ob9cN721Qc9+u0+lVdy1BQAAAADOilALAC7Q0dxS3fL2Rj31n70qaeYHsFtHxOnHeWM1uvGPZ1l7pGW/s+7oHyVd83fJZLJxxQAAdGAj7pcSx1u3Hf7BuJikQWywjz66Z6Seu+YSeblbfxUym6X31yfrZy+v1oqD2XYoGAAAAABwvgi1AOA8VdfW6/UVSZr6tzXa3MxQRRH+npp/13C9cP0ZwxxVlUpf3CPVVVsfcN0bkk+IjasGAKCDc3GRZr4l+YRZty/5rZSxo1E3k+Zc1lWLHxmrIfFBTZ4mo6hSd7+/Vf/v0x3KK62ycdEAAAAAgPNBqAUA52HHiZO6+u9r9eKSw6quq2+y/4YhsVoyb6wm9D5jQnqzWfp+npR3yLr9sv8ndZ9gw4oBAOhE/LtI179l3VZfI31+l1RZbNXcLcxXn//8Mv3vtD5N7tqSpO92ZWjyy6v1xbY0mc1mW1YNAAAAAGglQi0AaIWiiho9881eXf/Geh3KLmmyPy7EWx/eM0IvzRqkIB+Ppk+wbb60Z6F1W5eB0sTf2qhiAAA6qR6TpcsftW47edy4uOSMcMrVxaT7x3bXj4+O1eU9ms61VVheo8c+36XZ72zSkZym7/8AAAAAAPsi1AKAszCbzfpiW5omvrhKCzaknPlbmFxM0n1juunHR8dqTM/w5p8kY6e0+AnrNs8AadYHkpunTeoGAKBTm/i0FDvcum3vF9K295vtnhDqq4/uGam/3jhQgd7uTfavP5qvqa+u0Z8WH1R5ddN5NAEAAAAA9kGoBQAtOJBZrFlvbdBjn+9Sfll1k/19owL09cOX66np/eTj4dbMM0iqKJQW3tl0Hq1r/yGFJLZ90QAAQHJ1l254V/IKtG5f/BspbVuzh5hMJt00LE7LfjVOMwZGNdlfU2fWm6uPavJLq7V4TyZDEgIAAACAAxBqAcAZiitr9Nx3+zTj72u1Jflkk/1e7i76zVW99e0vLtfA2KCWn8hslr55WCpMsW4f9bDU75q2LRoAAFgLTpCued26ra5aWniHVJrb4mHh/p56/bYhenfOMEUHejXZn1FUqQc/3q4739us43llbV01AAAAAOAsCLUAoEF9vVkLt6Zq0kurNX9dsurqm16B/bN+kVo6b5weGt9D7q7n+Cd07SvSwe+t22JHSFOea8OqAQBAi/pdI43+hXVbcbr0xV1S3dmHEZzUN1LLfj1OD47vLndXU5P9a5LydOUrP+lPiw+qpLKmLasGAAAAALSAUAsAJG08lq+rX1+r33yxW7klVU32x4f46L25w/T2ncMUF+Jz7ic8/KO0/PfWbd4h0k3zjSGRAACAfUx+Tuo6xroteY20/NlzHurj4aYnruqjxY+M1eU9Qpvsr66r15urj2rCi6v06eYTzV4QAwAAAABoO4RaADq1lPwy/fzDbbrl7Y3al1HcZL+Hm4sendxTS+aN1cQ+ka170txD0pf3Smr8w5ZJuuFfUmBsm9QNAABaydVNunG+5B9t3b7+79Ler1r1FD0i/PTRPSP1+m2XKjLAs8n+vNJqPfnVHk1/bY3WH8lri6oBAAAAAM0g1ALQKRVX1uiFRQc05eWf9MO+rGb7TOwToWXzxunRyb3k5e7auieuKJQ+vVWqOiMgm/w7qcfkiysaAABcGL9w6eYPJVcP6/avH5IydrTqKUwmk2YMjNbyX4/X/WMT5ebSdEjCg1kluu2dTbpvwVbm2wIAAAAAGyDUAtCpVNXW6d21xzX+r6v01k/HVF1X36RPzwg/fXD3CL03d7jiQ1sx1OAp9XXSl/dIBUet2/vfKF3+6MUVDgAALk7sMGnqX6zbaiuMi1GKM1r9NH6ebvrfaX21ZN5YTe4b0WyfpfuzNeXl1Xrmm73NDmsMAAAAALgwbo4uAADsoa7erK93pOvlpYeVXljRbJ9gH3f96me9devwOLm5XkDmv/QZ6cgy67aoQdI1f5dMTa/mBgAAdjZ0rpSxXdq+4HRbSab06S3SXYslD99WP1ViuJ/emTNc647k6fnv9+tgVonV/tp6sxZsSNEX29J07xXddN/YRPl7Ma8mAAAAAFwM7tQC0KGZzWYtP5Ctaa+u0a8/39VsoOXuatJ9Y7pp1eMTdMeohAsLtLa8I2143brNN0K65RPJ4zzu9gIAALZjMknTXpISrrBuz9wlfXW/VN/0Du5zubxHmP77yzH648wBCvX1aLK/vLpOr604onF/XaV31x5XVW3dhVYPAAAAAJ0eoRaADmtLcoFmvbVB93ywVYeyS5rt87N+kVoyb5yemt5Pgd4XePX0oR+kRY9bt7m4G3N3BMZe2HMCAADbcPMw3qNDEq3bD34vrfj9BT2lq4tJt42M18rHx+uBcYnycGv6NaugrFrPf79fE19crc+3pqq2mSGQAQAAAABnR6gFoMPZklyg2e9s1E1vbtCW5JPN9hnRLURfPniZ3r5zmLqFtX6ooSbSt0tf3CWZz/hh6uq/SfGjLvx5AQCA7fiESLctlLwCrdvXviJte/+CnzbAy11PTu2rVY+N16xhsXJpZvTh9MIKPf7Fbk1+ebW+3JZGuAUAAAAA54FQC0CHsTW5QLe/s0k3vblB647kN9unTxd/zZ87XP++f5SGJgRf3AlPpkif3CzVlFu3j3tCuvT2i3tuAABgW2E9pVkLJJczphn+fp508L8X9dTRQd76y42D9OOjY/WzfpHN9knOL9evP9+lKa/8pK+2E24BAAAAQGsQagFo906FWTe+uUFrj+Q12yc22Fuv3DxI//3lGE3oEyGTqZlLp89HeYH08Y1SWY51+6BbpfFPXtxzAwAA+0gcL01/ybrNXC99cbeUsuGin75npL/evnOYvnzwMo3oFtJsn+N5ZfrVwl362Ss/6T87CLcAAAAA4Gzczt0FAJyP2WzW5uMFen3lEa1Jaj7IkqQIf089NL67bh0ZL08317Y5eVWp9PFNUt5h6/ZuY6WrXzMmoQcAAO3D0LlS4QlpTaNwq7ZS+vRm6a4fpMh+F3+KhGD9+/5RWnU4V68sPazdaUVN+hzLK9O8f+/S35cf0cMTeuiawdFyd+UaRAAAAABojFALQLtSX2/W8oM5emPVEW0/Udhiv1Nh1i0j4uXl3kZhliTVVEqf3Salbz3jhP2kmz8yJp8HAADty8TfSqXZ0o6PTrdVFkkf3SDds0QKirvoU5hMJk3oHaHxvcK18lCO/rYsqcVw69ef79LLSw/r3jHddPPwOPl48LUNAAAAACRCLQDtRHVtvb7dlaG3Vh9VUk5pi/3CT92Z1dZhliTV1RrDER1fbd3uH938ZPMAAKB9MJmkGa9KZfnS4cWn20sypA9nSnP/K/k3PzfW+Z/KpIl9IjWhd4RWHDTCrT3pTcOt9MIKPffdfr22PElzL+umO0cnKNiXi2cAAAAAdG6EWgCcWllVrT7bkqp31xxTRlFli/3C/T314Ljuum2kDcIsSaqvl755WDp0xsTxPqHSnV+3yRXcAADAgVzdpBvfkz68TkrddLo9P0lacK0093vJN6zNTmcymTSpb6Qm9onQ8gM5+tvyw9qbXtyk38nyGr2y7LDeXH1Ut46I171juik6yLvN6gAAAACA9oRQC4BTyiqq1Icbk/XxphMqLK9psV9MkLfuHdPNNndmnWI2S4sfl3Z/Zt3uGSDd/pUU3ts25wUAAPbl4SPd+pk0f6qUe/B0e+4BacF10pxvJZ+QNj2lyWTS5H6RmtQ3QssOtDzEckVNnd5bd1wLNiTrmkHRuuvybhoQy13iAAAAADoXQi0ATmVnaqHeW3tci/Zkqrbe3GK/XpF++vm47rp6kI0nUTebpUWPSVvesW538zJ+9IoebLtzAwAA+/MJke74jzR/mnTy+On27D3GUIR3fiN5B7X5aU0mk6b0i9TkvhHaknxSb6w6opWHcpv0q60366sd6fpqR7qGdw3W3Zd305R+kXKz5echAAAAAHAShFoAHK6mrl4/7M3Se+uOa0czVyY3NrxrsH4+rrsm9I6Qi4vJtoXV10uLfi1tfc+63cVNmvWh1PVy254fAAA4RkC0NOc76f1pUuGJ0+2ZO6WPbzTu1PYKsMmpTSaTRnQL0YhuI3Qgs1hvrT6q73Znqq6Zi322JJ/UluSTigny1pzLEnTzsHgF+rjbpC4AAAAAcAYms9nc8q0QaFNpaWmKizPm3UlNTVVsbKyDKwIcK7+0Sv/emqoPN6Qo8yzzZUnSpD4R+vn47hretW2H/GlRfb3033nStvet202u0g3vSP2vt3kJubm5ioiIsGrLyclReHi4zc8NAICzcOj74clk446t4nTr9pih0uwv2nwowpakFpTrnTXH9NmWVFXV1rfYz9vdVTcOjdWdoxPUM9LfLrUBzorv3wAAAB0ToZYd8aEakMxms7Ykn9THm1K0eE+Wquta/mHGy91F1w+J1V2XdbXvDzP19dL3j0jbF1i3m1ylG9+VLplplzIItQAAcIL3w/yjxhxbpdnW7ZH9pTu+lvzs975cUFatTzef0IINycourjpr3xHdQjR7ZLyu6t9Fnm42mncUcGJ8/wYAAOiYGH4QgF0UVdToP9vT9PGmE0rKKT1r36hAL905uqtuHRGnIB8PO1XYoLZa+vpBae8X1u0ubtKN70n9rrVvPQAAwLFCuzcMRThdKms0x1X2XiPsmvOtMVyhHYT4eujhCT1035hELd6bqffWJWtXamGzfTcfL9Dm4wUK8fXQTcNidduIeCWE+tqlTgAAAACwFUItADZjNpu1K61In2xK0be7MlRZ0/JdWZI0NCFYd13eVVde0kXujpjsvLpMWnindGSZdbuLm3TT+1Lfq+1fEwAAcLzw3tJdi6UPrpFKMk635ydJ711lBFvBXe1Wjoebi64dHKNrB8do+4mTem/tcS3em9XsvFsFZdV6a/UxvbX6mMb0DNPskQma1DfCMZ+1AAAAAOAiEWoBaHM5JZX6ZkeGPt+WqsPZZ78ry8PNRdMHRGnOZV01OC7IPgU2p7xA+uRmKW2zdbuLuzTrA6nPdMfUBQAAnENYT+nuhmCrMOV0e2GK9O6V0uzPpaiBdi9rSHywhtwWrIzCCn20MUULt6Yqr7S62b5rkvK0JilPYX4emnlpjG4aFqdezL0FAAAAoB1hTi07YkxvdGTVtfVacTBbX2xL08pDuc1eKdxYtzBfzR4ZrxuGxCrY185DDJ6pOEP68Hop94B1u7uPdPNHUo9JDinL4XOIAADgBJzu/bA4Q1pwrZR32Lrdw1+6eYHUfaJj6mpQXVuvJfuz9PHGE9pwLP+c/QfFBurGYXG6ZmC0An3c7VAhYB98/wYAAOiYuFMLwEXZl1Gkz7em6Zud6TpZXnPWvm4uJl15SRfNHhmv0d1DZTKZ7FTlWWTtNe7QKk6zbvcOlm77XIob7pi6AACAcwqIluYukj6cKWXvOd1eXSJ9fJN0zevS4FsdVp6Hm4tmDIzWjIHROpJTqk82ndAX21JVXFnbbP9daUXalVak57/frysv6aKbhsbq8h5hcnVxgs9pAAAAAHAGQi0A5y29sELf7crQNzszdCCz+Jz9Y4O9deuIeN00LFYR/l52qLCVDi+RvrhLqj5jiET/aOmO/0gRfRxTFwAAcG5+4dLc76XPZkspa0+319dKX/9cKk6XxvxacvAFPD0i/PTM1f30m6t66/vdmfpkU4q2nyhstm91bb2+25Wh73ZlKNzfUzMGRunawTEaFBvoHBciAQAAAIAItQC0Un5plRbtydQ3OzO0NeXkOft7u7tq6oAuumlonEZ2C5GLs13tu+lt6YcnJHO9dXtoDyPQCop3TF0AAKB98A6S7vhK+s/PpX1fWe9b8byUf1Sa8Yrk7vgLerzcXXXj0FjdODRWR3JK9cW2NH21PU05JVXN9s8tqdL8dcmavy5ZCaE+unZQtK4ZHK0eEcy/BQAAAMCxCLUAtKikskY/7svWt7sytO5I3jnnyZKkEV1DdOPQWE0bGCU/Tyf8J6auVvrxf6XNbzXdFzdKuuVjyTfM/nUBAID2x81TuuFdY0jCDa9b79v1iTHv1i0fS/5dHFNfM3pE+Ol/pvbRYz/rpTVJefp8W6qW7s9WTV3zn/NS8sv12oojem3FEV0SHaBrBxtDG0YHedu5cgAAAAAg1AJwhuLKGq08mKPFe7K04lCOqmvrz3lMVKCXbhhiXP3bNczXDlVeoNJcY7jB5DVN9w1omAPDCa6mBgAA7YiLi3TlH6TAWOmHJyU1CofSt0pvjzeCrZihjqqwWW6uLprQJ0IT+kToZFm1vtmZri+2p2lvestDS+/LKNa+jGL9cdFBDYkP0tT+UbqqfxfFhfjYsXIAAAAAnRmhFgDll1Zp2YFs/bA3S+uO5Ku67txBVoCXm6b2j9I1g6M1KjHU+ScTT9sqLbzTmOPiTOOflMY94fB5LwAAQDs26kFj+OKv7reer7MkU3pvqnTNa9KgWxxX31kE+3po7uXdNPfybjqSU6pvd2Xo253pSs4vb/GY7ScKtf1Eof6w6ID6xwRoav8oTe3fRYnhfnasHAAAAEBnYzKbzeceTwxtIi0tTXFxcZKk1NRUxcbGOrgidGaZRRVass8IsjYdz1crRhaUt7urJveL1DWDojW2V5g83VxtX+jFMpulbe9Li38j1VVb73P1kK79hzRwlkNKO5fc3FxFRERYteXk5Cg8PNxBFQEAYH/t7v0we7/06S1SYUrTfUPmSFP/LLk7/9B9ZrNZu9OK9M3ODH23O0O5Lcy/dabekf66qn8XTR3QRb0j/WXioiE4CN+/AQAAOibu1AI6CbPZrAOZJVpxMFvLDuRoZ2phq45zdzVpXK9wXT0oWlP6RcrHox39s1FVaoRZOz9uus8/Wrr5Qyl2mP3rAgAAHVdkP+n+VcYd4mcOebz9Ayl9u3TT+1JYD0dU12omk0mD4oI0KC5IT03vq43H8vXNznQt3pulksraFo87lF2iQ9klenV5kuJCvDWpT6Qm9Y3QyG6h8nBzseOfAAAAAEBHxJ1adsSVYrC3iuo6rT+apxUHc7TiYI4yiypbdZyHq4uu6Bmmqy7pop9dEqkgHw8bV2oDGTulL++R8o803dd1jHTjfMnPSa/wbtDurkwHAMAG2u37YV2N9OP/SpvfbrrPw88YjrD/Dfav6yJV1dZp/ZF8Ld6bqaX7s3WyvKZVx/l5umlMzzBN6hupCb3DFernaeNK0dnx/RsAAKBjake3XABojYzCCkuIte5Inqpqzz0/lmQMLTihT7iuvKSLJvaJkL+Xu40rtRGzWdr4T2np76T6Zn5kGf0LafJzkiv//AEAABtydZem/VWKHS5996hUU3Z6X3Wp9MXd0pEV0lUvSF4BDivzfHm6uWpCnwhN6BOh2rp6bTpeoMV7M/XjvuyzDlFYWlWrxXuztHhvlkwm6dK4IE3qG6lxvcLVLypALs4+PysAAAAAp8CdWnbElWKwhfLqWm06VqCfknK1JilPR3JKz31QA38vN03pG6kr+3fRuF7h8nJvB3NknU1JlvTNL6QjS5vu8/CTrvm71P96+9d1gdrtlekAALShDvF+mHvYGI4w90DTfUHx0sy3pYTR9q+rDdXVm7X9xEkt3pOlH/dlKb2wotXHhvl56IoeYRrTM1xjeoYpIsDLhpWis+D7NwAAQMdEqGVHfKhGW6ivN2tvRpHWJOVpTVKutqWcVE1d6/8an5rbYGKfCI1K7CBzG5jN0u6FxvxZlYVN90cPkW58VwpJtHtpF6ND/IgHAMBF6jDvh9Xl0qLHmp/rUybpikel8f8rubXDYZ/PcOZcrrvSCnU+3zr7dPHX2F7hGtszXMO6Brf/C6/gEHz/BgAA6JgYfwtwcmazWakFFdpwLE9rkvK07kheq+cukCRXF5OGJgRrUp8ITeoboe7hfjKZOtDwLiXZ0vePSocWNb//8kelCU91iB+IAABAO+bhI133T2Nuz0WPS9UljXaapbWvSIeXGHeWxw51WJltwWQyqV90gPpFB+gXE3sqt6RKKw/laPmBbK1JylN5dd1Zjz+YVaKDWSV6+6dj8nRz0cjEUF3ePVSjEkN1SXSA3Fw7wEVZAAAAAC4IoRbgZMxms04UlGvTsQJtPJavjcfylVFUeV7PEejtrvG9wzWxT4TG9QpXkE8HDHTMZmn3v6Uf/keqONl0v1+kNPNNqftE+9cGAADQksG3GkMN/udB6cR66305+6R3J0sjf25clOPp55ga21i4v6dmDYvTrGFxqqyp06bjBVp+IFs/Hc5Vcn75WY+tqq3XT4dz9dPhXEmSv6ebhncL0ajEkIaQK1CuzMcFAAAAdBqEWoCDmc1mpeSXa9PxfG1sCLIyzzPEcnMxaUh8sMb0DNOYXuEaENPBv9znHpL++2speU3z+/vfaEzM7hNi37oAAABaI7irNPd7af3fpRX/J9U3ugvfXC9t/Kd04HtpxitSz8kOK9MWvNxdNa5XuMb1MoaPPJFf3jA3bK7WH8lXSVXtWY8vqarVioM5WnEwR5IRco3oZgRco7uHqm9UQMf+HAwAAAB0coRagJ3V1NXrQGaxtqWc1LaUk9qafFJZxecXYklSYpivEWL1DNeo7qHy8+wEf52ry6Wf/mr8AFTfzBCMvuHS9JelftfYvzYAAIDz4eJqzKPVY5Jx11b2Huv9RSekj2+Q+l4j/ez/pOAEh5Rpa/GhPro9NEG3j0pQTV29dqUWGndmJeVpd1qh6s8xF1dJVa2WH8zR8oaQy8/TTZfGB2loQrCGJgTr0vjgzvE5GQAAAOgk+HQP2NjJsmptP3HSEmLtSitUZU39eT9PiK+HRiWGaEzPcF3RI0xxIT42qNZJmc3S/q+lpc9IhSea73PJ9dK0FyXfULuWBgAAcFG6DJDuX2lctLPqT1JdlfX+A99KSUukK+ZJlz8iuXs7pk47cHd10bCuIRrWNUS/+llvFZZXa92RfK0/mqeNx/J1NLfsnM9RWlWrNUnGXLSS5GKS+nQJ0LCuRsg1rGuIYoI67n9DAAAAoKMzmc3mc1z7hraSlpamuLg4SVJqaqpiY2MdXBHaWm1dvY7klmrniUIjxDpxUsda8eW7OaG+HhqVGGqZL6BHhJ9Mpk44lEraNunHJ6XUTc3vD4yXpv1F6j3VvnXZQW5uriIiIqzacnJyFB4e7qCKAACwv071fph/VPr2l1LK2ub3B8ZLU56TLpkpdcLPhTnFldp4vEAbjuZr07F8Hcu7sM/ZUYFeGhIfrIGxgRoUF6T+MYHczdUB8f0bAACgYyLUsiM+VHcs9fVmHc8v0560Iu1KK9SetCLtyyhWRU3dBT1fmJ+nRjYEWKMTQ9Q9vJOGWKcUnpCWPy/tWdj8fhc36bL/J419XPLwtW9tdtKpfsQDAKAFne79sL5e2vGhtOxZqaKg+T7Rl0qTn5MSx9m1NGeTXVypjcfytfFYvjYczVdyfvkFPY/JJPWM8NPA2CANigvSoNhA9ekSIA83lzauGPbE928AAICOicvRgFYwm81KO1mh3WlF2p1eqN2pRdqbXnTOiazPJjHcV0PjTw2DEkyIdUpxprTmJWnb+83PmyVJCVdI01+SIvrYtTQAAACbc3GRhs4x5ghd+UdpyzuS+YyhqzN2SAuukbpPkiY/K0UNdEipjhYZ4KVrB8fo2sExkow7ubalnNTWhmVfepFqzzUpl4yRrg9nl+pwdqm+2JYmSfJwdVHf6AANjg3UgNggXRIdoB4RfnJ3JegCAAAAHIlQCzhDdW29juSUan9msQ5kFmt/RrEOZBWrsLyFgKUVvNxdNCjWesLqEF+PNqy6AyjLk9a+YvxwU1vZfJ/grtKU3xsTphMAAgCAjsw7WJr2V2nIHGnxE80PSXh0ubH0vVoa+5tOG26dEhHgpakDojR1QJQkqaK6TrvTCrU15fT8tkUVrftMX11Xr12phdqVWigpRZIRdPXq4qd+UQHqFxWgS2IC1aeLv/y93G30JwIAAABwJkItdGony6qN4KphOZBZoiM5Jaqpu7hROWOCvHVp/OkQq29UAFd1tqQoTdrwD2nbB1JNC/MieAZK4x6XRtwvuXnatz4AAABH6tJfmvu9dOBbafnvpfwjTfsc+M5Yek8zhmaOGWL/Op2Qt4erRiaGamRiqCRj+PCjuaXafuKkdqUVaXdaoQ5mlrTqbi7JCLr2phdrb3qxVXtCqI8uiTaCrn7RAeoV6a+YIG9GYQAAAABsgFALnUJJZY2SckqVlF2ipOxSHW5Yzyxq4Y6g8xDu76lBsYEaGBukAbGBGhgTqFA/gpdzyjkgrXtV2vO5VN/CMI6uHtKwu40fZ3zD7FsfAACAszCZpH7XGqHVjo+kVS9IpdlN+x1aZCyJE6TLfmEMT0iwYuHiYlLPSH/1jPTXzcONtsqaOu3PLNau1ELtTivSrtRCHctr4UKrFqTklyslv1yL9mRZ2vw83dQjwk+9I/3Vq4u/ekUa6+H+noRdAAAAwEUg1EKHciq8OpJdqsPZJTqcU6oj2SXKaIPwSpKCfNw1MDZIA2MCNbAhyOoS6NUmz90pmM1Syjpp/evS4cUt93Nxky69Qxr7mBTIhM4AAACSJFd3adhd0sBZ0sY3pPV/lyoLm/Y7ttJYwvtKox+SBsyS3PnM2hwvd1cNiQ/WkPhgS1tRRY32pBVpV5ox/OD+zGKlnaw4r+ctrarVztRC7UwttGoP8nFXrwh/9erip16R/uoV6a8eEX4K9fUg7AIAAABawWQ2my9unDW0WlpamuLi4iRJqampio3lx/oLUVdvVkZhhY7mlup4XpllOZpT2mbhlSTFhXirb5cA9W0YRqRfVIBigxlG5IJUlUi7PpO2vCvlHmi5n8nV+JFm3G+kkET71eekcnNzFRERYdWWk5Oj8PBwB1UEAID98X54FpXF0ua3pQ2vSxUnW+7nEyYNv1caOkcKiLZffR1IUXmNZcjyfRlF2p9RrCM5pa0euvBcArzclBjup8RwX3UP91NimK8Sw/2UEOojL3fXNjlHZ8P3bwAAgI6JO7XglMxms/JKqxsCq1IdyyvT8VwjvErJL1d1XX2bncvDzUW9I/3VLypAfaP81S86UH2i/BXAhM8XL+eAtOUdI9CqLm25n5u3NOQOafQvpOAE+9UHAADQnnkFGHe2j3zAuHho/d+l8rym/crzpNV/kn76i9TrKmnIHKnnFMmFsKS1An3cNbp7qEZ3D7W0VdXWKSm7VPszjKBrX0axDmWXqKSyhaG1z6K4svk7u1xMUkywd0PQZYReiWG+ig/1UVSgt1xduOAOAAAAnQuhFhympq5emYWVOlFQrtST5TpRYCypBeU6nld2QV8Gz8bVxaSuoT7qFemvnhF+6hnpr95d/JUY5is3V5c2PVenVl4g7f1S2vWplL7t7H29gqQR9xs/xDBnFgAAwIXx9JeueNT4TLX739KGf0p5h5r2M9efnncrIEa69HZp8GwuKrpAnm6u6h8TqP4xgZKMO4LMZrOyiit1OLtUh7NKjCHRs0t0OLtUFTV1532OerOUWlCh1IIKrTqUa7XP3dWkuGAfxYX4KCHUR/EhPkoI9VV8iLHu7UFoCQAAgI6HUAs2YzabdbK8xhJUWT2eLFdGYaXq2mi4jsZcXUxKCPUxxqqP9FOPSOOxW5ivPN34YmcTtdXSkaVGkHXoB6m+5uz9Q7pLI+4z5s3y9LNPjQAAAB2du7c0dK506Z3S0eXGsITHVjXftzhdWv1nY4kbJQ24UbpkJhcaXSSTyaSoQG9FBXprXK/TQ2TW15uVXlihQ1klOpxTosNZJTqUXaqjuaWqrr2wUShq6sw6llemY3llze6P8PdsCLuMoCs22Fsxwd6KCfJWl0AvuXNhHwAAANohQi1csMqaOmUVVSqjqEIZhZXKKKxQRmGF0hseM4sqVV59/lcjtpb/qXHnw3zVLcxXXcN81TPCGJKD8MoOaquMH0n2fysd/L75ScobM7lIvadJw++Ruo2XXPgSDQAAYBMuLsbwgj2nSFl7jXm39n7Z8nDQqRuNZfETUveJRsDVe6rkFWjfujswFxeT4kKMu6om94u0tNfVm5V+skJH80p1LLdMx3IbHvNKlV1cdVHnzCmpUk5JlbYkN51vzcUkRQZ4KSbICLqig7wt6zEN676e/FwAAAAA58OnVLToZFm1ThSUW4KqzKLGwVWl8kov7ktWa3i4uahrqI+6NUyU3C3M1xJihfh6yGRiDHm7qi6Tjq6U9n8jHf5Bqio+9zG+EcZ8WUPvkoLibF8jAAAATuvSX7rmNenKPxjB1rYPpIztzfc11xl33x9ZKrm4S12vkPpMNy5MCoyxb92dhKuLSfGhPooP9dGE3tb7Sqtqdbwh4DraKPBKyS9T2UVePFhvljKLKpVZVKmtKU1DL0kK8nFXTJAReEUHeqlLoLeig7zUPdyvYchFAAAAwP4ItdCil5Ye0kcbT9j8PP6eboprGPc9PtTHsp4Y5qvoICY/drj8o1LSUilpiZS8VqprRZjp6mn8ADLoVuNqX1f+qQEAAHAoT39jaMKhc6XM3dL2D4yQq6L5QEP1NdKxlcay6DEp+lIj3Oo+SYoeLLkwMoKt+Xm6aUBsoAbEWgdIZrNZBWXVSiko14n8cqXklyuloEwn8o2h3nNK2ubiw8LyGhWW12hfhvWFbBP7ROi9ucPb5BwAAADA+eKXZrQoOsi7TZ7H1cWkmCBvxTcMtxEX4m2ZvDg+xEeB3u7cceVMyguklPXS8Z+kI8ukgqOtPzZulDT4VqnfdZJ3kK0qBAAAwMWIGihNf0m68gVj7q09n0sHF0m1FS0fk7HDWFb+QfIOlhLHGwFX94ncxWVnJpNJoX6eCvXz1JD44Cb7y6trlVpQoZT8Mp0oMEKvxiN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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 469,
+       "width": 858
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "tau = 1\n",
     "tau2 = 2 * (tau**2)\n",
@@ -205,7 +300,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -278,7 +373,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -348,9 +443,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4000"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "tau_s = 50e-6             # in seconds\n",
     "tau = int(tau_s/d.clk_p)  # in raw timestamp units\n",
@@ -367,7 +473,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -387,16 +493,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_65165/287442945.py:59: RuntimeWarning: invalid value encountered in divide\n",
+      "  ED = np.mean(kde_adi / (kde_adi + nbkde_ddi))  # (E)_D\n",
+      "/tmp/ipykernel_65165/287442945.py:60: RuntimeWarning: invalid value encountered in divide\n",
+      "  EA = np.mean(kde_dai / (kde_dai + nbkde_aai))  # (1 - E)_A\n"
+     ]
+    }
+   ],
    "source": [
     "fret_2cde = calc_fret_2cde(tau, ph, mask_d, mask_a, bursts)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -405,9 +522,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1488, 1488, 1488, array([1488]))"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "len(fret_2cde), len(fret_2cde_gauss), bursts.num_bursts, ds.num_bursts"
    ]
@@ -421,9 +549,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 450x450 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 426,
+       "width": 440
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.figure(figsize=(4.5, 4.5))\n",
     "hist_kws = dict(edgecolor='k', linewidth=0.2,\n",
@@ -431,7 +575,7 @@
     "\n",
     "valid = np.isfinite(fret_2cde)\n",
     "sns.kdeplot(x=ds.E[0][valid], y=fret_2cde[valid],\n",
-    "            cmap='Spectral_r', shade=True, shade_lowest=False, n_levels=20)\n",
+    "            cmap='Spectral_r', fill=True, thresh=0.05, n_levels=20)\n",
     "plt.xlabel('E', fontsize=16)\n",
     "plt.ylabel('FRET-2CDE', fontsize=16);\n",
     "plt.ylim(-10, 50);\n",
@@ -444,9 +588,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 612,
+       "width": 619
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "valid = np.isfinite(fret_2cde)\n",
     "x, y = ds.E[0][valid], fret_2cde[valid]\n",
@@ -454,7 +614,7 @@
     "                facecolor=sns.color_palette('Spectral_r', 100)[7])\n",
     "\n",
     "g = sns.JointGrid(x=x, y=y, ratio=3)\n",
-    "g.plot_joint(sns.kdeplot, cmap='Spectral_r', shade=True, shade_lowest=False, n_levels=20)\n",
+    "g.plot_joint(sns.kdeplot, cmap='Spectral_r', fill=True, thresh=0.05, n_levels=20)\n",
     "g.ax_marg_x.hist(x, bins=np.arange(-0.2, 1.2, 0.0333), **hist_kws)\n",
     "g.ax_marg_y.hist(y, bins=70, orientation=\"horizontal\", **hist_kws)\n",
     "\n",
@@ -501,7 +661,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -543,9 +703,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2859, 2656, 2859)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "ALEX_2CDE.size, np.isfinite(ALEX_2CDE).sum(), np.isfinite(ds1.E[0]).sum()"
    ]
@@ -559,9 +730,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 612,
+       "width": 616
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "hist_kws = dict(edgecolor='k', linewidth=0.2,\n",
     "                facecolor=sns.color_palette('Spectral_r', 100)[7])\n",
@@ -578,9 +765,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of bursts (removing NaNs/Infs): 2656\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 612,
+       "width": 616
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "valid = np.isfinite(ALEX_2CDE)\n",
     "print('Number of bursts (removing NaNs/Infs):', valid.sum())\n",
@@ -596,7 +806,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -606,9 +816,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'matplotlib.figure.Figure'>\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x700 with 4 Axes>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {
+      "image/png": {
+       "height": 607,
+       "width": 745
+      }
+     },
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x700 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 607,
+       "width": 745
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "alex_jointplot(ds2, vmax_fret=False)"
    ]
@@ -617,7 +866,7 @@
  "metadata": {
   "hide_input": false,
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -631,7 +880,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.13"
+   "version": "3.9.19"
   },
   "toc": {
    "colors": {
@@ -655,5 +904,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
diff --git a/setup.py b/setup.py
index 42f2124..2fc8500 100644
--- a/setup.py
+++ b/setup.py
@@ -8,7 +8,7 @@
 from Cython.Build import cythonize
 ext_modules = [Extension("burstsearch_c",
                          [project_name + "/phtools/burstsearch_c.pyx"]),
-               Extension("fretbursts.phrates_c",
+               Extension("phrates_c",
                          [project_name + "/phtools/phrates_cy.pyx"],
                           include_dirs = ["."],)]
 

From 41517895bb62c2305372af35d2902b99b1dd3550 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Thu, 30 May 2024 09:04:44 +0300
Subject: [PATCH 083/142] Revert to fretbursts._c for cython packages

---
 .github/workflows/tests.yml                               | 2 +-
 fretbursts/phtools/burstsearch.py                         | 2 +-
 fretbursts/phtools/phrates.py                             | 2 +-
 notebooks/Example - Customize the us-ALEX histogram.ipynb | 8 ++++----
 setup.py                                                  | 4 ++--
 5 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index d9a880b..1ac41d5 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -53,7 +53,7 @@ jobs:
       - name: Install Dependencies
         shell: bash -l {0}
         run: |
-          conda install cython numpy nbconvert pytest jupyter scipy pandas matplotlib pytables phconvert lmfit pybroom seaborn setuptools build
+          conda install cython numpy numba nbconvert pytest jupyter scipy pandas matplotlib pytables phconvert lmfit pybroom seaborn setuptools build
       - name: Install project
         shell: bash -l {0}
         run: |
diff --git a/fretbursts/phtools/burstsearch.py b/fretbursts/phtools/burstsearch.py
index 523ca6e..8ac3c5c 100644
--- a/fretbursts/phtools/burstsearch.py
+++ b/fretbursts/phtools/burstsearch.py
@@ -191,7 +191,7 @@ def mch_count_ph_in_bursts_py(Mburst, Mask):
 #
 
 try:
-    from burstsearch_c import bsearch_c, mch_count_ph_in_bursts_c
+    from fretbursts.burstsearch_c import bsearch_c, mch_count_ph_in_bursts_c
     bsearch = bsearch_c
     mch_count_ph_in_bursts = mch_count_ph_in_bursts_c
     print(" - Optimized (cython) burst search loaded.")
diff --git a/fretbursts/phtools/phrates.py b/fretbursts/phtools/phrates.py
index e0e2779..abb943c 100644
--- a/fretbursts/phtools/phrates.py
+++ b/fretbursts/phtools/phrates.py
@@ -31,7 +31,7 @@
 import numpy as np
 
 try:
-    import phrates_c as cy
+    import fretbursts.phrates_c as cy
 except ImportError:
     has_cython = False
 else:
diff --git a/notebooks/Example - Customize the us-ALEX histogram.ipynb b/notebooks/Example - Customize the us-ALEX histogram.ipynb
index 06b6150..bed7d37 100644
--- a/notebooks/Example - Customize the us-ALEX histogram.ipynb	
+++ b/notebooks/Example - Customize the us-ALEX histogram.ipynb	
@@ -47,7 +47,7 @@
     "import matplotlib as mpl\n",
     "mpl.rcParams['font.sans-serif'].insert(0, 'Arial')\n",
     "mpl.rcParams['font.size'] = 12\n",
-    "%config InlineBackend.figure_format = 'retina'"
+    "# %config InlineBackend.figure_format = 'retina'"
    ]
   },
   {
@@ -448,7 +448,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -462,7 +462,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.13"
+   "version": "3.9.19"
   },
   "toc": {
    "colors": {
@@ -493,5 +493,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
diff --git a/setup.py b/setup.py
index 2fc8500..1c63469 100644
--- a/setup.py
+++ b/setup.py
@@ -6,9 +6,9 @@
 project_name = 'fretbursts'
 
 from Cython.Build import cythonize
-ext_modules = [Extension("burstsearch_c",
+ext_modules = [Extension("fretbursts.burstsearch_c",
                          [project_name + "/phtools/burstsearch_c.pyx"]),
-               Extension("phrates_c",
+               Extension("fretbursts.phrates_c",
                          [project_name + "/phtools/phrates_cy.pyx"],
                           include_dirs = ["."],)]
 

From 5cfacddb6e82b822c3a74c79f7d0f6573a718103 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Thu, 30 May 2024 09:32:50 +0300
Subject: [PATCH 084/142] Moved assumed location of pytest call

---
 .github/workflows/tests.yml | 1 +
 tests/test_burst_plot.py    | 2 +-
 tests/test_burstlib.py      | 2 +-
 tests/test_burstlib_ext.py  | 2 +-
 4 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 1ac41d5..6977c8d 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -94,4 +94,5 @@ jobs:
           cd notebooks
           python nbrun.py .
           cd ..
+          cd tests
           python -m pytest
diff --git a/tests/test_burst_plot.py b/tests/test_burst_plot.py
index 2044907..4e299ef 100644
--- a/tests/test_burst_plot.py
+++ b/tests/test_burst_plot.py
@@ -42,7 +42,7 @@
 
 
 # data subdir in the notebook folder
-DATASETS_DIR = u'notebooks/data/'
+DATASETS_DIR = u'../notebooks/data/'
 
 
 def _alex_process(d):
diff --git a/tests/test_burstlib.py b/tests/test_burstlib.py
index fee877a..f3e4f0e 100644
--- a/tests/test_burstlib.py
+++ b/tests/test_burstlib.py
@@ -44,7 +44,7 @@
 
 
 # data subdir in the notebook folder
-DATASETS_DIR = u'notebooks/data/'
+DATASETS_DIR = u'../notebooks/data/'
 
 
 def _alex_process(d):
diff --git a/tests/test_burstlib_ext.py b/tests/test_burstlib_ext.py
index a3e9c2c..bfb3844 100644
--- a/tests/test_burstlib_ext.py
+++ b/tests/test_burstlib_ext.py
@@ -44,7 +44,7 @@
 
 
 # data subdir in the notebook folder
-DATASETS_DIR = u'notebooks/data/'
+DATASETS_DIR = u'../notebooks/data/'
 
 
 def _alex_process(d):

From c781a90aca10084ac981c1c1408c8f6efae1b1b2 Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Thu, 30 May 2024 11:06:54 +0300
Subject: [PATCH 085/142] Update build_wheel.yml

---
 .github/workflows/build_wheel.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/build_wheel.yml b/.github/workflows/build_wheel.yml
index 237671d..18f0951 100644
--- a/.github/workflows/build_wheel.yml
+++ b/.github/workflows/build_wheel.yml
@@ -2,7 +2,7 @@ name: Build
 
 on:
   workflow_run:
-    workflows: [test]
+    workflows: [Tests]
     types:
       - completed
     branches:

From 2d9c7d1b629e9126eb226aadb22b886446dc077a Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Thu, 30 May 2024 11:28:39 +0300
Subject: [PATCH 086/142] Update build_wheel.yml

---
 .github/workflows/build_wheel.yml | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/build_wheel.yml b/.github/workflows/build_wheel.yml
index 18f0951..263f30e 100644
--- a/.github/workflows/build_wheel.yml
+++ b/.github/workflows/build_wheel.yml
@@ -26,5 +26,6 @@ jobs:
         #   CIBW_TEST_COMMAND: python -m pytest {package}/tests
       
       - uses: actions/upload-artifact@v4
-        name: cibw-wheels-${{ matrix.os }}-${{ strategy.job-index }}
-        path: ./wheelhouse/*.whl
+        with:
+          name: cibw-wheels-${{ matrix.os }}-${{ strategy.job-index }}
+          path: ./wheelhouse/*.whl

From c4dd0912a25c4765a7a7fcfe951a89ca82f30ab5 Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Thu, 30 May 2024 11:44:30 +0300
Subject: [PATCH 087/142] Deprecated python 3.6 support

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index b0b28fd..e7ce9cb 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -27,7 +27,7 @@ classifiers = [
     "Programming Language :: Python :: 3.11",
     "Topic :: Scientific/Engineering"
  ]
-requires-python = ">= 3.6"
+requires-python = ">= 3.7"
 dependencies = [
     "importlib_metadata;python_version<='3.9'",
     "numpy>=1.19", 

From 9abec1623737ad21068e03f4674c754a208b9883 Mon Sep 17 00:00:00 2001
From: harripd <harripd@gmail.com>
Date: Thu, 30 May 2024 12:58:27 +0300
Subject: [PATCH 088/142] Skip pure python cibw builds

---
 .github/workflows/build_wheel.yml | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/build_wheel.yml b/.github/workflows/build_wheel.yml
index 263f30e..53b8688 100644
--- a/.github/workflows/build_wheel.yml
+++ b/.github/workflows/build_wheel.yml
@@ -20,10 +20,11 @@ jobs:
 
       - name: Build wheels
         uses: pypa/cibuildwheel@v2.18.1
-        # env:
-        #   CIBW_TEST_REQUIRES: pytest, numpy == 1.20.1, matplotlib, scipy, pandas, tables, numba, seaborn, lmfit, phconvert
-        #   CIBW_BEFORE_TEST: python -m pip install pytest
-        #   CIBW_TEST_COMMAND: python -m pytest {package}/tests
+        env:
+          CIBW_SKIP: "pp*"
+          # CIBW_TEST_REQUIRES: pytest, numpy == 1.20.1, matplotlib, scipy, pandas, tables, numba, seaborn, lmfit, phconvert
+          # CIBW_BEFORE_TEST: python -m pip install pytest
+          # CIBW_TEST_COMMAND: python -m pytest {package}/tests
       
       - uses: actions/upload-artifact@v4
         with:

From f373b04a9e111ab81b12e664882b9566ea07a57f Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Thu, 30 May 2024 20:47:55 +0300
Subject: [PATCH 089/142] Require matplotlib tests

---
 .github/workflows/build_wheel.yml |  5 ++---
 .github/workflows/tests.yml       | 17 ++---------------
 pyproject.toml                    |  2 +-
 tests/test_burst_plot.py          | 25 ++++++++++++++-----------
 4 files changed, 19 insertions(+), 30 deletions(-)

diff --git a/.github/workflows/build_wheel.yml b/.github/workflows/build_wheel.yml
index 53b8688..204fd75 100644
--- a/.github/workflows/build_wheel.yml
+++ b/.github/workflows/build_wheel.yml
@@ -1,10 +1,9 @@
 name: Build
 
 on:
-  workflow_run:
-    workflows: [Tests]
+  label:
     types:
-      - completed
+      - created
     branches:
       - master
 
diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 6977c8d..ef817c6 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -1,23 +1,13 @@
 name: Tests
 
-on: 
-  push:
-    branches:
-      - plots
-      - master
-      - gitactions
-  pull_request:
-    branches:
-      - plots
-      - master
+on: [push, pull_request]
 jobs:
   build:
-
     runs-on: ${{matrix.os}}
     strategy:
       matrix:
         os: [ubuntu-22.04, windows-latest, macos-latest]
-        python-version: ["3.7", "3.9", "3.11"]
+        python-version: ["3.7", "3.8", "3.12"]
         exclude:
           - os: macOS-latest
             python-version: "3.7"
@@ -41,9 +31,6 @@ jobs:
       - name: Upgrade pip
         shell: bash -l {0}
         run: python -m pip install --upgrade pip
-      # - name: Windows 3.6 Oddities
-      #   if: matrix.python-version == 3.6 && runner.os == 'Windows'
-      #   run: python -m pip install pywinpty==1.1.6
       - name: MacOS install hdf5 dependencies
         if: runner.os == 'macOS'
         run: |
diff --git a/pyproject.toml b/pyproject.toml
index e7ce9cb..0997873 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -47,7 +47,7 @@ Issues = "https://github.com/OpenSMFS/FRETBursts/"
 Repository = "https://github.com/OpenSMFS/FRETBursts/issues"
 
 [project.optional-dependencies]
-plot = ["matplotlib>=3.0.1"]
+scientific = ["jupyter", "matplotlib>=3.0.1"]
 gui = ["matplotlib>=3.0.1", "PyQt5"]
 
 [tool.setuptools.packages.find]
diff --git a/tests/test_burst_plot.py b/tests/test_burst_plot.py
index 4e299ef..17bd746 100644
--- a/tests/test_burst_plot.py
+++ b/tests/test_burst_plot.py
@@ -13,14 +13,9 @@
 import numpy as np
 
 
-try:
-    import matplotlib
-except ImportError:
-    has_matplotlib = False  # OK to run tests without matplotlib
-else:
-    has_matplotlib = True
-    matplotlib.use('Agg')  # but if matplotlib is installed, use Agg
-
+import matplotlib
+matplotlib.use('Agg')  # but if matplotlib is installed, use Agg
+import matplotlib.pyplot as plt
 # try:
 #     import numba
 # except ImportError:
@@ -37,8 +32,7 @@
 from fretbursts import select_bursts
 from fretbursts.ph_sel import Ph_sel
 from fretbursts.phtools import phrates
-if has_matplotlib:
-    import fretbursts.burst_plot as bplt
+import fretbursts.burst_plot as bplt
 
 
 # data subdir in the notebook folder
@@ -149,14 +143,17 @@ def data_alex(request):
 def test_mch_plot_bg(data_mch):
     d = data_mch
     bplt.mch_plot_bg(d)
+    plt.close()
 
 def test_mch_plot_bg_ratio(data_mch):
     d = data_mch
     bplt.mch_plot_bg_ratio(d)
+    plt.close()
 
 def test_mch_plot_bsize(data_mch):
     d = data_mch
     bplt.mch_plot_bsize(d)
+    plt.close()
 
 ##
 #  Timetrace plots
@@ -175,6 +172,7 @@ def test_trace_single(data, ratetraces):
     for i in range(d.nch):
         for ph_sel in ph_sel_list:
             bplt.dplot(d, ratetraces, i=i, ph_sel=ph_sel)
+    plt.close()
 
 @pytest.fixture(scope='module', params = (bplt.timetrace, bplt.ratetrace, 
                                           bplt.timetrace_bg, bplt.timetrace_fret,
@@ -186,7 +184,8 @@ def test_trace(data, timetraces):
     """Test general time trace type functions"""
     d = data
     for i in range(d.nch):
-            bplt.dplot(d, timetraces, i=i)
+        bplt.dplot(d, timetraces, i=i)
+    plt.close()
 
 
 @pytest.fixture(scope='module', params = (bplt.hist_size, bplt.hist_width,
@@ -204,12 +203,14 @@ def test_hist(data, hists):
     bplt.dplot(d, hists, i=None)
     for i in range(d.nch):
         bplt.dplot(d, hists, i=i)
+    plt.close()
 
 def test_hist_S(data_alex):
     d = data_alex
     bplt.dplot(d, bplt.hist_S, i=None)
     for i in range(d.nch):
         bplt.dplot(d, bplt.hist_S)
+    plt.close()
 
 @pytest.fixture(scope='module', params = (bplt.hist2d_alex, bplt.hexbin_alex,
                                           bplt.scatter_alex, bplt.scatter_naa_nt))
@@ -223,6 +224,7 @@ def test_ES_plots(data_alex, ES_plots):
         bplt.dplot(d, ES_plots, i=0, color_style='kde')
     for i in range(d.nch):
         bplt.dplot(d, ES_plots, i=i)
+    plt.close()
 
 @pytest.fixture(scope='module', params = (bplt.scatter_width_size, bplt.scatter_rate_da,
                                           bplt.scatter_fret_size, bplt.scatter_fret_nd_na,
@@ -235,3 +237,4 @@ def test_scatterplots(data, scatterplots):
     bplt.dplot(d, scatterplots, i=None)
     for i in range(d.nch):
         bplt.dplot(d, scatterplots, i=i)
+    plt.close()

From de4d5b2c565b6912fb2cf50212a5417e7c8677f0 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Thu, 30 May 2024 20:49:33 +0300
Subject: [PATCH 090/142] Install pyqt in tests

---
 .github/workflows/tests.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index ef817c6..87a1d5b 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -40,7 +40,7 @@ jobs:
       - name: Install Dependencies
         shell: bash -l {0}
         run: |
-          conda install cython numpy numba nbconvert pytest jupyter scipy pandas matplotlib pytables phconvert lmfit pybroom seaborn setuptools build
+          conda install cython numpy numba nbconvert pytest jupyter scipy pandas matplotlib pytables phconvert lmfit pybroom seaborn setuptools build pyqt
       - name: Install project
         shell: bash -l {0}
         run: |

From 57f5935226d54b0b3b8cdd1e23f1f05707a403f4 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Fri, 31 May 2024 11:55:20 +0300
Subject: [PATCH 091/142] Updated documentation with env yaml files

---
 docs/source/absolute_beginner.rst  | 18 +++++++++++--
 docs/source/downloads/frbcmplt.yml | 23 ++++++++++++++++
 docs/source/downloads/frbmin.yml   | 19 ++++++++++++++
 docs/source/installation.rst       | 42 +++++++++++++++++++++++++-----
 4 files changed, 93 insertions(+), 9 deletions(-)
 create mode 100644 docs/source/downloads/frbcmplt.yml
 create mode 100644 docs/source/downloads/frbmin.yml

diff --git a/docs/source/absolute_beginner.rst b/docs/source/absolute_beginner.rst
index 4844d2e..b379d39 100644
--- a/docs/source/absolute_beginner.rst
+++ b/docs/source/absolute_beginner.rst
@@ -6,8 +6,9 @@ Getting started for the absolute python beginner
 Before running FRETBursts you need to install a python distribution that
 includes the Jupyter/IPython Notebook application.
 
-You can find a quick guide for installing the software and running your first
-notebook here:
+We recomend using Anaconda, which you can find instructions for downloading and installing here: `Anaconda installation instructions <https://docs.anaconda.com/free/anaconda/install/index.html>`_.
+
+You can find a guide for jupyter notebooks here:
 
 - |jupyter_quick_guide|
 
@@ -31,6 +32,19 @@ The installation should take a few seconds.
 If you notice any error please report it by opening a new issue on the
 `FRETBursts GitHub Issues <https://github.com/OpenSMFS/FRETBursts/issues>`_.
 
+Alternatively create an environment from one of our yaml files where we have verified compatibility of all versions of the software: :downlaod:`frbmin.yml <downloads/frbmin.yml>`
+
+First download `frbmin.yml`
+
+Then run the following in your terminal::
+
+    conda env create -f frbmin.yml
+    conda activate frbmin
+
+.. note::
+    You may need to replace frbmin.yml with the path to the file you downloaded.
+    :ref:`instalation` provides other yaml files for more complete environments.
+
 Running FRETBursts tutorial notebook
 ------------------------------------
 
diff --git a/docs/source/downloads/frbcmplt.yml b/docs/source/downloads/frbcmplt.yml
new file mode 100644
index 0000000..447709a
--- /dev/null
+++ b/docs/source/downloads/frbcmplt.yml
@@ -0,0 +1,23 @@
+name: frbcmplt
+channels:
+  - conda-forge
+  - defaults
+dependencies:
+  - python=3.10
+  - importlib_metadata
+  - pytest=7.4.0
+  - cython=3.0.10
+  - ipython=8.24.0
+  - jupyter=1.0.0
+  - numpy=1.26.4
+  - numba=0.59.1
+  - pytables=3.9.2
+  - matplotlib=3.8.4
+  - pandas=2.2.2
+  - scipy=1.13.1
+  - seaborn=0.13.1
+  - pyqt=5.15.9
+  - lmfit=1.2.2
+  - phconvert=0.9.1
+  - pybroom=0.2
+
diff --git a/docs/source/downloads/frbmin.yml b/docs/source/downloads/frbmin.yml
new file mode 100644
index 0000000..53452bf
--- /dev/null
+++ b/docs/source/downloads/frbmin.yml
@@ -0,0 +1,19 @@
+name: frbmin
+channels:
+  - conda-forge
+  - defaults
+dependencies:
+  - python=3.10
+  - importlib_metadata
+  - ipython=8.24.0
+  - jupyter=1.0.0
+  - numpy=1.26.4
+  - pytables=3.9.2
+  - matplotlib=3.8.4
+  - pandas=2.2.2
+  - scipy=1.13.1
+  - seaborn=0.13.1
+  - pyqt=5.15.9
+  - lmfit=1.2.2
+  - phconvert=0.9.1
+
diff --git a/docs/source/installation.rst b/docs/source/installation.rst
index a47769a..4355c55 100644
--- a/docs/source/installation.rst
+++ b/docs/source/installation.rst
@@ -17,9 +17,8 @@ Installing latest stable version
 
 The preferred way to to install and keep FRETBursts updated is through
 `conda`, a package manager used by Anaconda scientific python distribution.
-If you haven't done it already, please install the python3 version of
-`Continuum Anaconda distribution <https://www.continuum.io/downloads>`__
-(legacy python 2.7 works at the moment but it will be discontinued soon).
+If you haven't done it already, please install the python3. We recommend using `Anaconda <https://docs.anaconda.com/free/anaconda/install/>`_.
+
 Then, you can install or upgrade FRETBursts with::
 
     conda install fretbursts -c conda-forge
@@ -33,6 +32,35 @@ and how to launch it please see:
 
 See also the FRETBursts documentation section: :ref:`running_fretbursts`.
 
+Install from yaml file
+----------------------
+
+With anaconda, you can manage different environments, allowing specific versions to be installed ensuring compatibility.
+Which packages you need will depend on your use case.
+
+Environments can be build from yaml files with::
+
+    conda env create -f <envfile.yml>
+
+And activate with::
+
+    conda activate <envname>
+
+Below are environment files that we have *_**verified to work**
+#. Minimal environment: :download:`frbmin.yml<downloads/frbmin.yml>` which will create an environment named `frbmin` which contains just the essential packages for running the notebooks
+#. Complete environment :download:`frbcmpt.yml<downloads/frbcmplt.yml>` which will create an environment named `frbcmplt` which also includes cython, testing packages and numba, which are not necesary for running FRETBursts, but can come in helpful in other circumstances
+
+For packages build off of FRETBursts, check their respective documentation for similar yaml files.
+
+To create an environemnt from a downloaded yml file (like those above) run the command in your terminal::
+
+    conda env create <filenam.yml>
+
+Then simply activate the environment with::
+
+    conda activate <envname>
+
+
 Alternative methods: using PIP
 ------------------------------
 
@@ -57,13 +85,13 @@ containing the fretbursts (do it only once after installing Anaconda)::
 
     conda config --append channels conda-forge
 
-Then create a new conda environment with python 3.7 and FRETbursts::
+Then create a new conda environment with python 3.10 and FRETbursts::
 
-    conda create -n py37-fb python=3.7 fretbursts
-    conda activate py37-fb
+    conda create -n py310-fb python=3.10 fretbursts
+    conda activate py310-fb
     conda install pyqt   # optional
     pip install pybroom  # optional
-    python -m ipykernel install --user --name py37-fb --display-name "Python 3.7 (FB)"
+    python -m ipykernel install --user --name py310-fb --display-name "Python 3.10 (FB)"
 
 The last command installs the
 `jupyter kernel <https://ipython.readthedocs.io/en/latest/install/kernel_install.html>`__

From 312b6eef2179c8e76f3145085d65340ee8d9a05b Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Fri, 31 May 2024 13:11:48 +0300
Subject: [PATCH 092/142] Use importlib for docs version

---
 docs/source/conf.py | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/docs/source/conf.py b/docs/source/conf.py
index ba61559..60af79e 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -54,10 +54,9 @@
 # documentation root, use os.path.abspath to make it absolute, like shown here.
 sys.path.insert(0, os.path.abspath('../..'))
 import fretbursts
-
-from fretbursts._version import version as __version__
-version = ".".join(__version__.split('.')[:2])
-release = version
+from importlib.metadata import version as get_version
+release: str = get_version("fretbursts")
+version: str = ".".join(release).split('.')[:2])
 
 import sphinx_bootstrap_theme
 html_theme = 'bootstrap'

From 392b539c540200a3f0d9d169697a5f87d2e74b8a Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Fri, 31 May 2024 13:44:09 +0300
Subject: [PATCH 093/142] Update release notes for version release

---
 docs/source/releasenotes.rst | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/docs/source/releasenotes.rst b/docs/source/releasenotes.rst
index e6c3002..90779f8 100644
--- a/docs/source/releasenotes.rst
+++ b/docs/source/releasenotes.rst
@@ -1,6 +1,15 @@
 FRETBursts Release Notes
 ========================
 
+Version 0.8. (Jun. 2024)
+------------------------
+
+- Removed support for Python 3.6, as Python 3.7 now in end of life and 3.6 not supported
+- Switch to using setuptools_scm for version managemnt instead of versioneer
+- Updates for newer numpy compatibility (deprecation of np.float)
+- Introduce :func:`burst_plot.scatter_burst_data` function for scatter plotting (currently now used in :func:`scatter_naa_nt` and :func:`scatter_alex`) to normalize scatter ploting.
+- use of :func:`burst_plot.scatter_burst_data` enables KDE density estimation with keyword argument `color_style='kde'` 
+
 Version 0.7.1
 -------------
 

From 43c6c8507972f502ad550f4d065a25c87db0620c Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Fri, 31 May 2024 17:16:35 +0300
Subject: [PATCH 094/142] Update release notes

---
 docs/source/releasenotes.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/releasenotes.rst b/docs/source/releasenotes.rst
index 90779f8..5573286 100644
--- a/docs/source/releasenotes.rst
+++ b/docs/source/releasenotes.rst
@@ -1,7 +1,7 @@
 FRETBursts Release Notes
 ========================
 
-Version 0.8. (Jun. 2024)
+Version 0.8.0 (Jun. 2024)
 ------------------------
 
 - Removed support for Python 3.6, as Python 3.7 now in end of life and 3.6 not supported

From b6e1c4755b3206e1783faa1231f7632c654842b0 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Fri, 31 May 2024 17:33:12 +0300
Subject: [PATCH 095/142] Update build_wheels.yml

---
 .github/workflows/build_wheel.yml | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/build_wheel.yml b/.github/workflows/build_wheel.yml
index 204fd75..ccfbca5 100644
--- a/.github/workflows/build_wheel.yml
+++ b/.github/workflows/build_wheel.yml
@@ -2,8 +2,10 @@ name: Build
 
 on:
   label:
-    types:
-      - created
+    push:
+      branches:
+        - created
+  pull_request:
     branches:
       - master
 

From 8decc1f3d599135cda99d2cf0e38faadbce44c66 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Fri, 31 May 2024 17:36:40 +0300
Subject: [PATCH 096/142] Update build_wheels.yml

---
 .github/workflows/build_wheel.yml | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/build_wheel.yml b/.github/workflows/build_wheel.yml
index ccfbca5..41d6e1b 100644
--- a/.github/workflows/build_wheel.yml
+++ b/.github/workflows/build_wheel.yml
@@ -1,10 +1,9 @@
 name: Build
 
 on:
-  label:
-    push:
-      branches:
-        - created
+  push:
+    branches:
+      - created
   pull_request:
     branches:
       - master

From 555cdca34d7357cd1de2a9d2b90e01d60119b82d Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Fri, 31 May 2024 17:39:08 +0300
Subject: [PATCH 097/142] Update build_wheels.yml

---
 .github/workflows/build_wheel.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/build_wheel.yml b/.github/workflows/build_wheel.yml
index 41d6e1b..ae7c176 100644
--- a/.github/workflows/build_wheel.yml
+++ b/.github/workflows/build_wheel.yml
@@ -3,7 +3,7 @@ name: Build
 on:
   push:
     branches:
-      - created
+      - master
   pull_request:
     branches:
       - master

From ddd9b4832049b176da4e9bbcc614afdd74004c50 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Mon, 12 Aug 2024 13:48:07 +0300
Subject: [PATCH 098/142] Allow reading ph_times_m from tables array in
 _compute_acceptor_emission_mask

---
 docs/source/downloads/frbcmplt.yml | 2 ++
 fretbursts/loader.py               | 2 +-
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/docs/source/downloads/frbcmplt.yml b/docs/source/downloads/frbcmplt.yml
index 447709a..6442eff 100644
--- a/docs/source/downloads/frbcmplt.yml
+++ b/docs/source/downloads/frbcmplt.yml
@@ -5,6 +5,8 @@ channels:
 dependencies:
   - python=3.10
   - importlib_metadata
+  - setuptools
+  - build
   - pytest=7.4.0
   - cython=3.0.10
   - ipython=8.24.0
diff --git a/fretbursts/loader.py b/fretbursts/loader.py
index bfacfc7..a037515 100644
--- a/fretbursts/loader.py
+++ b/fretbursts/loader.py
@@ -230,7 +230,7 @@ def _compute_acceptor_emission_mask(data, ich, ondisk):
         mask = (selection_mask(det_ich, donor) +
                 selection_mask(det_ich, accept))
         data.detectors[ich] = det_ich[mask]
-        data.ph_times_m[ich] = det_ich[mask]
+        data.ph_times_m[ich] = data.ph_times_m[ich][:][mask]
         if 'nanotimes' in data:
             data.nanotimes[ich] = data.nanotimes[ich][:][mask]
 

From bc98c1474468deb5d490c08e9817122a4ddaa320 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Wed, 21 Aug 2024 16:32:44 +0300
Subject: [PATCH 099/142] Replace np.asfarray with np.asarray for numpy 2.0
 compatibility

---
 fretbursts/bg_cache.py               |  2 +-
 fretbursts/burstlib.py               |  8 ++++----
 fretbursts/burstlib_ext.py           |  2 +-
 fretbursts/exptools.py               |  4 ++--
 fretbursts/loader.py                 | 14 +++++++++-----
 fretbursts/phtools/burstsearch.py    |  2 +-
 fretbursts/phtools/burstsearch_c.pyx |  2 +-
 7 files changed, 19 insertions(+), 15 deletions(-)

diff --git a/fretbursts/bg_cache.py b/fretbursts/bg_cache.py
index bc6bc45..544704b 100644
--- a/fretbursts/bg_cache.py
+++ b/fretbursts/bg_cache.py
@@ -197,7 +197,7 @@ def _load_bg_data(d, bg_calc_kwargs, h5file):
     for node in bg_group._f_iter_nodes():
         if node._v_name.startswith('BG_'):
             ph_sel = Ph_sel.from_str(node._v_name[len('BG_'):])
-            bg[ph_sel] = [np.asfarray(b) for b in node.read()]
+            bg[ph_sel] = [np.asarray(b, dtype=np.float64) for b in node.read()]
 
     Lim = bg_group.Lim.read()
     Ph_p = bg_group.Ph_p.read()
diff --git a/fretbursts/burstlib.py b/fretbursts/burstlib.py
index 095ee05..5db8350 100644
--- a/fretbursts/burstlib.py
+++ b/fretbursts/burstlib.py
@@ -222,7 +222,7 @@ def burst_ph_stats(ph_data, bursts, func=np.mean, func_kw=None, **kwargs):
     burst_stats = []
     for burst_ph in iter_bursts_ph(ph_data, bursts, **kwargs):
         burst_stats.append(func(burst_ph, **func_kw))
-    return np.asfarray(burst_stats)  # NOTE: asfarray converts None to nan
+    return np.asarray(burst_stats, dtype=np.float64)  # NOTE: asarray converts None to nan
 
 
 def ph_in_bursts_mask(ph_data_size, bursts):
@@ -2629,7 +2629,7 @@ def _update_leakage(self, leakage):
         """Apply/update leakage (or bleed-through) correction.
         """
         assert (np.size(leakage) == 1) or (np.size(leakage) == self.nch)
-        self.add(_leakage=np.asfarray(leakage), leakage_corrected=True)
+        self.add(_leakage=np.asarray(leakage, dtype=np.float64), leakage_corrected=True)
         self._update_corrections()
 
     @property
@@ -2679,7 +2679,7 @@ def _update_chi_ch(self, chi_ch):
         """Change the `chi_ch` value and recompute E and S."""
         msg = 'chi_ch is a per-channel correction and must have size == nch.'
         assert np.size(chi_ch) == self.nch, ValueError(msg)
-        self.add(_chi_ch=np.asfarray(chi_ch))
+        self.add(_chi_ch=np.asarray(chi_ch, dtype=np.float64))
         if 'mburst' in self:
             # Recompute E and S and delete fitter objects
             self.calc_fret(corrections=False, pax=self.pax)
@@ -2697,7 +2697,7 @@ def gamma(self, value):
     def _update_gamma(self, gamma):
         """Change the `gamma` value and recompute E and S."""
         assert (np.size(gamma) == 1) or (np.size(gamma) == self.nch)
-        self.add(_gamma=np.asfarray(gamma))
+        self.add(_gamma=np.asarray(gamma, dtype=np.float64))
         if 'mburst' in self:
             # Recompute E and S and delete fitter objects
             self.calc_fret(corrections=False, pax=self.pax)
diff --git a/fretbursts/burstlib_ext.py b/fretbursts/burstlib_ext.py
index 6838438..85dd8b5 100644
--- a/fretbursts/burstlib_ext.py
+++ b/fretbursts/burstlib_ext.py
@@ -248,7 +248,7 @@ def calc_bg_brute_cache(dx, min_ph_delay_list=None, return_all=False,
     if min_ph_delay_list is None:
         min_ph_delay_list = np.arange(100, 8500, 100)
     else:
-        min_ph_delay_list = np.asfarray(min_ph_delay_list)
+        min_ph_delay_list = np.asarray(min_ph_delay_list, dtype=np.float64)
 
     base_name = 'bg_%s_%ds_%s/' % (dx.bg_fun_name, dx.bg_time_s, error_metrics)
     store_fname = dx.fname[:-5] + '_BKG.hdf5'
diff --git a/fretbursts/exptools.py b/fretbursts/exptools.py
index bd39927..556b7f4 100644
--- a/fretbursts/exptools.py
+++ b/fretbursts/exptools.py
@@ -45,10 +45,10 @@
 
 
 def weighted_median(data, weights=None):
-    data = np.asfarray(data)
+    data = np.asarray(data, dtype=np.float64)
     if weights is None:
         return np.median(data)
-    weights = np.asfarray(weights)
+    weights = np.asarray(weights, dtype=np.float64)
     data_argsort = data.argsort()
     sorted_data = data[data_argsort]
     sorted_weights = weights[data_argsort]
diff --git a/fretbursts/loader.py b/fretbursts/loader.py
index a037515..606b308 100644
--- a/fretbursts/loader.py
+++ b/fretbursts/loader.py
@@ -246,6 +246,8 @@ def _compute_acceptor_emission_mask(data, ich, ondisk):
         _append_data_ch(data, 'A_em',
                         selection_mask(det_ich, accept))
 
+import re
+mask_rgx = re.compile(r'markers([1-0]\d*)')
 
 def _photon_hdf5_1ch(h5data, data, ondisk=False, nch=1, ich=0, loadspecs=True):
     data.add(nch=nch)
@@ -259,12 +261,13 @@ def _photon_hdf5_1ch(h5data, data, ondisk=False, nch=1, ich=0, loadspecs=True):
     # Load photon_data group and measurement_specs (if present)
     ph_data = h5data._f_get_child(ph_data_name)
     meas_type, meas_specs = _get_measurement_specs(ph_data, h5data.setup)
+    det_specs = dict() if meas_specs is None else meas_specs.detectors_specs
     # Set some `data` flags
     data.add(meas_type=meas_type)
     data.add(ALEX='ALEX' in meas_type)  # True for usALEX, nsALEX and PAX
-    data.add(alternated=data.ALEX or 'PAX' in data.meas_type)
+    data.add(alternated='alex_excitation_period1' in ph_data)
     data.add(lifetime='nanotimes' in ph_data)
-    data.add(polarization='2pol' in meas_type)
+    data.add(polarization='polarization_ch1' in det_specs and 'polarization_ch2' in det_specs)
     data.add(spectral='smFRET-1color' not in meas_type)
 
     # Load photon_data arrays
@@ -275,7 +278,6 @@ def _photon_hdf5_1ch(h5data, data, ondisk=False, nch=1, ich=0, loadspecs=True):
         _load_nanotimes_specs(data, ph_data)
 
     # Unless 1-color, load donor and acceptor info
-    det_specs = meas_specs.detectors_specs
     if data.spectral:
         try:
             donor = np.atleast_1d(det_specs.spectral_ch1.read())
@@ -297,6 +299,8 @@ def _photon_hdf5_1ch(h5data, data, ondisk=False, nch=1, ich=0, loadspecs=True):
     if data.spectral and not data.alternated:
         # No alternation, we can compute the emission masks right away
         _compute_acceptor_emission_mask(data, ich, ondisk=ondisk)
+    
+    
 
     if loadspecs and data.spectral and data.alternated and not data.lifetime:
         # load alternation metadata for usALEX or PAX
@@ -346,12 +350,12 @@ def photon_hdf5(filename, ondisk=False, require_setup=True, validate=False, fix_
         d = group_data([photon_hdf5(f) for f in filename])
         return d
     filename = str(filename)
-    assert os.path.isfile(filename), 'File not found.'
+    assert os.path.isfile(filename), FileNotFoundError(f'{filename} does not exist')
     version = phc.hdf5._check_version(filename)
     if version == u'0.2':
         return loader_legacy.hdf5(filename)
 
-    h5file = tables.open_file(filename)
+    h5file = tables.open_file(filename, 'r')
     try:
         # make sure the file is valid
         if validate and version.startswith(u'0.4'):
diff --git a/fretbursts/phtools/burstsearch.py b/fretbursts/phtools/burstsearch.py
index 8ac3c5c..05d875e 100644
--- a/fretbursts/phtools/burstsearch.py
+++ b/fretbursts/phtools/burstsearch.py
@@ -182,7 +182,7 @@ def mch_count_ph_in_bursts_py(Mburst, Mask):
         A list of 1D array, each containing the number of photons
         in each burst counting only photons in the selection mask.
     """
-    return [np.asfarray(count_ph_in_bursts(bursts, mask))
+    return [np.asarray(count_ph_in_bursts(bursts, mask), dtype=np.float64)
             for bursts, mask in zip(Mburst, Mask)]
 
 
diff --git a/fretbursts/phtools/burstsearch_c.pyx b/fretbursts/phtools/burstsearch_c.pyx
index d9f9a12..3dc813b 100644
--- a/fretbursts/phtools/burstsearch_c.pyx
+++ b/fretbursts/phtools/burstsearch_c.pyx
@@ -129,5 +129,5 @@ def mch_count_ph_in_bursts_c(mburst, masks):
     num_ph_list = []
     for bursts, mask in zip(mburst, masks):
         num_ph_list.append(
-            np.asfarray(count_ph_in_bursts_c(bursts.data, mask.view(np.uint8))))
+            np.asarray(count_ph_in_bursts_c(bursts.data, mask.view(np.uint8)), dtype=np.float64))
     return num_ph_list

From 28e0e4d42696168cb641f81b1ac4abd54d7a6501 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Wed, 4 Dec 2024 15:42:46 +0200
Subject: [PATCH 100/142] Changed markers to non-photon

---
 fretbursts/loader.py                              | 6 +++---
 notebooks/Example - Burst Variance Analysis.ipynb | 6 +++---
 pyproject.toml                                    | 5 +++--
 3 files changed, 9 insertions(+), 8 deletions(-)

diff --git a/fretbursts/loader.py b/fretbursts/loader.py
index 606b308..ab8b255 100644
--- a/fretbursts/loader.py
+++ b/fretbursts/loader.py
@@ -30,6 +30,8 @@
 import logging
 log = logging.getLogger(__name__)
 
+import re
+mask_rgx = re.compile(r'non_photon_id([1-9]\d*)')
 
 def _is_multich(h5data):
     if 'photon_data' in h5data:
@@ -246,8 +248,6 @@ def _compute_acceptor_emission_mask(data, ich, ondisk):
         _append_data_ch(data, 'A_em',
                         selection_mask(det_ich, accept))
 
-import re
-mask_rgx = re.compile(r'markers([1-0]\d*)')
 
 def _photon_hdf5_1ch(h5data, data, ondisk=False, nch=1, ich=0, loadspecs=True):
     data.add(nch=nch)
@@ -265,7 +265,7 @@ def _photon_hdf5_1ch(h5data, data, ondisk=False, nch=1, ich=0, loadspecs=True):
     # Set some `data` flags
     data.add(meas_type=meas_type)
     data.add(ALEX='ALEX' in meas_type)  # True for usALEX, nsALEX and PAX
-    data.add(alternated='alex_excitation_period1' in ph_data)
+    data.add(alternated='alex_excitation_period1' in meas_specs)
     data.add(lifetime='nanotimes' in ph_data)
     data.add(polarization='polarization_ch1' in det_specs and 'polarization_ch2' in det_specs)
     data.add(spectral='smFRET-1color' not in meas_type)
diff --git a/notebooks/Example - Burst Variance Analysis.ipynb b/notebooks/Example - Burst Variance Analysis.ipynb
index c4aff3c..edb71b9 100644
--- a/notebooks/Example - Burst Variance Analysis.ipynb	
+++ b/notebooks/Example - Burst Variance Analysis.ipynb	
@@ -266,7 +266,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -280,7 +280,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.13"
+   "version": "3.10.14"
   },
   "toc": {
    "colors": {
@@ -304,5 +304,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
diff --git a/pyproject.toml b/pyproject.toml
index 6193bf0..ba83b74 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -2,8 +2,9 @@
 requires = [
     "setuptools>=64", 
     "setuptools_scm>=6.0",
-    "cython>=0.29", 
-    "oldest-supported-numpy",
+    "cython>=3.0",
+    "pybind11>=2.12", 
+    "numpy>=1.19",
 ]
 build-backend = "setuptools.build_meta"
 

From 62e74e51a6028b7e773af945dab9ab7430610ef1 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Wed, 18 Feb 2026 15:23:39 +0100
Subject: [PATCH 101/142] Updated tests to download files using pooch

---
 .github/workflows/build_wheel.yml |   4 +-
 .github/workflows/tests.yml       |  47 +----------
 fretbursts/burstlib.py            |  24 +++---
 tests/conftest.py                 | 128 ++++++++++++++++++++++++++++++
 tests/test_burst_plot.py          | 100 -----------------------
 tests/test_burstlib.py            |  94 +++-------------------
 tests/test_burstlib_ext.py        | 116 ---------------------------
 7 files changed, 156 insertions(+), 357 deletions(-)
 create mode 100644 tests/conftest.py

diff --git a/.github/workflows/build_wheel.yml b/.github/workflows/build_wheel.yml
index 5c25324..dc7bbd5 100644
--- a/.github/workflows/build_wheel.yml
+++ b/.github/workflows/build_wheel.yml
@@ -14,9 +14,9 @@ jobs:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        os: [ubuntu-22.04, windows-2022, macos-13, macos-14]
+        os: [ubuntu-latest, ubuntu-24.04-arm, windows-latest, windows-11-arm, macos-15-intel, macos-latest]
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
         with:
           persist-credentials: false
           fetch-depth: 0
diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 231b3e3..2a63f0e 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -6,22 +6,13 @@ jobs:
     runs-on: ${{matrix.os}}
     strategy:
       matrix:
-        os: [ubuntu-22.04, windows-latest, macos-latest]
-        python-version: ["3.7", "3.8", "3.12"]
-        exclude:
-          - os: macOS-latest
-            python-version: "3.7"
-          - os: windows-latest
-            python-version: "3.7"           
+        os: [ubuntu-latest, windows-latest, macos-latest]
+        python-version: ["3.9", "3.11", "3.13"]
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
         with:
           persist-credentials: false
           fetch-depth: 0
-      # - name: Setup Python ${{matrix.python-version}}
-      #   uses: actions/setup-python@v4
-      #   with:
-      #     python-version: ${{matrix.python-version}}
       - uses: conda-incubator/setup-miniconda@v3
         with:
           auto-update-conda: true
@@ -40,41 +31,11 @@ jobs:
       - name: Install Dependencies
         shell: bash -l {0}
         run: |
-          conda install -c conda-forge cython numpy numba nbconvert pytest jupyter scipy pandas matplotlib pytables phconvert lmfit pybroom seaborn setuptools pyqt
+          conda install -c conda-forge cython numpy numba nbconvert pytest jupyter scipy pandas matplotlib pytables phconvert lmfit pybroom seaborn setuptools pyqt pooch
       - name: Install project
         shell: bash -l {0}
         run: |
           python -m pip install . 
-      - name: Download files Unix
-        if: runner.os != 'Windows'
-        shell: bash -l {0} 
-        run: |
-          cd notebooks
-          mkdir data
-          cd data
-          wget -N http://files.figshare.com/2182604/12d_New_30p_320mW_steer_3.hdf5
-          wget -N http://files.figshare.com/2182601/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5
-          wget -N https://zenodo.org/record/5902313/files/HP3_TE150_SPC630.hdf5
-          wget -N https://zenodo.org/record/5902313/files/HP3_TE200_SPC630.hdf5
-          wget -N https://zenodo.org/record/5902313/files/HP3_TE250_SPC630.hdf5
-          wget -N https://zenodo.org/record/5902313/files/HP3_TE300_SPC630.hdf5
-          cd ../..
-      - name: Downlaod files Windows
-        if: runner.os == 'Windows'
-        shell: bash -l {0}
-        run: |
-          cd notebooks
-          mkdir data
-          cd data
-          curl.exe -L --output 12d_New_30p_320mW_steer_3.hdf5 --url http://files.figshare.com/2182604/12d_New_30p_320mW_steer_3.hdf5
-          curl.exe -L --output 0023uLRpitc_NTP_20dT_0.5GndCl.hdf5 --url http://files.figshare.com/2182601/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5
-          curl.exe -L --output HP3_TE150_SPC630.hdf5 --url https://zenodo.org/record/5902313/files/HP3_TE150_SPC630.hdf5
-          curl.exe -L --output HP3_TE200_SPC630.hdf5 --url https://zenodo.org/record/5902313/files/HP3_TE200_SPC630.hdf5
-          curl.exe -L --output HP3_TE250_SPC630.hdf5 --url https://zenodo.org/record/5902313/files/HP3_TE250_SPC630.hdf5
-          curl.exe -L --output HP3_TE300_SPC630.hdf5 --url https://zenodo.org/record/5902313/files/HP3_TE300_SPC630.hdf5
-          cd ..
-          cd ..
-
       - name: Test project
         shell: bash -l {0}
         run: |
diff --git a/fretbursts/burstlib.py b/fretbursts/burstlib.py
index 5db8350..61582cb 100644
--- a/fretbursts/burstlib.py
+++ b/fretbursts/burstlib.py
@@ -483,15 +483,22 @@ class DataContainer(dict):
     """
     def __init__(self, **kwargs):
         dict.__init__(self, **kwargs)
-        for k in self:
-            dict.__setattr__(self, k, self[k])
-
+    
+    def __getattr__(self, attr):
+        if attr not in self:
+            raise AttributeError(f"Attribute {attr} has not been set in {type(self).__name__}")
+        return self[attr]
+
+    def __setattr__(self, attr, value):
+        if hasattr(self, attr) and attr not in self:
+            super().__setattr__(attr, value)
+        else:
+            self[attr] = value
+        
     def add(self, **kwargs):
         """Adds or updates elements (attributes and/or dict entries). """
         self.update(**kwargs)
-        for k, v in kwargs.items():
-            setattr(self, k, v)
-
+    
     def delete(self, *args, **kwargs):
         """Delete an element (attribute and/or dict entry). """
         warning = kwargs.get('warning', True)
@@ -501,11 +508,6 @@ def delete(self, *args, **kwargs):
             except KeyError:
                 if warning:
                     print(' WARNING: Name %s not found (dict).' % name)
-            try:
-                delattr(self, name)
-            except AttributeError:
-                if warning:
-                    print(' WARNING: Name %s not found (attr).' % name)
 
 
 class Data(DataContainer):
diff --git a/tests/conftest.py b/tests/conftest.py
new file mode 100644
index 0000000..1a3b763
--- /dev/null
+++ b/tests/conftest.py
@@ -0,0 +1,128 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Feb 18 12:02:27 2026
+
+@author: paul
+"""
+import pooch
+import pytest
+import os
+
+from fretbursts import loader, bg
+
+
+# data subdir in the notebook folder
+DATASETS_DIR = u'data/'
+if not os.path.exists(DATASETS_DIR):
+    os.makedirs(DATASETS_DIR)
+
+alex1c = pooch.create(path=DATASETS_DIR, base_url='doi:10.6084/m9.figshare.1019906')
+alex1c.load_registry_from_doi()
+
+phdf5 = pooch.create(path=DATASETS_DIR, base_url='doi:10.6084/m9.figshare.1456362')
+phdf5.load_registry_from_doi()
+
+mphmm = pooch.create(path=DATASETS_DIR, base_url='doi:10.5281/zenodo.5902313')
+mphmm.load_registry_from_doi()
+
+def _alex_process(d):
+    loader.alex_apply_period(d)
+    d.calc_bg(bg.exp_fit, time_s=30, tail_min_us=300)
+    d.burst_search(L=10, m=10, F=7)
+
+def load_dataset_1ch(process=True):
+    fname = phdf5.fetch("0023uLRpitc_NTP_20dT_0.5GndCl.hdf5")
+    d = loader.photon_hdf5(fname)
+    if process:
+        _alex_process(d)
+    return d
+
+def load_dataset_1ch_nsalex(process=True):
+    fname =mphmm.fetch("HP3_TE150_SPC630.hdf5")
+    d = loader.photon_hdf5(fname)
+    if process:
+        _alex_process(d)
+    return d
+
+@pytest.fixture
+def dataset_1ch_file():
+    return phdf5.fetch("0023uLRpitc_NTP_20dT_0.5GndCl.hdf5")
+
+def load_dataset_8ch():
+    fname = alex1c.fetch("12d_New_30p_320mW_steer_3.hdf5")
+    d = loader.photon_hdf5(fname)
+    d.calc_bg(bg.exp_fit, time_s=30, tail_min_us=300)
+    d.burst_search(L=10, m=10, F=7)
+    return d
+
+@pytest.fixture
+def fake_pax_file():
+    return phdf5.fetch("0023uLRpitc_NTP_20dT_0.5GndCl.hdf5")
+
+def load_fake_pax():
+    fname = phdf5.fetch("0023uLRpitc_NTP_20dT_0.5GndCl.hdf5")
+    d = loader.photon_hdf5(fname)
+    d.add(ALEX=False, meas_type='PAX')
+    loader.alex_apply_period(d)
+    d.calc_bg(bg.exp_fit, time_s=30, tail_min_us='auto')
+    d.burst_search(L=10, m=10, F=6, pax=True)
+    return d
+
+    
+def load_dataset_grouped(process=True):
+    fn = ['HP3_TE150_SPC630.hdf5', 'HP3_TE200_SPC630.hdf5', 'HP3_TE250_SPC630.hdf5', 'HP3_TE300_SPC630.hdf5']
+    fn = [mphmm.fetch(f) for f in fn]
+    d = loader.photon_hdf5(fn)
+    if process:
+        _alex_process(d)
+    return d
+
+
+@pytest.fixture(scope="module")
+def data_8ch(request):
+    d = load_dataset_8ch()
+    return d
+
+@pytest.fixture(scope="module")
+def data_1ch(request):
+    d = load_dataset_1ch()
+    return d
+
+@pytest.fixture(scope="module")
+def data_1ch_nsalex(request):
+    d = load_dataset_1ch_nsalex()
+    return d
+
+@pytest.fixture(scope="module")
+def data_grouped(request):
+    d = load_dataset_grouped()
+    return d
+
+@pytest.fixture(scope="module", params=[
+                                    load_dataset_1ch,
+                                    load_dataset_1ch_nsalex,
+                                    load_dataset_8ch,
+                                    load_dataset_grouped
+                                    ])
+def data(request):
+    load_func = request.param
+    d = load_func()
+    return d
+
+@pytest.fixture(scope="module", params=[
+                                    load_dataset_8ch,
+                                    load_dataset_grouped
+                                    ])
+def data_mch(request):
+    load_func = request.param
+    d = load_func()
+    return d
+
+@pytest.fixture(scope='module', params=[load_dataset_1ch,
+                                        load_dataset_1ch_nsalex,
+                                        load_dataset_grouped])
+def data_alex(request):
+    load_func = request.param
+    d = load_func()
+    return d
diff --git a/tests/test_burst_plot.py b/tests/test_burst_plot.py
index 17bd746..f9ac8b3 100644
--- a/tests/test_burst_plot.py
+++ b/tests/test_burst_plot.py
@@ -35,106 +35,6 @@
 import fretbursts.burst_plot as bplt
 
 
-# data subdir in the notebook folder
-DATASETS_DIR = u'../notebooks/data/'
-
-
-def _alex_process(d):
-    loader.alex_apply_period(d)
-    d.calc_bg(bg.exp_fit, time_s=30, tail_min_us=300)
-    d.burst_search(L=10, m=10, F=7)
-
-def load_dataset_1ch(process=True):
-    fn = "0023uLRpitc_NTP_20dT_0.5GndCl.hdf5"
-    fname = DATASETS_DIR + fn
-    d = loader.photon_hdf5(fname)
-    if process:
-        _alex_process(d)
-    return d
-
-def load_dataset_1ch_nsalex(process=True):
-    fn = "HP3_TE150_SPC630.hdf5"
-    fname = DATASETS_DIR + fn
-    d = loader.photon_hdf5(fname)
-    if process:
-        _alex_process(d)
-    return d
-
-def load_dataset_8ch():
-    fn = "12d_New_30p_320mW_steer_3.hdf5"
-    fname = DATASETS_DIR + fn
-    d = loader.photon_hdf5(fname)
-    d.calc_bg(bg.exp_fit, time_s=30, tail_min_us=300)
-    d.burst_search(L=10, m=10, F=7)
-    return d
-
-def load_fake_pax():
-    fn = "0023uLRpitc_NTP_20dT_0.5GndCl.hdf5"
-    fname = DATASETS_DIR + fn
-    d = loader.photon_hdf5(fname)
-    d.add(ALEX=False, meas_type='PAX')
-    loader.alex_apply_period(d)
-    d.calc_bg(bg.exp_fit, time_s=30, tail_min_us='auto')
-    d.burst_search(L=10, m=10, F=6, pax=True)
-    return d
-
-    
-def load_dataset_grouped(process=True):
-    fn = ['HP3_TE150_SPC630.hdf5', 'HP3_TE200_SPC630.hdf5', 'HP3_TE250_SPC630.hdf5', 'HP3_TE300_SPC630.hdf5']
-    fn = [DATASETS_DIR + f for f in fn]
-    d = loader.photon_hdf5(fn)
-    if process:
-        _alex_process(d)
-    return d
-
-
-@pytest.fixture(scope="module")
-def data_8ch(request):
-    d = load_dataset_8ch()
-    return d
-
-@pytest.fixture(scope="module")
-def data_1ch(request):
-    d = load_dataset_1ch()
-    return d
-
-@pytest.fixture(scope="module")
-def data_1ch_nsalex(request):
-    d = load_dataset_1ch_nsalex()
-    return d
-
-@pytest.fixture(scope="module")
-def data_grouped(request):
-    d = load_dataset_grouped()
-    return d
-
-@pytest.fixture(scope="module", params=[
-                                    load_dataset_1ch,
-                                    load_dataset_1ch_nsalex,
-                                    load_dataset_8ch,
-                                    load_dataset_grouped
-                                    ])
-def data(request):
-    load_func = request.param
-    d = load_func()
-    return d
-
-@pytest.fixture(scope="module", params=[
-                                    load_dataset_8ch,
-                                    load_dataset_grouped
-                                    ])
-def data_mch(request):
-    load_func = request.param
-    d = load_func()
-    return d
-
-@pytest.fixture(scope='module', params=[load_dataset_1ch,
-                                        load_dataset_1ch_nsalex,
-                                        load_dataset_grouped])
-def data_alex(request):
-    load_func = request.param
-    d = load_func()
-    return d
 
 ##
 #  Multi-channel plot functions
diff --git a/tests/test_burstlib.py b/tests/test_burstlib.py
index f3e4f0e..a03483d 100644
--- a/tests/test_burstlib.py
+++ b/tests/test_burstlib.py
@@ -43,43 +43,19 @@
     import fretbursts.burst_plot as bplt
 
 
-# data subdir in the notebook folder
-DATASETS_DIR = u'../notebooks/data/'
-
-
 def _alex_process(d):
     loader.alex_apply_period(d)
     d.calc_bg(bg.exp_fit, time_s=30, tail_min_us=300)
     d.burst_search(L=10, m=10, F=7)
 
-def load_dataset_1ch(process=True):
-    fn = "0023uLRpitc_NTP_20dT_0.5GndCl.hdf5"
-    fname = DATASETS_DIR + fn
+def load_dataset_1ch(fname, process=True):
     d = loader.photon_hdf5(fname)
     if process:
         _alex_process(d)
     return d
 
-def load_dataset_1ch_nsalex(process=True):
-    # fn = "dsdna_d7_d17_50_50_1.hdf5"
-    fn = 'HP3_TE150_SPC630.hdf5'
-    fname = DATASETS_DIR + fn
-    d = loader.photon_hdf5(fname)
-    if process:
-        _alex_process(d)
-    return d
 
-def load_dataset_8ch():
-    fn = "12d_New_30p_320mW_steer_3.hdf5"
-    fname = DATASETS_DIR + fn
-    d = loader.photon_hdf5(fname)
-    d.calc_bg(bg.exp_fit, time_s=30, tail_min_us=300)
-    d.burst_search(L=10, m=10, F=7)
-    return d
-
-def load_fake_pax():
-    fn = "0023uLRpitc_NTP_20dT_0.5GndCl.hdf5"
-    fname = DATASETS_DIR + fn
+def load_fake_pax(fname):
     d = loader.photon_hdf5(fname)
     d.add(ALEX=False, meas_type='PAX')
     loader.alex_apply_period(d)
@@ -87,58 +63,6 @@ def load_fake_pax():
     d.burst_search(L=10, m=10, F=6, pax=True)
     return d
 
-def load_dataset_grouped(process=True):
-    fn = ['HP3_TE150_SPC630.hdf5', 'HP3_TE200_SPC630.hdf5', 'HP3_TE250_SPC630.hdf5', 'HP3_TE300_SPC630.hdf5']
-    fn = [DATASETS_DIR + f for f in fn]
-    d = loader.photon_hdf5(fn)
-    if process:
-        _alex_process(d)
-    return d
-
-def normpdf(x, c=0, mu=0.5):
-    return np.exp(-(x-c)**2/((mu**2)*2))/(mu*np.sqrt(2*np.pi))
-
-@pytest.fixture(scope="module")
-def data_8ch(request):
-    d = load_dataset_8ch()
-    return d
-
-@pytest.fixture(scope="module")
-def data_1ch(request):
-    d = load_dataset_1ch()
-    return d
-
-@pytest.fixture(scope="module")
-def data_1ch_nsalex(request):
-    d = load_dataset_1ch_nsalex()
-    return d
-
-@pytest.fixture(scope="module")
-def data_grouped(request):
-    d = load_dataset_grouped()
-    return d
-
-
-@pytest.fixture(scope="module", params=[
-                                    load_dataset_1ch,
-                                    load_dataset_1ch_nsalex,
-                                    load_dataset_8ch,
-                                    load_dataset_grouped
-                                    ])
-def data(request):
-    load_func = request.param
-    d = load_func()
-    return d
-
-@pytest.fixture(scope="module", params=[
-                                    load_dataset_8ch,
-                                    load_dataset_grouped
-                                    ])
-def data_mch(request):
-    load_func = request.param
-    d = load_func()
-    return d
-
 
 ##
 # List comparison functions
@@ -163,8 +87,8 @@ def list_array_allclose(list1, list2):
 # Test functions
 #
 
-def test_bg_compatlayer_for_obsolete_attrs():
-    d = load_dataset_1ch(process=False)
+def test_bg_compatlayer_for_obsolete_attrs(dataset_1ch_file):
+    d = load_dataset_1ch(dataset_1ch_file, process=False)
     attrs = ('bg_dd', 'bg_ad', 'bg_da', 'bg_aa',
              'rate_m', 'rate_dd', 'rate_ad', 'rate_da', 'rate_aa')
     for attr in attrs:
@@ -200,9 +124,9 @@ def isinteger(x):
     assert (hist_ac > 0).all()
 
 
-def test_time_min_max():
+def test_time_min_max(dataset_1ch_file):
     """Test time_min and time_max for ALEX data."""
-    d = load_dataset_1ch(process=False)
+    d = load_dataset_1ch(dataset_1ch_file, process=False)
     ich = 0
     assert d.time_max == d.ph_times_t[ich].max() * d.clk_p
     assert d.time_min == d.ph_times_t[ich].min() * d.clk_p
@@ -238,8 +162,8 @@ def test_aex_dex_ratio(data_1ch):
     assert (1 - aex_fraction) == 1 / (1 + aex_dex_ratio)
 
 
-def test_burst_size_pax():
-    d = load_fake_pax()
+def test_burst_size_pax(fake_pax_file):
+    d = load_fake_pax(fake_pax_file)
     aex_dex_ratio = d._aex_dex_ratio
     nd, na = d.nd[0], d.na[0]
     nda, naa = d.nda[0], d.naa[0]
@@ -1170,7 +1094,7 @@ def test_norm_pdf():
     x = np.arange(0, 1, 0.01)
     for c in np.arange(0.1, 1.0, 0.1):
         for mu in np.logspace(np.log(5e-2)/np.log(10), np.log(0.5)/np.log(10), 10):
-            assert np.allclose(normpdf(x, c, mu), norm.pdf(x, loc=c, scale=mu))
+            assert np.allclose(norm.pdf(x, c, mu), norm.pdf(x, loc=c, scale=mu))
 
 if __name__ == '__main__':
     pytest.main("-x -v tests/test_burstlib.py")
diff --git a/tests/test_burstlib_ext.py b/tests/test_burstlib_ext.py
index bfb3844..fb86fab 100644
--- a/tests/test_burstlib_ext.py
+++ b/tests/test_burstlib_ext.py
@@ -43,122 +43,6 @@
     import fretbursts.burst_plot as bplt
 
 
-# data subdir in the notebook folder
-DATASETS_DIR = u'../notebooks/data/'
-
-
-def _alex_process(d):
-    loader.alex_apply_period(d)
-    d.calc_bg(bg.exp_fit, time_s=30, tail_min_us=300)
-    d.burst_search(L=10, m=10, F=7)
-
-def load_dataset_1ch(process=True):
-    fn = "0023uLRpitc_NTP_20dT_0.5GndCl.hdf5"
-    fname = DATASETS_DIR + fn
-    d = loader.photon_hdf5(fname)
-    if process:
-        _alex_process(d)
-    return d
-
-def load_dataset_1ch_nsalex(process=True):
-    fn = "dsdna_d7_d17_50_50_1.hdf5"
-    fname = DATASETS_DIR + fn
-    d = loader.photon_hdf5(fname)
-    if process:
-        _alex_process(d)
-    return d
-
-def load_dataset_8ch():
-    fn = "12d_New_30p_320mW_steer_3.hdf5"
-    fname = DATASETS_DIR + fn
-    d = loader.photon_hdf5(fname)
-    d.calc_bg(bg.exp_fit, time_s=30, tail_min_us=300)
-    d.burst_search(L=10, m=10, F=7)
-    return d
-
-def load_fake_pax():
-    fn = "0023uLRpitc_NTP_20dT_0.5GndCl.hdf5"
-    fname = DATASETS_DIR + fn
-    d = loader.photon_hdf5(fname)
-    d.add(ALEX=False, meas_type='PAX')
-    loader.alex_apply_period(d)
-    d.calc_bg(bg.exp_fit, time_s=30, tail_min_us='auto')
-    d.burst_search(L=10, m=10, F=6, pax=True)
-    return d
-
-def test_load_group():
-    """Smoke test to see if loading groupd data works"""
-    fn = ['HP3_TE150_SPC630.hdf5', 'HP3_TE200_SPC630.hdf5', 'HP3_TE250_SPC630.hdf5', 'HP3_TE300_SPC630.hdf5']
-    fn = [DATASETS_DIR + f for f in fn]
-    d = loader.photon_hdf5(fn)
-    assert d.nch == len(fn)
-    
-def load_dataset_grouped(process=True):
-    fn = ['HP3_TE150_SPC630.hdf5', 'HP3_TE200_SPC630.hdf5', 'HP3_TE250_SPC630.hdf5', 'HP3_TE300_SPC630.hdf5']
-    fn = [DATASETS_DIR + f for f in fn]
-    d = loader.photon_hdf5(fn)
-    if process:
-        _alex_process(d)
-    return d
-
-
-@pytest.fixture(scope="module")
-def data_8ch(request):
-    d = load_dataset_8ch()
-    return d
-
-@pytest.fixture(scope="module")
-def data_1ch(request):
-    d = load_dataset_1ch()
-    return d
-
-@pytest.fixture(scope="module")
-def data_1ch_nsalex(request):
-    d = load_dataset_1ch_nsalex()
-    return d
-
-@pytest.fixture(scope="module")
-def data_grouped(request):
-    d = load_dataset_grouped()
-    return d
-
-@pytest.fixture(scope="module", params=[
-                                    load_dataset_1ch,
-                                    load_dataset_1ch_nsalex,
-                                    load_dataset_8ch,
-                                    load_dataset_grouped
-                                    ])
-def data(request):
-    load_func = request.param
-    d = load_func()
-    return d
-
-@pytest.mark.parametrize("data_ch, process", product([load_dataset_1ch, 
-                                             load_dataset_1ch_nsalex, 
-                                             load_dataset_8ch],
-                                            [True, False]))
-def test_group_data(data_ch, process):
-    if data_ch is load_dataset_8ch:
-        orig_d = data_ch()
-    else:
-        orig_d = data_ch(process=process)
-    n = orig_d.nch
-    d = bext.group_data([orig_d for _ in range(8)])
-    assert n*8 == d.nch
-    for field in Data.ph_fields:
-        if hasattr(d, field):
-            assert len(d[field]) == 8*n, f"{field} not correct length"
-    if not process and orig_d.alternated:
-        loader.alex_apply_period(d)
-        for field in Data.ph_fields:
-            if hasattr(d, field):
-                assert len(d[field]) == 8*n, f"{field} not correct length"
-    d.calc_bg(bg.exp_fit, time_s=30, tail_min_us='auto')
-    d.burst_search(m=10, F=7, L=10)
-    for field in Data.burst_fields:
-        if hasattr(d, field):
-            assert len(d[field]) == 8*n, f'{field} not correct length'
-
 def test_join_data(data):
     """Smoke test for bext.join_data() function.
     """

From 9918f25dac73256b5c745edc8e500d3a4b73b353 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Fri, 20 Feb 2026 09:45:35 +0100
Subject: [PATCH 102/142] Switch notebooks to use pooch

---
 .github/workflows/build_wheel.yml             |  2 +-
 ...art here if new to Jupyter Notebooks.ipynb |  6 +-
 notebooks/Example - 2CDE Method.ipynb         | 10 +--
 .../Example - Background estimation.ipynb     | 17 +++--
 .../Example - Burst Variance Analysis.ipynb   | 11 +--
 ...le - Customize the us-ALEX histogram.ipynb | 11 +--
 ...ting Burst Data Including Timestamps.ipynb | 55 +++++----------
 .../Example - FRET histogram fitting.ipynb    | 67 +++----------------
 ...le - Plotting timetraces with bursts.ipynb | 15 +++--
 ...Example - Selecting FRET populations.ipynb | 15 +++--
 ...- Working with timestamps and bursts.ipynb | 14 ++--
 ...Example- Combining Technical Repeats.ipynb | 26 +++----
 ...ursts - 8-spot smFRET burst analysis.ipynb | 22 +++---
 notebooks/FRETBursts - ns-ALEX example.ipynb  | 31 +++------
 ...rsts - us-ALEX smFRET burst analysis.ipynb | 46 +++----------
 tests/conftest.py                             |  4 +-
 16 files changed, 130 insertions(+), 222 deletions(-)

diff --git a/.github/workflows/build_wheel.yml b/.github/workflows/build_wheel.yml
index dc7bbd5..170c3f8 100644
--- a/.github/workflows/build_wheel.yml
+++ b/.github/workflows/build_wheel.yml
@@ -21,7 +21,7 @@ jobs:
           persist-credentials: false
           fetch-depth: 0
       - name: Build wheels
-        uses: pypa/cibuildwheel@v2.18.1
+        uses: pypa/cibuildwheel@v3.3.1
         env:
           CIBW_SKIP: "pp*"
           # CIBW_TEST_REQUIRES: pytest, numpy == 1.20.1, matplotlib, scipy, pandas, tables, numba, seaborn, lmfit, phconvert
diff --git a/notebooks/1. First Steps - Start here if new to Jupyter Notebooks.ipynb b/notebooks/1. First Steps - Start here if new to Jupyter Notebooks.ipynb
index 024b58a..c3a20b8 100644
--- a/notebooks/1. First Steps - Start here if new to Jupyter Notebooks.ipynb	
+++ b/notebooks/1. First Steps - Start here if new to Jupyter Notebooks.ipynb	
@@ -117,7 +117,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [default]",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -131,7 +131,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.13.0"
   },
   "toc": {
    "colors": {
@@ -155,5 +155,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
diff --git a/notebooks/Example - 2CDE Method.ipynb b/notebooks/Example - 2CDE Method.ipynb
index 0ef5ed9..7679765 100644
--- a/notebooks/Example - 2CDE Method.ipynb	
+++ b/notebooks/Example - 2CDE Method.ipynb	
@@ -120,10 +120,12 @@
     }
    ],
    "source": [
-    "url = 'http://files.figshare.com/2182601/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5'\n",
-    "download_file(url, save_dir='./data')\n",
+    "import pooch\n",
     "\n",
-    "filename = \"data/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\"\n",
+    "phdf5 = pooch.create(path=\"../tests/data\", base_url='doi:10.6084/m9.figshare.1456362')\n",
+    "phdf5.load_registry_from_doi()\n",
+    "\n",
+    "filename = phdf5.fetch(\"0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\")\n",
     "\n",
     "d = loader.photon_hdf5(filename)\n",
     "loader.alex_apply_period(d)\n",
@@ -880,7 +882,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.19"
+   "version": "3.13.0"
   },
   "toc": {
    "colors": {
diff --git a/notebooks/Example - Background estimation.ipynb b/notebooks/Example - Background estimation.ipynb
index 3049701..148314b 100644
--- a/notebooks/Example - Background estimation.ipynb	
+++ b/notebooks/Example - Background estimation.ipynb	
@@ -66,11 +66,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "url = 'http://files.figshare.com/2182601/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5'\n",
-    "download_file(url, save_dir='./data')\n",
-    "full_fname = \"./data/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\"\n",
+    "import pooch\n",
     "\n",
-    "d = loader.photon_hdf5(full_fname)\n",
+    "phdf5 = pooch.create(path=\"../tests/data/\", base_url='doi:10.6084/m9.figshare.1456362.v15')\n",
+    "phdf5.load_registry_from_doi()\n",
+    "\n",
+    "full_name = phdf5.fetch(\"0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\")\n",
+    "\n",
+    "d = loader.photon_hdf5(full_name)\n",
     "loader.alex_apply_period(d)"
    ]
   },
@@ -361,7 +364,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -375,9 +378,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.13"
+   "version": "3.13.0"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
diff --git a/notebooks/Example - Burst Variance Analysis.ipynb b/notebooks/Example - Burst Variance Analysis.ipynb
index edb71b9..b4f98b1 100644
--- a/notebooks/Example - Burst Variance Analysis.ipynb	
+++ b/notebooks/Example - Burst Variance Analysis.ipynb	
@@ -55,10 +55,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "url = 'http://files.figshare.com/2182601/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5'\n",
-    "download_file(url, save_dir='./data')\n",
+    "import pooch\n",
+    "\n",
+    "phdf5 = pooch.create(path=\"../tests/data/\", base_url='doi:10.6084/m9.figshare.1456362.v15')\n",
+    "phdf5.load_registry_from_doi()\n",
+    "\n",
+    "full_fname = phdf5.fetch(\"0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\")\n",
     "\n",
-    "full_fname = \"./data/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\"\n",
     "d = loader.photon_hdf5(full_fname)\n",
     "loader.alex_apply_period(d)\n",
     "d.calc_bg(bg.exp_fit, time_s=50.1, tail_min_us='auto', F_bg=1.7)"
@@ -280,7 +283,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.13.0"
   },
   "toc": {
    "colors": {
diff --git a/notebooks/Example - Customize the us-ALEX histogram.ipynb b/notebooks/Example - Customize the us-ALEX histogram.ipynb
index bed7d37..a2acb5a 100644
--- a/notebooks/Example - Customize the us-ALEX histogram.ipynb	
+++ b/notebooks/Example - Customize the us-ALEX histogram.ipynb	
@@ -63,9 +63,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "url = 'http://files.figshare.com/2182601/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5'\n",
-    "download_file(url, save_dir='./data')\n",
-    "full_fname = \"./data/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\"\n",
+    "import pooch\n",
+    "\n",
+    "phdf5 = pooch.create(path=\"../tests/data/\", base_url='doi:10.6084/m9.figshare.1456362.v15')\n",
+    "phdf5.load_registry_from_doi()\n",
+    "\n",
+    "full_fname = phdf5.fetch(\"0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\")\n",
     "\n",
     "d = loader.photon_hdf5(full_fname)\n",
     "loader.alex_apply_period(d)\n",
@@ -462,7 +465,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.19"
+   "version": "3.13.0"
   },
   "toc": {
    "colors": {
diff --git a/notebooks/Example - Exporting Burst Data Including Timestamps.ipynb b/notebooks/Example - Exporting Burst Data Including Timestamps.ipynb
index 564ede2..13a2781 100644
--- a/notebooks/Example - Exporting Burst Data Including Timestamps.ipynb	
+++ b/notebooks/Example - Exporting Burst Data Including Timestamps.ipynb	
@@ -71,10 +71,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The full list of smFRET measurements used in the [FRETBursts tutorials](https://github.com/OpenSMFS/FRETBursts_notebooks) \n",
-    "can be found on [Figshare](http://dx.doi.org/10.6084/m9.figshare.1456362).\n",
+    "> **Note**\n",
+    ">\n",
+    "> Figshare now blocks downloading through standard python.\n",
+    "> Since downloading files is not a core part of FRETBursts, we now use pooch to download files\n",
     "\n",
-    "This is the file we will download:"
+    "The full list of smFRET measurements used in the [FRETBursts tutorials](https://github.com/OpenSMFS/FRETBursts_notebooks) \n",
+    "can be found on [Figshare](http://dx.doi.org/10.6084/m9.figshare.1456362).\n"
    ]
   },
   {
@@ -83,40 +86,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "url = 'http://files.figshare.com/2182601/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-success\">\n",
-    "You can change the <code>url</code> variable above to download your own data file.\n",
-    "This is useful if you are executing FRETBursts online and you want to use\n",
-    "your own data file. See \n",
-    "<a href=\"1. First Steps - Start here if new to Jupyter Notebooks.ipynb\">First Steps</a>.\n",
-    "</div>\n",
+    "import pooch\n",
     "\n",
-    "Here, we download the data file and put it in a folder named `data`, \n",
-    "inside the notebook folder:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "download_file(url, save_dir='./data')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "> **NOTE**: If you modified the `url` variable providing an invalid URL\n",
-    "> the previous command will fail. In this case, edit the cell containing \n",
-    "> the `url` and re-try the download."
+    "phdf5 = pooch.create(path=\"../tests/data/\", base_url='doi:10.6084/m9.figshare.1456362.v15')\n",
+    "phdf5.load_registry_from_doi()\n",
+    "\n",
+    "full_path = phdf5.fetch(\"0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\")"
    ]
   },
   {
@@ -139,7 +114,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "filename = \"./data/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\"\n",
+    "filename = full_path\n",
     "filename"
    ]
   },
@@ -764,7 +739,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -778,7 +753,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.13"
+   "version": "3.13.0"
   },
   "nav_menu": {},
   "toc": {
@@ -802,5 +777,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
diff --git a/notebooks/Example - FRET histogram fitting.ipynb b/notebooks/Example - FRET histogram fitting.ipynb
index 092fec2..5910c7a 100644
--- a/notebooks/Example - FRET histogram fitting.ipynb	
+++ b/notebooks/Example - FRET histogram fitting.ipynb	
@@ -64,9 +64,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "url = 'http://files.figshare.com/2182601/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5'\n",
-    "download_file(url, save_dir='./data')\n",
-    "full_fname = \"./data/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\"\n",
+    "import pooch\n",
+    "\n",
+    "phdf5 = pooch.create(path=\"../tests/data/\", base_url='doi:10.6084/m9.figshare.1456362.v15')\n",
+    "phdf5.load_registry_from_doi()\n",
+    "\n",
+    "full_fname = phdf5.fetch(\"0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\")\n",
     "\n",
     "d = loader.photon_hdf5(full_fname)\n",
     "loader.alex_apply_period(d)\n",
@@ -242,63 +245,11 @@
    "source": [
     "E_fitter.params"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With [`pybroom`](http://pybroom.readthedocs.io/) we can get a \"tidy\" DataFrame\n",
-    "with more complete fit results:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pybroom as br"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = br.tidy(res)\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, for example, we can easily select parameters by name:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.loc[df.name.str.contains('center')]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.loc[df.name.str.contains('sigma')]"
-   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -312,7 +263,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.13"
+   "version": "3.13.0"
   },
   "toc": {
    "colors": {
@@ -343,5 +294,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
diff --git a/notebooks/Example - Plotting timetraces with bursts.ipynb b/notebooks/Example - Plotting timetraces with bursts.ipynb
index 754c41d..5fb7369 100644
--- a/notebooks/Example - Plotting timetraces with bursts.ipynb	
+++ b/notebooks/Example - Plotting timetraces with bursts.ipynb	
@@ -64,9 +64,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "url = 'http://files.figshare.com/2182601/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5'\n",
-    "download_file(url, save_dir='./data')\n",
-    "full_fname = \"./data/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\"\n",
+    "import pooch\n",
+    "\n",
+    "phdf5 = pooch.create(path=\"../tests/data/\", base_url='doi:10.6084/m9.figshare.1456362.v15')\n",
+    "phdf5.load_registry_from_doi()\n",
+    "\n",
+    "full_fname = phdf5.fetch(\"0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\")\n",
     "\n",
     "d = loader.photon_hdf5(full_fname)\n",
     "loader.alex_apply_period(d)\n",
@@ -159,7 +162,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -173,7 +176,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.13"
+   "version": "3.13.0"
   },
   "toc": {
    "colors": {
@@ -204,5 +207,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
diff --git a/notebooks/Example - Selecting FRET populations.ipynb b/notebooks/Example - Selecting FRET populations.ipynb
index af91990..ab0632e 100644
--- a/notebooks/Example - Selecting FRET populations.ipynb	
+++ b/notebooks/Example - Selecting FRET populations.ipynb	
@@ -63,9 +63,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "url = 'http://files.figshare.com/2182601/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5'\n",
-    "download_file(url, save_dir='./data')\n",
-    "full_fname = \"./data/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\"\n",
+    "import pooch\n",
+    "\n",
+    "phdf5 = pooch.create(path=\"../tests/data/\", base_url='doi:10.6084/m9.figshare.1456362.v15')\n",
+    "phdf5.load_registry_from_doi()\n",
+    "\n",
+    "full_fname = phdf5.fetch(\"0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\")\n",
     "\n",
     "d = loader.photon_hdf5(full_fname)\n",
     "loader.alex_apply_period(d)\n",
@@ -183,7 +186,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -197,7 +200,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.13"
+   "version": "3.13.0"
   },
   "toc": {
    "colors": {
@@ -228,5 +231,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
diff --git a/notebooks/Example - Working with timestamps and bursts.ipynb b/notebooks/Example - Working with timestamps and bursts.ipynb
index 9291913..564117a 100644
--- a/notebooks/Example - Working with timestamps and bursts.ipynb	
+++ b/notebooks/Example - Working with timestamps and bursts.ipynb	
@@ -46,7 +46,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "filename = \"./data/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\"\n",
+    "import pooch\n",
+    "\n",
+    "phdf5 = pooch.create(path=\"../tests/data/\", base_url='doi:10.6084/m9.figshare.1456362.v15')\n",
+    "phdf5.load_registry_from_doi()\n",
+    "\n",
+    "filename = phdf5.fetch(\"0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\")\n",
+    "\n",
     "d = loader.photon_hdf5(filename)\n",
     "loader.alex_apply_period(d)\n",
     "d.calc_bg(bg.exp_fit, time_s=30, tail_min_us='auto', F_bg=1.7)\n",
@@ -672,7 +678,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -686,7 +692,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.13"
+   "version": "3.13.0"
   },
   "toc": {
    "colors": {
@@ -710,5 +716,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
diff --git a/notebooks/Example- Combining Technical Repeats.ipynb b/notebooks/Example- Combining Technical Repeats.ipynb
index 9b1ea75..64c8703 100644
--- a/notebooks/Example- Combining Technical Repeats.ipynb	
+++ b/notebooks/Example- Combining Technical Repeats.ipynb	
@@ -43,12 +43,18 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "urls = ['https://zenodo.org/record/5902313/files/HP3_TE150_SPC630.hdf5',\n",
-    "        'https://zenodo.org/record/5902313/files/HP3_TE200_SPC630.hdf5',\n",
-    "        'https://zenodo.org/record/5902313/files/HP3_TE250_SPC630.hdf5',\n",
-    "        'https://zenodo.org/record/5902313/files/HP3_TE300_SPC630.hdf5']\n",
-    "for url in urls:\n",
-    "    download_file(url, save_dir='./data')"
+    "import pooch\n",
+    "\n",
+    "mphmm = pooch.create(path='../tests/data/', base_url='doi:10.5281/zenodo.5902313')\n",
+    "mphmm.load_registry_from_doi()\n",
+    "\n",
+    "filenames = [\"HP3_TE150_SPC630.hdf5\", \n",
+    "             \"HP3_TE200_SPC630.hdf5\", \n",
+    "             \"HP3_TE250_SPC630.hdf5\", \n",
+    "             \"HP3_TE300_SPC630.hdf5\"]\n",
+    "\n",
+    "filenames = [mphmm.fetch(fn) for fn in filenames]\n",
+    "filenames"
    ]
   },
   {
@@ -68,10 +74,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "filenames = [\"data/HP3_TE150_SPC630.hdf5\", \n",
-    "             \"data/HP3_TE200_SPC630.hdf5\", \n",
-    "             \"data/HP3_TE250_SPC630.hdf5\", \n",
-    "             \"data/HP3_TE300_SPC630.hdf5\"]\n",
     "d = loader.photon_hdf5(filenames)"
    ]
   },
@@ -192,7 +194,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -206,7 +208,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.13"
+   "version": "3.13.0"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/FRETBursts - 8-spot smFRET burst analysis.ipynb b/notebooks/FRETBursts - 8-spot smFRET burst analysis.ipynb
index c865622..945d9b5 100644
--- a/notebooks/FRETBursts - 8-spot smFRET burst analysis.ipynb	
+++ b/notebooks/FRETBursts - 8-spot smFRET burst analysis.ipynb	
@@ -89,16 +89,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "url = 'http://files.figshare.com/2182604/12d_New_30p_320mW_steer_3.hdf5'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "download_file(url, save_dir='./data')"
+    "import pooch\n",
+    "\n",
+    "alex1c = pooch.create(path='../tests/data/', base_url='doi:10.6084/m9.figshare.1019906.v26')\n",
+    "alex1c.load_registry_from_doi()"
    ]
   },
   {
@@ -114,7 +108,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "filename = \"./data/12d_New_30p_320mW_steer_3.hdf5\""
+    "filename = alex1c.fetch(\"12d_New_30p_320mW_steer_3.hdf5\")"
    ]
   },
   {
@@ -599,7 +593,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -613,9 +607,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.13"
+   "version": "3.13.0"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
diff --git a/notebooks/FRETBursts - ns-ALEX example.ipynb b/notebooks/FRETBursts - ns-ALEX example.ipynb
index abb7ab3..6748268 100644
--- a/notebooks/FRETBursts - ns-ALEX example.ipynb	
+++ b/notebooks/FRETBursts - ns-ALEX example.ipynb	
@@ -72,25 +72,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "url = 'http://files.figshare.com/2182602/dsdna_d7_d17_50_50_1.hdf5'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "download_file(url, save_dir='./data')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "filename = './data/dsdna_d7_d17_50_50_1.hdf5'\n",
+    "import pooch\n",
+    "\n",
+    "nsfshare = pooch.create(path='../tests/data/', base_url='doi:10.6084/m9.figshare.1456362.v15')\n",
+    "nsfshare.load_registry_from_doi()\n",
+    "\n",
+    "filename = nsfshare.fetch('dsdna_d7_d17_50_50_1.hdf5')\n",
     "filename"
    ]
   },
@@ -499,7 +486,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -513,9 +500,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.13"
+   "version": "3.13.0"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
diff --git a/notebooks/FRETBursts - us-ALEX smFRET burst analysis.ipynb b/notebooks/FRETBursts - us-ALEX smFRET burst analysis.ipynb
index cf30f84..49a86aa 100644
--- a/notebooks/FRETBursts - us-ALEX smFRET burst analysis.ipynb	
+++ b/notebooks/FRETBursts - us-ALEX smFRET burst analysis.ipynb	
@@ -117,6 +117,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "> **Note**\n",
+    ">\n",
+    "> This section is essentially deprecated, only included because needed for testing.\n",
+    "\n",
     "The full list of smFRET measurements used in the [FRETBursts tutorials](https://github.com/OpenSMFS/FRETBursts_notebooks) \n",
     "can be found on [Figshare](http://dx.doi.org/10.6084/m9.figshare.1456362).\n",
     "\n",
@@ -129,40 +133,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "url = 'http://files.figshare.com/2182601/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-success\">\n",
-    "You can change the <code>url</code> variable above to download your own data file.\n",
-    "This is useful if you are executing FRETBursts online and you want to use\n",
-    "your own data file. See \n",
-    "<a href=\"1. First Steps - Start here if new to Jupyter Notebooks.ipynb\">First Steps</a>.\n",
-    "</div>\n",
+    "import pooch\n",
     "\n",
-    "Here, we download the data file and put it in a folder named `data`, \n",
-    "inside the notebook folder:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "download_file(url, save_dir='./data')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "> **NOTE**: If you modified the `url` variable providing an invalid URL\n",
-    "> the previous command will fail. In this case, edit the cell containing \n",
-    "> the `url` and re-try the download."
+    "phdf5 = pooch.create(path=\"../tests/data/\", base_url='doi:10.6084/m9.figshare.1456362.v15')\n",
+    "phdf5.load_registry_from_doi()\n",
+    "\n",
+    "full_path = phdf5.fetch(\"0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\")"
    ]
   },
   {
@@ -199,7 +175,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "filename = \"./data/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\"\n",
+    "filename = full_path\n",
     "filename"
    ]
   },
@@ -1915,7 +1891,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.3"
+   "version": "3.13.0"
   }
  },
  "nbformat": 4,
diff --git a/tests/conftest.py b/tests/conftest.py
index 1a3b763..cfd913f 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -17,10 +17,10 @@
 if not os.path.exists(DATASETS_DIR):
     os.makedirs(DATASETS_DIR)
 
-alex1c = pooch.create(path=DATASETS_DIR, base_url='doi:10.6084/m9.figshare.1019906')
+alex1c = pooch.create(path=DATASETS_DIR, base_url='doi:10.6084/m9.figshare.1019906.v26')
 alex1c.load_registry_from_doi()
 
-phdf5 = pooch.create(path=DATASETS_DIR, base_url='doi:10.6084/m9.figshare.1456362')
+phdf5 = pooch.create(path=DATASETS_DIR, base_url='doi:10.6084/m9.figshare.1456362.v15')
 phdf5.load_registry_from_doi()
 
 mphmm = pooch.create(path=DATASETS_DIR, base_url='doi:10.5281/zenodo.5902313')

From a5324e58d4e6c26553cf32197ea7f3b3c6c9239a Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Fri, 20 Feb 2026 09:59:50 +0100
Subject: [PATCH 103/142] Deprecate support for python < 3.10

---
 .github/workflows/tests.yml | 2 +-
 pyproject.toml              | 8 +++-----
 2 files changed, 4 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 2a63f0e..43a30a0 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -7,7 +7,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest, windows-latest, macos-latest]
-        python-version: ["3.9", "3.11", "3.13"]
+        python-version: ["3.10", "3.13"]
     steps:
       - uses: actions/checkout@v6
         with:
diff --git a/pyproject.toml b/pyproject.toml
index ba83b74..0e5dd25 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -21,16 +21,14 @@ keywords = ["single-molecule FRET","smFRET", "burst-analysis", "biophysics"]
 classifiers = [
     "Intended Audience :: Science/Research",
     "Operating System :: OS Independent",
-    "Programming Language :: Python :: 3.7",
-    "Programming Language :: Python :: 3.8",
-    "Programming Language :: Python :: 3.9",
     "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
+    "Programming Language :: Python :: 3.13",
     "Topic :: Scientific/Engineering"
  ]
-requires-python = ">= 3.7"
+requires-python = ">= 3.10"
 dependencies = [
-    "importlib_metadata;python_version<='3.9'",
     "numpy>=1.19", 
     "scipy>=1.2", 
     "pandas >= 0.23",

From bd23819499b77adade6b1055142439d345848317 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Fri, 20 Feb 2026 10:22:29 +0100
Subject: [PATCH 104/142] Exclude cp310-win_arm64 from cibuildwheel and fix
 name setting from list of files

---
 .github/workflows/build_wheel.yml | 2 ++
 fretbursts/burstlib.py            | 5 +++--
 2 files changed, 5 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/build_wheel.yml b/.github/workflows/build_wheel.yml
index 170c3f8..3bdd1c1 100644
--- a/.github/workflows/build_wheel.yml
+++ b/.github/workflows/build_wheel.yml
@@ -12,6 +12,8 @@ jobs:
   build_wheels:
     name: Build wheels on ${{ matrix.os }}
     runs-on: ${{ matrix.os }}
+    env:
+      CIBW_SKIP: "cp310-win_arm64"
     strategy:
       matrix:
         os: [ubuntu-latest, ubuntu-24.04-arm, windows-latest, windows-11-arm, macos-15-intel, macos-latest]
diff --git a/fretbursts/burstlib.py b/fretbursts/burstlib.py
index 61582cb..d6922ca 100644
--- a/fretbursts/burstlib.py
+++ b/fretbursts/burstlib.py
@@ -2951,9 +2951,10 @@ def status(self, add="", noname=False):
     def name(self):
         """Measurement name: last subfolder + file name with no extension."""
         if not hasattr(self, '_name'):
-            basename = str(os.path.splitext(os.path.basename(self.fname))[0])
+            fname = self.fname if isinstance(self.fname, str) else self.fname[0]
+            basename = str(os.path.splitext(os.path.basename(fname))[0])
             name = basename
-            last_dir = str(os.path.basename(os.path.dirname(self.fname)))
+            last_dir = str(os.path.basename(os.path.dirname(fname)))
             if len(last_dir) > 0:
                 name = '_'.join([last_dir, basename])
             self.add(_name=name)

From 4375ef6c8ff1b21d2c9d3f2c983f773a7e1185f6 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Fri, 20 Feb 2026 10:42:12 +0100
Subject: [PATCH 105/142] Fix doi in notebook

---
 notebooks/Example - 2CDE Method.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/notebooks/Example - 2CDE Method.ipynb b/notebooks/Example - 2CDE Method.ipynb
index 7679765..7313506 100644
--- a/notebooks/Example - 2CDE Method.ipynb	
+++ b/notebooks/Example - 2CDE Method.ipynb	
@@ -122,7 +122,7 @@
    "source": [
     "import pooch\n",
     "\n",
-    "phdf5 = pooch.create(path=\"../tests/data\", base_url='doi:10.6084/m9.figshare.1456362')\n",
+    "phdf5 = pooch.create(path=\"../tests/data\", base_url='doi:10.6084/m9.figshare.1456362.v15')\n",
     "phdf5.load_registry_from_doi()\n",
     "\n",
     "filename = phdf5.fetch(\"0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\")\n",

From 3c378c4ce4f17d900750588bba7f6d8a76a7ca24 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Fri, 20 Feb 2026 13:54:08 +0100
Subject: [PATCH 106/142] Fix DataContainer for propper deletion of attrs

---
 .github/workflows/build_wheel.yml |  4 +---
 fretbursts/burstlib.py            | 28 ++++++++++++++--------------
 2 files changed, 15 insertions(+), 17 deletions(-)

diff --git a/.github/workflows/build_wheel.yml b/.github/workflows/build_wheel.yml
index 3bdd1c1..1d778c4 100644
--- a/.github/workflows/build_wheel.yml
+++ b/.github/workflows/build_wheel.yml
@@ -12,8 +12,6 @@ jobs:
   build_wheels:
     name: Build wheels on ${{ matrix.os }}
     runs-on: ${{ matrix.os }}
-    env:
-      CIBW_SKIP: "cp310-win_arm64"
     strategy:
       matrix:
         os: [ubuntu-latest, ubuntu-24.04-arm, windows-latest, windows-11-arm, macos-15-intel, macos-latest]
@@ -25,7 +23,7 @@ jobs:
       - name: Build wheels
         uses: pypa/cibuildwheel@v3.3.1
         env:
-          CIBW_SKIP: "pp*"
+          CIBW_SKIP: "pp* cp310-win_arm64"
           # CIBW_TEST_REQUIRES: pytest, numpy == 1.20.1, matplotlib, scipy, pandas, tables, numba, seaborn, lmfit, phconvert
           # CIBW_BEFORE_TEST: python -m pip install pytest
           # CIBW_TEST_COMMAND: python -m pytest {package}/tests
diff --git a/fretbursts/burstlib.py b/fretbursts/burstlib.py
index d6922ca..f8466ec 100644
--- a/fretbursts/burstlib.py
+++ b/fretbursts/burstlib.py
@@ -503,11 +503,16 @@ def delete(self, *args, **kwargs):
         """Delete an element (attribute and/or dict entry). """
         warning = kwargs.get('warning', True)
         for name in args:
-            try:
+            if name in self:
                 self.pop(name)
-            except KeyError:
-                if warning:
-                    print(' WARNING: Name %s not found (dict).' % name)
+            elif warning:
+                print(' WARNING: Name %s not found (dict).' % name)
+    
+    def __delattr__(self, name):
+        if name in self:
+            self.pop(name, None)
+        else:
+            super().__delattr__(name)
 
 
 class Data(DataContainer):
@@ -1311,9 +1316,7 @@ def delete_burst_data(self):
             if name in self:
                 self.delete(name)
         for name in ('E_fitter', 'S_fitter'):
-            if hasattr(self, name):
-                delattr(self, name)
-
+            self.pop(name, None)
     ##
     # Methods for high-level data transformation
     #
@@ -1348,8 +1351,7 @@ def slice_ph(self, time_s1=0, time_s2=None, s='slice'):
 
         # Delete eventual cached properties
         for attr in ['_time_min', '_time_max']:
-            if hasattr(new_d, attr):
-                delattr(new_d, attr)
+            new_d.pop(attr, None)
         return new_d
 
     def collapse(self, update_gamma=True, skip_ch=None):
@@ -1688,11 +1690,9 @@ def _clean_bg_data(self):
         # Attributes specific of manual or 'auto' bg fit
         field_list = ['bg_auto_th_us0', 'bg_auto_F_bg', 'bg_th_us_user']
         for field in field_list:
-            if field in self:
-                self.delete(field)
-        if hasattr(self, '_bg_mean'):
-            delattr(self, '_bg_mean')
-
+            self.pop(field, None)
+        self.pop('_bg_mean', None)
+    
     def _get_num_periods(self, time_s):
         """Return the number of periods using `time_s` as period duration.
         """

From 8391964fa3ce6e9d8da293d8696916bb746b440b Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Fri, 20 Feb 2026 15:15:18 +0100
Subject: [PATCH 107/142] Docs cleanup

---
 .github/workflows/tests.yml       | 2 +-
 docs/source/absolute_beginner.rst | 2 +-
 fretbursts/burstlib.py            | 6 +++---
 tests/test_burstlib.py            | 4 ++--
 4 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 43a30a0..2ac9b8f 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -7,7 +7,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest, windows-latest, macos-latest]
-        python-version: ["3.10", "3.13"]
+        python-version: ["3.10", "3.12"]
     steps:
       - uses: actions/checkout@v6
         with:
diff --git a/docs/source/absolute_beginner.rst b/docs/source/absolute_beginner.rst
index b379d39..1b2331a 100644
--- a/docs/source/absolute_beginner.rst
+++ b/docs/source/absolute_beginner.rst
@@ -32,7 +32,7 @@ The installation should take a few seconds.
 If you notice any error please report it by opening a new issue on the
 `FRETBursts GitHub Issues <https://github.com/OpenSMFS/FRETBursts/issues>`_.
 
-Alternatively create an environment from one of our yaml files where we have verified compatibility of all versions of the software: :downlaod:`frbmin.yml <downloads/frbmin.yml>`
+Alternatively create an environment from one of our yaml files where we have verified compatibility of all versions of the software: :download:`frbmin.yml <downloads/frbmin.yml>`
 
 First download `frbmin.yml`
 
diff --git a/fretbursts/burstlib.py b/fretbursts/burstlib.py
index f8466ec..8616705 100644
--- a/fretbursts/burstlib.py
+++ b/fretbursts/burstlib.py
@@ -1407,9 +1407,9 @@ def collapse(self, update_gamma=True, skip_ch=None):
             dc._update_gamma(np.mean(self.get_gamma_array()))
         return dc
 
-    ##
-    # Utility methods
-    #
+    ###########################################################################
+    ### Utility methods
+    ###########################################################################
     def get_params(self):
         """Returns a plain dict containing only parameters and no arrays.
         This can be used as a summary of data analysis parameters.
diff --git a/tests/test_burstlib.py b/tests/test_burstlib.py
index a03483d..547a216 100644
--- a/tests/test_burstlib.py
+++ b/tests/test_burstlib.py
@@ -1069,9 +1069,9 @@ def test_collapse(data_8ch):
     bursts2 = bl.bslib.Bursts.merge(d.mburst, sort=True)
     assert bursts1 == bursts2
     bursts2 = bl.bslib.Bursts.merge(d.mburst, sort=False)
-    indexsort_stop = bursts2.stop.argsort()
+    indexsort_stop = bursts2.stop.argsort(kind='stable')
     bursts3 = bursts2[indexsort_stop]
-    indexsort_start = bursts3.start.argsort()
+    indexsort_start = bursts3.start.argsort(kind='stable')
     bursts4 = bursts3[indexsort_start]
     assert bursts1 == bursts4
     indexsort = np.lexsort((bursts2.stop, bursts2.start))

From fa6022fca85b2744912b86b7ef0aad59c4361604 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Sat, 21 Feb 2026 09:11:03 +0100
Subject: [PATCH 108/142] Test caching in github actions

---
 .github/workflows/tests.yml | 22 ++++++++++++++++++++++
 tests/conftest.py           |  2 --
 tests/fetch_files.py        | 27 +++++++++++++++++++++++++++
 3 files changed, 49 insertions(+), 2 deletions(-)
 create mode 100644 tests/fetch_files.py

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 2ac9b8f..eb80d92 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -2,6 +2,21 @@ name: Tests
 
 on: [push, pull_request]
 jobs:
+  download:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v6
+      - uses: actions/cache@v5
+        id: filecache
+        with:
+          path: tests/data
+          enableCrossOsArchive: true
+          key: testfiles
+      - name: Download Files
+        run: |
+          cd tests
+          pythom -m fetch_files.py
+      
   build:
     runs-on: ${{matrix.os}}
     strategy:
@@ -13,6 +28,13 @@ jobs:
         with:
           persist-credentials: false
           fetch-depth: 0
+      - name: Restore Cached Files
+        id: filecache
+        with:
+          path: tests/data
+          key: testfiles
+          enableCrossOsArchive: true
+        uses: actions/cache/restore@5
       - uses: conda-incubator/setup-miniconda@v3
         with:
           auto-update-conda: true
diff --git a/tests/conftest.py b/tests/conftest.py
index cfd913f..a840fee 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -14,8 +14,6 @@
 
 # data subdir in the notebook folder
 DATASETS_DIR = u'data/'
-if not os.path.exists(DATASETS_DIR):
-    os.makedirs(DATASETS_DIR)
 
 alex1c = pooch.create(path=DATASETS_DIR, base_url='doi:10.6084/m9.figshare.1019906.v26')
 alex1c.load_registry_from_doi()
diff --git a/tests/fetch_files.py b/tests/fetch_files.py
new file mode 100644
index 0000000..1844347
--- /dev/null
+++ b/tests/fetch_files.py
@@ -0,0 +1,27 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Feb 21 07:24:07 2026
+
+@author: paul
+"""
+
+import pooch
+
+# data subdir in the notebook folder
+DATASETS_DIR = u'data/'
+
+alex1c = pooch.create(path=DATASETS_DIR, base_url='doi:10.6084/m9.figshare.1019906.v26')
+alex1c.load_registry_from_doi()
+_ = alex1c.fetch("12d_New_30p_320mW_steer_3.hdf5")
+
+phdf5 = pooch.create(path=DATASETS_DIR, base_url='doi:10.6084/m9.figshare.1456362.v15')
+phdf5.load_registry_from_doi()
+_ = phdf5.fetch("0023uLRpitc_NTP_20dT_0.5GndCl.hdf5")
+
+mphmm = pooch.create(path=DATASETS_DIR, base_url='doi:10.5281/zenodo.5902313')
+mphmm.load_registry_from_doi()
+hp3_files = ['HP3_TE150_SPC630.hdf5', 'HP3_TE200_SPC630.hdf5', 'HP3_TE250_SPC630.hdf5', 'HP3_TE300_SPC630.hdf5']
+for fn in hp3_files:
+    mphmm.fetch(fn)
+

From 2c1f11f2324266efeaeb074a3cee03e2697f251f Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Sat, 21 Feb 2026 09:15:20 +0100
Subject: [PATCH 109/142] Fix types in github actions

---
 .github/workflows/tests.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index eb80d92..9b49fc2 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -15,7 +15,7 @@ jobs:
       - name: Download Files
         run: |
           cd tests
-          pythom -m fetch_files.py
+          python -m fetch_files.py
       
   build:
     runs-on: ${{matrix.os}}
@@ -29,12 +29,12 @@ jobs:
           persist-credentials: false
           fetch-depth: 0
       - name: Restore Cached Files
+        uses: actions/cache/restore@v5
         id: filecache
         with:
           path: tests/data
           key: testfiles
           enableCrossOsArchive: true
-        uses: actions/cache/restore@5
       - uses: conda-incubator/setup-miniconda@v3
         with:
           auto-update-conda: true

From b8604dc7494d94bea325d35f0b93dc430ac3ab26 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Sat, 21 Feb 2026 09:19:43 +0100
Subject: [PATCH 110/142] Cache download installs pooch

---
 .github/workflows/tests.yml | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 9b49fc2..303353b 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -6,6 +6,16 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v6
+      - uses: conda-incubator/setup-miniconda@v3
+        with:
+          auto-update-conda: true
+          activate-environment: test
+          channels: conda-forge
+          python-version: ${{ matrix.python-version }}
+      - name: Install Pooch
+        shell: bash -l {0}
+        run: |
+          conda install -c conda-forge pooch
       - uses: actions/cache@v5
         id: filecache
         with:

From 3586ea050442ed64281bd0e89a286c10c5491343 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Sat, 21 Feb 2026 10:15:22 +0100
Subject: [PATCH 111/142] Test cached tests

---
 .github/workflows/build_wheel.yml | 34 ------------------------
 .github/workflows/tests.yml       | 43 ++++++++++++++++++++++++++-----
 2 files changed, 37 insertions(+), 40 deletions(-)
 delete mode 100644 .github/workflows/build_wheel.yml

diff --git a/.github/workflows/build_wheel.yml b/.github/workflows/build_wheel.yml
deleted file mode 100644
index 1d778c4..0000000
--- a/.github/workflows/build_wheel.yml
+++ /dev/null
@@ -1,34 +0,0 @@
-name: Build
-
-on:
-  push:
-    branches:
-      - master
-  pull_request:
-    branches:
-      - master
-
-jobs:
-  build_wheels:
-    name: Build wheels on ${{ matrix.os }}
-    runs-on: ${{ matrix.os }}
-    strategy:
-      matrix:
-        os: [ubuntu-latest, ubuntu-24.04-arm, windows-latest, windows-11-arm, macos-15-intel, macos-latest]
-    steps:
-      - uses: actions/checkout@v6
-        with:
-          persist-credentials: false
-          fetch-depth: 0
-      - name: Build wheels
-        uses: pypa/cibuildwheel@v3.3.1
-        env:
-          CIBW_SKIP: "pp* cp310-win_arm64"
-          # CIBW_TEST_REQUIRES: pytest, numpy == 1.20.1, matplotlib, scipy, pandas, tables, numba, seaborn, lmfit, phconvert
-          # CIBW_BEFORE_TEST: python -m pip install pytest
-          # CIBW_TEST_COMMAND: python -m pytest {package}/tests
-      
-      - uses: actions/upload-artifact@v4
-        with:
-          name: cibw-wheels-${{ matrix.os }}-${{ strategy.job-index }}
-          path: ./wheelhouse/*.whl
diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 303353b..7dc0de2 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -4,6 +4,7 @@ on: [push, pull_request]
 jobs:
   download:
     runs-on: ubuntu-latest
+    python-version: "3.12"
     steps:
       - uses: actions/checkout@v6
       - uses: conda-incubator/setup-miniconda@v3
@@ -16,18 +17,19 @@ jobs:
         shell: bash -l {0}
         run: |
           conda install -c conda-forge pooch
+      - name: Download Files
+        run: |
+          cd tests
+          python -m fetch_files.py
       - uses: actions/cache@v5
         id: filecache
         with:
           path: tests/data
           enableCrossOsArchive: true
           key: testfiles
-      - name: Download Files
-        run: |
-          cd tests
-          python -m fetch_files.py
-      
+
   build:
+    needs: download
     runs-on: ${{matrix.os}}
     strategy:
       matrix:
@@ -40,7 +42,6 @@ jobs:
           fetch-depth: 0
       - name: Restore Cached Files
         uses: actions/cache/restore@v5
-        id: filecache
         with:
           path: tests/data
           key: testfiles
@@ -76,3 +77,33 @@ jobs:
           cd ..
           cd tests
           python -m pytest
+
+  build_wheels:
+    needs: build
+    name: Build wheels on ${{ matrix.os }}
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [ubuntu-latest, ubuntu-24.04-arm, windows-latest, windows-11-arm, macos-15-intel, macos-latest]
+    steps:
+      - uses: actions/checkout@v6
+        with:
+          persist-credentials: false
+          fetch-depth: 0
+      - uses: actions/cache/restore@v5
+        with:
+          path: tests/data
+          enableCrossOsArchive: true
+          key: testfiles
+      - name: Build wheels
+        uses: pypa/cibuildwheel@v3.3.1
+        env:
+          CIBW_SKIP: "pp* cp310-win_arm64"
+          CIBW_TEST_REQUIRES: pytest, numpy == 1.20.1, matplotlib, scipy, pandas, tables, numba, seaborn, lmfit, phconvert
+          CIBW_BEFORE_TEST: python -m pip install pytest
+          CIBW_TEST_COMMAND: python -m pytest {package}/tests
+      
+      - uses: actions/upload-artifact@v4
+        with:
+          name: cibw-wheels-${{ matrix.os }}-${{ strategy.job-index }}
+          path: ./wheelhouse/*.whl

From 0c8e96b313d365933f0f8229577685bb7fa13555 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Sat, 21 Feb 2026 11:34:30 +0200
Subject: [PATCH 112/142] Fix cache python version in tests.yml

---
 .github/workflows/tests.yml | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 7dc0de2..0d160e0 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -4,7 +4,6 @@ on: [push, pull_request]
 jobs:
   download:
     runs-on: ubuntu-latest
-    python-version: "3.12"
     steps:
       - uses: actions/checkout@v6
       - uses: conda-incubator/setup-miniconda@v3
@@ -12,7 +11,7 @@ jobs:
           auto-update-conda: true
           activate-environment: test
           channels: conda-forge
-          python-version: ${{ matrix.python-version }}
+          python-version: "3.12"
       - name: Install Pooch
         shell: bash -l {0}
         run: |

From f4193b7a56c181069b9736ca50a2caf42aef5d04 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Sat, 21 Feb 2026 11:37:42 +0200
Subject: [PATCH 113/142] Update tests.yml

---
 .github/workflows/tests.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 0d160e0..1040c1b 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -17,6 +17,7 @@ jobs:
         run: |
           conda install -c conda-forge pooch
       - name: Download Files
+        shell: bash -l {0}
         run: |
           cd tests
           python -m fetch_files.py

From 40f59788d70736e867fea13528801963795d33ef Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Sat, 21 Feb 2026 10:45:30 +0100
Subject: [PATCH 114/142] remove -m from python to allow running of
 fetch_files.py

---
 .github/workflows/tests.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 1040c1b..0e478bc 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -20,7 +20,7 @@ jobs:
         shell: bash -l {0}
         run: |
           cd tests
-          python -m fetch_files.py
+          python fetch_files.py
       - uses: actions/cache@v5
         id: filecache
         with:
@@ -99,7 +99,7 @@ jobs:
         uses: pypa/cibuildwheel@v3.3.1
         env:
           CIBW_SKIP: "pp* cp310-win_arm64"
-          CIBW_TEST_REQUIRES: pytest, numpy == 1.20.1, matplotlib, scipy, pandas, tables, numba, seaborn, lmfit, phconvert
+          CIBW_TEST_REQUIRES: pytest, numpy, matplotlib, scipy, pandas, tables, numba, seaborn, lmfit, phconvert
           CIBW_BEFORE_TEST: python -m pip install pytest
           CIBW_TEST_COMMAND: python -m pytest {package}/tests
       

From 321c214c1befcd4e3e323ada0baba3ff5a15b190 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Sat, 21 Feb 2026 12:22:07 +0100
Subject: [PATCH 115/142] Rearange cache

---
 .github/workflows/tests.yml | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 0e478bc..317cc7c 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -6,6 +6,12 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v6
+      - uses: actions/cache@v5
+        id: filecache
+        with:
+          path: tests/data
+          enableCrossOsArchive: true
+          key: testfiles
       - uses: conda-incubator/setup-miniconda@v3
         with:
           auto-update-conda: true
@@ -21,13 +27,7 @@ jobs:
         run: |
           cd tests
           python fetch_files.py
-      - uses: actions/cache@v5
-        id: filecache
-        with:
-          path: tests/data
-          enableCrossOsArchive: true
-          key: testfiles
-
+      
   build:
     needs: download
     runs-on: ${{matrix.os}}

From 97890aa68a0fa97df07b733dbaf246e1fd85f7c7 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Sat, 21 Feb 2026 14:03:47 +0100
Subject: [PATCH 116/142] Update test.yaml for CIBW_TEST_REQURIES syntax

---
 .github/workflows/tests.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 317cc7c..4153cb4 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -99,7 +99,7 @@ jobs:
         uses: pypa/cibuildwheel@v3.3.1
         env:
           CIBW_SKIP: "pp* cp310-win_arm64"
-          CIBW_TEST_REQUIRES: pytest, numpy, matplotlib, scipy, pandas, tables, numba, seaborn, lmfit, phconvert
+          CIBW_TEST_REQUIRES: pytest numpy matplotlib scipy pandas tables numba seaborn lmfit phconvert
           CIBW_BEFORE_TEST: python -m pip install pytest
           CIBW_TEST_COMMAND: python -m pytest {package}/tests
       

From 27a0470fdf170e72bfdf0612d5f122424f5dd5cc Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Sat, 21 Feb 2026 15:30:11 +0200
Subject: [PATCH 117/142] Test how cache works

---
 .github/workflows/tests.yml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 4153cb4..5df530f 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -46,6 +46,8 @@ jobs:
           path: tests/data
           key: testfiles
           enableCrossOsArchive: true
+      - name: test move
+        run: cp tests/data/* *
       - uses: conda-incubator/setup-miniconda@v3
         with:
           auto-update-conda: true

From e02ce1a8fcc78962c3643b16eb707a4f2f879b55 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Sat, 21 Feb 2026 16:34:59 +0200
Subject: [PATCH 118/142] Update tests.yml

---
 .github/workflows/tests.yml | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 5df530f..f6091cd 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -9,7 +9,7 @@ jobs:
       - uses: actions/cache@v5
         id: filecache
         with:
-          path: tests/data
+          path: ~/tests/data
           enableCrossOsArchive: true
           key: testfiles
       - uses: conda-incubator/setup-miniconda@v3
@@ -27,6 +27,8 @@ jobs:
         run: |
           cd tests
           python fetch_files.py
+      - name: test move
+        run: cp tests/data/* *
       
   build:
     needs: download
@@ -43,11 +45,9 @@ jobs:
       - name: Restore Cached Files
         uses: actions/cache/restore@v5
         with:
-          path: tests/data
+          path: ~/tests/data
           key: testfiles
           enableCrossOsArchive: true
-      - name: test move
-        run: cp tests/data/* *
       - uses: conda-incubator/setup-miniconda@v3
         with:
           auto-update-conda: true
@@ -94,7 +94,7 @@ jobs:
           fetch-depth: 0
       - uses: actions/cache/restore@v5
         with:
-          path: tests/data
+          path: ~/tests/data
           enableCrossOsArchive: true
           key: testfiles
       - name: Build wheels

From 2c7f6c7a44e3297c3f79dd7cbefc809cae47d287 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Sat, 21 Feb 2026 16:38:25 +0200
Subject: [PATCH 119/142] Update tests.yml

---
 .github/workflows/tests.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index f6091cd..8061ffe 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -28,7 +28,7 @@ jobs:
           cd tests
           python fetch_files.py
       - name: test move
-        run: cp tests/data/* *
+        run: cp ~/tests/data/* *
       
   build:
     needs: download

From 8fb7f8f2505f8effac3f1421617093de9fa40fd1 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Sat, 21 Feb 2026 16:41:57 +0200
Subject: [PATCH 120/142] Update tests.yml

---
 .github/workflows/tests.yml | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 8061ffe..e0b3b61 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -9,7 +9,7 @@ jobs:
       - uses: actions/cache@v5
         id: filecache
         with:
-          path: ~/tests/data
+          path: ./tests/data
           enableCrossOsArchive: true
           key: testfiles
       - uses: conda-incubator/setup-miniconda@v3
@@ -28,7 +28,9 @@ jobs:
           cd tests
           python fetch_files.py
       - name: test move
-        run: cp ~/tests/data/* *
+        run: |
+          ls
+          cp ./tests/data/* *
       
   build:
     needs: download
@@ -45,7 +47,7 @@ jobs:
       - name: Restore Cached Files
         uses: actions/cache/restore@v5
         with:
-          path: ~/tests/data
+          path: ./tests/data
           key: testfiles
           enableCrossOsArchive: true
       - uses: conda-incubator/setup-miniconda@v3

From c69d640bdd14a5e83c11a3f34698bf5a84b45483 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Sat, 21 Feb 2026 16:52:55 +0200
Subject: [PATCH 121/142] Update tests.yml

---
 .github/workflows/tests.yml | 23 +++++++++++++----------
 1 file changed, 13 insertions(+), 10 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index e0b3b61..3c242d4 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -6,12 +6,6 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v6
-      - uses: actions/cache@v5
-        id: filecache
-        with:
-          path: ./tests/data
-          enableCrossOsArchive: true
-          key: testfiles
       - uses: conda-incubator/setup-miniconda@v3
         with:
           auto-update-conda: true
@@ -29,9 +23,14 @@ jobs:
           python fetch_files.py
       - name: test move
         run: |
-          ls
-          cp ./tests/data/* *
-      
+          ls tests/data
+      - uses: actions/cache@v5
+        id: filecache
+        with:
+          path: ./tests/data
+          enableCrossOsArchive: true
+          key: testfiles
+  
   build:
     needs: download
     runs-on: ${{matrix.os}}
@@ -45,11 +44,15 @@ jobs:
           persist-credentials: false
           fetch-depth: 0
       - name: Restore Cached Files
+        id: cache-action
         uses: actions/cache/restore@v5
         with:
-          path: ./tests/data
+          path: tests/data
           key: testfiles
           enableCrossOsArchive: true
+      - name: Check Restore
+        shell: bash -l {0}
+        run: ls tests
       - uses: conda-incubator/setup-miniconda@v3
         with:
           auto-update-conda: true

From 3393dfc90327128bbe9c998562edfdd0b94935c1 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Sat, 21 Feb 2026 17:07:32 +0200
Subject: [PATCH 122/142] Update tests.yml

---
 .github/workflows/tests.yml | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 3c242d4..38d4a99 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -30,6 +30,9 @@ jobs:
           path: ./tests/data
           enableCrossOsArchive: true
           key: testfiles
+      - name: Check Saved
+        shell: bash -l {0}
+        run: ls tests/data
   
   build:
     needs: download
@@ -52,7 +55,7 @@ jobs:
           enableCrossOsArchive: true
       - name: Check Restore
         shell: bash -l {0}
-        run: ls tests
+        run: ls tests/data
       - uses: conda-incubator/setup-miniconda@v3
         with:
           auto-update-conda: true

From a8fe8f2b8a27282eddefe37e0242044d986309f8 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Sat, 21 Feb 2026 17:48:09 +0200
Subject: [PATCH 123/142] Update tests.yml

---
 .github/workflows/tests.yml | 11 ++++++++---
 1 file changed, 8 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 38d4a99..0d479c6 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -100,11 +100,16 @@ jobs:
         with:
           persist-credentials: false
           fetch-depth: 0
-      - uses: actions/cache/restore@v5
+      - name: Restore Cached Files
+        id: cache-action
+        uses: actions/cache/restore@v5
         with:
-          path: ~/tests/data
-          enableCrossOsArchive: true
+          path: tests/data
           key: testfiles
+          enableCrossOsArchive: true
+      - name: Check Restore
+        shell: bash -l {0}
+        run: ls tests/data
       - name: Build wheels
         uses: pypa/cibuildwheel@v3.3.1
         env:

From 7c493f2075549780bf08c026ff9b7a8623925f43 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Sun, 22 Feb 2026 16:57:20 +0200
Subject: [PATCH 124/142] Update tests.yml

---
 .github/workflows/tests.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 0d479c6..f1a9ef8 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -116,7 +116,7 @@ jobs:
           CIBW_SKIP: "pp* cp310-win_arm64"
           CIBW_TEST_REQUIRES: pytest numpy matplotlib scipy pandas tables numba seaborn lmfit phconvert
           CIBW_BEFORE_TEST: python -m pip install pytest
-          CIBW_TEST_COMMAND: python -m pytest {package}/tests
+          CIBW_TEST_COMMAND: python -m pytest {package}/tests/test_ph_sel.py {package}/tests/test_exp_fitting.py {package}/tests/test_Bursts.py
       
       - uses: actions/upload-artifact@v4
         with:

From 5f850a64ae812aec9fb85cd1d9e89bc7ffffbc27 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Mon, 23 Feb 2026 10:18:54 +0100
Subject: [PATCH 125/142] Tests rely on pre-download through fetch_files.py, do
 not use pooch directly

---
 .github/workflows/tests.yml                   |  3 +-
 notebooks/Example - 2CDE Method.ipynb         |  9 ++---
 .../Example - Background estimation.ipynb     |  9 ++---
 .../Example - Burst Variance Analysis.ipynb   |  9 ++---
 ...le - Customize the us-ALEX histogram.ipynb |  9 ++---
 ...ting Burst Data Including Timestamps.ipynb |  9 ++---
 .../Example - FRET histogram fitting.ipynb    |  9 ++---
 ...le - Plotting timetraces with bursts.ipynb |  9 ++---
 ...Example - Selecting FRET populations.ipynb |  9 ++---
 ...- Working with timestamps and bursts.ipynb |  9 ++---
 ...Example- Combining Technical Repeats.ipynb |  9 ++---
 ...ursts - 8-spot smFRET burst analysis.ipynb | 34 ++-----------------
 notebooks/FRETBursts - ns-ALEX example.ipynb  |  9 ++---
 ...rsts - us-ALEX smFRET burst analysis.ipynb | 13 ++-----
 tests/conftest.py                             | 27 +++++++++++----
 15 files changed, 48 insertions(+), 128 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index f1a9ef8..09e18b6 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -116,8 +116,7 @@ jobs:
           CIBW_SKIP: "pp* cp310-win_arm64"
           CIBW_TEST_REQUIRES: pytest numpy matplotlib scipy pandas tables numba seaborn lmfit phconvert
           CIBW_BEFORE_TEST: python -m pip install pytest
-          CIBW_TEST_COMMAND: python -m pytest {package}/tests/test_ph_sel.py {package}/tests/test_exp_fitting.py {package}/tests/test_Bursts.py
-      
+          CIBW_TEST_COMMAND: python -m pytest {package}/tests/ 
       - uses: actions/upload-artifact@v4
         with:
           name: cibw-wheels-${{ matrix.os }}-${{ strategy.job-index }}
diff --git a/notebooks/Example - 2CDE Method.ipynb b/notebooks/Example - 2CDE Method.ipynb
index 7313506..70ec873 100644
--- a/notebooks/Example - 2CDE Method.ipynb	
+++ b/notebooks/Example - 2CDE Method.ipynb	
@@ -120,12 +120,7 @@
     }
    ],
    "source": [
-    "import pooch\n",
-    "\n",
-    "phdf5 = pooch.create(path=\"../tests/data\", base_url='doi:10.6084/m9.figshare.1456362.v15')\n",
-    "phdf5.load_registry_from_doi()\n",
-    "\n",
-    "filename = phdf5.fetch(\"0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\")\n",
+    "filename = \"../tests/data/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\"\n",
     "\n",
     "d = loader.photon_hdf5(filename)\n",
     "loader.alex_apply_period(d)\n",
@@ -882,7 +877,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.0"
+   "version": "3.10.14"
   },
   "toc": {
    "colors": {
diff --git a/notebooks/Example - Background estimation.ipynb b/notebooks/Example - Background estimation.ipynb
index 148314b..3bd2a51 100644
--- a/notebooks/Example - Background estimation.ipynb	
+++ b/notebooks/Example - Background estimation.ipynb	
@@ -66,12 +66,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pooch\n",
-    "\n",
-    "phdf5 = pooch.create(path=\"../tests/data/\", base_url='doi:10.6084/m9.figshare.1456362.v15')\n",
-    "phdf5.load_registry_from_doi()\n",
-    "\n",
-    "full_name = phdf5.fetch(\"0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\")\n",
+    "full_name = \"../tests/data/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\"\n",
     "\n",
     "d = loader.photon_hdf5(full_name)\n",
     "loader.alex_apply_period(d)"
@@ -378,7 +373,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.0"
+   "version": "3.10.14"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/Example - Burst Variance Analysis.ipynb b/notebooks/Example - Burst Variance Analysis.ipynb
index b4f98b1..4b185eb 100644
--- a/notebooks/Example - Burst Variance Analysis.ipynb	
+++ b/notebooks/Example - Burst Variance Analysis.ipynb	
@@ -55,12 +55,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pooch\n",
-    "\n",
-    "phdf5 = pooch.create(path=\"../tests/data/\", base_url='doi:10.6084/m9.figshare.1456362.v15')\n",
-    "phdf5.load_registry_from_doi()\n",
-    "\n",
-    "full_fname = phdf5.fetch(\"0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\")\n",
+    "full_fname = \"../tests/data/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\"\n",
     "\n",
     "d = loader.photon_hdf5(full_fname)\n",
     "loader.alex_apply_period(d)\n",
@@ -283,7 +278,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.0"
+   "version": "3.10.14"
   },
   "toc": {
    "colors": {
diff --git a/notebooks/Example - Customize the us-ALEX histogram.ipynb b/notebooks/Example - Customize the us-ALEX histogram.ipynb
index a2acb5a..15a0560 100644
--- a/notebooks/Example - Customize the us-ALEX histogram.ipynb	
+++ b/notebooks/Example - Customize the us-ALEX histogram.ipynb	
@@ -63,12 +63,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pooch\n",
-    "\n",
-    "phdf5 = pooch.create(path=\"../tests/data/\", base_url='doi:10.6084/m9.figshare.1456362.v15')\n",
-    "phdf5.load_registry_from_doi()\n",
-    "\n",
-    "full_fname = phdf5.fetch(\"0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\")\n",
+    "full_fname = \"../tests/data/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\"\n",
     "\n",
     "d = loader.photon_hdf5(full_fname)\n",
     "loader.alex_apply_period(d)\n",
@@ -465,7 +460,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.0"
+   "version": "3.10.14"
   },
   "toc": {
    "colors": {
diff --git a/notebooks/Example - Exporting Burst Data Including Timestamps.ipynb b/notebooks/Example - Exporting Burst Data Including Timestamps.ipynb
index 13a2781..895b6bf 100644
--- a/notebooks/Example - Exporting Burst Data Including Timestamps.ipynb	
+++ b/notebooks/Example - Exporting Burst Data Including Timestamps.ipynb	
@@ -86,12 +86,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pooch\n",
-    "\n",
-    "phdf5 = pooch.create(path=\"../tests/data/\", base_url='doi:10.6084/m9.figshare.1456362.v15')\n",
-    "phdf5.load_registry_from_doi()\n",
-    "\n",
-    "full_path = phdf5.fetch(\"0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\")"
+    "full_path = \"../tests/data/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\""
    ]
   },
   {
@@ -753,7 +748,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.0"
+   "version": "3.10.14"
   },
   "nav_menu": {},
   "toc": {
diff --git a/notebooks/Example - FRET histogram fitting.ipynb b/notebooks/Example - FRET histogram fitting.ipynb
index 5910c7a..b7c0cf7 100644
--- a/notebooks/Example - FRET histogram fitting.ipynb	
+++ b/notebooks/Example - FRET histogram fitting.ipynb	
@@ -64,12 +64,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pooch\n",
-    "\n",
-    "phdf5 = pooch.create(path=\"../tests/data/\", base_url='doi:10.6084/m9.figshare.1456362.v15')\n",
-    "phdf5.load_registry_from_doi()\n",
-    "\n",
-    "full_fname = phdf5.fetch(\"0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\")\n",
+    "full_fname = \"../tests/data/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\"\n",
     "\n",
     "d = loader.photon_hdf5(full_fname)\n",
     "loader.alex_apply_period(d)\n",
@@ -263,7 +258,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.0"
+   "version": "3.10.14"
   },
   "toc": {
    "colors": {
diff --git a/notebooks/Example - Plotting timetraces with bursts.ipynb b/notebooks/Example - Plotting timetraces with bursts.ipynb
index 5fb7369..c62243b 100644
--- a/notebooks/Example - Plotting timetraces with bursts.ipynb	
+++ b/notebooks/Example - Plotting timetraces with bursts.ipynb	
@@ -64,12 +64,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pooch\n",
-    "\n",
-    "phdf5 = pooch.create(path=\"../tests/data/\", base_url='doi:10.6084/m9.figshare.1456362.v15')\n",
-    "phdf5.load_registry_from_doi()\n",
-    "\n",
-    "full_fname = phdf5.fetch(\"0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\")\n",
+    "full_fname = \"../tests/data/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\"\n",
     "\n",
     "d = loader.photon_hdf5(full_fname)\n",
     "loader.alex_apply_period(d)\n",
@@ -176,7 +171,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.0"
+   "version": "3.10.14"
   },
   "toc": {
    "colors": {
diff --git a/notebooks/Example - Selecting FRET populations.ipynb b/notebooks/Example - Selecting FRET populations.ipynb
index ab0632e..9acc3f3 100644
--- a/notebooks/Example - Selecting FRET populations.ipynb	
+++ b/notebooks/Example - Selecting FRET populations.ipynb	
@@ -63,12 +63,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pooch\n",
-    "\n",
-    "phdf5 = pooch.create(path=\"../tests/data/\", base_url='doi:10.6084/m9.figshare.1456362.v15')\n",
-    "phdf5.load_registry_from_doi()\n",
-    "\n",
-    "full_fname = phdf5.fetch(\"0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\")\n",
+    "full_fname = \"../tests/data/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\"\n",
     "\n",
     "d = loader.photon_hdf5(full_fname)\n",
     "loader.alex_apply_period(d)\n",
@@ -200,7 +195,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.0"
+   "version": "3.10.14"
   },
   "toc": {
    "colors": {
diff --git a/notebooks/Example - Working with timestamps and bursts.ipynb b/notebooks/Example - Working with timestamps and bursts.ipynb
index 564117a..23b3031 100644
--- a/notebooks/Example - Working with timestamps and bursts.ipynb	
+++ b/notebooks/Example - Working with timestamps and bursts.ipynb	
@@ -46,12 +46,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pooch\n",
-    "\n",
-    "phdf5 = pooch.create(path=\"../tests/data/\", base_url='doi:10.6084/m9.figshare.1456362.v15')\n",
-    "phdf5.load_registry_from_doi()\n",
-    "\n",
-    "filename = phdf5.fetch(\"0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\")\n",
+    "filename = \"../tests/data/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\"\n",
     "\n",
     "d = loader.photon_hdf5(filename)\n",
     "loader.alex_apply_period(d)\n",
@@ -692,7 +687,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.0"
+   "version": "3.10.14"
   },
   "toc": {
    "colors": {
diff --git a/notebooks/Example- Combining Technical Repeats.ipynb b/notebooks/Example- Combining Technical Repeats.ipynb
index 64c8703..01597a1 100644
--- a/notebooks/Example- Combining Technical Repeats.ipynb	
+++ b/notebooks/Example- Combining Technical Repeats.ipynb	
@@ -43,17 +43,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pooch\n",
-    "\n",
-    "mphmm = pooch.create(path='../tests/data/', base_url='doi:10.5281/zenodo.5902313')\n",
-    "mphmm.load_registry_from_doi()\n",
-    "\n",
     "filenames = [\"HP3_TE150_SPC630.hdf5\", \n",
     "             \"HP3_TE200_SPC630.hdf5\", \n",
     "             \"HP3_TE250_SPC630.hdf5\", \n",
     "             \"HP3_TE300_SPC630.hdf5\"]\n",
     "\n",
-    "filenames = [mphmm.fetch(fn) for fn in filenames]\n",
+    "filenames = ['../tests/data/' + fn for fn in filenames]\n",
     "filenames"
    ]
   },
@@ -208,7 +203,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.0"
+   "version": "3.10.14"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/FRETBursts - 8-spot smFRET burst analysis.ipynb b/notebooks/FRETBursts - 8-spot smFRET burst analysis.ipynb
index 945d9b5..c20309d 100644
--- a/notebooks/FRETBursts - 8-spot smFRET burst analysis.ipynb	
+++ b/notebooks/FRETBursts - 8-spot smFRET burst analysis.ipynb	
@@ -65,36 +65,6 @@
     "import phconvert; phconvert.__version__"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Downloading the sample data file"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The complete example dataset can be downloaded \n",
-    "from [here](http://dx.doi.org/10.6084/m9.figshare.1019906).\n",
-    "\n",
-    "Here we download an 8-spot smFRET measurement file using \n",
-    "the `download_file` in FRETBursts: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pooch\n",
-    "\n",
-    "alex1c = pooch.create(path='../tests/data/', base_url='doi:10.6084/m9.figshare.1019906.v26')\n",
-    "alex1c.load_registry_from_doi()"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -108,7 +78,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "filename = alex1c.fetch(\"12d_New_30p_320mW_steer_3.hdf5\")"
+    "filename = \"../tests/data/12d_New_30p_320mW_steer_3.hdf5\""
    ]
   },
   {
@@ -607,7 +577,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.0"
+   "version": "3.10.14"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/FRETBursts - ns-ALEX example.ipynb b/notebooks/FRETBursts - ns-ALEX example.ipynb
index 6748268..29b42cb 100644
--- a/notebooks/FRETBursts - ns-ALEX example.ipynb	
+++ b/notebooks/FRETBursts - ns-ALEX example.ipynb	
@@ -72,12 +72,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pooch\n",
-    "\n",
-    "nsfshare = pooch.create(path='../tests/data/', base_url='doi:10.6084/m9.figshare.1456362.v15')\n",
-    "nsfshare.load_registry_from_doi()\n",
-    "\n",
-    "filename = nsfshare.fetch('dsdna_d7_d17_50_50_1.hdf5')\n",
+    "filename = '../tests/data/dsdna_d7_d17_50_50_1.hdf5'\n",
     "filename"
    ]
   },
@@ -500,7 +495,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.0"
+   "version": "3.10.14"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/FRETBursts - us-ALEX smFRET burst analysis.ipynb b/notebooks/FRETBursts - us-ALEX smFRET burst analysis.ipynb
index 49a86aa..d046e57 100644
--- a/notebooks/FRETBursts - us-ALEX smFRET burst analysis.ipynb	
+++ b/notebooks/FRETBursts - us-ALEX smFRET burst analysis.ipynb	
@@ -122,9 +122,7 @@
     "> This section is essentially deprecated, only included because needed for testing.\n",
     "\n",
     "The full list of smFRET measurements used in the [FRETBursts tutorials](https://github.com/OpenSMFS/FRETBursts_notebooks) \n",
-    "can be found on [Figshare](http://dx.doi.org/10.6084/m9.figshare.1456362).\n",
-    "\n",
-    "This is the file we will download:"
+    "can be found on [Figshare](http://dx.doi.org/10.6084/m9.figshare.1456362)."
    ]
   },
   {
@@ -133,12 +131,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pooch\n",
-    "\n",
-    "phdf5 = pooch.create(path=\"../tests/data/\", base_url='doi:10.6084/m9.figshare.1456362.v15')\n",
-    "phdf5.load_registry_from_doi()\n",
-    "\n",
-    "full_path = phdf5.fetch(\"0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\")"
+    "full_path = \"../tests/data/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\""
    ]
   },
   {
@@ -1891,7 +1884,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.0"
+   "version": "3.10.14"
   }
  },
  "nbformat": 4,
diff --git a/tests/conftest.py b/tests/conftest.py
index a840fee..f4cf456 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -24,20 +24,33 @@
 mphmm = pooch.create(path=DATASETS_DIR, base_url='doi:10.5281/zenodo.5902313')
 mphmm.load_registry_from_doi()
 
+fetchers = dict()
+
+def fetch_if(name:str, doi:str)->str:
+    fn = DATASETS_DIR + name
+    if not os.path.exists(fn):
+        if doi not in fetchers:
+            fetch = pooch.create(path=DATASETS_DIR, base_url=doi)
+            fetch.load_registry_from_doi()
+            fetchers[doi] = fetch
+        fetchers[doi].fetch(name)
+    return fn
+
+
 def _alex_process(d):
     loader.alex_apply_period(d)
     d.calc_bg(bg.exp_fit, time_s=30, tail_min_us=300)
     d.burst_search(L=10, m=10, F=7)
 
 def load_dataset_1ch(process=True):
-    fname = phdf5.fetch("0023uLRpitc_NTP_20dT_0.5GndCl.hdf5")
+    fname = fetch_if("0023uLRpitc_NTP_20dT_0.5GndCl.hdf5", 'doi:10.6084/m9.figshare.1456362.v15')
     d = loader.photon_hdf5(fname)
     if process:
         _alex_process(d)
     return d
 
 def load_dataset_1ch_nsalex(process=True):
-    fname =mphmm.fetch("HP3_TE150_SPC630.hdf5")
+    fname = fetch_if("HP3_TE150_SPC630.hdf5", 'doi:10.5281/zenodo.5902313')
     d = loader.photon_hdf5(fname)
     if process:
         _alex_process(d)
@@ -45,10 +58,10 @@ def load_dataset_1ch_nsalex(process=True):
 
 @pytest.fixture
 def dataset_1ch_file():
-    return phdf5.fetch("0023uLRpitc_NTP_20dT_0.5GndCl.hdf5")
+    return fetch_if("0023uLRpitc_NTP_20dT_0.5GndCl.hdf5", 'doi:10.6084/m9.figshare.1456362.v15')
 
 def load_dataset_8ch():
-    fname = alex1c.fetch("12d_New_30p_320mW_steer_3.hdf5")
+    fname = fetch_if("12d_New_30p_320mW_steer_3.hdf5", 'doi:10.6084/m9.figshare.1019906.v26')
     d = loader.photon_hdf5(fname)
     d.calc_bg(bg.exp_fit, time_s=30, tail_min_us=300)
     d.burst_search(L=10, m=10, F=7)
@@ -56,10 +69,10 @@ def load_dataset_8ch():
 
 @pytest.fixture
 def fake_pax_file():
-    return phdf5.fetch("0023uLRpitc_NTP_20dT_0.5GndCl.hdf5")
+    return fetch_if("0023uLRpitc_NTP_20dT_0.5GndCl.hdf5", 'doi:10.6084/m9.figshare.1456362.v15')
 
 def load_fake_pax():
-    fname = phdf5.fetch("0023uLRpitc_NTP_20dT_0.5GndCl.hdf5")
+    fname = fetch_if("0023uLRpitc_NTP_20dT_0.5GndCl.hdf5", 'doi:10.6084/m9.figshare.1456362.v15')
     d = loader.photon_hdf5(fname)
     d.add(ALEX=False, meas_type='PAX')
     loader.alex_apply_period(d)
@@ -70,7 +83,7 @@ def load_fake_pax():
     
 def load_dataset_grouped(process=True):
     fn = ['HP3_TE150_SPC630.hdf5', 'HP3_TE200_SPC630.hdf5', 'HP3_TE250_SPC630.hdf5', 'HP3_TE300_SPC630.hdf5']
-    fn = [mphmm.fetch(f) for f in fn]
+    fn = [fetch_if(f, 'doi:10.5281/zenodo.5902313') for f in fn]
     d = loader.photon_hdf5(fn)
     if process:
         _alex_process(d)

From e1a1f029c0e0a81fd594a17e81fb1ce2156efbfb Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Mon, 23 Feb 2026 10:29:03 +0100
Subject: [PATCH 126/142] Add file needed for notebooks to fetch_files.py

---
 tests/fetch_files.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tests/fetch_files.py b/tests/fetch_files.py
index 1844347..a47f359 100644
--- a/tests/fetch_files.py
+++ b/tests/fetch_files.py
@@ -18,6 +18,7 @@
 phdf5 = pooch.create(path=DATASETS_DIR, base_url='doi:10.6084/m9.figshare.1456362.v15')
 phdf5.load_registry_from_doi()
 _ = phdf5.fetch("0023uLRpitc_NTP_20dT_0.5GndCl.hdf5")
+_ = phdf5.fetch("dsdna_d7_d17_50_50_1.hdf5")
 
 mphmm = pooch.create(path=DATASETS_DIR, base_url='doi:10.5281/zenodo.5902313')
 mphmm.load_registry_from_doi()

From 486de20a4c11cf828ea51c43c15122ac5c638275 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Mon, 23 Feb 2026 10:56:30 +0100
Subject: [PATCH 127/142] Reduce necessary file downloads

---
 .github/workflows/tests.yml                   |   5 +-
 ...le - Plotting timetraces with bursts.ipynb |   2 +-
 ...Example - Selecting FRET populations.ipynb |   6 +-
 ...- Working with timestamps and bursts.ipynb |   2 +-
 ...Example- Combining Technical Repeats.ipynb |   5 +-
 notebooks/FRETBursts - ns-ALEX example.ipynb  | 425 +++++++++++++++---
 tests/conftest.py                             |  16 +-
 tests/fetch_files.py                          |   9 +-
 8 files changed, 373 insertions(+), 97 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 09e18b6..7b9ba6c 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -21,9 +21,6 @@ jobs:
         run: |
           cd tests
           python fetch_files.py
-      - name: test move
-        run: |
-          ls tests/data
       - uses: actions/cache@v5
         id: filecache
         with:
@@ -32,7 +29,7 @@ jobs:
           key: testfiles
       - name: Check Saved
         shell: bash -l {0}
-        run: ls tests/data
+        run: ls tests/data -l
   
   build:
     needs: download
diff --git a/notebooks/Example - Plotting timetraces with bursts.ipynb b/notebooks/Example - Plotting timetraces with bursts.ipynb
index c62243b..53e3f07 100644
--- a/notebooks/Example - Plotting timetraces with bursts.ipynb	
+++ b/notebooks/Example - Plotting timetraces with bursts.ipynb	
@@ -171,7 +171,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.13.0"
   },
   "toc": {
    "colors": {
diff --git a/notebooks/Example - Selecting FRET populations.ipynb b/notebooks/Example - Selecting FRET populations.ipynb
index 9acc3f3..2d6c940 100644
--- a/notebooks/Example - Selecting FRET populations.ipynb	
+++ b/notebooks/Example - Selecting FRET populations.ipynb	
@@ -89,7 +89,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [],
    "source": [
     "ds_no_Aonly = ds.select_bursts(select_bursts.size, th1=30)\n",
@@ -195,7 +197,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.13.0"
   },
   "toc": {
    "colors": {
diff --git a/notebooks/Example - Working with timestamps and bursts.ipynb b/notebooks/Example - Working with timestamps and bursts.ipynb
index 23b3031..3bfe398 100644
--- a/notebooks/Example - Working with timestamps and bursts.ipynb	
+++ b/notebooks/Example - Working with timestamps and bursts.ipynb	
@@ -687,7 +687,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.13.0"
   },
   "toc": {
    "colors": {
diff --git a/notebooks/Example- Combining Technical Repeats.ipynb b/notebooks/Example- Combining Technical Repeats.ipynb
index 01597a1..3355d7a 100644
--- a/notebooks/Example- Combining Technical Repeats.ipynb	
+++ b/notebooks/Example- Combining Technical Repeats.ipynb	
@@ -45,8 +45,7 @@
    "source": [
     "filenames = [\"HP3_TE150_SPC630.hdf5\", \n",
     "             \"HP3_TE200_SPC630.hdf5\", \n",
-    "             \"HP3_TE250_SPC630.hdf5\", \n",
-    "             \"HP3_TE300_SPC630.hdf5\"]\n",
+    "             ]\n",
     "\n",
     "filenames = ['../tests/data/' + fn for fn in filenames]\n",
     "filenames"
@@ -203,7 +202,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.13.0"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/FRETBursts - ns-ALEX example.ipynb b/notebooks/FRETBursts - ns-ALEX example.ipynb
index 29b42cb..e8e5b01 100644
--- a/notebooks/FRETBursts - ns-ALEX example.ipynb	
+++ b/notebooks/FRETBursts - ns-ALEX example.ipynb	
@@ -35,16 +35,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/paul/Python/FRETBursts/fretbursts/__init__.py:67: UserWarning:  - Cannot import QT, custom GUI widgets disabled.\n",
+      "  warnings.warn((' - Cannot import QT, custom GUI widgets disabled.'))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " - Optimized (cython) burst search loaded.\n",
+      "WARNING: QT not installed. No GUI scrolling available.\n",
+      "--------------------------------------------------------------\n",
+      " You are running FRETBursts (version 0.7.post189+g4375ef6c8).\n",
+      "\n",
+      " If you use this software please cite the following paper:\n",
+      "\n",
+      "   FRETBursts: An Open Source Toolkit for Analysis of Freely-Diffusing Single-Molecule FRET\n",
+      "   Ingargiola et al. (2016). http://dx.doi.org/10.1371/journal.pone.0160716 \n",
+      "\n",
+      "--------------------------------------------------------------\n"
+     ]
+    }
+   ],
    "source": [
     "from fretbursts import *"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -68,11 +94,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "filename = '../tests/data/dsdna_d7_d17_50_50_1.hdf5'\n",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'../tests/data/dsdna_d7_d17_50_50_1.hdf5'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filename = '../tests/data/\"HP3_TE150_SPC630.hdf5\"'\n",
     "filename"
    ]
   },
@@ -87,7 +124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -97,9 +134,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Perfect, I found the file!\n"
+     ]
+    }
+   ],
    "source": [
     "import os\n",
     "if os.path.isfile(filename):\n",
@@ -119,7 +164,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -129,27 +174,62 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(2268.2743759426116)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "d.time_max"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[array([6, 6, 4, ..., 6, 6, 6], dtype=uint8)]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "d.det_t"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Detector    Counts\n",
+      "--------   --------\n",
+      "       0          1\n",
+      "       1          1\n",
+      "       4    3345380\n",
+      "       6    6371466\n"
+     ]
+    }
+   ],
    "source": [
     "print(\"Detector    Counts\")\n",
     "print(\"--------   --------\")\n",
@@ -159,7 +239,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -168,18 +248,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[array([ 372,  579, 1926, ..., 2553, 1182,  316], dtype=uint16)]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "d.nanotimes_t"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "bpl.plot_alternation_hist(d)"
    ]
@@ -195,9 +297,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "# Total photons (after ALEX selection):     8,862,749\n",
+      "#  D  photons in D+A excitation periods:    2,866,446\n",
+      "#  A  photons in D+A excitation periods:    5,996,303\n",
+      "# D+A photons in  D  excitation period:     4,093,146\n",
+      "# D+A photons in  A  excitation period:     4,769,603\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "loader.alex_apply_period(d)"
    ]
@@ -211,27 +326,78 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " - Calculating BG rates ... Channel 0\n",
+      "[DONE]\n"
+     ]
+    }
+   ],
    "source": [
     "d.calc_bg(fun=bg.exp_fit, time_s=30, tail_min_us='auto', F_bg=1.7)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'data_dsdna_d7_d17_50_50_1'}, xlabel='Time (s)', ylabel='BG rate (kcps)'>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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++1iyZInSyFSKc3aUV/6zhoeH44MPPsAnn3yCGTNm6FW26OhoLFq0CM+ePUNmZiZ4PB4CAgLQt29fTJgwQSUYLSkpwcaNG3H48GGkpqbCx8cHo0aNwujRo3Xa78GDB7F//37ExMQgNzcXNjY2aNy4McaPH4+OHTuqrB8TE4OffvoJN27cgEgkQrNmzTBv3jw0bNhQp/3GxcVp7Kvi4+OjcgwQQkwbBQ6EEEIIIYQQRtTHgRBCCCGEEMKI+jgQQggHxGIxxGKx1nWqS6fSyvqspaWlWpfz+XzGGamNqTLKJ5FIIBKJtK4jEAg4n3tBJBJBW8KCbEABQkj1QqlKhBDCgTVr1mDt2rVa19G1L4SpWrBggcpIP+Xp2heCibZceZnp06fj008/5XS/utDWXwIA3nvvPSxdupTTfR4+fFhpZnR1FPu6cGX06NG4deuWxuXUf4GQ6okCB0II4UBycjJSUlK0rlO3bl29RmIyNXFxcfIJ3jSRzXzNleLiYsZgxN3dXWV27Yr08OFDrcudnJw4DxwzMzMRFxendZ2aNWvC1taW0/1GRkYiLy9P43KhUMgYSBFCqh4KHAghhBBCCCGMqHM0IYQQQgghhBEFDoQQQgghhBBG1WOIjwokkUggFldOdhefz6u0fZOqiY4Zois6Zoiu6JghuqJjxvTw+TxWo69R4KAjsViCjAzNHcKMxcyMDycnG+Tk5KO0VPswiIQAdMwQ3dExQ3RFxwzRFR0zpsnZ2QYCAXPgQKlKhBBCCCGEEEYUOBBCCCGEEEIYUeBACCGEEEIIYUSBAyGEEEIIIYQRBQ6EEEIIIYQQRhQ4EEIIIYQQQhhR4EAIIYQQQghhRPM4kCpHJBIhPPw20tJS4erqhhYtWkEgEFR2sQghhBBCqjUKHEiVEhp6HsuWLUZycpL8NQ8PT8yfvxAhIT0AUGBBCCGEEGIMFDiQKiM09Dzmzp0JiUR5mvqUlGTMnTsTy5evBgDGwIIQQgghVR9VFFY8nqT8UxjRSiQSIyMjr8L3K5uiPTMz762col0kEqFPnxClgEARj8eDvb0DsrOzAUhUlgHA8uWr36rg4W0/Zoju6JghuqJjhuiKq2OGTQYCYc/Z2QYCAXPXZ+ocTaqE8PDbGoMGAJBIJMjOzkL5oEG2DJC2RIhEIiOVkBBCCCEVQZaBUP65QJaBEBp6vpJKVv1R4ECqhLS0VIPeL5FIkJychPDw2xyViBD9iUQihIXdxJkzJxEWdpMCWkIIYUkkEmHZssUqacsAVRRWBOrjQKoEV1c3TrZjaACiiHIriT6oeZ0QQvTHJgNBVlHYunVbrdui+7juKHAgVUKLFq3g4uKK9PQ0g7bDVQBCD39EH2w6+NPxQwgxdZX5wM22ApBpPbqP64dSlUiVIBAI4OPjq3aZrPOzg4Oj/N/q1vHw8ESLFq0MLgvlVhJ9UPM6IaQ6CA09jz59QjB58lh88cVcTJ48Fn36hFTYvY9tBaC29eg+rj8KHEiV8PjxQzx4cA8AwOMpH7bu7h745Zdf8c03//tvufrgYf78hQbXiFTHhz/Kt1dmrO9Dl+Z1QqoCkUiEW7du4ujRo7h1S79zha4/VYspPHC3aNEKNjY2GpczVRRWx/t4RaJUJWLyJBIJVq/+Rf73vHlfoHbtOmqbSJcvX63S9AgAc+d+wUnTI5e5laaAq6ba6pInasyma66a16uj6nL8ANx8lqrwfXAxGSelilQtTA/cPB4Py5YtRpcuIUY9XnNyclBSUqJxuUQi0VpRWN3u4xWNAgdi8q5fv4Zbt24AAHx9/TBs2AiYmwvVrhsS0gNduoQgPPw2jh07jJMnjwEA3rzJ4aQs1enhj6t8++py8zd2/wMumtero+oUvHLxWarC+cTFZJzU36fqMZUH7l27tqG4uBgAYGVlhYKCAqXlPXv21nrsVKf7eGWgVKVqgqm5t6o2B4vFYvz66wr53598MlNj0CAjEAjQunVbfPrpbPlrFy6c46Q81eXhj6umWlNotuZCRTRdN2vWAkKh9mPX0tIKzZu31HsfVQ1Xx09l51zLymDoZ6kK5xObc+V///sGc+Zo/hwXLpylVJEqyBQeuNPT0/DHH3sBAEKhEIcOncSWLbswb95C8PnSR9obN/5Ffr7miXqry328slDgUA0w3TS5uqlWRvBx/vwZPHv2BABQr14D9OzZh/V7PTw80bRpcwBARMRLREZGGFyeFi1awcPDU+NyLjthGxMX+fbVKU9Ul+9D3/Ngy5YN8loyTQoLC7Bv326dyl5VVafglYvPUlXOJ0Mn45RIJJg3bxb196mCTOGBe8eOLSgslLYwDBkyAt7ePmjdui1GjhyDPn36AwCys7Nx8OB+jdtguo8DqPD7eFWq3KXAoYpjummuXPkz65uqtgO3Mmr0SkqKsXbtKvnfM2Z8Jq9RYKt7917yf1+4cNbgMgkEAkyd+onG5Uy5lVxjc7FRt05qquE1R6bY2Vffiy/bGrLLl//S6zz4+++/sHnzegDS4NLJyUlpuYODo/zfq1Ytx9WrV1iVh4kp34yqU/DKxWcxxfNJnYpM36hqqSKmfL5xoeyBW/0AJIBxK85SUpJx4MAfAABLS0tMnDhFafn48ZPl/96zZweKiorUbkcgEKB7955a91WR93FTaDHVBfVxqMLY3DR3797OqiPT5cuhGvNRAVRKLuqhQwcQFxcLAGjbth3ateug8za6d++J5cuXAJAGDtoe+tnSdjOzt7dH69bvGLwPGW1522xyoU+fPo2vvvpaaR1HRyfGdC8ZbTVHptBsrciQ3HC2NWR79+5SeU3deaD4u4lEIixZ8j/5+rNmzcWoUeNUftdNm9Zh8+b1EIvFWLDgM+zYsQ9ZWZl65+xXVN8BffsW6HL8aNpHRedcayrHq1cvWH8WfZbps56xVGT6RlVKFamovimV2ZdHIBBg/vyFmDNnhoY1jFtxtn37ZnkwMHz4ByrHR1BQLYSEdEdo6AWkpqbi+PEjGDbsfZXtZGZm4tSp4xr3Y2tri44dO3NbeA2qYl8fChyqMKabJgC1QYPisuTkJKxevRx79uxUe+DOmTMDtrZ2FTaKguyiGB8fh3XrVstfnzHjM43DrGrj4eGJJk2a4cGDe3j16iVev45EzZqBepevtLQUhw79CUBac7x06S8QicTYt283Hj16gJycHPzyy1J8//1ivfcho+1GBDAHc3w+D599NkNlnaysTFb7Z6o5MoVmaxlDL77SCQZdkJ6ervO+2QThMt2798KYMRPA4/FUHmQ/+mg6IiNf4eLF88jNzcX777+nVGOpy0NIRXV8Z/uwVP5hx8/PH3/++Tvj/gEgLOwWVq1arrKPefO+wNOnj1ltg+3Dtq6Buru7B1q2bI2LF9nVDGo7F0zpfNKmRYtWcHR0RFZWlkHbcXJyQlZWlsZ7FJc112wetg15IK+ohz9TGEggJKQHPvpoOjZuXKuyzN7eHu3bv8u6HLpISkqU33utrKwxbtxktetNnDgVoaEXAEjTmt57byjMzJQfdZctW4TMTOl9MCSkO95/fxTS0lJx8OB+3LkThtzcXJw5cxKDBg3hrPzqvnNpWSp/lCpdUeBQhXFV87R79w61r8sO5tzcNxrfy2WNnrqLIgA0adIMDRs21nu73bv3ks8BceHCWUyZMk3vbf3zz9/y8nXq1EXe56JFi5YYMqQf8vLycOzYYfTo0RsdOuh/AdV2I5ozZwYcHBy1tjR9880X8nxiTfh8PsRiMXg8ntr12rR5R+vFqkWLVrC1tdN4fPB4PLi7e7C++et7cwcMv/jyeDzY2tprCBx4UJevXX4/yclJ2Lx5PTZvXq/xe+/aNURjAMzn8/HDD0vx+PFjJCbGq6Q5sH0I4WrIRKaHoTFjJqht0SxfTnXntaZjTp3Dh/9UeS05OQlz585k9X6A3cO2voH6mTMnGbfN5lyQpYEkJydD3fGm6/lkLGlpqRr76sh+VwcHR+TkZKv9jWWfY+7cBZg/f7bGY4Grmms2D9uGPJDrcr4BqPTghIvgw9PTS/7vAQPeQ3R0NO7fD0dOTg727t2FSZM+YrUdXWzdulE+BOsHH4yEs7Oz2vUaNGiE9u074t9/ryIhIR5nz55Cv34D5cuvXLkkP2ft7R3wxRffyK8PPj6+GDNG2kKxZ89ODBw4WK8Ky/I0fefvvtvZJEap0hX1cajCFPOiK5uhQYymvhoA8ODBPYNy/RRzGQ3t53DgQFkt6bBhH8j/7enphc8++1z+9w8/fIPc3FzG7anLiWWTgibtfKhZXl4e8vPzta4jFovx8cefwt3dQ+3yM2dO4tGjhxrff+9euNaRKwD2N382OZ6a1tmyZYPBueGHD/+J6OjXAKBSXg8PD4wcOZbxMwDApk3rtD4Qr179i9a8Z6HQAiUl6h/K2ObsV0TfAYlEgj17djD2Lbhw4aza81q2joWFJQDNkzZygc3Dtra+YnPmzMD//vcNY6CjqaJA9tmYzgVZGoimILWi+0+pU1paigUL5sivLRYWFkrLmSbjVPwuunfvheXLV2u8/pSf6FMfbDrOG9q5nu35tnXrRr3z2E1tIIGEhHj5v7t164lvv/1B3v9w586t8tp8Q8nuj3v37sbhwwcAADY2NhgzZoLW902cOFX+7+3bN0MsFgMAcnNz8eOP38mXzZ27QKlSoUmTZkoDqly/ftXgz6DpO09OTtLagVtRZacnlkctDlVURkaG2qbC8qS1yhJouhlZWFho7ECki9LSUqPUGAMwuLnO09MLTZo0xYMH9/Hy5QtERUWiRg3d05ViY2Pw77/SC4mPjy/at++otHzw4GE4f/4Mbt68jqSkRKxc+RN69eqn8+RHPXv2YUxB44q/fwBOnw5V+k2uXbuCnTu3/feQ8Bl+//0w7OzslN6XmpqCzz//TH5Btra2UQoiHB2d8PXX33OWVgOor/FNTk5idR4Ami++qakpWLWqbILBdeu2QCAQKP1u4eG31fZv0BVT7ZHsd9CETQ2UIX0H2rRpAwC4c4c5DVL222sr5/z5s7U+cDs42GPevKVYvnypynkwcOAQbN68jvFzdO/eExcunAePpz4109PTU2tgwkWgDgDjxk3C4MHDsGTJD0q/gbu7B+va3JCQHpg06SNs3bpRZVlgYBCCg7szbsOYNm1ah7t37wAAvLy8sW/fQbx+HYGCghxYWdmjadMWWifjLP9dKM67k5aWipcvX2D79s0AgB9//BbNm7dUGUiALTa/6+LF32ts8WDbOsf2fNuwYY3Ka2xbC7joy8Pl5G2KgYO3tw8CA4MwaNAQHD58ALm5udiyZYO8pU5fmjIQ2rfvCEdH7cdEy5at0bx5S9y9eweRkRHYsmUD/P0DcObMKaSkJMu307//IJX3jh49Dvfv3wUgbXUwJPVK23eui8pOTyyPAocqQHpzD5NfnB0cHDFr1ieIjY3R+B7ZjXL06PHYvXs7AOWLo2z5hAlT1F7QdPXDD99g+fIlyM7Olr/Gtjl48OBhRm+u6969Nx48uA9AOqfD5Mkf67wNxdqBoUNHqIzwxOPx8M03P2Do0AEoKMjHoUMHcOjQAflyNpMfJScn/fd7VQxXVzf5vBcyzZq1QHj4bTx4cB9xcbH48cdvsXTpL/JjpqSkBPPnz5bfMNu2bYc1azZh3brV2LVrGwBgypRprB6U2Nzcv/hirtLfhnxWdZYtWyxPtxow4D288057lXVkaSQpKckay2FmZobS0lLGchi7g2z5WmBNHj58qLbvwMKFX+D0acNHIAOYf7OUlBQ4OjqpBK8tWrTC+fNnWO0jOLg7evXqqzEd6sGD+/j11xWYNWuu2vez6SvGRlpaKnr37odOnbqiXbvmKC0thbe3D06cOK9ThYdiDfyAAYNx48Y1pKQkIzIyAqGh59Gtm/bRYIzlxo1/5QGNQCDA0qW/wMnJGW5urnByskFmZh5KS8uCyfJBgabKJMXrT69eEkREvMTff19CRkY6fvrpByxdugJM1FVIsfldmfo0sbn3PH36hLF82rbPZXDC1Sh4TPdZ5cDBG4C0j9bp0ydQWFiIP//8HR9+OBq+vn6syl2epvsjIL1/h4aeZ7y/TJw4FdOnS0ddKv+MIxQK8dVX36utUOjatRt8fHwRHx+H69ev4eXL56hdu65en8PQa4uppCeWR4GDiWPKD3Zzc8OoUeOwb98ejTU7TZo01Vjz06VLCA4fPqDxgYjH48He3gHZ2dkaa/QAoLi4WCXvlW2NMdvAxZDmuu7de+KXX5YCkM4NoWvgUFRUhGPHDgEAzM3NMXCg+k5TPj6+6NWrD44cOaiyTPZ9LFmyHD//bHgthCY8Hg9ubu7g8Xhaf1dNFyRzc3MsWfILRox4D7m5b3Du3Gm0bt0WAQE1kJaWitDQC/JaRw8PTyxZ8guEQiE6duwkDxwSE+NVtqsOmwsr09wHbNjZ2av9rFeuXJanrzk5OeGzz+arfb8sjWTu3JkqNZSym8/kyR+zOpaN2UE2OjoKv/zyE6tt7Nun2oKSnJyEmTPZ9x3gQlpaqkrwCuj2XbRu3VblIbWgoACzZk2DWCzGzp1b4ePjixo1aqo8xCYnJ3LyOWTlNTc3h4+PL6Kjo5CZmanzENKKD2V9+vRDSEg3zJwp7Ze1cuXPePfdLqyCQ0NH31F8v1AoxKJF38uP++nTZ8lTOrRR97tqw+Px8NVX3+Pu3XDk5GTj7NnTCAnpoTSsdnnq7pGurm7w8FCfAqUPda1zDRo0ws8/L8bRo4cM2jabB3YuOs6npqaw2gab+6zsGHVycoK1tQ0A6TPFqFHjsHXrRpSWlmDt2lVYuvQXbZtRi6mWnm3LiGyuB3WKi4vx9OljeHv7qCwTCAQYOXIMli2TDnDy22+79B7sRJdnFk33lcpOT1SHAgcTpinqlv3t5eWFHTv2wdPTS+3wjrKDjanmh+mBSJavqq61YMaM2fjhh29RWFioUn7Ztr7//mudOkNqYkhznaHpShcvnpOPItKtW0+NHbNEIhGuXftH7TLZ51+wYI5uhS9HWzAn+80+//xL+ahK+lyQfHx88e23P2DevFkApGkD5fH5Aixfvlr+Xfj4+MqXxcezCxy4zt3UdJy9eZOD33//DaNGlfVVyM/PUxomdc6cBVqbwENCemhNv2AThLPtIKutZQMAIiJeyZviZee0UCjErFnTOMsvZqItDZLH48HR0ZFVWTSd10zfRfnvU91D6ueffyX/jRct+k5pmYeHJ3r06M2qc7M26n5XLy9vREdHoaAgH9nZWYypFYrKp4H4+fmjbdv2uHnzX8THx2Hfvt1K49Wrw6YDrK4jSMm0b98RY8dOZP15dOXm5o4FC77CwoXzAEh/N4FAgKKiIrXlVHePTEtL5fTa8uDBfZXWOfUtjMoDKehy39NW3vr1G7Jq0Xzy5LHKdaFFi1aIj4/Dzp3bWJWD6T5bUlIs/x68vJQfvMeNm4RDh/YjMzMTZ8+eQqtWrWFjY6tT4MpVWtbPPy/RuA2m4GPQoCFYv34NcnPf4PTpE/j009k6P3+IRCJcuHCO1boff/wpDh8+oDWtz5RQ4GCi2OTGiURiuLm5A2Cu2dG2nOmBSHbgqgs+wsNvqw0aFOXkZGtdzoSr5jrp6Er6pSspDh05fPgHGtcLD78tz6Hkgq7BnOJvZmbGx+bNm1XmcWB7QerevRfateuA69evqV0uFouUPquHh6f85iabf4MJl7mb6i6+NjY2yMuT9r1YvnwJSktLMXq0NMjeuXMrEhMTAABt27ZH374DGPdhaBDOtoOsum0oWrr0B6xevRwFBepr1WrVqo0PPhiNzZvXqzw89us3ENu2bWL8rB9+OBq///4bAPXBKVMa5MKF32L58qV6B1JsWnmYvs8RIz7EtWtXcOXKZZVlyclJ2LNH/Yhy5cvJFKiXL4diTWZCQrxOgYPsmOTxePD09AKPx8PcuZ9jxIj3IBaLsXXrRvTvP0jjucO2z5CuI0jJ9OjRW+dWFF317t0PFy+ex19/XUBWVhY+++xTlXJ26RKCn35apHeFlKxlFpDWxmvbzu+/71F5TfYQb25uju++WwxLSwu11+PBg4cZ1BIpkUiwaNF3rNIgV65chvXrf0VRUdk92dbWFoWFhYzvZ3ufTUpKkn9XipVFsn1NnvyxvLZesSMy25GbTCEty9raBkOHjsDOnVtRUlKC/fv34ZNPNLfElg/C69Wrjy+/nK/2uqNI9p1PmvQRJk36qNLm59AVBQ4mik0KR0pKMmfDdLHJR1UXfBi7xpjL5rpu3XrK0zguXDjLOnB48eK5vLNUrVq10axZC43rcvl9sKmFYPrN+vTpgzZtOuLWrVs6X5BEIhEiIl5pXF6+1kYgEMDLyxuxsTFISIiT5+9qw6ZWmenmru3i27x5S2zdulHegXrVqp+xZcsG5OUpj3gVHNyN9cg+XATh2mjahoeHJ+rVa4C///4LADQGDUFBtbB9+17Y29tj0KAhevcdaNy4KVq2bK31s2hLgwwJ6QE+n2/Qg7+h36dIJMKzZ08ZP2tQUG1ERLw0KFBXVD5waNCgEWMZFNcHIG9FAoDatetiyJDhOHDgD+Tl5WHdul/x7bc/qLyXTQfY//3vm//6ouk21LPMhg1r0L//IKM+1PB4PHTq1AV//XVBZVlychLmzJkBX18/1pU0mn7Xzz//EgAYA3Vt7O0d0KtXHwgEArXXYwBaWyIB6SzImlK/9u7dLW8VEwqFsLOzR3p6mny5h4cnGjVqLJ+7QDFoAKA0up+TkzMyMzNU9qHLfbZ8i1h5Li6uat+nGLj27Kk59YyLtCwugo8PPhiF337bidLSUhw48DsmTJgCKysrlfXUtc4ptg5pGvZc3XduSkOuakOBg4mqjFlEdc1HBYxfY8xlc52XlzcaN26Khw/v48WL54iOfo2AgJqM7ys/BKu2B0y234e2yY90qYVg85vp87sCzK0n6mptfHx8ERsbg9zcXFYpGoq1yuWxubmzufh+9NF0mJmZYe3aVQCgEjQA0hp8V1dXTo4ztp1C9dkGAHTt2l5rK15ubi5sbKR5x8boO6BLGiRXgZS+3yfbFsAFC75CTk62Xq2u6spRPnBgq7CwUP5QWP6h7OOPZ+DMmZPIzc3FkSMH0KhRE1hbW+k8k7amEaLYjiBVEePKi0Qixlp6tq2aI0eOxcWL57T+rpoC9f79B6kd4UpRenqa/PvQdK1lakUsLCzEzz8vxsKF3yrdX8LCbmLlymXyvxcv/hldu3ZTe10IDu6g9beztrbG8eNncevWDYPOR22Bg0gkwooVy8q/BYByR/Bu3TSPDqZriqI6XAQfslTG06dPICsrC+vWrULDho2VzjdNrXuyoMHS0hKrV29Abu4bg75zU0OBg4mqSrOIGrPGmOvmuu7de+LhQ2m60vnz5zB5svaJavLycuVT01tZWTOms7C96Gma/MiUaiH0CV6V+znEsUrRkD1czps3S2mITzY3d7YX3/HjJ2Pnzq1a59bgcoZOfYM1pm2Ehd1kTP1jerBjc4wqztprSBokwE0gpe/3qcsx3Lt3P71aXdVRzP2WpR6xobhu+YcyZ2dnTJkyTf5g9sMPX8uXeXh44oMPRrGewdpQxh5XnquRrgCgS5dgfPbZfK2/q6ZjlG3rHNP3oSmAdnR0Qk5ONsRiMQ4c+AO2tnZo374j0tJSwecLsGTJ/+RzM0yYMFk+opa66wJTwJefn49nz57KP+uwYQMQGRkBADh06CRsbW1ZfVZtgQPbFKE7d26jZ89gteuwqUxiahnhIvgApEOznj59AoC0k7SMh4cn5s5dgOXLl2ptpbKxsUWrVm00tkaZaioSEwocTBRXB76xsclDNrTGmEvduvWU33iPHTsEX19ftSexLGfx5Mlj8smO+vbtz3hxZZuXLb2R8E26FkKf4FVx+L24uDjWM36/8057edAQEFATX331HeubO9uaZ21Bg6nO0FkeFy2R7M5Zbkfy4CKQ0oeuxzBX5dS3xUFxNDJ1aSAeHp5q35ecnIRVq5brUELDGLvCiu1xbm/vgDdvchjvkfq2zHJZgafp+nXu3Gl8+eV8SCQS7NixBTt2bFF5b7t2HfDJJ7M0blvX64JAIECDBo3kgUNCQjzq1GE35Ki2wIGrTImQkB74+edVmDt3FhRT6tjeH7noHwVoPneTk5PkA4dow6Y1qiqimaNNVNksotpn3zSFiFVWm1J+BlB3dw/5xDZs1qkI0lFKAgBIm7rVzeCpOEPxsWOH5e/196/Bah9sP2tISA+cPh2KLVt2YcmS5diyZRdOnw41iaABKAteNaVmla+ZBgBv77IWh4SEONb7UpyTpFmz5vILbXmyi2/v3v00rqNOZaT+GQNXDzKajlEPD09s3rxZabb1qkyfY5gLbm5uMDMzB6Bb4KC4rpeXt9IybWkgiriYcVnzto3zfZXH9jgfOXIMAOPdI7k+ftRdv/r06Y9Bg9QP7y3Tu3c/rZ9Dn+tCQEAN+b9jYqJYvR+QtiTLyOZwMKQcmjRu3AyyoKFu3fo63x8NfeaQ9RcylKnfU/RBLQ4mjIv84IrCpjaYi5QFQ4WGnkdsbLTK67KOW2PGTMDu3dvV1mCtXLkMPj4+nOZlm3IthD61Nr6+ZYED2xxkQDr/gIziDY0rVSX1jwmXLZHqjtE2bdrA1dUemZl5jO+vCriqedQVn8//b6CAaCQkxLMaKAAwLA1EZtKkqfK8/PKfVyKRwMHBETk52TrP21ORFVZsj/NJkz5CrVq1jXaPrIjjRyQS4d9/r2pdZ9261ejbd4DG/ehzXVCsCFO8/jKRHaPOzi6wsrLWqxwtWzJfn2JiXsv/3bp1G73uk4a2UnORLmfq9xR9UOBg4mQH/v374fKZo5s2bWESLQ3lGbOjLhe01SDILnKaggYZXfLgTTkoYEvX4LV8Hwe2YmLKgjl//wADSqxeVUn9Y8L1g0z5Y1QgqH6N0JVVAePtLQ0c8vLy8OZNDuztHRjfoxg4+PjolwYSGBik9fMC2tNGdR1Byhh0Oc6NXSFl7OOHzQMqUxqlPteFgICy66zi9Veb4uJi+URymiZPY+oIzvb6FBUVJf93jRrMg5hoYuz+UZpUlXuKPihwqAIEAgHatGkLJycbZGbmobRUzPwmooLNBVpb0FBV8uC5psuN2cHBEba2tsjNzdUpcFCs8WKbEqaLyqp5Noaq1BJpKiqjtbN8PwddAwdPT/3TQJhGw9J33p6KPD90Oc6NXUljzOOHy34BulwXFCto2AYOSUmJ8munusBBWzkcHZ3w9dffs74+RUWVtTgYoxWaiS4tBVX9nqIrChzIW4OrXMPqmLPIhO2NmcfjwdvbFy9ePENiYgJEIhGrC6dijq2fn78hRdWoOj1wm0LaX1VT0S2A5QOHevUaML5HNqqSm5sbLCwslJZxMZO2jL7z9lQ0UzrOjfV9VEQHbHXfl7W1Ddzc3JCamso6VYlpDofy5Vi/fg22bZOmzY0ePV6na6xy+qr+LQ760mWExOXLl1b5e4ouKHAgbw2ucg2rY84il3x9/fDixTOUlpYiOTlJ6w1GRlbj5eHhqXaSHa6Y0oOIoUzhwY5opnjcx8czd5AuKipCaqq0UqJ8x2jA+GlqpqqqlFNfXKdR6vJ9+fsHIDU1FRkZ6Xjz5g3s7Oy0rs82cJCVo3v3HvLAITr6tdb1y5Otb21tLR/SvSLpMkJicHD3anFPYav6JbQSogHTCBmAtFMjULEjsFQ3irnZbNKVsrOzkJWVBaBimqT1HZmJEF0oPvyzmcshKUnzHA4ypjI6HeFOZY6g6O9fVpPPZmQlxeu5Yn82TQICaso/g2zoVzZKSorl+woIqMFqYAFjYHu+vW33FGpxIG8NNjUIo0ePx+7d2wG8XTmLXPLxKZvLIT4+jrH2y9gdowmpDLrO5aDYKsEmDeRtquGs7iorjbJ8PwemeXd0aXEAACsrK3h7+yA+Pg6vX0ewHl0sNjZWaV6fykTnmyoKHMhbhc0FukmTptUiD76y6DqyknLHaAocSPXg5uYOMzMzlJaWsgoctM0aXV51T995G1XGA6ryXA7MHaS1zTOiSc2aQYiPj0NeXh5SUpI1TmKoyNjDc+uKzjdlFDiQtw7TBZpqGAyjPHs081wOijcsU7hJEMIFgUAAT08vxMXFsgocdK3NJdVPRT+gKlbUsOkgLTtGXVxcYWlpyWofgYFBuHr1bwBARMQrVoGD4ohKhgzFSoyDAgfyVmK6QFMNg/6UO4Uytzgo5tYaYyhWQiqLl5c34uJikZv7Bjk5ObC3t9e4rnJtLgUOxPj8/PzlabtMLQ5MczhoEhgYJP/369eRaN++I+N7FDtSU2WS6aHO0YQQTllYWMhHwdAlVYnP5yvNPE1IVaf4gMXUQVqfNBBCDGFhYQFPTy8A0uuwtnmMdEmlU6QYOERGvmL1HlNLVSLKKHAghHBO1s8hPT0NBQUFGtdTrOny9vaBubmwQspHSEXQpYO0PmkghBhK1sr75k2OfHQ7dZRHVGIfONSsqRg4sBtZSZaq5ObmBhsbW9b7IhWDAgdCCOcU+zloa3VIT09DXl4eAKpZItUP28ChuLhYPrEk9W8gFUl5ZKUojevp2wfHzs5OPpxpZOQrra0aAJCTk43MzAwAlT+iElGPAgdCCOeUH5g0Bw40FCupzpTnctAcOCQlJcofqChNiVQkxQobbR2kDem8L2t1yM4uCwo0USwDdYw2TRQ4EEI4pzgkq7aRlWgoVlKdKQfQmvs40IhKpLKUn8tBE0OOUcV+DhER2vs5UP8G00eBAyGEc8qpSpprWmkoVlKdubt7yIdx1paqRIEDqSxs53JQPEY9PXVrFQsKUh5ZSRvFoVgpVck0UeBACOGc8iRwbFscahixRIRUPDMzM3l+t7ZUJX1HrCHEUN7ePvLgVluqkuz4dXV107nzvnIHae0tDsqBQw2d9kMqBgUOhBDOubm5w9zcHID2ztGyznhmZuaU202qJVkgkJ2djdzcXLXrUIsDqSzm5ubw9pZW9MTERKvtvFxYWIjUVP077wcG1pL/m2lkpbJ7gplSBRQxHRQ4EEI4JxAI5IFAXFyc2puRWCxGbGwMAMDPz49m5ibVkvJcDupbHZTncPAyepkIUSSr2S8oyJdP8qYoKcmwFjEnJyc4OTkDAF6/1hw4iMViebqUr68fzMxojmJTRIEDIcQoZP0cCgrykZmZqbI8OTkJRUVFAKhjNKm+2AzJKnvdyckZVlbWFVIuQmSYOkhz0SIWGBgIAEhNTUV2drbadZKTk1BYWAiARlQyZSYVOLx+/RqrV6/G8OHD8c4776B58+YYOHAgNmzYgPz8fNbbefToESZOnIiWLVuiefPmGD16NMLCwoxYckJIeUz9HKhjNHkbKA/JqjqyUklJMVJSkgFQmhKpHEwdpLkJHJjTlRT7N1CfN9NlUoHDoUOHsHPnTvj6+mLatGmYP38+atasiVWrVuH999+XR6LaPHjwACNHjsTr168xbdo0fPbZZ8jKysK4cePw77//VsCnIIQAgI+P9kngaChW8jZQnGVXXYtDcnKyPJWPAgdSGRSvv+o6SHMTODDPIE1zOFQNJpVA1rNnT0yZMgX29vby1z744AMEBARg48aNOHToEEaOHKl1G4sWLQKfz8dvv/0Gb29pTc+gQYPQt29ffP/99zh79ix4PJ5RPwchRPmBSd1cDoqzlFKLA6muZB1PAfWBA3WMJpVNl1QlfTsssxlZKTq6rMWBAgfTZVItDo0bN1YKGmR69+4NAHj+/LnW98fGxuLevXvo1auXPGgApFOeDxs2DFFRUbh//z63hSaEqMU0lwMNxUreBu7uHuDzpbdapsCBRhYjlcHT0wtCoRCA+hYHxeu3vscom0ngaCjWqsGkWhw0SU6W5n+6uLhoXe/BgwcAgObNm6ssk7324MEDNGvWzKDymJlVfLwlEPCV/k8Ik8o+Zvz9/eX/TkiIVTlvZCMqWVlZwcvLU/5wRSpPZR8z1ZGZmQXc3T2QlJSIxMQElfNAccQaPz/fSrm/GIKOmarPzIwPPz9/RES8QlxcDPh8KF2PZcGtu7s7rK11m8NBxsvLE3Z2dnjz5g0iI6WTwJU/ZmStHXZ2dnB3d6PsEBNl8oGDSCTC+vXrYWZmhv79+2tdVxZgeHh4qCzz9PQEACQlJRlUHj6fBycnG4O2YQh7e6tK2zepmirrmHFysoGDgwOys7ORmJigdN6UlpbK05dq1KgBFxe7SikjUY+uM9wKCPBHUlIiMjMzIRQCNjZl50JaWrL83/Xr167U+4sh6Jip2mrVCkJExCsUFxejoCAbvr7SlKSCggKkp6cBkFYGGXJ81q5dG+Hh4UhIiEdeXh7s7cu2VVBQIA9QgoKC4Oxsa8CnIcZk8oHDjz/+iHv37mHWrFny4bw0KSgoAAB5k5siCwsLAGDVwVobsViCnBz2IzxxRSDgw97eCjk5BRCJxBW+f1L1mMIx4+3tg+zsbMTHxyM1NVs+LndMTDRKS0sBAL6+/sjMzKuU8hFlpnDMVEfu7p7yfz958hK1atWW/x0VVZZTbmvrVOXOBTpmqgdv77LU0vv3n8DGxgmAckdmDw8vg47PgIBAhIeHAwBevXqFmjXryI+Zly9fyAcJoHtC5bC3t2LVcmjSgcPKlSuxb98+DBs2DB999BHj+lZW0hqP4uJilWWygEHXqdLVKS2tvIujSCSu1P2TqqcyjxkfH188ffoEIpEI8fEJ8o51kZFluax+fgF0TJsYus5wy8urrNNzTEwsatQoy/eW5Y87OjpCKLSqst87HTNVm69vWWrp69ev0aZNOwBAbGzZiHient4G/cY1a5ZV/r58+RL+/rXk24uIKAtQ/P1r0LFkwkw2KXHNmjXYuHEjBg0ahP/973+sct1kKUqylCVFstdkKUuEEONTHFFGcWQlGoqVvE00zeVQUlKC5GRp+iyNqEQqk6a5HLgYUUlGcWSlly9fKi1TvCcEBNCISqbMJAOHtWvXYu3atRgwYACWLFnCutNk48aNAQB3795VWSZ7TbYOIcT4lEdWKqu5Uh6KlW4SpHrTNHt0SkoyxGKxyjqEVDRNczkoXrcNPUYVR1Z68eKF0jLFEZVoKFbTZnKBw9q1a7FmzRr0798fS5cu1Rg0pKSkICIiQt6vAZB23GnSpAnOnj2LxMRE+eu5ubk4ePAg/P39DR5RiRDCnmINlaYWh4AAanEg1ZumwIHmcCCmws3NHVZW1gCUK3a4PEa9vLxhaSlNKVdtcVCcNdofxHSZVB+HvXv3Ys2aNfD29kaHDh1w8uRJpeWurq7o0KEDAGDFihU4cuQIdu/ejbZt28rX+eqrrzB69GiMHDkSo0ePhrm5Ofbv34/U1FRs3ryZhvcipALJRuYAgIQExRYHaVO4ra0tnJycK7xchFQkT09P8Hg8SCQSChyISeLxePD3D8Dz508RHx+HkpISmJuby49RHo8HT08vg/bB5/NRs2ZNPH36BNHR0SgqKoJAYA6grDLJ09NLHsAQ02RSgcPDhw8BAAkJCViwYIHK8jZt2sgDB02aNm2K3377DatWrcKaNWsgFovRqFEj7NixQynAIIQYn5eXj/yBKS5OGjgUFxfL87z9/WtQME+qPXNzIdzc3JGSkqzUx4EmfyOmRBY4iEQiJCTEIyCghvwYdXNzVztipa4CA2vh6dMnEIvFiIp6jaCgOsjMzER2djYAmvitKjCpwGHp0qVYunSpwes2adIE27dv57JohBA9CIVCuLt7IDk5SZ4rGxsbIx92jzpGk7eFt7cPUlKSkZGRjoKCAlhZWVGLAzEpitfjmJhouLt7ICMjHQB3x6diP4fIyAgEBdVRSlOiPm+mz+T6OBBCqhdZP4fMzAzk5+cpjdhBtUvkbaH44CVrdVBsfVAcspWQyqB4PY6OjlI6Pg0dUUlGcWSliIhXAJQ7RtM9wfRR4EAIMSrFG058fFy5oVhrVHyBCKkE6gIHWYuDg4MDbG1pplxSucq3OHA5opJMUFBZ4PD6deR/+4qSv0YjKpk+ChwIIUalPLJSnNJNglKVyNtCsQ9DQkI8SktLaQ4HYlIU04Sio6OMkkrn4+MHc3Nph+iyFocohTLU4GQ/xHgocCCEGFX5uRxoKFbyNio/JGtKSjJEIhEASlMipsHR0RF2dvYApK0AxggczMzM5K0KUVFRKC0tlacqCYVCGiSgCqDAgRBiVMqpSrHyPg5OTk6wt3eorGIRUqHKBw6K+ePe3vSwRCqfbEhWAEhKSkRUVKR8GZetYrIO0qWlJYiOjkJsrPSe4OcXAIFAwNl+iHFQ4EAIMSrFwOHlyxdITU0BQP0byNtFsSY1MTGeRlQiJkmWKiSRSHDnThgA6fwLnp6enO0jKKiW/N/Xrl1BSUmJ0r6JaaPAgRBiVK6ubvLxv+/duyt/nfo3kLeJUCiEm5sbAOlcRcqBAzcj1hBiKMXrcm5uLgDA3d0D5uaGz+Egozgk66VLofJ/16hRg7N9EOOhwIEQYlR8Pl/e6lBaWiJ/nWqXyNtG1pchLS0Vr19HyF+nFgdiKtRdl7k+PhVbHO7dC5f/m1qhqwYKHAghRqeuRpVuEuRto/gAdufObfm/qUMoMRXqWoK5DhwCAmrI+zLIJgMFaCjWqoICB0KI0fn6qgscKFWJvF0UH8BkfX3s7OxhZ2dXWUUiRIm6Ch2uAwehUKh2RD2aNbpqoMCBEGJ06mYd9ff3r4SSEFJ51I2eRGlKxJTY2dnB2dlF6TVjHKN16tRR+tvBwQFOTk6c74dwjwIHQojRKc7lAABubu6wtrappNIQUjnUPYBR4EBMjZ+fcqWOl5cX5/uoXbu20t8uLq7yeU2IaaPAgRBidOX7ODg5OdNNgrx11AUJ1L+BmJLQ0PN4/vyp0mtffjkfoaHnOd1Pfn6+0t+RkRHo0yeE8/0Q7lHgQAgxulevXij9/eLFM7pJkLeOpyelKhHTFRp6HnPnzkRhYaHS62lp6Zg7dyZn1+sLF85h27ZtKq+npCRzuh9iHBQ4EEKMKjT0PL7+eoHK63STIG8bS0tLuLi4Kr1GgQMxBSKRCMuWLVYa5aiM9LVlyxYb3FIsEomwdOkitctk++ZiP8R4KHAghBiNtpsR3STI26h8ahKXM/ISoq/w8NtITk7SuFwikSA5OQnh4bc1rmNK+yHGQ4EDIcRo6CZBSJnQ0PN4+VI5bW/mzGnU6kYqXVpaKqfrVfZ+iPFQ4EAIMRq6SRAiJcsfLyoqnz+eSil7pNK5urpxul5l74cYDwUOhBCjoZsEIZSyR0xfixat4OHhCR6Pp3Y5j8eDh4cnWrRoVSX2Q4yHAgdCiNHQTYIQStkjpk8gEGD+/IUAoHK9lv09f/5CCAQCg/ezYMGXRt8PMR4KHAghRlNRNyNCTBml7JGqICSkB5YvXw13dw+l193dPbB8+WqEhPTgZD/du/fE5s2bjb4fYhxmlV0AQkj1JrsZLVu2WKnW1d3dA/PnL6SbBKn2KGWPVBUhIT3QpUsIwsNvIy0tFa6ubmjRohXnlTt9+vRBmzYdcevWLaPuh3CPAgdCiNFV1M2IEFMkS9lLSUlW28+Bx+PB3d2DUvaISRAIBGjdum212Q/hFgUOhJAKQTcJ8raSpezNnTsTPB5PKXiglD1CSFVCfRwIIYQQI6uo/HFCCDEmanEghBBCKgCl7BFCqjoKHAghhJAKQil7hJCqjFKVCCGEEEIIIYwocCCEEEIIIYQwosCBEEIIIYQQwogCB0IIIYQQQggjChwIIYQQQgghjChwIIQQQgghhDCiwIEQQgghhBDCiAIHQgghhBBCCCMKHAghhBBCCCGMKHAghBBCCCGEMKLAgRBCCCGEEMKIAgdCCCGEEEIIIzNd31BcXIzr16/j5s2bePXqFdLT08Hj8eDs7IzatWujTZs2aNeuHYRCoTHKSwghhBBCCKkErAOH9PR0bN++HYcPH0ZWVhYkEgnMzMzg4OAAiUSCZ8+e4cqVK9i+fTscHBwwZMgQjB8/Hq6ursYsPyGEEEIIIaQCsAocNm3ahM2bN0MsFqN79+7o0qULmjVrBm9vb6X14uPjcf/+fVy6dAn79u3D77//jqlTp2Lq1KlGKTwhhBBCCCGkYrAKHPbs2YNPP/0Uw4cPh7W1tcb1fHx84OPjgz59+qCgoAD79+/H1q1bKXAghBBCCCGkiuNJJBIJ00qFhYWwtLTUaweGvNcUiURiZGTkVfh+zcz4cHKyQWZmHkpLxRW+f1L10DFDdEXHDNEVHTNEV3TMmCZnZxsIBMxjJrEaVcmQB//qFDQQQgghhBDyttJ5VCVNiouLcerUKWRnZ6N79+7w8fHhatOEEEIIIYSQSqZX4PDjjz8iLCwMx44dAwBIJBKMGTMG9+/fh0Qiwdq1a/Hnn38iMDCQ08ISQgghhBBCKodeE8Bdu3YN7du3l/8dGhqKe/fu4ZNPPsH69ethYWGBLVu2cFZIQgghhBBCSOXSq8UhJSUF/v7+8r8vXbqEgIAATJ8+HQDw6NEjHD16lJMCEkIIIYQQQiqfXi0OIpEIfH7ZW2/evIm2bdvK//bx8UFaWprhpSOEEEIIIYSYBL0CBy8vL9y7dw8A8OLFC8TFxSkFDunp6VrneyCEEEIIIYRULXqlKvXp0wfr169HZmYmXrx4ATs7O3Tq1Em+/OnTpwgICOCskIQQQgghhJDKpVeLw9SpU/Hee+/h7t274PP5WLZsGezs7AAAb968wV9//YV33nmH04ISQgghhBBCKo9eLQ5CoRCLFy9Wu8zGxgZXr16lid8IIYQQQgipRjibAE6Gz+fLWx8IIYQQQggh1YPegUNhYSF27dqFCxcuIDY2FgDg5+eHHj16YMyYMdTiQAghhBBCSDWiV+CQnp6OMWPGICIiAra2tvDz84NEIkFUVBRWrFiB48ePY/fu3XB2dua6vIQQQgghhJBKoFfgsGzZMkRGRmLBggX48MMPIRQKAQDFxcXYt28ffvrpJyxbtgxLly7VedubNm3C48eP8fjxY8TFxcHHxwd//fWXTtsYPXo0bt26pXbZunXr0K1bN53LRQghhBBCyNtMr8Dh8uXLGDp0KMaNG6f0ulAoxLhx4/Dy5UtcvHhRrwKtWLECjo6OaNCgAd68eaPXNgDAyckJX3zxhcrrjRo10nubhBBCCCGEvK30ChyKi4vRoEEDjcsbNWqE06dP61Wgixcvws/PDwDQr18/5Ofn67Uda2trDBw4UK/3EkIIIYQQQpTpNY9D48aN8eTJE43LHz9+jCZNmuhVIFnQwAWxWIzc3FyIxWLOtkkIIYQQQsjbSK8WhwULFmDcuHGoU6cO3n//fZibmwMASktLsXfvXly4cAE7d+7kspw6S05ORvPmzVFYWAgLCwu0bdsWM2bMQOPGjQ3etpmZXvGWQQQCvtL/CWFCxwzRFR0zRFd0zBBd0TFTtfEkEolE1zeNGTMGSUlJiI2NlY+qBACxsbHIzc2Fv78/PDw8lHfE42HXrl067UeWqqRr5+gFCxbA3d0dderUgVAoxJMnT7Bnzx4UFRVhy5YtaNeunU7bUySRSMDj8fR+PyGEEEIIIVWRXoFDcHCwXjvTNQDQN3BQJzIyEoMHD4aHhwfOnTun93ZEIjFycgoMLo+uBAI+7O2tkJNTAJGIUq8IMzpmiK7omCG6omOG6IqOGdNkb2/FqhVIr1QlLh7kK1pgYCB69+6Nw4cPIyoqCjVq1NB7W6WllXegi0TiSt0/qXromCG6omOG6IqOGaIrOmaqprcqwczHxwcAkJGRUcklIYQQQgghpGrRK3B48uQJ9u7dq3H53r178fTpU70LZSxRUVEAADc3t8otCCGEEEIIIVWMXoHD2rVrcfnyZY3Lr1y5gnXr1ulbJtZSUlIQERGBgoKyPgfZ2dkoLi5WWffhw4c4e/YsateuzemQr4QQQgghhLwN9Orj8PDhQ4wePVrj8tatW2P37t16Fejo0aNISEgAIE0pKikpwfr16wEA9vb2GDVqlHzdFStW4MiRI9i9ezfatm0LAAgLC8M333yDXr16wd/fH0KhEM+ePcPhw4dhZmaGH374Qa9yEUIIIYQQ8jbTK3DIzMyEo6OjxuX29vbIzMzUq0CHDh3CrVu3lF5bvXo1AGkfBcXAQZ2aNWuidevW+Oeff5CWloaSkhK4u7tjwIABmDJlikGdogkhhBBCCHlb6RU4uLi44NWrVxqXv3jxAg4ODnoVaM+ePazXXbp0KZYuXar0WlBQkDzQIIQQQgghhHBDrz4O7du3x4EDBxAREaGy7NWrVzh06BDat29vcOEIIYQQQgghpkGvFoePP/4Y58+fx+DBgzF48GDUrVsXAPD06VMcPXoU5ubmmDZtGqcFJYQQQgghhFQevQIHf39/7Ny5E1988QV+//13pWW1a9fG4sWLqS8BIYQQQggh1YhegQMANG7cGCdPnsTTp08RFRUFiUSCwMBA1KtXj8vyEUIIIYQQQkyAXoFDTk4O7O3tAQD169dH/fr1VdZ59uwZBRGEEEIIIYRUE3p1jv7oo4/UTrIm8/LlS0yYMEHvQhFCCCGEEEJMi16Bw/PnzzFnzhxIJBKVZRERERg3bhysra0NLhwhhBBCCCHENOgVOPz666+4dOmSyizMUVFRGDduHIRCod4zRxNCCCGEEEJMj16BQ4cOHbBo0SL8/vvv2LhxIwAgNjYWY8eOBY/Hw+7du+Ht7c1pQQkhhBBCCCGVR+9RlQYOHIjk5GSsXLkSfD4ff/zxB0QiEfbs2QM/Pz8uy0gIIYQYTceOrVive+DAcXh5sasYi4mJxvnzZ3Dr1g0kJMShqKgYPj6+6No1BMOHfwgrKyv5uhKJBOfPn8G///6DZ8+eIi0tFQ4Ojqhduw7GjJmIhg0bqWx/z54deP78GZ4/f4bExHh4enrh4METWssUFnYD+/fvw5Mnj1FYWAAXF1c0bNgYX375HczNzVl/D+Ttkpubi4EDe6KoqAgLF36LPn36q11v6ND+SEpKlP9taWkJOzt71KwZiJYtW6N3735wd3fjvHxff70Aly5dhL9/APbtO6S07O7dO/jrr4u4fz8ciYmJsLAQws/PH4MHD0e3bj3B4/FUtvfs2VNs3rwejx8/gFgsQd269TBp0kdo1qwF52WvavQOHABgypQpSE5OxooVK+Di4oLdu3ejZs2aXJWNEEJINSUSiRAefhtpaalwdXVDixatIBAIKqUsX3/9P6W/o6JeY8+eHejUqSs6d+6qtMzR0Yn1dk+dOo7Dh/9E+/bvonv3XjA3N0N4+B1s2bIBf/11EZs374CFhSUAoLi4GD/88A2CgmojOLg7fHx8kJaWhmPHDuOjj8bjq6++R8+efZS2v2nTOtjbO6BOnbrIzX3DWJ49e3Zg06Z1aNGiFcaOnQAbGxtkZGTg3r27EIlEFDiYAIlYBLx4DElWBniOzkCdhuDxK+e8UHThwlkUF0sD35Mnj2kMHADAxcUV06bNAACUlBQjPT0d9+/fw6ZN67B793Z88cXXGDbsPc7K9u+/V/H333/BwsJC7fING9YgOTkJnTp1xZAhtVFYWIDQ0Av4/vuvEB5+G59//pXS+k+fPsb06VPg5OSMsWMnQSg0x/HjRzBz5sdYvvxXtG7dlrOyV0U8iboezuWsXbtW4zKJRIKdO3eiQ4cOqFOnTtmGeTx88skn3JTShIhEYmRk5FX4fs3M+HByskFmZh5KS8UVvn9S9dAxQ3RVUcdMaOh5LFu2GMnJSfLXPDw8MX/+QoSE9DDaftkKD7+NGTM+wvjxkzFx4lS9t/Ps2RP4+PjBzs5O6fXNm9dj9+7tmD17PoYMGQ4AKC0txYMH99CihXLrR3p6GsaMGQGBwAxHj54Bn1+WYRwfHwcfH18AwOjRw1FQUKCxxeHOnTDMmjUNo0aNw9Sp3N2b6TrDHcmdaxDv2wRkppW96OQK/odTwWvZofIKBmDixNGwsrJCSEgP/PLLUuzbdxD+/jVU1hs6tD+EQqFKrT8APH/+DPPnz0JWVhb27v0NNWrUMfiYyc/Px+jRw9GxYydcu/aP2n2Hh99GkybNYGZWVlcuFovx6adTcf/+XezevR+BgUHyZVOnjkdk5Cvs2XMAnp6eAKQtLqNHD4eFhSV+//2Q2laKqs7Z2QYCAXMPBlYtDtoCB5nz58/j/Pnz8r+ra+BACCFEf6Gh5zF37kyVUflSUpIxd+5MLF++2iSCB02SkpJQVFQIHx9fpQcRderVa6D29eDg7ti9ezsiI1/JXzMzM1MJGgBp7W3Tpi1w5colZGZmwMXFVb5MFjSwsXv3djg4OMoDofz8fFhYWOjcypOenoY9e3bg2rWrSEtLga2tLerXr48RI0ahZcs2AIDp06cgKSkRq1dvwJo1K3D37h2IxRK0bNkK06fPhq9vWTqzRCLBgQO/49Sp40hISAAggZOTM5o2bY65cxfIW2SqO8mdaxCvW6S6IDMN4nWLwP/ky0oLHl69eonnz59i4cJv8e67XbBmzQqcPHlc3qrAVt269bBw4beYM+dT/Prrr1ixouzZMisrC9nZWXBxcYWtrS3rbW7ZsgGlpaWYMmUarl37R+066s4rPp+PLl1CcP/+XURGvpIHDvHxcXj8+CH69OkvDxoAwNbWFv36DcSOHVvw+PEjNGrUmLFsL148w+7dO3D//l3k5r6Bk5MzGjduiilTpsnP3Y4dW6F3737o0aM3tmzZgIiIl7C2tkFISHdMmfKJ0gilOTnZ2LVrG/7552+kpaVCKLSAh4cHgoO7Y+zYiay/M0OxChxCQ0ONXQ5CCCHVnEgkwrJli9UO5S2RSMDj8bBs2WJ06RJSaWlLTH788RvcuxeuU1+H8lJTUwAATk7OrNc3NzeHra0d88pqFBQU4N69cLRt2x5nz57Cjh1bkJycBKFQiDZt3sGnn37GKghJTEzAxx9PRGZmBnr16ou6deujuLgQL148RVjYTXngAACFhQWYMeMj1K/fEFOnTkdcXAyOHDmIJ08eY/v23+DqKs1z37VrG7Zu3YgOHd7FwIFDwOfzkZychGvXrqCgoPCtCBwkYpG0pUEL8e+bwG/+TqWkLZ08eRRWVlbo0iUE1tbW6NChE86dO4UpU6YxBs/ltW3bDp6enrh16xYKCgpgbi5NLzp0aD927Niitf9EeU+fPsahQ/vx7bc/wsaGfbAho+48fPr0MQCgUaMmKus3btxUvg5T4HDt2j/46qv5sLKyRr9+A+Dj44eMjHTcunUdkZGvlM63Fy+e4fLlUPTvPwi9evVFePhtHDy4H69evcSvv26UtzJ+/fUC3LsXjoEDB6NWrTooKipCbGw07t69Y3qBg4+Pj7HLQQghpIo4f/4sNmz4FXl5uqVtFhcXIysrU+NyiUSC5OQkhIR0hFAo1LlcNjY2mDZtBrp376XzeyuKSCTCzp1bIRAI0KMHczn//fcqnj59jJ49e2vM4WYSFxcLkUiEJ08eISzsBt5/fxTq1WuAly+fY+/eXXj6dCJ27vydMZD55ZelSEtLxcqVa9G69TsAylKV0tPfQKyQdZKVlYVhwz7AzJlz5K81bdoCX345D9u2bZLnlV+5cgk1agTip59WKu2Ly3SqiiIJ+wfio78Bhfm6vbGkBMjN0b5ORhrEs0YCuvZDsbQG/73R4LXqqNv7/lNcXIzz58+ic+dgee13nz79cOnSRdy4cQ0dO3bWeZu1atVBUlIS4uPjUKNGEPMb1CgtLcVPPy1Cq1Zt9GqhTE1NwfHjh+Ht7YOmTZsrvJ4KAHBzc1d5j+y11NRkrdsuLCzEkiXfw8bGFjt3/g5X17JWwvHjJ0MsVk7Pioh4hcWLl6NTpy4AgMGDh2HVquU4ePAPXLx4Dj169EZubi7u3AnDe+8Nw2effa7z5+WSQZ2jCSGEvH127dqG168jjbZ9bcEFk127ths1cFi7drNB71+1ajkeP36IyZM/VpsjrigmJgo//PANXF3dMH36bL33mZ8vfZDNysrEvHkLMXDgYABA585d4enpiaVLf8T+/fvw0UfTNW4jJycbN29eR5s27eRBgyI+n6/yQDRq1Filvzt37gp//wD8889leeBga2uH58+f4v79e2jatJnen9EUiM8eAhJjjbcDpuBCrXSIzxyEQM/A4cqVS8jJyVZqBWjTph1cXFxx8uQxvQIHGxsbAFCqeJg4capO/Yn++OM3xMREY9GiZTrvv7CwEAsXzkVBQQGWLPlFqdWkqKgQANRWXMheKyws1Lr9mzevIysrC1OnTlcKGmQU+ykBgL9/gDxokBk1ahwOHvwDV65cQo8e0koDodACjx8/RGJigt6tnVxgFTisXLkSkyZNUungxSQnJwfbtm3D7Nn6X/AIIYSYlnHjJmL9eu5bHGQcHZ30bnEYO3aCzu+rKJs3r8eRIwfQv/8gjBmjvZwJCfGYNUta6758+a+s05rUkbVU8Pl89O7dT2lZr1798PPPSxAeflvrNuLiYiGRSFCrVm1W+7S1tVPqjyETEFAT//xzGbm5ubC1tcXUqdOxcOEcfPLJJLi4uKJ585Zo164DunbtptcxUJn4vYdCfGSPcVocAMDWXr8Wh95DdXuPgpMnj8HR0Qlubu6IiysLilq3bosLF84iPT1N7e+sjey6IQsgdBUfH4cdO7ZgzJjxOvXzAYCioiIsWPAZnj2T9tlo3ryl0nLFUc7UvReQDjGrTVxcDACwPlcCAlRHI3V1dYWtrR3i4+MAAObm5pg5cw5Wr16OYcMGoEaNmmjRohXefbez2kDemFgFDidOnMDevXsxaNAgDBo0CI0aqY4nrejhw4c4duwYjh07Bnt7ewocCCGkGunevZdetfoikQh9+oQgJSVZbT8HHo8Hd3cPnD4darJ9HPS1bdsm7N69Hb169cW8eQu1jsqSmJiAGTM+Qn5+PlatWsf6AUQTd3dpioWdnZ3Kw7iZmRkcHByRk6P9wZV5/EVlmj+f5L/l0r8aNmyEP/44ilu3buDu3dsID7+NixfPYceOrVi/fgucnV1023El4rXqqFfNvkQsgnjeeOXRlMpzdgV/2Y4K7eOQmJiAO3fCIJFI8MEHg9Wuc+bMSYwaNU6n7b58+QLm5uY6P/TLrF27EnZ29ggO7o7ExAT56yKRCKWlpUhMTICFhYXKsVNUVIQvvpiLO3fCMG/eQpUgGgDc3KR9b2T9HxSlpcnSmDy0lo/FYKVKtJ0rissGDhyMjh074fr1q7h37y6uXLmMw4cP4N13u2DRomUqLRnGwipwOHv2LHbs2IHt27dj7969cHJyQpMmTeDn5wcHBwcAQHZ2NmJiYvDw4UNkZmbC3t4ekydPxrhx44xZfkIIIVWEQCDA/PkLMXfuTPB4PKUbrOwGOX/+wmoXNGzfvhk7dmxBz569sXDht1pv8ElJiZgx4yPk5r7BypXrNI7MpAtnZxd4eXkjKSkRBQUFShPPFRUVISsrU2mkI3X8/PzA4/Hw6tULVvt88yZHbW10dHQUHB0dlTqzWllZoXPnsjkzTp8+gcWLv8eRIwcNGg63quDxBeB/OFX9qEr/4X8wtcI7Rp8+fQISiQTz5i2Evb29yvKdO7fi1KnjOgUON278i+TkJHTs2BFWVlZ6DceamJiItLRUfPjhELXLhw0bgDZt2mHFijXy14qLi7Fw4TyEhd3AnDkLMGCA+nkk6tdvCAB49OiByjoPH97/bx3t56QsBfHVqxdo1455JKyoKNW0z7S0NOTm5sLbW7mPsYuLK/r1G4R+/QZBLBbjp59+xKlTx3HvXrja0aOMgVXgIBQKMXXqVIwbNw4nT57EmTNnEBYWhsuXLyutZ2tri1atWqFXr17o06dPlWtmJIQQYlwhIT2wfPlqlXkc3N09TGYeB210GY4VAHbs2ILt2zejR4/eWLjwO8ag4dNPp+LNmxysWLFW/hDDhV69+mLHji04fPhPjBxZ1vfg8OEDEIvFaNdOe025vb0D3nmnPa5fv4awsBsq6RHqall/+22XUufov/++hJiYaPTvP0j+WlZWFhwdHZXeV7dufQDSfhVvC17LDuB/8qXqPA7OrtKgoYKHYhWLxTh9+gRq1AiU94kpLz4+Dhs3rmXdP+X582dYsuR/MDMzw6effqq0TJfhWGfM+Ay5ubkqry9btgjm5uaYPXs+nJ3LUvuKi4vxxRdzcevWdcyZ8zkGDVIfcADSIY7r12+IS5cuYuLEqfDwkA7JmpeXi1OnjsPHxxcNG2ofUalNm3fg6OiI/fv3oXfv/ir9HGQjyMnExETjypXLSv0c9u7dBQDo1EkaTMv6VSimSfH5fNSuLZ0/rSLPFZ06R1tYWGDIkCEYMmQIxGIxEhISkJGRAR6PB2dnZ3h5eVVYUwkhhJCqKSSkB7p0CTGZmaN1octwrIcO/Ylt2zbBw8MTrVu3xcWL55SWOzs7yx/A8/Pz8OmnHyExMQFDh45AbGwMYmNjlNZv3bqtUvrF2bOnkJSUCED64FVSUoKdO7cCkKYlDRkyQr7uBx+MwuXLodi4cS1iY2NQr159PH/+HKdOHUNQUC0MHToCTGbPno8XLyZg7tyZ6N27H+rWrY+SkmK8fPkULi7u+OijsodBR0dH/P33X0hLS0Xz5i3lw7E6O7sotSKMGjUUDRo0RoMGDeHq6obMzAwcP34UAoEA3bv3ZixTdcJr2QH85u+YxMzRYWE3kZychPHjJ2tcp0uXEGzcuBanTh1TChzy8vJw7txpAEBJSQkyMqQzR4eF3YC1tTX+978laNasGTIzy/pI6TIcq6aa9V9//QVCoVClo/H//vcVbt78F61atYGVlbW8bDJBQbWV0gFnzZqHTz+dik8+mYyhQ0fA3Nwcx44dRnp6Gn7+eTXj5G+WlpZYsOBrfPXV5xgzZgT69RsIX18/ZGVl4tatGxgx4kO8+25ZGYOCauGHH75G//6D4Ovrj/Dw27h8ORTNmrVAt249AQCxsdGYPn0KOnXqipo1A2Fv74CYmCgcOXIIrq5uaNWq4maz1ntUJT6fD19fX/j66pejRggh5O0lEAjQunXF3ewqw7NnTwAAyclJWLToO5XlzZq1kAcO2dnZSEyMBwAcPLhf7fZ+/XWjUuBw8uQx3LsXrrTO1q0bAQCenl5KgYO1tQ3WrduCbds24cqVyzh79hScnV0wZMgITJgwRSl9SRNvbx9s3boHO3duxfXr13D27CnY2dmjQYP66NNngNK6lpZW8gngNm5cC4lEgrZt22H69NnyORwAYMSIUbhx4xoOHtwvnySrfv2G+PbbH1lNslXd8PgCoF4TVPa8xCdPHgMgDQ408fX1Q1BQbVy6dBGzZs2FtbW0s3N6ehp++OEbAIBQaAF7e3vUrBmIqVM/QZ8+/eHmpltnakM9e/YUAHD79i3cvn1LZfn48ZOVAoeGDRth3brN2Lx5PbZv3wKxWIR69Rpg5cp1rNOBOnbsjPXrt2LPnh04deoY8vPz4eTkjCZNmiEwsJbSunXq1MP06bOxefN6HD16GDY2NhgyZDimTv1EXhnv7u6Bvn0HIDz8Dv75528UFxfD1dUVvXr1xahRY3WaNM9QPImuvTjeciKRGBkZuo0kwgXZWNmZmXkGT9FO3g50zBBd0TFDdKXumJHNHH3w4IlKLh0xRXSdKSObOfrLL7+r7KLA2dkGAgFz1hDlFRFCCCGEEEIYUeBACCGEEEIIYUSBAyGEEEIIIYSR3p2jCSGEEELKW7t2c2UXgZAq4epV7TO2myJqcSCEEEIIIYQwMqjFITY2Fjdu3EBaWhr69+8PX19fFBcXIy0tDa6urjQBHCGEEEIIIdWE3oHDzz//jJ07d0IkEoHH46FZs2bywKFv376YOXMmxo0bx2FRCSGEEEIIIZVFr1SlP/74A9u2bcOHH36I7du3K001b2tri+DgYFy6dImzQhJCCCGEEEIql14tDvv27UOPHj3w5ZdfIjMzU2V53bp1ERYWZnDhCCGEEEIIIaZBrxaHqKgotG/fXuNyJycntQEFIYQQQgghpGrSK3CwsLBAfn6+xuUJCQmwt7fXu1CEEEIIIYQQ06JX4NCkSRNcuHBB7bLCwkIcO3YMLVq0MKhghBBCCCGEENOhV+AwceJE3Lt3D3PnzsXTp08BAMnJybh8+TJGjhyJ5ORkTJgwgdOCEkIIIYQQQioPT6I4JJIO9u/fj0WLFqGkpAQSiQQ8Hg8AYG5uju+++w6DBw/mtKCmQiQSIyMjr8L3a2bGh5OTDTIz81BaKq7w/ZOqh44Zoqu3+ZgJD7+NGTM+kv/N5/NhbW0NFxdX1K5dF127hqBjx84QCAScvE9XixZ9hzNnTmpc7uvrhz/+OKL0WlxcLDZuXIPw8DsoKipEYGAtjBo1Fp07Byutl5iYgGHDBqjdrq2tLc6evaxxv2/zMfM2yc3NxcCBPVFUVISFC79Fnz79Gd8TExONDz8cAgD49deNaNGiFQDDj5nnz59hx47NePDgPgoLC+Dj44v+/QdhyJARSudZaWkpVq5chqdPnyA5ORH5+flwdXVD/foNMXr0ONSuXVdpu+XPZUW1atXBzp37dC5rVeLsbAOBgLk9Qe95HEaMGIHg4GCcPXsWkZGRkEgkqFGjBnr37g0PDw99N0sIIeQtIJKI8DDjPtKL0uBi4YrGzk0h4Bn2cM2FkJDuaN/+XUgkEhQUFCAuLgbXrl3FxYvnUL9+Ayxa9DPc3VXvcfq+j62BAwejVas2Kq/fuROG06dPoEOHd5VeT0xMwEcfjYdEIsHw4R/A0dER586dwZdfztf44NepU1d07txV6TUzM3O9y0x0JxFLkB6RjcKcEljam8MlyAE8Pq+yi4ULF86iuLgYPj6+OHnyGKvA4eTJY7CysoK1tQ1OnTomDxwMce9eOD77bDpsbGwxdOgIODo6ISzsJn79dQVev36Nzz//Ur5uSUkJnj17iiZNmsHbuw+sra2RnJyMU6eOY/LksVi+/Fe159SAAe+hadPmSq9Rv90yBs0c7ebmhtGjR3NVFkIIIW+BK0mXse7JKqQWpshfc7N0xycNZqGTZ5fKKxiAWrXqomfPPkqvTZ8+G3v37sLGjWsxb94sbNu2B2ZmZpy8j61GjZqgUaMmKq+fO3caANC370Cl1zduXIvs7Gxs2bIL9eo1AAD06zcIU6aMxZo1K9GlSzCsrW2U3hMUVEvlM5CKk3A/DQ8PR6Iwq1j+mqWjEI0HB8K7qWsllkwaBDRt2hwhIT3wyy9LERMTBX//GhrXLy0txblzp9ClSwicnJxx6NB+zJ79OWxtbQ0qx+rVy8Hj8bBx43b4+PgCAAYPHoZlyxbh+PEj6NWrL5o2bQYAsLKywrZte1S2MXDgYAwZ0g979+5SGzg0atSEzgMt9OrjUL9+fZw4cULj8tOnT6N+/fp6F4oQQkj1dCXpMr4LX6gUNABAamEKvgtfiCtJlyunYFrweDyMGjUOISE9EBHxEn/9pX5wEF3fFx8fh+joKL3LlZSUiNu3b6Fhw8YIDAySv15QUIB//vkbzZq1kAcNAGBmZoahQ9/Hmzc5+Pffq2q3WVRUhIKCAr3KI5FIcPz4EUyePBbdu7+L7t3fxZgxI7B160b5OqdPn0DHjq0QFnYT27ZtwpAh/dC1azuMGTMC58+fVdnmw4f3MXfuDAwY0BNdu7bDgAE9MWvWNNy/f1evMpqqhPtpCNv+TCloAIDCrGKEbX+GhPtplVQy4NWrl3j+/Cn69OmPbt16QigU4uTJ41rfc/36VaSnp6N3737o06c/ioqKcOGC6u8LANHRUYiPj2Msx5s3b/Dy5Qs0bdpCHjTIyFpATp/WXi4AcHZ2gYWFJd68eaNxncLCQhQVFTFuS52SkhLs3bsL48Z9iJCQDujZszMmThyNQ4f2y9fZtm0TOnZshcjICKxa9TMGDOiJ4OD2mDx5DMLCbqhs899/r2L69Cno168bgoPbY+DAXvj889mIjIzQq4yG0CtwYOoWoWe3CUIIIdWYSCLCuiertK6z7skqiCSiiimQjgYOlPbdu3btH07eN3Pmxxg5cqje5Tl16jjEYjH691dubYiIeIXi4iK1LRSy154+fayy7I8/9qJbt47o3v1dDBzYE2vWrEB+Pvs+fT/88A2WLVsEgUCAMWMmYNq0mWjRojUuXw5VWXfDhjUIDT2PQYOGYuLEqSgtLcX//vcVTp48Kl8nJiYKs2d/gpiYaAwdOgJz5izAsGEfwNLSEi9ePGddLlMnEUvw8HCk1nUeHY6ERFw5z1YnTx6FlZUVunQJgZ2dHTp06IRz506htLRUy3uOwcvLG82bt0SNGjVRv34DnDx5TO26I0cOxcyZHzOWo7hY+iBvaWmpsszCQvrakyePVJaJRCJkZWUhIyMdz549wf/+9zXy8/PQrl0HtftZvXo5unXriJCQDhg2bAB27tyq9bMqKikpwWefTceGDWvg7OyCSZM+xpQp01C3bj38/fcllfV//PFbPH78ECNHjsHo0eORmpqKuXNn4tatsuDh7t07WLDgM+Tm5mLkyHH47LP5eO+9oSgsLERsbAyrcnFJ71QlWWdodV6/fg07Ozt9N00IIcSEXU78CztfbEG+SPN8PuoUi4qRU5KtdZ3UwhQMudgPQoFQ53JZC6wxvs5kdPYKZl5ZD7Vq1QYAnW/W+r5PG7FYjNOnT8DKyhrBwT2UlqWlSVtz1PWpkL2WklLW4sPj8dGiRSt07NgZ3t7eyMnJwb///oP9+/fhzp3b2LBhG6ysrLSW5+LF8zh//gx69uyDL7/8Dnx+Wb2kWKzaATY7Owu7dv0hT10ZNGgoxo59H2vXrkJISE9YWVnh5s0bKCwsxPffL0b9+g1ZfjOVJ/5uGp6diUZpoW6Br6hUjJI87Q+mBVnFOPPVTQjMdKvvNbMUoH6fAHg30y/Vqbi4GOfPn0XnzsGwtrYGAPTp0w+XLl3EjRvX0LFjZ5X3pKWl4caNfzFmzAT5s2Lv3v2xYsVPePXqJerVq6vyHjacnJzh4OCAx48foqioUB4sAMDdu7cBSEf4LC86+jXGjHlf/re1tQ0+/HAMxo6dqLSemZkZ2rd/F+3adYCbmzsyMtIRGnoeW7duxIMH9/Hzz6sYBzn48899uHv3DsaMmYApU6YpLVN3HggEAqxfvxXm5tK+RH37DsDIkUOxYsUy/P77IfB4PFy9+jfEYjFWrlwHJycn+XvHjZuktSzGwjpwOHLkCI4cKRuxYcOGDfjzzz9V1svJycGLFy8QHGycCzchhJDKtT9yL2Lyoo22/ZySbKBEv/fuj9xntMBB1idAl1p4be87eFBzyi+TsLCbSE5OQr9+A+UPdDKFhYUAIH8YUSQUSgOyoqJC+Wuenp749deNSuv16dMfO3duxdatG3HgwO8YM0b7EOvnzp0BAHz88QyloAGAyt+ANFBQzHe3tbXFoEFDsGnTOoSH30aHDu/Kl1+5chmBgbVgYWGhtQyV7dVfcchN1i/Ni42SvFLdT4ts4OVfcXoHDleuXEJOTrZSZ+g2bdrBxcUVJ08eUxs4nDlzEmKxGL169ZW/1q1bT6xduxInTx5DvXrzlda/evU2q7Lw+XwMG/YBtm7diIUL52PSpKlwcHDE7du3sG3bJggEAqXjWsbLywcrV65DaWkJ4uLicOHCWRQWFqCkpESpz1GTJs2wbFkzpfcOGPAeli79ASdPHkNo6AX06NFLaxnPnz8LW1tblaBEVv7yRoz4UOk8dXf3QPfuvXD8+BG8fh2BwMBasLWVVsRfunQRAwa8p3c/Ka6w3ntOTg7i4qQ5aDweDxkZGSp5kDweD9bW1hg8eDBmzZrFaUEJIYSYhvcDR2KHkVocAMDe3EHvFocRgR/q/D62ZA/+5TsVG+t92sjSPvr1G6SyTJbKUVKi+pgpy9tWrK3VZNSocdi1axuuX7/KGDjExsbAyckZrq7sHlBr1Kih5rWaACDPd+/WrScuXDiHPXt2YP/+fWjYsBHatHkHISE94O3tw2o/Fal2iC+enjZOiwMAmNuY6dXiUDvYl3lFDU6ePAZHRye4ubkjLi5W/nrr1m1x4cJZpKenwcVF+Tc/deo4goJqQyKRKL2nceNmOH/+DGbMmAVAv3NhzJgJKCoqwv79+zB58lgA0uN9+vRZ2Lp1E0Qi1e/eysoKrVu3lf/dt+8ATJgwCnFx87BixVrGfY4bNwknTx7D9etXGQOHuLgYnYLcgICaKq/VqBEIQHoeBAbWwpAhw3H16hWsWPETNm5ci8aNm6Jt23bo1q0HnJ1dWO2HS6wDh7Fjx2LsWOmPVK9ePSxcuBD9+zMPx0UIIaR66ewVrFetvkgiwoeXhqh0jFbkZumOfV0PmcTQrOW9fPkCABAQUKNC3qdJdnYWrl79GzVrBqJRo8Yqy11d3QEAKSmqaRupqbI0JnfG/ZiZmcHV1Q2ZmVmM6+rat1FdurNsE4rzQq1YsQbPnj3BzZvXcf/+XezYsQU7dmzBF198g+7dtT/EVTTvZq561exLxBKc/z5MpWO0IitHIbp/27pCh2ZNTEzAnTthkEgk+OAD9XNznTlzEqNGjZP/fe9eOOLipCl577//ntr3/P33JQwfrt9cX3w+H1OnfoLRo8f/1zFYgqAgaSrgL7/8hIYNVc+H8qytrdG5c1fs3bsL8fFxKh2ty3N394BAIEBWVqZeZdZGfdq/RGmZvb0DtmzZhQcP7iEs7Cbu37+LdetWYdu2jViy5BdOhrnVhV7tHaGhoXB2dua6LIQQQqoxAU+ATxrMwnfhCzWu80mDWSYZNADA8eOHAQDt23eskPdpcvbsKZSUlKB//0FqlwcF1YJQKMSjRw9UlsleUxxtSZOiokKkpCSr7WRdXkBADVy5chlpaWmsWh1ev36tkuYSHf0aAFRaE+rVayAvb1paGiZOHIWNG9eaXOCgLx6fh8aDAxG2/ZnGdRoNDqzw+RxOnz4BiUSCefMWqp3HYOfOrTh16rhS4HDy5DGYm5vjq6++V5uas3Llzzh58pjegYOMtbW1UtB86dJFSCQSvPNOe1bvl7W85eRkMwYOCQnxEIlErGr3/fwCEBsbjaKiIlatDlFRkfI+UGWvqZ4HfD4fzZq1QLNmLeTrTJw4Ctu3b67wwEGvUZV8fHwYO0oRQggh5XXy7ILvWiyGm6VyjbebpTu+a7G40udxUEcikWDv3l0IDb2A2rXrIDi4Oyfv03c41lOnjsPc3Bw9eqgfa97KygodO3bGvXvhePbsqfz10tJSHDy4H7a2dkpBTHq6+qE+N2/eAJFIhA4dOjGWqWfP3gCADRt+VekEqq414ujRg8jNzZX/nZubi6NHD8HW1k7+IJSVlaXyPldXV7i4uCInJ4exTFWJd1NXtJ5QD5aOyil6Vo5CtJ5Qr8LncZB1vq9RIxADBw5G167dVP7r3r0XYmNjcP/+PQBAXl4uLl8ORatWbRAS0kPtezp16oKwsFtISEiQ74vtcKyaZGdnYdOm9XB0dMSgQWWjlGVmZqrtkJyenoZLly7CysoaNWsGKb1enkgkwsaN0nSmjh2Zz4MePXohNzcXu3ZtU1mm7jzYv3+fUkphSkoyLlw4Bz8/f3nZ1J0Hfn7+sLa2QU4Oc+on1/TuYZGdnY2DBw/i/v37yMnJUflxeDwedu3aZXABCSGEVC+dPLugg8e7Jjlz9KtXz+WTqhUU5CMuLg7Xrl1BbGwM6tdviMWLf1Y7soo+75s582MkJSWy7hwKAI8fP0JkZASCg7vD0dFR43pTp36CO3duYc6c6Rg+/EM4ODji3LnTePHiGRYs+Ao2NmUdk5cvX4qMjHS0atUG7u4eyM19g+vXr+HevXA0btwEQ4YMZyxXSEh3/PXXRZw7dxrx8XHo0KET7OzsEBsbg1u3rmPPHuXBVBwcHDFlylj07TsAEokEp0+fQHJyEhYs+EpeMblr1zaEhd1A+/Yd4eUlrX29ceMaXrx4hsGDh7H+zqoK76au8GrsYhIzR8s6348fP1njOl26hGDjxrU4deoYmjZthgsXzqGwsBBduoRofE/nzsE4evQQDh8+jJEjxwOQDsfq6enFarCA69evYt++PWjdui2cnV2QnJyEEyeO4s2bHCxdukLpnLhw4Qz+/PN3dOrUBV5ePjA3N0NsbAzOnDmFN29y8PnnXykN7Tpv3kzY2zugSZNm8lGVLl0KxatXL9C5c1etn0tm2LAPcO3aP9i9ezuePXuKNm3aQii0wOvXkYiJicbq1euV1heJRJg2bRK6d++J/Px8HD16CMXFRZg9e748Vemnn35EamoKWrduC09PL5SUFOPSpVBkZmbggw8qfhJmvQKH+Ph4fPDBB0hJSYGdnR1yc3Ph4OAgDyCcnJyoRYIQQohGAp4AzVxaVHYxVISGXkBo6AXw+XxYWVnBxcUVdevWx0cfTUfHjp01Dseo7/t0JesUXX7uhvJ8fHyxYcN2bNq0Fr///huKi4sRGBiEH35Yiq5duymt26HDuzh37jROnjyK7OxsmJubw88vAB9//CmGDftAPhITk2+/XYQmTZrj1Klj2LlzC/h8Aby9vVX2BwAff/wpHjy4h0OH/kRmZgZ8ff3wzTc/KnU+fffdzkhPT8Nff11ERkYGhEIhfH39MGfOAgwYoD5/vqrj8Xlwre1Y2cWQH2faHpZ9ff0QFFQbly5dxKxZc3Hq1DEIBAK8+67qSEsyzZu3hL29Aw4fPowPPxync7k8Pb1hbi7EwYP7kZOTDQcHR7Rq1Rpjx05Umcm6SZPmePr0Ca5d+wcZGekoKSmBs7MLWrVqg2HD3kfjxk2V1g8O7o5r167g8OEDePMmB5aWlggMDMK8eQvRv/8grdMQyEj75azFH3/8hgsXzmHz5vX/Hbf+SiNTyXz11fc4duwQfvttF3Jz3yAoqBa+/PJbtG79jnydXr364PTpEzh79hSysjJhY2MDf/8a+PbbHyslXY8n0WO2tnnz5uHixYvYsGED6tSpg/bt22PHjh1o1qwZNm7ciFOnTuG3336Dp6enMcpcqUQiMTIydBuKjwtmZnw4OdkgMzMPpaWqTW+ElEfHDNEVHTNEV7oeM6dPn8Dixd/j1183VnhuNjENdJ2Rzhy9Y8cWHDhwHF5e3pVdHACAs7MNBALmHgx69XG4fv06hg0bhnfeeUcpArOyssLs2bNRp04d/Pzzz/psmhBCCCGEEGKC9AocsrKyULu2tBe4bOIK2YQzANChQwf8+++/HBSPEEIIIYQQYgr0ChycnZ3x5s0bAICNjQ0sLCwQHx8vX15SUqIUSBBCCCGEEEKqNr06R9euXRvPnz8HIB09qUmTJti3bx+Cg4MhFouxf/9+BAYGclpQQgghhFRtffr0V9tJlJC3ycSJUzFx4tTKLoZe9GpxCA4ORnh4uLxVYdq0aYiOjkZISAi6d++O6OhoTJs2jdOCEkIIIYQQQiqPXqMqqfPgwQOcOnUKfD4f3bt3R4sWpjfMHhdoVKXKJxFLTGKca1NHxwzRFR0zRFd0zBBd0TFjmtiOqqRzqpJIJEJycjKsra2VJtpo0qQJmjRhnpaeyaZNm/D48WM8fvwYcXFx8PHxwV9//aXzdh49eoSVK1fi3r17EIvFaNSoEWbMmIHWrVsbXEZSeRLup+Hh4UgUZhXLX7N0FKLx4MAKn1mTEEIIIeRtonOqkkgkQrdu3XDw4EFjlAcrVqzAzZs34e/vDwcHB7228eDBA4wcORKvX7/GtGnT8NlnnyErKwvjxo2j0Z6qsIT7aQjb/kwpaACAwqxihG1/hoT7qtPFE0IIIYQQbujc4iAUCo06M/TFixfh5+cHAOjXrx/y8/N13saiRYvA5/Px22+/wdtbOrHGoEGD0LdvX3z//fc4e/YsqxkAiemQiCV4eDhS6zqPDkfCq7FLhaUtUcoU0RcdO0QbOj4IYYfOlYqn16hKnTp1wpUrVzBy5EiuyyMPGvQVGxuLe/fuYfDgwfKgAQDs7OwwbNgwrF27Fvfv30ezZs0MLCmpSOkR2SotDeUVZBUjPSIbrrUdjV4eSpki+qJjh2hDxwch7HBxrlDgoTu9RlWaN28ekpOT8cUXX+D58+coKiriulx6e/DgAQCgefPmKstkr8nWIVVHYU4Jp+sZglKmiL7o2CHa0PFBCDtcnCsJ99Nw/vswXFv7CHd2P8e1tY9w/vswOs8Y6NXi0L59e/B4PDx79gxHjx5Vuw6Px8OTJ08MKZtekpOTAQAeHh4qyzw9PQEASUlJBu3DzEyveMsgsp7ubHq8qyMRS5D2KhuFOcWwtBfCtVbViqptnCxYr2fM30ciluARU8rUkdfwa+5W6d+voccM4Zaux05lnLN0zBhO39+tKl1bFL3t9yaiOy6OGUPPlfh70sCjPFng8c7kBvBpRi186ugVOAwaNMhk+wgUFBQAkPbFKM/CQvrwacis1nw+D05ONnq/31D29rr3LXl9Kwk3dj9FXkbZ57ZxtsQ7Y+qjZhtPLotnNKVuzC0JlvZC1GrtDb4RbzoJT9JRwJQylVmEwuQieDdwMVo5dMF0zIjFEiQ9y0BBVhGsHC3gWc/ZqN+hqTPW96HLsVOUW1Kp56w+1xljqirHqCHX2qp4bVGk7phh+t2qw71JUVU5Tk2lnPpeZww9V8RiCc4e0h54PDwciQad/Tn7XkzlO+eCXoHD0qVLuS4HZ2SdtouLVQ8qWcBgaWmp9/bFYglycnTvsG0ogYAPe3sr5OQUQCRSHfdYU61N/L003Nii2vKTl1GI0FV3dYqqK6tmqDCnGH/9cpdxvZLCUsQ+TYW9t/ECu7T4HNbrWXnpf5xxgemYAaS1LvcPvFK6CFs5CtF0WK23srbFmN8H22PnzuGXSH6SqfJ6RZyz6o4Zpm1wcV3Qtg2ufhNjX78MvdZWpWuLIk3XGabfjct7kymoqGupocexKVzz2dybtDH0XEl9kaUUrKqTl16IV2EJcKvjqHP5ymPznZtCy5u9vZVx5nEwdbIUJVnKkiLZa7KUJX1V5oQlIpFYZf+aOgg1GlQTj46+1rq9+wdewb2BE+MBWlkd9sSlYlzf/AQFmdJ+NDZulhAVi1GYXVYOvhkP4lIJRMVi/LP2ITp+2hgFWUVG6exkbsPulDG3MauQ44RNx67SEhGSn2eqrCPLES2vIKsYN7Y8QesJ9apcBzM25dC0DpffhzoCKwGr9dQFDYoq4pyVXWeYtsHFdUHbNgBw8psY+/olEUtw78Arresw/W6mdm3RleK9ielcajIsEM9Ox2jdHtvjnCuGXMPYXjsMvU6yOY617cPY1zhdqXueYaMgR3trg4ymcyUvk12/3LzMIjgZeK6x+c4BVKkBEapd4NC4cWMAwN27dzF8+HClZXfv3lVapzrQdFAWZhXj9s7njO9nMxKRtn2EbX/G6cVG8aJnYWeO2NspyHgtrV2wdBCi46dNYGFnrnRhtPexwfX1j5EVm4vC7GKELr4DicK5zuUJ6BLkAEsHoVLgUp6VoxAuQfrNQVIe002A6WLz+lYS/t35WKmmg21QqTi8raHlMPSzssGmHIYE2bLvA4DOwYmNmxWen9X+oMRWRZ2zTNsICvZBxF/xrPaha7Am24aZpfZgi80QzLp8F0zHoKblXIz65hLkAEtHodbt8M14sPWwrpBAXd99sBk6+8EB7cuBsu/LJcjB6J/VkGsY26HCJRLg0RH9r5NsjmNA8wOoV2MXkxvSnIm6YzDmVjIeHWE+frTdhy3tzVntn+16ms4VNsfG/T9eoTi/VOV1YzxfcaVKBw4pKSl48+YNvL295SlK/v7+aNKkCc6ePYsZM2bAy8sLAJCbm4uDBw/C39+/2gzFyuagZEPbSEQVOX+Cuou3DN+cjzYT68PSQdp3pfzN952PGuLyT3dRmFOsFDQA3J6APD4P7g2cEHNdtUVLptHgQE4uvPrUxCp+VoGArzYVQNegsji/1KBycFkjrO8DqOymamiQ/eJ8DKKuJ+scnIAHQMK4C3g1cUbigwzG9bg6ZwHlIMijrhPrbURcUg0a1O0j8WG63sFaaaFI63LFh3F1xwYA1t+FpnKyaV3Jic/Tug8Zbb8bj89D48GBao9RGXGpBJeWhgM8Hopzy7bFde2kIQ/SbIIotuLvpeHOby+MWilhaJDNNmi8vUP/fXDxAOrdwpXTIc2NHbyqOwYFQj5ExexaADy1PJNY2AkZr8lsKwC1nStCazPG71zdb6bI1II5wAQDh6NHjyIhIQEAkJGRgZKSEqxfvx4AYG9vj1GjRsnXXbFiBY4cOYLdu3ejbdu28te/+uorjB49GiNHjsTo0aNhbm6O/fv3IzU1FZs3bzbZjt264uoCrS2qrqj5EzRdvGVqtPeAU4CdxuVCazNIGJ7MuDgBxSIxUp9laS6HrTk86jvpvX0ZppuZubX2Uzd87wvwYPhx/uRUNDJfv9G7HFzWCGttLTii/QH07r4X4PENHyno2ZlYreUE1AcnskPT3NoMtYJ98PpqotLnsHIUotF/Nxo2gQMX56y6IMjKUYj24xqisKSE+drCEAjJ9qHpO2MTrLHx4kIc3iQX4MWFWJVjw/8dD1bfxbOzMXhxTvNvy9S6whZTDaZTDfXXOAtbc4hEEpQWlKI4z7i1k4Y+SHM5JHbUVdUREHVtKdL2YMdFLTwXn5dpH2zOaaYH0IRwdkOMsvk8xk7903QMKgYNNTt5wSXQHo+Ovlb73URdS4R7PUd4NlLuHJ2XVoh/1z9ivH75tXZnlQ6q7VzR9szCVkXOT8WWyQUOhw4dwq1bt5ReW716NQDAx8dHKXDQpGnTpvjtt9+watUqrFmzBmKxGI0aNcKOHTuUAoyqjosLlpmlQGtUzfX8CfrWCibeT0ejQZpr8tMjslHEUAam2kk2AUXc7VR5fwv3+o6oHeKLguxivAqNQ05CPopzS/DiQhzq9w1g3Bag//dRwnCTEBVxkwOtLmjQpRxMFz02NWkPD0VCLJLgzi7VB022D6ClhWIAxs0LZxOsCcz5qB3ii9ohvhqbtpnSVQTmfDj42Wo8hnNTC1iVV90DfUFWMUJX3eWsduvZWdV9cC31eRZSn2epvF6YVYwXLPevLmhQxNS6wgabGswUhf4tvq3c4NHAWf7bFmYX48IPtyERaX7iYZteqImuLcwSsQSpL7KQIcqGSCCBYw07sGpaAyC0NUNxrvbrB5tyaGspArS3iHo0cDK4YiwnkV1rkyH7yGeZk88FpuDW2KnLbI5Bc2szNH5P+jzg3dRVKb058UEaXv+TBIkYCNv5HO0+aghIJCjMKQGPBzw+/lqeamzlbAFJqQSFavpMxN9NQ51e/hBoGN6dTTkzo7XfP9mqiPmpdGFygcOePXtYr7t06VKNIzw1adIE27dv56pYlUrdxVkiAZKfMtdMMiktFCHicjxqBfuqX16sPVVAhk0uoKZaioB27GoFtV1Y2Z5Y+RlFeteWSMQSvLwYJ/+7bk9/ONe0BwA4+tri0rK7kIgkeBkaB99WbrDzsNZaFk3l8GvtzllTvynQ9tuwqUkrzC5WGzSYGjbBWmF22XGs7lhmk64iKhHj0k93IS4Ro+iNQrqKgxAeDZ0QdydVr/IrkojZPfwxb4ibzVQ6Fp/Ds4kzkrS0FrFJYUx6XPb+wE7eSjWWeWkFWoMGgF16obZael1amNXtQ2hjxuqeYeUoRMP3AtWm77BVkFWM65seq20BlveRsdDeR4ZpIAL59nJKVL4v50B7PD8Xi5cX4pg3oOc+XIIc8CY5n7P+UQILvtbrFFNwa0gaJNvKOTbHYEl+qfw6yuPzlK6lrrUcUJwnQnx4KsQlYlxb81DtNuw8rdHh08YQWpspBR5PT0tb2fPSChFxKR51uvvpXU6usO1rUVFMLnAgytQ9XFrYm8PcUoDcFOb5KGQX6PKdssysBCgtkF7gHx+LAt+cj5odvJRO9IKsYjw8FMG4DwuFmnJtn0NTLcVzNTWf6mh7AGV7Yj04GKE2R5JNbUnig3Tkpkhrc11q2cuDBkB6EaoV7IOXF+IgEUnw4M8ItJ/eSGNanLbvg6sbkaH82rgj9laKwdvR9tuYWk1KRWD6zN5NXdF6Qj31D2VFYohLxSjIUK2BLMwuRvS/mvve6Eo2WplGDDnCPD5U+hvpSmhtpjUFo1aID16FGtYawBPwGB/I2fBp5ga/Vu6qednmfLQYXYd5JKsSsbzlxMLWHI5+tkrL2Z4rj09EISs6V+V1Np1oHX1tVd6nzt0/XiE/TfX+oy6NSp1G/wUwPDXHuZWjEF5NXRH5dwLjdrSljQJAaRG7ii8mOYm50ppqhXLyzfkQl3DXiplwLw2Pj71WGnjD3EqA0mIxJ8cnANQO9lHb0ijDFNwakgbJNpXJ0CwHHp+HFiNr401yHnLiNQ+dH9TFGxa20nuTYuAhtDHD5Z/vARLgxflY+LV2h5Wj6gS0bMvp385Da59Ic2szrS33XA62whWdAoeUlBTweDy4ubkBAIqKirB7926V9by9vdG3b19uSvgW0/RwWZRTUpaWw3Dzll2gvZu4qET/Ly7EyofEe3gwEs9ORaOkQPcLrUQsQeGbYlg5qJ/dmatO3NoeQNmMSAKAsWOVplxTiUSCFxfKLrjqaiHq9PBDfHgq8tOLkPYqG3G3U+HX2l1lPa6+D20sHczB4/G0TpKjKahUzLc3NHBguuhVVE2KpYM5AB7jaFiavg//dh6sA1zGsrD4zN5NXeHVWPWczUnKx98/32V8IHeuaYcMhjQzJjU6eGl9eAvqqj7vX6ZOTz+Dv7Om79cCoPqQKztGtQY2LNXp4cvJb2tpbw7X2o7wauyC1BdZuLn1CcQlEoAHeDRwZnx/2qts+fXJo6HqMKRszxV1QYMibZ1ok7LYtWKrCxoU8c35aDosCE9PR6v93WQPj5qO8/SIbFaBAxfMLASMAcbLC6rHuWLQ0GhQTVg6Wai9djT8bxAApntT4oN0ldcU78dM9zc2D6B1evjDzstG7SAktbv7cvZQz9QXzL+l6n1RhosRj3h8HmMa3POzMfBv66Fynjn42KJmRy+8/icRomIxHh99jVbj6uldTr+WbvCo76TxGgZo6Bf3H64GW+ES68AhMjIS/fv3x8yZMzFlyhQAQH5+Pn755RfweDxIJGUXcDMzMzRo0AA1a9bkvsRvCTYPlzw+0OHTxih6U6LxoJRdCMo35wHSVBtRiVhew60paHCtbY+A9l7S2hCFfchq6opzS3Fj42N0mN4YOQl5eg1VyITpAZRNioeNhyXykrXf8DSlRKU8y0J2nDSX1cHPFm51HVXeayYUoMmQINzYLB3J6OGRSJhZCiAqFnP+fTDVxDYeEqRxVCUZbUEl23x7pnIwXfTYBHxCGzPWNZmaNB4SBID5Aq3p+wCA6HI1aOWxDU7Y1h6pO2dL8kpY1eLX7xugNpVElyDIq7EzXILstV5bnGvYaVzu1diF8TtjCl5l1y91D5c8Pg9pL7OYvwwA9Xr7qe0IzracuozAwuPz4F7PCT7N3RB7KwWiYjFSnmXK0zc0SXpU9tDu0VA10GBbOcKEqRMtF8QlYlg7W6DHt60Z01XUHedsPquZpYBx1C02agV7a62FZyK0MUNgZ29pvr2GaymPz9OpE315Ags+Qr5ogZTnWQY/gCoGa0mPMxBxSRqgsRkZjIuKnkeHI+HX3E3jcja/PdN1ND0iW+t1GNCe/lyvTwDiw1NRnFeK+LtpCOiQBbdy61k6WDC2qsrKyePzNF7DAKhtYS5/DTQlrAOHQ4cOwcHBAePGjVNZ9vnnn6NBgwYAALFYjJkzZ+Lw4cOYM2cOZwV927B5uJSIAYlIorHWhk2UWq+3PyL/TtBaE5+bWgifZq7waeaqtA8bVytc/fUB8jOKkJOQj3Nf34JYoUlVOuJNIJKfGN4Xg03UrSnFQ7F28s5u5lx5dbUqL5VaG3w1piB5NHSGV1MXJN5PR0leKW5tfSpfZukoRK2uPki4x250C22YamK9m7rCzIyPkFnNVeZxYBNUyl5nCsY0lQMAbN2tGC96PD4PjRhynZsMC2KssWP7AMrmAq3p+2D6LtgGJ4bUHunSjO/b0k3tdQFgDoKsnCxY3fCYrj1M3xlT8Cqj6Tdh+5BRp4c/6vTw17ucTK0r6n5X72au8ha7hLtpWgMHiUSC5P/6N/AFPLUVE2zOR/933BFzw/D0Qm1qviutjWVSmFOi8XdjwuazBnX1NrilSFstvJWjEF5NXBB5RftnLc7TnG8vo+3exCaQFxWJkRWby3i+sX0AlZXTJcgBCffSUZBZhOSnmchLL4SNi+ZZyV2CHGBhb844EIk2BVnFSHuVDWcX9WlxbH57puuooelOQmszNOhfA/f+kE7q+OBABBoPCURxbiks7c0hsBDg5uYnjJU4iuXUdi4Y8gxXGVgHDjdv3kRwcDCEQqHKsrp166JNmzbyv3v37o3r169zU8K3lK4Hvr4X6IzIHMb0ncIszR06233cCH8vvyvNvS6Xhykd8YZ9LYu2WkG2Ube2E5Bt7WT5WpX0iGykR0gnobP1sGKsOXSv54jE+6rNzoVZxYzDhipi830wXWxqtvGEQ5Ct2pmj2WAKxsqXoyC7GE9PRqMgswi5KQXIjs+Fg4/2vGlzDR0YFffBVGPH9gHUkAs02+/CmLVHujbj6xsENR0axOqGx7Sc7XdmzAdMxZu3IeXU1rqi7nd1r+so70uW9CgDohIxBObqR2h5k5gvH63NpbYDzC3V35qZyim0NuMkcAjs7I2E+2ka98EmcDC0dprps7JpKWLbIqrpuhB/Nw1gCBwAdvdrrftggc29XtfrG4/PQ432nnh6KhqQANH/JqFB/xoay8Dj82DrbmVQ4CD9LNorRaVBmGptPtvrKBfpTv5tPRD1bxKyYnKRm1yA6+sfq9+GgxASiUTpO9Hneq/vNbAysA4coqOj8d5777FaNzAwEKdPn9a7UIT7mQ01MTQyt3G1BN9cABg4/CebWkG2DKmdFNqYqTSBvlDorFy7m6/W8kjEEjxnGNpRWkgwpj6w+T7YXGwMvSCxuRkp7qO0QIQHB6Wd6l//k4hm79fWuv1XCkNd1u3lB1t3a7U121w9gBryfbD5LoxZe8RFM76sjGq/TycLtB/bAI617VBayk3HT2PXprE9Ngwtp66fg2/Gh1djF8TeSkFpkUhrupLiaEqeatKU2JaTTXohG16NndFoUE2998FVh05DW7TYtMzKqLsucH0frqx9aOP/jgeenY2BRCRB9I1k1O2teQjSN8n58kq08nRJg7S0V62AVhR/J1UeNHg1dYF3U1edrhtcXCelAaULsmI09xmycbNEp9lNYW5lVmVaC7jAOnAoKCiAtbXy8JIODg44ceIEfH2Vh/K0tbVFfr7m3uyEGVcPCEwMvWilR2QrzWCqCdPIAmxqBQ3FpnayOL8UsWEp8GvtjvSIbGREvUHKU+mQfdbOFvBtqTk3E2A/RFudbr5KAUl5FfF96EKXm5Fvazc8ORGF0iIR4m6nosGAmhBqmCguOz5PPpKMtYsl6vb013jBNZXm3IoI1rRt19BmfBl136dHXSc4u9giM9PwsekVGbs2jatjw5DWFXV8WKYrJSsEDmw6UhuSXsh2FBdD9sFlh04uWrT0PTYq4j5cUfd6TSzthfBu6oL48DQU55Yg8V4afFup77z87EyMvMKrXl9/uNS01z0N0lEI11raP0v0zbJnhbo9/eHgY6PTZ+LiGJWIJYhkaFkTlYhhbmVWpVoLuMA6cLCzs0NqqvL44Hw+H7Vrq9YmpqWlwc7O8Bnz3mYVdXE29KLFtsXCrbaj1pEFKqoDkKYbjcCcD1GJGJAAd/e9xMPDkSod79zqOoIv0D77MNvvw87Lpsp1iGLL3NIMfm3cpaNSlIgRezMZQV191K4bcbmstSGoizfj8fy2XaDV4aqGHVD9PqtyLZkpHhtudR1hbiVASYEIiY8yICoWQSBUTs0ryi1BRpR0BCw7T2vYuGrOMWeD6fgADO+Hw+UxaChdW0R1URH34YoOxNSp0cEL8f/NLP36WpLawCE7LhcJ/6VVWdiaI6izj9p5Mgz9LNnxeciOldbyO/jZ6hw0yBh6jLKaY4hhfqnqinXgUKdOHVy9elU+opI2V69eVRtQEN1UxMXZ0IuWLi0WsqEKK7vGWN2NxrmmPR4de43X/+WzqhutI/p6MtzrO2n93qvi92EMih0oX19NlI86oqggu0g+WZm5lQD+bT0qvJxVlam0vhDt+GZ8eP6XriQqEiHlWRa8mii3OiQ/yZDX4qobTUkfXHWiZbOPrKg3EIh48slJK+MYNGbQWBH34coOxFyC7GHnaY03SfnIiMxBTkIe7L2VH9ifno6W/7t2d1+Nk+tp+iwAILQ1Z+wjGKPQ2hBg4D3BkOukoWnc1RnrwKFHjx748ccfERoaipCQEI3rXbx4EWFhYfjqq684KeDbriIuzoZctHRtsTCVWkF15Wg0qKY0H1nLEH+a5nmQqarfB9fsPKzhWscBaS+ykZdWiJTnWfCo76S0zusrifKJjWp08GKc5ZUoq67HTnXj01whXelemmrg8Lhs9mLPhsrniCEqYhQXHp8HtzqOcHKyQWZmHmf9YkxNRQTqlVkZwOPxUKOjJx4elA4B//pqIpoOryVfnhGVIz9OLR2FqNHBS+v2yn+WV5fikB2bh+LcEiTcT0eAmvmNAEBcKkbcbem5wjfjwYchNZjVZ9PzOllR/UyrItaBw7Bhw7B3717MmjULkyZNwtChQ+HjU5Z+EB8fjwMHDmDbtm2oWbMmhg0bZpQCv40q4uKs70XLFJpZuZIRmcM4Lri2sZ+B6vV9GKpmRy+kvcgGALz+J0EpcCgtEiHq3yQA0vlAanbSfiMipKpyq6M5XUlcKp3jAZD2PXCqYa9tU5yiwFM3FfF9VeZv4tfaHU+OR0FULEbs7VQ0GFBDPrrX05NlrQ11e/prHB1MkeJnEdqY4foG6ahEr/6Kg38r9QFB0qMM+Zw9Xk1cNfaNqwiV3ffElDH/+v8RCoXYtGkTfH19sWHDBnTr1g2tW7dG165d0bp1a3Tr1g0bN26Ej48PNm3apHbYVmLaZCe6b0s3+ZjUbMhaLCwdlX9zK0chWk+oV2Xy9blqmqwu34ehPBu5wMpROpt48hPpGOEyMbeS5R00fVu4aZx1nJCqjm/Gl7cyyNKVZNIVKis86juBL6j+FQrENJlbmsHvv74Nov8GtgCA1BdZSHsprQCycbWEf1vNsz5r4lbXUd5XISsmF2mvstWup5im5P9O5aauyioBtXlbKgHL0ymc8/Pzw9GjR/Hnn3/i3LlzePnyJVJTU2FjY4OWLVuiR48eGD58OCwtDevcRaqe6pBzzWXTZHX4PgzFF/BQo0PZGOFRVxPRcGBN6WgVlxPk6wV19a7EUhJifN7NXBFzU5qCEX+3LF1JaTSlRtz0byBEXzU6espbgl+FxsHMUoAX58uGV63b259xgBB1eDweagX74M6eFwCkQ5zXaaM8YEZBlnQSOkA6LLRb7cqvya/sviemSud2IAsLC4wePRqjR482RnlIFVbVm765bpqs6t8HFwLaeeD52RiI/xsjvF5vf+kMpWnS1ge3Oo6ME8QRUtW51XGUD4Oa9CgdomIR+OZ8JD2SBg48PuBRj7v+DYTow8HHFjZulshLLUR+RhHC/3vQB6R9G3xb6N/nwLu5K578Nzlo0uMMZMa9AWzKgpDYsBT5IAH+bdxNppKNKgFV6R46alBUVISMjAzmFQkxUdQ0yT0LOyG8m0trZUryS/H0TAyenCjLlw0KVj9MKyHViXQyOGmLgqhYjOSnmchNKZAH0C6BDjCvxHxuQgAg4X4a8lIL1S4rzCpG4sN0vbfNF/AR1KWsdfnBydfyf0skEsTcKEtT8jOxEfb0TeOurnQKHJKSkrB7927s27cP6enSA+jVq1cYNWoUmjdvjg4dOiA4OBhnzpwxSmEJMTbqn8C9mu+WdXyO+CseeakFAKQX49Ii7Z3RCakuvJuX1dYm3EtTTlPicDQlQvQhEUvw8HCk1nUeHY6ERCzRex8B7TxhbiUdGCDiWgIKsooASAcmkQXRrrUdYONC6e6mjHUVR1RUFIYNG4bc3FxIJBKsW7cOu3fvxvjx45GdnY06deqgtLQUUVFRmDNnDlxcXNCmTRtjlp0Qo6CmSW4VaEj9kogluL3jGXgUkJG3gFsdB4V0pQzkpxfJl3E1fwMh+mIz4RnTqIJMzCwEqNHBCy8vxkEskuDVpXjU718D0TdMp1M0Yca6xWHLli0oLi7GwoULsWrVKtjZ2WHmzJmwsLDAmTNncPToUZw8eRKHDx+Gvb09tm/fbsxyE2JU1DTJDYlYgkdHjFuLRUhVwBfw5ZNfiYrFyIyWzhZt7WIBW3eryiwaIRU24VlgZ2/wzaT304h/EhB/N1U+EajAkg/vJtoniCOVj3XgcOvWLQwbNgyjR49Gr1698MUXX+DVq1cYO3as0nwOderUwbBhw3D//n2jFJgQUnXoUotFSHVn6aA6THlRbgkSH+ifO04IFypqwjNLeyH820hbFURFYtze+Vw+ESjEkI+sREwX68AhOTkZdevWlf8t+3dAQIDKujVq1EB2Nj0IEPK2q6haLEJMXcL9NKWhLWVERWKEbX+GhPtplVAqQqRkowpqw9WEZ/beNmpfFxXTuVAVsA4ciouLleZnsLCQTthkbq4afZqbm0MiodQDQt52FVWLRYgpq4iOp4QYoqJGFZSIJXh5UTWAVkTngmnjbDhWQggpryJrsQgxVZSyR6qCihhVMD0iW+OAGTJ0Lpg2nQaOvnz5MpKSpLMKFhYWgsfj4fTp03j06JHSek+fPuWuhISUI5KI8DDjPtKL0uBi4YrGzk0h4Akqu1hEDVktVtj2ZxrXobkxSHVHKXukqjD2qIJ0LlR9OgUOp06dwqlTp5ReO3DggNp1eTx6ECCqDH3ov5J0GeuerEJqYYr8NTdLd3zSYBY6eXbhZB+EW7JarIeHI5VqXa0chWg0OJCGYiXVHqXskapENqqgMdC5UPWxDhx2795tzHKQaoDpgZ3NQ782V5Iu47vwhSqvpxam4LvwhfiuxWIAMGgfxDhobgxSHehbKSFL2dOWrlQVU/ZEEhEept1FYWYuLEW2aODQROdKGqroqZoMOResHIVa05V0ORfo+Kl4PAn1YtaJSCRGRkZehe/XzIwPJycbZGbmobRUXOH7Z8IUFGh66Jf5rsVirQ/2IokIH14aorT98uzM7PGmNEfvfVQ3pn7MVGdV9Wb2th8z2n43Qys+Eu6naU3Zq2oz0xv6fXC1jbeNKVxbDP3dkh9l4MaWJxqXsz0X6PjhlrOzDQQC5q7PFDjoqDICB5FEhCfZD1Ao0FyrU5kXE6agYEGTb7Dx2RpkFWsen9nN0h37uh7SWOZ76eH47OZ0g8rJtI+qSNvvzvYh0BRuRNVJVU6nq6zAwRS+D22/GwCDKj5kEu6nVYuUPbYVQUyBGBff6dvEFB6UufjdzMz4yHr5Bv/ufKzU8qDLuVDdjh9TuAZS4GAkFR04sLlQcHUx0efAZdMSwNaKtmvR2Lmp2jIcfL0f65+u5mQfzVxaMK5XUScx034MqQFl8xBYmcdOdcTmZgawS6cz5NjQl7ECB2PW5LPZBxOm383e3B45JZpbM3WplJCIJRWSsmesc5LNNd/N0h0f15+BDU9/Vfu7dvB4l9U29nU9BAAmcW2p7GucKTwos/3tmc4F2XUmIz0Xyc8zdT4XuCqHbFuVfXyZQkAIUOBgNBUZOLB9COHiYqJvLenDjPsGtwTIDAr4f3t3Hh5FkfcB/NuTO0DInSwB5BAIRyAJYjgEOQQBhSCCyJEFxRUFBQVWAY9lVZBdF0EuAV0OA7jwcrgsCh4oqFkFdAPhUFEOAYWEHBBCyDn9/jHOmCHTx0z3THqS7+d5fCTTPd013dXV9auq7hqBjOzP7dIQGRiFlKhu+OiXPSg3y7/CTY3nEueiX6MBsuuovYi1VuzUDO/S0gLat3Ff2UqgXjciTxZ6RijkpdKg5mamVAG1HnMteUNLHk2KTkJkeIhTgYOWfA54rvySS78ejR/WRgkj5FE9rkmp36FH72+3qB746nKG4noTWj2C98/vrPGGDU/dE+TSr1dFWQu1516pgU5rb7he6TDCvcsIAaEVAwc38VTgoKagqO/bAIIg4JrGljAtraQdwjris4ufyP0UQ1FTkKi5iLVW7JT280DzMdhyZpPkcjUtoFv675CsBOp1I9Kz0HP3w/VqudI6/nj8k7hY/CveOvmmpn1bW2pfynxech2lvKE1j87pOhudQ7rZ8oyW3gKl/BHkE4QblTdkj4e786AeFWEAmNPpL/D3CTB874maa1LqvI69dTz2/boXh/P/pyot7qR3UKm1YqdXsO+IXhVlrfb++hHmHZ6ruN5ziXPR+w/9NA2jdXy8ojC82QP4JvcQvs09qCkdnqywS943HPTK3cyTQ6wZOLiJpwIHvW5mgHxhokcrqRoN/UNxteyKpm10juiCb/MOSS5Xk877bhmByW2nAoJQrTABoLoLXkvF7sWkV/Dmd2/gcsllyXUECBCh7dJ8o/ty9G3Vy2Hh7OyNyNFNFVB3vNQUeu5+uF4tLa3jerH2XkhRyhta86gAAS/dNh89ou/UdDye7/RXrPjuDeSX5Uuuo4bW8ktpyMv2M1uw7LvFmtIIAKH+YbLPcbm791dtr5fSNal0rRlF1d/hrkq/2oqdXsG+1O9wpsKu1KMOuB6Yqr1v9Izpje+vHre7xzkzjFavPDim5Xh8/Mtut1fYtTzLo4a7A0IrBg5u4qnAQW1BoYZc1K1ngCJFTcGqxuspy1BYXqi5Yteywa24UnYFeaW5dtu4q9EAvHt6g+L3/Ux+KDdLT06jR6VfDy8mv4SRCfchN78QmTmZdud+38W9qvKXXCvq4CZDsf7HtxW3obWX54XEl7HsxCIUyFRA9RgPrZSOYN96KK7w/BvVXBFgCkSpuURyuQkmmOG4pU+AgKjAaDzW9knZa1aPBgU15CpDassvR0NeIgIi0Ta0Pb7KyUClWKFbeqW4u/d3UvwU/HztLNJPrVVMi9Q1qXbYlh5lXD3feriu8XqSuyfoVelXQynYVyL3TMhDrf+Ez37di0O5XytuZ+HtS9ExItFtPbeVYiUe/PQ+u3uns5SG0er53KRWaoYfyh1PNc/yqKE2INSKgYObeGOPwx9bPozdv+yqnrHbTsOPhSex8dR6XfYjRalVp1dsH2w7u1lxO9aLx5WLuFt0D3xw/j+o8EAFwSgmtpmEDn+Ix/yvX7V/biQgCs0bNMchFV29oX6huFJ+RVM65Ao9PW8SasdDu9p7ooY3BRdKlIYRecqUttMwrNkIh9f8Rxd2Y0HWy25Pg1KQFBEQqaoypbX3pL5vfRRVFCknWIFUY5LaZ9YebJ6Gf51J15SGCa0ewToVDQ9ybo/qhoOXv9K0DQEmiBJBtLdpExKP3NLLyCvNs32md8/ty5kvahqerDSMVm3dZ0yL8dh02r11l6FN7kNiRDLe/H6pS42VHcIScKzgqOZ0sMfByxnpGYfIgChAAHJlhrx4yv3NHsDnl/a5VGlTe7PS+kakzLxvMfPAVEP0BtQlcufNEz1egHLwqrb3RMn9zR7AtrNbJJcrVUAb+oXiqsZArTYK9glGcWWx7e/IgCikRHdDxqUvcKVceniQWt2j78B/c76UXK70NqzyyjLMOzJXcT969J7oYWiTYfjq8n+r/ZbkiNvw4S8fKH7/ucS58DP5ax63nZH9hVuvx9rE3+SPMg0vCHm+01+x4vslyK8SVNxMqVesrLIMoz8b7rD315nzJjeM1plhWVJ5sGdMb2z/Wboc1osnel2N+IyD6pmjybN8BB9MafeUbDT7RPunAXhm3LWSHjG98FjbJxWHifgIPtUqkQnhnRAVGK14o7G2DCtxtA9An+51vSp2Ss98yA0lATxTYEUGRCG3VFtQqnTetHR5O2P5icUwi2aHQxIul+ToVknpEdMLCeGJLg+nm9ZhpmKlSylv1PdtgKKKa84n3g2U8rnafFw1aACA3NLLeP/8Tq3JsxnR/EEMaDxYcQhHj5iekkM+1YgIkH4/vV7XQj3f+riu0Cux8/x71T67XJKjKmgALL8jMSIZPWJ6OpxjyCSYZPP5lHZPwUfwQa/Y3g6PKQB8cFPvoVF5KtifkzgXgOPg9c7YPth2dovs/e2VI39R3MflkhwczT8i2dDz6cWPbUFDz5jeuK/ZiGpDYNXIK5EOXuSukZvXs+bBm/PPvot7gZ9VbUYTTwzVtF4rRqIcWlCN6RXbG3OT5yMqMNru86jAaFsLqtw69zQZqm4/Mb1ll4f4hcgut1YOrRX2fo0GIDEiWXVmtwZJcvS4ePS4MU/rMLPasb6ZSeGyigqMxrT2M2XXGdH8Qdnl0xNmSeeNpHkY13K87PetJrR6RDJ/TYqfomobcia3nSZ73tTeJBr6h2pKx+WSHLx6+K+atqHEeh30iu2NTX224fWUZXgucS5eT1mGTX22KV6vc5Pno/cf+ipeC8p541nNebSebz3Z5Wqoyedy+fj5xJdUpaNVg9aa0glYyga582YlVcZZGz/kKAXRaq8FJQMbD9ZlO1Kq/g4fwQdJkZ0xuMVgJEV2th0PNfcuK0fHVM09oVdsH82/paFfqOZt6HFPUKPcXCaZR7vH9NStJ13qPimKIrae+X1I8QMtRlc7b6or/YERkssSwjshPEB6OVA9D7qaDjmDGt+reRuApSFHTlRgNF5MekXVtWIU7HEwOGuLjNzM0VKtNvsu7lXVKtfzD73RN26Ay62kelTqrTcad75uU21hojRWXqk1bUTzB2XfoPH7duR/b7uwDi63gJaL0g9vVxVXrzE29dmmqRXV0fGyUnroNCG8k2LPhl4PMKo9JlKUWserXgdSvV6A9PV6c6VLS97QkkcFCHim0xwsO/6Gpretqc3ngON8fDT/iKqHZx9vN1XyAVm1QyesZYPceZOjpodYqZxMCO9UbUiWK+R6ve6OuwcbVDw8LUdtea+Uz9V8X+46CPELweeXPnP5dwDqevjkHlzW656ghlwe1bPnVuo+mZn3LU5f+wkA0Da0PdqHJVRbR+3ogY4RiZLLfQQfNAqKkx1SpeZaUpMOufNabi7D7gu7JL+v1ojmo2Sf5bHmoZ6xd9b4/C9qMXDwAtZWHbnXlzkqTPTo8rNmXHdX6gHtNxolaguTsbeOx9hbx7u1Yqfm96o5HlIVHWfOvdQ2XD1el29cxuoflgMA/nlyFXrG9oafyc/h930EHySEd8RnMl3cShVQT42Hnp4wC4C6WZ+VKFVQteYNV/NodGAMZnedhc4h3SCKgmxlSO3xcDUfq60M5ZXmol+jAS4PeXFmGKQcqWMOAMkRtynmj7ySXMUx7Grmb7EeW8khHCqofWZNiauBmJVc3qkUKzVXDtVU+tVU7LTcE9Q+E6JHb5XS0EG5/Ww98y/bv0dK9HqqCaAntn5U9p7+3ZUTOHYlC0D14cVq86DaQF7uvKptOJNjvT82a9BCsZzUeq14Eh+OdpInZ46uSu1Mi1XpPdukEWZE1cqTE5bV5PGynvvcksuSXdh6vR7y5uMliiJmHpyKzLxvAQBPtpuO+5qNcPj9c0Vn8acvxzt8va2eb0RSOw5ZzZuZvOk60DpztJY5BbTSc3ZYT87Maj0eF66fw4oTS1BiLoEJJrzV8x00b9BC8nuvHnkJH/+yB0D1N1qp7f3Va7K711OW2Xp9lM6rK/cmvag9t56aXNLVe4LWPKr2Xq/Ucyv18P65orOY8PkYAJbGhY29/w8+Jul2Z0fH0yq16XDMSHzGYZ4RRRFPfT0ZRwuOAACeaPs0WoS0dPuM347oMcdV1fPmDfcNvlXJTbwpcAA8f9P0Bp6cZr4mZeTsx4vfzJEMHPSeebWq76+cwOT/PgIACPUPxYbe/4fgm8arm0Uznv56iu0m8UDzMega3d0tczBYJt1T96YXwPW5ILydo3Kmpm54ejZ81NQ1v+nUO3j7h5UAgK7RPTD/ttccrnfy6vd4LONhAJbKyLo738XZa2dcmq1bjt6NSUDNBg6A5yr97qb1d2iZ7M5qZPMH8XjbqdU+X3TsNfzn3A4AwGPxT+CBFmMU01P1eJrgg79nzUOpuQQCBKzs+U90b9GlWp758tJ+vPi/2QCAxvWaYk3PDfCVCVDUcPcs7IA+vdBGwMDBTbwtcADqTkXZGTV9k/AEX18Tvi38qto8Dp66qb6U+YJtaMT4VhMxvtVEu+Xvn9+JhUcXAAD+ENwI/+y5AYE+garTdDOjzD7tzWq6EngzT/YQukNpZSn+uH+ULU866h0RRRHTDzyBI/mZACzzVtzffJTsdt1dGXLmOjBCnqkt5bnW36H2Xl91PxXmCiw8+jdUiOUQIGBJt5V2zy9cLbuKBz8dhlJzKYJ8grG573uo71ff6d+2+fQmrPp+GQCgTcN4bB76LxReLbHlmQpzBR7+fCwuFJ8HALzceQF6xPRyej96q8leV09j4OAm3hg4ALUnY5N61jzjaOZoT5z7C9fP46HPx6BSrESQTzDSe29BeEA4ACC/NA8T9o+xvTb0tdvfQOfILpr36akhCbWVESqBN/P2c7bnwvv4e9Y8AJYK0/Lub8Mk/H5zzsj+HC98a3leJC64Mdb02ij5TJBe9DymRswzdZkr9/p/ndqA1T+sAAA0rXcLVt+xDv4+AQCAjT+txz9PrgIADG82Ek+0e9qldFWYKzDpywk4U3QaADAnZQ4Gxgy15ZkdZ7di6YnXAQAdwxOxKGU5BEFwaV96qyv1JwYObuKtgQPVPUbIM28c+wf+fW47AGDYLSMwtf10APa9EQPiBmFWpxc8lqa6chNwhRHyjCPefM4qxUpM+nICTl87BQB4IfEl9Gl0F4DfWlm/GIcL188BAP6a/Cp6xt7psXTpcUyNmmdIvUpzBZ74ahJ+uPodAGB0izT8Kf5xlJvLMeaz+5FXmgsBAtJ7b0Gj4DiX93M0/wimff04AKC+X32k99mMhr5hKCovwrh9I1FYfhUAsKL724gPbaf9h5FTOAEcEdW4tFYP4cNfdqOk8gZ2/rwd7ULb46fCH21BQ4hfQzze9kmPpsmb3l5BFt58znwEHzwaPwWzDlmC5rd/WIkeMb3g7+OPned22IKGhLBOuMODQzO8+ZiSvnxMvnim43OY9OUEVIgV+NfpjYgNisWpaz/Z3m7WI6aXpqABsLypb2Dje7DnwvsoKi/CsmOLcU+TVPzfmXdtQUO/RgMYNBgcAwcicpvwgAg80Hw03vlpDcwwY/4R+0nY+jXqr3lyNyKj6xKZguSI2/C/vG9w8cavWPndUrQMaYU1P6y2rTO57VTDDM2guqd5gxZIa/UQ1p58CyLMWHTc/kH+1g3b6LKfSfFT8N/sL1BYXoi9v36Mvb9+bLc8IUz7q5HJvThzNBG5VVxwY8llO37eis8v7fNcYohqgCAIeLTKTOzvnduGhccWoLjSMuy1Y1gntAltW1PJIwIANAluKrlszcnVupTVDf1D0fe3oXqOLD7+Gu8JBsfAgYjcplKsxNsnV8qus/zEYlSKlR5KEVHNuHTjouSyrIIjrCxRjaoUK/Hm90tl19GjrK4UK5GR/aXb90Puw8CBiNzmaP4R2XfGA8DlkhwczT/ioRQReV6lWInlJxbLrsPKEtUkT5XVvCd4PwYOROQ21gfr9FqPyBuxskRG56mymvcE78fAgYjcJiIgUtf1iLwRK0tkdJ4qq3lP8H4MHIjIbRLCOyEqMFp2najAaCSE800aVHuxskRG56my2rofAdJvEOM9wdgYOBCR2/gIPpjS7inZdaa0e8prJvMicgUDaDI6T5XVPoIPpnaQn32a9wRjY+BARG7VK7Y35ibPr1ZxigqMxtzk+egV27tmEkbkIQygyRt4qqy+s1EfvN77dd4TvJQgiqJY04nwJpWVZuTnX/f4fn19TQgLq4eCguuoqDB7fP/kfYyWZyrFShzNP4K80lxEBEQiIbwTK0oGY7Q8U9t8fmkflp9YbPegdFRgNKa0e8prK0vMM7WPu8tqa57JzS9EZk4m7wkGER5eDz4+yv0JnDmaiDzCR/BBYkRyTSeDqMb0iu2NHjE9GUCToXmqrOY9wTsxcCAiIvIQVpaIyJvxGQciIiIiIlJkyB6HXbt2Yc2aNfjpp58QFBSEHj16YMaMGYiLi1P8blpaGg4ePOhw2fLly3HXXXfpnVwiIiIiolrPcIHDhg0b8PLLLyM5ORmzZ89GQUEB1q9fj0OHDmHr1q2IiYlR3EZYWBhmz55d7fMOHTq4I8lERERERLWeoQKHgoICLFy4EO3bt0d6ejp8fS3J69mzJ0aOHIklS5Zg3rx5itsJDg5Gamqqu5NLRERERFRnGOoZh71796K4uBhpaWm2oAEAEhIS0KVLF+zevRtlZWWqtmU2m1FUVASzma+HIyIiIiLSylA9DllZWQCApKSkasuSkpJw8OBBnD59GvHx8bLbyc7ORlJSEkpKShAQEICUlBRMnToVCQkJuqTT19fz8Zb13bpq3rFLBDDPkPOYZ8hZzDPkLOYZ72aowCEnxzIpTmxsbLVl1mcbsrOzZQOHuLg4JCUloXXr1vD398eJEyeQnp6O0aNH46233kK3bt00pdFkEhAWVk/TNrQICQmqsX0bhVhZibITWTDn58EUHgH/dh0h+PA96FKYZ8hZzDPkLOYZchbzjHcyVOBw48YNAIC/v3+1ZQEBAXbrSFmwYIHd3wMGDMDQoUMxfPhwzJ07Fx9++KGmNJrNIgoLizVtwxU+PiaEhAShsPAGKivr7vCrykNfomLjSiA/9/cPwyPhO/Yx+HS5o+YSZkDMM+Qs5hlyFvMMOYt5xphCQoK8b+booCBL9FlWVobAwEC7ZaWlpXbrOKNFixYYNGgQtm/fjrNnz6JZs2aa0llRUXMZvbLSXKP7r0nitxkwL3fwcHx+LiqWvgLzlOcgdO4B0VwJnDwO8Uo+hNBwoHV7CKa62yNRl/MMuYZ5hpzFPEPOYp7xToYKHKKjowEAly5dqla5z87OBgBVr2N1xDoHRH5+vubAwRt5e2VaNFfCvGmV7Drmd1dBMJsh/ustoMDSIyECQFgkTGMmQejcQ/c0efMxJSIiInKGoQKHhIQEbN68GZmZmdUq95mZmQgODkaLFi1c2vbZs2cBAFFRURpT6X3EbzMslW6FyrTWirAeFWnJbZw8bku/pPxciG++Wv3zglyYl8+D6bceCT2oOaZ1KbAwym/1lnSoSadRfgsRkVGxnPQ8QwUO/fr1w7x585Ceno4hQ4bYXsl69OhRHDp0CMOHD7c9/5CTk4Nr166hUaNGtuFLV69eRVBQULVnJI4ePYo9e/agVatWaNKkiWd/lIdIXTySw3tuqkyrDS4k96/x+3LbEEY+DPHIAVXbkGN+dxVMSV01FypqjikAzcdDVVoMUGjqce6NlA7NAbRCOlQFnQY5poBn8pgR8rGR8Hjoi8ezdjJSOVmXCKIoijWdiKrWr1+P+fPnIzk5GampqSgoKMC6devg5+eHbdu22YYqzZo1Czt27MA777yDlJQUAMAnn3yCF198EQMHDkTTpk3h7++P77//Htu3b4evry/Wrl3r8FWvzqisNCM//7rm3+ksX18TwsLqoaDgerUxgTdfPAAsFe4H/2Q3bMeh8EgIo/7kuKX+NyaFZwckK9I3fR9wIcDRmemZBRDiO7r8fdFcCfOfH5I/pvUbAEXXpNOgU8+H1Hm3FprWPJOfV4iKE0fd0vrtzLl3J73SoXRMAfnjpZQOYeD9EPdsk00nAFW/RY9ei5uX+7ZLQHhEiK2c0Xo81KRDr30o8dQ29A48AdRIAKx2Hz6nTiC4vBjFfsGobNnOcD18ao+nUfKYt9DyW+XqM6r3r1eZb5BzZoR0hIfXU/VwtOECBwDYuXMn1q5di1OnTiEoKAjdu3fH9OnT7XoLHAUOp06dwpIlS3DixAnk5uaivLwc0dHR6Nq1Kx599FFdnm2oicBBrnDWp8It4LdY3TFrcHFzEOJEcGL6+1og82vXAxydCI8+A1PX3i5/X/w+C+a/z9KWiN+Oh5pCQUuF3S+lJ4K++wZXVi22fwuVTOv3zcsB7YGp1t+q5nuKwZyKdKg5pgAkjxeSuiqnQxAAuSI3IMhyOZbIvD1O5npUc95kz314JEInPYUbbW9D+YEvNB0PNekAlIMkNfsA9Kk8ytFyrTjVc6tD0KhX8KGYVgMEOFrzl9pyUEs6jBYAa6X1eGkNHJwp8wG4tVzQg1HS4dWBg5F5OnCQLRTVVFSMokc/IGOvtm30vRf4dJemTQgTpsHU626Xx6Cbv/wY4ppF2n4H1PV8aKqwh0XCd+yjqFg2X3IVPVq/1dDyW9VUDnHyuKpgzpoOh9sAlK+leg2A69I9SejcA/g2QzEd7qbmvCmde58/zUTlljXA1XzpHanoWVNKh+IxVdl7p0flEdAWqDuzH0fUVoaUgkbdgg8NPWueCnAUG86U8pcTPe5a0uFMAKy4nxquYOrR0q85cFDZgCcMGwdx/x7N5YIejN5rDzBwcBtPBg6KhWLPu4EvtM1L4U2ER5+B4OfnsJXU4c3UEZMPcHsv4IcsoCDv989VtMILd/S3FEKFBbr8FlPX3u4duqXUuq20PDAYgCjf+q0mGQq9PKorIRLnBa3aAQc/V07HhGkQ6tV3fG7vHAjxvQ1qfo7xBQUDEIAbnh9SaSe4viV/uTMdDcOA4eOBtYul1wmqJ58GPXpEA4Msv7W0RHE/Uq3DevRmCo/PVj88VSn40NJgpWOAI0VVoKWGjy9QWSG9XKHlWs/hq54I1gDP9O4CkNyH2sBBsgFvzzaIW/6pmF5ZKoJKvXrLPdVrrxUDBzfxVOCgW6GopEEIcK3QvfvQiVyLsV6VbaWWWN10uA1C5+4Qd26q0aFbniDX4+BUK6tMq6Aqvr5AhUwFgequDp2BY9+6fTdy14L5630QV/9d4w5MgFnbe/EVy0A9GqyGjAI+fR+4XiS9jlKFXY9hoyrJtVyLECAuf0XbDhQCLb2CNUBbr4UeLf1Vn7+TCxwcpzMCaNkO+F+G5nyuhlKdQzqd6gNkZ9Lhbgwc3MRjgYOHCkVVLVRa+QcAZaXatqEy6pYaty2MfATizz8CSkGBUis8APyhCXDxvPRypZaMuiQsAqbX1mlvZfX3B8rKdE6cGwQEyrc6CyZAlLnhhUVY/l+1N4xqDbneN09WhGWpKQM9RKoCKjzwCMSTx4DPtA1d1YUnjtfd9wEf7tC0CT2G0+kR3Fqfv5MLHDz1shQlwvgnIdQPcXkYpF71K63PZqqlNnAw1OtY6XfiFZmxxVUpVcqlWiHCI2Ea/VtEbDK59SIVBo/UPAzENHqSqq46oXMPmJK6Om6hahgKs1LgoOIGIIybDKG4yGGAYhqtXJjAzx8od3Ml2Cg9SRExlsqyBNX5XGvQcEtL4OdT2rahgjBohGxeF+6+T/65kjGPAdD+XIkuGjQErl2t6VTUKkJouOQysUVrwMcHqKz0YIocJcQYQQMAx9dSQS7EVQv02UH9EKBIYznpieOlMWgAAPPbCy1Bjtw6Cq8sF0LD5V6joi4d766C2KW75HI1k70CAJK6AZlfaUyNPDF9haW34Wa/vXodwfXlv79ygS75Q67cqAnKoQXVCLUZRRg8Una5afQkmLr0hOm1tZburkefgemZBZbW+99aFoTOPSytEWGR9l8Oj4Tw+Ozqn99Mar3wSEsLxr2jtG/DiYeDBJMPhPiOMHXtDSG+4+9dimorqUquFliOmcQxlTuepinPQZj0rD7pkCGMmwKEKxxzmQo9AEvrt7UFXIpkHvntBvXTCYj7PpBOgp4FYv9h0sf87vu1b79eA/nl4ZEQ7h0le+5ND0yUzxsK+UfV9ajmvCmd+/AoCOMmy6+jdDzUpKO+wjaU9tEgRH65GkHB2reh8lqxPojv0M539QkaGjTUvg0l/gHu34ceFPKXkDZF+XpS46a5owyptET5ebX8XMtkqxJEjc+7Wfch/nBMermayV4BmPqnSpeTw8ZpTORvHAUNVRXLDLUD9AkqlcqNGsAeB6Nq3d5yQSiNabx3FIS4WyRbv23BgckHiO8IqfYG2ZZ6hR4JW89F5+6S4wBNYyZp3oZWerSWWLcDyB9T2eP59T7tiVDRkyT4+qBiqfTYWz1av6XOm3jtqu2ZBPHd1RBbxEO4pWW176u6Eakc+mVK6gqMmug4/3yfpercOxwaobInydYrJnPuAfm8YUuHlutRxXlTOve+YydBTOpu2ZeLPWuq8s/4qZZ1XNyHMG6K8lAAhQdThbuHa+8RVdNTlNBFesjeD0ch7t7628ZMlpbwwiu/r6D2BRB6PQ+kQI9eZF16tO7oD3z5seRipfylW4/7nYOBj9+TXq7H8FVP9FTD0sDm6J4mfndEt3wl14intoFPvJIPU9feDstJANXL8ZsplAto0QY4/YOqtLiT2tEWnsRnHJxkpLcqqXmNoK5pUQhOPLENLVQ9iKs0Bl2HNxzoMZZZzRs0fH1NjudxqHrTVHFOXD1v5k0rIX6y0/JHdCMILyyCcP6MfXCx+jX5N5pA/ZtiZN9Y4873fnswD1vpcd4cL49C6KRpuNH2NtvYY6cfDHQh/2jZh8uvwrQGJ1oePFXzW638/GF6fhGEJs3tPhaLi2B+cQqQfxkAIIx4CMLA4Zpe3Sj5rJea4ENlGejwLVQeDnDk3rSnNn8BMs/G9VL3tjXTMwuA69dcH76q5rcOG+eRN78Jf34VQpsO9g1BJhPERS/+/vxWy7ZAfo79s1hOHC+/2X9DRPfujie0VXl/VHpgWGu5IJaXa39ZAaAcIKssWzyBD0e7iSHmcaiBDAXUjslr9JjZV+tx1/S+difOvSdmjpb9neXlML86Ezj7o+UDuQecW8ZbKk433YicqRzq8a712jbTqNaZoz2VDq370CU4cdOrLs3ffPn7/DN/aALTC4shBAbZ1jev+hvEA/stf7RJgOnP85UnKlRxT3D17XPOlIFSk5NqDnCcqLArvfVGLZfnd6nSaOFKAKw20NIcrKl98ULLtkBeDnCl6npVJolNTIFp8nOASXDteAkm+C1YjYh2bRyWM+YD+yGu+pt8GjW+LEVNuaDXZK9q5wkxwn2FgYObGG3maHKeay2x+gZrelZUpGidZEcPYs5FmF+cLP8Af5sEmGbOt9ybNFYOFdNjoEDciIyQZ1zllhmIdcgbYnkZzPNmAOd+ezi/W1+Yeg6AeCUf4i8/A+9vtnweVA+ml5ZDiIhW3qabf6szx0Iqz2gJcADnKuzupOcEXXpMAubOSQoVNWlu6TXzk36mQ9UbkcKjEPm3ZSj0q2/XqGW+dAFIXyHf4wV9jrma7+k2P4eX3HcYOLhJTQQOgHff0I1Iy6ypuqXBzYWJEfKMaK6EedoY+fG9YZEwvabPRDtq01TTLTtGZYQ8U5PclTfE7F9gnjsVKJV+pkeY9CxMKXdq3pfqNOlUBsrlGS3H00gz6nqsMckDDSNS6+C2nsBH7wFyT4MpvF5bNh2hEZbnd6xD8kJCIQoCcFViQtX4jkD2L5K90J6gdbiTmh5AI2Hg4CYMHEhP7ixMjJBn9BqvSp5hhDxTW1W+u1r2AVpPVoT15M48Y6SWWo80JnmoYcRhL8/J47qW1Q73ca3Qsg+5eZAAoNPtME39i6XnoYYr21qHQXoTzuNA5AWU3nbl7Zx5Q0ZtPQZEorkS+OZL2XWU3qFfF6l5A5nH0uKBslqPfajZhqN1zDqX1Q7T0TAMwsx5EP/8kPyrTs+fBkSzIe6Pqt6CZ4B0ehIDByJyG7WvwDXaBDdEulLzbnrrO/TZ82anrlXKaoqnymoh+1fHk6pVZbBrgXnQHieAIyL3sc5HIseAE9wQ6cmZnjeiGuGhsprXgvdj4EBEbiOYfGAaM0l2HSNOcEOkJ7WttOx5o5riqbKa14L3Y+BARG4ldO5hefvEza1Z4ZFe+0AokVPY80ZewCNldev2lrwuh9eCofEZByJyOyM95EjkadbWXNlXO7LnjQzA3WW1YPKB79jHULH0Fcl1eC0YG1/H6iS+jpW8BfMMOYt5xr2M9HpRvTDPkLN8fU0I+u4bXFm12PIgtJWXXwvejq9jJSIiMhD2vBFZBHa/E/5tkuxmjua14B0YOBAREXkIX+1IZCGYfCDwWvA6fDiaiIiIiIgUMXAgIiIiIiJFDByIiIiIiEgRAwciIiIiIlLEwIGIiIiIiBQxcCAiIiIiIkUMHIiIiIiISBEDByIiIiIiUsTAgYiIiIiIFDFwICIiIiIiRQwciIiIiIhIEQMHIiIiIiJSxMCBiIiIiIgUMXAgIiIiIiJFDByIiIiIiEgRAwciIiIiIlLEwIGIiIiIiBQxcCAiIiIiIkUMHIiIiIiISBEDByIiIiIiUsTAgYiIiIiIFDFwICIiIiIiRQwciIiIiIhIEQMHIiIiIiJSxMCBiIiIiIgUMXAgIiIiIiJFDByIiIiIiEgRAwciIiIiIlLEwIGIiIiIiBQxcCAiIiIiIkUMHIiIiIiISBEDByIiIiIiUsTAgYiIiIiIFDFwICIiIiIiRQwciIiIiIhIEQMHIiIiIiJSxMCBiIiIiIgUGTJw2LVrF4YPH46OHTsiJSUF06dPxy+//KL6+8eOHcPEiRPRuXNnJCUlIS0tDYcOHXJjiomIiIiIajfDBQ4bNmzAjBkzEBAQgNmzZ2P8+PHIyMjAgw8+iOzsbMXvZ2VlYezYsThz5gwmT56M6dOn48qVK5gwYQL++9//euAXEBERERHVPoIoimJNJ8KqoKAAffv2RfPmzbFlyxb4+voCAI4ePYqRI0fi/vvvx7x582S3MWrUKJw8eRLvv/8+GjVqBAC4du0a7rnnHgQFBWHPnj0QBMHlNFZWmpGff93l77vK19eEsLB6KCi4jooKs8f3T96HeYacxTxDzmKeIWcxzxhTeHg9+Pgo9ycYqsdh7969KC4uRlpami1oAICEhAR06dIFu3fvRllZmeT3z58/j8OHD2PgwIG2oAEAGjRogJEjR+Ls2bM4cuSIW38DEREREVFt5Ku8iudkZWUBAJKSkqotS0pKwsGDB3H69GnEx8e79H3rOomJiS6n0WQSEB5ez+Xvu8raSdKwYRCM00dERsY8Q85iniFnMc+Qs5hnjMlkUjcax1CBQ05ODgAgNja22rKYmBgAQHZ2tmTgYH0GwrpuVdZtXrp0SVMaBUGAj4/rQ520MpkM1UlEXoB5hpzFPEPOYp4hZzHPeCdDnbUbN24AAPz9/astCwgIsFvH1e+XlJRoTicRERERUV1jqMAhKCgIABw+x1BaWmq3jrPftwYMgYGBmtNJRERERFTXGCpwiI6OBuB4OJHcMCSrqsOZpL7vaBgUERERERHJM1TgkJCQAADIzMystiwzMxPBwcFo0aKFy9+vug4REREREalnqMChX79+CAoKQnp6OioqKmyfHz16FIcOHcKgQYNszy/k5OTg1KlTds88NG3aFB07dsSePXtw8eJF2+dFRUXYunUrmjZtqumNSkREREREdZWhJoADgPXr12P+/PlITk5GamoqCgoKsG7dOvj5+WHbtm224UizZs3Cjh078M477yAlJcX2/SNHjiAtLQ2RkZFIS0uDn58fNm/ejFOnTmH16tW44447auqnERERERF5LUO9jhUAxo8fj7CwMKxduxbz589HUFAQunfvjunTp8s+32DVqVMnbNiwAYsXL8bSpUthNpvRoUMHrF271i7AICIiIiIi9QzX40BERERERMZjqGcciIiIiIjImBg4EBERERGRIgYORERERESkiIEDEREREREpYuBARERERESKGDgQEREREZEiw83jQNXt2rULa9aswU8//YSgoCD06NEDM2bMQFxcXE0njTygTZs2kssOHTqEkJAQ29+5ublYuHAh9u/fj2vXrqFZs2ZIS0vDAw884PD7zFvebdWqVTh+/DiOHz+OCxcuIC4uDp9++qnk+u7MH85um2qGM3lm6dKlWLZsmcNlf/zjH/Hcc89V+5x5pnY5c+YMdu7ciYyMDJw7dw6lpaVo2rQpBg4ciPHjxyM4ONhufZYxtR8DB4PbsGEDXn75ZSQnJ2P27NkoKCjA+vXrcejQIWzdulXVpHjk/W677TaHhWNQUJDt34WFhRgzZgyys7Mxfvx4NG7cGHv37sULL7yAnJwcPPHEE3bfZd7yfq+//jpCQ0PRrl07XLt2TXZdd+YPZ7dNNceZPGM1e/ZshIWF2X3WsmXLausxz9Q+27Ztw8aNG9GnTx/ce++98PPzw4EDB7B48WLs3r0bW7ZsQWBgIACWMXWGSIaVn58vJiYmivfdd59YXl5u+zwrK0ts06aNOGfOnBpMHXlK69atxWeffVZxvX/84x9i69atxQ8//NDu80mTJont27cXz507Z/uMeat2qHpO77nnHrFPnz6S67ozfzizbapZzuSZJUuWiK1btxbPnz+vuF3mmdopKytLvHr1arXPX3/9dbF169bihg0bbJ+xjKkb+IyDge3duxfFxcVIS0uDr+/vnUMJCQno0qULdu/ejbKyshpMIXlSWVkZioqKJJf/5z//QePGjTFgwAC7zx966CGUl5dj9+7dts+Yt2qHJk2aqF7XnfnDmW1TzXImz1RVVFSE8vJyyeXMM7VTQkKC3XBYq0GDBgEAfvjhB9tnLGPqBgYOBpaVlQUASEpKqrYsKSkJ169fx+nTpz2dLKoBH374IRITE9G5c2ekpKTgueeew+XLl23LL1++jIsXLyIxMbHad5OSkiAIgi0/AcxbdY0784ez2ybvk5qais6dO6Njx44YPnw4Pvjgg2rrMM/ULdnZ2QCAiIgIACxj6hI+42BgOTk5AIDY2Nhqy6xj/7KzsxEfH+/RdJFnJSQk4O6770azZs1w48YNHDhwANu3b0dGRga2bNmC6OhoWyHuKK/4+/sjLCwMly5dsn3GvFW3uDN/OLtt8h4NGjTAyJEjkZycjNDQUJw7dw4bN27E008/jbNnz2Ly5Mm2dZln6o7KykqsWLECvr6+GDJkCACWMXUJAwcDu3HjBgDLhXGzgIAAu3Wo9tq6davd30OHDkWXLl3w7LPPYunSpXj55ZdRUlICwHFeASz5xboOwLxV17gzfzi7bfIeEyZMqPbZgw8+iBEjRmD58uVITU21vf2GeabueOWVV3D48GE89dRTaNGiBQCWMXUJhyoZmPWNOY7GmpeWltqtQ3XLsGHDEBcXh3379gGA7a0WUs8llJSU2NYBmLfqGnfmD2e3Td4tMDAQEydOREVFBTIyMmyfM8/UDYsWLcKmTZswcuRIPPbYY7bPWcbUHQwcDCw6OhoAHHbBWbvu+MrMuisuLg4FBQUAfs8HjvJKaWkprly5YtfNy7xVt7gzfzi7bfJ+1l6G/Px822fMM7Xf0qVLsXLlSgwbNgwvvfQSBEGwLWMZU3cwcDCwhIQEAEBmZma1ZZmZmQgODrZ1E1LdIooizp07h8jISABAVFQUYmNjcfjw4WrrHj58GKIo2vITwLxV17gzfzi7bfJ+P//8MwDYyh+Aeaa2W7ZsGZYtW4ahQ4fi1VdfhclkX31kGVN3MHAwsH79+iEoKAjp6emoqKiwfX706FEcOnQIgwYNkhzzR7VDbm6uw8/T09Nx6dIl9O3b1/bZvffeiwsXLuCjjz6yW3ft2rXw9fXF4MGDbZ8xb9U97swfzmybvENFRYWtR7OqwsJCrFq1Cn5+fujZs6ftc+aZ2mvZsmVYunQphgwZggULFlQLGqxYxtQNgiiKYk0ngqStX78e8+fPR3JyMlJTU1FQUIB169bBz88P27Zt43CSWm7evHn46quv0Lt3bzRq1AilpaU4cOAAPvvsMzRr1gzvvvsuwsPDAQBXr17F/fffj9zcXLuZNT/77DNMnjwZ06ZNs9s285b3e++99/Drr78CsMzCWl5ejoceeggAEBISgnHjxtnWdWf+cHbbVHPU5pnCwkLccccd6N+/P9q0aYPQ0FCcP38e27ZtQ15eHubMmYPx48fbbZt5pvbZuHEjXnrpJTRq1AhTp06tFjRERkaiR48eAFjG1BUMHLzAzp07sXbtWpw6dQpBQUHo3r07pk+f7vJEPuQ99u7di02bNuHHH39EQUEBBEFA06ZN0a9fPzzyyCNo0KCB3fo5OTlYtGgR9u3bh6KiItxyyy0YO3YsRo8e7XD7zFveLS0tDQcPHnS4LC4uDp9++qndZ+7MH85um2qG2jxTVlaGv/71r8jKysKlS5dQXFyMkJAQdOrUCePHj0e3bt0cboN5pnaZNWsWduzYIbn89ttvR3p6uu1vljG1HwMHIiIiIiJSxGcciIiIiIhIEQMHIiIiIiJSxMCBiIiIiIgUMXAgIiIiIiJFDByIiIiIiEgRAwciIiIiIlLEwIGIiIiIiBQxcCAiIiIiIkUMHIiIiIiISBEDByIicsmsWbPQpk2bmk6GrLy8PHTu3BmbN2926nuiKGLIkCF4/vnn3ZQyIiLv41vTCSAiImNwJgjYu3evG1OinzfeeAMNGzbE8OHDnfqeIAh48sknMW3aNIwbNw7x8fFuSiERkfcQRFEUazoRRERU8/7973/b/X369GmsXLkS/fv3R//+/e2W9e/fH35+fjCbzQgICPBkMlXLzs5G3759MX36dEycONHp74uiiH79+qFDhw5YsmSJG1JIRORd2ONAREQAgNTUVLu/Dxw4gJUrV6JNmzbVlnmDzZs324YcuUIQBAwdOhRvvfUWcnJyEB0drXMKiYi8C59xICIilzh6xsH6WX5+PmbNmoWUlBQkJSVh8uTJuHz5MgBLhX7QoEFISEjA3XffjY8//tjh9j/44AOMHj0aSUlJ6NSpE0aOHIk9e/aoTt/u3bsRHx9frcIviiLWrVuHIUOGICkpCUlJSbjrrrswa9YslJSU2K3bu3dvVFRUSKaRiKguYeBARES6e+SRR3D9+nVMnToVo0aNwv79+zF58mS8/fbbWLNmDe677z7MmDEDFRUVeOqpp3D+/Hm77y9atAhPP/006tWrh2nTpmHmzJkICgrCtGnTsHHjRsX95+Xl4fTp0+jUqVO1ZStWrMCrr76KuLg4zJw5E88++ywGDx6M48ePo7i42G7ddu3awd/fHwcOHNB2QIiIagEOVSIiIt0lJyfbvZFIEASsWbMGubm52LVrF+rVqwcA6Nq1K1JTU7F582bMnDkTAHDs2DGsXLkSjz76KGbMmGHbRlpaGiZPnoyFCxciNTUV9evXl9z/jz/+CABo2rRptWWffPIJbr31VqxcudLu8+nTp1db19/fH7GxsbbtERHVZexxICIi3aWlpdn9nZycDMDyHIU1aACA+Ph41K9fH+fOnbN9tmvXLgDAsGHDkJ+fb/df3759cf36dRw+fFh2//n5+QCAhg0bVlvWoEEDXLp0Cd98842q3xIaGmrbHhFRXcYeByIi0l3jxo3t/g4JCQEAxMXFVVu3YcOGKCgosP196tQpAMDgwYMlt5+bmyu7f0EQAFieZ7jZjBkzMGXKFIwdOxZRUVFISUlBr169MGjQIPj7+1dbny8fJCKyYOBARES68/HxcerzqqwV9dWrV8PPz8/hOrfeeqvsNsLDwwEAhYWF1ZZ16tQJH330ETIyMnDgwAEcOHAAu3btwooVK7Bx40ZERkbarX/16lXb9oiI6jIGDkREZCjNmjXDF198gdjYWJdnpm7VqhUEQcDPP//scHlwcLDd/BTbt2/H7NmzsWnTJkydOtW2XllZGS5evIi+ffu6lA4iotqEzzgQEZGhDB06FIDlzUoVFRXVlufl5SluIzw8HLfeeiuysrKqLXP0vEL79u0BWHoXqjpx4gTKy8tx++23q0o7EVFtxh4HIiIylI4dO+LJJ5/E0qVLMWzYMAwcOBAxMTHIycnB8ePH8fnnn+PYsWOK2xk4cCBWrFhRbfK2wYMHIzExEQkJCYiJiUFeXh62bNkCX1/fapPF7du3D76+vtVmziYiqosYOBARkeE88cQT6NChA9LT0/HOO++guLgYERERaNWqFebMmaNqGyNHjsSbb76JXbt24eGHH7Z9/vDDD2P//v3YsGEDrl27hvDwcHTs2BELFy5EYmKibT1RFLFz507069cPMTExev9EIiKvI4h8XQQREdVSL774IjIyMrBnzx7JB62lfPzxx5g6dSq2b9+Otm3buimFRETeg884EBFRrTVt2jRcuXIF27dvd+p7oihi6dKlGD58OIMGIqLfsMeBiIiIiIgUsceBiIiIiIgUMXAgIiIiIiJFDByIiIiIiEgRAwciIiIiIlLEwIGIiIiIiBQxcCAiIiIiIkUMHIiIiIiISBEDByIiIiIiUsTAgYiIiIiIFDFwICIiIiIiRf8P4d4VbUmWg2IAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 900x450 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "dplot(d, timetrace_bg)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(-50.0, 50.0)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x450 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "dplot(d, timetrace)\n",
     "xlim(1, 2)\n",
@@ -240,16 +406,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " - Performing burst search (verbose=False) ...[DONE]\n",
+      " - Calculating burst periods ...[DONE]\n",
+      " - Counting D and A ph and calculating FRET ... \n",
+      "   - Applying background correction.\n",
+      "   [DONE Counting D/A]\n"
+     ]
+    }
+   ],
    "source": [
     "d.burst_search()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -258,36 +436,102 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'matplotlib.figure.Figure'>\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x700 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "alex_jointplot(ds);"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   - Applying background correction.\n",
+      "   - Applying leakage correction.\n"
+     ]
+    }
+   ],
    "source": [
     "ds.leakage = 0.05"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'matplotlib.figure.Figure'>\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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AiofOYp3fCH657jTzl+1h/rI9PPjh5naJTR+ZiN+Ampuqiu2fozq714RwIYQQortoUVJ//fXXs27dOq655hruvfdeIiIimDRpUl17ampq3VCZlliwYAELFizg2muv5emnn0ajqR9mZGQkMTEx7N27t8F79+7di6qqDBkypO612v/fs6dhScA9e/bg5+fXqnh9iaPgNKrLicYvCG1wVLsd5/LLp6HV6jh69DAnThxvt+N0Zs6yAkrWvIYj7wSKVk/AyGmEzXwAU+LghjcA/q27CW6K/9BL0QaG466qwHqofW4ehBBCCOFdLUrq77zzTh588EH0ej0DBw7k5ZdfrhvWUlJSwr59+7j44otbFNCCBQt46aWXuOaaa3jmmWcaJPS1Zs6cSWZmJmvXrq33+ttvv41Op6s3kfeyyy7DbDazaNEinE5n3ev79+9nx44dzJgxo9Hx+V2NPacmwTZE92rX+uVhYWF1w25Wr17ZbsfprOy5JyhZ+wauimI05kCCp9yK/6CLUZSOHzuraHUEjK75fag+vpvMzMwOj0EIIYQQ7atFY+oB7r//fu6///4Gr4eGhrJly5YW7XPJkiW89NJLxMXFMXHiRFaurJ8MRkREMHHiRADuvvtu1qxZw29/+1vS0tLqVpT99ttvue++++oWnoKaBPNXv/oVTz31FPPmzWPWrFmUlJTwzjvvEBERwUMPPdSieH2RPfeHpD624cqkbW369KvZtGkDW7ZsYu7c2wkLC2/3Y3YGtuxjlG94D9XlRB+ZiHnAeHShDSsvdSRjXB+MCQOoPn2AhQsX8te/PuPVeIQQQgjRti44qbdarYwaNYoHH3yQ++67r02D2b9/PwDZ2dk8+uijDdrHjh1bl9QHBwezdOlSnn/+eT788EMsFgs9e/bk8ccfZ+7cuQ3eO3/+fEJDQ3n77bd56qmnMJvNTJgwgYcffrjbjKd3V1fiLM4BOiapT0npTf/+Azl8+CBr1qxi7tx57X5Mb7NlH6Nsw1JwuTDE9yN40s04CjO8HRYAASOnY8s8zOHDh9m+fSujRo1r/k1CCCGE8AkXnNT7+fkRFBREWFhYmwfzzDPP8Mwz59+DGBUVxdNPP33e21977bVce+21LQmtS6jtpdeFxqAxdcyiUFdddQ2HDx9k3bqvmD37xlZXGerMHEVZlH//AbhcGBMGEDTxJhRtix+GtTltQCjm3qOh+ABLlixi6NCR6PV6b4clhBBCiDbQogG+48aNY8eOHW0di2hn9twTABhi2r+XvtbIkaOJjo7Faq1kw4ZvO+y4Hc1lKaHsuyWoTjuGmF6dLqGvZe47hqCgIHJzc/nmm6+8HY4QQggh2kiLkvrf/OY37Nq1iwULFmCxWNo6JtEOVFXFnpMOtN/QG1V1U1hYQF5eXt1XQUEBEyZMwuFwsmLFcnJycnC7u1adadXpoOy7JbirLehCYwi6eG6nTOgBFJ2B66+vWYRq+fIPsVorvRyREEIIIdpCizKPO++8E5vNxssvv8zLL79MWFhYg0WdFEXh66+/bpMgReu5KopxW8tRtDr0P6wi29bc1nJ+ufJAg4WYVKcfJRY3J0syuOWfb/Pho3d1qXkMlQe+w1mWj8YcQPAlt6PRG70dUpMuueQSvv76G7KyMvnssxXccsvt3g5JCCGEEK3UoqQ+Li6ureMQ7cxRcBqoWYxI0bXfOOrahZh+zDxgPNYDG7BnHWm3Y3tDaupeqk+momi0BI2/Aa1f+9WbbytarZbbbruDf/7zKVatWskVV0wnPDyi+TcKIYQQotNqUVK/aNGito5DtDNHfk1Sb4jt7ZXjm/uMxXpwE47ibI4fT+8SPfXFxcUsWbIQAL8BEzqkolBrqaqb/Px84uPj6dkzmfT0Y7z11hvcccdP67aJjIxsdH0IIYQQQnROnXPgr2hTTqcTR8EZAPReSjy1fkGYkodRdWwHa9Z8wYQJE70SR1txu938978vUVlZiS44Cv9hl3s7pPPitpYzf9F2tAEhOP0HU1qyj5NrvmGNPQ5dSDQuSymL75zaJW66hBBCiO5EuuO6gZMnT6C6HGhM/uhCvJes+Q2ajKIoHDyYxvHj6V6Loy2sXr2StLRU9Ho9AWOu6rQTYz2pHSJlTBiIudcIFI2WqiPb0PiHNpgPIYQQQgjf0KJMpH///iiK0uQ2NcnbwRYFJdrW4cM1n4M+pheK4r37OF1gOIb4/mA5zSefLOORRxouMOYLTp48wfvvLwHghhtu5o3cQC9H1HL+wy6j+kwa9twT2HPS0QV1j1V/hRBCiK6mRUn97NmzGyT1TqeTM2fOsG/fPvr168eAAQPaJEDRerVJvSHGO+Ppz2XuNw5l92l27drB6dOn6NkzydshXZDq6moWLHgel8vJ6NHjmDhxMm8s3+vtsFpMGxCKX7/xWA9upHL3GoIuvsXbIQkhhBCiBVqU1De16uvu3bv5+c9/zuOPP97SmEQbslgsZGTUTpL1/kROXWA4I0aMZv/+vXzyyXIeeugRb4fUKLfbTUFBQb3X3ntvERkZGQQHBzN79g0UFRV6Kbq24zdoMlXHd+Esy8eWcQAY5e2QhBBCCHGB2nwg8MiRI7n++uv517/+xZIlS9p69+ICHTx4AFVV0QaGdZpyi9OmXcX+/XvZtm0zJ09eR3JyL2+H5FFBQQG3v/Nt3ThzW/YxKratASBo4FXct/oY9vwMDFGJXoyy9TQGM/6Dp2DZ9SXWg5uorr7Z2yEJIYQQ4gK1ywDrpKQkGU/fSaSm7gXAEJXk1TjO1aNHPBMmTAZg6dLOXR61ru6+Rkdl6jcoGi3+g6dg7jUCbUAYWv/OcaPUWuY+Y9EGhOK2Wfnmm7XeDkcIIYQQF6hdkvrt27djNHbuVTW7A1VV2b9/HwD6qPZZRbal5syZi06nIy0ttS7GzkpV3VRs+RjVVoUuLA7/oZd6O6Q2p2h1+A+/AoCvv15LSUmJlyMSQgghxIVo0fCbTz75xOPrpaWlbNmyhQ0bNnDTTTe1Ji7RBvLycikoyEer1aKPiPd2OPVERUVzxRXTWb16JUuXLuLvfx/SaRc8qjq0GXvuCRSdnqCJN/pU+coLYUwchD4sFru9jPfeW8R99/3C2yEJIYQQ4jy1KDt59NFHURQFVVUb7lCn48Ybb+TRR32zXGFXUtsDnpzci1KdwcvRNDRr1g189906Tp8+yYYN33HJJZ2vB9xZkotl39cABIy6Cl1QhJcjaj+KouA/9FKU1BVs3LieqVMvY8CAQd4OSwghhBDnoUVJ/bvvvtvgNUVRCA4OJj4+Hj8/v1YHJlpv//5UAAYMGMieCi8H8wNVdVNYeLaizGWXTWPFimW8887/SEzsib9/AACRkZFe77mvrq6mYucqcLsxJgzElDLSq/F0BF1oDBMmTGb79i289dYbPP30v9HpuuaTCSGEEKIradG/1mPHjm3rOEQbc7lcpKXVJPX9+w+CHZ1jjLTbWs4vVx6oqyijusMoc5lxZhZwx1OvEDDiClyWUhbfOZXoaO+tfgvw4YdLcVlK0AaEEjhuVrMLrnUV1157PYcOpZGVdYYvv/yCmTNneTskIYQQQjSjcw5iFq12/Hg6VVVV+PsHkJDQuUou1laU0QaEoQuKJHD8DSgaLbaMNFzW8rqE35s2bPiW7du3oigKQRNvRGM0ezukDuPv78/cufMAWL78QwoK8r0ckRBCCCGac0FJvdVqZd26daxfvx673Q5Afn4+v/vd75g0aRIjRoxg3rx57N69u12CFeevdjz9oEGddwJqLUNUz7qhLRWbP8btsHk1nqysTN566w0AzAMmdKpyoB3l4osvoV+/Adhs1bz++iu43W5vhySEEEKIJpx3tpefn8/MmTN54IEHuPfee5k1axYFBQXceeedfPrpp9hsNYnYjh07uPPOOzl06FC7BS2aV5vUDx06zMuRnJ+AUTPQBoTiqizFmvqt1+KoqqrihRf+jd1uo1+//pj7ds+hZhqNhrvvvg+93kBa2n7WrZPa9UIIIURndt5J/Ztvvklubi7z58/n17/+NeXl5Tz00EOUlpby/vvvs2PHDvbs2cPrr7+OVqvltddea8+4RROqqqwcO3YUgMGDh3o5mvOj0RsJHH89KArVGWls2bKpw2Nwu928+OLzZGWdISQklPnz70JROvdTjvYUGxvH3Lm3AzWLhOXl5Xk5IiGEEEI05rwzlu+++47rrruORx99lLvuuos//elP7N69mzvvvJPhw4fXbXfxxRdz4403snPnzvaIV5yHtLQDqKqb6OhYoqK8O9n0QhiieuI/+BIAPvhgMUePHunQ47/11lvs2rUTnU7PI488SlBQcIcevzO68soZ9O8/EJutmpdeeg6Hw+HtkIQQQgjhwQUNvxk69Gyvb+3/9+3bt8G2ffv2lRUpvSg1dS8AQ4cO92ocLeE3ZAqGuN44nS6ee+4f5ObmdMhxv/jic5YvXw7Avfc+QEpK7w45bmdTW3I0Ly+PvLw8CgoKuPHGuej1Bo4cOcwbb/yXvLw8GWMvhBBCdDLnXdKyqqqqXv15s7mmGojRaGywrdFolH/0vehsUu8b4+nPpSgaAkfNoMfJNeTn5/Lkk4/z5z//tV2fOHz11ZcsXPg2Op2Gm2+ey4QJk9rtWJ3dj0uO1rInTKJ86yec/PhzPkiv5MPf/dTrJUeFEEIIcVb3HTDcReXm5pCfn4dWq2PgwMHeDqdFFJ2B++//JXFxPSguLuTxx//AyZMn2vw4LpeLRYve4fXX/4vT6WDatGlMnDiprpf63EWyupNzS47Wfpn7jMF/0MUoGi3Wg9+TlZXp7TCFEEIIcY4LWnxq3759aLVaACorK4Gaajc/Hmqzb9++NgpPXKh9+/YC0Ldvv7qnKb4oKCiI3//+cZ555m9kZmbwxBN/ZP78u7jkkkvbZBEou93OggUvsHTlatDoMPcZzbslcSz6YM/ZbfIzMER1rhr/3uQ//HIcxdnYc9J57bUF/POfzxMcHOLtsIQQQgjBBSb1S5YsYcmSJQCoqgrAK6+80iDJUlW126y+2dns378X8M3x9D8WFhbG448/yYsvPkdq6l7eeOMVtm/fym233UF8fEKL93vixHH++9+XOHXqJIpWT8CYa/DrN67BdtrK0lZE3/UoGi3Bk2+heNXLFBcX8+9/P8Mf//iExyF4QgghhOhY553UP/300+0Zh2gDDoeDgwfTABg2bLh3g2kjfn7+/Pa3f+CLLz7jww/fY9++3aSm7mX8+IlcfvmV9Os34LxvIM+cyeDzzz9h48YNgEpgYCCBA6ZjThnVvifRhWiMZoLGX4ffwc84fvwYzz//Tx555Hfo9QZvhyaEEEJ0a+ed1F933XXtGYdoA8eOHaG6uoqgoGASE5O8HU6b0Wg0XHPNbEaPHst77y1m585tbN78PZs3f09oaDhDhw6jT5++xMcnEhISgp+fPy6XE6vVSnZ2FqdOnWTPnl2cOJFet8+JEy9m2rSreWBNehNHFp5oA0K5554HeP31l0lN3ctLL73AQw89Ujc0TwghhBAd74KG34jO7dxSlhpN15sDHRsbx8MP/5b09KN8/vmn7Ny5nfz8PL7+ei1ff91wxVOdTse5nfiKomHMmHFcc81sUlJ6y2JKLaSqboKDg/nJT+7m1VdfZMuWTTidTm6//c56iX1kZGSXvA6FEEKIzkiS+i6kdpLskCG+V8ryXLW10htjs9lZZU9AM24AjsIzOAoycJUV4Kooxm2zorpd4HbSLzaChIQEevRIYNCgIYwYMYrQ0NAOPJOu6dyyl/aUS6nY9hkn137LpwcyCRhzNYpGi8tSyuI7p0rZSyGEEKKDSFLfRRQXF3H69ElA8cn69OdqrFZ6rdqqNNqAMHTBUQ3GxKsuJy5rKc/dNEqSynZSW/bS3GcsGlMAZZs+wp57AsuuNQRPvtnb4QkhhBDdjjwb7yL27NkFQO/efbpEmUFPtdLrvvyDmnyvotWhKHJpdxRjwkCCL74VRavHnn2U0u8Wozrt3g5LCCGE6FYk8+kidu7cAcDIkaO9HInojoxxfQieOg9Fb8SRd5KyDR9QVlbq7bCEEEKIbkOS+i6gurqatLT9AIwePdbL0XQOtePya1eH9fTVXVeMbS+G6CRCLrsTjckfZ1k+zz77jKw8K4QQQnQQGVPfBezfvw+n00FUVDQ9esR7O5xOoblx+SArxrYHfXgPQq/8P0q+fovi4mL+8pff8+tfP0r//gO9HZoQQgjRpbWop95isXDHHXdw8ODBto5HXCC3282GDd/hcDjp27c/+fn50iP9gybH5Z/H2HzRMtrAMIIvvoXk5F5YrZU89dQTbN680dthCSGEEF1ai3rqHQ4H27dvp6ysDKhJ8u+77z4effRRBg6UHrmOlJeXx/tr16O6HBTnm/hs2Z4G20iPtOhoisHErbfewcqVn7Jv3x6ef/5fHD16hOnTr663ArDUshdCCCHaxnkn9TfccAOjRo1i9OjRJCcn12v7cZIvOs7JkydQXQ40Jn+MPYegaBqu6qmtLO34wES35raW85s15WhCxmINq6Lq+G4WLH6fN77dTcDIaSg6g9SyF0IIIdrQeSf1ffv2Zf369bz77rsoioKiKLz//vtUVlbSr1+/9oxRNGH37p0AGHv09ZjQC+EttcOfgibcgD4qiYodK7HnHKd888cET7m1yfkOQgghhLgw553UP/300wAUFRXx/fff8+ijj7J7927WrFmDRqNBURQ+/fRTXC4XI0aMwN/fv92CFjXcbnddfXpj4hAvRyNE48y9R6ENiqD8+/dxluRS8uVrBIyeAYzwdmhCCCFEl3DBg1nDw8OZMmUKAP/85z/ZsGEDf/zjH1FVlXXr1vGzn/2McePGMWfOnDYPVtR3+PBBysvL0OiNGGJTvB2OEE0yRPUkdPo96EJjcFdXUv79h3z11Ze43W5vhyaEEEL4vPNO6m+++Wb+8Y9/sG7dOkpKSgBQFIWoqChmzJgBwIsvvshXX33FE088QUqKJJntbcuWTQAY4nqjaKU6qej8tP4hhF75M4xJQ1BVlU8//Zh//vPvslCVEEII0UrnnQnGx8ezZs0a3n777box9e+99x5Wq5W+ffsCNUl+QkICCQkJ3HDDDe0WtACXy8X27VsBMMT393I0Qpw/RWcgaMKN6IIj0WVuIzV1L7/73cP89Kd3M3bsRd4OT/zA7XZTUNB8OVypYCSEEJ3DeSf1zz77LAAFBQVs3LiRxx57jJ07d7JmzRq0Wi2KovDZZ5+hqiojRozAZDK1W9CiZsGpiopyAgICKI+QcpXCtyiKgilpKL+99XKWLn2XrKwzvPDCvxgz5iJ+8pOfERIS6u0QuzVVVTl8+DD3vbMK3G7c1RWoTgeqywFuFUWnR9HpUVWVv1w/hT59+hIVFY3ZbPZ26EII0W1d8JiNyMhILrnkEgD+/e9/07NnT7766iuefvppvvzySz7++GN0Oh1Dhgzhvffea+t4xQ++++4bAEaNGkuOIr1kwjfFxfXgqaf+yccfL+Ozz1awY8dW9u/fxzXXzOaqq67BaDR6O8RuweVycfx4Oqmpezl+PJ0TJ9IpLi7GUmJtsqqW6nby8st70On0AERERNKjRzw9evQgPj6RpKRkgoKCpTdfCCE6QKsHYsfFxXHttdfy9NNP8/LLLxMbG8v27dvZsWNHW8QnPCgvL2PXrpqf7/jxE1m5tdDLEQnRcnq9gZtvvpWLLhrPG2+8yokT6Xz00Xt89dWXTJlyKePHT2oyuZeE8cK53W7S049x6FAaBw+mcfjwQaxWa4NttIFh6IKj0PqHoOiNKFo9aDSoTjuqw46jKJPsqgrUyirc9mpOlpxmx7HT9fajMfpx45RxjBgxkr59+5GQ0FM+LyGEaActSuqNRiPXXXcdUVFRDdqSkpJISkqS6jftaOPGDbhcTpKTU4iPTwAkqRe+r2fPZP7616fZunUT77+/hJycHP712hto3nkPU69hGHsOQesfXO89soDV+XO5XBw7doS9e/ewdetmNqYeBM3ZfwI0ehP66J7ownrUVCiyVxMak4w2IKzRfdrzTqD1r1mPwF1dibM0F2dpHs6SXJzF2TjLCnDbKtmyZSM7dmwDIDAwkEGDhjBo0BAGDhyMXq9HUUBp4omjTqcQHJzcaLsQQogWJvV+fn51deuh6SRftC1VVfn223UAXHLJpV6ORoi2pdFomDBhMmPGjOPTT1fwr0Uf4a6yUHVsJ1XHdmKI6YWp1wgM8f3R6GVoTmNqJ7mWlpZy6FAaaWn7OXLkEFVVVQA4nQ7Q6NBHJmKM7YMhrje6iPh6ibU978QFHVNj8scQk4Ih5mzlM7e9mqpj2ygoycNdWYqjOBu1JI/UjDxY/TWKTo8uJBZT8lAMPfqiKIrHfbsspax+ZCYmU1ALfhpCCNE9tEkdxB8n+aL9HDiQSlbWGUwmMxMnTqaiwuLtkIRoc3q9gcmTL+GN3EBcJflUHduOPfdE3Zei1aGPTUEfmUhlZV9vh9tplJWVcuTIYXbs2M67azfgqiyr164xmNBHJaGY/AmbNAZ9REK7xqMxmNBHJGDqOQRtQBiqy4mj4DS2rKPYMw/jspTgyD+BozADw5le+A++BH1Mr0aTeyGEEI2T4uY+ZtWqzwGYMmUqfn7+ktQLn6WqbgoLGy+ZWFhYgKJoMCYOxJg4EJellOqTe6g+lYqrvAh75hFsGWn85jffMnjwUIYMGc6QIUMJCQlFp1OorvajrMyK06l2yXH3FRUVZGaeoqAgh4MHj3Ds2FFycrIBcDicuCqtKFodurA4jHF9MMT1RRceh6JosOedQGPq+FW/Fa2urjdfHTkNR34Glalf48g/jSP/NKXfLEQflUTg6KvQhcZ0eHxCCOHLJKn3IVlZmezbtwdQmD79am+HI0SruK3l/HLlAbQBIR7b7fkZGKLOlmvVBoTgP2QqfoMvwVWWjy3jIFXHtnG6pITTm3bwxaaayeO64CgMMb0wxKagDYnCXVnuM+PuPdWGt1qt5ObmkJubTU5ONrm5ORQVFVJSUgwo6HQanE43oAIKCQmJ9OiRQGGOgilpmFeS9/OhKBoM0Ukw9FIUjY7qjDSqj+3EkX+K4tWvYu47Fv9hl8kwKyGEOE+S1PuQTz/9GIBRo0YTHS29WML3aQNCGp2Iqa0s9fi6oijoQqLRhUSjj+6J6nbhLM7BnnUER2EmrooiqiqKqDq2A405AH1EIvv3hxESMqVTl8hUVZWjR49wz2srcFdbcJXl4ywvxF1dWX9Dl5OEEDM6nZ6oqChSUpKJioolNjae5OQU/P39KSwsYNV3mZ02of8xjTmAwFEz8Os/HsvuNdgy0qg6shV79lGCxl+Pxhzg7RCFEKLTk6TeR2RnZ7Fp0/cAzJ59o5ejEaLz0AWGY4ztg/+gi3FXW7BlH8OeeQR77nHcVRaqT+/ntdeO8+67bzF48FCGDRvOwIGDiY9P8OrYbYvFwvHjx+rqwqenH6OoqIjKH9WGVzRaNH5BNaUlgyNBdVPqH4o+JpkKvZHjAGVAmQsOHwUaPuXwFVr/EIIn34w95zjlW1fgqiim5Ku3MPcehds9wdvhCSFEpyZJvY/4+OOPUFU3I0eOJiWlt7fDEaJT0pgCMPcagbnXiJpJmfmnqDqxhzClkIqKcvbu3cXevbsACAwMZuDAQQwcOIiUlD4kJCSg1xvaJS6rtZLMzMy65P348XTy8nIabKcoCrrgSAzRvdCFx6ELiUEbHFlvCMq5ZSQb09hTDl9hiE0h7Or7sexcRfXJfViPbOUf//gHDz/8O/z9pQKOEEJ4Ikm9D0hPP8bmzRsBuOGGm70cjRC+QdHqMMT2RhsYxhM3DMfhsLN7907S0g5w5MghKirK2LZtM9u2bQZAq9WRkJBIcnIvYmJiiYqKJjo6mtDQMPz8/NHr9R6P43K5qKqqwmKpID09ndLSEkpLSygpKSY/P4/c3BxKS0t/2FqtjQ6AqKgoEhOT6NkziZ49kzGZTDy2Ob/JhL270BjMBE24AUNcH8o3f8zBgwf57W8f4YEHfsmAAYO8HZ4QQnQ6ktR3cm63m3fe+R+gMmnSFJKTe3k7JCF8iqq6KSoqJCIikosumshFF03E6XRy+vRJjh49wvHjx8jJycFqtXDq1AlOnWpYn11Va2ro6/WGHxZKqknKbTYbdrsdqKn9fqasqt6CTufSmPxRDP7oI3pgiOmFLjSWCoOpZvhMEVBUjj3/gE8Om2lPpqShKAYzPYq2kJ2dy5NPPs4tt9zGzJmzpPSlEEKcQ5L6TsztdvPxx8s4cuQwJpOJK66YTl5eXr1tmioJKIRorspOD5z+Zl745W1oNBoyMk6TlZVJUVEhBQX5FBYWUFlpweFoOmGvOZALbWAE2qAItH5BaPyC0AaGoQuKRBschcZganbojK8Pm2kvuqBwHn/wcRYtWsr336/nvfcWceTIIe655wECAwO9HZ4QQnQKktR3Yvv3p/LMq28A4D90HA+ubdiD6KsT4oToSE1V2XFVlvKrL9J+SPo1QCL4J4I/kFTT02/PPk5oSCQa4w/VZNSaYTSK3oCiN6HojTgKTjc71l20nMlk4oEHHqJv3/4sXPgmu3fv5NFHH+a++37BoEFDvB2eEEJ4nST1nZTNZvth2A0Y4/rgP/TSeku415KePSFar6mkH8AdUi4Juxepqpv8/HyMRiuDBw/joYd+w1tvvU5+fh5PPPFHrrhiBldffQ0xMbFdbpExIYQ4X5LUd0Jut5uXX36BzMwzaAwmAsdf7zGhF0KI7sBtLWf+ou31hlCpfWdSWf1dTcnSD5bzv8+/4vlHH2bChIneC1QIIbxIMsVOxuVy8cYb/2Xnzu3odDoCx81C6ycl3IQQ3Vvt05TaL11IDMEX30LwlFvRmgNxWct5/vl/8vrrr1BcXOTtcIUQosN1u6R+5cqVXH/99QwdOpRx48bx8MMPk5WV5e2wAKiqquKFF/7F+vXfoCga5s37CfqIeG+HJYQQnZYpcRBh1/wCU8/BAHz33Tp++cv7WbTobUnuhRDdSrcafrN48WL+9re/MXLkSB577DFKSkpYuHAhO3bsYNmyZURHR3sttkOH0njttZfJz89Dp9Pz4IO/JDExGU7u8VpMQgjhCzRGPwJGTuNXIwJZu3YVR48eZvXqlXz55SpGjBjJJZdcxtChwzEY2mdxMSGE6Ay6TVJfUlLCs88+y6BBg1i0aBE6Xc2pT548mZtuuokXX3yRv//97x0ak6qqHDqUxqpVn7N7904AIiIieeCBX9G3b78G5SuFEEI0LiWlN3/5y5McOJDKJ58s59ChNHbv3snu3TsxGIwMHTqM/v0H0rdvf3r2TGp0QTEhhPBF3SapX7duHVarlXnz5tUl9ABDhgxhzJgxrF69mr/85S/t2pNjs9nIzs4iM/MMhw8fIjV1D0VFhQAoioapUy/j1lvn4efn324xCCFEV6Sq7rp1O6KiYrj77vvJzc1h06YN7N27m5KSEnbs2M7OndsB0Gi0xMTEEhfXg9jYWEJDw+q+goND8Pf3x2w2o9VqvXlaQghx3hRVVdXmN/N9f/7zn/nggw9Ys2YNSUlJ9dqee+45XnvtNT799FP69+9/wftWVZXS0jLcbtcP5avVutdVteYfG7fbjacftaIoGI0mj/94uFwuiqx2aKpEm8sJiqb9t+mo43SmWOScu0cscs5dJxao2c4D1eUkQK/gcrlwOp243e7G93UORVHqfYFC7UK2tSvael7ZVjlnH6DXGzAajXWv1f6912gUWRlXCNEmuk1PfX5+PgAxMTEN2mrH0ufl5bUoqVcUhdDQkFbF55meeLOpHfYrhBBCCCG6km5T/aaqqgrA4/Ca2t6T2m2EEEIIIYTwJd0mqTebzQDY7fYGbTabrd42QgghhBBC+JJuk9RHRUUBkJub26CttsqMN0taCiGEEEII0VLdJqkfMmQIAHv2NKz7vmfPHvz8/OjVq1dHhyWEEEIIIUSrdZuk/rLLLsNsNrNo0SKcTmfd6/v372fHjh3MmDFDFiYRQgghhBA+qduUtARYuHAhTz31FCNHjmTWrFmUlJTwzjvvoNfrWb58uQy/EUIIIYQQPqlbJfUAn332GW+//TbHjx/HbDYzYcIEHn74YRISErwdmhBCCCGEEC3S7ZJ6IYQQQgghuppuM6ZeCCGEEEKIrkqSeiGEEEIIIXycJPVCCCGEEEL4OEnqhRBCCCGE8HGS1AshhBBCCOHjJKkXQgghhBDCx0lSL4QQQgghhI+TpF4IIYQQQggfJ0m9EEIIIYQQPk6SeiGEEEIIIXycJPVCCCGEEEL4OEnqhRBCCCGE8HGS1AshhBBCCOHjJKkXQgghhBDCx0lSL4QQQgghhI/TeTuArkBVVdxu1dthtAuNRumy5/Zj3eVc5Ty7FjlP36bRKCiK0q7H6Mr/RrWXzn69dcR146vcTheVGXmt2od/YjQanbaNIuo4ktS3Abdbpbi40tthtDmdTkNoqD/l5VacTre3w2lX3eVc5Ty7FjlP3xcW5o9W277JmaIoXfJn11584XrriOvGV1Vm5PNx3ztatY/rjy4isFdsG0XUcWT4jRBCCCGEED5OeuqFEEKILszpdHo7BCE6jgKa1o6c8dGHINJTL4QQQgghhI+TnnohhBBCCNFlaDQ+2tXeSpLUCyGEEF1YZWUl8s+96C4UQNvK4Te+eksgw2+EEEKILqy6utrbIQghOoDcugshhBBdmKp23nrrQrQHGX4jhBBCCCGEL1NA09pxKD56TyDDb4QQQgghhPBx0lMvhBBCdGEy/EZ0JwqgaeVquz7aUS9JvRBCCNHVORx2FEX+yRfdQ6uH3/iobnraQgghRPdhsVi8HYIQop3JbbsQQgjRxVVUVBAYGOLtMIRof0rrh9/46vgb6akXQgghuriioiJvhyBEh1AAraZ1Xz6a00tSL4QQQnR1WVmZ3g5BCNHOZPiNEEII0cWdOnXS2yEI0WFk8SkhhBAX5PHH/0BWViZvvLEQgKeeeoL09GO89dbi83r/qlWf89RTT9R7zWQyERERxYQJE7n11juIiIhs87gvxKpVn2OxVDBnzq0dcrzNmzfy6afLOX48nZKSYvR6Az169ODGG2/gyitnotXq623vdDpZuvRd1qxZRXZ2FmazHyNGjOTuu++nZ8+ketsWFhayfPkHHDlymKNHD1FaWsqMGTP5wx8ebxBHeXk5X375BVu2bOT06VOUlpYSHR3N8OGjuPPOu4iOjmnHn0LbO3AgFVVVUZTumewI0R1IUi+EEC104EAqU6ZcWvd9aupeLrpo4gXv5/rrb2LQoCEAVFVZOXgwjWXLPmD9+m9ZtOgD/Pz82yzmC7Vq1efk5uZ0WFJ//Hg6Wq2OmTNnER4egc1Wzf79+/j73//OmjVf8fzzL9clpqqq8thjj7BlyyYmT57CDTfcTGlpCStWLOOee+7kv/99i+TkXnX7zsg4xaJFbxMVFU3//gPZunVzo3EcPHiABQueZ+TI0Vx33U2EhIRw8uRxPv30Y7799qsG++7siotLOHHiOCkpvb0dihDtSwGNtvX78EWS1AshRAsUFhaSm5vD0KHDASguLiIz80zd9xdi6NDhXH75tLrvZ8++kcDAQD74YCk7d+7g4osvaZugAavVip+fX5vtr63Nm3dng9duueVWoqIieO+99zhwIJUhQ4YBsHHjerZs2cS1117Hb3/7h7rtp027ijvuuJkXXvg3//nPK3Wv9+8/gM8//4rQ0FBKS0uZOfPyRuPo2TOJpUuXEx+fUO/18eMn8atf3c+bb77Gk0/+o5Vn2zGMRiMAX3+9RpJ60S3I8BshhBBNslqt2O12AHbs2ArUJH+lpaVs27al3vc6nY6AgIAWHys8vGbYjU539s/07t07+cUv7uX3v/8LV111Tb3t//73x1m9eiUbN+6se+2BB+4mNzeH//znv/z3vy+ya9dOKirK2bhxJ6qq8tFH7/HFF5+RnZ0NqISGhjFs2Ah+/etHMRpNTJo0um5f5/7/Rx99RmxsHPv372Phwjc5evQIFRXlBAYG0atXCj/5yf8xbNiIuu1Pnz6FTqejR4/4Fv884uNr3ltRUXHOz2MXAFdddW29bXv0iGfYsBFs376V3NxcYmJqhsr4+fmf91OP2Ng4j6+PGTOOoKBgTpxIv+Bz8Baz2QzAhg3fcc01s4mL6+HliIQQ7UGSeiGEOE/PP/9PVq9eWe+122+/qd738+ffAsDw4SNZsOD189qv1WqltLQUgOrqKg4dSuO99xYRH5/IqFGjm35zM6qqrDz44D0MGTKMu+++j5KSYgAWLnyT//3vVSZOnMysWTeg0WjIy8tl06YNVFVVYzSa+NOf/sq7775FWVkpDz74cN0+Q0JCycg4xa9+dT9hYeHceOPNhIWFU1JSQlpaKkePHqmX1N92243ExMSybNnn5x231VqJ3e7Aaq3k0KEDvPHGGwQFBTF48JC6bex2G1AzD+HHjMaa1w4dOlCX1LcFi8WC1VrpU0Nv9Ho9o0ePZufOHfzvf6/yxz8+gaa7LrkpujyF1q8o66v9/JLUCyHEebr11ju48soZAPz1r39i2LDhzJp1AwBPPvkXhgwZWvd9YGDQee/3n//8O//859/rvTZkyFD+9rd/1CWnLVVWVsZPfjKHu+66p97rGzZ8S1JSL/7xj+frvX7PPffX/f+0aVfx+eefYLPZmDbtqnrbbdu2lerqap544ikGDBjUqhg9eeqpJ/juu2/qvh88eDAPP/w7goKC615LSqpJrHfv3kHv3n3qXq+urubgwQMA5OXltmlcCxe+idPpZMaMq9t0v+1t3ryfsH//fg4fPsiyZR8wZ85cb4ckRPtQlDZYfMo303pJ6oUQ4jwlJ/ciObkXubk5lJQUc+WVVzFmzDgKCwsoKirkiiumM2bMuAve7x13/JQRI0YBYLPZOHnyOB98sJSHHvo5L7zwCpGRUa2K++abb2vwWkBAIEeOHGLfvr0MGzb8gvdZO7Row4bv6NWrd924bU/OHRJ0vn7yk7uZNesGSktL2Lt3FxkZp+oNvQGYNm3GD08cXsNkMjN69FjKykp5883XKCsrBWoS/Laybt1XvP/+YsaMGddgyE9nFxsby09/ejf//e+LfPLJMoKDgxvcqAkhfJs8fxNCiPNQO0SmtLSUDRu+Q6PRkJLSm9LSUr7/fj2KopCS0ofS0lIsFssF7btXrxTGjBnHmDHjmDTpYubN+wnPPPMsp0+f4tVXX2pV3CEhoR7H9t9zzwOYTCbuv/9nzJo1nccf/wNr1qyqmzPQnMsvn8bYseNZtOhtZsy4lAcfvIdFi94mOzurVfHWSknpzZgx47jiiuk89tifmD17Nr/61QOkpu6t2yYoKJjnn3+Z2Ng4/vnPvzNnziz+7//mU1lZyW23zQfA37/l8xrOtWXLRp588s/06dOPv/3tHz45fGXy5Clcf/0coOaJw8cff4Sqql6OSoi219oVZX2V9NQLIcR58DSe/uabZ9f7/pZbrgMubDx9YwYPHkpAQAA7dmyve62pGuMul8vj657GmwMMGjSY99//hO3bt7Jnz052797J11+v4e23/8crr7xBWFh4k/Hp9Xqee+4lDh8+yLZtW9i3bw9vv/0Gb7/9Bo899meuuGL6eZzl+Zs1axZPPPEEn366vF6FoT59+rJw4XucOZNBUVEh4eERJCQk8sor/wFoUKu+JbZu3cwf/vBbEhOTeP75Ba2aAO1tN9wwB4fDzueff8KyZe+TmXmGn/3sHq+WTRWiLSnQ6uE3vjn4RpJ6IYQ4L+eOp//973/D1KmX1SWuf/rT75g48WKmT68ZZ30h4+mb4nK5qKw82+tfO568vLyswbYt6SE3m81MmTKVKVOmAmcXw1qxYlndGPzmFivq338g/fsPBGrKfN511+28+uqCNk/q7XY7breb8vJyj+0JCYkkJCTWfb9162b8/f3ryl+21LZtW/j9739DQkIi//nPfwkODmnV/rxNURTmzp1HVFQ0b7/9P7Zu3cSxY0e4554H6k1CFkL4Hh9+yCDOl9ut8t22M3zyVTo2u+fevB25BSw5lI7F7vDYfuREMYtWHKSotMpje05mGR+9u4eczIbJxvmwVNp5//PD7DqQ16L3C9HekpN7MWbMOKKjY6iqsnLFFdMYM2YccXE9sFgsXH75tLohNP37D2j18bZu3UxVVRX9+p3dV2xsHFqtlp07t9fbNjV1H2lp+y9o/7XVds5Ve6xzbxrMZj8slooGwzQ8vT8iIoLw8IgGiffp06fIyso8r7iKigo9vr5o0SKAukW6mrJs2fucOHGcOXNurSvn2BLbt2/lscd+TUJCAv/5z6uEhIS0eF/e5HK5yMvLJS8vr+5r8OBh/PGPjxMVFU1RUSFPPfU4r7zyIsXFxd4OV4jWUWqq37Tmy1e76qWnvotLP13K0s8OkZFdM8Fs8+5s5lzVl9FDakq8ZVsqWXwonbSiEgA2ZedyQ59kpibU1GguKqnitSV72ba3poLElj3ZzJiSzPSLk9DrtVRZ7Xz5ySG2rD+F262yY1MGEy5JZtqs/pj9DM3G53arrN92hk++Ok5llYOvN2cwoHcYc6/pT1yU7z7iFl3X3r270Wq1DB48rN73Q4e2vEc4NXVv3fCZmomyJ/j88xXo9fp61Wj8/Py46qpr+PzzT/jLX37P6NGjKSjIZdmy5aSk9CE9/eh5H/P2229k4MAhDBw4iIiISEpKivnss0/QarVcccWMuu0GDhzE5s3f88IL/2LQoCFoNBomTryYhQvfZMeOrUyYMInY2Jq651u3buLo0cNcf339Mp8XUtLyjjtuZujQ4fTt25/IyEhKS0vZuXM7O3duJyWld4OqLb/+9S+Ii+tBUlIvFEVh+/atfP/9d0yYMIn58+9qsP933vkfUPNzBjh+/Fjda71792XSpIsBOHz4II8++gigcvXV17JtW8PVZ31lommBxcZt7++u95rLUsriO6fy9NPPsnTpu6xbt5aNG9ezY8c2rrlmNjNmXI3Z3HkXKROiKbL4lOhy3lp2gC27szm3g624tJpXl6YyICWThImhfJmRieucDSodTt49eIz1mTlMIIKVa49TbTvbu293uPn06+Ns3p3NtFFxfLvyCJUVZyfWud0qG785wZ4dmdzyk5EMGNJ4feiCYiuvLN7HmZz6FS0OpRfzxItbuOqSZGZdLqsfis5l797d9O8/sK4HeO/e3fTp069VEzI//vgjPv74IwA0Gg0hIaFcdNEE5s37Kf369a+3bW29+PXrv2Xjxg0MGjSQf//7BVasWH5BSf3NN9/O1q2bWLbsAyyWCkJDwxgwYBB/+cuT9YZhzJlzK5mZZ/j66zV1Eys/+ugzJk+eQlFRId988zXFxcUYDAbi4xN45JFHufba61r8s7jxxlvYvn0rH3/8EeXlZRiNJnr27MnDDz/MNdfcgF5fv8rO4MFDWbdubd18h549k3n44d8xa9b1aLUN14r/3/9erff90aNHOHr0CAAzZsysS+pPnDheVwf/xRef8xirryT1aDRoA8I8NpnNZu666x6mTLmURYve5tixIyxb9j6rV69k2rSrmD79ap+eQyBEd6KoMvW91VwuN8XFld4Oo4F7/vgVLlfjH2/IlUEU2Bov99Ynw8ypk40Ppxka6U/GoYJG2ydd1ovZtwxttH3foQJeendPo+0xkf48+fDERtvbkk6nITTUn5KSSpxOd4cc0xvkPLsWOU/fFxbmj7ady21klli4/s1t9V5zWYpZeOMIoqOj615TVZUtWzbx8ccf1s3RMJvNXHHFdK64Yjrh4RHtGmdn4QvXW0dcN76qOjOX1Gv/r1X7GPrZG5ji227Ruo4iPfVCCCGEQFEUJkyYxEUXTWDbti2sWLGMzMwMPvtsBZ9//imjR49l2rQZDBgwqNkJ1EKIjidJvRBCCCHqaDQaxo+fyLhx49m1awdffvkFhw6lsWPHVnbs2EqPHglMnXoZEyZMIiQk1NvhClGPQuvH1PvqLask9UIIIYRoQKPR1FV0OnMmg7VrV/P99+vJyjrD4sXvsGTJuwwaNIRJkyYzZsw4mVgrOgcFNA2n01zwPnyRDMjyUXvzC8kob3zVypOWEuITGp/cFBdlpn+ovtH2UL2enhorjd3s+pl1JPfVoNN7voR0eg0xyQ7ceF6d0q2qZDorCQ5svEJOWLCJ7PwLW5lTCCFE20tISOSuu+7h5ZdfZ/78n9G7d19U1c2BA/t49dUF3HPPT3nxxWfZtWsHDofn0shCiPYlPfU+JrfSyuJD6ewvLEajwNSEOG7ok4y/viZBL3fYWJl1mN0l2TAWBqSEkLPTTmlZTRUHs1HLjZf7MWtiHjpdIZcXhPPGQQOZlpoJszqNwiWWKka9sxi9pZKBA4ewttdEjhfUvF9RYNLYQGbPyiMwsIDxl/jx+bshpO0prYtxwPAQpsy1EBh5nEI1kwB1CGY1GeWHW9+jJaUsOniMjIpKDCMV+pSGcfJACc4fJvVGhZsxGrQcTC/iiRe3cOn4RK69LAWzSS5XIYS4YG43Lkv9+vMuSymFhY0XOmjK8OEjGT58JAUF+ezcuZ0dO7aRn5/H999v4PvvN+Dn58eIEaMYM2YcvXr1RqO5sP7DyMjIC36PEOfSdtOSllL9pg10RPUbu8vFivRTrD2VifNHH1mgXs8NfZJQjDa+yk3H5q6/wJTOrSXqdAhhbpWfzigiLMhWr93pUlhxKo69J+xc/NGXBB053uD4x6fM4FCPFGbNqiQpuaRB+5Hd4WxYpWXUlS4ShjRcvESnhkHVUD46XMiWnPwG7RGqgfAsLTqXQvrpUtw/uiqDAw3cOKMv40fENfozag1fqHbQFuQ8uxY5T9/XIdVvCkuZ+Y+PGryu8QtCUVp/bFVVcZXmYTtzCFvWEdzVZ/891JoDMSQMwJgwAF1Q89Vzauvnn1uVp6P5wvUm1W8aZ8/K5cice1q1j34fvoahh1S/Ee0k11rFqpNnPLZVOBwsPZJOcITVY7tT4yI7uYi/jXKjVRoOh9FpVW5KyaLPv3dQdeSkx32krF/N1GVz0Sd6XlG238gi4gdGYlU9r0boVIpJLTrGlhzPj2ULFTumWDN5G0s9tpdV2Plo1dF2S+qFEKLL0uowRPdq10PoAsMxJgxEVd048k5SfTIV25mDuG1WqtN3UZ2+C11oDKakoRh7DkHrH9yu8QjRHUlSL4QQQog2oSgaDDEpGGJSUMfMxJZ9FNupVGxZR3GW5GIpycWyZy36qCRMSUMwJg5GYzR7O2zRlSig0bZy+E0r3n7y5Ek+++wzNm3aREZGBjabjcTERKZPn878+fPx8zs7ofyll15iwYIFHvdzxx138Ic//OGCji1JvRBCCCHanKLTY0ochClxEG5bFbYzaVSfTMWRf6ruq2LnKoxxfTAmDcXYo5+3QxZdhDenZCxfvpwlS5YwdepUZs6ciV6vZ9u2bbzwwgusXr2aDz/8EJPJVO89jz32GKGh9cvDpqSkXPCxJakXQgghRLvSGM2Ye4/G3Hs0rsoybKf3U30qFWdJLrbMw9gyD6PojRiikzk82CSTZYXPmjZtGnfffTdBQUF1r82dO5eePXvy6quvsnz5cm677bZ677n88suJj49v9bHlN8ZHmLVaDE38gYvy0xKkb/weLURRcVU1PuHHWa3i0DReXlJVwHLM85j9WsYKW5Pt0f5aNDQ+LzssREFvaPyZl9lfT6XN2Wi7pdJOaXnTMQghhPAurX8wfgMnEXbVfYRdfT9+gyaj8Q9GddiozkhjwYLnefDBe3n//cVkZWV6O1zhgxSN2qqv1hgyZEi9hL7WjBkzADhy5IjH91ksllaXg5Weeh8R6WfmmcljWXo4nZ15hXWv6zQwa6CZwMBCNGgorwpmQ04p6g8DwhRV5XZDJReX7UazzY6t53D0PQPqLcxw4ksLO//4PVWZRcRMHkBgWRaKrbqu3R3dg9zMao5e+z5xN4yk/9N9MfU4m3xryzX4HziDoXgjoaHJZPdNoDrg7C+FDjMRxjASE0t4MzqAhbv1fJdxdv+BBg2X9tVTrcsj+ecm8nb5s2vj2eoJOp2GHv3COVxk5aG3dnLb5CQuGRRdt0y5262yfnsmn6xNx+V2M/PSXlw+sSc6qQwghBCdmi4kmoDhV+A/7DIcBWeoOrIVsz2LkpIiPvtsBZ99toJevXozefIUxo+fSFCQTLAVzVCg1UWdFMjOzmbevHmNbrJu3boL2mVeXh4A4eHhDdpmzZqFxWJBo9EwYMAAfvazn3HVVVddWMxIScs20RElLc+VVljM4kPpJIW7GdjDgp36PehGAjhSrCPQUsa86oP4V9bv6XCZwlD7DKayArb+YR95Xx+o124IDyJuVCKGsnwq/aLJ+u4AnHOZaP1N9P71JHrdH0HAiUpMGftR1LNlNFVFS0X8EHKTgwkxx+JnrERRzmlXIac0lOe3OugdacA/qAS7Wr8qT6AjhN1fqKD1I9ftpshSv71PbCB3XZaCu8rJe58fJiO7ol57TKQfc2f2Z1Df5kuogW+UMGsLcp5di5yn7+uQkpYlFq5/c1u7HqOtuCzF/G/WYLKyzvD999+xd+8e3D+UadZqdYwZM44rrphG//4D6zp22povXG9S0rJx9pxcTs5rXUnL5EWvUai62yypd7lc3HrrrRw4cIDPP/+cXr1qqlG98847pKenM3LkSEJCQsjIyGDJkiVkZGTw0EMPcd99911Q3JLUt4GOTuoBqlxWNlu+bnKbG/d+i6I2PlxlxR+dVBzLabQ9ZGQfSncfa7R92msTiQ7JaLTdOuIy1ERjo+2nywJZl+u5BCaAagng008bX/VWr1Xwy2t6SNC/H7uYkCBTk9uAb/wRbwtynl2LnKfv65CkvqicWS+taddjtBWXpZQXZg4mIiISgPLycnbv3sG2bVs4c+bsvzcxMbFMmjSFceMuwmz2a2x3zfI0dt8XrjdJ6htnz8nl1B13t2ofSe++jiG27erUP/HEEyxdupRf/vKX/PznP29y2+rqam688UZOnjzJ2rVr6dGjx3kfR4bf+Kjz66Fo+n5N/fEKTz/WTHtr+0iau51svr35+9HmTlEIIbo81Y2rstTbUZwfBR7+NgNFOfcJcxikXI0zLLem/n3mIU6WHGfLoeMoby/ClDwMU8pItObACzpUZ1joSnR9zz//PEuXLuWmm27i3nvvbXZ7k8nEXXfdxaOPPsqmTZuYM2fOeR9LknohhBCiK+uAxac6gjYgDGPCQNz2aqpP7qP62A6cZflUH99D9clUTMnD8B88BW1AaPM7E12WQuvH1LfVwK6XXnqJV199ldmzZ/PXv/71vIeM1fbOFxc3PprBE0nqhRBCCOEzNAYTfv3GYe47Fnv2UawHN+HIP0X18d1Un9qHuc9Y/AddjMbk7+1QhZdoWlnBpi0sWLCABQsWcO211/L0009fUInW06dPAxARcX7zAmv5RFL/2muvkZaWRlpaGpmZmfTo0YNvvvnmgvdz4MABnn/+efbu3Yvb7Wbw4MH84he/YMyYMe0QdftS1fMY56do4JwJrA2bm75jbK5dbe5etpnfqeZuWDXNHF+jKChK08N0mh1iJIQQwicpioKxRz+MPfrhKMigMvUb7LknqDq8heoTewgYcSWmlFHtNqFWiMYsWLCAl156iWuuuYZnnnnGY0LvdDqpqKhosOhUeXk5r732Gnq9nsmTJ1/QcX0iqX/uuecICQlh4MCBVFRUNP8GD1JTU5k3bx7h4eHcd999GAwGPvzwQ+68807eeOMNJkyY0MZRt58iRz5Hq/cToAnChZMqd/3JokHaAEb6qaijJuE6ko6u/Ey9dpcxDJfdwCV3FbJjdT9y19evmWqICMa/VyyW9EyiJg0mf1NavcxZF2AmaEgv1v9pD5P+NJzY6KwG1W+sjngqnlmJ4SfTMYwOQtHUr35jdwSj0xQwPDiYwxXVVLvrV7cJqg7h0IdWRpoNZPrpyf9RDfzECD8cLjfGACMmi52cvPoTlaPCzIQ43fzvuU3MvnkI/QbLmEkhhOiq9JGJBF86H0fucSx71uIsyaVi22fYTh8gcMINFzzeXviwNipp2VJLlizhpZdeIi4ujokTJ7Jy5cp67REREUycOBGr1cqUKVO44oor6NevHyEhIZw5c4bly5dTVFTE73//+wue7+ET1W/OnDlDQkICADNnzsRqtV5wT/3NN9/M0aNH+eKLL4iLiwOgoqKCq6++GrPZzJdfftniu/mOqn5T5a7kaFUahc7ceq+HaMOocJWjUTQMMvnTx1hYb4lke7YdzfF9KE47DmMcmowjKO6zVXEycuPY/V4+1swiQkf3oyz1OK7Ks3XkA/omgAKWo5mEjulH5fEc7EVlde3RY+KY+EgiAUoWNn085d9n4zydV9euxEdjfmgm2jgnbpc/xbZqqtTSunaXS09GRQQnKksxY6b4Ox2H1p1t1+g0hI2J44DFhkmvJSbExJFzSlgqqsqQcH+KMstxuVR6hfuRnZaH6jp7aQ8aHsO1c4YQHun5cawvVDtoC3KeXYucp++TkpZtT1XdVB3ZSuW+dahOBxpzIMEXz0UfUX/FTpelmIU3jmiQOPnC9SbVbxrnyM0l8/9+1qp9xL/xP/QxLat+8+ijj7JixYpG28eOHcuiRYuw2+088cQTpKamkpubi9VqJSgoiGHDhjF//nzGjx9/wcf2iaT+XC1J6s+cOcPll1/O9ddfz9NPP12v7aWXXmLBggV88MEHDB8+vEUxdURSX+WuZGvFt7jx/AfGjJErg+2YtJ5LWLodYF+zA215nsd2p1vLuvdDKNx6tNEYwicPoej7/Y22T3z0IvyO7m20Xb1nFmUjtY3eAeefiWDxkxW4HJ4vyaD4II6FGql2eP4Z+Ou1JGdVUFFc5bFdp9fwmycu85jY+8If8bYg59m1yHn6Pknq24+zvJDyDe/jLMtH0RsJmToPfWRiXbsk9V2Tt5N6b+oWV0RqaioAI0aMaNBW+1rtNp2VU3U2mtADOHE0mtADaPSg2Bqv6a7TuHBV2xptB3BXN718sT3fczJdF6OlsslHWu4qR6MJPYDdYm80oQeodLio/tEiVfWO73BjtzX+MxJCCNF16IIiCJn2f+ijk1EdNso2vIfLWu7tsEQHUDSt+/JVPjGmvrVql+b1NDYp5oc7sdzc3AZtF0Kna9+rQNfM/df5DBxqbnRRc/to9v3NtjczMbcDJjNptRqPn1Vtj0dX7/mQ8+xa5DzFeXG7cVkurDReVxI4+irKNryHs6yAii0fEzjuWqCmTn1JSSE6Xf1/e2rzArnefJMCXEChmUb34Yu6RVJfVVXTg2wwGBq0GY01K55WV1c3aDtfGo1CaGj7ls5SbHYoa2qD89hHM0lzc3/AdDpt69qb2X9zx2+uGg40f2MRFGRu8rMKCjI3e4yuQM6za5HzFE3ypcWn2onfwImUb1qGLfMQxl7D0PoFgwIPfX0aRTlbTMJlKWX1IzMJCfGX6034nG6R1JvNNb+YdnvDoRm1ybzJZGrx/t1ulfLyxoe2tIUKZ9NDW5orHwk1K7A2lfO6XE2PHXQ6Gy+PeV7tzey/ueO7z6M8ZXMzRMrLqygpaXhzp9VqCAoyU15e1WwcvkzOs2uR8/R9QUHm9u8R7iKLT7VKdC+qT+7HkX8KXO4mfx4WS01e0Jmvtw65bnyYovjUdNE20y2S+tphN7XDcM5V+1pMKydEtPdkGqPqR5KxDxm24w3G1mvQ43AH8nUJTAwqwfyjsfWOSpXT72ZjPx1K4iAVo9ZSr92lakjbH4+qq8ScGEVVRn79gysKw24bQI94JwcMiWRszGgQX9ykflQXOTBGpmAqOt6g3TCiD+ERNkxFwRSGuuBHve4WWwCHjSYG3+7PoQ8KG4ytN0WYUS+Kpa9Oy+njZdgc9W8g/Mwa+gzyRzfMjOWrfKqL69/A6XQaLpnWm7AIP4+fldut8u2ao2zbeIrpswfQIzGkwTZdicvl7rQTwNqSnGfX0l3OU7QPbVA4jvxTuKstTW5Xm8jL9eajvFzS0pu6RVI/ZMgQAPbs2cOcOXPqte3Zs6feNp2VRtGSYhpArD6RY9UHKHTmgQp6wsm1leFQi8kBjlcbGBcYzFC/IjQayF5TQdZrW3AWlQKQts9E1GW9iYs9hUZxcyYvlt3vFWI5UfNzUPQ6wsYPonRfOm6rjaTJPRk8QUFffASKYORAhb7jB7F1SQaW7AqCekWRMDgS5/ET2Eog/xj4DUoh2FSK3lKEJiqE4NlDMRlyUZw5RGXlEFIcS06PGCr9nDhdOtKKIkktLcNNGcRDr18H4Nys5eT6UjR6DUEXJ7APJ9UVNU8rQuNMpGh1HD9RhoLKwEFBFOorOVZVc45+VwTSu9xI7pocVLfKwGExzLq58XKWp9KL+OT9/WSernn/4QN5XHRxEtNnD8Q/oGGvvhBCCN/jttaMYdUYZFiN6Jq6XFKfn59PRUUFcXFxdcNuEhMTGTp0KF9++SW/+MUviI2NBcBisbBs2TISExNbXM6yo/lp/RnmP448Wzbbyg9R4Sqq117ttrO+zE6aNYTBT36DdV96vXa3tZrczw9Q3COKUl0QZ1bVL1GpOpwUb0nDEBnMuJ8PIqJiPxSf7alQVJXA0sNcfr0fp23jqNpxCOfxE/X2YU07QZVeT8L8iUQNL0Oj5tRrN1TlkJiey/HwUSyrNlHpKq3XXqZaYTwMGhbNpmMmDlnr97qXVDsowUGvfsH4BVVz0loK5xTmsTpdpPpZib4lmtvDExk2NK7Rn+cXy9P4bs2xesN2VBW2rD/Fvp1Z/PSBi0jqHd7o+4UQQnR+7moL9ryTAOijeno5GtG+VBRNa4ff+ObwHZ9I6j/55BOys7MBKC4uxuFw8MorrwAQFBTE7bffXrftc889x4oVK3j33XcZN25c3et//OMfmTdvHrfddhvz5s1Dr9fzwQcfUFBQwOuvv+5zy0gH6EKpcDVeG7/QUYE17WSj7fasfIorG58cbC8oI0BvAdXzo0fFZsWg2rA6PJe5VB0OtH5uNKrnMpkKKlVVVirdjT8jK9XbyLM23p5RXkWQrvG5Bnm2ahL6hjXaDnD6RHGj4/CtlQ7ycy2S1AshhI+rTP0GXC70EfHoQmSFcdE1+URSv3z5crZv317vtf/85z8A9OjRo15S35hhw4axePFiXnjhBV566SXcbjeDBw/m7bffrpf8CyGEEKLrsJ05RNWxnQD4D7/Sy9GIjuDLteZbwyeS+kWLFp33ts888wzPPPOMx7ahQ4fy1ltvtVVYQgghROfXjevUO0vzKNv4AarbhTllJFr/oCZ/Fi5LaccFJ9qHAprWDr/xrcEbdXwiqRdCCCFEC3WTOvWuynJemjOBiIhIAM6cyeCllxYSGqinb9/B3H//L9Bqm097IiOj2jtUIdqFJPU+yOV2s/FMAQGmUCyUeNwmQh8Kl46Er3Z6LN7uPziZBI2eo9lFuO3OBu0hQxLRRoVApRmqG45bdwVFYivSoQkOwl3WcNltbVgwJMXiMrjQ2hvG6NYYCAnxJ6YygFxbw/JiWjQEaPzoE27mWFHDNQAUYECUH+YAPemlnpf9HhEVjr9e77Gt1rDRPThzqhSHvWGN/ejYQBKTQ5t8f3tyu1W2rD9JQJCRYaN6eC0OIYSP6yZ16l2WYiIiIomOjubo0SO89toCHA47/fsP4LHH/oSf3/ktEqlp7XKkwutk+I3wCQeLSlh86BhZlppEd3SPKOKjLFjVmu8DtQFoUSh0lFB4Z2/iLkmg5zu7cB09DYA+Opy4scGEKTVVcZIfimbXRjP5W2q+N4QHMmRuCn37nEGjHEcdEEG1JRB3+iEUVUXVGynS9+bwB0dx24vQBpiIHNUP55F0cLlAqyX0xvFE/zwJXaAGq2sAujNWTLlpKG4HKgq20BQsPf0xGau4zV3N/sJovs+rospdM+k2QhfC0UwNqRVWUKwMjQ8hu9hNobWmPTHEhJ+/gyxbEZRCQqA/DpebXGvNzUeMn5nbBvRmaGTzE1wnTu3FoGExrFyWxt4dWQCYzDquvKY/Ey/t5bXFPU4cLeST91LJzqy5Ydk64BSz5w4lOjbQK/EIIYSv2LhxA6+//gpOp4OUlD4XlNAL36fQ+sWnfHT0DYqqNrcGp2iOy+WmuLjxSjRtocrp5M0DR9iRW9CgTa+By/uaCAt0UuDwMFZQVRm4uZSkIxnE+R1GS8Oe+YzyZPKLzAwfk49R37Aqjl0XTUWegUMrs7CcKW3Q7pcUQ+SYRGLu7Yu5j4fa7lWgO1NKVYQBZ3DD41c7DHyfFcWBLB2pOQ2fDOgUDfHGMFTcnK4qbvAbpwB9Q4MZGhnG9KQEdBfY06LTacg5U86WDSeZOqMPgUEtX2G4NWw2J8ve3cue7ZkN2jRahUmX9uLaOS1fU0Gn0xAa6k9JSWWXXlRFzrNr6crnGRbm3+6dB5klFq5/c1u7HqMzcFYUcpMpg2+++QqAUaPGcN99D9WVtz5fvnC9dcR146tc+TkUPfKTVu0j/Nm30UbFtlFEHUeuCB9RUFXtMaEHcLjh23Sb54QeQFE4ODGUHuEZHhN6gMSgk4ye5vSY0AMYnHmcPuTymNADWE/lEjy7j+eEHsAM1uQeHhN6AJPeTpJflceEHsCpuilylXK6umFCDzUVZY+UlHFFYvwFJ/S1Bg6N5frbhnktoQcoL632mNADuF0q33/dcLVeIYTo7lSnHcv2laxZsxqAa66Zza9+9dsLTuhF16BoWvflq2T4jRBCCCF8lstaTtn6pTgKz6CNCOSee+5nypRLvR2W8KLWLz7lmySpF0IIIYRPchRlUbbhPdzWcjQGM7/4xcNMmDDJ22EJ4RWS1AshhBDC59gyDlK+eTmqy4EuOIqAMVeRktLH22EJb1Og1QWMfHSmrCT1PiJAryfEaKDUZvfYHqLzw1XtQmtqWB4SIBYFtUcPSPdc/tHqDqAsw0h8IxUcVaOZqKmJ5K8/4rFEpsakQxNuwu1WPf4yud1wqNBAUpgOg87zuPrs4kD8tE6sLofH9mBMaDUOSt02j+2RqoHsXAvJ8cGeT8IHmP30BIeaKSvxPLcgJi6ogyMSQvi8Lrb4lKqqVB3bjjVtIwCG6CQCxsxEtXn+uym6Hxl+Izq1MJORZyaP5dP006w9nYnrh8TapNURoQnhQFYlR7MDmJgSTkx8Joq2pu66QVWY7rLSs/wwJLtxJE/BtX0fmrJSAFyqwpHc3hz6+ASO8p3EXNyX0Vc5CfYrqzu2OuwitD39SFaqib3kRlKf3k/el4fr2mP/7yLiH+6DxlSGzRWA3hWATn+2tvzpMn8+OqpwurKEYH0oV/fUMjw2t649pzSINzfGsSOzGn+9kb7RQWRUF1H7K+mv6IiwBHD4VAU6rYZ+KaGc0ZfhoKYqgVHVkFLqx4m0Ep76fhtTxsYz+8o+BPg1XaO+MwoINPLbv13GN6uOsn5tel3lBZNZx7RZA5g4tevXmhZCtDEfXnzqxwtKOZ1O3n9/MVuzd0OoH1OmTOX66+eg1WoBiIyM9Ga4QniVlLRsAx1R0vJc2ZZKlhw6RoVVy7E8G5YfLZwUatYxqb+bS0OzGVVxDJ2zrF67W2PEXh5A7rdZ7P6ikoqj2fXaNSY9fW8cxNBrjZjH9UP50QJXKgolp/w4/GYaiX8aiyHOw+JQrgjsbi2fnzCxLb+EH19kvQICmNGzio2HI1hxwInDXX+LHkFGQgNdmKu0HD9uxfqjcwwLNBDTU4+h2k1RmoXyivpPMAL89Fx3ZW8mj4lHo2n+OVpnLGFWmG/h0w/2ExhoZMb1gwgMMrZ6n53xPNuDnGfX0pXPU0paNs1lKWbhjSOIjo7GYrHwwgv/4uDBAyiKhjvu+CnTps1o82P6wvUmJS0b5yrIoeKP81u1j8AnF6KN9L2SltJT74PiAvy5c+AAfrpir8f2kionn++BR0fsQaHhSqkatw1TgI1dn1dgSc9t0O6udnB48V4G//q2Bgk9gIJKWFIl/V+4GKfR8yNdVVvItqwebM33vOLtCYuFpakRbD3seahNVrkNl11P6ekKj+3FFXbKD9rxz/f8uNVidbDok0MM6R9JWLD3SlS2RkRUAHc9ON7bYQghhNcVFxfx1FN/JTs7E5PJzIMPPsyIESO9HZYQnYok9UIIIYTotAoK8nnjjVcoKMgnNDSc3/72MXr2TPZ2WKKT6s4rykpSL4QQQohOyVleyPPPL6WqqpLo6Fh+//s/ExkZ5e2wRGcm1W+EEEIIIToPZ2ke5Rs+oDxAQ3JyL37/+z8TEtJIiTYhBDLLwkfZnG50TUwANWo1qJrGq7+oaND6NTfxUtt0s+fKlHX0zUxQNTRzS2nQadA2sQ+TTotB3/glrNMq6LTevd2uKK/26vGFEMIXuSyllH67CLejmsTEnvz5z3+ThF6cN0WjturLV0lPvY9xuVW+PJbH4n2ZhJn1BBl1pBfXrz4zvqeJG0ZncdzQj7icSvzL0uu1V/vFczQyhrAPHJifTyTvrc2ozrMTaoMHJzL26fH499CAmoRalYlyTgbvUv2w783DtP1NbFdNwzEsGDTnTHhVDVTbg+gVlsWNShTrMt2U2M/WljdotAwPDyQsII+UkFC+OeDP6dKz7VpFYXiiCZt/HtFxJiqP+3Ei++w5KsDACH+cafnoFAVTQjDpWfXr7w/uG8EtM/sRFND6ijEtUV3lYO1nh9n47QlS+kYwe+5QomMDvRKLEEL4End1JaXfvovbWo4uMJz77/8lAQEB3g5L+BClm3ZZS1LvQ9LyK3htxylOltQkuBa7i/xKO33C/SmpcmDWw23jKugdexoAB3C6p5lAywhisjPROG1kRPXmTIAKuMCowfznZJJuiafkz3uoPHCGIY9OYdCdEWh1CqCCUo5qDsdpV9C5CrDlaFFXr0ax21AA8xdfYvw+GOtNM3DHOHE6wylzlOFWikCB5LB85gdr2ZMTw+Y8C70CA0kKrUCjLQAgOLSI2ZOKycjswVcH3cQGGQiKKsOiFNWcg94C/S2Mig/nxEE3/jotEYVVlGzMqPu5WIqr6NsrlFKdBhW4+ep+DB/onTGXqqqyc8sZVi1Po6K85kbl2KECnnviGyZe2osrr+2PyeR79fOFED7MhxafUp12yjZ+hLM0D405AP9hl+Pv7+/tsITwCZLU+4gzZVU8uvagx7ZjRZWEmOCP12Sg1TZ8bFQR4KSidwzl1WbsGg8lJPvqCX1/LJfZpxIY1LDmvEbjQGOCyq+K0KZubzB/RFNeRsCb71Nw042UJxY2mGCi17oYG59F39BoMm2FDfavKCo9EzK5PSKEb3MK8LQmbmFAEXHDDFjeqqDEw5Ox/BMlKFqFJ56dgZ+/wcMeOsb2jaf56N29DV53uVQ2fHWcovxKfvLARR0fmBCi+/KRxaecFaWMtR/lBOX49YjgV7/6LbGxcbKglLgwCtDaobcyUVa0J6e76TFeLjceE/o6GnA1N8Y9oOnLQbE3M4i+wRJT9emaig/QaJte5EPF3eQhVJeKVufdZ27NLVTSWRcyEUJ0YVodhujOvRq1qqpYDy/lRMUp/Pz8+MMfHqdv337eDkv4KOU8Fp3sirrpqCMhhBBCdBaVe7/ClnEQRVF46KFHJKEXogWkp14IIYQQXmM9vBnrwY0A3HrrHYwcOdrLEQmfp+2efdaS1PuIZh8kdcSTpnYeoqY0t4XSfADefuCmNBejtwMUQohOpPrkPiy7vgTAb9Akxo+f6OWIRJcgw29EZ9YzxMyDFyUTbGx4H5ak0TLyWBWf/yeKiny/Bu1Ol5mc8hjKbf4o7oZlwYyKkWGBSej8g3FoGk5Icjuhel8RGnsRruQBDdpdipavxl7P8yUR7M+Nw+1u+MvkdkTgpIp4UygaD5ddoCaIIKOTmT2DCNQ3PMcwqz/KTj8ihsUQEGZu0B4U4UfY0GhefHcPWXkNp9qWVjv4z5YT/HLVftLyKxq026qdfLBwN089tpYDe7IbnqPTzbdrjvH079eydcMp3D+a46CqKjs2nea7Ncfo1TcCjYdJOgOGRHPd3GENXhe+ZffWMzz9+69Y+9lhHA5X828QQnhkz0mnfOsKAMz9x2PuM9bLEQnh2xRVVX23yn4n4XK5KS6u7JBjWexOlu7L5IujefihMLbcTc7evLokU6fXMGFaCCOvLkCrVymvjiK7ugI3Z5OPCF0Yen0JquKgtzmeOD83Gs3ZSbAOux+KrQAtVtzZDhw7dqCpPFsOzRkUj1pUjqYwm8MpY/kgeQQ5jrN15mNMflye6CQ2qAjVFYjVqadKPVtHXocJm8OfAkcZRowYtWbKXaXntOspswbzfW4pRqeekPRgjuwvpfZKNeg19IkOIPdAPopGIWpgFEdzK+omoWo1ClMvSuDaK1IwGnR8cTSPpfsyqTwnAbskKZyfjEwkzM/A7q1n+GJ5GmWlZxeK6jcoilm3DCEqJpAjafl8+n4q+blnbxbie4Zw3a1D6dkrjMzTpaxYuo/TJ0rq2kPDzfgHGMk8XUpElD/X3jyEgUNjLvjzbms6nYbQUH9KSiq79KTd9jjP7DNlrHgvlZPHiupeC4vw49o5gxk8Iq5NjnGh5PP0fWFh/mjbeahAZomF69/c1q7HuFCOoixKv34b1WnHmDSEoAk34K4sZeGNI4iOjvZqbL5wvXXEdeOr3MU52J69q1X7MD7yJpqw2DaKqONIUt8GOjKpr3Uku4yl/95EZYXdY3tImIlL/6ClSq3y2K5T9MyIicbf0LCEJYCqaqhen4b2yFbP7YqG1X1nsUJtfNXZ65NCCA7IpbGSNTpXFNm2Utw08kfTEsKnbzqxVnmuuhMRasLpVCmtsHlsDwwwoB0ZwZEiz5+NWafhylKVw7sa9swDaLUKw8fGs2vLGY/tigJjJiSyY3MGjf0WjboogZvuGI5O38zqvB3EF/6xagttfZ77dmWx5PWdDZ7Q1Jp8eQqzbh7S6uNcKPk8fV+HJPVF5cx6aU27HuNCuCwllK1/D7e9Cn1kIkETrkfRaHFZSll851RJ6s+DJPWN685JvYyp91HRJn2jCT1AaXE11U0k3E7VgV7T+NABRXGDpbTxdtVNkVYPTfzBs7tdNFWD0oWz8YQesNsdWKsaj7GkrBpXE39vKyx27JWN/4yqnG5Kijzf9EBNbfmigsZv1lQVCgsqG03oa/bh7jQJvWi50qKqRhN6gOImrhMhvK4T1Kl3VZbz0pwJ6PV6nnvuQ4r8FRIH9OcXv/g1JpOpbjupSS9aT2mDMfW+OSZfknohhBCiK+sEdepdlmL8/f159dUFlJeXER+fwJ///DeCg0O8Gpfogrrx4lPy7EYIIYQQ7Up1OXn99VfIyDhFUFAwjz76J0nohWhj0lMvhBBCiHajqm4sO1dxrCqLwMBAHn30j14fNy+6NllRVviMKqudb788Rq8+4Wg8XLg6vYYew2Moz4lCg+fx3O7ySL7aZ8Dl9nxfl1MewJeGAbj8wz2226N60S9WJcbUsIQm1FTAiTI7MStBHtt1mAjSq4TrQhpp11PuDKD3wFCP5ekNeg09e4eT0jccna7hZazRKCQPCKdHkAm/Rsa0X5IUzpTLUwgKMXlsj+8ZjEJNhRNPouICqTJqie7h+Rwjovy56OIkj20AFeXVLFu0l+2bTtOS+epOp5tvVh/li+Vp2Ko9TyYWbWPg0BiS+3j+XQiL8GPCJckdHJEQvkFVVSw7V2PLPoZOp+Xhh39HUpJ3hwKJbkCrad2Xj5Keeh+iqirbN55m1YqDdZNkw6P8MRp1ZJ8pAyCudxgn/PUcr3aw+QuIjwpj1hVuXOaaMnwGVxDffW8iNd0BlLPoOy1P3BJE37hyFAWqHCYW7oPP0kuBAN4zTOc3wWcYmrsFxe3AZQ6heMQISpIMhFDObZEVpOXHsj6niiqXE7NWxyVxZgZG5qDR1CSqJkcEFa4qnNhQ0BCkCUWnK0TRWIg1QKg9jGwrWN1WFBTctgg+PuigtLoKIiEuIgjDcTdZWTUlJZOTQshwudlTXFO5JzIugJ4onM6o+RnExweRF6Fnq8MJ+RUEGbUMjAzgUIEFFegV6sfdY5IYFBUIwLBhsXyz+ijr16bjdLoJCTUTGGLkzMlSADQa6NU3nKyMMmzVTvwCDIT0DOFQXgVqjgUFlQGDoijLKKWywo7BqOXyq/tx8RW9Pd5wuJxuNn5zgq9WHqa6ysnWDafYtuEUs+cOJSEp9LyuhUP78/j0/VQK82smaO7aeoaZNwxi5EUJF3RNifMTGRPA/b+dzO5tZ1i5LI3y0mr0Bi1Tp/dh6vQ+6GUytBAeVR3eTNXRmnKad9xxF4MHd3yVKCG6Cylp2QY6oqRlZYWN/724hTOnSj2290wJoyjESFoj1V4mDjcQHqzhs/XVHtsvHmDm0osMPL+zBIeHKh8D9HYeis7DMtCIW9/w/VUOI0cKoukXmYdZ76HEpFuHyxWFRluKomlYRlN1KxRa4lh5WOFQQcNzUFSVfvYgygrdHC/1fA59Q81oAnTstXsucZkQZOLK3lFcOyAGzY+6/3U6DTari6Vv7+TA3mxczoY/g4BAI7EpoaTlV2K1N6zKY9ZrGZcSysxrBhDiYYEsgKKCSt58aSv5OQ0XwFIUmHxZCtc2UxrxrQVbObgv12Nbcu9w7vrFRZjMHj4kfKNUW1toz/O0VdfciA0ZFUdYuOenOB1FPk/f15Xr1FefPkD5xg8B8Bs0mQ8f/ZlPDLvxhetNSlo2Ti3Jxf7qva3ah+HeV1FCvb+2zIWSnnofUVZW3WhCD5CZWcYJfUij7Zv22gk0N/5xbzhURU6A6jGhBzjkMJDZO4Egfb7HdrPexphYKw7Fc0KNxokJGw4PCT2AolGpdlRzqMBzj6eqKGSaqsgvbfwe9GhJFarZ81AagDPl1QyPDW6Q0NeK6RGEy+X2mNADWCpsWBQ8JvQAVQ4XBJsaTegBCvIsHhN6qCmReTA1t8mk3u1WG03oAU6mF1FpsTea1IvWM5p0TLmyt7fDEKJTcxRkULHlYwDM/cZhShnp5YhEt9GNq99IUi+EEEJ0ZW43Lktx89u1EVdlGWXfLcbtsGGITcHcbxzuyrIOO74Q3ZUk9UIIIURX1k6LT9UuKBURcXbBKLvdznPP/YNMfw2JA/rz0EO/wWg0ArKwlOhAre2p91GS1AshhBBdWTstPuWyFBMREVk3Tl5VVV577WXy8nIICwvj97//C+HhEW1+XCGapCitL2nZyDDdzk5mWfiIoGATcQnBjbb36BHEAH9Do+2JEX70aGKsd3KCnqQoBY3ieTx5rxAt4SYNqJ4vmWqHgd25ZmwOzzHY7Vo27A6mtMzfY7vbrXCmOJCoJsbEJ5Xr6ePX+Dn2jA8kObTxc+yp15N+pBB3I/MGAPoOjELroWINgH+ggQG9IzAbPd8LG/Qa7A4XZRWNzCugpsxlRJTnn4GiQP/BTU8i02gU+g2KarQ9MTkUvyaug+4gJ7OMrRtONfk5CyHa3vffr2fDhm9RFA0PPviwJPRCdDCpftMGOqL6DdRMkty64RRffnIQa6UDgNAIP/z8DGRllALQo284x0xaCn6YzBnspycmxMSR7JrJmUmR/lQ5XOT9UEEmwF9h4sU6So2FuFEJ0QZQWGTmSEFNu1mn8MA4LQNiclEUF6rbTJXDj0p3zfhM1a1wqCCGb7NtWF0O/HV6LokzMiAiF+WHkpZpR+L46Fs9heV2jDoNV19kYPKY0+j1NVUFMooi+STNTGZFNVpFoXdIEBnlFVS5atrjHAYiDtjIOfFDycr+kRwyKBT/cI4hQQZC4vw5XFLzGfQO86Pc5iT/h0pAgRoNg+waTh0tQlUhKT6IW68dQK9zbpLOrXaQk1XOpx/s5/D+PKAmkZ4wNZlp1/bH7GegrMLGstVH2bo3h9rfnpTEYAqKqyi32DEbdcy8rBeXTUhE56E6gdPpZsNX6Xz9xRHstppziO8Zwuy5Q0lKCTuva+HA3hw++2A/xYU1E48Dg4xcdf1ARk9IRGmih8EXqjq0VJXVzppPD7P5u5O43SqJyaHMnjuUxOTzKxPqi7ry53murnyevlz9xmUpZuGNI4iOjqaoqJDf/vaXVFVVMWfOrcyefUObH6+j+ML1JtVvGqeW5eFYeH+r9qGf/zJKcOev1PRjktS3gY5K6mtVWuysWnGQgtwKTh8vxuWq/xHqDVoiB0VSFmbmRL6Fakf9P0pajULfuEDC4x1o4oqocjcsIRmpDSdc0XD1wCIUxdKg3e0MJb1Uz6pTWrKrGp57D7M/F4UprN3gx4HTDSveRIcYuO4SJ4csBnbkNHx/oEFPnMlMwP5qMncXNOh1NZp0hA6MpCTBn8MVVdhd9c9Rp1HoHxGAqcRG0ZESrFX1F2dSFJgwMo6bZ/bDz6T3+Ec8bV8Ou7ac4YqZ/YiNb/iUJP10KZ+vS6fMYiczp+HPKDbKn3mzB9K3kaSyrKSK1SsOktQ7nLGTenpcSKwpDoeLb788RpXVwZXX9Mfs13zFG1/4x6oldm3J4LOPDtSt33CukRfFM/uWoV3yCUZX/Tx/rCufZ1dI6qOiovjHP54kNXUvvXv35S9/eRKt1nfXbvCF602S+sZ156Rerggf5B9g4Iqr+3LiaFGDhB7AYXeRvSeXIzkVDRJ6AJdb5VBmOca4Mo8JPUCBq4jL+lR4TOgBNLoSDhUbPSb0AFlVlaw/4DmhB8grtfPNAbPHhB6gwu6grKiKjJ35HodR2Kqd5O3PI7W0skFCD+B0qxzIr6DseFmDhB5qykdu2pXNmWzP5SUBBg2L5Y57x3pM6AF69wyhT1KYx4QeICe/ku+2nml0/8GhZm756SguujjpghN6AL1ey5XX9GfWzUPOK6Hvyr5aecRjQg+we2smGSdLOjgiIbqPPXt2kZq6F51Ox733PuDTCb3oIjSa1n35KJkoK4QQQogWcbmcLFmyEIAZM2YSF9fDyxGJbk8BpZvWqffd2xEhhBBCeNWuXTvJyckmMDDYp8fRC9EVSE+9EEII0ZW10+JTzooS1q3bCMCMGVdjNvu1+TGEaJHWlrT0UT6V1K9cuZK33nqL9PR0zGYzEydO5JFHHqFHj+Yf96mqyqeffsp7773HqVOncDgc9OjRg6uvvpp58+bh7++5zGBnpTfo8PPX11XB+bHAICOh/noKGxlnbDZoMagGwPOYd62iwebUY25sbqGqYGpmko4fGhQFGpuKbdJr0WkUnI2UHvQ36LCbdVR7GBMPNWU+Q016Sqob+RkYtQQF6Slv5Gdg0GuwWD2/F2omQGdkV5DcRClRt93Z5DmGhjReohMgM7eCiFAzpkbKZLY3p9NNblY58T1DvHL8thISZqYw3/P8DJ1eQ0CgsYMjEj9WmG/BbNbjL59Fx2vjxadqF52yWkN57rllGAwGLr98WpvtX4hWk8WnOrfFixfzt7/9jZEjR/LYY49RUlLCwoUL2bFjB8uWLatb/KIxzz33HK+//joXXXQRDz74IBqNhs2bN/P888+zceNGFi9e3EFn0jb8Aww8+vcrWL3iIFs3nKpLKrU6DfF9wzlcXAWZFQztG86hQiuOHyaTKkC/HkFkFVtZ84nKxZOjcYYXYVPPJs4xxhAqndX871gF0+LjGBxajKKprmt3OILZXqCjWM1nTFQIJ8vcFNqq6trDtWZMuxQyNp9mSM9gynoEcrr4bHuIv564JCPplBBmMBBsMHKy/OyEVT+dlpQyA3lrczGb9ST1DuP08eK6c9TpNcSOieRAjB2jppJhYaGk5Vrrbg40ClyUZCahZzaK4iDhYCS7N1Zi+6EEJkCvxGCKSqp5dek+xo+M45aZ/QgNPXtjd/h4Me99fpisPAtD+0VwyzX9iQo/2wuVfaaMFe+lcvJYESk9Q6j005FTcPYGKSLUxJyr+zFykOfrsqLSzoo1x/h+ZxbBgUZumtGXccNjm/3c29KRA3l88v5+CvIsDBkZx7VzBhMa7ps9bf/3ywls/vYkaz47VO8mcPCIWK65aTDhkb51096V2GxOvl55hA1fH8dg0DJt1gAmXJLcosnhooXaePGp2kWnvv56N4oCo0aNJiAgoM32L4RoGZ8oaVlSUsKll15KcnIyH374ITpdzb3I/v37uemmm7jhhhv4+9//3uj7nU4no0ePplevXixbtgzNOTOb77vvPtatW8eqVatISUlpUXwdXdLyx7IySlnxXip2p5sCjUJBef3Fj0LCzBhiAmrqvquQWVy/dz40SMu4ixWcpkrMWgO5trJ67f5aDbMS/YkJKOdEaRA7iss4dxaJoiqojgiOl1URkWPi1CcFuB3nXFYKRI6I5YQCcXEmMo1l2NX6FWuSgwKosDuJdOqwfJVPdXH93vXo2EBUwBCiJ7OfhgJt/d77MIMZxeaHqrgZ0bcUrV/9c1CsJvK3BpOf7cBo0HImp37VG7NJx62zBtI/OYQPvjjCzh9q1NfS6TRcOaknl46L55svjjZc3EiBHoOjyaq0c/GYeKZPScKgb1gBwu1W+W7rGT75Or1BVZ6+yaHMvaY/CbGBDd7XVnQ6DU67m3de3caBPTn12vQGLZfO6MMl0/qg9xC7L6got7Hq4zROnyjhjrvHEp8U3GlL0rWFzl56b8+2TFYuO0BZaXW912Pjg5g9dygpfc9vcaLOfp6t4YslLWtLWf7730+TlXWGhx76NePGjW+z/XubL1xvUtKycWpFPq6PftmqfWhvegElsPGFHjsrn7gi1q1bh9VqZd68eXUJPcCQIUMYM2YMq1evxm73PMQCapL66upqIiIi6iX0AFFRNR+a2dz4SqSdXY/EEP7vVxM5aLE3SOgBSouryD9YQLnV0SChBygpd/HlSidat75BQg9Q6XKz9GQFazPC2FFczo+nhauKCoYCko8YOPFRfv2EHkCFgt059AlWOWEoaZDQA5wstxBYplL4QWaDhB4gL6eCCqedPQPdDRJ6gGJ7FUVKEeOGZzZI6AFUv2oiL81Dq6NBQg9QVe3kzQ9S+e+SfQ0SeqgZprLqu5Msent33eJGPz7HrP15TEgM4drLUzwm9AAH04tY+vlhj2U2j54s4dUl+zy+ry29/crWBgk91JRCXfPpYXZtabwMZ2cXGGTk5jtH8vunrmDIiDhvh9Ot5edWsOR/Oxsk9AA5meW8/twmL0Ql2kplZSVZWTV/KwYMGOjlaIT4Ea2mdV8+yieG36SmpgIwYsSIBm0jRoxg+/btnDhxgv79+3t8v8lkYsSIEXz//fe88cYbXHnllWi1WjZv3szHH3/MTTfdRFxc6xIAnc67F4HL3f69Ca19WN7EQqfn53yeKSnNbdfMTpqLsZl2RWn6Wmh2yEEz728trVbT6Pj/WhqN4vXrubVqe7C6ek9WZz7P5mJSOf9rvTOfZ3eVmXkKUIiNjSUsrGut2izXm/BVPpHU5+fnAxATE9OgrXYsfV5eXqNJPcCzzz7L7373O/7973/z73//GwCNRsMDDzzA/fe3buUxjUapNx7bG6ptnieTnqu5pFqr1UAT9wY6nRaaOIyumSEbzS1I0twf0PMZg9vcFj9+UnOhMeh1TZ+D0ahv8loIDGx8sSvoHNeSn5/B6zG0laAg330CdyE643lWVTb9N0mBC77OOuN5dldWazk6nYaUlOQu8/fix+R682EyUbbzqqqqmWRpMDQsxWI0Gutt0xiTyURycjJxcXFMnjwZjUbD119/zYsvvojb7ebBBx9scXxut0p5uecqMh3FZm8+qW+uh9blYWXWczmdrqbbHU23u1zNtTd9fE8ry/5Yc1u4m3mi0VwMjmZ+Bjabg5KSxudXVFQ0HIpwLrdbbfL9rXU+PU9Wq71dY+gIWq2GoCAz5eVVzX6mvqwzn2d5edN/k1U47+usM59nawUFmX2yR/jMmWycTjeBgaE+//fix3zhevPV66ZDKKC0diJ+K95+8uRJPvvsMzZt2kRGRgY2m43ExESmT5/O/Pnz8fOrX5CisLCQZ599lvXr11NRUUFSUhLz5s1jzpw5F3xsn0jqa8e72+12TKb6JQJtNlu9bTypqqpi7ty5DBo0iOeee67u9auuugqj0cjLL7/M5ZdfzoABA1oco7cn07hcKhqN0njiq4BGaTrl9TDUvX57c8M29M29v+nfkuZ64jXN/AFTAFxKkzNFlGZ+U1v7J9JN09eCq5kbH0XpgGupmZ+z0+VuMgaH3YXe4BsTaV3NnEtX0RnPs7m/F1qt5oJj7ozn6RPauE69y1JKgasAh8OBn59/l/1M5HoTLbF8+XKWLFnC1KlTmTlzJnq9nm3btvHCCy+wevVqPvzww7pctry8nFtvvZW8vDzmz59PfHw869at409/+hP5+fk88MADF3Rsn7jNq53Mmpub26AtL69mUmNTJS3XrFnDqVOnmD59eoO2q666ClVV2b59extF6x0GvZY/PXARfZJCGrRF9wgksHcYWo2G3jENy45FRRgZMNaPvae0+NmiG3R3+2kMxBlDOVJRjplwlB8l5zq0ROnD0I4rZfJDEfgF10/4NHoNwZf3ZBsaYrUR+OvqP3FRgEviY/nVtJH87KHxREY3jDF6YhRllwYS5+9HnH/DsotxmgCCcvzZuDoCR37DihraQj8K3w1Ek1HBgOgAlB+dZJi/nhER/lQcLGBITECDJ3d+Jh19kkI4WFxF7PBY9Mb656gzaIkbHsO6wwW8szyNisr6k33dbpXv1x3nw1d3MDjIRHhAw6dOKYnBVFU7+cdr28nILm/Q3lr5RVaef2sX+4qtxA+NbtCT4edvIKl3GJ9/eIC1nx3G8aMbEFu1k5XLDvDnX63i46X7sFY2PjldiIgof37+m0nExgc1aOs7MIpf/emSjg+qu/qhTn1bfaHAF8eKOFlU3uxTciG8Qqu07qsVpk2bxvr163nuuee44447mDt3Li+88AL33nsvR44cYfny5XXbvvHGG5w+fZp//etfPPzww8yZM4fXXnuNqVOn8uqrr3LmzIUVrvCJnvohQ4bwwQcfsGfPHpKSkuq17dmzBz8/P3r1arwGb23i73Q2HKJS+1pzQ0N8QUJsIL+7Zyxb9+Sw7Muj2N0qoUnBHMq1oP4w5r7IYqdXVACWagfl1Q76D/HnhK2M05U1Se6ODCfxgVHERtqo0paRHBhFZmUx2bYSANIriwnWmYky6bGqZUTqQyl3VpLvqOkFskQUM/TXBuz7gtmzvJiQEdGciDJxqKomAUzLq8Ss09M/JpAz1cUkBwcyb2AfkoNryjj2HxxN78cj2fBVOuu+OIoxzoR7QjAHqivBDiV2OwrQJySY7MpKNE6FkAp/jpw+O1b9i69gUFI8vUcUo9HasG0I4tDG4rqnGJa0fPrGB2Ex68grtzEwwp+cY0Wc/KGO/ZkD+fSM9IdIMycKrPRJCiE7z8KxU6UAHMosIzgukDiTnpxDBcT0jyDX5uJgZk0ivnFnFrvT8ph9eW8uuSiBk8eK+OS9VHKyatozTxSj02sY1jeCQ0WVhASbMBq0HM+oqdpTVmHnyZe3cfGYHsy+sg8Bfs08AmmGze5i1XcnWPP96bpepwNWBzF9wgm0OSnMKCU5JZzszDJOpdd8jms/P8yOzRlcO2cwQ0bG8f/svWe8HGd59/+dtr2fPb3rqHfJvfduyw0DBhySQBJCnicBHv4JJKRAAnGeJyEhkE4CBowB27hj4yb3ItlWL0fS6b1u7zsz/xcrnaKdOUe2ZEtHnq8/euFz7c7MPXPv7m/uua7f9c4bfTz+4B7ih51MXtvcxY6tA1x360rOvrDZ8hy3MKRtaZgv/vllvPZCF79+ZB8Op8LNH1vNasuZ6IPlBPvUA2Q7toG4ICSEhcUHypo1awz/ft111/Hv//7vtLe3T/3tscceo6GhgauvvnrWa3/rt36LzZs38+STT/K7v/u7x7zvBfGJvOKKK/jmN7/Jj3/8Y2666aZZPvVbt27ltttum8q3Hx0dJZFIUFdXN5WSc8R//qGHHuL666+fte0jd0xr1679oIbzvnPuhlrWr6zk89/fytBwsizeOZpEEgUWnWHnQDpaFu9PZBhIwBUrK+hMjJbFY8UMsWSGtf4wY4XxsniOPKzLs6Sqmcf25CEze0U3U9TY1p/mvMZavnruEoSjKnhlWeTy65ay6IwqvrFtB2p2dr6mDhyMxnDLMrF2GCmUF5/u6U5xoM/JmnGdiZGJsvhIfxxBgJWtQXr3lI9xciwFYylWnVnHnu7ycxRL5Ikl8qw8t4G9h8ofa6czRX762H727Rqm663BsnixoNGzZ5RFi4IciGTL0qY0TeeFN/vZfWCCu//4orL3vxv++Z53aO+MlP19eCLNMLBhaSWd+8fK4pGJNPf82xZWrathz47yp2SpZJ4HfrydsZEkN92x+riO0eL0RRQFLrx8ERvObsBmlxZsDwSL2QhKKX0gm7VW6i1OQU7AQtPg4CB33XWXafy55557V9s7ssBcUVEBwNjYGENDQ9x4441lr92wYQOCIEy5Px4rC0LUh0IhvvjFL/Ktb32Lu+66i5tvvplIJMIPf/hDwuEwf/RHfzT12m9/+9s89NBD/OhHP+Kcc84B4LLLLmPt2rW89NJLfPKTn+Sqq65CEASeffZZtmzZwmWXXcaZZ555sob3vuCwy6Tz5k8fVE0nr5nHdaA4T5K9Nk88P08Of0HTywT9TGwuBXWOxNxsQSVbMM8gK6ga+ax5AbGul/LD5zzGefIpi/MUUWXScxcwFwrqnAXAmTmO/1iZbxvzFUBnMoU549l54hYWUOqCbXH6INhKoj6dtkS9xSmGAMLxut+c4IfPqqryr//6r8iyzE033QRMi3wjZ0ebzUYwGDRMO5+LBSHqAT796U8TDAb5wQ9+wLe+9S2cTifnn38+X/rSl+bMp4eSleKPfvQjfvKTn/DEE0/wve99j1wuR3NzM1/84hf57d/+7Q9oFBYWFhYWFgsf0VGqfYpGy58CWlicDtTV1b3r1Xgz/uZv/obt27fzhS98YSpdPJstpbMaOTtCyd3xyGuOlQUj6gE2bdrEpk2b5nzN3Xffzd133132d6fTye/8zu/wO7/zO+/X4VlYWFhYWHwokLwhAMbGytMXLSxOLgLM05PmmLZxgvjHf/xHfvrTn3LHHXfwuc99burvRxxw8nlj04lsNks4XG78MRcLwv3GwsLCwsLC4tRB9pXygsfHx04LowmL0wxROL5/J4jvfve7/Pu//zu33HIL3/jGN2alHB/JMjFKscnlckSjUcPUnLmwRP0CZHw0xQ++9wYP/2wnmXR5PnMskeN/HtjNSr+DgIFzikOROCPgpOpAkSq7oywuCwKrq7wkcjpVDr/hMVSKFWzvkfBg3B7crYXoHRNYUekxvN9tDTmwBSJsHulENcjNHxxN8vP797FC92E36ETrt9loS7tYU+HC5yx/4OSySZzpcxAIOgmEynsYyIpI/foaItUuKuu8ZXFBgIZVVYwrIo0GlnwALc0BxkSBVgMbUSi5ESXRaVpRaegOE652YxdgZYMPRSn/KIaDDj59+0rDbQPE0wX+/emDfPuxfYzFyx/R5fIqv/z1QQRBoKqi/BxIosCVFzSx6Y7VtC6pMNzHxnMauOmO1azeUGsYX7aqikuvWWJ6jBYnH03TeXVzJ9+7+yUO7isviAbYvnWAf/7Wi2zf2v8BH53FQkV0BxAUG8Vikb6+npN9OBYWpxzf+973+N73vsemTZv427/927KO9pWVldTU1LB9+/ay927fvh1d102ddMxYUOk3H3byuSLP/eoALz59aMqacPuWAa6/bSVnXdCEpuk8/3ofjz7XMVUcqdgk1iwNsW8sRVHVWVXpRt09xnikVNwkbhNYc00tB905sqrKooAHwZliRBuFFAgpneXBCtJ6goyWJyh7GR6188JEqelX+zic1VBFXWWCjJ7BKbjo6Any0oEikAfyNPmdaLpOfzxLwCGzvEkgIg8yATwxGGHLRD+3NKxgma+SbK7Io8918NxrvaiqDgfAG7LRdJaXg9kEsiCwWPTS93KErmzJ2cfhkGlaHGLfaBJN01ld6Sa3c4SxWOkYJUlg0dIK+rqjFPIqdYtDdLoVOrKlGyLBp3BWbR3J9nFSyTzVjX5GK528mi5AorQCtXJpBamRJNFYjsoKJ4Qc7IhMC+m2xSHEaJbR8TR+r42KgJPOvpJFZT9QuyhIIK8y1BvD6VKoqffRfWii1KCnN051hRN7g5+OgTiKLHLtJS1cd0krNgOnEFXTeWbHED97rYfU4ev8Tuckt57TyKYzG1Bkka07h7n/VweYjJWOURRgaWuQvqEEmWyRFYtDfPzG5dQf7gnwB398Ee+82cfjD5RsK+sa/dx659opsf+bnz+HA3tHefi+nYwOJwmFXWz62BpWrzcW+xanBl0HJ3jopzsYPGy3+h/ffpW1Z9Rx0x2rCVa4GB6I89B9O+loL7lY/eQ/3+L1F7u55c611NYb38xaLEBOcPOpI0ieCiBGe3s7LS0n1jLTwuK4OO70m+Pje9/7Ht/97ne56aabuPvuu8sE/RFuvPFGvv/97/P000/PsrX8wQ9+gCzLZY6N8yHo+nx9/yzmQ1U1Jiff3zbZsWiG7/7tS0QnjZ0GWtpCjDokBkeMjyMYdhIWBMb2lVtQArgqHVR8tII+3TjuECVaHCFe6UqgG6y92yS4oMHP49vz5AyexArAuS0eEq5BCoKxG8t6pY5tj0aIJYzzy5pW+klG80wOGp+Diio3/rzKpIHFJIDPb0dZHuadmHHhid8mscpn57XxtGHcJgmsr/Hy9mAc1eBTI6JzTrWPQwfGyReMXXE2NPgYOThBOmXsGNO0qoo7f2M9laHyBlsAuq7z//14Gz1jxte52u9gqSKz08CiEsDtUrjr1lWcu77GsFNiLlvkwN5RVq2vNXy6oBY19uwYYsXamlPamlCWRYJBN5FI6rTuCDnXOJ96eC/PPnHA8H2KTeKci5p57YUuNIPJLEoCt31iHede3PJ+HPa75nS+nqGQG2mebtnHS/94lBv/7v4Ttj01Fee7Hz2fLVve4Omnn+Scc87jC1/48gnb/slmIcy3D2LeLFT09Bj6C39+XNsQLv1rBFfle3rvvffeyze+8Q3q6ur4wz/8wzJBHw6HueCCCwCIxWLcfvvtjI+Pz+oou3nzZj7/+c/Pcnc8FqyV+gVCKpk3FfQA/X0xxn1203hkPINrDmvD9FgWn5At6yZ7hKymMhoXDAU9QF6FjmGZnGosyHVAk/Omgh6gPxI3FfQAk91pkgbpRkeYGE0hxnOm8Xgsx1w9UGN5lbE5hGpe1UkKGAp6AA2BdFE1FfQA8WzRVNADpMdSpoIeSufRTNADjMSyOOfYfypdoNagY+8R7A6ZNRvNGwNJssjaM+pN4xanDgOHnxQZUcirDPbGDAU9gKbqDPWbv99igXGCm0+pyUnC4UouvvhSnnnmSXbu3E6hkEdRLNtSC4tdu3YBJZ/7r3zlK2Xxs88+e0rU+/1+fvrTn/KP//iP/OIXvyCZTNLc3Mxf/dVfceedd77rfVui3sLCwsLCwuJds2hRG8FgBZHIBLt372LDhjNO9iFZWFByvzneYtf3/n4zF0Yzqqqq+Nu//dv3vL+ZWM9uLCwsLCwsLN41giBw5plnA/Dyyy+czEOxsJiNKB7fvwXKwj1yCwsLCwsLi5PK5ZdfAcCWLW8Si0VP7sFYWHzIsUT9AiEYcrFqnbFfqSgJnH9JCxeeWY9g8MRIQGdltYeaeh+iSevk6iUh5IMOFNU4p9yn+cmnHbhl45zJapeTS1uqqPOWW2QCBJ0SiiASksrtI6FUiFvfoLDibLdhXJZh0aVuVlxk7sixpCVA1YpK0/bQ4TXVVHjt2Ezii2q8OG0SHrvxOajz2HBnioQNLDQBAm4bulMhXGOcs+6yS/hlkbpG4zFIdglpWZjX242LXDVN5/UXujjLbUMwKX5YFXJRHXYjm42x0sXOV3tJp42rC3oH4/z88XYiJsXEx0suV+TXj+xjz44hw7ha1Hjx6UNseaUHq4b/+Nh4TiMud7mlLUBNvY+N5zUa2r1Cqag8HstaefUW89Lc3Epb2xI0TeXpp5862YdjYVHiQ7pSb7nfnAA+CPebI+zfPcIjP9vF2EjJznHx8jC33LmWmrqSUOzqi/HTR/fRddjCrj7owJspMnz4/0MVLlweG/09UQB8YRdynZfOwQQAXo+NxrMcDIYjIIBDsCPGKtnel0YHnIrI8moHfdlJNHTsksimtmaubWlEFkUKqsbD+4b5xe4BskUNWYT1jU4GszGyqooALA97ScoRsnoBAZ1lgQBZEqS1ktCsVIMcfLrI6EDp/xef6UVdmiNSLBUK18he4lt0Bg+V/r+uyo0ODI2WrkFVyEmgqDHaWWpf7q3zkmsNcHCi5GoTdNsI++wcHCqN2e9UqA052T9QOkduu0RT2M3+gTg64FJElrvtdHVG0DQdRRFpag2yK5ahoOrIIiyt89MxnCBX1BAFWFntZaxjkmymWLqpqvES6YqSTpXG1LwoyOREhsRh8VyxtpoeRWTicHxNU4DfunwRjRWlm5zOA+M8dN9Ohg5fx3Ctl3ith4OHxXmd10Ywp9F/+DoG/XYCXvvUPAi6FRoUib7DzkAer53rbl3B2Rc2IwgCyXSBh54+yEtb+tF1sNskbrhsEVdf2Iwsn5gvuG1v9vP4A7uJRUtjXr6mmps/tobKw4W7My0zAZpag9z6ibU0thj3QpiLheBecSKYb5zpVJ4nH9rLGy91o+vgcMpcc/MKzr+0FUkSKeRVnnvyAC/8+iDFgoYkCTS3habsX0VR4PxLW7nm5uU4XSevCPJ0vp4fiPtNJMlt//3mCduempzkno9smGqe8+abr/Od7/w9TqeLf/7nf8PtNi/GXwgshPlmud+Yo6fH0V/7+nFtQzj/LxFc766b66mAJepPAB+kqAcoFjVefq6DUNjFOgMnEl3XefXtQba91E3v/jGMrnBjSwA96KB9KEnB4EurocGDe4ObdwaKJHPljjW1XjsbGp18bGULIUe56854KsdPd/fSm51gLFO+6uuSJVbXOnF4k0wUEmVxSRAJTlYQUzQGtPLVQhGBhmKA9NsiHb1RwzEurvehexV2TGZQtfIXtFS68ToVDg4lyBbKfThrg05q7TKjXRESBo41Qb+dQEuA/niO8US5647HIbPSbUPrjDI2VD5Gm02iYUkFfQE7hybKbTQlUeD69bVIhyJs22LcFKh+eZhc0EFnR+mG42ia630EdZ2B/eMU8uVjbGwJsOaSVh7d3GnoLFQddvHZj66htdG4CdmxEI9m+cl/baXzwERZTJJFLr16MaPDCXa9U756Lwhw9oXN3PEbG97VPhfCj/KJ4FjHOdAbZduWAS65ejFeA5esibEUTz68l+5Dk4YuW26vjY98av2czkjvJ6fz9TwdRL2maXzlK/+H/v5ebr75dj72sU+csH2dDBbCfLNEvTl6Zhz99W8c1zaE8/4CwbnwRL3lfrMAkWWRy+bo4ikIAheeWc9j33/LUOwC9HVHUZQKQ0EP0N+fJNDsNRT0AEOJHDe6agwFPUDYbWd5tZO39xuncaSLKplikZSBoAdQdY1MKMVAzDhNREOnX4wy2WP+pXZoII7Q5DMU9ADdYymaK92Ggh5gKJKhxucwFPQAkVgOX1EzFPQAyWyRgiQxaSDoAfJ5lcl0nkMmLdZVTeeFncOEdhun4wAM7B8nW+81FPQAPQNxbLJoKOihNA8y/iFTq9CR8TT7OiaPS9SPDCUMBT2U0m22vtZDPGp8DnUd3ny5h9s/td7QN9/i2KhvClDfFDCNV1S6qar2sn3LgGE8lciz653BkybqLY6TE9x8Sk1GGR+f/b10xRVX8/3v/zsPP/wAK1eupqqqmsrKStOmOxYW7ysf0nlniXoLCwsLC4vTGV1DTUVP3PYE+NLmXgRh+gmirkNCqSQ/2sPv/e13ca29nHt/6/Kp1XwLC4v3H0vUW1hYWFhYnM6c4OZTZnjPu43JX/0LhfE+1IhxMbyFxfuPcAJW6hfmk+EP5/MJCwsLCwsLixOK7AvjXn0pAMkdzzIxMX5yD8jiw4lAqfnU8fxbmJreEvWnKz0DcSrrjO0joZRD21xvbg/pcSsEEEzntUMW0ZUUqm6ck19UNeKjWeySsT2kACgFO07R3FHDLToJKMYWmQB1Ljf1tS7TeDjkpNprnPMP4HcreD3Gln8ATpuEzaWY5nIrsognIGAzcYcRBLB5FZwu8314ww6CHvNzUFXhpMLEIhOgqt5NXZ35OQj67LhNbAsBnG4Fu1sxtEIFsCkiuViWokndwbEwOhzHY1CceYSG5uCUA44RVbVe+rojpvGR8TR7Drx38ZDNFdmyYwhVff8K4vbtGmFi7IMrpn8v1Db4kBXjuSyKAg3NgQ/2gCwWJK5VF6FUNqEXC9xzz3+jmtQMWVhYnHgs95sTwAftfjMXyVSeXz59iJe39oOus6LGS6wnSipRKjhVbBJXXL+US69ejKxI7Nw/xs8eb2f0sPuKKAosbg7QOxgnm1OpXRxkMmyjNzFd8Lq+wUmwdpSinCGkeLgyvJoWV+VUfM+Bce57fD/DY2m8IRsVZ3k5mJ0uFq1zuigMi/T0Z/C4BS64WCZqH0c77L3ulZykYh52DWVRRFhT72a4MEnh8A2ER7axIezE7SwJufFdlby+OUMqUyrqddgkmuq9HOqJomnQuDjIYEFj8rBdpCwJLG7ysz+bIavptLkcaLECQ4ddPwRgWb2Pgck0iUyRBq8dX05lYHB6DIsW+yiuyhC3Z3GrTrQeH/u7p+dAU9hFUdUYjGTxySJrdIGBvaNThcuhajfqmR4OOXIogkhj3k97R4KCWnpBwK1QUeNmTzKNpMP5RZH4thGyh8fodCmsuMaPvn4SRFA7wux4MU/syHWWRVob/XT1RSkUddoq3ehjKSbHp69z3cpKdqKTKGo0+ey400UGD9tJQsk9KNsXIzmZIVzlZtPH1rByrXGvBCMG+2I89NOddB2awGaXaGgO0NMxiXp4jMEKFzd/bDWrN9RRLGq89MwhnnviALnDxdkut0J1rZeuQ5MIApx1fhPX3bZqyr0lly/yxOYunn6lh2JRY92KSj5+4zIqQ65jcq/QdZ03tg3xwFMHiSVy1Fa5ufOm5axcXHHMY5yPseEkD/98J+27R5GVUoH75dctRbEZ3+y+W060S8fkeIpHf76b3dunUydaF1dwy51r5iy0fb9ZCG4k75WF6H4zH2oywsRj36HFq7Bp0y3cdddvfWD7PhEshPlmud+Yo2cn0N+5+7i2IWz8CoLjxP0WfFBYov4EcCqIel3XeeGNPh5+poNUZraTidMmsdTvwO9S2PTR1QRCs1d2C0WNp1/uZvveUZKZAmMTR1naCdCwvoqYV6S5KUHeUe6isMRdw0apjcef6mXb3tGyeP1yH1qrhJxR2LMnjq7PXhpubVJYfkYRVVXY0pUhf9T3aIVToblKosKlU+2LIIqzXXnUrELnKyGiYwIj42kSqdmuOYoiUre0gpQoMKFoDOdmnyMRnY1BP+lIlnxBo3+y3GJyVciFkM7jWqUz4i+32QxlgkQ7FWySRPtgueNNi1OhNpZFbHSwsyKLetTqeFC044o4kBR56oZjVlwQ2DBRxOtRcV+WRHMe5VhTkEhsCzM5AJFolkh8tqOMJMKqag9kVfq8NrqPakAl6Dqrw270WA5nujDl8z+TFWuqueXOtVRUGjcJA8ikC1Pe6Ee78oTCLjxeO8tXV3PZdUtQlNniNhbN8Pj9e4hGMgz1x6ZuYo7gcMpcs2kF9ko39z/ZTiQ2e4yyLHLtRS1suqqN6iqf6Y9y31CCex/Zx6HD/RpmcsbqKj52w3JCAfOnRPORzxV55vF2Xnq2A/Wo/QcrnGz66JoT4iTzfomP9j2jPPtEO+de3MIZ5zaesO2+VxaCyHqvnI6iHiDd/gbhg8+hKDKf+9z/5uKLL/1A9388LIT5Zol6c/TsBPr2/3tc2xDW//GCFPXWjDhNyBdU7n10f5mgB8jkVXaMpbj5k+vKBD2UVnVvuGwRoiiUC3oAHfq3jbKuxVjQAxxMDfPoGwcNBT3AwP44SofI7t2JMkEP0NVbYN9bTl7pKBf0ABOZAv1jBWoDY2WCHkByFFhy5Qi9g/EyQQ9QKGj07Bkj4qRM0ANoCLwViSPKoqGgB9gzmcZ/rmwo6AEmnRFC9RgKeoDuTIGxFT62hcsFPUBEy5EO59meTpcJeoCIrrM5JOK8PlIu6AEUFe/ZI0Tj5YIeQNVg51CSkXpvmaAH0AWBXRNp/LpuKOihlEby+ovdhrEjHNg3ymsvdBnabE6OpykWNa7etLxM0AP4A05u++Q6ug5OlAl6gGymyCM/38W9j+wtE/RQ6uHw+OZO+kxsRI/w5ItdhoIe4O3do7z2zuCc75+Pvp4om586WCboASITGR66b+dxbf/9ZtmqKv7gjy86JQS9xcLEXr+Ua6+9AYDvf//f6eg4dJKPyMLi9Mdyv7GwsLCwsDidOcE+9ceCmoxy9sXnMjDQx759e/l//+9bfP3r36K6+thT+Cws3iuCWaHYaY4l6i0sLCwsLE5nTrRP/bEgwJdf7EeVlrC6epLR0WH+9m+/wde//i38/sAHeywWHzI+vJaWlqi3sLCwsLA4nfmAfOqNEASRP/joH/G97/0jo6Mj3H333/AXf/ENnE5z1y4LC4v3hpVTf5ogCgIOu7mjhk0RkU2sFwFUTUeax5GjWJz7HlAQ536/YmINeQSbTZxzQsqijKaa70PPSrjmGIMkCThk82MQdB2bNPcxKsbp9lP43HPXnYfcIGD+GhsitjlOgt8pImE+RkEXcfjM46IITs88KxBucwvOEnOPMSMy5yKHooiGueZHECUBu918rtnsEi6n+TFKAhQMaktm4p7j/QAu5/GtdzgcsqkVKjCnzSlANlMgbVAbciI5XovNyETasG7ig0LTdKIm9S8WpxY+n5+vfvUv8Hr99PR0cffdf0MmY107i/cJgdKP3fH8W5gL9Zb7zYngVHC/AYjGczz41AHe2D7EzKt65ppqPnr9MlM3j129UX7wfAfD0QzLw26GD06Sz097C4cbvaQbXPQksmxscuKpHkaVposUbTkXk/uC7OtOsbTKQ3YoQSwybYHpdsnUVnro6I1SV+Ml7pDoT0y/324TWL7GQ1cuRsjpwCGJ9Camz6cILAn66YkncSoSF7aKBAPTBbm6rpPaHmTbYynUokbFoiB7R5LM1Bs1y0IM+yQiuSJLK9y0jycozNCVLS4H9rRG32iSZfU+esZSpHPT56DWIdMczzPUGaF5XYjJM/Mk7NPCMWhTWFvpIK5N4tECvP6KQkffdKGnzy3w0etklMAYNty81e1k29D0OZARaFYDHOhI4nUoeKoc7E9OFy2Lgs5lq2RE/zgCAo0uN0ltcpa/vFwMcjBaJK0WqI752fdMjOKMWtPm1W5s61Qmi2lqtQq2vZEnnZk+CVVhJ3LITlcszVqPE14fJDs5fR19fjvBChc9nRFWrKnm5o+vIVw17S+fyBX58Y4+fn1wlCa3g8oDUeIHpwtuZUWkqTVIb1eEYIWLWz6+lmWrqjAiFs3wxAN7eOfN/ll/X39WPTd+ZDU2l8yjz3aw+fU+1BkXelGlC8YzxCMZrrt1JRddsQjRwCFC03Re3trPQ08fIpmevo6VIScfu3EZ61cYH9e7Yag/xkP37aTzwMTU3yRJ4MIr2rjqpmU4HOXCXtd13nqtl1/9ci+qpnPtzSs49+IW0xuE9+LSMTqc4OH7dnFg7ygbzm7gxjtW4Q+Y9zI4mky6wNOP7uPVF7qoa/Bzy51raWkLHfP73wtHj7PjwDgP37eT0aEEF13ZxlU3LsfuWJgPnk9X95sjqMlJ7vnIBqqrq+nq6uRb3/o6qVSSJUuW8ZWvfO2UXLG33G8WNnpuEnZ/+/g2svpLCPb393vt/cAS9SeAU0XUH+Fgd4T7HtuPqurcedNylpv84I7Hc/zoxU5eP6pxT9CtUG+TGRtKULmxmncmE7MEstsmceYiEd03jNBbwzu7MxRmNO5RJJEVYReDByZoqffTP5wgk51Wl6Io0LooyIF0nqZFTqLODNH87FXJtoCP8UyWgM1GplhkNJOdFV8SdLOxJYk0qbHvYYH+zviseLjGQ9FvJyKCsCRAe2z2qlDYZSPkVBiJZ2kRbRzojc1af/Y4ZBoqXPQMJdggSwzvHZu1umyzSzRcGKJ/aYKNNT5UKUpRnzFGBOREJU8+V+Dys+w0Lp6kyOwxioUKfrVPx5FxMtGjMnaUY82iWg+Tik5Fpc6ilgRRdbajS9jmIWDTKagao0knPanZ5yAoO5HabYz35mi6zEkvsx1tPJIN+4SffXvyNCzyszeSQp3xdeCURdZqEqlXB2hsCdDXHaUw42ZPlkUuvmoxl12/hBd6J/nxjn4SuRnnQIC1LifaqwNUh1zEIhli0dnXcfX6WjZ9bDWhsLFFZtfBCR766Q50HW65cy1ty8Kz4gMjSe57dB8jI0kaFIm+Q7OLAQMhJzfdsZp1Z9Ybbj+ZLvDIM4d4fdsg117cyjUXt6DM8UTrvbBtSz+P37+b6joft3x8DVW1xk3h+nuiPPTTHfQc5TxU3+Tn1jvX0mLgn/9uxEc2W+CZx9p55bmOqV4BAHa7zBU3LOXiqxbP+TRP13W2vlq64UjOuCkXBDjj3EZu+MgqvL73bgM6F0fG2dVZEvPbtw7MivsCDm68fRUbF6Bbz4dJ1AOzhP3ixUv5yle+hstlbpF7MrBE/cJGz03C3n88vo2s/KIl6j+snGqiHkorkbquz/mh/9I9b9M3bv4ItGVFkP1zjGuDy8m+LmN7R4ANYTed+807fbasDXAwaL7/apeDkXTWNG5DIPDLCHPN4IlrmxifIxVjOTa6R5Km8Qslib49xjadAOf/TiWZpgnTeIM9RFE0d51ITwb50S9NwwR8EksuN98+QCLlIKOW2z8eodruYSRnPkbbZAO7hsyvw+VpGNg6ZBoPX9LIq5jvf40ok9/cZxqvqHTz1W9dZRo/kuIxVzrL//3zZxkdNh/j737xfJauNF99LxS1Ey7mZ1IsqMgGFp5T+y+o/OkfPGY6lwUB/uLvr5tqvHWEdyM+fv7Dd9j6aq9p/Jqbl3PVjctN49u29HPvf71lGm9dXMEf/MlFcx7De+XIOP/sjx6j38SKFOC3/uAcVq2vfV+O4f3iwybqAbq7O/nmN0vCvrm5lT/5kz8jEAielOMzwhL1Cxs9Nwn7v3N8G1n+RwtS1Fsz4jRFFIV5P/Dz5cPOly47f3ye7c+Tm63Ot31Nn1PQH9M25tnA/Pe888SF49v+sdxzH+95nvf98/ymqfNMhPnGoM2zA1EU5hT0pW3Mt4+54++noAfmFPQA+jxzWddLrzketHk+DPPG5z3H77/4Od7rbHFq0NKyiK997a+mcuz/8i//lKGh4+sNYWFhYYl6CwsLCwsLiw+Y5uZWvv71b1JVVcPY2Ch/9Vd/xsGD7Sf7sCxOBwShlP95PP8WqM/9wqwssrCwsLCwsDg2TkLzqSOoySjj42OGMUEQ+YM/+AL//u/fpbe3h7/4i6/y8Y9/inPPvWDW6yorKxGP23fc4kPFh3S+WKJ+gaLr+knvmDbf3t/v+LE8ZzrufSwAhHlGMe85mGceCfOkEB0/x38VTvZn4Xg5puM/3iEe94dhvlSzhX0NTmtORvOpIwjwpc29CEK/6Uv0pitJjD5JfugQf/3df8P5qzdwrb4EQZRQk1F+8puXzcrJt7CwMMYqlD0BfNCFskfs3IIVLjZ9dA3hqnfnHDARzfCLJ9rpGkkihF10HuVX7bbLLLdJpHqjCOfXs21ydgGiTRJZ7nUy3JegIehi/2B8Vn69KMDKag+R7ih1lR66+mMUCrPzbVtbg/QWi9QvdjIkJkkXZxdaNnm8jEUEQm6ZlJAkmpvtHFPncOLbmcUeVcnnVMZHZ48hVOlk3S0O1Dp4aU+A9rHZBbd+u0ydz8F4LEu1JnFoYLazjEORaGny0pvOsH5SZXDnyKycZ0kSaD4vzNiaDOvqXGSYRGP2GN1amFcPqZzfYiPon0A9qpg0rPhZHEjw1p4Q9z4nEknNLuhtqnIhVatUVml4Ayni6uxi1grFxeV1OiIaD3c46EvPPgde2Y5T9TKeKFAbhoHs7KJmhyhT5/QylMygx8LsHEjPkm02SWC5z81Id4zVmsDQntFZrimCAC2LKxgdSaKcXcMOrUhWnX0OVvpdFNonaVAUot1R0snZ17G2yU/UJlFd5+VjNyyjMjTb3m6wP8bD9+1E03RuvXMt9U0BjBgejPPwfTs5dFRhtsutEGwNMpQp8JHrl3H22lO3Rf3BfWM8fN9ORoaOcjmqcnPzx9ewYk35sb+bgr5kIseTD+1lyys9s+eyLHLxlW1cecMyQ1tIXdfZ8koPTz2yj8pqDwO9MXLZ2XN51boaNn1sDRWV74+LyZFxtu8b5sGf7KD9qOJ1p0vhmptXcP6lrfPWX5xqnO6FsseKrmukd71IatdmAJTqVvwXfhS9mCsrtH2/sQplFzZ6PgId/3J8G2n7AwTbqVO8faxYov4E8EGJ+lgkw+MP7GHblukVD1kWueTqxVxx/VJsczTsgZLDxlMvdfPki13kZ4jsxrYgg6pGJJVnTdhNdvsQucS0wAysrqS/yc1AKsfyoJvoYIpofFqc1QQc2BWJnrEUrWEXwmSWiRkiO+CzE/I76OyLURV2oQbsdM6wNvS4JNpWOTmUjhGw23HqLnYPTsftksC6JgfdqQgOUWJRRGHomaGphUNBLDlvDPbFUFWdDVcHCV0YQZCnp/bQcC3P7BaJZVWWV3romkyRnvFlvcTjpBgpMDSZZkmjj369QKQwbd+4WJCo7Ygz2henYVmA1Lk6Eff0MVY77CyvkIlrUdyCl/YBB3tHp23/qlwS16+U0JRxnKKTJT6RCve0AM1kFX75QhNPbM2W7DRb7XTpsanVU1mAc1qdpOQJBOC8Sg8bq0eQxNIYdA3eGqrlyd48GbVIvS3EnoHsLJG9rNKFZk8TK2Ro9QQZziRJq9PX2a8FGBpy0hfNsTToJjmcZjI6PYZ6h0xjLMdQV5Taeh9FVWNshuOMM+xEPaeWXckMtW47VZECwzN86l02icV+B/3t47i9dtwNPvbPcB5SZJFrLm7h+ktbKeZVnnpkH2+82D1V/CgIcO4lrVx3ywpcbhtG7Hh7gMd+sZt4NMui1dXsm0iTmmGzuWxRkE/ctJz6GmNbyZONqmq8urmTpx/dj6bpXHnDsjmtJt+L+OjtivDQfTvp64qwfHU1N39sDZU1nnlfewS310ZllYeezknCVR5u/vgalq9+fwXX0ePcvX2IR3++i8hEmrMvbOb6W1fi9trn39ApiCXqZ5Pr20f89V+iF3KILh+e9Vfx09/fZIn6o7BEvTl6PgJd/3p8G2n9vCXqP6x8EKJ+cjzF3//V8+RnNESaSbjKzVe+aW4LCPD1f36dvqNWAY+gKCJLgk4G95rkPooCgStb2N4bN4wDnFXr5cAuc/vHpSvCvD2WRDN5zr9ssZd96Tx5ExeOZo+M/+VhcnFji0qXW+HiLyoIAWMbTLUo8czWJvab2HjKAqyu8LB93NgaUdB1rmlS6PKZj3GVp5IXOxLoJmO8fYWT61f0IorGPxQH+iv59psKOd34Ole4JL5+xSRuu/EYMwWF//diPV3RjGFcEQXOXuzmQMz4OqNDaKyebbvNrUov9drp2mruVFF3Tj37u6KmTiSLajwMjKfJmfxYVle4kAfipJLGHVVdHht/+rdXGTZvAsjnivzrPe+wuytqGJdEgT/6rY2sNPB9P1VIxLNoqo4/OHdTqPcqPnRdZ7AvZvrkA2DHWwP85D+3mrryrNlYyyd/56w5ve1PFEbjLBRUJsZS1NT53vf9v59Yor6cYnSU2Cs/R42NoWsqv3/nHfzmb34GSZrbRepEYYn6hc2HWdRbM2KBkM0WTQU9UNbUx4hoImcaKxQ01Lh5XNd0Chnz/QMU5zg+gKyqmwp6gFRKNxX0AImUZiroAdKpAlLAPC7JKqmC+TEWdZircbkuCOSCc98DT6QxFfQAebVoKugBfL6sqaAHiKSLpoIewKkUSMxxHQqaTqZo7imPcAzXsTiPrWAsN6e1YKqomQp6gHg8ZyroAdLJPGrB/P02u0x6jmNUNZ3EHNs/FfD6HPMK+uNBEIQ5BT1AIpad02azUNA+EEFvhqJIC17QWxgjB6oIXft7ONo2AvDUU0/wN3/zl0xMmPc9sbCYQqBUKHs8/xZWFt8Ulqi3sLCwsLCwOKUQZBu+c2/Be+b12O122tv38cd//EVefPH5Y+rfYfFhRjh+Ub9AVb0l6i0sLCwsLCxOSeyNK/jKV/6ctrYlZDJp/uM//oW///u7mZw8ORadFhanMpaot7CwsLCwsDhlqays4q/+6pvceeddyLLMtm1v8Sd/8kVeeeUla9XewpjjbT61QLF86hcIlVUeLr6yjVc2d5a1c3e6FK69ZYXpewsFlc1PHqTNqXCoqJLMzs6ZFkWBS85uYFW9jyce2ENkojyzvHZFJdlUgQavnX6D3Pymeg8Rr0y1FmDkULQsHq7xkLeJLKn1ctCgWDccshFsgo2ak+19aTR99ofK5xBZXKfg9Ncz9tQQam52TrXskqi4tpb9vdBWN4Ykz86t13WBodEaasM6qaLEZGr2ORDQuWKtA583RlGw0TlWnld+0VKZxdVpnEk3HfHywuhVQSdnVyWpcdl5pqP8HC0KKNSGdIbi1dR4RhCOuqUuFu0UdRe/sd7Oz3bmyB+VNh5ySPzp+T48mo+U0IMuHDVGTSCTr+RzZxf4n7dFBpOzNyCi85mNdtY15Lh3n41D8fK88tpIgOxwniafnV6DGouGOg8jXpmAVkV0R3nBcKDGQ8Eh09bkp6O3vNjW53cg13hornTR1z5RlnvvdirccnUb6niG5548QCE/+zooNonLrl2C08T95gg3XdnGfY/uZ9RgLp+1toblbaE53z8XA71Rnn5sP2ec28jaM+rL4hNjKZ58aC+LloY59+KWMovFI9aSgZCTy65ZgqzMLv7L5oo8vrmTYkFj01VtuEwKgs3QdZ3X3hlk5/4xbrlqCbXv0vIWoKdzkj07hmloDtDfEy2L+1o9jK2383L/EBfW15T57EfjWX7560PUVbu58oJm5KMK+tLZAo8+24Ekidx0+SIc8zh3WRwnJ7H51PEys3nV2WefR2NjMz/+8f/Q29vLd77zD2ze/Bwf//in8Plm11dYDassPoxY7jcngA/Sp36mH7cgMK+d2+5tgzz6i91MHnZ8cboUKhYF2TuSRNNhSUuAT2xaQWNtyeKvkFd5/qkDbH7qIMWCRkWDj2LQSc9hIS6KAq2LgrSn86TyKn6fjWCdm/2R6fGv9LvI7Z8kOZnD4ZQJLwqybzQ55WXfUuUmk1MZiWWxKQIrVnvoLsYpaCURWuNykUvb6BjPIQo66xtdjObjU172IZuduh6NkddKX/Q1l1TTV6cTzZdEqleRubBFIVwxgiBAIhHi9S6Z4VSpwNQuidQ7A2zvzVDUYFmNxPLFaSbVkggVEfAXq3h+JyRzOi0VEpevy5IWp38UfUKIHWNZooUCIbvM9c12FNt0EZdWCPHwboVDkwU8isimVTYU5zj6YS97n+RhSaCAzxFF10QmM5WM5+Noh73sRd3Fm11+NnflEIDf2+Dn+sV5ZKkk5FVNIVlUyYp9AOTyIVLFHKpwWMTqMgeGq7lne4GCBhc1KnxyQwK77bDQ1kV6IlX8oD1LuqjhzzoRdtnp7ipdZ0GA1kVBDmXyJPIaPo+NcKObvTPm+UqvE9tbI2SGU9icMhXLw7QPJKaEekONh0JRY2Q8jSSLNCyroH1i2vWm1u/AX1AZ6o0jCHDRWQ3cdvViPIcFe2QizWP372bn2yWnndUbatn0sTWEKmZ72RshyyIut4Of/HIXT77QRaGoUV/j4c6blrN80XsT9OlUnicf2ssbL3VPFZAuWVHJLXeupbrWSyGv8tyv2nnh6UMUDxfy1jf5ufUT62hpC6Fp+pRdZSZduo4VlW42fWw1q9bVAvDm9iHuf/IA0cM3VF6PjduvWcIFZ9QZNqg62qWjZyDOTx/dN3VDJUkCV57fxE1XtB2TcE7Eczzx4B7efr13aoyNLQGS8RyRyQyKSyZ0XQ37hBTq4Re0+X3ctXIJrX4vRVXjmVd6ePz5TnKHb8hqKt3cedNyVi2pQNd1Xn17kAd/fXCqWDngs3PHdUs5Z32t6XEtBDeS98oH4n4zHuXGv7v/fd3H+4no8iHMWAXRNZXMwa1k9r+OrmmINgfudVdgb1gOcNwNqxbCfLPcb8zRC1EY/P7xbaTuswhK4EQczgeKJepPAB908ymAnW8PEKxw0dhibrn0/X9+nf27Rgxj4RoPF9ywlIvObTKMT46n+OWvDrC1fRzVwMnE7VKoXlfJO6kMObX8S88hi2y0OzkwmCCZLXdbkUSBNYv9jHkSRHLGTiRLfUFixSwjaWN7xkV2N6qg05M19qxp8jmpcMpsGzG28axwOFhVJzMpDRn2ynSKNqqoQHMOlzWWApAFmTolSNg7jka5646ASCRWg6rEKGDsLNTkCIGYIo/x/NHzlZxZ4cbrMB5jvuikJxMlx4Tx+1UPasFLbWDQsO5HUx389Ik6Xn0+Oqux1BFcDpna9VVsy2TIGPy42SSR85HpOxQlmS4/B6IosHRJBR35IuMm7ktr633cdVErLfXGTiYH942haTrLVlUZxo2Y+aM8NJqkvTPC+Rvr3nNjokP7x/jRf2wta54FJeF88VVtbN86QGSifK4KApx9UTO9HRGGBowtYVeurWbcLnNghh/8TBY1+vk/nz0Du222MJ85zgeePMATmzsNHWv8Xjv/+9MbTM8xwL5dw9z7X2+RzZR/XmVZpPncavY1FIkVjOY6XFldx/7NowyPGc/VM9fWMBnJ0NlnbJe6pCXA//nsmWWr+keP81QVWe8Vy9LyvVOMDBN//ZcUI8MA2JtW4T3rxuNuWLUQ5psl6s3Ri1EY/J/j20jdbyPIgRNxOB8o1jPPBYrRY/+jObjX3E99fDjJ0mbzG4JQ2E1ax1DQA6TSBdLohoIeIFvUSDl0Q0EPpe2mtYKpoAeYLGQYz5jbN/bkUsxlvtgbzxDLmqdpTGSzYBfQTRweM1oery9LxGSMRb2I31E0FPQAOhoBT47hnLlVaLyYw2EzvyF02ON4HeZC1CZnyM9oUnU0gpSkxolpXJSypIaLhoIeIJ0tkkE3FPQAeVUjIWMo6AE0TSeWLTCeMr/OQ4ncnGJzyYpK09ixUBlylXWqfbcM9MYMBT2Aquoc2j9uKOgBdB0O7R+f1ZDtaA7sH2fcZ948qbMvRiarlon6mezvmDS1oIwlcgyOJOc8z31dUUNBD1AsaqQUzVDQQ6kP3L7BCCMmgh5g36EJUibzBOBgd5RiUTMU9RYWRsjBGoLX/C7pPS+T2vMiud49FEa7ca2+BNhwsg/PwuIDx/r2tLCwsLCwsFiQCJKMe+1lBK/5XeRAFVo2RWLLY/zgB/9FImH8lNbidEcotZo/nn+WpaWFhYWFhYWFxQePEqojeO3ncK2+GEEQePvtrfzxH3+Bt9/eerIPzeJkcNw+9QuThXvkFhYWFhYWFhaHESQZz7or8V/yCWpqaojFovzDP9zNv/3bd0mlkif78Cws3ncWlKh//PHHue2221i7di3nnHMOX/rSlxgYGDjm96uqyn333ccdd9zBhg0b2LBhAzfddBP/8i//8j4e9cljrrz72gYf+3YOl1kKHmFoII5T17EdZbd3hIDXTiBZxG0S99gk3KpO0MR60CaL+IoSVQ6HYVwUICw4aHCZ2/E1e/20eM1zhBcH3SyusJs+RKv3OgkoNiSTj4FfdlDvElAE4zxmh2gj7HAgY5wLLSJR6XDhEo3zuQUEJBwoeE3HkCv4iGbN88FzRRd2IYBhpS8g6z7QnKAbn4Vi0Y2tyobNZnwdfV4bLhd4TZxTXIqEt1ogGDA+B7Is4vHYqQ06DeOCAOcsCRsf/GF2vj3A9q3mn/OOA+O8/mKX+Vzuj/Hi04coFIwrMCYn0jz3qwOkTfL+U8k8YyMJvH7juarYpJKrVKXxXBUlgVXra2mZw0azocnHojrzedBS62Xv9kFTT+6+7gh+RUSWjK9zVYWLpjm2H03lGVBV3F7jz6vdIePOCoTtJtdZEPDa7NSY2GcKApyxuopVSypMj2HdikoUeUH9JFmcosjBGv7kT/6cG2+8GRB4+eUX+OM//hLbt79zko/M4gPjuNNvFiYLxv3mJz/5CX/913/Nxo0b2bRpE5FIhHvuuQebzcYDDzwwb5V7oVDgf//v/83LL7/M9ddfz8aNGxFFkYGBASYnJ/mbv/mb93xsJ8P95lg51D7GI/ftmnLd8PrshMJuejpL9oy1DT5uuXMtbUtLwiqTLvDrR/fx2gtdaKqOP+xCqfdx6PD7ZVlkaY2X0b2jFPMqdr8d6cJ6dqQyaHpJjK/2uojvGieTLKAoIrXLwuwfS1I4XIy5rNpDeiBBPJotWWSeH6LHliajlkRXo8tDsk9nYLhUJLtyhYeII00sXyqyq3Y6KWbtHBwrFaCuqHagKhnGs6XXhxw21jcKyO6S5SU5P/sHnPTGS0V8bkXmzEYbdu8wCGAXnOSKDgayUQAUQeLssJezqoeRJY1M3sGO8SBd6VJcQGCFt5LzanWcik6hKNAV0xkrjKAfVtdBqZK2gIJD0VA12DehcDAxinq4tNcr+inoBbJ6GtCpsQWR5DgqJWEp6V72RmQOJUurS5dXVXJFnYhNKsWLqo2EmiUvDgFQKPhJFnSKwmHrUd2OW/JgV8ZL30+qG1BAKo1B12V29tfwr1sL5DUIoFA9qNB5sBSXJYHWVUE6AmlygoZTkqkQA+weTqHppWzDdQ0uNN8YOSGHpIlU9wU5+E6cwmE7x+YmP0PojKULCMDyeh+94ylSudI5WFbn47cvb2NRtcdw7o4MJXj4vp0c3Fe6jm3Lwtx651pqDhd7xiIZHrt/95Tgr2vwcesn1tG6pAJZFrEpCvf9YCuvbi4J/lDYxaaPrmb1hrrD50xl81MH2fzUQQp5FbfHxnW3ruTsC5sRRQFN03njpW6eengv6VQBxSbS2BKkpzOCerhwuGlRkOhEmngshyBCa1sFA30xcocLxNuWhbnlzrXU1vvQdZ133ujj8Qf3kIiV5m5ltQdZERnqL32+qpeEmNBhPFqay0GfnWpFYri9ZJfa2Brk1jvX0tRaKnLPZgo881g7Lz17CF0Hf5UbsdpD12ELWrtN4vpLW7n6ohZDwaxqOk9uG+QXr/WQyau4JIH1osTQntGpm6SWthDjoymSiRySTaTymloOOKddr5rdXiY6VUbHcwjorKnwMN4XmyqKbWnw8YlNK1jU6Adg255Rfv7EfsYjpTFWV7j4+E3LWLPMvCB6IbiRvFc+EPebiTg3f/fX7+s+TiXUZJR/unE14XAlnZ0d/OQnP2B0tGQccf75F3HbbR/B4TBeaKisrMRmk0/5+Wa535ijF2Mwdu/xbaTykwiy/8Qc0AfIghD1kUiEyy+/nNbWVn7xi18gy6VVw127dnHHHXdw++23881vfnPObfzzP/8z//Zv/8Z//ud/ctFFF53Q4zuVRT0w5Y+9Z/sQvZ0R8vnyFcsNZzeweEWYJ3+5j6SB9WD14hCCz066N0Z8vNzhwrckSH5FiEJfkom+8uKkQMiJt96LGs8x2FNuaecO2Kg5x0cmJbBvf/ljUodNZPlaNwVBZJtBcypZhPWNTqpDebzBERBnj1HXoZCsJpmRCVWMglTuwuFXKhB1OL9mEp+BheR4KkBHzMOZVQpV7nKXkGRWoidZoMZlo8JVHk/lJHaMayQLBeJatCyuIFPt8DKagTcmIuhHPWNwiCJ3NtfS6FFJC90gzP6x0TWBbCGMqgm47JOIooGTiVpBLO/g2y/r9MbL50Gz6sI9ITBWWWBMKF+5DttdOHUXNn+cuFh+HT15B+4DbmIFkfZIuRuMxy7RWu3lkpVVXLyyytB7XdN0Hn9gN68+31nmyiNKAhdc2orX7+DZJ9rJ54zn8pIVlTz58N4p8TyTZauqOOO8Rp56eN9U/4aZNDQHuPSaxTz/5EEGDewXgyEnwbCLbKbAYF+5RaXbY6Ou0c85FzWz/qyGsng2W+DpR/fT1x2l+9BEmWONKIvUrqoCUWBkT+nmeSaCAGed30R9c4BfP7KPdKp8LtcsrSDQ6Oe265YSMnnCcHAozr/++iD9Bg26GpwKizJF8om8oQ2nu8aJ8/IwyYRI+8Hyz6vbJrHSY+fc1dVcdGZ92XUuFFSeeqkbSRK4+sIW5HlW6C1Rf3wsdJ/698JMb3u9WCC99xUyHaWVesnpxb3xGmxVzbPec8Tfvr6+9pSfb5aoN+fDLOoXhKXlc889Rzqd5q677poS9ABr1qzhrLPO4sknn+Qv//IvsdmMHx2n02nuueceLrvsMi666CJ0XSeVSuHxGK8Qnm6IosBFV7Tx3BMHDAU9wLYt/Qz1xwwFPcDIoUla2kIMG4gggPjBCHVuO3v7jd0GopMZwh4bhwy6jAKkonmSe/IcyBpb3mXzGof2ZRn3CBhVpRc1eKsnwyeXjE+ths9EEMDmHaEp4CapGu8jVpjg1iYXDpvxGMPuKE3uSpx2Y5tNj0NlqeRHUYzPgduu0uRVeONwd8SjKVCkP53l7cms4RizmsYPuvr58jpj60RB1HHax0rpNkaCHkCa4H/eWkxv3PgYe6Q0S5f5GYsY36SO59JsaBDoLxhfx6Qti73VTfubxseYzKkkMgUuWWX+ZC2XLfLSMx2GMU3Vefm5TlxuxVDQQ2kuD/bHDAU9QPueUbLZgqGgB+jvifLaC12Ggh4gMpnBH3QaCnoopesUi5qhoAdwOBQuuWoxf/3HxiunWlFjYMcwkixOPRGYia7Dlld7CR0YNxT0AMMHJrju6sWmgh7glf1jhoIeoD9ToM5lY2T/uGE8NZzBsSNLu4ndayqvMqDrXGxyDhRF4qYr2kyPzeIEI8nYqhed7KM4qfjOvx1H2xkk3ngINRkh8fpDOJeejXv9VYiKuZ2sxUJEOAEpNAvT/WZBiPqdO3cCsGFDue/shg0b2LJlC52dnSxfvtzw/W+//TbJZJK1a9fyd3/3d/ziF78gmUzi9/u56aab+PKXv4zTafwo7liZb6XplGCeOWq0avru3j/f/ud+wXG+/ZiYd4jzHeO8p+g4xzhP/Jg4zo0c73UQ5zsHgjDn5+VEfJaO+zrNNw/mOURBmHsc0okY4zxjkCRxzmMQj3euH0P8RH0vHlmRtFYmLY4HW3ULwes/T2rb06WOtAe2kB88iPfcW7FVtwAgy4I13xY6AsfvYLMwNf3CEPVHcuFqamrKYkdy6UdGRkxFfWdnJwD33HMPkiTxhS98gaqqKp599ll+8pOf0NHRwQ9+8IP5Ra0JoigQDJoXdJ4qzPcjPt8XmCwbF1Mec1yZe/vz7f9Yro8gYFo0CqVOtnMhHu85mGeM871fmO8czBktIQqiQf/bY+eYrrN5L6l5BaskiXN+Xuy2OTZ+mPnmgjjPF/r8Y5zn/dL852iuMeraMczleeKiSVHsETxu+9zn2aHM+f55z5EklR6RmR7f3Nf5veDzHd/ii4WFqNjxnn0T9qaVxN94GDUZIfrcD3AuOxfn4jPw+11T88yabxYLjQUh6jOZ0qN8o/Qa+2E3hiOvMSKVKqUSxGIxHn30URYvXgzANddcA8Cjjz7KK6+88p5z7TVNJx4376R4qqDNUz6hmjxKP0KxOFf/1mOIF+be/nz7L5V/zC1k5qsQMeuQewRN1eb0hCoWVWTzJrUUCxryHFppvnOkz3cO5oyW0HTtuFYZjvc6G6WMzIqrGhGT9B4oFWvPx3ylQJp2vHN5nver85+jucYYi5l/Xx1hvmutmXQBPkIylZvzGHImqW5HmPcczXMOtHmu87tBkkR8PifxeGbe74mFhs/ntFaETwK2mjZCN/wvkm8/SbbjHTL7XyfXu4ft54dxuwOn/Hyz5s08LGAHm+NhQYj6I6kx+Xwex1EWiLlcbtZrjDjynrVr104J+iPcfvvtPProo7z55pvHVUB7qhbTzCRc5SYZN84zdjhlnC5zNSrJAjb73Ct3TkFAQC8r8DyCWwBFEqZccI7GY5dwqxopE+vBgNdGwaYTzRrni1fJMsRc4DfOF5dREDQbYCw0FFEinrNRaXIadE0gWtCoknVEkxV/LZtBl8y/T5zJFDYN8ibxSodCha3IRN54jA1OB6oqIEnGN5FCXEdIZNFM3ExVTWZxWGDniHFcFsBtnoaNgE7YJTCQx/RpgCjasMl58iafieoaSOSyOCXju6PBVAp3rZPUkLHw9fntON0203xyu0vB7nfAgPE8EG0iWtB8kIIAim3uH4RwpYe+rqjp595ul0nEc6afKUkS8AUcxKPG9RmhsAvFJjEyaFKf4bVRUeliYsxkLttEopNp0+NTixpyIo+g6+gGTz0EXUdxKIiSYHrzIHvs2JM5cib78HhtTCRz+E2eCIwNJxElwdQKNBrPkUjlaaydtuJUVW1BfNdaLAxExY7v3FuwN64k8eYjqMkI//f/3s1HPnIHv/M7v23NN4sFx4IQ9VVVVQAMDw/T0tIyKzYyUlInc1laHknbObKdmVRWlmzUYjHjorjTic//fxfx2gtd/PqRfbNWQ1uXhBgZStJ5cILmRUEmJzIkYtNio6k1SDya5cCeMeqb/GTTBSZmFBnW1HtRizqH3hlica2HvM9Oz8S0IKvx2wkWNXreGqQ+5MRW4+HA6LQY8XvtVAQdHOqIEHAptDX62DWemhIbTodMfYuPvdEktqLI6iov+8YSHNEaNkFgIzL9eyfZvFtn/VnV+DZOgHJYGOsCLjFIbzpBTovS7AyBmKQ4I4ek0RlEkrLsjkWoSNewOJDAZZs+xlQ2yEhWJ6f3Mpjxs8jnIeCcvvlQMwJidASnNoGa8KH66pFm1GGrqSIc7CAc7+Qm2cPB4DJ22qeFo1OSWRtw4LKPsDKscChSweN98amSX4cock2dm4BznMkcuIQqXPZxBPHwD04RbG9Esb/2OhSyFNacSe6KBnR36RzqGsSyVYzl0iyu6ebLoTAP7BLpjk7fPCyrdKHb0/QVhlnT5GUyKjEw4yZwRaWd5Q1ZUvog57vdjCRtHIxNO594dA/JMT8vjWWoaHDSIMh0dk1/rsJBibMuhHGpn//qHeGi0HLW+5qnUmnShSIPHermud4BpAudLMuHGHtqCDVXGqMkCTS3hejrjpJM5GldUsFQf4xs5vAYBKhfXU13NMuO0RQrz6wn1jFBKjI9l8Nrq+m2ibyczNF2QSPO7iiJGeK/tsFHoaCyf9coVbUeRFFgeEa8pt7HLXeuYfGySq64YRmP/Hwn+2bcIfn8dgIVLvbtGuHvvvYs19++krPObypLF3K6bPzJX1/JM4/v5+VnO6ZcfhSbxOXXLeHSa5YgCgIvPdfBs4+1k8uVxiiKAudf2so1Ny/H4VR4540+nnhwL/EZn9fmthCT4yke+PEO9uwY5uaPrSU8wz/+wN5RHv7ZLkaHEpzZ6GO80kXXjO+DRq8dT6bIrvZxKheFqNR1Rg5NTl/nag+FJUHeHE8TcCs0V7k5MOPmw+tWqGrysHMyxe89uoNPrG3ghqXVU6lvuWyRZx5v5+XnOhAEuPTqxVxx/TKUw70SiqrGs6/28PjzneTyKhef1cAd1y9dECmOFgsTe/1SlBv+F/HXH0RP9vDwww+xa9d2fud3Pk9TU+vJPjyLd82Ht1B2QVha3n///Xzta1/j7rvv5tZbb50Vu+uuu9i9ezdvvvmmqftNf38/V1xxBWvXruX++2fber3yyit85jOf4fd///f5whe+8J6O71S3tDyaVCLHrx7aS0/HJLpe8gOfic0m0dASJB7N4PHa6e6YnBUXRYGWxSEiE2n8AWdZHKBpaQXDRY0au0x/+3hZY6D6liATMviCLroH4mWuPLXVHjIeGVulk+5cnsRRK9fVbhteu4wrWSTfmSASm73i6fPYWHeJDe/iHJN5ncnC7OtjE2SaXT7scpaQXSKlzXYyERFpdPqp88QZz7hIaJGyMVbI1bR6JZR4AiXfh3BUwkRBrkH3BdD7R1GGdyLos8eQcdbxdqCBYNBHpWcCQZi98lwsenh5yIVDElgSiKMLs8co6g7ckgt33zCOp99CmhyaFdftbrIXnUdifT3DWRsZffYYBSTGIpX8+hBUBmEgO/vGVkSgwR5iOFrg7BaBlFjuhOITQ+wdLZKNhtjem6V41HVuC7hQIzlWrtTIBscp6LOvc5XNxxUVq+mezHP/gc6pXgRT59hmp7ZbQxkskEzkiE7OXr13eWxU13hI5FVSboWhsdlPMOw2ibZKN6mJFOnmAIeOcnuRRIE1ISe0T+Dz2enpmH2dBaEkkhOxLBde3sb5l7WWPfLet2uYxx/Yg9tjo687SuGoudzUGuTWT6ylsSVYdv4ARocTPPKzXdjsEjd9dA2hitnNxmLRDE88sIdoJMOtd66ltmHaZk2WRew2hZ/d8zb7dg4jyyKD/bOvsyyLXHL1Yjae28BTD+9j1zuz54kgQP3KKjptArU2hc7OybI0trZ6H+pIEnFRkN2xTNnTtuZKNwVVxV3hpCOTJX3U07aWgJPfPauFQleMxx/cU/aEIhByctNHVyN77dz3eDvDRz2BcLsU7rp1FWevrTZtMrZQ+UAsLSNJbvvvN9/XfZwOqMlJvrhY56GH7iedTqLrcPPNt3PLLbfPct47FbAsLc3R1QREHzy+jQRuR5DMG/adqiwIUT85Ocnll1/OokWLDH3qb7vtNr71rW8BpaLaRCJBXV3drJScT33qU7z11lv87Gc/Y/369UApL/fzn/88zz//PD/72c8M3XWOhYUm6o/wf//8WUaHzVtnN7YE6OuOmsZbl1TQdXDiPcdrllSw38DL/AiBGjftFeZfpDJQsWfuJyzrfxfSmnHKEcCFld4ysTuTJqefvGA+huW6m2qt3zReSLmQh/eaxrOhJhJLzLviaprCRM7EnvIwi777K4S8+Xnc8Xu/SVY2j7/RV82+mPk5uLDWTwpja0OA+Gg9v9ppfowbWmQ8tYOmcZvmZFvH3D+YVb+MzlkvIS0OMWGSygIQXhGmwyRVBeACWaJ/96hp/OIr29j0sTWm8d3bh/jhv5iLppp6H1/+q8tN4++Vmf7t//TNF+g+VH6DfYSWtpDhDfgR6tbXsLfffB40Lqlgd8S8dqiuxk2nbp6nHxIEKl8wnwcA6VoveZP0O4BvfPF86qpOLytiq/nUqcORplVut5MHH/wZb765BV2H+voG7rzzLlpaWqmsrJy3EP+DwBL15nyYRf2pdetpQigU4otf/CLf+ta3uOuuu7j55puJRCL88Ic/JBwO80d/9EdTr/32t7/NQw89xI9+9CPOOeecqb9/7Wtf4xOf+ASf+cxn+NSnPkVVVRXPP/88r7zyCrfffvt7FvQWFhYWFhanNLqGmoqe7KM49RHgS5t7EQQR3b6R/CIXqR3P07X7AK/82Z9jr1/GT7/xZZqbW072kVrMh1Uoe2rz6U9/mmAwyA9+8AO+9a1v4XQ6Of/88/nSl740Zz79EZYvX87Pf/5zvvOd73DfffeRTqdpbm7mq1/9Kr/xG7/xAYzAwsLCwsLiJGA1n3pPyCsuwNG6juQ7vybbtYPcQDt//dd/we/+7uc566xz3rMNtsUHwCnwNOVksGBEPcCmTZvYtGnTnK+5++67ufvuuw1jS5Ys4Xvf+977cWgWFhYWFhYWpxmiw1PqRrtoPfHXfkksFuOf/un/sWHDmfzmb36GyspyAw4Li5PFh/NW5kNAsahRmCM3VdP0eZsEKcrcFpbHGzezhZx6vygw10tssog8RwMeURSQ5pni83c/necjos/jmy/MfQ7mez+6OPdrdAFdMrci1REQ5zkH8jzXYT4XAGm+RlDzNT0T5r4KsiggzzGXBAGU+ebyPPH5GjnNx3zblyRhTm/9Ql6d0zpP0/QpBxwz5vs8CfOMcb73z9NvC/s8+b2SJM65D0kSUGTzuCCAZtkLWpwkbDVtBK74NNdeewOSJLNt21v8f//fF3jiiUdR5+nZYPHBIggCgiAd57+F+RRmQRTKnuqcaoWy+3YN88jPdqGqOps+upo1G+tmxXs6J3nopzuZHE9RXeej+9DErCLEyhoPt3x8LYuXh3nl2U6efHjvLMHhCzi48fZVrDurnlef7+Tpx/ZP2wpS8ry/etNyLrhsEdu3DvDEA3tmWe7JskjToiC9XREqmgNMAOMzihwFAZY1+IkenMBW52Z0SYCe5OwiyBWVLrJSEqWgE+gV6emeXeDX0OAlswhyLpV1TSJj2uxi17DNxdmVGhXOJJFMmOFcFH2Ge41DdLA6EKTRV6A3JjGUG0Zj+otbQqFhLEPDwVdR285ACYuITBfk6kgUigGUeDcFAojJcaRCbEZcpOhtRZZi5HxVJCp9IM8uWhajbtybX6XgcDNy3gby9tlzzJ73ULtnF8pklMKkDal3/6y4Vt2MeP1l6E1BumIqY4VRZrY0UnDilRVUIcb24RpeHU5S0Kevs1tSqHS46UlFOKsqAHKMwoxCSFmQyUareOQdlUVBN6OpPJHMjLgosL7RQcY9TIPXhWLLESvOLrSsoJKXd4m4FQW/r8hAavYYW31eYvkcUkqjoV2jf9/sQs/mthC33rkWf4WLXz59kFfeGpg1l+uq3Hz8puUsag7w4Bu9PP72AMUZzi0Bl0KDXWawY5KVVR5GD02Sy86ey3UNfro7JthwdiM3fGQVPr+xx/2+XSM88rOdjM+waxUEaFlcwfBAjMpqL7fcuZam1tkuODveHuCxX+xGUSRu+fgalq2enU7Y0T7Ow/ftJJXMc8NHVnHGuY3T51gWScTy/PDf3qC/J0J9U4DuQ5OzHGL8QScsCrAvnedMh42xPaOzHHqcboVQa5B9w0naWgIMjqZIzbC4tCkizWsCHPQmqRV8jPUUmEhM28FKosC162u54/xmumNp/mNrD11HFdSuCLqZ6E/hVzXqJnMM90RnxetaAoyLkEOgwu+gs292AXxdpRtfrkh8JMW1t6zg3Itb5r0JWShY7jcLBzU5yT0f2UCxWOD73/8P2ttLJghNTS189rOfY/HiJR/YsViFsnOgJSH5xPFtw3MDiAuvKN8S9SeAU0XUj4+mynyzAZaurOKWO9fgdNl44sE9vP167yzhU1VT8uOOTGa48sZlXHxFG5IsTjlrHNg/wi/v3cGBvaNcdGUbV924HLtjOnMrEc/yxIN7efv1Xs48v4nrb1uF12efimezBZ55rJ1XnuugrtFPIj7bmlCURWpXVXFoLEVlwIE9nmNipguHAKELGtjjFHDbJSr8GoPZ2SK+reAmuS+DWtQIrfZwwDZbIC8J26mqzJLRspwZcrMoMIIoTp+EouplPOdhMhtjkbuKVWEVRZqOZ/IiHbECUXWccNrOor2vYM9NW4Fqdjfa8jNRnHGKeggxMYJUnI7rukRBCyInelDtlQhOCZkZ70cgUbmUnL8ARQXnlg4c7btmjSG6/jzGl1chAOGeCUKd78yK5/VKtN5JhEwC/bIrUc5pmrUCHctKdMVTpPUEPimAJI5N+9wDiaybl/oC7I8laHEHGMjEyWnT4s+ryGysdJHQJ3EUK3jyHYXh+PT77ZLAkrCH9rEkiyocuCtjpITp6yACK4IBYloUl+iks8vDnoHZK1zrGhzEtCSKKOKzKXTFZ1/HpRk7bE1QzKlcf/sqzjyvcdaKSldfjJ8+uo+hsRR3blrJBWfUzmrNOhTJ8IPNHezqjbIi7GHo4MQsget3yjQ7FfoOTtDSFmJkKDGryZXDKXPljcu46PI2wyddxaLGS88c4tkn2qkIuymqGmMzHKYEAc66oJnrb1tJMpHjkZ/t4uC+sVnbWLW+hk0fXYMkiTz+wG62bx2YFW9ZXLqRCVa4eOax/by6uWuWiK8Iu7A7FUaHE1SvquKtfJH8jHjYJrM0V2To4CQNSys4lMiRzs0Q+Q6JhhovHb0xWtr8jNQWiM6wW1UQaSr6ae9IsqTWy2cub6MxPO0hr2o6Tx0c4Sc7+vHbFZxpjf7B2ddxvdtGsSOCKArYajwcHJ39/dlY6yWbK5LOFGnx2xnYMzrrOtY1+rn1E2tpXVxRdg0WGpaoXzgcEfXV1dVomsZLL73AvffeQyqVBASuuupaPv7xT+B0uubd1vFiifo5sES9xfFwKoj6QkHlz//wCdNH+JIk4PU7yny+Z/L7X76AtmWVU/8/0y6vWNRIJfO4Pca9AIB541te7eEXP9xmGq9t9jPUGwOTGempc9Fznh3N5AUKYBMkUrrxo1ABna9flcdhM7fBrLMvwec0P0fp7b24O141jRdaz8SujJnG83INtuKwaTyn+eD1rYiaSSdQuxMpbEcq5g3jGgLq1R9HnsOJqzeZJ100t2/c3NXIWxNR03iNWsuL+81tQs9qtRN3DZnGw6Kf13eLaCZpRUGHhOJNUjSZB7IO/+/Ccwh5jbtI67pOrqDSWB+cmrtH85+/2MWWbebHuKHWS9cuk7a7wPmXtnLbJ9eZxg/sHeU///E103go7CIayZh3a1VEFFkikzG2iBQEqK71MmzScRbAc2Eje8bNLSjPDjppP2hucbn0/Ar2KObbXx+q4Itnm9t8do2n+JMfvWNqReqSBVxj5Z73RxAFaAKiY+Zj+N9fvZjmRSHT+ELAEvULh5mi/gixWJR77/0Rr7zyIgDBYIhPf/oz73shrSXq50BLQurJ49uG+7oFKeqtGXGaoGn6nDm5qqqXNcU5mvm+IOYS7McSny+3Wi9opoIeQM2rpoIeoAAUBPO4joBNmvsc2OcpHbepxmL6CIJq7tMNIAhz5wRLhZypoAcQc1lTQQ8goiM65x7E0U2yjkafJ57Nz30dtXnGmFM1U0Ffiuumgh6gKIBinyvHXsDjmnsuMk8DI3We+NHN0o5mvnqVfF41FfQAxYI2p1+7rjNnHEqfh7mY6xwDqPOVe0hzb8AmCHP2Fsipuqmgh9Il0gpzz6W56oYsLD4I/P4An//8H/LVr/4l1dW1RCKT/NM//T/+/u/vZmzMfPHE4v3kcEfZ4/m3QDvKLij3GwsLCwsLC4t3iaahJs2fylgcG2oyyvi48ZPYqqpq/s//+Qq//vWveOaZp9iy5Q22b3+HG2+8mUsvveJdNaw6VRpcWSw8LFFvYWFhYWFxOmM1nzoxTDWnMu8iDk0U191OavszFEYGaP/ve/jOg0/h2XA1cmB++0s1GeUnv3nZMfXfsZiDD+lNkSXqLSwsLCwsTmes5lMfKJInhK22jWzHOyS3PY0aHyf28s9wrbgQ9+pLEGRzG2KL04P/+I//YM+ePezZs4f+/n7q6+t5/vnnDV/73e9+17SH0m/8xm/wZ3/2Z8e8X0vUnwaMjyZ55Oe7aG4LMTmWIhGfXcTo9dm5/vZV+Px2Hvn5bkaHZhe/2R0yV924jMaW2VZ7RygWNZ5/8gDbt/RzzS0rWLWudlZc13VefXuQZ17p4aoLm7ngjLqyAqE924d44elDLFpaQfehCY5OGw8uDjFU6SK0KEDy7SEKmdme3KGwC6dLIdwh09GiEzsqN94r23DrPlRVp6ikiRRmF7u6bRLLa+w8cEDiskaZSk90VlzTJdLFWl4cjLIq6KHeNzsjWdehWPQhrl1Jwe1B2bO5/DwtvwiprYVCIo08/A4CswdZcNYiOO3k1SUo8S4EffYYi656WLwYvX4d+rOPIiYis4+xqhH9qmspCALilueQYrMLPdMZB2896iT53Yc4658uJbx+drWsruroQzGasuMMeALEHbNtQtEFQkotn1xRoK7Xz2O9swuKBeDjbZVcWK/yfLubv9ucRD0qN/6OdQIfPWOEnribH7WnyR2Vm16R9pDbLnKuV2aXrJI6Kl7ltuG3KyiSk6QQJ3lU/YDXplDjcvIfO/fxyeVLqPXMdplI54rc/3ovB4bi/NEtq6nzGufW33zlYrLZIjvbx8tiS1oCDCdy1K2qZHDvWFmdR+2yMHsm0/z6pW6uuKAJ+ahalK6DEzx+/24WLa2gvztaln/vq/eSbgngFQUKO0fJxmZfB0/IiaPRj6brFAcTxI8qdnU4ZWob/GQzBapqPIwOz3aWkewSgTPqGNF0WqvcdB3lLCMKOqtW+4jYcrT6g3S9PXueASxq8pNsz7Ci1UO7K4F2VH5pncNHx6DOvXI/H1lVh/2oGoKB3igP/2wXF9gldqKTOKrep9ou05YuIgYc9BdUIqnZnzeXWyHYGiBd0PD1xoj3zXa7sttlrrhhKS1tC9/9xuL0RBBEnIvPxFa/jOTWJ8j17SW95yVyfXvwnf8RlIr6k32Ipzfz9Zh5n/n2t79NIBBg5cqVJBLmhgMz+epXv0owOFuHtbW1vav9Wu43J4CT5X6TyxV57okDvPTMoakiWZtNoqElQHdHKX/ywssWcfWm5TicpZUBtajx8vMdPPNYO/lckY3nNHLD7avwBcq9t2VZpLczyo//a8ssW77la6q5+WNrqKz20NkX475H99E1w4JyUaOfO29aTmujn7HhJA//fCftu6cLhkJhFy63Qn9PDEfQgby6ir1jqSnt5HcpLAbG3xnG7pCobwrQ0zGJeriozuGUqTg7zK6KLAgCDY4K9g1nyB4+B4oosKrWxVBhkqKmsqbOQ4wY6cNFrAJwRtjPufVjOJUcmVwlA5kiWW36RqDaFmJdpYLPrlIsOhHQkeRp8VXMKAhvv448chC1sg1h43kozmlhUiza0EYGUGIdqLIXzVuDIkwLSBUXWk5GSfWgSW7U+g0o7iJH7oU0TSLfPoLw8uOgONCuvBm52YNwuBBY10WKgymUt55Ey+fZu6WK3f+zj0KsNA8FSWTRZy9m419vwBFSUMezSKMHkNTp65h2NjIQUMjLRTxiBQG7hChNn4NE1sdP9hfYG81wRtjHbyx34XFMx1NZJ995UeRX+7OsrhH48hVJgt7pfFNVdbG518/T/SkcRQX/QS8Hdsemiic9bgX/kiDb8lnsssjiCg/7x5MUDwt9hyyyosZBf3YSHZ0lQT898STZw41eJEHg6uYGbl7cjEOSeHHvKPe+3EX0sEAUBLhsVTV3XthCwG0s7nfuH+Nnj7czOpGmrsqNDgzNEMENVe4pi1V/tRuhykP3jJvimko3d964jFVLw8SjWR5/YDfvvDn9aN7rsxMKu+npnERxyXg21rFrMj1ViOuySaxwKky8NYgoCNSsruLgSJL84QJRRRZZUuNldO8oxbxK65IKRgbjUzabggBLVlTR3xMhnSpQsbaaHkVkIjV9M7Sk1st4PEcklae12QmVKiOZ6evY5HRT3JtjrDdNVYUTu02mb8YYq6tciEsVeqUMPtmOU/Owb3T6RqPKbeO3z2jmgqYQ6VSeJx/ayxsvdU9dZ6dbwbs8zFupHIoocqZNZmTPKMUjY7RJ1C6pYO94ClWHpmUVHIpnp2w2JVFgTYWL9DtD5FIFNpzdwI0fWVXy3z8NsNxvPhzk+vaS2PoEWiYBooh77eW4Vl5Y1uDQyGHHCMv9Zg60FOSNV8WPGdvlILrnf50JfX19NDaWeorceOONpNPpeVfqn3vuORoaGt7zPsES9SeEkyHq06k8//D1zcQixvaLwQoXv/W/zqGuwW8Yj8eyRCbSc9rBPf7Abl749SHDmCSLrL18ES9sGzR0uBAEuHxdDdtf6EY1ceVpWl/DVlUja+JgsTLkRNg1RjJhbJ9YUefh0PoKRlPGbjAhp8LaFVmGcsYWlk5J5uZWF7GjGlMdQUTk0upmQm7ju2xdBzWqYPfmp8T20WRjArbMIQSMu4HmpXqUigCiaBwvZG3odhBl47hWVHjinJ8Q3dFjGLeFfdzyzPW4MM4B1QWFSOOZKO64cVwXiKVrqfWlMDIv0nXoHnfg9XaYOvsc6KzjX/6tQCZrPIaGFj+dFQqRrLFfS7XHRjCYYzSTNYwH7DYqJ73s6I4axl12ia9/dC0tVcb2ZIWixs8f38+LW/pN5/LathC7uyJTN5ZHc+Gaag6+3jercdVMGpZXsMspE88Yx+sDDuxjGSZiJmP02qlTNQZ7TeayS8F+Zh07RpOGcZsssm69lz0Z42JJUYB1aT973xqf5Xk/kyXnVLM1kyenGl/nSyp9xJ/qIp00/jzWNPlJJ3LEIyZjDDmZWFZBv8l3mt+l8IeXtLJ25emVa2yJ+g8PWi5DYsuj5Hr3AKBUteC74CNILt/UayxRfwI4BUT9TN6NqA8EAtjtdhTlvaVoWek3C5R8rmgq6AEiE2mqaszNyn1+h2lnzCMcnaYzE7WoMTSWMrWs03UYGU6aCnqAVEElayIQAGLpArKJoAeYHEszmjIf42SmQFI1FhAAGbVIVjM3/tPQEARzyzxBAMHlQBDMj1EUNVNBX4qrpoIeQLSp6LL5fbcoF0h2zuGLPx4vNcEy+aQLegF5DptPQdCpcOqGgr4Uh0pvntwcNpZqNk8ma76PWCJHZI7vzpFknuLRqUIziObyFOb4LKRzKpFUnhaTuCKLCHPYL+o6ZIq6qaAHGB1Jmgp6gHiqgPFtU4mxRA6niaAHiCZyeHLm5zCTLpAtmsfzRY3sHHNZ0yFXUE0FPUAqo5kKeoDh8RSYCHqA+ESGtMkNOEB0MsPYHJ/3WLpAcI7vNAuLUx3R7sR34UfJdm0n+davKIx2E3nqP/BffCdK+PhWaC1mIAjHn37zPvYYMOPmm28mmUwiiiIrVqzgs5/9LNdff/272oYl6i0sLCwsLCwsPgAEQcC5aANKZRPxl+6jGB0l+uz/4D33Fhwta0/24Z0+nAD3m8HBQe666y7T+HPPPXfc+wDwer3ccccdbNy4kUAgQG9vL/feey9f/OIX6e7u5vOf//wxb8sS9RYWFhYWFqczlk/9KYcgCPgu+AiJrU+QH+4k9vLPUZOTKOGmWV74lmf96c9v/uZvlv3t4x//OB/5yEf4l3/5F26++Wbq64+tsNoS9RYWFhYWFqczlk/9KYtrTcniMte7h+TbT+Jacxlf2uxDEPotz/rj4QS439TV1Z2w1fh3i8Ph4DOf+Qxf+cpXePXVV/noRz96TO+zRP0CxeO1c85FzWx5pacsF1iSBC68og1JMs4J03Wd17cNMTCS5MbLFuF0lE+DfbuG0fRS7n3cINe3qs4LokA46GDcoPAtHHSg2iSq6ryMDpbn5nt8dmw2mcUehUNj5UXGLptEldeOZ2kFfQcnysYoyyJtF4UINDvY2pVDPeoFogDLK724Cgo5aZy8Vp5P3Ob1o2k6sqRQ1Mtz612ijwPRPGfYbMhyeS6wqjrQgKLqRJbKc7pVVUGbHKWgBFDEaFlcw0ZO8aAV3djl8nOg6yKF3ji624lcLZel+Ok6qBkby37vbPZ97zW0bPkxNly9kmJER6myI+rl+coFRx0yDopaEUEsj2uqi4KWRirakeXyeLFooy9ZJOT0I8vlRZyaZqd7wk1zk0CPQZGn3SbRtMKBx+1kR3/6aPdIZFFgfaMDl0dm/2iS4lHXWQDaXH40v0QiXTQsul5c7+Gt6AitOTcBu70s3tEbZSKaoSLgYCJaPpdrKt1ccnYD+YJK90B5ZrzPayNf56I2H2ToULk9pMMpU+GxsSrsYo/BXFckgeVhNzaXnY6eaFleuyBA24ogTlRyb4xRyJePsWFZBXGngsdRIGmQ298SdmEb0wiF7Ezmyq9jrduBP6xSM+hieCRdFg/47TiCsEhz0jlZPtc9Ngmn10lwRZjBfeU2oYpN4pKr24hOZmY54xxBFAVq11fjqnZzqDdOwSB3f12tj7ee7SB84zKcrnI3o/Y9o+zeNsiVNyw7bZxxThiWT/0pja26jeQ7T5HZ/zqZA29ib1iBrar5ZB/WwuYkW1qeCI6szk9OHvtTNsv95gRwsiwtAfq6Izx83056OktiYsmKSm65cy3VtcYFZT0DcX766D46Dgssn8fG7dcu4fyNJW/58dEUj/x8J/t2jgAlD/uG5gDdhyZQVR23x4a/JcC+4QQ6ApIIbc1Begbi5PIqdptEc72Pjp4IqgYCOitqvES7o6STeSRJoGFZmPZIhuxhS7uGRUFGdI3xRB4BWF7tIdYTI3W46K6pwoU9nmVsqOTs0bQygHhpgbSvJMBsBS/9vSHaR0tio8nvRNN1+uOleJVXYlWbymCh9MGosDtZ5BcpStHSGAWZRpeflFayTlQEO7rmoj9bOqc2QeTq2nqaAzkEQUPTJNSiG1mJIwigazpq0YtNSCOKRXRdoDCpIh18CUEtCahCeBmSX0Qig45ATqmn4APk0jkQ1QAOQUGWSsecj4lozz6FOD4AgNqyAuHSi5FdJcFWzDuQov3IhdJj2lg/vPHX3Qw9tQsA79I6zvxYkLrKYQA0tw9h7XoUeRwBHU3yolYuwRYqiSNNF0gWbejSBIKgo2sKBdWGKgyDoIMu4KAVp01DFFV0XWQwbmPrxCDFw378S70+6jxRBDGHrgv0DNTyf+8XmIiXvmKWBp0UxzNMHC5qXbLCT2p5krRSus4+zc/osJuewzeJSyudeCpjpITSdfeJLvSMm85o6f/rnC4KwyI9/aXt+ZwytUEnBwYT6EB10EmwQaRXKwlxpyxxS1sLVzXXI4kisUSOB586yOuHHZwkSaCtKUD3QJx8XsVhl7jp8rYpL3pN03nl7QF++euDJFMFZEmgcXkFO4QimcNfoxt0CWX3BLHJDIIALW0hRoaSUwWiwcUVjIUdDBy+eVhS5SY/nCJ6WChXVjhxzLCUrK/3kG8TGRRKrw+pMs2HdPp3lxybQlVuiouC7IqW3u+ySzSH3bQPxtF0CLgUGu0yvQdLc1+xizSfF+KQkKSgabhkiVW1dtL20dJ11gQ83WH2v54mnSmiyCItawIc9KYoHHZ4anIG6B3XmMwUEAVYUemlJ5ImefiGarUg4d0XYXKk9J249ow6Nn10NYFQqafAQG+Uh+7bSfeh0jHVtAboafHQffjGu9KuUFUU6Ti8GFAfcFA1mWXyQGnMbq+N629dydkXNiMIApPjKR79+W52by/1bTjiYX/xVYuR5VP/h91yv7EA0HWN+Mu/INe3F9HtJ3TD/0LPpUydcCz3mznQ06C9enzbEC8AwTX/646B+dxvzLj//vv52te+xje/+U0+8pGPHNN7LFF/AjiZoh5KK+9vvdaL3SGz9gzzvKv7HtvP86/3Grp8tDX52dDg54VfHZjyvJ9JKOwiWOdl73iajMFKoc9jo6HWQ/9QkriBA4bLJrG8yk1/PMtovDwuyyLNK8IkJzOMDJSv7AvorKn34lxdINJo7COiJGsYGXaxZ8zY1m9Vg8Ka1gKCbRTdwIIy7PASlBx0pSco6uXnoMrm4tr6GjyOtKFjjaZKaCkZqeNNpMRQWVwXbRQa15MPu8Befg50XUDOhRHf2Ya0+w3DMRTPuwappRpb2thqtOdljcgbIyxf0o1kMEatrhX72WdB2IVg8CQnryoki1AQB0EoP0ZBcyJoTbw1MU6sWL6iaxMk2hx+fvJrka0HyueJLMKGKjd6Y5oxr4G7kg6hQg26qBKRjV19qsQQuVE7u3bF0fXyMTSFXVRUKXQI0akbjpnUuV1colTxxFOdZHLl19HvtbF6aZhbr15CwFe+sp/KFPjl8x28kkgxWCx/vx24KCuS64sbPqUSRIHw2XWkizoDJjaci5r8UCdxwJZAN3BhaMva8Y5KbEnmKBp8nmuDDmpdNvr2jZc1vwIIVNlpusiH6h+lIJZfZyVvQ24P02nPMWkwDxRRpNEWZihWYCBR/nRD0uESXebGs5pYsqLScIxvvd7LEwdHeUs0duVZ6nFSPZln7M0BdANXnsbWICvXVPPckwemPO9nUlnt4a7PnWVq63uqYIl6iyPoxTyTT/wLajKCc/l5uJadY4n694KeAf2149uGcD4IJ+aJ31yivlgskkgkyppOxeNxbrvtNoaHh3nuueeOOQXrfU+/icfjvPjii4yMjLB48WIuvfTS93uXHzoEQeCsC+Z/VLdl57CpbV9Hbwx5NGUo6AEmx9PIDT5DQQ8QT+YpFjVDQQ+QzqvERcFQ0EOpa21mNM3IiLEg1xHozWWpajS/eSp4hjk0WWMa39Nf4JwVSdImJ2E8mwBFMhT0AKP5NMliAZ+JBaUoqWiRcUNBDyBoebRcEuzGHztB0Cmmh3CaCHoA6fWnsVWdZxpvvkikJhtDTBuPURzsgsA1CJKxdaBNKqAVNUNBD6CLGbrjcUNBD5DXVV7aL7P1gLFVaFGDUaUARoIeQICUfYycbm6/OKpNMnEgYCjoAXrH09hbHRTTxtdxMJXmnaFRQ0EPEEvkqQy5DAU9gNupULu8gsHXjT3jc8CgU0A1EPRQerKT2j/B4ByryJ29MfRFdtMxdjhyeCW3oaAHGIpkqcxqhoIeIDqaY5Uty5iBoAco2PIoS1Qm+0zimkaOHAMJ47gqQLtf5osmgh5g/TkNfL3D+LMCcCCZwbcvYijoAfq6IhQLqqGgBxgbSdLXFTnlRb2FxREE2YbnrBuJbf4x2YNbcbSsOdmHtHA5yek3Dz/8MIODg0ApfaZQKPCv//qvAPh8Pj71qU8BkE6nueSSS7jqqqtYtmwZgUCAvr4+HnzwQSYmJvjTP/3Td1VTcUJE/TPPPMODDz7I17/+9Vk737NnD5/73OcYHx9H13UEQeDcc8/lP//zP9+zsb6FhYWFhYWFxemIrXYxcqiO4uQg+YEDwAUn+5As3gMPPvggW7ZsmfW373znO0ApV/6IqHc4HNx0003s3LmTl156iXQ6jc/nY926dXz605/mvPPMF/GMOCGi/sknn6S7u7vsbuKrX/0qY2Nj3HDDDWzYsIHnn3+e119/nZ/+9Kd8+tOfPhG7trCwsLCwsLA4LRAEAXvTSoqTgxTG+0724SxMPvi+UWX8+Mc/PqbX2Ww2vvnNb56w/Z6Q5xN79uwpS6vZs2cPBw4c4IorruAf/uEf+NSnPsV///d/s3LlSp588skTsVsLCwsLCwsLi9MK2V8FgJoyTvGzsDDjhKzUT0xM0NTUNOtvb731FoIgcOutt079TRAErrnmGr7//e+fiN1avEuWtQZ5a9eIYSzsseF1KRhHweuzs7glSMdQAlUtz3F1KCJeDeyySM4gL1+WBBa3BhmZzJAwyLsXBFjaEqBQUBkzsMwD8Nb5seUU8vaoYdyRCNDstHMgafz+Go+NifEAzgrjvHxJczIedYMrVnICOXr7okwsV6RKE5BFgyJUTaDgCyDZPEj58toADYGiL4yuSYb2kQDagRgFuQKlOGEYHxqrQd6iU3+28VJEMW1Dq6hGTBtbYGlVDSA50PWcYRdsrQj2jEraJYDBGPNFic5JO7oiI5jUFtRXC9RWSAxNGOdz19bJZBUnkYLxdfJl/BSEIlG7cUF0vctN1XKZrduN919bo1AdkBhM6+gGSzZ+VSKggiSCgXMidptIPJkjly9it5V/RRZUjdHRND5ZIl4sH6Og63jcdtRqN8kR47nmX+NHzWqMdBnHV25wIdbKbBssL0IFqHQ58NcrjBhYcAIEvDbEShfCQNwwJ93ulokl3QjOSXSx/CSIugiqDZecI20wRhGdYFwgLEmMq8bXudJloyeapjlg7CDRPhllcdjOoXHjz0K914F9eYjEG4OG8VDYxaIlFQwPxA1rhdxeGzV1PsP3AuRyRbZv6WfdmfU4nKd5OqjVfGpBoeVS6JqKXsgyPj5mNaB6l5S+D47fA8boN/JU54S432zcuJEvf/nLfOITn5j625e//GWeeOIJXn75ZcLh8NTff/nLX/IXf/EX7N69+3h3e8pwst1v3g0794/xs8fbGZ0oFTraZIHlFW4G28cpFjVq6rxoms7ocEmUHvG8v/KGZThdCsNjKe57bD97Dk6LzuXVHpJ9MZLxHF6fHU+jj30zxMyqJRXcedNyairdpLMFHn2mg81v9KEeFhst9T4+sWk5i5oCFIoav365m19t7iR/uADOX+WCxT4OxDKIAmxsdOKpGUKVSjcHcsFO+kCYnQdT6MDiei/Dosp4viT6HLJAW8hD+3iSoqaztNLBuqURJOdh0aiJpOM1vNWfIVtUqfU4WF1fQHAe8RzXWe4J4rLH0cij4KTZXUGNd1pUZvN2VOIIYhZdk5FGcji6tiAe/mLJVS4m39KMoGTQdRGtGECUIwiHbx60gRza/VsRO/ajixLC0pU47KOIWknwJDNe3nrGycCz+wBovGUDZ/9pI97qw+8vihQG8shDexHQKLoa0CaiSPHR0ghsdvTLb8He5EEQNFTVWXLbkUvzQNd0CgkdKdODSB5V8JBweck7p4s9dwxX80S3RrSQwyPb2BB24HZOTH3xeUQfKkUyWhoJmcmBCu5/Os8RTdjcaKN6RYGRYgJJEGh2BehPx8gfLkx2F52o3T7ae1MIwKrFHgr1k2QPX2e3LNPmdzF8xJqUILvekBgYPnyd7QIbz7UzLE+g6jr1bj8TkyJ9h4WvpMPaCQdjb46TyxYJVboh7KRzbLrod3GTn5GJDIlUnqDPzkeuW8o562un4tu6JvnB5k6GIhlcdomGBi+7kym0wzcPTS47SlxlYCKNIoms9juIvT2ImiudBF+zB/0CP5250hiX2nwMvR4jkywVFldUKZx/s0LSVRqjhxCvHYLh5OExSiLNPi+HonFUXafJ7SHVD32DmanPa9viIO3JNLmiRr3HTmgsx2jn4RU/ARrWV3EAlUS+SNilsHZxkYJ7+nbeXQhzaFhnMpvHo8jUud0ciE6vGDZpLrT2PCOjaWyKSN3yCt7R8hwpja7x2HEpEp2RNJIgcMOyaj65th7X4RukiUyW+9o72Dpccjda5PXTOaIymS6dI48i0RRwsX88gabDMq8Dz45xkn2luajYJC67dgmXXbsERZHo7Yrw0H076esqfV5FUeC8S1q49pYVhn72ANu29PP4A3uIRTJ4/Xauv20VZ57XiHASfsU/EPeb8Sg3/t397+s+LE4c2Z7dZPa9ilLZhHvDNdz7W1eUpTdb7jfm6HoaneNzvxE4H+EEWVp+kJwQUX/TTTexYcMGvvGNb0z97bLLLkPXdV544YVZr73nnnv4r//6L1555ZXj3e0pw0IS9QCFosYzr3Szb+cIqb4Y8aNW+0r+2hU4HDKf+t2zcXuVMlecd/aM8NzmToTJDMN95SuqNY1+9AonV166iA2rqsriA8MJHnr6EGuXV3LhmfWI4uwf08lolp8/2c6QS2B3KkvhqNVGj03izDYRMamxbWe+zJXHJossavKRd4kMJLLEjmrIIwpwXquT+uoUOwdgNF2+Wri60sP65jRNvhxFwaCBlhikzetFFPMIUvljUr3gRO4bplhVg+ApX5XWNQdaVobH34FXX0U4qkGW7vYhtrXSvk1j/8/2oR51jJLbwco/PI/VtwWQ+vYhFmbPQV2QKDibIRjGtmEpkjzbkUbXoVD0QiGNkBhE1qNlx5iTKukQnDzY7aAjUf70od7pYV2VjtcmEFfLGy/ZdBc7d/go+GCQSbSjVk+8so2Q4iLZY2fXvkxZ0yGXTWLlKjtVi/LE9Rg5bfYYJES8qTAT4xL5yjiJ4uxzJACN9grSnRraG4kp7/SZNLSFSLkU8prOgIH70tLWINdf2cbjO4Z4u7N8tbMm6MQRUrAVBA72xsrWhyrcNlo1DbFVYb+YKmug5ZZlGjMOWmoLiA0RisyeqxISQi7MoWGFoVSGRP6ocyAItDn9JMZhDJ3RdPmTsFV+F7aJHBNBhV4DC8rlVU6aG5NMxhU6IuXnqNbtwqmJKJ0anQeiZfFQ0IG8yIvms7N/LFnWDC7gUPj0hgZSpHm8s5f8UdfZIUnUOfyk0xK9sQzJoz7PsihwlsdFw0iGG29bRahi9o+trutsfa2X3duGuPaWFaaONyNDCR78yXY6D5Q/CWtpC3Hbp9Z94G45lqWlxdFEN/+Y/OBB3OuuwNGyxtDW0hL15nyYRf0JmREXXXQRjz32GC+++CKZTIYf/vCHDA0NccUVV5S9ds+ePdTW1hpsxeKDQpFFrr90EepYqkzQQ0nsdR2a4MIrF9HQFDDcxsZV1SypcBsKeoDhvhhLKlyGgh6gvsbL//qNDVx8dkOZoAcIBRzceP1StiUyZYIeIJlXeWV/gdfeyhjabOaLGvs7owwlcmWCHkDT4dXODPuHFENBD7B7LEnIJhgKeoCkFiFRMBb0AIKSodDUbCjoAQQxizA0ivDyS2WCHkBIxcnuPMie/9leJugB1FSWXX+7Gal7V5mgBxB0FVu6E2XdsjJBD6WbN5uSgHTUUNAD2NUxtg0bC3qAgUySZE42FPQAeSFN64os/UyUCXqARDHPWCLHO7tThl1E03mVt7alGVfHywQ9gIpG1D2KXp0sE/RQegDbm5vAsT9rKOgB+jsmscuioaAHONAV4aFXegwFPcBwJIOU0jhgIOgBJlJ5BvwSu4XyjrgAqWKRbkcKrWGsTNCXxqhStI8wbCDoAVRd50A6SsouGgp6gD2xNNlap6GgB9g/mmFywmMo6AGGUmnkqG4o6AEmI1n0iTx7RhNlgh4gmi1wz/Yefnmwu0zQA2RVlc7UJB2RdJmgByhqOq/HU1z6sdVlgh5KqZ1nX9DMb/+vc+cU5Vte6TEU9ADdHZO8/kKX6XstLD4IivFx8kMdANibVp3ko1m46Mf530LlhOTUf/azn+WRRx7hc5/73NTfvF4vn/nMZ2a9LpfL8cILL3D77befiN1aWFhYWFhYWJwW6LpOavuzoGvY6pch+8JWLcR7RDfpN3PMLMB8ejhBoj4UCvHggw/y/e9/n+7ubhobG/nt3/5t6urqZr1u+/btbNy4keuuu+5E7NbCwsLCwsLC4rQg172TXN9eEEQ868ozHSws5uOEdZStqanha1/72pyvOeecczjnnHNO1C4tLCwsLCwsLBY8hfF+Em8+CoB77WXIQfPu6Bbzs5BTaI4Hq8riQ4zHZzeNSZKAy23sHAGlx4TaPI+nvF7z7QMMjaZQjTwFD5NO5nDI5lPUZ5fxOMzvS502CbcimcYVUcAumMcFQJ4jPv0qc3RdRNfNX6M7nOjyHHZ6Xh9ywG0aVgIeNNm8mEcXFTTd/BzqOuj63GO0z2OlJs7TjtspiUhzuIq4FAmHzfwYfE4Zp2g+FxVBwjHH+oQkCMhzWBYKQqmwei5sytxxuyIhGdSGHMElStjmOI9uWcEmmB+jgoxbNh+jIgp47Ob7FwVwzOPsYpfniUvSnBZvboeITTJ/gccm45TNr7NbkfHZzcfokEWymnH9C5QMC0aGjOtfpo5hju+8Y4lbWLwfFKOjxF68F10tYKtbgmvVRSf7kBY8uq4d17+Fyglxv/mws9Dcb45QLGq89Mwhnn2inXxuujhtyYpKbrlzLfWNfoJBN5FIapb7Te9gnHsf3U9HT5SlVW7yw0miM7zlKypdbProGlatNy6Ijidz/PLXh3j17QFqK93cedNyViyumIpnc0Uef76TZ1/tweG3YV8eYm9s2nZQFgXWuJ2Mbx9FFAQqF4XYO1qyv4OSzF5a52MwkiZT0Fi0OMD+eHpWgd6SoIv0cIZILMuq1T6GxCTp4nSRYlvAySfXx2kNTxJPhxnJ5SgyXWRow0W1U8LjmERUK1AkGVGaPge6ZkPXXIhyFE11ARKiNC04dF2mUHCiioMwrqI81I64d9d0XFboPeNCHlzWhJTUWPGDdlIPvAna4TGIIs1XL2dVUz82u4rtwvXYhQEEffo65iuXUVjbiu6WUNQqHHIOUZiOF/J2GDyElB6g4GlBtuUQZ4wxh5+XM3725DLIapCOqMpkfnqMfsXOkoANVZ4krHjw2SCtTRebyoJC2OZBF8fI5N28PezmUGL6HCiCSI0SYtdACgcKwbib/T3TcUkUWFHlZvRQBEnW2XCjkwnPbAedmmyQ7l8kSI0XaL6tkv6KBPkZ56DWHqCjW2Fgssi5RYnMjjHSM/okVNZ6yXlt9E5maG3wE01kicSmhWMo4MBW5aZ9Mk1z2EVe1RmKTJ8Dn1OhNuikfTBOjd+BwybRPTb9XeBQJFa6bUS2DuCscmC7tIIDM3oYSILAEslL36sRHA6Bi261k/FNzFpjcqcqePXhHMmERvN5IQ4JSQra9FxeXeOjPhxBk9LERut5eb9GqjB9Dlq8DlwDaSb6EtSvDtNlh0h2uuC21munqSFHVJykUgwwHhMYTU/PA7+i0DgkMvLiCKEGH3mfnf7R6TE6HTIbL3JhWz6GVnCx71Alu4amz5FNEljXaCflGsYmyriLAfaPJ6bGKAArKr3EpQjq/9/efYdHdZ2L/v/uPb1Ko94lBEg00cEYXAHbGFNc4haHOMnJidOLfW7K+Z3cnBsnjs+9cZodJzk5cYlLsW+F9AAAfHBJREFUYtw7LtjGHbBN702AhHrX9LJ/fwgJiZmRgJGQRryf5/HzmFkze9bae2nm3WvWelckgsWTw+YjPkK9FshPybViymjGq3m5ILOEy3LGYdaduAHYv6eB5/6xjbpj7cyaV8hVn5uMw2kmlp1ba3nhiW009mqDK93KihumUDEzL+ZrhpJkvzm3BZuqaXv7ESJ+D/q0PFIXfxnVcOLmMtzZLNlvTpOmeQhG3knoGAb1kqTMfiNB/SBI1qC+W1uLl5ee2kHlgSaWXz+FqbPyAdDr1T5Bfac7wLOv7+fdjVV9Nnsx6BQmZthorGzhwsVjueSKrvzRJwuHI7z98VGef/MA3pMy0syaks0NV5Wxv7KVJ1/dS2t73xG57HGpNKYZselU1EMdtNb2Pd8Z2TbCqWaCmoaqKBxt8vQpd6UYcebaaPEHSQurVB7pm7XHYdNTPs3GMV8H102CBWOq6D0AHYnoaHJn0hLqIN1gJ83agNJ70x5NQRfJw6D3oYXtKPo2FKXv3X4klIqiegiHzQRpgJM2oFJ2+DE8u57O1EyemzWVI9a+I7f5+33k/uYjTOEQ087XcGl9N+VR8nIxzShE1QXwT5mKlnvS6HbEhJk0DGqAUGMr+oatfYsVI+GUYlS1gz2hbNZ2eunTAk1FC6azt9XNuBQbemMzmtI3U0mRJQ1N6SRFZ8dgaEFT+mZraejI4MOaMFbVzJH6CE3evuV5qg3PMQWToqA0+2iq73ud80vNFC+EkBLC/47C4Y/6Zh5y5JlJW+Gk1eRH8aSxfm/fbC8ORWF2a5i2Ay2kFqeys7ajzwZVBr3CmMJUqus6yS50sr3FQ7DXSVCACflOqpo9FKRbqazvxBvoe53H5zpobPeTZzUQ3t6At6Vv9qPMWWk0lRuxqHp827w0VfctHzvRwqTFYUBj3zoDu7f27cupWWYcM210KAGmFWqolsY+5VrIxOHKXPbUBhkTVKne1tBnHxaDSUfmjEz2+4NMLtTTZq7pc6OkopClZHCoyUdJwEzzmlqCnl5/rwrkTcyiyhOgZJyFjPNa0Mx9s+74W7P4aI8Jl1WHOb0Fj9K3DWk6B53tRlQVLLYALZG+I+x2zU5nYwrt3gjF+X5adH0XCzr1Jq7KL2csabz45Ha2fFLdp9xs0XP58gksWFgaM/AJBcOse30/6948wIJLx7BwSRmGfn4tGkpnJahvamflva8N6XuI0xeoO0THhpfQQgH0rhyc869DNfa9GQ13tvLoly6VoP40aJqHQOTthI5hVC+VoP5clexBfbdIROuTXvLkoP5vq7fx0aaauK9fsaiUFYvHxS3/aNMx/rY6/qZjBTl2qmpjpxUEsNuMdLpjp+zr5sux4g/F79I5LjO1LbHT+gH86Xsh0lLr45ZrwQwUQ2Pccl04D6MxfhuCQSshtTZueavbxv/sj19/JaLx44cf7nfenHLPN1CM0akPu9mPtqF0VsUtX2OawZ4YKTK7ZerTaAjFz8hQak3BaYmdNhCgpT2Fv26MH0CZNR3hd2OnAe2W5wvi6YzfxsCyEg51xJ+qcZ5PpfJA7DScAMWTM9kaJ8UlQHmegz3H4k/1KLMZ8X8a/2/F6rJwrJ+pKtB1gxHspy9f9V0dHiX+dapek8XePbFTzgJMvTqFI8b413HMISeHXm2IWz7pshRMF8U/B7qwlU/q418jRTt+r9HPaXDqTbTHSFXaLe91M8f2x04pC3DDl2Ywd0Fx3PKTP/OGg2w+de7RNA3/oc14924ENPRpeVjK53Pf5y8mIyMz6vmxdpSVoD6+czmoH7SFsiL5DfTlNtDt30B3hwO9PkY6+tM7ANAVIfTzvAEOoSr9P2HAHScTK6af6e9d5copLIQZqI4DXsf+Xz/QORgwRBroCQNcAziVvphYoDbw6wc6xwNVUBvwGAN294HO00AHGLAzDvT+A/WTgV4/QPkpPGfA0zzAh8pwB/RnjU6PMbt0uGshgEjAR8f65/Ef2YGiN2IZPxv7rKVEvO1kZGRGjciL09eVaT6xefFawt8iw0OCeiGEEEKIIRaoq6T9o6eJuNtA1eGYvRTL+DnDXa1RKZkXuyZCgnohhBBCiCGihUO4t76FZ9cHoGno7C6c86/DkFk03FUTo4wE9eKUDfRT9YCzJgacDTDA1JeBfio/hd/KBnpKeIA8nZrW/zHCYaCfDJUDTTFSBphPoKKgqSpK5MxHISIo9LsksJ8UnDDwrIyBa3YK03cGmEWV6BSgAbJwog7UFxPsq6eUKVVVINzP+4Shvws50DkYaBrXQOeov1Stp1KuaEpXLul+nqYM0JkGnOIjxDALNlXTsf55Qi1da6nMY2din3Vlnww3YvBJnnohBnDT8nIWzS+Kysdt1CtMzbaz4cXdrHluJwF/38w24XCEd9/cz5pHNjE1w4otRpaJidl21KPtVKSacdmio+L8klQCWVZcE9JJz47O256VaydlfBppDhPFmdGLWzIcRsZm2/GHIozPtUeVO+wGyqdm8IPnnazZNobIScF9OKKnriObvZ2dNHbmEIn0/dMJhVVe3lHCt16GBzdm4fb3vV+ORDTavXbq/a20ezMIh6PT7RkOhih64T1+dHgHY0PhqPIZLif/a2oKyt3fIDJ3VlS5NrEcfvktNJ1KOOSMKu8MGHj7qIMHAqlsMU2MCr7bw1b+0HA+D2w24GnJjQruFU0l4s/k3SoPQV8mysn57TUFJZjBmsognx3LIRCK/tI6Up/D01vNlKY4SDNF554vcthwWE1kX+Eie0z0dU4vcGA9P4faC3NJnZoVVW7NtGG5oAhfrY9pLkdUud2oY1Kmg+12hYJZ2VHBt0mvMi3bhm9DNfPNBownlesUmJ5upW1vMzNTLNhj9OXyPAe1EQ3rhUVYM6L7YsGFLkyfNzPxZhu5YyxR5RnFTsznZaPMzCCrNCWqPNNpYrLdxI57Q+j3pUaVK14TDW9nU13lY3xJdLndrmPpF+1kFTcxPzsFw0mRsYpCjpLFVkeErM8VYHScNPajQMbsPD5oMHJ4YwGaP/o6e1uyeWODCxrysWvRf28ZuhRUtwujJ500XfR1SvVZSVmfSudrerLbos+BPWAiY0saVSGFvElZUTcGZoueMePTeempHax7fX+/+2EIMRQiQT8dn75Ky2t/JdRSi2qyknLRzTjnXS0B/VkgeerFGRst2W9OdnL2m27VtR08/uJu9hxsYUK2HU91O+2tJzLKpKZZWH7DFKbNymf/7gae/cdW6nplCrFYDaSVuthZ20FuqgWnP0zt0RMZLAwGldyyDHY1urE6TZhz7eztldpQVWBStp2G/S0oOoX0MS521nX0WTRXluugvs2HJxBiXI6TfTXtBHuNeo7JsuH2h2jsCDB2XCoHPD48vXJ7j00z8bULmpmQ10CbJ5N6v5cQJ7JwGLCQbTHgMDexrTqbx7eaqHWfKHcaDdw42cil4xoJhCy0BHwEOZGJRNH0WNVULMZGdG0a1o8qMdTs7SnXVB2HC2fzj/QcLBYT15c4SbO29oxMahpEalX4n+fA54N/uQ51jB6l188lkVAKiuInrPjY1pDCtrYWQr1yuGfqbFys1ZITrOVV91QePWqhI3jihizbaqaiMIxqaUIXSmN/a5DW4Inr7NAbKXOZieib0IedHGlXqfP1ytGu6jk/x0qRq54Ot4O39znZ29x3v4GxqU4OtrZjNRjIMJs50NbrHAFlRic1H7cRiWi4pmayrcPT59eOyQ4Lho21BFr9pM7KY1ubj2CvAK4k20aHReGYN8CkTAeVrZ4+Odzz7SbSGwPUHWhlQrYdd1U7HW0n2uh0mVHHutjSGWB8qgWtxUtD04nMPDaLgcwiJ1ub3OS4LBh0KkcaT7TRpFeZ7DDRuvEYjgITjqV2jvbqByoKhUEXe9/oRFFU7BUZbG/z9BljmpxixbezmWBnkPI0C9V7mwj3+nscW5FG2mV+Qk4f/p2ZfPaBG1+vfSdys2woQE1DJ/OvcOCY0I5fO5FFyqpaaPFY2NnSQZbORV2LRqP3RF92GAyU1OuofbuW1DEumrNtHO2VptNi1HH+DCPO0hrCARs79mWws+5EuV5VmF5kxns8T705mMKephOfBwowIcNBp76VSChM+sFU9mzpuuY9bRzvJFzho0P1k1PlYt+n7QR65RotyLJhbPfTXNXOmPHp1B1rx+M+kXknO9fB1TdPZfzE6Mwiw03y1I8+/qo9dHzyUtfcecBcMhX7rCWo5ugb3G7x8tHHI9lv4otobryhxFK4WvRXoCrxN34cqSSoHwTnWlDf7Zl/buXDtQfjvr5iZi7bPouf1m9MWTqH9zf3+fLuLXNMKlttBgIx3hsgxaInFNFw+6NHtQHMBpU0u4ljLbHTI+pUhaLyVHbFuXYK8J/LIphs8dtw4FghT++Kf+1vnGxhVvHhuFMMbK1G8ta8ihIJxSwPpOYSvv4iFCV2G7WIDiJ6FH3stH+aprLmsIUaf2vMcgVoq8vj45r46Rvnl9jZ542f2rDcmc6e9vgpLIvNaWw64iMc56Mm12ql2e/DH2c01abX09Ziod0f+xwZdSolfpUjJ+1N0E1VYMykNHY0xm/j5R6FwxuPxS0vnp3HlsrWuOWlZelsbvHEzcYyvtBMpKyZUJwRILtqYvduZ58bjt7MepWZ21tpqovd13Q6hbQZ2eyrjJ3CUlFg2ZccdDrjX0d/Wz7rDsVPUTlBTWXTto64P2qPL7azKxDss2lUb3lOA1jc+MKx22jV67B/Eumz+VdvBoNKXo6Nw0dj11FRYHqGjUO74rfxxi/NYE4/KS6Hg+SpHz0ivk7cW9/GX901QKOzOrFNX4wxe8yAr42Xjz4eCerji2huPKE1CR3Dql+SlEG9zKkXZ0wd4Haws6P/nPJBfzhuQA/g84cJmOJPGu70h+jvV3VfMIInEDsQBAhHNHz9HEADwlrsAKSnDoH+T0IoEu5/cnPIHzegB9CFvUTiBPQAihrudwq8okTw9XN8DeiIXwyAb4D5+4EBpjZ4ApG4AT2ALxSKG9ADuEMhOvu5joFwBG8w/vEjGvjj3Bh2C8UJpnvKA/2XB0KRftMr+rQIaj8/6XrDwbgBPYAvFMHn7acvhzW8nviv1zRAP8B17CcnPoAv0v8sVbdfI9TPSfAEw6jG+HX0hMIo3vjlwWAEXz/lmgbBAa6Tu7P/z6RRS4sQdrcOdy2SUtjdzr03zI+ZP75bKBTi7bffZM1HL+P3+1HSbCxadBlXXrkck+nUp9pkZo68X5KSU+IpLQdePTYySVAvhBBCjGaSp/6MhTub+80fv3nzZ/z97w9SW9v1S195+QT+9V+/TnHxwKPzQgw2CeqFEEIIIU5DbW0NjzzyEJs2fQKA05nC5z+/igsuuDhq91dx9p2rM8slqBdCCCGEOAVer4fnn3+GV155kVAohKrquPLKZVx77eewWKKzXYnhkfj0m+SUVLeTL730Etdeey1Tp07lvPPO4/bbb6e6uvqMjvW9732P8vJylixZMsi1PHecf8kYxpZnxCwbNyGDJSsnMm12fszyzGw7KDBmfHrM/PepLgupZgMz06wYYiwGcloM/Ovi8Xxl4Vhs5uh7U5NBZZbLwrgwuKzRKTJ1qsKkAidmr0auPXrOowJMzLSzdocFQqkx2xD2plG5V2GMLTolH8CYFAtj0n0YtIyY0/N0mg1zZhaByeejqdFtCJtTWF8wh92NqWgnp48ENE1PIOAgGDShRaLTQ2qaSlNnKql6PTZddOpEBcg1pTE5z0uRM7ocYE6Ok+vG65mQEp1WEKDEZmd+voeprtjl6QYr3moD4y2p6GIkFU81GslsNjABJ8YYo1s2g545RVYumqjHESN9pFGnMneMhaKJkO6IPgeqAtMybGRWdlBgiz23dVK2jbYxKjkx0kcC5BWn4g5HKC2MXZ6bYcXa6mNqhi1m3vQMh4kUt0qRNw19jOTvNp0BV3saU502bIboNpr1KlOyHOinZeJIiU6FqtOrFE7LIZxiIiMtxnVWYGJFCh1KCGeM9JEAtkgaR+t1lDiiU6ECFDutjBvnZtYEW8wlIlmpJlKyNKbl2dDH+HtOs+hZMlljUakNky66jRa9jnJXCgUzUrDHSGmr16uUzchGKbST5oo+B4oC40tS8Zh0ZObGbmPFzDymzymIWSbEQEKhEK+99irf//63eeGFZwmFQkydOp3/+q/fcMstX5SAXowISZP95tFHH+XOO+9k5syZrFixgpaWFh5++GGMRiNPPfXUKa8YB3jnnXf4xje+gdFoJDc3lzVrElslfa5mv+m25ZNqXnxyO63N3j7pLLv1TmtpsRrIyXdyaN+JbCmZWTb0Rh01Ve0YDCqFY1wcPthM+PjCPWumjUh5Onsa3agKXDE9jxvnF/cE8+2eII+/X8lb22vRNJiUaUPb0YCnuSvrjd6swzkrj22tXkJhrSedZf3xLBuqqjB+fCr7PT7cgTAFTjM6VeFw64msOZ+frTK9tJ6I4keNWPn4ExevfHxi0d34sTb8qQEafT6cRgPLyhWmFVT1bOCj07qCpbDSjqLpsesySDG76Y5jteYQhg93oT+2B03VUVk0m3+m5dB5PEDKNBl70loChENOgjSCejz9ombAoOWg07ejKBq+gJNNTR5agl1tUDQFHWnU+lsJaWFcBgd2vUaQrowwmgaNTdm8VxmkMxgiz2bmq7NMzC48cQ42HDPx5P52mgM+nAYjC/MNjM+o7Smv7UjjzSMGarwezKqelE4Xn2700p3sJDfbREqRyhFPJ3pVYTwODn/YTNDf1bdSMkykzrKz39eBAkzJdqBzNBBUulIT6iJG3HW5fHrYS0SDyTlWjOnNePAcL1dJbcxk2y4vwXCE8Vl2LJUttB3pygijqAqpFxWwTa/hDobJc5pwOcLU+E9kU5ncYca7oZWOVh8pLjPmXAd7eqVTLcp14PWHaGj2YrPoKU61cGx7Xc9NW0pxCu35DiqbvZj0KuXpVqr2nEhBmZFvIudCA1WRNlQU8rR0Nq8P4PZ0ldutBrKLHexs7kSj68aypsNHq69rkawVmNsJ9VvqCIci5JW6qHaZOeLu6os6BaamWTlW2YrPHyY/z0bW+SHcru6sOBrj7WmE6cSvBbAoVg4ccfHunhOLcCflmAnqvTQd78szC/QYHHU9NyzhVhdbN1o4XO/FbNQxvtTGIV0rkeMnIc1oQfFb2dfkQa8qXDLexMTSKnT6ro4QCprZdTSNTXWdKMD41BSOud10Hk+nakFlTLOVAztaiEQ0ikpSOZKqo+54uQGYqRo5truJQDBCfradcDhC7fFUogoak3IcNB9qwesOkpXr4JoRms4SJKXlSBfqaOL28fDaa6/0zJvPycnjllu+yMyZswfcCG+oSPab+MJaJx3BFxM6hsOwHJ0SPwXpSJUUQX1LSwsLFy5kzJgxrF69Gr2+K5jbtm0b119/Pddddx2//OUvT+lYbrebZcuWsXDhQt5++22MRqME9XGcalAPEPCH2PrpMabOysNoijHqHI6w9uU9vPfWQby98kf3Nn5iJnU17bS3xk5plz01mxtumkpxZuw0UwdqO3jysS007m6MWW7PscPkTHZUx07757QbKRybwme1bTHXvadY4KYpep54zYMvRhP0OrjyEjsXV1RiNsZ4ggZmJYc0sx+9PnYmE/8+H0+3KBzQx876M8PlZHGBH01tiVmuRJwc7XCyrzN2ikkdJuxGF63+2pijyuGQAUsonyvLO4kxaEwgpPD2ET15zqMYYrQhElH4pLKIF14P09oeOxPJtAkO2va101Lni1leVOHENduHVxc7daExkILPa6ZRid1Ga8iCa5uN6o9i/4pnSjFhX1rInkBjzOts0hSm11vZvr81ZkYYVYGKsWnUbavDF6cv584roLrBQ3tb7DaWzU2h1g9Hq2NnY8nLtqKlGjjUGjsda4Gio9gHn8YpTzHrmDvJQGthbczsSyZVR3Ykm6c2hIiVEVavwmWTzKRkVYMao69q4D5awNZWLx2R2OegzJnKtDHNWKyxU4m6O118dNDEMXfsVKTZmgmj28oWX+xzmKHTMdmnsHtvnH5g0nHFrHyWXFmGTj9ygx8J6keuYONROja8SEGkBYNBj8ORwrXXXs+iRZf1xCHDRYL6+M7loD4pesTatWvxeDysWrWqzx9SRUUFc+bM4dVXXyUQOLVUZb/73e8IBoP84Ac/GKrqnpOMJj2z5xfFDOgBdDqVtAxb3IAeoKPNFzegB/BUtsYN6AHG5jhoOxg72AXorO2kuZ+Udu2dAToDobiJrNq8sH67MWZADxAKQ67FFzugB1BAr4biBvQAHfkpcQN6gE0tbXEDegBNbeeoJ/4NZhg/Cv6YAT2ATh/kgpJIzIAewKjXmJ4ZjBnQA6iqhjUQihvQAzRVB+IG9ACNh9xxA3qAgLENjz72jRmAR+8ldCx+ub/NT0Tzxb3OfkWjw6zETfEY0SDYGYgb0AOEajvjBvQAx3b74gb0AMfqPBxtj//6Ki1MVT8p19p8YXRF/rjpVP2RMPtrDTEDeoBQBMymYOyAHkABc0b8gB4grHfHDegBrNbWuAE9QJ3i52g/6Vwbw2E63PHPoccfxpljH9EBvRiZwh3NtL33BC2v/ZVgUzUGg4GVK6/jd7+7jyuuuHLYA3oxEG0QdpQd8ePdMSVFz9y6dSsAM2bMiCqbMWMGGzZs4ODBg0yYMGHA4zz66KPcc8892O2DewemH4VfHN2jAIM1GqDq+v+Z8lR+xRzoPA90iITLB3jCUJefDcpAZ2HARiRUfApPGNhAbUj4Og1cgWGXaF8e8PgDHiDxkzDwder/CapOHZWfzactEiHc2TzctRjxwt4OvLs/xn94W0/2FGPuWH7+sx8wYcLEYa6dOB3976oxeiVFUF9fXw9ATk5OVFn3XPq6urp+g/pQKMRPf/pT5s+fz9KlSwe1fqqq4HIl385jp8oZZxHl6bLFWajYTY2xgK5P+amc5wGCgIFuLAa6gdHHG8LuLu9nlB1AP1AbYyyk7O1UwiRVVUlk4f9AQZBer4N+NqzS61T6q4A6wDk+lWBTURXoZ5+hga6zXq+Dfn7cG/A6DlCuO4W+PJCBnjFgXx3oOg3UxgH7ev/vrzuFNg5koKB9oHNgsxpH9WfzKZPNp2IKu9t59LbFGI1GXnrpJd5a/xbBYBAybFRUVHDTTTdRVFREdna2pKkUSSEpgnqvt2veqNEYnd2ie7e27ufE88ADD3Do0CHuvffeQa9fJKLR3h7/Z+RkpdOpOJ0W2tu9hAfYNfRUuN3xp9YAROJsH99THtFoaRlg7cIAN+eRcP9PGKidA+48GhqgfKA29rPrKJzaD4KRAXaAHUgoFIHoP7Ve5QO1sf/3jwxQfiqrfLR+diLueo8BdvodqA0JlodPoS8PFLYPdBoG7KuJtjEYpr+wfqB1NuEBrtGpGGjJ10DnwO0JDPyZMcycTsvQz42WzadiCjZX8/LLa/jww/fx+7vu8idPruDmm29h3rzZPd99bW39xxfD4az0m2SlcXwKTWLHGAm/uJ6upAjqLZaukeJAIIDZ3Dedmd/v7/OcWI4cOcIf//hHbrvtNoqKioakjgN9wSWzcDiScPvcnQEO7WsiPctGU330l6zBqGPanHwstgYOxlj4pqiQnedg04YqKmbmxXyP9ZtryJqQQdW2upgRUX5RCmooQoMCwRjlRZk2nEGw6lQ8MYKFTJsRbDqcdiPtMebmOyx6tu+3kpPlJDM9ek53wKdnzbs2Zk81MH5c9FzjSATMip7zM+x81BhdrmmQocvgk2MhZuXUEGtQ/0BLJh1+E3q9H0WJbqRJcRDUTCiaDi3GfGW/z87qbXBDhQ6XNbq8xavjo1qNQqcVmzH6RtbrNbL/iJU8V4hjLdFzwg0GhcxxJpwWJ1U7Y897L8x0Yq+20JEfewGkoTMHi8+A11CLFqONqZE0QlPM6OvchHzRbXDlOTDXKVhdOjwxhvvTNCN6n0KKw0RbR/SNqN2swwKkZ1ppaog+BzqLDsNMB/m1Aar3RK8N0KkK50/L5XAwxMb9sds4c6YFbCof7wsQ0aK/WaaiIzMUoUmBWFP/87KtNNQZ0WV2EIkxL94QsuML6kkx6WnzR5dnO1SyUsOEVAvuSHRAo0dHik1hQqaJ3Q3R50ivQLrDgN/vwmSKXgOiadC0I4Myv4F9xg60GCPyY4M2dEGVVmL/KDPOaMBhiWA0qASC0X+veVk2SvKdcT+7Wpu9fPjOIeZdVExahozmn0siAR/e3R/i3vEurzsMGAx6SkvHceONtzBlSgWG479SDcZ3nxgeMv1mBMvKygKgtraWkpKSPmV1dXUA/aa0vPvuu3E6nSxdupSqqqqex7s2jlCpqqrCbDaTkRE757o4c5GIxsfvVrLmuZ143EFUVaG0LJ3qI634jwdcU2flsfz6KbjSrSy+agKfrT/KS0/toL21KyjMK3QS8IfZv7uR/bsbKZuUydU3TSXreD7qozUd/OPF3ew91BU85E7IwOYJ0Xi4FQBnqpkUl4Wjh1rgSBuT0i2ES1LZfnyRnc2soyjDzp7qNo40uHFaDJTm2djR4UZTFIyqQlmmnT0NnTS4A1hSVMryXOzf30okoqEqUJ7npLLBzQe72vl4j53LZ2WweP4RTKaugGnHp7m8/EqYto42Xl0LC2anc9O17aSkdC00DAWtBOlANdSwIB+mZ6Tx0mGNo96ugMmpOuj06djV0cauVlhfm8vKsV4KUrra2OK18Xa1g4PudsCNy2BljF2PomsDQIeRcNjO4UAz0IFFNZFpchKg65yFQ3oO1WaxvspNWGvh7UodN0xysmJSJ3qdQjAM71QZeK++gaAW5tMWlempuZSn1aPXhdEisHFrIc+9B+3ezq7c6PlOjjS6cR9fjVk+3obb4We3vwUyYewV6bR91knb8aCwOzXhnoMtcBCKi9NJnefD6+i6CTQEHFQdSWNvgxfwU5CSQ16ej7bjC4ctmoVQSzrrazyAj6yFBYxtDNC8oQYAk9WAqzyDvdXtRLY2YbMaKJriYI+5K6g0aArjO2xUbm9lZ7ATo0GlbIyLA0daCYe7rvOkbDtNB1vYX9URsy/nXJTN0XyNLYEOyIHxJek0beigo6Wrr00Ym8bNyyeQn921pmdzZQsPvnWAYy1dgXNxoZHsSUHqgg0AzJ9up77Gyt7arn6Uq6pMOObj2J4mqoApGVYYl8bm41lw7FYD2UUOdrZ0cvBgiNRjWcwYqxF01KAooEZ0BBvz+eiQn1CkA4teZXKWg90NHYS1rqw3K2fpsKTW06aFUCMqWYZ0moIthI9PqcowuOgIu2mKNFCUDxNyM1i3L0yLt+sclGVYwezhkK+RQ9UwzpHB2DQ3qq6rjr5aJ5+8YeBoddcNT3G+ncBYlWNK1997pmYk5YjK4cqu6zox3Uq42M6u4wM4aTod4z1QuaORXUCKw0hBroWDR7r6usWkZ/nisSw6vzDmSGYoGGbd6/tZ++peAv4w7765n0uXjGfhkjIMMfZCEKNHJOjHu3c9nl0foPm9aJEw+flj+OIXv8yMGbOGLT2lEIMlKVJaPvnkk/zHf/wHd999N9dcc02fslWrVrF9+3bWr18fc3oOwMqVK9m9e3e/73HBBRfwt7/97YzqJyktY6utbucfD3xK9fEv296sdiNFY1xcfNm4mPmj/b4Qr7+4m8r9TRyOkdFGp1O4YFEp7UY96zZUHZ/OcIKiwPh8Jw5fiCMHmgnGGskbm4a7JIUDvQLP3gozrFhzLBzt9NHsjc7ykW0zkR/R0dzqo7Y1elTaZTdwzTyNzR9HOFAZ3T8sZj2fv9bGvLkdaLroEVtNUznSlsVrlUYOdLRGjTsowNxMJ1l2jU+a2nqCrt6KLankWlVq/W0EtegR2TS9E6/Hwtv7Q7QHottY6LBw4zQjHzc30xKMHpV26E1MNth4+W0TB2ujR3TtZj0lOTbCWUEOu6NHrY2qyriwnUhlkAMx+omqKkye4aQzz8zGQ37CMT6uKnKt2KwRNh8O4IvRT8scZgqP+TjU4MEd4zrm5Nhwlppp2NlJS4zsS2mpZnJTzPir22msjf4FxeYwkj0uhbpJRip90dfZrNMxPmznkvxcZldErwsKhSO89Fk1lfoaqmmKeZ3zDOlE1kPDp7WEYvTl/HFptE1IY7fPhzvGFLHSNDNjcsNsOkjsvmw3MTFXobSkEY8Wow2qCafeTiASoDUUfR0Nih5/RwaNwTDVvtaocr2iMsOVQtN6hU2b26KmWCkKjJ3oQjUoHNzeQijG9KlxZeloFh1Vu5vwx/p7zXVQnOfgmivGk+KIvX7n0P4mnnjwMxpj/FroSrdww60zz3o+e0lpOfS0UBDv/o14drxH5PjfqC4lE8v4OTz+nevJzc3t8/xEv/vOBklpGV840kFz4KmEjpFm/Bw6NfZGdiNZUozUL1q0iF/+8pc88sgjLF++vE+e+o0bN3Lttdf2BPT19fV0dHSQl5fXMyXnJz/5CZ2d0V/GP/3pTzEYDPzv//2/SU9PP3sNOkfs29UQM6AH8HQGujaeifMFajLrmXtBMete3x+zPBzWeO+tQzSnRu8uCV0/7++taqcwGI4Z0AMcO9CMmm2LGdADHG30MDbDFDMIAqhz+8m1WWMG9AAtnUHWf2alqjL2FAuvL8TOfT7OOz92uaJEyLA2s78jThuB9Q3tFASIGdADHPa2YjHYYgb0AM2hdqob7LQHYs8ZPdrhZUOdRgux14x0hPx8dtQVM6AH6PSF8CpBamIE9ACBSIRavHQciX1THIlobPu0jQZ/VyrJWLbVeMiyGWMG9AB7O3w4IWZAD1Bb68ZhNsQM6AGaW33km/QxA3oAd0cAt4WYAT2ALxymxR6KGdBD18LipbPy+Pct22OWa0B1sAn7TmIG9ADV+5sJlqfEDOgBDjb7MOnsNHtjt6Gu08+l6WrMgB7AF/Fj16wxA3qAoBbCaPVQXRe7H4S0CNvrOqnZFLv+mgb7d7ZgNOhiBvQA+/c2kZ1hjRnQQ9cvdssWlsYN6AG2b6qJGdADtDR52fpp9YjdpEqcPi0cwrv/Uzw73yPi6Zrup3OkYau4FFNJBRF3qyyAHaWSYLx6SCRFUJ+WlsYPfvAD7rrrLlatWsXKlStpaWnhoYceIiMjg+9973s9z/3Nb37Ds88+y9///nfOO+88AObNmxfzuHfddRdGo5HFixeflXYIIYQQYmhpkTC+g5twb19HxN01sKTaUrBVXIJ5zHQUVaZZidEpKYJ6gFtvvRWXy8WDDz7IXXfdhcViYf78+dx+++39zqcXQgghxOinRSL4Krfg2fYO4c6uaZuqxYF18kVYxs1C0SVNyCMSoKGhJZLXmeRdaJtUPXzFihWsWLGi3+fcfffd3H333ad0vLfeemswqiWEEEKMXKN88ylNixCo2oNn90cngnmTFUvZXMxjpqHo9ES80dm2wp2tZ7mm4myR6TdCDLKMbBt6gxp3HnBugbPf11usBlJSzbTFmbOekWFF5zDR0BJ7Hm9aiomUsEZHW+y50ja7kYwMK4eaY88XNxlUnOGuhYqxPh50ioLFqqLXKTHnASuA1anHbNLhizMPWDWaiERMqGrsOmoRKy6jkZZA7HOQZrTg1Cs0BmOnh7SoRpSICYg9l1qPHrMW/2NArygoYQOqTiES4ywogEmvw6RX8ceZ024367EZ9LiDsef1uwxG9M4QLe2xz0Fetg1rqpVDLbGvU5rZQLpRT7079m5SDqOe/GwzR+Os7zAYVIwWPYoSO0e+qiqYTDr0ejXuojmT3oBFH8EbJ/d7imKgtd1HqjP2+gidopBlslHvjz3fO1Vvxpau0BnnHNnsRpR+Ni4z61WcViVuX9arCpFOHXp77OsMEO40oDfpCMXZ9cugmbDpQrjDsdcupJgt+F1hmmOkOgXISLNgNOo4FmftQlqqmfwcO3WNsfuBxaSnPU4ZdGW9CQXDqKoStbAeuhbrZuf1/5mUtJJ486mwu517b5hPRkb0WodIJMKWLZt4+eUXqK2tAQPYCjJZvPgKLrro0p59bPqTmSlrKMTokRTZb0Y6yX4TX1ODmxee2MaOLbU9j2XnObjm5qmMmzDwh6nfH2Lty3tY98YBwsfrYLEayMl3Urm/CUVVyJ2SxYFGT0/gbDCojM+2U7ejnnAwQsnYNBrr3XQezzmuqgrnX1zCFSsnYrUZ2XSomQffPkhNr5uDiRk2tF2NeJs8OMvTqB/n5HDniYBqXLoVTB6aA17SVDOmJhMHjp0IRvLTLZjzNGoibpzoya01cnBPa095mstM1mwDta5WUgw6vjTRSJ6zvie3vBYxsqPJxZpj7ajoSFXSONDRTvD4xlJGVUeBxUmluwUNjQmuVNxaO75IV0ClouAig42HAnhDEeYVWchKb8OnnQioLL4M3ngLGlvCjCmyoGSFqe21idsYp4P2QIAmn5+iVCMluSGaQicCY5fqoHKzhb0HA7hsRjKcJvbVnFhImZVqwlWg46jWgVWvo8BhZ19LW0/I6DQYKK5VqX2nDr1RR9akTPbVdhLsvs5mPSsWjWXh+YWgKLyyt47HtlbhDhy/zqrCFJuF+s/qCAUi5E/J4JAJWnzHz4ECV4zP4gvTCnCaDOzc38Q/XtxNTa+FkiXFKVRHNBq9QQodJuzeUJ+gsjDbjr7FS8uxDlLTLDicJo5WnriO6Tl23PkO9rgDOO16SiaaOeA50UaX0UhGk57Kz1oxGXUsW1jKZQuKY+7GGtYivFtfyZu1+/FHjrdRUcntTGXXG+2EAhEmZjtoO9KKuyPQ05fHT8zk8MFmfN4QrpnZHMw2U+85cYNTkWtFTWvEh4+USCq1NVaO9LpRHu+0oB7qoLXWTdFYM9OXQIf+RBttmp0D7xrYtdlLRraB81ca6LScGPW1KBZqGp1sOOrFalCZmGumyt/cc3Ng0xlwaClsr+lEj8L4DgeHt7X05JY3GXXkj3GxtdlNRNOYmm6j4Uh7z8JmnU7h8gtKuPX6qfi8ftZvruGJl/fQfLwNigLjC5x07G/B2+GntCyda26eSm5BSk8dd2yp4YUnttPU4CYjy4bBqKOm6sSNcPHYNK65eSoFxalR12WoSfab/oU7m3n4czP6TLPVNI3PPvuEJ5/8J0eOVAJgtdq46qoVLFlyVb/71pwKyX6T3EKRdhp8/0joGJnmm9GryXeTL0H9IJCgfmC7t9ex5rldzJxXwIJLS0/7w6ihrpMXntiGzxei7lg7Hnff0UBrign7GBdhDfxVbXQ29R29N5l15BeloigKK2+qIK/XFz6ApsAb2+t5Y+NRXI0emveclJFGAdcFBVSnGnA5NapipO0rVpx01EZIzdJzkNao3eiKIlbUAyFSCvXUFbYQVvue00mpJm4YH6YzqOfZIz46Qn1Hti2KCTVsJxJRaA546Aj1HZm26PSMT7UR0sLsP6ajuq1vuVmnsLjcjNUSYPMGE5t39x31VRWNyVOceC0BTDodh9qjM53MLLBgtXtwH7Pz4UdetJMaWZJlIxTWcGXqOKRrixr1zbFaMOpUnB0KzWtqCbr7ttGRYcWY7yQ7x87nlozHae870tbmC/LwpqMcaXQT2ddKe0Pf62ww6cickUXAouN/LZ5ItlHXp++GwhHe+vAIH3x2jKDDyL6TfuVRNI0pGTaCzT5cChzb2RB1DgqKUwkEwyg5dj7x+ImcdA6KCyyYcjXsPh1HPmwm6O97nbMzrNy8fAJTymLvi9Ee9PFS9R4aWj1UrfPTUnvSdTbqGJ9iJuwO4PeHaKzr+9mjGlScFxdSb1fJyvHSqp6UElYDVzCH6jqVjPYQx3ZEZ1+ae7Gd7Gk+2g9aePfldrSTNsCaMsvKmAVBvH4La/f5CJw0eJ/nMJHhimDAwK4aH56Tfq1zaQayagxoIYXKYIhWX99+YDfqKLMYMesUbl42gYJcR5/PokAwzCvvHGLLznpM7X4aT/oVRlUV5l8yhjkLClnz3G52bauLamNxqQu/P8zFl49j9vmFw5ajXIL6/p0c1O/du4d//OMR9uzZBYDZbOHKK5exdOkybDb7oLynBPXJTYJ6kRAJ6s+O9jYfP/+3Nf0+x2DUETw5wujljp9d2mcEr1t3W//f/3mT7Ztq4r4+67oCdsSZygJQlprC3tbY0zwAyjIstOjiz23NMJtQjfH7kqZBQ0f/s+bCbSk0+2JPRQHIb3ex72js6ToAU+fY2e+J34bScCrbd8dObQgwoczGEUP84xcEjPhfrI9bbrbo+cUflsUtB/j+nW/T6Yk9zQPgp9+Zx+xp+XH77u9e3s0Hu6MD9m7zTQaqe/26dLKMWbl8Gme6D0BpipmmA9H7K3RLdZr49U8ujlsO8PX/eCNuekeAUkWhuSF+Xyn8ZiZHiX8dcvels2tTa9zy8SWp7KuMX140NYUDrthT3wAyLSYavLGnC0HXr0mdO/oPSu7/1zlkOs1xP4vu/3/vxdyBulvJuDQq98f/e1t4ZRlLr53Ubx2GmgT1/esO6kOhIE888RiffLIBAL3ewJIlS1m+/BocjsHNJz7SvvtikaA+vlCknTpvYkF9tiU5g3qZUy+EEEKIESns7eDxx//OJ59sQNMiKIrKJZcs5LrrbiAtTfaXEbGFtXNzd2AJ6oUQQggxokQCXjw738ez4z0+TDFhMOiZPXsuN954C/n5BcNdPSFGJAnqhRBCCDEiaOEQ3n0bcG9fh+b3okXCjB07jq985WuUlZUPd/VEEtCIvwP56RwjGUlQL5KGXq/2m1bQYNRhMuvjzqlXVAgG48+3B9Cp/f9kpw8r0M9mhCr9z3HUDzAH0qCoRIj/gaJDj574aQVVTUHtr4KA0dR/G3XhAc7BAAsKdQOdA0UloCpocT51jcfXRRiMsdsRDIYxGvp/j2CcNKrdDOEB5sla+j+HhgG2ljfo+j9HRkNXP46VBQfAFwpjNOsJuWOvG1AV0JkGmE8b0vX7Ca8McA5V0wD9aKB+EtGhEj9FplFVMRrUniw4J9PpFPz9XCdN09DFOX/dzGZDv+Um8znyFZgEeeo1TSNYexD39nU9ueb1jnRMpdP5/ve/RE5OzjDXUCSTyDk6/UYWyg4CWSh79jTWu3n+n1ujsllMnZXH8uunoDeovPz0Tj796EifnON5hU4C/jAd7X4WLyvnokVj+wQE9TUdPP/ENvbvaaS41EVVZSuBXjcH9nQLpoIUDh5rp7TcRXW2nw5OZOyw6PSkqansqHUzLt1K0OCmLXgibaBRpzAlz0pNsJkMk5WwptHQKye5Ckx0pdIWacWqM2JU9dQH+i5ytAUy2VMTBkWhME1PXaTvAsF0r4vqXSod3hBjS60cVFv7hFN5NiuqolDv9VJicLJzayeB4IlnZDiMpFiNHKzvZGKpkzpLO16t1zlQ9KR32tld2cG4XAcN7X5aey0WNRtUSrPt7DnWQWGWFS0zSEPkxEJKvQZlnXaObG8j1WHC6Q/RcNJCzDHj0qir7cRk0rPihilUzMzrU75pZz1PvLQHrz9EXpYtaiFnfradcCRCS5uf66+awMVz8/vkpqk91s7z/9zGgT2N5E3OYlMkjKfXYtRUh5HUfBt7WtxUOCwoH9Xg65VJyWQ14CrPYG91OwV5Dup1CnW9zoFBpzAlxcLRQy1kuCwowLFeKTQVBcYVp1Jd24nDZuSm5eVUlPdN7fre4SYe+PQIwXCEsSGV6m0Nfe7yCnMd+AMh2jsDjMu0cWx7PZFef58pRU7aC5xUtfmoKLfRnt1ASDlxHa2aFX+Ti111XqakWOnc1oin48TNg8VhwFmRwfZ2D2VOK+G9rbQ39joHepUJaRaq9jaRUeCgtcJElfHEOVBRKAmncOCghxSbAWeBQlWk1+JyDYqsLg41hNChkKPoOVjZd2F2SZGDFr2GOxjmlmkFLJ+YS0a6veez6MihFp59fAu11e0UjnFx5FBLn/0wMrPtrLypgrJJWXy07hBrntuFt9fCakeKiWXXTWbW+UUMt7OyULaxlWX/9eSQvsfpODn3fHV1FU8//QR79+4BwOFwsHz51cybtwBVVcnMzEQd4EZ6MI3E776TyULZ+IKRdo52PpHQMQrtN2JIwoWyEtQPAgnqz76dW2t54Ylt6HQqV988lfET+wZGhw808+w/ttLW6iMt3cLhg30zkWTl2Fl501QKS1J57fldfLSuss+GNA6nibQMG9VVbV350+s6+4z+Wsx68iuc7LN0km9NY29doCd/OnTlUJ+c2xXEj88049d30h46kQlEAUpsLmq9HWRZzZiMAVpDfTfOyTWl0hHyEgkaqW+wcri9b3mx04bR5iMYCBOudLLnSN8+mJtmxp6v0Kr6yLdb2dfa3ifId5mMODrNHDzoYVyOk3017QR7Bbh2s56SUguHlTaKjwdp3l5tNOlVxubY2XOsnXE5DmpafbT3Cpx0qsLEUgfHzO3kBc14d/lobO6bLWV8gRN3ZSt2ix5FUag71jerTtmkLK6+uYKITuWfL+1m+0mZTnIzbSgKdLgDZGfYOHCktc/NXHaGlRuvKqesOJXXX9jN+28fJNKrjTaHEUtZBlt8AUrHudjT6emziZZFrzJV09H+XhU55RkcbvP35E8H0OsUikvT2NnuY0yKGW+dm5a2Ezdz3UH8sXo3LqeJYCgStXnStImZ3LSsHI8K/73xMFvr+t7MlTjNWKs8hFoDpLvMHDwpfWNOho2UUJiWmg4sM3LZ3uTu89Ozy26gdKKOFkcLNk82W474CfQ+BwYdE3QGjm1pIG9aJrvDQdy9ftEy6hSmWMzUbapnnMuKu6qdjpPaWDgzk91FYVx6K+1VGrUnbTBVXuSgLcWDyWDA7zFReVIq0dIUK6FmP6FQBHuOhb0nbTRW6rLyw8UTSQ9rvLB6Gxs/7HvTnuIyk5Jqoe5YB4uuKuOiy8b1+RXE3eHnlWd38ulHR1mwsJTLlpcPOIp/tpyL2W+6M9qYzSZWr/4n77yzFk2LoNcbWLp0OStXXptwrvlEjOTvvm4S1McXjLRT2ZFYUF/ikKD+nCVB/fAIBcMoqhL3gy0S0bj7/3uD5n52mSwsSe2zmdDJcmfmsivOTqQAReflsLEzflq/88aaaDXHT5FZaLXj77XRz8lMmNi83xh3fqCqgHLARIc39m6tAOPnmKn2xO+fxZ2p7DocP0XllKIUtvdzDiYVpLCzqp80nmlW6vpJO5jpNBE61BJzN1fomobRnmWLO6VGUSAzzUp9U/zrPDXVwpH98evgWFrKJ+7413Ge3sShLfHTcI4tSuFAP+coN8vWZ9Ork5kteqrG2gnHOQmqAuOP+miNszsyQPqEDA42xn+P8ZPS2NYUPx3rtGwHW+ri94N56GlZdzRuecb4dD7tZ8ZOqsNIvS3+XFedomAxqHT2k5L2okMe6vo5z6tum8O02flxy/2+0IibcnMuBvWhtnpWpTXx1ltv4PN1/d3Nmzefm276AllZ2QO8euiN9O8+kKC+P4FwO4c6Vid0jDGOGzDqki+oH1mfbkKcBr2h/zm/qqr07EIbz0Af2OEBVtv0k0YcIO584hPlA7y/Ful3wU9EA22ASsQLFE+1PDLAOYgM+PoB2hiOxA3ou8q1fq+TpnUdoz+hUP9rKQa8TgOdg4H6yUD9MBzp9zpEtK7z0O97DHSdE+3LA71/JAL9BBmhiNbvPNewphEcqI4DrIlRB1gTM9IC+nONpmn4j+yg85OXed4UwmDQU1o6ji984UtMmDBxuKsnRNKTTzghhBBCDKlgSw2dn7xCsP4wWiRMSlYuX/ziV7jggovO6nx5cW5INPtNspKgXgghhBBDIhLw4t7yFt59G0GLoOgMWMrm8L9/+K8UFQ3/QmUxOsnmU0KMRgOkXxygGGWg1w/09gOUD/SMU/lYSrQOSoJHGPDVCV6D7mP0t/wn0es4cAWGuHwQqpDoAQa+jok9YTD68il1FjEiaFoE38HNuDe/QcTXtdbDVDQZ+8wrQItgMpmGuYZCjD7ym5cY1b749TkUFKdGPW6xGhgzPh2fN0RBcUpUucGgUjQ5i4Y2H2OLostVVeHSeYV8a1EZKybkoIsRbExxWAi/3khpSyoGJfpPbZw9nZsKp3N55lQsanQmDhc21HeMTKk1kmY0RpWnG01MOWag3BcmL8UcVe4w65mQ58RbBYVWW1S5SadSak+jVokwPt8RVa5TFaZnWNG21DI1wxYVTynAxGw77qPtTM22Y4gxn3pstp22cJgx5elYYsxnzs2x4xufgvPCAhwx2pCWbqWgOIVxDhOZruhsGE67kUnZNlK8IQqyottoMeu58apybvnKTCZOjV6Ap9d3XefOfW1UpFijgkpVgSvGZXHbVRO44qISdDHyz48pTKG9w0/ZmFTMMXLrjy9J5eufn8aXrpuMwx59HfOz7Xz/1pn89JIycu3RgU662cD5XsjSIDfTGlVutxooG+NCbfZQmhl9Dox6lZlpVswf11Jhjz6HqgLLyrP53vxSlpdnE2taeoXdQnBfC6Vl6Rhi5LfPHZNKo8PAhHwnNlP0dc53WcgNacxAT7oluq9nWYyc1xlhWn2AfFv0OXCa9EzLS6GmzEl2SWpUucmk56rrJjNxquQyHwmCTdW0vPZXOj5+jojPjc6ZSeqiW0m58EZ0ttThrp4Y5TQgkuB/yTp7R7LfDALJfjOyRSIaG94/zKvP7sTjDlAyLp26Y+14em3sUzTGRUe7n5YmDwVj06gJh2nqPFFekGPvSUc4viSVz6+YSGHuiUD4cKunJx1hns1E4VE3rdsaesqdBWZSlzk5oraRajCzPH8C01y5PeXecID3m/ewuf0wRkWP46CdXasbexaQ6s06MpbksNvgRQHKAxYa1tQQ9nddF0VVSJ+dx25/EG8gzIQ8J5UNbjy9MolMnGCnzeKlNRBgrDOFA3Vhmt0nysfZzASbA9S1+hibbsVS2UpH9YlsKCnFKbTlOzjc7CXfZcHmCVLXqzw1zYIxx86++k4yHCZSrAYO1J3ItuI06hhrMXDoYAs2q4GU8S42BXxox+8WrMB5bqjbXIdOp1BQnErlgeaeBZqqTiF3SjYHGtwEwxHK8pw07mog6Due+UeBvEmZHO0M0OkJcun5xaxcXIqtVxC5a1stz/9zG431bvJLXdRFNBo7T+RYzyxJoT3HzJEOH+UZdr4+p4Rx6ScC5doGN/94cTc79jWR4bJgs+g53CsNp8NmJCvdwoEjbaQ6TVx/ZRnnTT9xnT2+IC+8eYC3PzqK0ahj5eKxXDqvsCeLRTAc4ZmdNTy5/RgRTWOGwUjnu0cJ+8I9bcyfkk1lqxePL8T4EhfH6jvp7NWXC8a6qAlHaO4MMCHDhrqnEXfDicxAqVMyqC6yU+X2MyXLwW1zSihxnbhZqGzx8JeNlWyv7yDfZiL/SCdt2xtPXMcUE6npVo4cbMHpMqOWutjSK1e/zaSjMMPGnmPt2Ex6xthNHNnT2PMtqTeqZM/IYrvXh4LCNFVPx7qjRIIn+nLqRYVs00fwhSJMyLRzqNmNp9dn0JyIDm1rIx1tPmbMLWDZ9ZNJSR2+FIiJOCvZb5raWXnva0P6HgCRgA/PjvfwVW4FQNEbsE6Yj3nsDBT1xA1vuLOVR790KdnZw5/pJpZk+O6T7Dfx+cPt7GlLbF+G8pTrMSVh9hsJ6geBBPXJweMO8ND96zkYJ72i3qCSX5HNljgp8xQFViway/JFY+O+x8sfVPLOI1viZqSZsiSPz18zC6MaO3PPoaYGHvz1BjyNsXcStRdYIAKdx2KnXzQ5TQSnZXGoIXZ6R7NRoWx6OhsOt8cs1wEXh/RUfxAndaECBfOL2L63Me5QxrgpmWyp7yRel5mQbWefGsQdZyXTGFVH2qcNdLbHTt9oSzFjcRhprIrdBqNFz823zWXhJWNj9t1QKMJjq7fx3kkbmPVu4+VLx3P9gpK403beeK+SJ9fsJV5in4ryDG67eSrmGKPWAMfqOrHbDDhjjMwDNLj93H/P+3QcjZ1i0mwzYBmXxqHq2OUGg0p5roOqz2KnU1VUhQu/UMGKC0tjNwB46f1DrHtka9ydf4umZrM+FCYQp3xcpo3Oyja83th9OTXTgqXZi7cudl81uky4LyzgYFvscitwx7Qi5lbkxixPFsm2+dTJG0dBV1abzz77hKee+icdHV19cs6c81i58jpSU1NjHudsbyh1OpLhu0+C+vj84Q52tSbW3yemXo9JF/0L9kgnc+rFOcNqM/YZnT9ZKBihs5+0fZoGxhjTK3pLDUT6TTEZPBqKG9AD2EOWuAE9QGdV/FzqAP52P5395Kz3BTTa3PG/pMIAHYG45WgQ6Qz0+9uk3xeKG9ADtEYiuPs5QL0WwRgnoAdwt/nQ+klRGfCGcMWY5tJNr1fx95f6UANrQOt3Hr7FYogb0AP4/KG4AT1AXrY9/ouBTJsJT038gQKfO0jYE/86B4MRtFZf3HItopHq7z9YSfVH4gb0AO5wJG5AD9DpCcYN6AE6Gr0Q5+YTINDip9Uf//UewBJjupGIQafHmB3/Bu50hDubycjI7Bllb2io58EH/8bmzZ8CUFxczL/8y21MnDh5UN5PiDMh2W+EEEIIIU5BJBLhtddeYfXqf+D3+9Dr9axYcS0rV16DwRD/ploIMXQkqBdCCCHEKWtqauQvf7mP3bt3AlBePomvfvU28vMLhrlmQnT9kJxoSstkHeiXoF4IIYQQA9I0DV/lNu666wEikTBms4Vbbvkil166eMTOjxfnpnN1+o38FYpzyqVXjMPmiP3T8Ox5RVxxUQkZMVInApSNcTF9YmbMsm4TpmQzPs5zUtMsLFgYf15rpzvAmg+PkFeRjRJjzrfOpCN9QSHp8wvRmaLn5SuqQsZ5+WSnWrDGKAcoz3OS4oP0OPO9p+c6ufiSMWTnxl4gVFCcyqILixkXI00oQIbdiKvVz9QYaQkBUg0q41r8nB9WUWOMhVgVOK9Do7QsHTVG+khVp5A/LZvUsgyMlug2KArMWVBETl78BU47t9QSqe0k0xG7jmOLUpg5OSvu6wEmjktj8vj0mGUup4nLLiiO+1qvJ8DzT2xj3ev7CYfjz2u/fPkEDHHWcIwZn0a+XsVhjl1eWphCKNWMPT06BSZAboGT/bsaOHY09qLwozUdbDraRlapK2a5K93KZReWMK8sI2a5w6xnxbwiLjmvIGZqeYNeYWxJKnnTc9DFSJGp6Lr6crFiwKqP/TV1SUk6Y1yx2ycGX8Tvoe3dx+nc9Dp+v5/y8kn86lf3sGjR5RLQCzFCSPabQSDZb5KL1xPgted38+E7h4hENLJyHVx3yzTmXTCGlhY3Xm+QNe9W8uq6QwSCEVxOE587KTXhQLZ8Ws2Lq7fT2uxFb1C55IrxLLqyLGaQFolorNtQxXOv78d9fGFhTroVhz9EQ2UrABnTsjmkV2k+njow3W6kJBChcWtXBhdXWTr1LjPH2roWR9rNegrSreyu7soQk+uyYNSrHG44vgmMXqWkyMlOj4egBlk2E1+dVcT5RWlAV5/+4K2DvP7ibnzeEFa7kaXXTGLuBcWox284Ptp0jKde3Udbhx+jXmFCuo1jexp7+kpuqYvDTiM1vhA6BWZbjLTuasB3fCFvZoGT+nFO9mpdi17nhnWEt57IeuNKt2KzG6k63HUOssel0aRAY0tXGx02I4V2I8d21oPWdcNxzeenUlyaFrPvNtZ38tw/t7H7eNYbnV4lvyydPc1e/KEIKQ4j1y0p4/wZuae8WdWmnfU88dIeGlu86HUKiy8oZtmlpTEXyWpaV2rVV57difv4YuTsXAdX3zw17o1ga7OHF5/cwZZPqrv6Rb6DSFijvrYrVajFaiCnPIMtVW1ENMhMs2Ax6zlyPM2mwaAyPttO/c4GQoEwjhQzaekWDh9sAbr2W5h3UQlLrp6I1WbE7Q3y/Bv7eWd9FZHjQ13j8p34jrbRebwvX7pkPAuXnOjL24608sBbB6hq8qAqcNnUXG66oBi7uSuV6JFj7Tz+wm72H7+OpYUpNLf6aO3ous5pKSay9Cq1e7qyUqWVp1ObaqbmeF92WPTk5jvY3tFJBIVSl5WvzSlhclbyZaaI5axkv2np5Nq/rT/j1wcbjtD2wZNE3G2AxvdvvYWbb/7CqA3mk+G7T7LfxOcLdbCp6emEjjEj/TrM+jP/jPnLX/7Cjh072LFjB1VVVeTn5/PWW2/FfX5jYyP33HMP69ato6Ojg5KSElatWsUNN9xwWu8rQf0gkKA+OdVUtXFwXxPzLizBZNZHtbWpxcun2+u4aG5Bv5lM4gn4Q3z8biWTp+eSHidLR1uHn98/9FlPEHayssIUmq16DjTFzhIyLt2KSVXY0RC7/+WnWUizG9lxtC3mz5EZThPzpmVz08xCTDFGRDvafXz60VHmXlCM1Rb9C4fPH+KFV/ay+8MjtMfItqLTqxRMzaazup2muug6KgoUTMsm0OKj7nDsUePCklQ8KSYOxEnfWJBlY9GMXBZcNKbnhuPkvvvemwd4+ekdMfuxI8XExPlFLF9aHnODrIEEg2HWbahiSlkGOXGus7vDz//c+zFHD7XELJ8+J58vfG1O3PfYv7uBN17aw4E9jTHLM3PsGItT2XWopScY783lNDHObuTw7kYCgejMQTa7kflXlfPqx0f65LzvZjSozB6Xzsory2L25XBE482tNZTlORmTFTuzz/sbq1n70RGO1sS+jiW5DkKpprh9uTjbzkWzcriqPAddf9mLksxIDuo1TcO75yM6N70OkQg6Zzr2GVfw2FevGLE55gdDMnz3SVAfny/UwSeNiQX1szMSC+rLy8tJTU1l0qRJ7NixA7vdHjeob29v53Of+xx1dXXceuutFBQUsHbtWt555x2+853v8O1vf/uU31fm1ItzVm5BCrkF0bvFdkt3Wbj8wpIzPr7RpOeiy8b1+5ymVl/cgB7gaKOHWlv8P9P9TR4s/aTZrG72omnx5xc2tvspS7HGDOgBHE4zl1wxPu7xzSY9RelWNsRJnxgORQg2uGMG9NCVJrStspX21vgpLI9WttIWYyfVblX1bsZPzu4J6GPZvb0u7pdzR5uffJfljAJ6AINBx+IF8afbALS3+eIG9AA7ttT2+/pxEzJ57H8+iVveUNuJPc0SM6AHaGn349erMQN6AHdngB17GmIG9ACBYISwSR/35lSnKlwxPa/fNlRMyOChZ3bELa+s6cBH/ADqcF0nM7JTRlVAf9ZEIoQ7m0/rJVo4ROdna/BX7QHAlF+GbcblaP7+0+oKIeDNN9+ksLAQgGXLluHxxE/f+9e//pXDhw9z7733cvnllwNwww038PWvf50///nPrFy5sudYA5GgXgghhBjNtAhhd2vc4pM3lGpvb+Mvf/kjh91H0WU4uPba67nookt7pqVlZva/tkiI4aQBkWHOfnOqQTjAiy++SEFBQU9A3+3LX/4yb7/9Nq+++ipf+9rXTulYEtQLIYQQo9kAm0/13lCqquoof/jDPTQ1NZKamsrtt/9QNpISSWcwst8cO3aMVatWxS1fu3Ztwu/R0NBATU0Ny5YtiyqbMWMGiqKwdevWUz6eBPVCCCGEoLLyIL/61Z10dLSTk5PH//pfPyE3t/9pVUKIM1dX15W4IScnJ6rMaDTicrmore1/emZvEtQLMYzSUszk59ipPp7N5GQV5emkATurYi8inVKUgtNi4MM4CyhLMm1MLEihprVrbv3JMuxGGg40EypNQ2+IPze/P3mFKThTzTEXyqqqgt1hIi3DSnNj9JxCRYHyKTnUH2vvychysglTsvGmmtiyO04bC5w47P3vYDl+Uib7dzcQDkefBEeKifyi1H5fnyhnipn8ohSqj8S+jnmFTvburKdsUuxUmof2NZGV46CjLfbag4wsGxaLgWOqEnNefVqKmbJJmdRUthKMMa/eajMwcXw6h5u8PRmYejMYVPT+EC1NHlxx0mQOZO/2Ogqz7Ryti93Xi3PsBFPN7Iq3UDbdSuX2OvIvKOl3/YQ4M4cOHeB//ufPeDxuSkvH8aMf/QcOx+jIMCTOPTE+6k9bXl7eoIzG98fn6/reNBpjf4eZTKae55wKCeqFGEapThM/+875vP3xUZ5/Yz8eX1e6x7xsG59fPpEJY7tSTH6wu56/rztEc+fxlJYOE7dePIbzy7vmti6qaOGBtw5Q3dy1iM1u1nPTgmIWT81FpypcMjmbB946wJ5jXSkujXqVKU4zLZ9U8+4nNex4/zArbqhg8mmk7exWNMbFj+5czBsv7eG9tQcIH1+Qml+UgtcTZO/OBlQVSsvSqT7Shv94G/OLUrjm5qmUjEtH0zQ++fAIrzyzk47jKS3TM6196rR1dwP/fGkP9cczATlsBq69YjwXzM4fMAXlJZePZ1JFDs/9cxt7d9YDoNMpXLBoLJctL8d8PP3iULE5THzv/7uE9e9V8upzu/Acv45pGVYsVgOHD7Tw37/9kIqZuSy/oYK044FzW6uXl57cwaYNVUBXfvlgIExjfVfga7boKR6Txt7d9Wj1bopzHYQzLRw5Hjjr9SpXXFjM0ktKMRl1zJ1XxIurt7Pts2NA103VeReWcOU1k7DZjVy4oJhnX9/Puxurem4Cx+Y78Ve1seO9w+xdX8WiK8u45Ipxp3wTWFPVxrP/2MrBvV0pKydNyqTKE6T9+DlIdZjINemo2d51XWZOzKTaYaDueD+wmfRMNOlp+KiK5z84yqfvHuaam6dSfPxvQyQu1FrPH//4IOFwiLKyCfzwh/+O1Rp7UbQQYvCYzWYAAoFAzHKfz0dGRuz9QGKRlJaDQFJaJr+R0NaOzgDPvbGfnEwbC88vjEpX5guGefrjI6iKwrXnFWI6KagKhSO8uukYta0+blpQjMPSN1DVNI0P9jTywbY6PJtqcMcYEZ0wJZvrvjDtjEdjG2o7efnp7bS3+TkSI9uL3WEiO8/BtNn5zLsoesTV5w3y+gu7sdgMXHLFeAwntTEYivD6e5W0dwZYuXgsVkvsYLy/67l90zE2b6zm8uUTyIqzydZQ8rgDvPrsTupqOqjc30TkpO5mMOq4dMl4DEYdb764B78/1KdcUaBkXBoKCrU1HT03CL3lTszEmmXjuivLyIpxLffurOfjdytZeGUZBTE2Ejtc3c5zr+0j0OCmbn901pT0TCvX3jKd8n426QoGwrz89I6e/SD6tNGkJ3NSJmhaTw793lSdQtqcfEIGFe/mGvwdfX89UBSYNa+QlTdVYLH2/yvNSDfcKS3Dnc00v/Iniq0wZcpUfvSj/68n0DhXjYTvg4FISsv4PKEOPqh9NqFjLMi5BmsCKS17685+EyulZUNDAxdccAHLli3jnnvu6VPm9/uZNm0aixcv5r777jul95KReiFGCIfdyKprJsUtNxt03HLhmLjlep3K8tkFccsVReGSKdnsfeMAO+NMcdi9vY49O+qZd1HJKde7t8wcOzPmFfLInzfGLO/s8OP0mJl/Sex2mC0GVtxYEff4Br3KVZfGX/B3KqbMyGPKjOGbJ2y1GVl8VTl3/vC1mOXBQJjXX9iNTq/2/OrRm6bBoX3NpGVaYwb0ADW7GvjyotKYAT1A2aSsuFN9AIrzncwqTuW5j47GLG9q8PDe2gP9BvUtTR7ef+tgzLKgP0TNlhq0OPFSJKzR+HEVzhRzVEAPXefgk4+OMntBEePKJRPLmYr4vbS+9QgRv4f88eP5t3/70Tkf0IvRYTAWyp4NmZmZ5OTksHnz5qiyzZs3o2kaFRXxvxNPJrd5QgghxGh2PE997/9CHY20vfs4obZ6VKOFb37ze9hssTcOE0IMnWXLllFVVcXrr7/e5/EHH3wQvV7P0qVLT/lYMlIvhBBCjGJaOMzvFhXicp2Ym/vaay/zor8WfaaT22//EePGxd9kTohkE04wT32innvuOY4d61q71NzcTDAY5P777wfA6XTyhS98oee5X/va13jttdf44Q9/yI4dO3p2lH377bf55je/eVo57yWoF0IIIUYxRacjMzOT9PSu6VKHDx/i9dfXYDDo+drXvsWcOXOHuYZCDJ7+dlE/nWMk4umnn2bDhg19Hvv9738PQH5+fp+gPiUlhccff5zf/va3rF69ms7OToqLi/nP//xPbr755tN6XwnqhRhEhw82U1jiGtEp99Iz42e1UFUFZ0pic2odDlPc+eAArjRLv69vanBjMOoSrsdIZjDqsNqNcefEO1PNGAw6muKsfTBb9KS6LDQ3xN56XK9XsTtMCdUxECP1ZW8DXUeTWY/FasDriZ4TD+BMMREJ05Pt6GRWu5HUNAvtbbHTuRmMOmy25F4kOxzC4TD//d9/IhIJM2fOPC65ZOFwV0mIUeeRRx45rednZWXxq1/9KuH3laBeiEFQX9PBc//cyt6dDeQVOLnm89MYMz59uKsV01e/M5+iUhcvrN7WJ+95aVk619w8ldyClISOX1qWwb/9bCHPP7GN3dvreh632o1cefVEzruwJObrAv4Qa1/Zy7rX96M3qFy2bAIXLCodlRkerDYjP/7FYtY8t4uP363syQ6j06tctHgsi68qR1UV3lqzl7fX7CMU7LpBUhSYM7+IK6+djM1uZP17lax5bhfuXjcHk6blsPLGin5v3vpTV9PBc//Yyr5dDWTl2FF1CrXVHT3lKalmll0/hRlz4y/KBkhxWfjRLxbzyjM72fjB4Z6RL51eobg0jaOHWlBUhdKydCoPNBM5nlhaVRXmXVTCkqsnYjLpee+tA7zx4p6eVKgAU2flseKGKaSmnVmWpnPZ2rVvcOjQAaxWG1/60leHuzpCDInIME+/GS6S0nIQSErL5HembfX5grzx4h7eX3sgamOjGXMLWHb9ZFJS+x/RPJt6t7Oz088bL+5h22fHuPKaSQMGaWdix+YaXnxyO2WTslhy9USscUZWt3xSzYtPbqf1eJ79blm5Dq6+qaLfTC2xJFPfPXa0jWcf34rRrOPqG6eSmdN3sWJzo5sXnthOW6uXaz4/jaIxrp4yvV5Fr9Px2N82sn9PIytuqGBiRfYZ1cPnDfL6i7v54K2DUX25ZGwaLc0eZs0rZNFV5ZhMpzcedORQC8/+YyuKBu1tPlpb+l7ntHQrVrsRg0HH1TdXRG0G5nEHWPPcbg7ta2TFjRWMnzh6Mt6clZSWTe10NjRgt6fw/e9/k7a2Vm699atcccWVQ/q+ySoZPj8kpWV87mAHr1c9n9AxLi9Yic2QfJuvSVA/CCSoT35n2tY3X97Dmud2xS2fMbeAW/519mBUcVCMxGva3OThrh+/3u9z/u9fVp7WlKaR2M6hMJjtfP3F3bz+wu645TPnFfD5fznzvtzR7uf/3PFqv8/55X3LYt4wjObreTaD+o8++ojHH/87mZlZ/PrXf8BgGNpN15JVMvQ3CerjO5eDepl+I0QiBrgllnvmUyDnaGQY6DIkfJlO4QDSFYZMJBLhtddeAeDqqz8nAb0Y1c7V6TcS1AshhBCj3Pbt22hqasRms7NgwYXDXR0hhozGIGS/GZSanH3y240QQggxmmkRPvroQwAuvnghRqNkDRJiNEqqkfqXXnqJBx54gP3792OxWFiwYAF33HEH+fn5/b6ura2N5557jnXr1nHgwAFaWlrIzc1l7ty5fPOb3yQ3N/cstUCMNqqu/5/4Ep3zGAqGUXXqiE6RmShVVVGU+LNwBjrHYnDoBjjPaoJ9WVWU/q+z2lUuBp8WibBz53YAzj9/wTDXRoihF07WofYEJc1I/aOPPsodd9yByWTiJz/5CbfeeisffPABN910E3V1df2+dsuWLdx9991omsYtt9zCT3/6Uy6++GJeeOEFli9fzv79+89SK8Roc/Hl41h5UwUWa9/5qTq9ysIrx3PtF6ad8bG3b67h//7vtfzhrnVUHmhOtKojVmqahW/96CLyi6JTaY6bkMHtP710VN/UjBSXLBnPihunYLb0HevR61UWLS3jms9PTej4NoeJ7/77xX2y93QbMz6d7//HJRhPM6uOOHWBQIC0tAxKS8cOd1WEEEMkKbLftLS0sHDhQsaMGcPq1avR67s++Ldt28b111/Pddddxy9/+cu4r6+qqiIcDlNcXNzn8Q8//JAvf/nLXHHFFfzhD3844/pJ9pvkl2hbOzv8Pfm4yydns/KmCjKz7QO/MIaG2k6e++dW9uyo73lMUWDWvEKu+txkHM4z35RpJF/TSERj/XuVvPrcLoxGHctvmMK0Wf3/ChfPSG7nYBqKdna0+3nlmR188uERJkzp6ssZWWfWl2PRNI2NHx7hlad3ouoUln1uMjPP638b9NF8Pc9GFpOjtQ188eYbWbz4Cr72tW8O6XuNBsnQ3yT7TXwdwQ5eqHwhoWOsKFmBQ7LfDI21a9fi8XhYtWpVT0APUFFRwZw5c3j11Vf52c9+FneeYEFB7Pzb8+fPJzU1lb179w5JvcW5w+4wccOtM7hixQRSXGeel7612cOv/89bUbuxahp88tFRdmyp5c7fX5VodUckVVU4/+IxTJudj16vyqjtMHE4Tdz4pZksWTkxob4cj6IozF1QTMWMPFRVwWSW6zzUtEjX7sATJkwc5poIcRZoiS+UTdaVsklxm7d161YAZsyYEVU2Y8YM3G43Bw8ePO3jdnR04Ha7SU8fmTt/iuSTaBAUDEaiAvrefN5gQsdPBlabUQL6EWAoAvreLFaDBPRny/Ggfvz48mGuiBBiKCXFJ2p9fdc0hJycnKiy7Oyu3RPr6uqYMGHCaR33/vvvJxgMcvXVVydcR70+Ke6PTkv3T3vnwk98I6Wt+lN4/0T62khp51CTdo4u50o7h46G0WgkPz8PVZVzOBDpb8nvXF0omxRBvdfbtaV4rOk1JpOpz3NO1SuvvMKDDz7IggULuO666xKqn6oquFy2hI4xkjmdQztiN5IMd1v93vCAzxmMvjbc7TxbpJ2jy7nSzqGQn59HenryzREeTtLfkpNG4kF9st4TJEVQb7F0/WEFAgHM5r6LBP1+f5/nnIp169bxwx/+kEmTJvH73/8+4ZGLSESjvd2T0DFGIp1Oxem00N7uJRwemYuFBstIaWt728A3py0tZ74oe6S0c6hJO0eX0dxOp9NyVkaEMzKyEvrsOJckQ387W/1GJJekCOqzsrIAqK2tpaSkpE9ZdzrL7mk4A3n33Xf59re/TWlpKX/7299wOAZn5GKkrpAfDOFwZFS3r7fhbmtKmoXl10/hjZd24/OG+pQ5U80su27yoNRvuNt5tkg7R5dzpZ2DToO0tHQ5d6dJ+lvyOlen3yTFbV5FRQUAmzZtiirbtGkTVquV0tLSAY/z3nvv8e1vf5uSkhIeeughXK7ofMlCDCdVVbj48nH88M7FzDq/EEXp2hTokivG8aM7FzNzXv+p/4QQ4mR6nUphYdFwV0OIsyaiJfZfskqKoH7RokVYLBYeeeQRQqETo5fbtm1j48aNXHnllT3z7evr6zlw4EDUHPv333+fb33rWxQXF/Pwww+TlpZ2VtsgxOlwppi5+Suz+PaPLuKO/1zIss9NkUwhQogzoiiKDGIJcQ5IiighLS2NH/zgB9x1112sWrWKlStX0tLSwkMPPURGRgbf+973ep77m9/8hmeffZa///3vnHfeeUBX8P/Nb34TTdO47rrreO+996LeY+XKlWetPUKcquKxcvMphEhcSkrqcFdBiLNCFsomgVtvvRWXy8WDDz7IXXfdhcViYf78+dx+++0Dzqfft29fz4LaX/3qVzGfI0G9EEKI0cpul8w34txxrs6pVzRNO0ebPnjC4QjNzaMvq0AybJU9WM6Vtko7RxdpZ/JLS7MNeRaTmpoa2tu9pKVlDun7jBbJ0N/ORr9JVm2BDh7e+0JCx7i1bAUpxuS7EU6akXohhBBCnBmTyTzwk4QYJZJ5sWsi5DZPCCGEGOVO3uNFCDH6yEi9EEIIMcoZjUbC5+pEY3FO0bRBWCibpH8qEtQLIYQQo5yiKCRvTg8hTs+5ev8q02+EEEKIUawroBdCjHYyUi+EEEIIIUYFDYWwltiNrEZy3ghLUC+EEEIIIUYNyX4jhBBCCCGESEoyUi+EEEIIIUaNc3WhrAT1QgghhBBiVNAYhJSWg1KTs0+m3wghhBBCCJHkZKReCCGEEEKMDhpEIokfIxlJUC+EEEIIIUaNc3VOvUy/EUIIIYQQIsnJSL0QQgghhBgVZKGsEEIIIYQQImnJSL0QQgghhBg1ztUdZSWoF0IIIYQQo4YslBVCCCGEEEIkJRmpF0IIIYQQo8K5vFBWgnohhBBCCDFqyPQbIYQQQgghRFKSkXohhBBCCDFqSPYbIYQQQgghkpimDcKc+iS9KZDpN0IIIYQQQiQ5GakXQgghhBCjRiSiDHcVhoUE9UIIIYQQYtSIhM/NoF6m3wghhBBCCJHkZKReCCGEEEKMCpqW+PQbWSgrhBBCCCGEGBYyUi+EEEIIIUYNWSgrhBBCCCFEkjtXg3qZfiOEEEIIIUSSk5F6IYQQQggxapyrKS0lqBdCCCGEEKOCpimDkP0mOW8KZPqNEEIIIYQQSU5G6oUQQgghxKhxri6UlaBeCCGEEEKMGpHIcNdgeMj0GyGEEEIIIZKcjNQLIYQQQojRQRuE7Dfa4FTlbJOgXgghhBBCjAoaic+pTzSmLy8vj1u2ceNGnE5ngu8QmwT1QgghhBBCDKLZs2dzww03RD1usViG7D0lqBdCCCGEEKPGSMh+U1hYyMqVK8/qe8pCWSGEEEIIIQZZIBCgs7PzrL1fUo3Uv/TSSzzwwAPs378fi8XCggULuOOOO8jPzz+l12/fvp3f/va3bN68mUgkwpQpU/jud7/LnDlzhrjmQgghhBBiyGkQHoSFsseOHWPVqlVxn7J27dp+D/Haa6/xwgsvEA6HSU1NZfHixXz/+98nMzMzsbr1I2mC+kcffZQ777yTmTNn8pOf/ISWlhYefvhhNm7cyFNPPUV2dna/r9+6dSurVq0iPT2db37zmxiNRlavXs2XvvQl/vrXvzJ//vyz1BIhhBBCCDEURsJC2YqKCq644gpKSkrwer2sX7+eZ555hg8++IDVq1eTlZWV4DvEpmiaNuIT97S0tLBw4ULGjBnD6tWr0eu77kW2bdvG9ddfz3XXXccvf/nLfo9x4403snfvXl5++WXy8vIA6Ojo4KqrrsJisbBmzRoU5cw6QTgcobnZfUavHcn0ehWXy0ZLi5tQaHTv5HCutFXaObpIO5NfWpoNnW5oZ8LW1tZiMjlG3bkbKsnQ385Gv0lWNR1u/uW5txI6xt+uXkiuwzZINery3HPP8aMf/YgbbriBO++8c1CP3S0pesTatWvxeDysWrWqJ6CHrjuhOXPm8OqrrxIIBOK+/ujRo2zevJklS5b0BPQADoeD66+/nsrKSrZs2TKkbRBCCCGEEEMvElES+m8oXH311eTn5/POO+8MyfEhSabfbN26FYAZM2ZElc2YMYMNGzZw8OBBJkyYcEav737O9OnTz7iOen1S3B+dlu5RgHNhNOBcaau0c3SRdopTJefu1El/S3aDEZgPTWCfn5/Ppk2bhuTYkCRBfX19PQA5OTlRZd1z6evq6uIG9XV1dX2e21v3MWtra8+4fqqq4HIN7s80I4nTOXQ5VUeac6Wt0s7RRdopBiLn7vTJORODSdM0jhw5QkZGxpC9R1IE9V6vFwCj0RhVZjKZ+jznTF/v8/nOuH6RiEZ7u+eMXz9S6XQqTqeF9nYv4fDInFc4WM6Vtko7RxdpZ/JzOi1nZUR4NJ67oZIM/e1s9ZukpEFkELLfnKnGxsaYgfsjjzxCbW0tt9xySwIV619SBPXdu28FAgHMZnOfMr/f3+c5A73+ZN3B/MnHPV0jdTHNYAiHI6O6fb2dK22Vdo4u0k4xEDl3p0/OWXIa7uw3f/nLX/joo4+45JJLyMvLw+/3s379et5++21KSkr49re/nVDd+pMUQX136p/a2lpKSkr6lPU3taZb7yk6J+t+LNbUHiGEEEIIIU7VvHnzOHjwIC+88AItLS0oikJRURFf//rX+epXv4rD4Riy906KoL6iooInnniCTZs2RQX1mzZtwmq1Ulpa2u/ru597ww03RL2+93OEEEIIIUTyGqoMNqdi0aJFLFq0aFjeOykmZC1atAiLxcIjjzxCKBTqeXzbtm1s3LiRK6+8sme+fH19PQcOHOgzx76oqIipU6eyZs0aampqeh7v7OzkqaeeoqioKKHMN0IIIcRIdaZ7sAiRlLRBSGk54ndwii0pgvq0tDR+8IMfsGPHDlatWsU///lP/vSnP/HVr36VjIwMvve97/U89ze/+Q1Lly7tSWPZ7T/+4z8Ih8PccsstPPjggzz66KPcfPPNNDQ08LOf/Uw+9IQQQgghRNJKiuk3ALfeeisul4sHH3yQu+66C4vFwvz587n99tv7nU/fbdq0aTz66KP87ne/49577yUSiTBlyhQefPBBzjvvvLPQAiGEEEIIMdQSzn6TpJImqAdYsWIFK1as6Pc5d999N3fffXfMsqlTp/LAAw8MRdWEEEIIIYQYNkkV1AshhBBCCBHPcKe0HE4S1AshhBBCiFFjOLPfDKekWCgrhBBCCCGEiE9G6oUQQgghxOigKYkvlNWSc6RfgnohhBBCCDEqnMtz6mX6jRBCCCGEEElORuqFEEIIIcToEUnWsfbESFAvhBBCCCFGBUXTUBMM6hUtOW8KZPqNEEIIIYQQSU5G6oUQQgghxKihhJNzpD1REtQLIYQQQohRI9HpN8lKpt8IIYQQQgiR5GSkXgghhBBCjA7aIIzUJ+lAv4zUCyGEEEIIkeRkpF4IIYQQQowKCqAkmtJycKpy1klQL4QQQgghRg31HM1+I9NvhBBCCCGESHIyUi+EEEIIIUaHc3ihrAT1QgghhBBi1JA89UIIIYQQQoikJCP1QgghhBBilNASzn6TrPNvJKgXQgghhBCjgkLi02+SNaWlTL8RQgghhBAiyclIvRBCCCGEGB20QchTn5yzbySoF0IIIYQQo4dkvxFCCCGEEEIkJRmpF0IIIYQQo0bi2W+Sk4zUCyGEEEIIkeRkpF4IIYQQQowK53JKSwnqhRBCCCHE6HAOZ7+R6TdCCCGEEEIkORmpF0IIIYQQo8a5ulBWgnohhBBCCDFKaKiRSMLHSEYy/UYIIYQQQogkJyP1QgghhBBiVFC0Qch+k5wD9RLUCyGEEEKI0SPh7DdJSqbfCCGEEEIIkeRkpF4IIYQQQowakv1GCCGEEKOOoiTr/phCnIFBmFOfpMlvZPqNEEIIIYQQyU5G6oUQQgghxKigMAjZbwanKmedjNQLIYQQQgiR5GSkXgghhBBCjBLaIKS0TM5J9RLUCyGEEEKI0UEbhOw3yRnTy/QbIYQQQgghkp2iaVqS3o+MHJqmERmlOVF1OpVwODLc1TgrzpW2SjtHF2lnclNVZchTTkYiEeSb/vSM9P52NvpNsgqHIzQ1uBM6RnqmDZ0u+ca9JagXQgghhBAiySXfbYgQQgghhBCiDwnqhRBCCCGESHIS1AshhBBCCJHkJKgXQgghhBAiyUlQL4QQQgghRJKToF4IIYQQQogkJ0G9EEIIIYQQSU6CeiGEEEIIIZKcBPVCCCGEEEIkOQnqhRBCCCGESHIS1AshhBBCCJHkJKgXQgghhBAiyUlQL4QQQgghRJKToF4IIYQQQogkpx/uCoiz76WXXuKBBx5g//79WCwWFixYwB133EF+fv4pvX779u389re/ZfPmzUQiEaZMmcJ3v/td5syZM8Q1Pz1n2s62tjaee+451q1bx4EDB2hpaSE3N5e5c+fyzW9+k9zc3LPUglOX6DXt7Xvf+x5r1qxhzJgxrFmzZghqe+YSbWc4HGb16tU888wz7N+/H4CCggKWLFnCt771raGs+mlJpJ2apvH888/zj3/8g8rKSoLBIPn5+Vx11VWsWrUKm812FlowsL/85S/s2LGDHTt2UFVVRX5+Pm+99dZpHydZPo/OtsH8TEhGhw4d4oUXXuCDDz7gyJEj+P1+ioqKWLJkCbfeeitWq7XP8xsbG7nnnntYt24dHR0dlJSUsGrVKm644YaYxz/Xz68YmRRN07ThroQ4ex599FHuvPNOZs6cyYoVK2hpaeHhhx/GaDTy1FNPkZ2d3e/rt27dyqpVq0hPT+eWW27BaDSyevVqDh48yF//+lfmz59/llrSv0Ta+e6773Lbbbcxb948zj//fFwuF/v27eOJJ57AYDDwz3/+k3Hjxp3F1vQv0Wva2zvvvMM3vvENjEYjubm5IyqoT7SdwWCQ73znO7z33nssXbqUmTNnoqoq1dXVNDc384tf/OIstaR/ibbznnvu4b//+7+ZN28el112Gaqq8uGHH/LGG28wZ84cHn300bPUkv6Vl5eTmprKpEmT2LFjB3a7/bSD+mT5PDrbBvMzIVn9+te/5rHHHuPSSy9l+vTpGAwG1q9fz6uvvkp5eTmrV6/GbDYD0N7ezuc+9znq6uq49dZbKSgoYO3atbzzzjt85zvf4dvf/nafY8v5FSOWJs4Zzc3N2vTp07VrrrlGCwaDPY9v3bpVKy8v1/793/99wGPccMMN2vTp07Xq6uqex9rb27ULL7xQu/zyy7VIJDIkdT8dibbz6NGjWmVlZdTjH3zwgVZWVqZ95zvfGfQ6n6nBuKbdOjs7tUsuuUT7+c9/rl166aXaFVdcMRRVPiOD0c7f//732oQJE7R33313KKuakETbGQwGtWnTpmnXXHONFg6H+5R94xvf0MrKyrT9+/cPSd1P15EjR3r+/6qrrtIuvfTS0z5GMnwenW2D+ZmQzLZu3aq1tbVFPf6b3/xGKysr0x599NGex379619rZWVl2muvvdbnubfddps2efLkPn1Vzq8YyWRO/Tlk7dq1eDweVq1ahV5/YuZVRUUFc+bM4dVXXyUQCMR9/dGjR9m8eTNLliwhLy+v53GHw8H1119PZWUlW7ZsGdI2nIpE21lQUEBxcXHU4/Pnzyc1NZW9e/cOSb3PRKJt7e13v/sdwWCQH/zgB0NV3TOWaDs9Hg8PP/wwl156KRdeeCGaptHZ2Xk2qn5aEm1nKBTC5/ORkZGBqvb9eM/KygLAYrEMTeVPU2FhYUKvT5bPo7NtMD8TkllFRQVOpzPq8SuvvBKAPXv29Dz24osvUlBQwOWXX97nuV/+8pcJBoO8+uqrPY/J+RUjmQT155CtW7cCMGPGjKiyGTNm4Ha7OXjw4Bm/vvdzhlOi7Yyno6MDt9tNenp6wnUcLIPV1q1bt/Loo4/y7//+79jt9kGvZ6ISbeenn35KZ2cnU6dO5b/+67+YPXs2s2bNYu7cudx55514vd4hq/vpSLSdZrOZGTNm8N577/HXv/6Vw4cPU1VV1bOO4Prrr+8TACezZPk8OtuG6vNvtKirqwPo+RxvaGigpqaG6dOnRz13xowZKIrSpx/J+RUjmQT155D6+noAcnJyosq65wB2f+DF0l0Wa75g9zFra2sTrmeiEm1nPPfffz/BYJCrr746ofoNpsFoaygU4qc//Snz589n6dKlg1/JQZBoO7u/ZB9++GFefPFFvv/97/OHP/yBiy++mEcffZRvfOMbaCNgedFgXM977rmH2bNn8+tf/5rLL7+cRYsW8bOf/YzbbrttxKwbGAzJ8nl0tg3V599oEA6Huf/++9Hr9Sxfvhw4cS5inS+j0YjL5erTj+T8ipFMst+cQ7pHI41GY1SZyWTq85wzfb3P50u4nolKtJ2xvPLKKzz44IMsWLCA6667LvFKDpLBaOsDDzzAoUOHuPfeewe/goMk0Xa63W6gK7PRCy+80LPQ+YorrgDghRde4P333+fCCy8c1HqfrsG4nmazmTFjxpCXl8eFF16Iqqq8+eab/OEPfyASifCd73xn8Cs+DJLl8+hsG4rPv9HiF7/4BZs3b+b73/8+paWlwIk+Eut8Qdc5692P5PyKkUxG6s8h3XNpY8338/v9fZ5zuq/v/tDrziYwnBJt58nWrVvHD3/4QyZNmsTvf//7qLnKwynRth45coQ//vGP3HbbbRQVFQ1NJQdBou3s7pdTp06NylzUfZO2fv36QalrIhJtp9fr5eabb6azs5P/+q//YtmyZSxdupTf/OY3XHvttfzxj39k165dQ1P5syxZPo/OtsH+/Bstfvvb3/L4449z/fXX8/Wvf73n8e4+Em8evM/n69OP5PyKkWzkRCdiyHUvlIv1k3R/P2V36++nxf5+wjzbEm1nb++++y7f/va3KS0t5W9/+xsOh2PwKjoIEm3r3XffjdPpZOnSpVRVVfX8FwqFCIVCVFVV0djYODSVPw2JtrO7X3Yfp7fMzEygaxR/uCXaztdee43KykqWLFkSVbZ06VI0TWPDhg2DVNvhlSyfR2fbYH7+jRb33nsvf/7zn7n66qv5+c9/jqIoPWXd5yLW+fL7/bS2tvbpR3J+xUgmQf05pKKiAoBNmzZFlW3atAmr1drzk+SZvL73c4ZTou3s9t577/Htb3+bkpISHnroIVwu16DXNVGJtrW6upr6+nqWLFnCokWLev6rq6vj6NGjLFq0iB/96EdDVv9TlWg7p06dCkBNTU1UWfdjI2EBdKLt7A4qQqFQVFn3Y+FweDCqOuyS5fPobBusz7/R4r777uO+++5jxYoV/OpXv4r6pTUzM5OcnBw2b94c9drNmzejaVqffiTnV4xkEtSfQxYtWoTFYuGRRx7p86W/bds2Nm7cyJVXXtkzT7C+vp4DBw70mRtYVFTE1KlTWbNmTZ/gqLOzk6eeeoqioqKYGQTOtkTbCfD+++/zrW99i+LiYh5++GHS0tLOahtOVaJt/clPfsIf//jHqP/S0tLIzs7mj3/8Y9TGK8Mh0XYWFBQwZ84ctm3b1ufLW9M0HnvsMQAuvvjis9OYfiTazrFjxwLw7LPPRh376aefBk7c4CSTZP48OttOpw+Ndvfddx/33nsvy5cv5+677447dXLZsmVUVVXx+uuv93n8wQcfRK/X90kgIOdXjGSyo+w55uGHH+auu+5i5syZrFy5kpaWFh566CEMBgNPP/10z8+GP/7xj3n22Wf5+9//znnnndfz+i1btrBq1SoyMjJYtWoVBoOBJ554ggMHDvDf//3fXHDBBcPVtD4Saee2bdu45ZZb0DSNO+64I+YI/cqVK89qe/qT6DWNZeHChRiNxhG1o2yi7dy9ezef//znURSFL3zhC2RlZfHWW2/x/vvvc91113HXXXcNV9P6SKSd4XCYm266ia1btzJ79mwuu+wyFEXhzTffZMOGDVx66aX8+c9/Hs7m9Xjuuec4duwY0LVDZzAY5Mtf/jIATqeTL3zhCz3PTfbPo7PtVPvQaPbYY4/x85//nLy8PL773e9GBfQZGRksWLAA6Jp6d91119HY2NhnR9m3336bb37zm3zve9/r81o5v2Kkkuw355hbb70Vl8vFgw8+yF133YXFYmH+/Pncfvvtp/RBNG3aNB599FF+97vfce+99xKJRJgyZQoPPvjggIHi2ZRIO/ft29ez4OlXv/pVzOeMpKA+0WuaLBJt54QJE3jiiSf4/e9/zz/+8Q88Hg/FxcX85Cc/4Ytf/OJZaMGpSaSdOp2Ov//97zz66KO8/PLL3Hffffj9foqLi/nBD37AV77ylbPUioE9/fTTUfP7f//73wOQn5/fJ6iPJ1k+j862c+UzoT/btm0D4NixY/z4xz+OKp87d25PUJ+SksLjjz/Ob3/7W1avXk1nZyfFxcX853/+JzfffHPUa+X8ipFKRuqFEEIIIYRIcjKnXgghhBBCiCQnQb0QQgghhBBJToJ6IYQQQgghkpwE9UIIIYQQQiQ5CeqFEEIIIYRIchLUCyGEEEIIkeQkqBdCCCGEECLJSVAvhBBCCCFEkpOgXgghhBBCiCQnQb0QQgghhBBJTj/cFRBCjD7r16/ni1/8Yr/P2bNnz1mqjRDibLjjjjs4cuQITz75JAA/+clP2LNnD88888wpvf6ZZ57hJz/5SZ/HLBYL2dnZXHzxxfzLv/wL2dnZg17v0/HMM8/Q3t7Ol770pWGthxCxSFAvhBgyS5cu5ZJLLhnuagghzoJNmzZx+eWX9/z7008/5aKLLjrt49xyyy1MmzYNAI/Hw9atW3nkkUd44403ePHFF7Hb7YNW59P17LPPUl1dLUG9GJEkqBdCDJmJEyeycuXK4a6GEGKI1dfXU11dzaxZswBobGzk8OHDPf8+HbNmzeKqq67q+ffNN9+M0+nkoYce4uOPP2bx4sWDVm+3243NZhu04wkxnGROvRBCCCFOm9vtprm5mebmZj744AMASktLaW5u5v333+/z746OjoTeKysrCwC9/sRY5Pr16ykvL485vefHP/4x5eXlfR5btWoVCxcu5OjRo3z3u99l7ty5zJw5EwBN03jooYdYvnw5M2bMYMaMGSxevJgf//jH+Hw+AMrLy9mwYQPV1dWUl5f3/FdVVQXAZ599xr/+67+yYMECpkyZwoIFC/jyl7/MJ598klDbhThVMlIvhBgyXq+X5ubmqMeNRuOw/oQuhEjcnXfeybPPPtvnsaVLl/b594oVKwCYO3cujzzyyCkdt/tmAbo+Q7Zt28bf/vY3SkpKmDdvXkJ1drvdfOELX2DWrFl8//vf73mf+++/nz/84Q9ceuml3HTTTeh0Oo4dO8bbb7+Nx+PBbDbzf//v/+XPf/4zLS0tfeb+p6WlcfDgQb7yla+QkZHBqlWryMjIoLm5mU2bNrFz505mz56dUL2FOBUS1Ashhsz999/P/fffH/X4+eefz0MPPXT2KySEGDRf/epXe4L2f/u3f2P27NncdNNNAPzwhz9k5syZPf92Op2nfNyf/vSn/PSnP+3z2IwZM/j973+P2WxOqM6tra1861vf4rvf/W6fx998803GjRvHn//85z6P33777T3/v3LlSp566in8fn/UtML3338fr9fLb37zG6ZOnZpQHYU4UxLUCyGGzOc+97k+c2O7paamnv3KCCEG1bhx4xg3bhzV1dU0NTWxYsUK5s+fT11dHQ0NDSxbtoz58+ef9nG//vWvc9555wHg8/nYt28fDz30EF/60pd46KGHEs6A8+UvfznqMYfDwY4dO/jkk0/OaFS9+6Zl7dq1lJeXYzKZEqqjEGdCgnohxJApLi4+oy91IcTI5na78fv9QNcot6qqlJeX09zczNq1a1EUpeffBoMBh8NxyscuKyvr87mxcOFC5s6dy0033cSvf/1r/t//+39nXO+0tLSYdbnjjjv41re+xS233EJmZibnnXceF110EVdeeSVGo3HA4y5dupQXX3yRP//5zzz44INMmzaNCy64gKVLl1JYWHjG9RXidEhQL4QQQojTEms+/clZabrTW57OfPp4ZsyYgcPh4MMPP+x5TFGUuM8PhUIxH7dYLDEfnzZtGq+//joffPAB69evZ/369bz00kvcf//9PPbYY2RkZPRbP6PRyN/+9je2bdvG+++/zyeffMJ9993Hfffdx69+9SuWLVt2Cq0UIjES1AshhBDitPSeT/+tb32LJUuWsHz5cgC++93vsnDhQq6++mrg9ObT9yccDtPZ2dnz75SUFADa2tqintudkeZ0WK1WLrvsMi677DLgxGZYjz/+eNQc/HgqKiqoqKgAutJ8Xnvttdxzzz0S1IuzQlJaCiGEEOK0jBs3jvnz55Obm4vH4+mZP19YWEhHRwdXXXUV8+fPZ/78+UyZMiXh93v33XfxeDxMnjy557GCggL0en2f0XvoSi25efPm0zp+rCxd3e/V+6bBZrPR3t6OpmkDvj4rK4usrCxaW1tPqy5CnCkZqRdCDJldu3bx/PPPxyxbtGiRpLUUIslt3LgRvV7PjBkzANiwYQM6ne6MNp3q9umnn/ZMn/H7/ezbt48nn3wSg8HQJxuNzWbjmmuu4cknn+T2229n7ty5HD58mGeeeYby8nJ27959yu+5dOlSpk+fTkVFBdnZ2TQ1NbF69Wr0en3PLxAAU6dO5e233+YXv/gF06ZNQ6fTcemll/KnP/2JDz74gEsuuYSCggKg60Zkx44d3HLLLWd8LoQ4HRLUCyGGzCuvvMIrr7wSt0yCeiGS24YNG5gyZQpWqxXoCvInTpyY0N/2Y489xmOPPQaAqqqkpaVx0UUXcdttt/UZqQd68sW/8cYbrF27lkmTJvGnP/2J1atXn1ZQ/5WvfIV169bx6KOP0tHRQVpaGlOnTuWee+5h+vTpPc+79dZbOXz4MC+99BKPPfYYmqaxdu1aFi9eTENDA2vWrKGxsRGTyURRURE/+9nPuPHGG8/4XAhxOhTt5N+QhBBCCCGEEElF5tQLIYQQQgiR5CSoF0IIIYQQIslJUC+EEEIIIUSSk6BeCCGEEEKIJCdBvRBCCCGEEElOgnohhBBCCCGSnAT1QgghhBBCJDkJ6oUQQgghhEhyEtQLIYQQQgiR5CSoF0IIIYQQIslJUC+EEEIIIUSSk6BeCCGEEEKIJPf/AxFrvd6fqrEfAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 700x700 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "alex_jointplot(ds);"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'data_dsdna_d7_d17_50_50_1, T=753μs, #bu=3912'}, xlabel='E', ylabel='PDF'>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x450 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "dplot(ds, hist_fret, show_kde=True)"
    ]
@@ -307,16 +551,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[array([ 372,  579, 1926, ..., 2553, 1182,  316], dtype=uint16)]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "d.nanotimes"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -334,9 +589,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "hist_params = dict(bins=range(4096), histtype='step', alpha=0.6, lw=1.5)\n",
     "hist(nanotimes, color='k', label='Total ph.', **hist_params)\n",
@@ -360,7 +626,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -380,7 +646,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -398,9 +664,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "hist_params = dict(bins=range(4096), histtype='step', alpha=0.6, lw=1.5)\n",
     "hist(bursts_nanotimes_t, color='k', label='Total ph.', **hist_params)\n",
@@ -430,7 +707,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -452,7 +729,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -461,7 +738,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -495,7 +772,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.13.0"
   }
  },
  "nbformat": 4,
diff --git a/tests/conftest.py b/tests/conftest.py
index f4cf456..e393b1b 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -15,14 +15,14 @@
 # data subdir in the notebook folder
 DATASETS_DIR = u'data/'
 
-alex1c = pooch.create(path=DATASETS_DIR, base_url='doi:10.6084/m9.figshare.1019906.v26')
-alex1c.load_registry_from_doi()
+# alex1c = pooch.create(path=DATASETS_DIR, base_url='doi:10.6084/m9.figshare.1019906.v26')
+# alex1c.load_registry_from_doi()
 
-phdf5 = pooch.create(path=DATASETS_DIR, base_url='doi:10.6084/m9.figshare.1456362.v15')
-phdf5.load_registry_from_doi()
+# phdf5 = pooch.create(path=DATASETS_DIR, base_url='doi:10.6084/m9.figshare.1456362.v15')
+# phdf5.load_registry_from_doi()
 
-mphmm = pooch.create(path=DATASETS_DIR, base_url='doi:10.5281/zenodo.5902313')
-mphmm.load_registry_from_doi()
+# mphmm = pooch.create(path=DATASETS_DIR, base_url='doi:10.5281/zenodo.5902313')
+# mphmm.load_registry_from_doi()
 
 fetchers = dict()
 
@@ -42,6 +42,7 @@ def _alex_process(d):
     d.calc_bg(bg.exp_fit, time_s=30, tail_min_us=300)
     d.burst_search(L=10, m=10, F=7)
 
+
 def load_dataset_1ch(process=True):
     fname = fetch_if("0023uLRpitc_NTP_20dT_0.5GndCl.hdf5", 'doi:10.6084/m9.figshare.1456362.v15')
     d = loader.photon_hdf5(fname)
@@ -49,6 +50,7 @@ def load_dataset_1ch(process=True):
         _alex_process(d)
     return d
 
+
 def load_dataset_1ch_nsalex(process=True):
     fname = fetch_if("HP3_TE150_SPC630.hdf5", 'doi:10.5281/zenodo.5902313')
     d = loader.photon_hdf5(fname)
@@ -82,7 +84,7 @@ def load_fake_pax():
 
     
 def load_dataset_grouped(process=True):
-    fn = ['HP3_TE150_SPC630.hdf5', 'HP3_TE200_SPC630.hdf5', 'HP3_TE250_SPC630.hdf5', 'HP3_TE300_SPC630.hdf5']
+    fn = ['HP3_TE150_SPC630.hdf5', 'HP3_TE200_SPC630.hdf5']
     fn = [fetch_if(f, 'doi:10.5281/zenodo.5902313') for f in fn]
     d = loader.photon_hdf5(fn)
     if process:
diff --git a/tests/fetch_files.py b/tests/fetch_files.py
index a47f359..2639fba 100644
--- a/tests/fetch_files.py
+++ b/tests/fetch_files.py
@@ -1,9 +1,8 @@
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
+# Created on Sat Feb 21 07:24:07 2026
 """
-Created on Sat Feb 21 07:24:07 2026
-
-@author: paul
+Pre-processor script to fetch files for tests
 """
 
 import pooch
@@ -18,11 +17,11 @@
 phdf5 = pooch.create(path=DATASETS_DIR, base_url='doi:10.6084/m9.figshare.1456362.v15')
 phdf5.load_registry_from_doi()
 _ = phdf5.fetch("0023uLRpitc_NTP_20dT_0.5GndCl.hdf5")
-_ = phdf5.fetch("dsdna_d7_d17_50_50_1.hdf5")
+# _ = phdf5.fetch("dsdna_d7_d17_50_50_1.hdf5")
 
 mphmm = pooch.create(path=DATASETS_DIR, base_url='doi:10.5281/zenodo.5902313')
 mphmm.load_registry_from_doi()
-hp3_files = ['HP3_TE150_SPC630.hdf5', 'HP3_TE200_SPC630.hdf5', 'HP3_TE250_SPC630.hdf5', 'HP3_TE300_SPC630.hdf5']
+hp3_files = ['HP3_TE150_SPC630.hdf5', 'HP3_TE200_SPC630.hdf5']
 for fn in hp3_files:
     mphmm.fetch(fn)
 

From c4b0e582248df5be24a9bb96abb93a45d6f2eed6 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Tue, 5 May 2026 14:50:12 +0200
Subject: [PATCH 128/142] Use dot slash notation in cache action of test.yml

---
 .github/workflows/tests.yml | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 4153cb4..b859964 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -9,10 +9,10 @@ jobs:
       - uses: actions/cache@v5
         id: filecache
         with:
-          path: tests/data
+          path: ./tests/data
           enableCrossOsArchive: true
           key: testfiles
-      - uses: conda-incubator/setup-miniconda@v3
+      - uses: conda-incubator/setup-miniconda@v4
         with:
           auto-update-conda: true
           activate-environment: test
@@ -43,10 +43,10 @@ jobs:
       - name: Restore Cached Files
         uses: actions/cache/restore@v5
         with:
-          path: tests/data
+          path: ./tests/data
           key: testfiles
           enableCrossOsArchive: true
-      - uses: conda-incubator/setup-miniconda@v3
+      - uses: conda-incubator/setup-miniconda@v4
         with:
           auto-update-conda: true
           activate-environment: test

From ae7154a829e25666967dbcc8acf0bc9340e684ea Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Tue, 5 May 2026 14:58:49 +0200
Subject: [PATCH 129/142] Update tests.yml

---
 .github/workflows/tests.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index b859964..b955e05 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -54,7 +54,7 @@ jobs:
           python-version: ${{ matrix.python-version }}
       - name: Upgrade pip
         shell: bash -l {0}
-        run: python -m pip install --upgrade pip
+        run: conda update pip -c conda-forge
       - name: MacOS install hdf5 dependencies
         if: runner.os == 'macOS'
         run: |

From febc3be4f2674adec1a99451dc1158d2985868e6 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Tue, 5 May 2026 15:02:24 +0200
Subject: [PATCH 130/142] Update tests.yml

---
 .github/workflows/tests.yml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index b955e05..bb61dfe 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -54,7 +54,8 @@ jobs:
           python-version: ${{ matrix.python-version }}
       - name: Upgrade pip
         shell: bash -l {0}
-        run: conda update pip -c conda-forge
+        run: |
+          conda install pip -c conda-forge
       - name: MacOS install hdf5 dependencies
         if: runner.os == 'macOS'
         run: |

From 2db78d238279d7433889417246c5d466712ed991 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Fri, 4 Sep 2026 08:26:00 +0200
Subject: [PATCH 131/142] Fixes to pooch download for github actions testing

---
 .github/workflows/tests.yml                  | 83 +++++++++++---------
 docs/source/index.rst                        |  5 ++
 fretbursts/phtools/burstsearch.py            |  4 +-
 notebooks/FRETBursts - ns-ALEX example.ipynb | 61 ++++++--------
 tests/conftest.py                            | 10 +--
 tests/fetch_files.py                         |  7 +-
 6 files changed, 83 insertions(+), 87 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 7b9ba6c..b360324 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -1,43 +1,39 @@
 name: Tests
 
 on: [push, pull_request]
+
 jobs:
-  download:
+  Download:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v6
-      - uses: conda-incubator/setup-miniconda@v3
+      - uses: conda-incubator/setup-miniconda@v4
         with:
           auto-update-conda: true
-          activate-environment: test
           channels: conda-forge
           python-version: "3.12"
+          activate-environment: test
       - name: Install Pooch
         shell: bash -l {0}
-        run: |
-          conda install -c conda-forge pooch
+        run: conda install -c conda-forge pooch
       - name: Download Files
         shell: bash -l {0}
         run: |
           cd tests
           python fetch_files.py
-      - uses: actions/cache@v5
+      - uses: actions/cache@v6
         id: filecache
         with:
           path: ./tests/data
           enableCrossOsArchive: true
           key: testfiles
-      - name: Check Saved
-        shell: bash -l {0}
-        run: ls tests/data -l
-  
   build:
-    needs: download
-    runs-on: ${{matrix.os}}
+    needs: Download
+    runs-on: ${{ matrix.os }}
     strategy:
       matrix:
         os: [ubuntu-latest, windows-latest, macos-latest]
-        python-version: ["3.10", "3.12"]
+        python-version: ["3.10", "3.12", "3.14"]
     steps:
       - uses: actions/checkout@v6
         with:
@@ -45,37 +41,35 @@ jobs:
           fetch-depth: 0
       - name: Restore Cached Files
         id: cache-action
-        uses: actions/cache/restore@v5
+        uses: actions/cache/restore@v6
         with:
-          path: tests/data
+          path: ./data
           key: testfiles
           enableCrossOsArchive: true
-      - name: Check Restore
-        shell: bash -l {0}
-        run: ls tests/data
-      - uses: conda-incubator/setup-miniconda@v3
+      - name: Install Conda
+        uses: conda-incubator/setup-miniconda@v4
         with:
           auto-update-conda: true
-          activate-environment: test
           channels: conda-forge
           python-version: ${{ matrix.python-version }}
-      - name: Upgrade pip
+          activate_environment: test
+      - name: Apple Silicon Oddities
+        if: matrix.os == 'macos-latest'
         shell: bash -l {0}
-        run: python -m pip install --upgrade pip
-      - name: MacOS install hdf5 dependencies
-        if: runner.os == 'macOS'
         run: |
-          brew install hdf5
-          export HDF5_DIR=/usr/local/
-          export BLOSC_DIR=/usr/local/
+          brew install c-blosc hdf5
+          export HDF5_DIF=/opt/homebrew/opt/hdf5
+          export BLOSC_DIR=/opt/homebrew/opt/c-blosc
       - name: Install Dependencies
         shell: bash -l {0}
         run: |
-          conda install -c conda-forge cython numpy numba nbconvert pytest jupyter scipy pandas matplotlib pytables phconvert lmfit pybroom seaborn setuptools pyqt pooch
-      - name: Install project
+          conda install -c conda-forge pip
+          python -m pip install setuptools build
+          conda install -c conda-forge cython numpy numba nbconvert pytest jupyter scipy pandas matplotlib pytables phconvert lmfit pybroom seaborn pyqt pooch
+      - name: Install Project
         shell: bash -l {0}
-        run: |
-          python -m pip install . 
+        run:
+          pip install .
       - name: Test project
         shell: bash -l {0}
         run: |
@@ -84,14 +78,27 @@ jobs:
           cd ..
           cd tests
           python -m pytest
+      - name: Apple Silicon Oddities
+        if: matrix.os == 'macos-latest'
+        shell: bash -l {0}
+        run: |
+          brew install c-blosc hdf5
+          export HDF5_DIF=/opt/homebrew/opt/hdf5
+          export BLOSC_DIR=/opt/homebrew/opt/c-blosc
+      - name: Install Dependencies
+        shell: bash -l {0}
+        run: |
+          conda install -c conda-forge pip
+          python -m pip install setuptools build
+          conda install -c conda-forge pytest numpy numba scipy pandas matplotlib pytables phconvert
 
   build_wheels:
-    needs: build
     name: Build wheels on ${{ matrix.os }}
     runs-on: ${{ matrix.os }}
+    needs: build
     strategy:
       matrix:
-        os: [ubuntu-latest, ubuntu-24.04-arm, windows-latest, windows-11-arm, macos-15-intel, macos-latest]
+        os: [ubuntu-latest, ubuntu-24.04-arm, windows-latest, windows-11-arm, macos-15-intel, macos-14]
     steps:
       - uses: actions/checkout@v6
         with:
@@ -99,20 +106,18 @@ jobs:
           fetch-depth: 0
       - name: Restore Cached Files
         id: cache-action
-        uses: actions/cache/restore@v5
+        uses: actions/cache/restore@v6
         with:
-          path: tests/data
+          path: ./tests/data
           key: testfiles
           enableCrossOsArchive: true
-      - name: Check Restore
-        shell: bash -l {0}
-        run: ls tests/data
       - name: Build wheels
-        uses: pypa/cibuildwheel@v3.3.1
+        uses: pypa/cibuildwheel@v4.2.0
         env:
           CIBW_SKIP: "pp* cp310-win_arm64"
           CIBW_TEST_REQUIRES: pytest numpy matplotlib scipy pandas tables numba seaborn lmfit phconvert
           CIBW_BEFORE_TEST: python -m pip install pytest
+          CIBW_CACHE_PATH: $GITHUB_WORKSPACE/tests/data
           CIBW_TEST_COMMAND: python -m pytest {package}/tests/ 
       - uses: actions/upload-artifact@v4
         with:
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 6674fb2..53bc940 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -64,6 +64,11 @@ Single-molecule FRET burst analysis
     <br>
 
 
+**Attention** FRETBursts will only be minimally supported from now on.
+
+The package `smfBursts <https://smfbursts.readthedocs.io>`__ is replacing the functionality of FRETBursts.
+Users are recomended to start migrating their piplines.
+
 **FRETBursts** is an open-source
 python package for burst analysis of freely-diffusing
 `single-molecule FRET <https://en.wikipedia.org/wiki/Single-molecule_FRET>`__
diff --git a/fretbursts/phtools/burstsearch.py b/fretbursts/phtools/burstsearch.py
index 05d875e..222a8e9 100644
--- a/fretbursts/phtools/burstsearch.py
+++ b/fretbursts/phtools/burstsearch.py
@@ -305,8 +305,8 @@ def from_list(cls, bursts_list):
         """
         bursts = cls.empty(len(bursts_list))
         for i, burst in enumerate(bursts_list):
-            bursts.istart[i], bursts.istop[i] = burst.istart, burst.istop
-            bursts.start[i], bursts.stop[i] = burst.start, burst.stop
+            bursts.istart[i], bursts.istop[i] = burst.istart[0], burst.istop[0]
+            bursts.start[i], bursts.stop[i] = burst.start[0], burst.stop[0]
         return bursts
 
     @classmethod
diff --git a/notebooks/FRETBursts - ns-ALEX example.ipynb b/notebooks/FRETBursts - ns-ALEX example.ipynb
index e8e5b01..5e63929 100644
--- a/notebooks/FRETBursts - ns-ALEX example.ipynb	
+++ b/notebooks/FRETBursts - ns-ALEX example.ipynb	
@@ -38,22 +38,13 @@
    "execution_count": 1,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/paul/Python/FRETBursts/fretbursts/__init__.py:67: UserWarning:  - Cannot import QT, custom GUI widgets disabled.\n",
-      "  warnings.warn((' - Cannot import QT, custom GUI widgets disabled.'))\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       " - Optimized (cython) burst search loaded.\n",
-      "WARNING: QT not installed. No GUI scrolling available.\n",
       "--------------------------------------------------------------\n",
-      " You are running FRETBursts (version 0.7.post189+g4375ef6c8).\n",
+      " You are running FRETBursts (version 0.0.0).\n",
       "\n",
       " If you use this software please cite the following paper:\n",
       "\n",
@@ -100,7 +91,7 @@
     {
      "data": {
       "text/plain": [
-       "'../tests/data/dsdna_d7_d17_50_50_1.hdf5'"
+       "'../tests/data/HP3_TE150_SPC630.hdf5'"
       ]
      },
      "execution_count": 3,
@@ -109,7 +100,7 @@
     }
    ],
    "source": [
-    "filename = '../tests/data/\"HP3_TE150_SPC630.hdf5\"'\n",
+    "filename = '../tests/data/HP3_TE150_SPC630.hdf5'\n",
     "filename"
    ]
   },
@@ -180,7 +171,7 @@
     {
      "data": {
       "text/plain": [
-       "np.float64(2268.2743759426116)"
+       "6749.839568650001"
       ]
      },
      "execution_count": 7,
@@ -200,7 +191,7 @@
     {
      "data": {
       "text/plain": [
-       "[array([6, 6, 4, ..., 6, 6, 6], dtype=uint8)]"
+       "[array([0, 1, 0, ..., 1, 0, 0], dtype=uint8)]"
       ]
      },
      "execution_count": 8,
@@ -223,10 +214,8 @@
      "text": [
       "Detector    Counts\n",
       "--------   --------\n",
-      "       0          1\n",
-      "       1          1\n",
-      "       4    3345380\n",
-      "       6    6371466\n"
+      "       0    4445435\n",
+      "       1    5143309\n"
      ]
     }
    ],
@@ -254,7 +243,7 @@
     {
      "data": {
       "text/plain": [
-       "[array([ 372,  579, 1926, ..., 2553, 1182,  316], dtype=uint16)]"
+       "[array([ 850, 2030, 1656, ..., 2144, 1805, 2358], dtype=uint16)]"
       ]
      },
      "execution_count": 11,
@@ -273,7 +262,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -304,11 +293,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "# Total photons (after ALEX selection):     8,862,749\n",
-      "#  D  photons in D+A excitation periods:    2,866,446\n",
-      "#  A  photons in D+A excitation periods:    5,996,303\n",
-      "# D+A photons in  D  excitation period:     4,093,146\n",
-      "# D+A photons in  A  excitation period:     4,769,603\n",
+      "# Total photons (after ALEX selection):     9,051,653\n",
+      "#  D  photons in D+A excitation periods:    4,812,416\n",
+      "#  A  photons in D+A excitation periods:    4,239,237\n",
+      "# D+A photons in  D  excitation period:     6,455,826\n",
+      "# D+A photons in  A  excitation period:     2,595,827\n",
       "\n"
      ]
     }
@@ -350,7 +339,7 @@
     {
      "data": {
       "text/plain": [
-       "<Axes: title={'center': 'data_dsdna_d7_d17_50_50_1'}, xlabel='Time (s)', ylabel='BG rate (kcps)'>"
+       "<Axes: title={'center': 'data_HP3_TE150_SPC630'}, xlabel='Time (s)', ylabel='BG rate (kcps)'>"
       ]
      },
      "execution_count": 15,
@@ -359,7 +348,7 @@
     },
     {
      "data": {
-      "image/png": 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pv8rWuSg9Zu9c5HOFRTh7/4zFh7Xzhc8LSXxIcI4XCxAefxYgvEjAIVd8jrM30pUeWAAAAG4WIFxq96233jJJ3lVa9nH+/Pk0ZIh9Y80f4FKXs2e73gMikTVrmEMekJ49e5M3IoUa8fjI8ezZU1GdzRpcLnTt2pUihKNHD9tjwH1uBg7sJ/7PFX2UhGtxcjx7p3jlWgksLORySgoVekEIEF7N5Zh6NtA++GAY9e//lti+cuWqVLt2XWrUqKnq79/TBLZ8lZLXLpX1gBAFUKAzHhAmU6hjHpCWr5IzsMjg85FX/3keSLChv337FhE+qCR/yBhJeEhCxFm4HC+HRxYvXpLGjv3SwsBft24Nff31BFq4cDkVKVLMJGm9V6/XaObMb2j8+K/tnosc8iSVFrYFhxwySs8XPi/MYeHC5xELJYZDJdmjOG3aJOrUqS0VLvyCyO3iRHq5RQEAAAA6EiC//vqrKLXbvn178VOunO0eCCdPnqRffvlF/ISGhkKA/Ad7GRzxNHCYUKtWTUS+g1weCOc65MyZizZt2qnLHBB3kSNHDpMcDnOjhyv4VKhQWfa1HNIkVcl6//0PbFbn4URvDkHhOPmpU2cpNpDUFri2fgypO5Ke5hXpn35aJ/qZHD36p4h955CThQu/p9mz5zndL0JLOMzJmqfBVolGkQPy/huG6leyhGenwK8Wap4DwivtHPbD5+prr3WQ3YYrTPFqvRrOnz8nckckr4MzcEneDz8cIbx+HB4ol8DOieBssBuLD8nzwKKF552Sc5FDEFO3yWXzmNRWhLd1vhg/x7kf9erVF4Lr2LGjtG/fHlqzZqUoDMDV81DJDgAAdCpAtmzZQgsXLqQFCxYIIRIWFkYVKlQQDQe5MRXDxt61a9eE+IiMjBTCo0+fPtSrl7qbLLCERcWoUR+KKlh8YzW+UUs3Wi7F60/ig+EVZBZep06dtHiOQy54nEqXtqx8tWDBdyKunfM4OHnXlgHClalYfHA1nmnTZlOJEqUVHx+fH/z9XLhwTtH2jx7FyK6Mc+hXarnr50YiJyFzeJbUc2XTpl9p/PjPRNK6MyE53gKLCi61K1sF6z8CX+vrkQR0/i547rGw5eugOZxzxBXM1AgQFgy8AFGjRi1VPXWshTp98MH7omEnh1FxrpQcLCasiR3OGeGFEQkunMCcOnXCogHoyZPH/9tGvgqdhBQCyecLe/TsceWKZTgrN0tkT6H5cfM51aZNe/HDuSRffvmF+A44OV2u2h0AAAD3oqiJAYd19O3bl/bt20dffPEFlSlTho4cOSK6nnOJXv7h///1119CmEyYMIH2799Pb7/9tteFhOiVZs2a03fffScMbmP4bz2X4JXK8LIRzUaLq2natLnolr537y6Tx1esWC4EGZfaNIaFBwsQjg//4INPFYkPFgYsvsuUSTWylMKlTWvVqiM8FUeO/KFoxXfZssUmf+/du1t0cDfuw8Bx8uZwdS2G8w78BS6xy6V2ue+HhefDQyV42bhlAcKJ2rzyzmFx5j/sBb1+/ZrI4VEC9wGZMOFz4f3o3dtUXPJc4HNLKplrD56Lo0cPF+J42rS5Qthag6ti8XGyqDA/HhYJxucDV6diEcJJ9sZ9ijhsjA19fr5sWdsVsFhc8fGsWPGDEBL2zhc+L9ibYd4XSGp+KuWdmOee8DlfvHgJvztfAADAa5PQOaGvY8eO4odvtByaEhERIVZ5w8PDKU+ePHBnu5FWrVpRjRr1dNEJ3Z1leLds2SiMf8nA4g7RvGrMcB5Sx45dDNvyKjKX4uTk8LNn/xErn1zv//ff91OvXm+ZlO1cvfpnEXrF5Uw5Fp/DlozhOSzFhXO41XvvvSPCaTp1+h9dvnyZTp06+18+Qiq8D3vhTkOGjKBz53qLXi3ct4CFAifscmnd3LnzmPSEYOOLhRR/t5zXIZXh5fcw9mp06/YqlSlTXhiAPAe4xO/69evEPGjWrCX5E0KEVK6lm07oR44cEgY4V22yBpflnTt3Jm3c+ItJEQHODdu6dZP4P895Lj/LIoXFK1fS+vTTcRZlbNWU4T179gyNGsUV1lJEQ81Dh3632Ma4EeJbb70jth8yZIDohM4eE85nWbdutbjO8/PGDB78vhDs/fv3oVdf7SLyL7hrOnv1vv56mt0mhJwjMmrURzRmzEjR7JP7h3DpX85/Y+HUpcvrJkKcK4GNHfuRyOHKn7+gCAnja0GlSlXEwgTD5Xa5xw4LEhZUvCjAzSDXrl0tzp1q1dCdHQAAvKoKFt+A8ufPL36AdrCRyYavL8MJvCxYjOG+Fwwb7cYChENcZs+eT999N4t+/XWdEA4sOrghH3c5NzfAGDYQx4371OJ92XCRBAiHFLJnhVm58ifxYw73GrAnQFgQff/9UiGgDh48IMQV91DgPBLzUJX06TMYGhGygcorvtz/gStuGTeD69Klmyjlyj0PpEZtvPr8ySdfKGr05msIsVGqAumhxzbPXUlkWION6qJFiwtvweDBw0UDPYYNdRbSDFdu4rnNRjOXV2ZxobRPjTUuXbooCjMw06dPkd3GWIDUr99AlNvlXBA+VhbGnCtStWp10T+jVCnTcETOTZo16ztR3GHBgnmUnJwkmn9y3pTSMCeuDjZ79veicScLtLi4OPG5K1SoZJGLUqJEKXFu8PtxwjwX1OjYsbMYL8mzyR5iFlt///2XWJjgJHlOVOfcL644Z695IwAAAPcQkKI288/PSUpKpogIdVWsXIGthFzg/WMvdUJftepXt+zfG8Gcx7hbQ+qE/uGHlgsJ3gzmPMbdn8B819fYh4eHUFCQoswMl6DdOwEAAAAAAAD8HggQAAAAAAAAgGZAgAAAAAAAAAD0n4QOAHAdM2d+h+EEQCG//fa8CSIAAADvAx4QAAAAAAAAgHd4QK5fv05//PGHaBr18ssvi5K8XOaQ/+ZSh2hCCAAAAAAAAHCJAPn6669p0aJFlJSUJBpMVapUySBAWrduTYMGDaJevXo5unsAAAAAuIiklCQ6GXGcHj59QNnSZafy4RUpKEDfTWwBAL6LQwLkp59+ovnz51P37t2pUaNG1Lt3b8Nz3NipcePGtHv3bggQAAAAwMOi48DdfbTj1jaKfhZleC5H+pzUv8xgqp/7eXd5AADQtQD54Ycf6KWXXqIPP/yQIiMjLZ4vWbIkHTlyxOGD2rBhAy1YsIAuXLhAGTJkoLp169KwYcMoX758il7PXpmff/6Z1qxZI/bBsHemRYsW1L9/f4ePCwAAAPAG9t3ZQ7POfEP34+/JPs+Pf/r3B/RplfEQIcCrgDfPjwXIlStX6PXXX7f6fFhYmKwwUcKyZcto7NixVKVKFRo9erTYz+LFi4WgWbVqFeXKlcvm6xMSEui9996j/fv3U6tWrahDhw4UGBhIN2/epNu3bzt0TAAAAIC3sOf2Lvr86BhF27JIqZvrRYRjAa8V1vDm+ZEASZcuHcXFxVl9/tatWxQaGqp6vyw2Jk+eTGXLlqWlS5dScHDq4b344ovUqVMnmj59Oo0bN87mPubMmUN79+6l7777TrwOAAAA8Cfx8cXRjxVvz4Ych2lVylbFrccFgCvEB3vtzIE3z4/K8FaoUIG2b98u+1x8fDz98ssvwoOhlp07dwphw7klkvhgypcvT9WrV6fNmzeLJHdr8GvZW8J5KSw+UlJS6PHjx6qPAwAAAPB0mMmxh3/Tzlvb6O8Hf4of/j8/xs9ZM9DY85FMyareixPTAdD7OTDzzFSb27NnxNq5AXzEA/Lmm2+Kn+HDh4sQJ+bu3bu0Z88emjFjhvg/ezLUcuLECfG7cuXKFs/xY4cPH6ZLly5RqVKlZF//119/CcHBAunLL78UeSD8d5YsWUSZYD5ezilxluBg7dunBAUFmvwGGHtfB3Me4+6vc37/nb30zYnJVvM3OORkYLkh1CBvI8NjbHixAeYI0QmRtOfODgpLFyb+jnwaSdnSZ6MK2Sr5RWgWrjX6G/e9t3bT9FNTrZ4DcvC2Sy8soKo5qimeu3zenHh4jB7GP8Sc15iAFHYTOMCKFStEOBTnXPAuuBQvkyZNGvr0008NwkQN77zzjqiedfz4cUqfPr3Jc8uXL6fPP/9chFY1aNBA9vXs/Rg/fjyFh4dTUFAQ9e3bl3LmzEk7duyg9evXU+3atWnhwoWGY3UE48/q7xw6dIh69Ohh+JtzbUJCQihHjhxUpkwZat68OTVp0kR8F654nVpu3Lgh9iNH5syZ6c8/LbspX716VYhnPkb25pUoUYL69Okjii4Yc/r0aVEsgfvg8PswBQsWFPO+c+fO4jwA4NGjR1SvXj0xlyZMmKDounj58mVRMINZsmQJ1axZ0+mBvHfvnriGnjp1SsxdDnd95ZVXaOLEiU4X8+CFJ66MeO7cOXr48KHo/1SgQAFq3749denSRYTsAuXsuLqDhu4ZSilk/dYcQKn3oEkNJlFY+jC6H3ef7j+5T5P+nKR6qAMDAik5Rd5jEpo2lLqX7k59KvShoEDfFyLAe84Be7CYblOkDTUo0EDYbRHxEZQjYw6qkrOKYS7z+0w8PJHuxt01vC5Xxlw0qsYoalqoKbmSpOQk+vve3+JcNT8Of8XhPiB8Y+Fyu1u2bBFeCf6CCxcuTC1btrSbKG6NJ0+eiN9yDQylm5i0jRyxsbHid3R0tBAcxYoVE3+zQcvwY7/99ptTuSHJySkUE2M9/8Vd8ApBaGgGiox+TEfv/a0Ltf7oUbz43bTpS1S3Lo9pCsXFPaHr16/RgQP7hYFepkxZmjhxEuXMmcvp16klOjp1rjRo0IgaNmxs8hwLhMjI1PlinLvUu3c3YknepcvrlDVrVtqyZZMoasDGY9OmLSkpKfVGPXv2XDp8+A+qX78htWrVllJSksWxs0jeunUbffPNLAhVF835mJgnhnGXIyU5hR5ciKb4mGeUPjQtZS+WhQIC9bFIsHr1anr69Kkw3H/6aQU1apR6LbLFsmU/Ck8ti/IffviJSpQo5/RxnDjxD82dO5dy5cpNpUqVoYMHD9DTp4kW54A07hkyBNM77/Sjgwd/p2bNXqIWLdpQYGAA3b59i65cuW7yuuPHT1FyMlHr1u0oW7Zs4vMeP35ULFBt3bqdZsyYg3NBKYEpwiCyZ3hJz7+/933V4VbmWBMfTMyzGJp1fBYtPbOM3q84ysTj4o/XGuD+cWePxPg/JjglPiQv3tJ/loofOe8h89Gfoy1ex2JkyJ4hNLbaBJfNdzlvTg4ZL6anx57/1jLKxqlO6LxizfkarkIKj+I8D3MPCN/UjLeRQ3oNh2BJ4kOiY8eOQoDwyrazyemJiZ65QLFa5xNTL9UfpElbtGgJataspclz/fsPpuXLF9PcuTNpyJCBNH/+86ICjr7O0eMrUqSYxfvIfY+zZk0X4nXevMXCSGNYXLz9di+xUlyjRj1Kly51/nXo0JlGj/6Y0qV7Pk9feaUzff75R7Rt22ZRha1OnXoOHTew/B6tnXO3jj+gk2suUXzU89yw9FnTUvkORShvxeweH8r169dRxYqVqUmTl2jy5IlisaZgwcJWS0mmJKXQ5s0bqGHDJhQWFk6rV6+gwYNHiP5KzlC8eEn69dftokJhVFQUtWnTVCwaWRtXLubx+++/0ddfT6OaNWtbPG/8utdf72nxPJ8fmTNnoXXrVtGxY8eofPmKTh2/v3Ay6qjJaqw9nBUfSolJiBbGmq+X7LV1rQHu41liAh29d1RcCyOfRqgKu1IL75vncmga24WSWDDUylHP6QVeW4nzH+ngnPLknHdI6pQuXZp+/fVXq89v2rRJbKMWDpdi7ty5Y/Ec55UwtrwruXPnNtmPuVhi2MD0RlhBs0vS/MSUqj/wJNcTHKbWrVsvYXhdvHiedu3a7pLX3bx5g65evaL6eFjA2vKe8XP79++lSpWqGMQHw+Knc+f/iXnDBplEhQqVTMSHROPGqW7bixdTQ1bswUbg+vVrqU+fntSs2Yvip0ePLvT993MN22za9CvVq1eNjhw5RPPnf0sdO7ahRo1qi+22bdtisc+TJ4/T8OEDqW3b5mI7/j14cD+xKu1LsPg4suCsifhg+G9+nJ/3JBcunKd///2HWrV6mZo2bS48uxs2rBfP8fn6+u6ONPTQABp37FPxm//+ftNcEcbUsmUb8Tqet9u3W37HDJ8HfD4oIWPGECE+lMDnAoez1qtXX4gPnqNxcZaeEnvkzZvXEIamBA7n5cWHXr1epyZN6lLz5g3ozTe7CxEmwfOfz4VLly7SN998LeZ248Z1qE+fHnTkyB8W++RzdsCAt4Xg4u3atWtBI0cOEa/XI+zZdiccupU3Q36HX48kX+COhdXO218xXAtn/zNdk0GOSYhRVB3OGZTkZc3y48R5hwSIvbQRB9NKRLUr5uhRS0OJH8uYMSMVKVLE6uvZ88HI9fuQHuMQAW+DJyercVsuSb1O4nbtUmPeOTzJFa8bNOhd6tr1VVX7+umn5dS0aT1h3Ldr15xmzJhiYVCxYHj27CmVK5c6h4ypUCF19fb06VN23+v+/fvit1Jjb+zYj+mrr8aJfJcePXpTv36DqEqV6rRnz06LbefMmUE7d26j9u1fpTff7EuJiYn0+edjaMOGdYZtrl27QkOG9Kdr167Sq692oWHDRlGnTq8J7+C5c/+Sr8BhV+z5sMWpNZfEdp6Cvxf22LI3g3OO6tatT1u3bqTdN3eKRQO5xYTlaxdTWM4wqly5KhUu/AKVLl2GNmz4RXb/fB7w+eBqWKhy8Q4Og5w58xtq0aIhvfRSA2rZsjFNnfqVyGeRg88p9q7cunVTCGMWE5kzh1K5cqnXdXviY+jQAWKOh4dno7feepfefrsflSxZivbu3W2x/RdffEKnT5+krl17UPfub4jzbvjwQSIsUuLo0b9o1Kih4rN07dqLhg4dQa+88qo4fg731CM3Hl936/4/qjyWqmav5vDreY4ef+hbCxlAfwuresHZ6nAsYOx9tvsuEDreisMhWLYSsTmJkm+4auGEYY4b5h4gXLVKCr05efKkaETICZxSfggnVfLKGq+ySWFZHGfN5Xo5uZjd/pUqVTIIIk7AZKwlsGtVn33RuXkUl6Quh+RZ0jPhArc3iTvuaENpgyzzZ2yRMSgjvVGiDzXIY5oj4SqKFSsufqu94Tv6OmMCAgKpSpVqVK9eAzFPYmJi6Pff99OKFT/QX3/9SXPmzDfMnQcPUi8Scjknkkft/n3bFxIuA/3DD0tEuAznhthj587tIlyrefNW9OGHn4pkfIlkDqo3Izo6ihYv/skQjsNCpGfP/wkjsUmT5uKzHDr0hzCwPvtsPJUuXZb0zs2jD+js5quUGG8pnvkSw7kcLCLM1zSSEpMpITbR5r6fRD2jzWMOUZDKqnXB6YOodKtClLeS4yFcHEbKRniDBo3FwgnTqlUb2r17B0395Sui1CgsE1Iep1DKxRRKrJ8oQmuCKIhatnyZpkz5UnhTpHPC3UgeRj5PeE726fMuZc+eg/bv30OrV/8sGtHK5TiNH/8Z7dmzy/A3exKHDRtJoaFZ7L7nzz//IAQDi3AWHsbInQss2GfP/t5Q7KF167ZCkE2Z8hX9+ONqcWy//bZXvHbq1FkmCwK9er1FeoS9YgvOzXPLvjncZGj5UeL/v15/vmDhCJ8dHUPDyo/y6VAsoI+FVU/DobFaCJiHfloGW7EAWbt2rfgxjhHmCinmsJHH1VA4QV0tXL1qyJAhopIV55a0a9dOVGtZtGgRZc+enQYNGmTYdsqUKeJ4zKvEjBkzRnRp5zLB3bp1E8bjrl27RPI554HIlfjVihWXltO12Ktu278QKQnqX7fi0g9uEyAc+sGoDeGw9rpVq6yH/smF5E2f/jyUieGwlkWLvhchTitX/igMHkZa1ZWrXpU2bWoBhPj4JzYrBn322YciSffjj8cqMrq2b98sfr/77kAT8cGY/y0JDuNcAP5/+/Yd6dtvZ9Hff/8pEvql5/ft2yNyX/RegejCrhv0+K71cXUWFimqT4loovO7bjglQPbt200xMdFivknUqFGbQsOyUOSRCEpT2HKeJZ9I5qB+ii8db2gMx6FbM2dOFV6QwYOHm2z/22+WVdxcQWxsnOFavmjRj/TCC6leZ/bkMFu3bhaeBvPckDfeeJvatetIUVGR9Pfff9GVKxfp8WNl4Vcs1nju9uz5psVzcucCF4kwPld54aBZsxYinPHy5Yti7mfKlLoIxqKvbdtXHM4l0wJnSugq4ePKX1DFbJVFmJ+zPEqIER48T8euA+9GiXfAk3B+LeflaSFgsjkpdLwVxVdkvhlJ5UZ5dSkiIsIipp4f59U+9lQMHjzYoQPq2bOnWK3icrksRHhVt06dOjR06FBF1bW4RwiXCJ42bRr9+OOPYlW6UKFCNHr0aJPSr57gf0W60kI3eUCY0DRZHPKAdCnyOrkLSUBIgsLdr1MC55gsXjyfDh78zSBApAIGHApijlQAIX16+QIIvMo6YcLnIlzsrbfeoZdeskx4l+PGjesi0ZjFtRK4ypzlYy+I31IuABus27dvpaVLF4oV7LJly1GNGrVETk3evPlIbxRvkp/+2eQeDwiTJiTYIQ9I8caOx8kzLBiyZg2jHDlyiu9Z4oXyRej4b0eFtyMgk6kHIelEEgXkDOASR3Tu6r+U/Ulq3lr58pWEp6xfv4GyFQJdjSRay5QpZxAfElzpigUIC15zAVK0aDHxw7AYWLduNQ0bNpBmzvxO5EzZ4saNa6oEc6FCqfPemMKFixjOBd5Xx46d6bff9gkPEhe14ER4PmauvsdhXlohV2yAE1uNH3dn4i0bUiw+XG3wsWCqm+tFv+gTAlzP/fjUcGW9wsV9nJ3bfK7z+WfrvMvhAqHj8wKEhQH/SEb+Bx98IMKk3EHbtm3Fjy24KpG1GvbFixenmTNnkt5gL4Mjnga+UfHK1YP4+1bdlTyJf2i0Wnc3g/Pnz4nfhQoV1uR1SuCVUA4piYyMMjyWPXtqmNW9e5YVaKTQKzYmzeHwvokTx9KWLRuFmFET3qE2V0ou7FHahXEfnilTZtDZs2fo0KGDIp5/4cJ54oerdrFhqCfYy2DN08ANP8PCQkTJV/MqHSxKtn12xCIB3ZgMWdNSs0+qa16Sl71gf/11RHy/r70m3/cj+WQyBdV+fq4mX0smikgtr5owN4Gm02TxYwyHQLGQdDdSyKGcMM6WLbthQcoeLVq0Fsb/L7+stitAXBMCnGLyHHshuaLdiRPHRAEHPhdmzfqG5s+fSxMmTBbhmVqEVbGhbl65sFGeprT79g5NVoAlQ8rVYR587Ksv/yzKwRsLKwDsCe/lFxaLyAu9MqbSZ0Jccxd2WwsH9uY9P87nn1wVLPPzU81+fQWHfNI7d+4U4VJAG3gScr3oj/+0P4n1xvr1a8RvtSVpHX2dEp4+jRdCwzjhnFdueXX51KkTFtufOJGaIMZJuXLig6tUcSKseey6PQoWLCQqbz148ECRF4RzqzifxZirVy+L3+beDY6/l6p58f7ffLObWAXWmwBxFBYVXGqXq11Zo1yHIh7pB8LzgefG++9/QKGhoRY9F8bO+oQSjyeYCpDjyURBREEvB1GWdFloUNnhokGcxNSpX9PGjes1ESBly5Y3qTxozN27qRUKlVz/OQ+GvYNKxEqBAoXo+vWrwtuoxAty5coli5yYK1cszwUO3+LKdvwjbcPnwoIF37ldgNgqv/nzZfcbX+Yl2pWGebyUr6U49vgk+6GRc8/OMPw/S5qs1DRfc2G4+YPxBCyRjOgDd/fRjlvbKPpZlIXw3nJjg90KVI7Cc7B01rL0T/Rpk/d2JF93xpmpssdvvnBgrxUCP/560Z70w8XFFhErQ8uPFM9bW6jwVIsFXQuQfPn0F8rh63CzmikNp+iqD4gt2ADjhGxOtC5evAQ1btzMJa/j8Aqu/qTEM/Lw4QPDiq0x3303R+RscFUiCQ71Y+Oe48XPnv2HSpVKLSPN77Vy5U/CkKxbt57JcX755RfCKOSQrr59n3eGVgr3JmEBMmfOdIskdN6/+Sov91TgKj5SngdX9+EwF451l4wprkLEDRSNYXHD46DXyj+Own0+qvcuZdEHhD0f5TzUB4QNbhYgHA4kVXIz5/d/f6OtyzdR8vVkCiwQSClPUyj5bDIFFA6goDJBNLzKBxbnM3tUOL+BS5RL5cY5WZy9efnyORcuZg4XbJCKeZw6ddJQxYrn5Jo1qXl/tWvXtXuerVr1k4mgscVLL7Wg2bOni9BIcyEvdy5weCEn+Et5ILygwKGHBQoUpBdeKGr1XODnOayT83PcaYDdfXKHZro4p4MTyW0Zbt2L9aaK4ZUo8lmE7Aqq0nCQ9yt8IETI8MMDVR1fdEIUrb6yQvzo4b7kjyvKnkTOiNZKeHcr2ouqZK8m66VgUTLu+KeqBMlvd/cqPn6pFYKtnKhMaSz7ODXM08QgPqwtVPh6rpXDWXncF2HVqlV0/PhxscJlXqmEbxhcSx64jqaFmlKlzDUMDXv0clG9cOFf2rp1k/j/kydxIlfowIF9wuDlSkzjx38tqta44nVcdvTOnduKEnAnTZpIEREPqVq1GiJJlRNiuQP0sWN/U/nyFUSMuDEsIv766zANGzaAOnd+nbJkySqO799/z9IXX3xBISGZDKFAs2ZNE3H+xYqVEHHy0ueQYKNQrqSvec+QvXubideysGJBxNXj+PMfPnyQli41LfLAx/P22z1FxR82ytjQ5RXpUaPGGKp5sQHH/RDYc5QnT+pCwR9/HKBz585Shw6dyNdgkZGnfDZ6eJE7oSdQ+tA0lK2o5zqhc6gPfydvvNHH6jZvvNxHCBBix1oBouTTyaJ4REjZEPqgyqeyNxs2tllsbtq0nnr3fls8xlWfcufOo7gwAxdfMM5p4spaE+eMpbjEWCperCS93qKH4VrCxTxee+11cS507NhFCAw+Nzn5nOef8dzmfjQcYlWiRCnRb4kN/yNHDotziT2LnTu/ZvfYuFQ051AtWbJALADUqFFTFH+4fPmSKCk9bdpsk+15AaFfv7eoWbPmIs+Px4bLaA8ZMsIgVniBgMMnq1evKcYpIeEZ7d69kyIjI+i111zXQFepAeYsUhUrR1dK1YSDcM6IPbFiC08bT3LfReY0odShcGfqVqynx++Z3oo1UWfNiNYCnqc9S7xp8p3y/7mIh8SQciPcfny2cqKuPkr1zhpz8N5vVPZmObt9T3w51yogxYGmHTdv3qTXXntNlMJlg4lXYrNkyWIQIpxEzgYRV5/yNbhrZESE+qZczmIrHt5TcCLqwIHvGP7mFXz+3tlYYWOkUaMmwqtgLiIcfR3z6qsvKxYgLBDYuOfeGCyYebWUQz2aNGkmDB65hF42dr79dqYo08shJEWKFKUePXpRx47tTMaem5uxkLEGN5Jjr4Y9+HxZu3YVbdz4i1jRDgwMEivQbHBKhiYLDS5xyuVEOZ6dvS5sROXPX4C6d+8tVo+Nx5aNsTNnTolCEfwZeTs2GrkSkNyY6hU9znl7fPTRKOFF43LJUkK2HD17vkaXrl+g4PeCKfGHREq5m0K9Z/Sl3hXlhQt74rjpHhf5WLlyvTCyuSGfGgHC21sjsHwg5emUVxigjfM3FuP+118naO7c2aI8LleAy5evALVt255effV/Jt46zi9iYcLJ9uxZ4AadHF7YoEEjsa0kju3Bwuinn5YJT8atWzf+m7sFRSUxSTxzI0J+vyVLVojcEhYUvLDAY82ek+rVaxn2t3fvLnHucP8brswVEhIiutCzF9GRUERbK+ruNMDMBYazK/tKwz1c8Zk8kZto77ilksTGn9UbrzVaY23evFt6IM35Z7rHKlopFbmLzy+gxedTF2HcxZSaM02Ej8S7B3rTv9FnRSPQ3Bny0O0nt1yyX2eQm/Ph4SEUFORQe0DtBMj7779PO3bsEKV4S5QoIapUcdUq7rsxd+5c2rhxIy1btswQKuBLQID4H56+OUkChEsKa5E4qxc8Pe7uJCk5kVpta0oJyc9DxzoU7kQDygzxuIE2ttoEal+2jcm4qzF63Rn6IgkQFmF58qR2WvdUTLuU81AzR23VIR5qmFRjuggvcSVKvyOeK5NPThSldx3F3Hhy5/yQCrYoMYaNDVdfvta4Ak96OKyhNsyP58ZruzuIYj7uon2hjlQ/dyOTOc15f222NaX4pHgKSxsuQiTV8mGlT6lJ3pd8ToA4FIJ18OBB6tSpE9WqVUv06ZDglS7u43H+/Hn6+uuvafJk0youAAAAiG7F3TIRH4w7b4xq+k1MPzmVXi79vJS0mgRJuW29NTnZXkiVcc6DO3HEYLGHeYiKNfj7zRScSXU+iDH77qR2sufv/sDd/W5NtlVTanjm6anis/H45syYgxpkeZ7XBLTrUaOU7Oly0KiKH1nNcbIHb8sLPO4UUuuurhY/xte8nOlzCvHBxCY+dmi/2Xy0T4hDAoRjfLnULSMlAUqN3Ji6devqsgwuAADogauPL7u8Lr6SleXjD4/aNdDuP71H7+97n1rlbUvH7h+jxRfmK4rxt7ZKqrfkZG9b8fW08eFsPohklHHPKbkeWK7MF1FTavjB0/smwirXsVw0oMxgqpvTtNKgv6PkmqEFA8oOcdoTyPOL55k7c7XMr3lc7UrimdmikxJ8uU+IQwKESzA+epTa4ZbjarlsIueFSHAzN2NBAgAA4DlXZAWI4zdEW56H2jnrGpIeN13foGh/269uFz/2mHLiS8oQlIEinj5UVPXJ08nJSkQbPzb11FekB/RgfChJXleCvQa8rki2dUas3Yu7Rx/9OVo3c1MPSCF4WsCGeov8rVWXuVUL74fnmXTec1jUxBNj3eaBVtJE2hZ6bbHgsRyQN998U5T2/PLLL8Xf3bt3p4cPH9L3338vkmr79OkjkiVXr15NvgZyQPwPxAdj3F3NuGOf0s5b28T/MwRlpCdJcRRIgbS1xR4KCgz22tV6pbA4WtFkHaUNTOuxcqlyoo0TlBOTE+0ay1qhJ2PY3VW+XJFsyyvMXXa2FyvQrpib/lwiWKvrSmjaUHq1cBd6rWhqFT5PjI0er6GhMsUSXInX5oA0btyYFi1aJLwc6dOnp379+tFbb71FTZo0Ec9zhZYZM543KAIAAPCcK/+VZQwMCBI32MP3D1IyJVPE0wjKkSG1E7lSg0svq/VqYAOx447WVD6skkXTMFdXfVJjcLirQZq1Xh7WmpvlTJ+L+pUZpBvxYW3lWPKsmSfoO4oz3dolgeSM+DCemy3ytzF4Dx3NO5Cbv1HPoiyqRsmt8mttiBu/H3+3M89MdXqf1sLuJHqX6EMDa/anmOh4gxGsND/JlWgVmsVVsFJI2Zr/x5W/cHnxCZ/wgMhx4sQJUf2KyzM2a9aMqlTRdgJpBTwg/gc8IBh3V8KiofXWppSUkigMzVo56tD662vFc7PqzBOdfJUaXFNPfuW0waVX+AbM3eCVJC2rrdKltFKSq5CO2diANz5O6fijEiKocI789ELakpSS5JleNo7Ax7/8wmJa5GSZU8kDYu/7lDPqPz86htyN2nAgtV4jyeOldTEHd3m3uILb48THVs9hqeS3XqqPmYswd4Zmebp8tR48IKoFCDeAunv3rgixMu8y6w9AgPgfECAYd1fe6KefmkQRRpWNQoJDKDYxtbfQp5XHUf08jezeHG/G3nDa2NM79lYLbRlrUn+CrGmzWhixxx7+TUMPDXD78YcEhdCAskMpV4Zcig1Gb73WuELUSUaXvUpZct83hy+yB1FPoXGOhPVI89aemHIkL0JO1DGuEI72ysdaE5R6n+/uCs1Sem1zJ14ZgsUCpGnTpjR06FARdgUAAMDxm5kkPhhrBpwW8fd6w16oAnt/OF/ji2MfWzzH42RuxElGm3n5Y3fxfsUPdRVC5U7UlL+1Bn83LD7kzhGpeEHDPE1oz+2dFs9rKT6kEr7mCfOuCGHizznt9CSXFXOw1ceGwwF5/flRYmpBIXcgFQXwRFiVnkOz+NrWPF8r+v3ebyY9drylSqDHBAh3p5U6nQMAgK9jblg4EhOutJb+3Sd3vSJBUg9w6Nm4Y58o3l4y2prlfd7jRC3srZLLWzHG34wIZ3M30gWmo9GVPqFaOetQl13tbW4rJz48AZfwZc9Bj+K9Xb5AoCaXRk4ISdg7JnfnO+mhgpurc594LGf/M90l+U7VctSg4RVG674Yge6S0OvXr0/79u2jrl27uv6IAABAJ9i7iSs1NpWuEF+MOa/LJmB6RWlCpzHbb212aeUuR0WpL+FM+Vsu40wpKU5Xr9IaDlsqnLmI+L+nFgjMhZCeFi18qXys5MHh8E1XiA/pnAnyUs+QRwXI+++/T71796bRo0dTr169qHDhwqIXCAAAaIUrqsTY2oeSm7h5KIS1/SltMsgGhatDW2xhnCshF6Zhq4KTEsZU+pxmnJnispu2JxlSfoShNKu/Gw7m8DxX06yQtw1LG0bnYv6lqIQo+vToh249PkfmrhJmnJpCiZRIehBC0iKIpyvj+bIHUKmnz171L1/xDnlEgNSpU0eU2j179iytW7dOdht+/syZM84eHwAAWGAt8VhNdSRblWZq5qit6ibO+0lOSZYtr8llVrfe2KRoP7EJz/NBnOmObivJ0Vo1HTao3yn9Hp2JPkHxQY8pbWJGSkpKMVnZl0sQlsP4uwgODPb4aqwzcILzmMqf+6RBpWWzwp7F36L8IfkNc+mrE+OEANEC7qfAyPV9cUaYPHzmeOiZK5EagnI57y03NprklWkJf8fdivX0Gc+Ho56+zkVet5nY70veIc3L8I4aNUoIDHtMmDCBfA1UwfI/9F6pw1exNu72PBPdi/WmiuGVZPsTSIYxiwUtynaqJTggmLa02CPKzzpTZpdL2EoVoNSGCNmb73LhR/x/HucsacMoR/ocFu/h7Un0zjbI85drjdKFAc2a3Jk1c5NbkFAqqkEqHQt3Ft+fK7qVe9t8V1LtTWk1N0+jhypYLusD4i9AgPgf3naR9OVxd1UPBzUNobRmdZMNdDLyhMMGWq/ib1nEhOthvtuqyKN3pHKi7sYXrjVKenho1YeF+1AoaeYWEJRCl5/9S5vPbaNtN7d41dz0hBjn79QVydPeON/tiWfjymR67ngfrAMB4lAIFgAAeAJX5UToSXywp+KFTEXpaMRf4u+9t3fT4gvzHdpX9nQ5qGuxnqRHpLwJKdTLvJHc7DPTLHJgfCHJ2t+wlx/j7rwm49XmitkqKz7m6rmrU7F0ZejtUv1dXvHIV5ByF/w5B8paaV4574Y/j5MSIEAAAH5R7lOvNM7TjLbcfJ4jMv3MZIf3NaDsEN2ssNlC7sb8Yu4Gbm2K5ihIGPXOc9jROHvjuZkuKL1X5y+pxV5ODHIXLEvz6tG74S1o52sBPgu7Gbk83c5b28Rv/hsAd3Aj9obPDeyaqyspzsmkUTaSteie60745s2hY/w5+PMYw393fuF1i8e1AEaXPr1J1uaEK88FabU7c5pQcheZgzMrEgbuhotT/NxkvdXzz9uvL65GEqocmsm/IT7UAw8I0CTpEABXzLXFOlsdd0WFJUe7OPcrPZDC0oX73OqbrdXFPqXedSiPxNbKrlSpbPftHbiO6ahkr2R0G39vchXcpDnhrpVono+ZgjPR8MMDHc4xk/ss0n2SP4s9z59UxcvRohRK4P2eiTyF1X2gGUhCVwmS0B1LxvJmvDFRzhcwHvenCQmaJa7aIkNgRnqSbL2+u1ZIlVbcITq8Zb6bJ3hyHolcGWTJyLPXOFAPCaPeMvbuvndI5VwZLb4TJZXf1Fx/jKvQScdt77MoWczjHh/cLd5deSlaFVvwt/muR4KRhA70jrWbspIOzfw83/h9ZWUWeA6tElftkSYomJ644D7JhkX93I1o9ZUVDr0eYUHW80isGXn2kkGRMKrPRF49JPEq6XOixPtv67MoySvgRphDyo1QlZfCx9SqQFtF3mMUWwBaghAsYBVbjdpyZchl1yDk548/PKqoDCLwX5SsPHs6+VzNTdwe0uouf2a1AoRXVQeXG+ETnkV3ABHhPXhbIq810WStsae75q+14zDG/JiYTdfX2+1fge7cQLcC5N69e6IBYY4cOcTfT58+pSVLllhslzdvXmrdurXrjhJobgjejL0hG5PKcaJqjKYxf42kLkW6+nR3VODanhDSKmLj/I1dsjKXMSgjxSXZDpvi93y39ECbzfv23N6p6n3N8w7MV0eVxsIbGxU/NV4nVkEB8AW8TTDqRTSZH4eSZqP2PDjwqgLdCpBLly7Ryy+/TIMGDaK3335bPBYXF0eTJ08WosS4n2FwcDCVKVOGXnjhBfccNXA57upUHJ/0RKwar73ys0lHWuC/2Jtr/DjfKIMCJ1D7sDbisTJh5Sg0TRaKSYi2aaD3KzOIsqXLZnEzlutKq3blUqkIkrwU9gwVpWEdEkPKj4D4AMDD6EU0qT0ONWFvAOhKgKxevZqyZMlCvXr1snhu5MiRQnAwycnJQqSsWbOGhg0b5tqjBYpQk0zJ22pRe59XgtnQksJP9JL0CfSVfGrMpGNfUs6s4bTj4m7RndiW+JAMdGs3UVesXCrxWJh7KVwRTgEDAQDgSx4cAFQJkEOHDlHjxo0pbVpL93/JkiWpRo0ahr9btmxJBw8exAjrZHWZa5i3L/QqVQyvJFaFJXftwXu/0fabW+0adq4k1RuykspkLUf/RJ+WDb3BSoxvIQlNqbOwUqITo6jvjr52t1M6b5xduVTisXDES+FIOAUAAHizBwcAxQLk6tWr9MorryjatkiRIrRp0/POvsAFBtyDoxQf+ZjSJ2WiMlkqyBok1laXHyXE0NILC2ipTr4IFjx/3D9gNfRGroShvxlg3ugdkjtmudAnV8Ieh6UNf9YsNMldYQwwCgAAAPgTigXIkydPKGPGjCaPcUjWr7/+Svnz5zd5PFOmTCI/BDiPrdrg5qumM89M9YkhH3v0I5MmTraMOy0MdX6PI3eO0JX7NyhrmnC7fQPsHZO955XUg9ebQJE7ZlvN31zdPEvLFT2EMQAAAAAaCZDMmTPT/fv3TR4LDAyk4sWLW2z74MEDsT1wDmseDclTkDE4hOISY31umI3Fh/Hn5fyR/CH5ba6uuzKMS8qPWXPlZ4tqRtY6J1t7XKqwZKvqEx+zve+cV98Zd39uNeLG2jG7W3x4skQvPBYAAACABp3Qe/bsKSpdyZXdNadHjx7it5JtvQ2tOqGr7bzqb9hbXXe0C7txedjNNzZQXKJ2njwOPTPv5Ky2pKxxkr+ry+LKjae7O/MqYUrNmYhpdiHoTuw5MPYYd38C891zeFUn9Jdeeom++OIL2rlzJzVp0sTqdjt27KAjR47QmDFjXHWMfoleOj+rJUNQBnqS9MTt72NvdV2uC7vxyr5coq+78xXsMe3UJBFSZAt7/Sw4yX/j9V9oQJkhJoLBlldDaVlcc1HHr5t68iu7x+xO0DwLAAAA8D4UC5BOnTrR8uXLafDgwfTWW2/Rq6++Svny5TM8f/PmTVq5ciXNnz9f9P/g7YHjeLrzs60V9scJMVZXydno57ClVZdX0OPERx47TvMu7PaMbC3yFezhKkP+Qfx9E8FgK6eEUVoW11jUqSmn607QPAsAAADw4RAs5vr166IJ4eXLl0XzQU4255/Hjx+LH95V4cKFad68eVSgQAHyRbQKwTr28G8aemgAeQrz/BK1SdBa9RexRYagjNQyf2uKTYylrTf9ryqblHvy+VHr3ki1eUTvlHqPwtKFiXK6ngy7Qslm94GwCM+Bsce4+xOY7/4dgqVKgDBPnz6ln3/+mbZu3Urnz5+n2NhYCgkJEcnoHKbVuXNnSp8+Pfkqvp4DIldhy5kqS+7qsA6UERKciWITH3uNWLKVA6O2czlwDBgFngNjj3H3JzDfPYdXChB/RysBwmgV5uJuw85WgnMABVhUvQL+hxQuZuxZy54hG2XKlJ6uP7xtUv4YuBcYBZ4DY49x9ycw3z2HTwkQ9oywNyQ8PDW511fRUoBIImTm6an04KlpCWQ1dCvay1AlyLgTuic6LZuHbkU9i7IZIgR8C3PBaSuUCjcnz4Bx9xwYe4y7P4H57t8CRHESOnPnzh3atm0bBQcHU/PmzSlbtmx04cIF+vTTT+nvv/8WOSB58uSh999/n1q2bOm+o/YjpKZnP15aQgv+naf69Wzg9Szxpm5WjuX6JwQGWHaWBr7JR5XH+n2XewAAAMDfUSxArly5IipbScnms2bNEn0+3njjDYqOjqYSJUpQYmKi2G7YsGFCnNSoUcOhg9qwYQMtWLBAiJsMGTJQ3bp1xT6Nq24pZdCgQbRlyxZRmYt/eyNstPcq+SaVz1OGxv8xwcRQt9cXwhuqBBl3lubP5ukEZ+A85lXFkDQOAAAAANUChCtbPXv2jD744APKkSMHffPNN8K4T5cuHW3evNkgDs6dOycaEbKAcESALFu2jMaOHUtVqlSh0aNHU2RkJC1evFj0Flm1ahXlypVL8b727NkjPDa+khTftFBTqpS5Bh29d9QkOdzdHcG19oykC0qvixKvTEhwCLUq0IZalmguchFmnpqmuPu53OOZ04RSnZz1fLYqFwuPoeVHuayIAQAAAAB8D8U5IM2aNaMGDRoYGgzu3buX+vbtSx9++CF1797dZNvJkycLsXDw4EFVB8Nio3HjxsJbwZW2ONSLOXnypPC+dOzYkcaNG6doX5yP0qZNG7G/3bt3U9q0aV3iAdE6B0RprKS9srjehrXeFXJGvbt6eHBy/oom6yhj2vSGsX+akCA7ztbGX+5xRk2FM3ufL3NwZnrkwp4rocFZKG1QWofyjibVmG7oveIsiA/2DBh3z4Gxx7j7E5jvnsOrckDu3r1LJUuWNPwt/b9QoUIW23IvEA7LUgt3WY+LixOCRhIfTPny5al69erC0/LJJ58IMWEP9tAkJCTQkCFDhADxdeRyK7wZ47Asc6O+T6l3LR5X28W8e7HeFEgBtPjCfKvbDCk/gtIGplU0zmofZ++UPS+P0maBHV7oIjqgu4qhFUbafU9rx1sxW2WXHQcAAAAAfBPFAoTDr4xDmTj0ikmTJo3FtvyYI8W1Tpw4IX5XrmxpxPBjhw8fpkuXLlGpUqXs7odDudgTw40SgXeixqg3Fixy5X6thaa9EFrUI+FrvG8uPWv+3tZKIstta9yzZdP19U4n8Zt/brn3tIU35BsBAAAAwPOoqoLlbu7dSzV0cufObfGclPvBnhhbAoQT4T/66COqU6cOtWrVym2uK62R3GJause8jWAKpGq5qomfAeUH0YmHx+j+k/sU9SySsqYLoxzpc1CFbJVMjOTG+RtTg3wNxLYP4x9StvTZLLZx19greW+l2w4sN4Q++nO0zfcLTZNF/I5JeO6dzJomjJrlf4nq5alvd2y4Azr/f/XllSYhYTnT56L3yg2mBnkbkSvBnPcMGHfPgbHHuPsTmO/+PfaqBAgndXMpXiY+Pp4CAgJo06ZNdOrUKZPt/vnnH4cO5smTJ+K3XIiV5HGRtrEGJ79fvnyZZsyYQe4gMDBAxM15itDQDB57b2+jcXh9l27rrrF3xXG2D2sjmvZNPDyR7sbdNTzOoqFNkTbUqGAjqpIz1Wv0972/6X7cfcqRMYd4LCgwSPF7NqWGNKjme6r34QyY854B4+45MPYYd38C890/x16VANm4caP4MWblypWy27I4UQuX3JUL95IaHRpvI8e1a9dEeWBOji9YsKDq91dCcnIKxcRYL3vrLlil8kSJiXkiEuEBxt6cqqG16acma6x6SWKi48XvYunKULFUPW94TC2u2Ic9MOc9A8bdc2DsMe7+BOa7vsae/9ZlEjr3/HA3OXPmFL/Zy8KJ7MZw6BVjqwzvxIkTKTQ0VIRe3bhxwyQsKzAwUDzGwiZ79uxOHadcFSqt4Iniyff3Z7xj7AOofNbnOVQpSUSJpPdj9oVx9z0w7hh7fwNzHuPubyR58P6qWIA42lRQDVztasWKFXT06FELAcKPZcyYkYoUKWL19Tdv3hR5JC1atJB9vkmTJlSvXj2aP9965SMAAAAAAACAnyShs0DgPh9Lly6ll19+2aQPCDci7NChgyE/hIXGo0ePKG/evIawLG5cyJ3azeGkdK7M9fHHH4sO7QAAAAAAAADPoCsBEh4eLvp2jB8/XvQCadeunWhOuGjRIhE2xZ3XJaZMmUJr164VoWE1a9YUj9WqVUt2v7w/Fi5NmzbV7LMAAAAAAAAAdC5AmJ49e1JYWBgtXLhQCAf2bnBJ3aFDh9rM/wAAAAAAAADon4AURzoG+nnCTkRErObvy71HuPxvZGQsEnIx9n4B5jzG3d/AnMe4+xOY7/oa+/DwEE2rYKGrHQAAAAAAAEAzIEAAAAAAAAAAmgEBAgAAAAAAANAMCBAAAAAAAACAZkCAAAAAAAAAAPy3DC8AAAAAHCclOYno3GlKiYqggKzhRCXKUkBgEIYUAKAbIEAAAAAALxcX0uPJR/8g+mM30aPo1O35n7DsFPh6XwqoWlfRPiBcgJ7BPPUNIEAAAAAAHWNPXATUbEAph/YSRT6Q30HkA0qeNY4C2nejlNjHivYhJ1wA8BRqBDbwDtCIUCVoROh/oFkSxt2fwHz33CpumrRpLJqDpfx1gJJ/+Na6uNCAwP4f+rRxhznv2XGPeBhDiWdOWvW8KT4H2r5OAbnzw6vnJY0I4QEBAAAANMTcoBKruJlCKaFuY3raoDGl5C3K64Op280a5/HvJvnHbymwci3kkQCXE//7Xnr27TdEEfKeN1XnwPofxOul84mKlCK6/K+ptyRTKAXUbkwBlWsJoSNwMuwQIWGOAQ+ISuAB8T+wOoZxdwd6vWn5+3x39/eiyKAKz04BXfpQyk/zPOr5MCZwxEQKKFWBfBF/n/OeOr8CH9ympDVLrW4b8O5o954DIZlTf8c+ev6YynAuWe+MF4SEBevAAwIBohIIEP8DNyeMu6vR803Ln+e7u7+X5MRnlDKsp2FF1qto1p4Cu7ypS9HsLP4857VEdTghezEex5Beww7tLSboOXQxGALE+4AA8T9wc8K4uxK937T8db67+3sR+188w2MGlUtIn5EoPu7532bhLN4qRvx1zmvl6aDQrJRy7pQIkfIaModSwOQlFBic1urnS37/DdtiKjw7BX61UJcV5oJ1IECQAwIAABohblq8AmgDxNtrjzu/F953yoYVlLJuGXk9xuKDeRxDKdvXiR+9ePCA59FD4QSneRRDKYO6UnK9ZvIC+9xp+58v4gGlnD1J9CTWMucrDOcLBAgAAGiFwpuW2M4H4u31turnju/F1mcUhtjyuURRD8nn+a/Ur6c9eBZlWyMeUMDjaKLMWYiyhKc+GRNlUSnp6cmjlHT9JqVkDtPvPPUC9FI4wSU8ibUqsIVnRwEpMz6nlKfxuj9fPAEECAAAaITimxYbs24/GvlQiaSgAHqSFE/JaTJSStEyigwxOSOcjv7hNat+jn4v1qpZcVgShWTyDa+HSpKXzKCAitWthq5oIWBlvxfzbYz6nyQd3kvP/qvCREbzlHjl2xsEtMZY+15EjtOSmeSTmAkG/txy88oCOfFhRLIfV5iDAAEAAI1QetMSN3UPhUokElG0CkNM1tjj6jLGlWVUrvopMTxd6V1RbEyEZrW/0vtfWJJbSZ+BKP6J9edr1Cc6vM/2PgICiFIUfWr1oSvDelJyt34UkDmLQdjKeR5sITuvFAhYVSvwkQ8oZctq2cfFPozmsZ4FtKux69WT+V5EI8vftnt3jpMCkn+YSwEZQiiFBWuGEOElcYoI3/F4qwVVsFSCJHT/AwmKGHdXoTZxUcn+HDXCk4/sp5Q5E5QduLmgkIQJ78eRcAsbn1FJJSq11arsjZOi70W8RzYKfP0dSqlY3aPVrAKGj6cAs7hyw7i+ZtQ/wfx5TqytlZo0nvIoWvn372pkvisTT9y9Wza9R8YC1iLZef5kokj3hrz5ctiMrXOL0U14VdvXifZv8+48E4m3hlNgrQaaetv0kIQOAaISCBD/AwIE467Haks2DQU7oSNCfMz9kq035z6MNU+HA30llCRrc18Aun1dsXGqRqyoWjm354FwMwFvj6DAWg0VCSubz3s4WTigfTcKaNPFIlzPLmHZKODNYZRy/DDRH7u1F4Is5P73ttWcEjWYCyhn9+coioolOHG+u5T/FjAEZmMnOycyhxJlzUF0/SLpkjRpiYKCTYs8uNnbBgHihUCA+B8QIL437p5Mjhar7fOnEB3cbfpEQIAwsAOr1bO/D3vGsrmhIOUlVKyhm3KYxka0MHy2rSOKe2zvVVYi+uWNE3sGlblYSV65kFI2rySPrOaq+E5c2RTQ/FwQnpEfv9Mucd7DYs6luKKJncL9WbuGOXJt87ZiCY6EcIqCBF+NIm8j0E3eNj0IEOSAAAD8Cms5CwHN2onVWHfmGtg0OFJSKKBgUbvvzdgrGWuxSinlJbg7N0EF/JmS2NDlMArz8q7WX2V/Ey59ycJjz2a7BpVxwrQQQreukqYYeaySlYaThGc3zANXIOZyqQqG5Hoh8arW0a50sK+IDys5TlaFghKP23/744UJKZ/GIBKNOoSb5GEc2qsqb0Y3VasCAu17ZBUKPPM5zaTwOROW3etCtpJ9OEkdIVgqgQfE/4AHxHfGXYnnILDXQIdzDeyuLNuJuQ/oPYQC6zWz/t7cGbhkeaK/Dij7wMA+mbNQQN2mJoabluFHJsm9CgxBLfMPrOWR0AuliM6fdj4B11exFSLGQuF/fUwEhF1cUDDAXMQYFjOU5D65mvDsFNClDwVnzUoZE+IoLk1GSoyKsnl9ND9fHEG151gnBLrQ46knDwgEiEr8TYB4TR1/NwIBov9xVzJPRYlIhYnDSnINbN0QZQ03JSt8JctRYLtunk0QBu7FTOQaE3D0dzFvkh/et3ydUYK5bkquejARH6jE/PrDQqhBC228XJkyE/UdQQGPHpnMIfNrvOx108Xz3tZ7SLlzyUf/INq/VTfeuYD/wlVdCQSIF+ILAkSpqFCcvOnjIgUCRF/jLutZMF9NlKuatHiGihKRCnINmJBMRE3bUkDxcobEUZeIByViBXglXMEqsEwlq3M+a2h6ijh0iBLu3zc00Avg0BEdXld1E74DdI01r53cNV4Le0LRgtWZY5Qy6QP1O2cvtYtLEQfCAwJ8QYAoFRX2SnRK7lyxUmDFxWzu7tXbzVMpECD6GXe1VXv4xsdoaiS5q78C8H7slFj2xmuNenHvIVp3SQ1f/O4r/R+rr2AnXFXP811xaW4Z20hVeJ09VJRlVwM8IF6INwsQpeU/FZXoVLtC68UNnPR8kfRWlFRwCc6WjcJr1qSomPjn7nm1QoL3nZgIgwNoQ/MORFvXOJy/4a3XGt2HYxkZcfDaaIOSnA29z3dVc8W8B4+LFr0CfbgKFnJAvFiAqHFVKlLzYdmIXmzu1hKd1i5Keg7j0vtF0tuw5oUzr+DCBGbLIYRrUvlq+jZwfAEpOXbFvNTuvMChBRZn4ti9+Vpj1+iq9iLRn/vJEyjpDROQOZRSHsEz4jQqcja8Yb5bLcRQsxEFZM9pNUTS2nUgoEYDSuGKd7F2kt3dnPMFAeKFeFqARDyMocQzJ62GPlnzMjgcz+gO/usmbJLwpeKzaI2rL5J6FlvuxuGVIV/qFaBDjBcGOBE6ccYXju3ILPxSq87Ubk+gffxI1aKKo+e4NxhktrAnvtzW+DBDCFHxskSX/zW9j9gw4uS8rQ937qDEZXO9rlSrx2Dju1s/h8OtvWW+O3o+2/L0p3CZ6+2/mAoRbnBZqzEFsG2ETuhATwIkwz9/UtS339hdnZStGb5kpv7Ky6XPaLf+v6PuR1ca+UqToW01ghKoEFveKFKsdfQ1dKjlx3l1HV4M/ZxnMgaa1WuNA4a4Q6KTS2FWqkl0YIdrEz4VXG9s5hG5uTqPNxlktlDanV32WmhnjsnCRtvkJYZeLs4Kv4RnCfaLXPxnKKakTUe0cQW5XEzpobwxF9iIfaxo3P15vjtDigfv8/CAeCGeECA8SQM2/UxJa5b6XxKsAxc5JYn21oxluYuA3M3JavK9TBiRWL3n7+NpvCKxpbb3hB7EittWNoHjmJWWVDLX5bytJkL66B8OGeJ254fZyh9jN2T0v14CiiqO/XcdCTh+RHnCdOYsFNhjgN2Gcq7E3wwy2cUauTnm5hh5e+NudbHp7AnXdtdWM6fdDC8o2CrRq8W4A/cBAeKFaC1AxI17+Vy7HX19GoVuXoNb01bvBvYOBQbavsEZdSfmm05A9EPKkBRPcdeuUfLBPe5bwVdy86nblALKVDLEnMrerDOFUkBtdW5cZ4wrJHU6gSMrvjbmjisqzzlqjNlDjehXUzRDbLdouk0Pr7GxpChh2gWru44Ag0xFqW0XeqAcHXdHKyU5tAjlgaR9RxcclIL57jkgQLwQLQUIDDsbmIkE4ZE4uEvByqbC/g6e6oqqxv2uJC9CQS6NM/1ehOjz1uRwFrZNXlbWiEvqlq0kedCR8B6Fq/KcX0GhYYoMeF8wCpQmdFuNqbZiLCkVN1qjp7HXG+70QDkz7vbmEnsSKE8B26VZrcxpcW/jMEh7oavmVSmdXNgwnv96HXfgHBAgXohWAsTVKys+iydEghcixecLzFcWbfV7kV4ntxKWLj1RcgpRwlPyOqSQHO7obO88M4sxlzV01c5TM4ND0aq8m+rB690oUFvtT/G2GnRd9vax9xecHXclc0mtB1CVyDHL+7TuHc+cuv5m7dql8fzHfPccECBeiGYCxNWxpe7Gl/JOfBVOKmQPkPHNR8n3Zi8Z0QsxWeFzYDVczphIOX7YMi9IJg/D2ZAjd+NPRoEe8qf8dez1hEv6bLl5LjkimG0VRHFECLkazHfPAQHihWglQJL/2EMp3LFVD9SoT3R4n30Xsw4S54AfoyTswFZIjgtWw9Wt1qfQw4vRFB+TQOlD01C2olmIjv7u8VV5GAWeA2OPcfcmwewsmO/+LUCCNXsnoAoRHuPpMTOu3179RfsuZrnkbjYKOUwnzrdW0IGHMes3YTPsQEFtdZ7DgQq8FPYQ25eqIDKNbHHr+AM6ueYSxUc9MzyWPmtaKt+hJOX5eqFPGRkAANeg9PoCgDeATujengPiaOiTlFRrXjLWhrGmZPXFmsvXXnUqpz4L8Cus9ZvwllVCFh9HFpy1+nz13qUob8Xs5CmwKuk5MPYYd38C891zwAMCrMIGE1chUlRdQ23ok0iqXZyaVPtqL8XGmpLVF2vbBLR9nVLyFbK5Om0vIdq3UFGNy9/gkrI1GlDK4b2mjfAUhiLpeZWQw67Y82GLU2suUZ7y2SggUI+fAAAAAHAehGDpGBEW0v9Dx0KfbBDY4z0K+K++vZbGmr0wFz4GjkRMmfulaUlBPWOvFK5592U1XZ69GQ696zuCAh49Sk12/Pl7uy8JaPM/0eNEmhNB/+tNIbcu0qPrNyk5c5juPBmOIHI+jMKu5HgS9Uxsl734fwmiAAAAgI8BAaJzJKM96OIZypgQR3FpMlJS0TImhpicYe/uxk2OYk/wBHKuCYsQV3hClOafKOmnYatrs41mTXLVj1IO76cUNQJETanhdBmInqr4LG4isOdACihb9Xk44fZ19rtat+9qOq8Dgyhd+coUl78EpfhIRSBOOHfldgAAAIA3AgHiLeFYpStShrAQio+MpWQZY8zcsBcBPlXr6DoWnmyIEFmvjpIcEbMu4Izd7uhvDLbdJIrJGEJB3d6hlKw5LMfRXgKzueBSWmAgcxYK7DHgebPFiAcU8DiaUh7cJeLcHSvlXhXl27gLGZGrJJxQvMYL5qazcLUrV24HAAAAeCMQID6MnmPh7WHVq6OgaZ65IWs1/8S4yhcLHhsGctZBo+lJ6WpWa8SrGmsWRmHZFTS/W2wIleN9G3+qFO6IbS2UTfq8y+cSRT0k1WTMpK5qmdIqUwrCCX0dLrXL1a5shWFlyJo2tSQvAAAA4KPoUoBs2LCBFixYQBcuXKAMGTJQ3bp1adiwYZQvXz6br4uOjqZ169bR3r176eLFixQZGUl58uShGjVqUL9+/cT/gfcg69WR84woMGLt5p/YMJCDu75D6es0oCeRrql+psgjYJSn44jgkT6vKm+ICi+KKICQM68qz5qrSt16M5xYXr5DEZtVsMp1KIIEdAAAAD6N7srwLlu2jMaOHUtVqlShtm3bChGxePFiSps2La1atYpy5cpl9bX79u2jvn37Uq1atah27doUFhZG58+fpxUrVlCaNGnop59+omLFinlFGV5zUK5OmzKrcvtOkzaNW7oTu6r5naPvE9DFso+GSdlljY7PH+c8l+L9c9FZk1oLwemCqHLX4h4twevr4653MPYYd38C892/y/DqSoCw2GjcuDG98MIL9PPPP1NwcKqD5uTJk9SpUyfq2LEjjRtnfdX4xo0blJSURIUKFTJ5/Pfff6c33niDmjdvTtOnT3fqGCFA/A93XiS16lnh6Pt4sqeGL9+cnsUl0ubRf5g8lrt8ONV8qwx5Gl8ed72Dsce4+xOY7/4tQHQVgrVz506Ki4uj7t27G8QHU758eapevTpt3ryZPvnkE+ENkSN//vyyj9epU4eyZs1K586dc9uxA6DnPB1H38eb84j0TNRVy6pmsffjPXIsAAAAgNZoJ3UUcOLECfG7cuXKFs/xY7GxsXTpku0mXnI8evRIvDZbtmwuOU4AAHCGiCsxFo89vv+EkpN045AGAAAA/MMDcu/ePfE7d+7cFs9JuR93796lUqVKqdrv7NmzKSEhgdq3b+8y15XWSG4xLd1jAGPvSXx1znM39HtnIg1/Zy0QQlHXYyklKYWeRj+jzDkzePT4fHXcvQGMPcbdn8B89++x15UAefIktYGaXIhVunTpTLZRyqZNm2jhwoWikhbnkDhLYGCAiJvzFKGhnjVO/BmMvXtITk6hO2cj6EnUU8qQNR3lLhUuzjNfHPfLh+/QH0vOUGzEU9nQq5THSRRW0nPXF2N8ady9DYw9xt2fwHz3z7HXlQDhkrvMs2fPKH369CbPPX361GQbJXA53hEjRlCZMmVo2rRpFBgY6BJjKSYmjrSGVSpPlJiYJyIRHmDsfYGbxx7Q8ZUX6IlRXwzug1GxUzEqWDWnpnOePRMPLkRTfMwzSh+alrIXy+LScrj8Wf+Yd8bi8YT4JMP/b1+MpNAimciT4Frjv2Pv7nPAn8fd2tj665jrYb77M0EyY89/+20Ses6cOcXvO3fuUOHChU2e49ArxlYZXvOSvAMGDKAiRYrQ/PnzKXPmzC47Tk9WhuGJgso0GHs9wjfShxf5RpogOnlzMz1bN1IuRSvXD4PFiGSol2tUyC1z3vxYn8Ym0qm1l0waBKYJCaYC1XJSnvLhdj+Lkvc7tvKC3e2ib+mn8pRW1xq188YXkcYgITaRsucLpfS50lFScoqmY8rn48k1ZudAxiAq0iAvlXypoNd9J46MgbvmvNzYckPSfFVy0M2/71s8zr2C8pTPZvP4lXw+d51brt6vK8ZdL9cRvRyHN9iUuhIgXO2Ke3YcPXrUQoDwYxkzZhSCwh779+8X4oP3sWjRItEPxFcQqyXno7xmcjtzonrbiezNGI91ukzBRAEB9PSRc8aLdCOV62vB78fb24IN9rAcIfTw9iMhBpTOD3vzRu5Y5WBj8NLeW+LH1mexhvFxPI15avf9mKhrltWxHHk/V5wvWlxrrM2bcu1foHSZ0njEuFKLs8chNwbsBSxnNN/UvIfac9HWYkBCXBL9u/k6Xdh1kwrVyq1KjLtjPjo7Bp6YV9bGlo/t4q6bso/z9mkyBlNCXKLs8d8+GUE3/rpHzx4nWv2OlYyBq671jizW8DjfPxdFEUnRlBiQLAS3muOwd0yOXLOdvT+6+zh8DV31AYmIiBB9QFhkyPUB6dChA40fP96QsM7VrfLmzWsSlvXbb7+JrufcC4QbGIaHh7v0GD3RB4Qnf9SVRxRxLprO7b9Fzx4neNXklrug3z750OaJqvREtmeE8mr6s8fPKG3mtJQhS1qHbiZ6rFXurGAw3ofczcwY6eaSu2yYeJ/46Odjmj5zGnp4KYb+3XLd6vuUaJGfshfNanJ8HHLw+6xTqj6zkvlhb0Xx3LZrdHaz9WO1R8mWBRStBisVOeYEpQ2k1l/VpoCAAFXGkC2jQPrelM4Pnu9R5x/R74tOm4TGqTn/nDHM5FBiXPHnzV81B4WEpzPMTUfOCyXYOnfUXJPtjQHPt8y5Qyy8c9a+C3vzu1qvkpSvcg6Lz7LtsyOK56qSFXpXG2JqjF5H5lWByjko/u5TOnfgJl07ctfm96lmzoux/fSIuGZqhTRn/lyobAyMsXeOP74bR/9utX39tPc9S/P04t7bJgJL7T6kY4q9H2dzziu9Zjt6f5SOlbE176r3LmUxh564wD7x5j4guhIgDIsGFhncCb1du3aiOSF7MbiT+erVqw0hWKNGjaK1a9fSkiVLqGbNmgah0rVrV+KPNGzYMFnPB+/TmwSIUkPGeHJ7EvMTKzbyKd38677JyWu+smNO0cb5ZFeGzD+rGiPU1s3TmgFvLyzCmgiQ+7+x0a72QqMkXMgYOcPT/DPa24c7CUoXSMmJKaLqkyPkqZSNbh976ND7Jj11XkCmz5KWyr1ifSVVjQEkR7U3SlHajMF055TljU/O6FL7fvZu7ndPRcjmqpgLStnjsxGyY36+/L38vGrDjM99xpHxVWrU2DMKlF6T2fDJlCOjzWuDI2Ng7Vp4YvVFehr9fHFKlgAWIaUob4Xn17/46Kd0+pcrqt87OH0QJRrlL0lzM23GIIcMQmvXU3sGpvk1z5ExTRsSTM9ird+TjOeeNWEld0+5/Nttun1c/bXKaXhonbDspO9IbqFQ7T6M76VszF87dIcS45Vdh40Xr6Qxlbvu2MOWh0bpAqnd97Bj1/Aca/55DbpzOsLqvtNruKAMAWKF9evXi8pVFy9eFN4NbiQ4dOhQKlCggGEbOQGyZs0aGj16tM1B//fff71GgKhaycmShqp0KylrbLojtMlVJ60jF082cl54MS+ds7MS4+jFwpqIEe9bP49V48uVK03OXGyB9kg3uFyls9JfS8/ZNWZcgRROcWrdZYfOOblFi+TEZNr6yRETL6sjsNgzDtlx1bWBz8GgNEFOGe3sBTAPQbF1npl73pwRl67G0WuhuXjQGhbx5Tva9mjpDV4UeGbDwLRngHobrliw4e85X1XrC4KewNhTKrdA6qqFKjkC0wRQcoJ9ZVhdgwVlCBAvRCsBotYtbgt7hrO9WH0lRrGvXXy1wtZqMgDuRFq0kLxzfDO+fuiu4tVJpbjzhq7l6jCfq9cO3tM0nMbX4TENoACbIZwA+Btp//OWBLqx5xwEiBeilQDh5M8DM9XFyDuLeYyivThNAAAAAADgWtJmCqaKnYu5zROiBwGiqypY4DnscdCak6svUpoMqbHnauI0AQAAAACAa3j2OFGEe+olv9cdQIDoFM6r0Jr46ATVlYkAAAAAAIDrObXmkihw4IstCLTztQBVcOIm52YAAAAAAAD/40nUM5GH64tAgOgUVrtSbWkAgPYUqGHaLwEAf0uEfaFBHkoTEuTpQwHAr4n3QEi+FkCA6BiO++P4P3hCgFblCbmHQ51+ZalO/3Li/5wIpwbu4Mx9XFTPWZ15l/lzVH6thDj/eFzcWdoTAHfAlQmDMzguHip2KUYVOhSlll/UolItn5fAB+79ztRcH7iktLM2AvdVcuY6ycfgzPzgktilWhWkki0KuG0cXbJPFuT18whh7g8h+X7ZiFDveLITelBSACUGJNORJf96XSnI4HSBlOihUpzcXTrpmW8n1JdtV1i4ah0t5aumOZh5J3RbTRztl29ObVxnr3Ovo2WezV/HN4/8CpqlGSf+pfbGOOyWEsnWmpsZw8ccVigzRVyNoYTYJNV9gRxtNKd0/Pnc5ruIq88xe30XbJUU5rn56E4cnd9+g7SgZPMCdjtEqzHInjrZh0U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       "text/plain": [
        "<Figure size 900x450 with 1 Axes>"
       ]
@@ -389,7 +378,7 @@
     },
     {
      "data": {
-      "image/png": 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+      "image/png": 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",
       "text/plain": [
        "<Figure size 900x450 with 1 Axes>"
       ]
@@ -448,7 +437,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 700x700 with 4 Axes>"
       ]
@@ -493,7 +482,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 700x700 with 4 Axes>"
       ]
@@ -514,7 +503,7 @@
     {
      "data": {
       "text/plain": [
-       "<Axes: title={'center': 'data_dsdna_d7_d17_50_50_1, T=753μs, #bu=3912'}, xlabel='E', ylabel='PDF'>"
+       "<Axes: title={'center': 'data_HP3_TE150_SPC630, T=1639μs, #bu=2982'}, xlabel='E', ylabel='PDF'>"
       ]
      },
      "execution_count": 22,
@@ -523,7 +512,7 @@
     },
     {
      "data": {
-      "image/png": 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33XczePBgnn/+eYKCgnB2dmblypW88847NdpHbXMPGjSI//3vf8ydO5e//vWvFBYWEh4ezv3331/eGTc9Pb3KC54LFTFV3UV2cXGhoKCg/Osff/yRhx9+mEmTJnHPPffQrl07nJyc+Pzzz1m4cGGN3idAWlraBbO0a9fuooXDggULaNu27UWHQL2Q9PT0Gn1/62PBggU4OzvXqtlWdaZMmcIrr7zC7t27ywsHHx+fSkUdlBagRUVFte4Y/VvOzs5MnjyZV199lZMnT9K1a1e8vb0xmUxV3nHNyMgAqPIpiKO60IXub13ojn9qaio///wzo0ePrtRk7EL69u1L586d2bNnT6XX3N3dyy/4Bw0aRL9+/Zg+fTpffvllrUdzq8nviNooKSnBbrdjt9vZvn07Dz/8MMXFxcTExJCdnU1UVBTFxcWYzeZKF8EX+5mry917Hx+fKkcZq6nw8HCg9FxMmDCB6667jhdeeIElS5YApX1LZs+ezeOPP8706dPLj/n444/zt7/9rd4FWFlzz8TERD755BM6duxYr/2JqHAQqQcvLy9MJlOVHV5/u2zlypXk5uYya9asCp09Dx48WOPjfffdd1gsFt59990Kd91r22G4NrkBJkyYwIQJEygsLGT37t28++67PPbYY+V37X18fGq8r5pasmQJHTp0YObMmRXuuv12uMvqlHUSrG2+mJgYYmJiuOuuu3B2dq7VMaH0j390dHStjlkb586dY/Xq1YwbNw4/P78G2ef5zr8g6969O9999x3JyckVLloPHz4M/Hpx1BDKzrWbmxudOnUqP8b5Dh06hJubW7O6CKpvU6UlS5ZQVFRU66dfdru9RneYIyMjMZvN5c1qylTVN+P8p39lqvsdURuXXXZZhYL+mWee4Zlnnin/euLEiQBcd911vPzyyxW2vdjPeVmBW/a7s7CwsMJ6ZTcZzlffpkrns1gs9O7dm2XLllVY3rdvX77//nvi4uLIy8ujU6dO7N+/H6Bew85mZGRw5513EhcXx8cff1zpibBIXahwEKkHd3d3+vbty48//shf/vKX8j9I2dnZlYZ7LLsgOn+MdLvdXmWzFRcXlyqfHphMJpycnCpcCOTn55ffvWqM3L/NNXjwYLy8vFi/fj0xMTFERUUxZMgQ3n//fQ4ePFjhj1N9JmIymUw4OztXKBqSk5P56aefarWfLl264O/vz9KlSys0Vzpz5gy7du26YB+HsuYJde10PGTIEJYtW8ZPP/1U4YlFQ01O9fXXX1NUVMT111/fIPsrU9ZM7fxmc+PHj2fmzJksXryY++67r3z5okWLcHNzq3I+gdooKiri+++/x9fXt8JcDhMmTOCTTz7h7NmztG/fHij9jK5YsYJx48bVuEmTI6hvU6UFCxYQEBBQafSdi9m9ezcnT56s0SR9W7duxWazVZpLIzk5mXPnzlUoTnfu3HnB/Vzod0RtvP322xQWFvL111+zatUq3njjDaB04IjOnTtz5513AlQ5clBOTk6VP3Nms7n8Irzsxs2hQ4cqfHbLJoI8X32bKp2voKCA3bt3V/oelyk793a7nY8++oiAgAAmTZpUp2OVFQ2nT5/mww8/pHfv3nXOLXK+5vNbV8RBPfTQQ9xzzz3ceeed3HXXXZSUlDB79mysVmuFR+PDhw/H2dmZRx99lHvuuYfCwkI+//xzMjMzK+2ze/fu/Pjjj3z22WdERkZiMpno06cPo0eP5qOPPuKxxx7jxhtvJD09nQ8++KBOEzbVNPcbb7xBQkICw4YNIygoiMzMTObMmYOzs3P5yC633347Cxcu5L777uPhhx8uH1Xp+PHjtc5VZsyYMfz4448888wzTJw4kYSEBN566y0CAgKIjY2t8X7MZjMPPfQQTz31FH/4wx+YPn06mZmZvPnmmxdsNlRQUMDSpUuJioqia9eudcp/7bXX8vHHH/PXv/6VRx55hE6dOrFmzRrWr19f5frLly8HSmdKhtIhKMsmvKrq4mHBggW0b9++zhft27dv5+233+ayyy6jY8eO5Z2j58+fz9ChQysMwxkeHs4NN9zArFmzcHJyok+fPmzYsIH58+fz8MMP16qp0ksvvURxcTEDBgygXbt25Z2jDxw4wEsvvVShqc7dd9/NN998w3333cdDDz2Es7Mzs2fPpqCgoFYjiEHppGdr1qwBKG+6s23bNtLS0rBarYwePbp83ccff5zFixfz008/XbTPQW2UNQuqiz179nDkyBHuv//+CzZlmjJlClOmTKFr164VOke3a9eufO4NgFWrVjF//nzGjRtHSEgIRUVF7Nu3jzlz5tCpU6dKTzRMJhN/+MMfuOeee3BxceH7778vvxv+zTffMGbMGD7++ONqf0fArzOVV3dnvmxEpX//+9+MHTuWPn36kJuby5EjR3jwwQcv+r308fHhmWee4ezZs3Tu3Jk1a9Ywf/58br755vKO0f7+/gwfPpz33nsPb29vgoOD2bRpEytWrKi0P19f3yoLlOrcdNNNjBs3jrCwMNq0acOZM2f4/PPPqxwt7/XXX6d79+74+/sTHx/PwoULiY6O5t133600UduaNWvIy8srH+r66NGj5b87Ro8ejdVqJT8/n7vvvpuYmBiefPLJ8s7XZdq2bUtoaGit35MIqHAQqbcRI0bwv//9j5kzZ/Lwww/j7+/PzTffTEFBQYU/EF27dmXWrFnMnDmTP/3pT/j4+HDVVVdxxx13cO+991bY5+9+9zuOHDnC66+/TlZWFna7nUOHDjFs2DBefPFFZs+ezf33309gYCDTp0+nbdu2/O1vf2uU3P369WPfvn385z//ITU1FS8vLyIjI/n444/Lm6j4+/szb948XnjhBZ555hmsVisTJkzg73//Ow888ECdvq/XX389586d44svvmDhwoV07NiR++67j4SEhCqHqb2Ysouh999/nz/+8Y+EhITwf//3f2zbto2tW7dWWv/HH38kIyOjXmPlW61W5syZwwsvvMB//vMfTCYTI0eO5LXXXuOmm26qtP5DDz1U4etPP/2UTz/9FKjcBGLnzp0cP36cP/zhD3Xu6Ojv74+TkxNvvfUWaWlpmEwmOnXqxIMPPshdd91Vab//+Mc/CAwMZN68eSQnJxMSEsLf/va3Gt3NPl94eDhffvklS5cuJTs7Gw8PD/r06cMHH3zAyJEjK6zbtm1bPvvsM1555RX++te/UlJSQv/+/Zk7d26tC7pz585V+h7PmjULKL0Dff7d5tzcXNzc3KqcYM0ICxYswGQyXfTpV7du3Zg/fz7JyckUFRUREBDAFVdcwR/+8IcKT9VCQ0Nxdnbm7bffLm/CExISwvXXX899991XqT1/UFAQ48eP5x//+AdZWVkMHz6cTz75hIcffphXX32VoUOH1uh3BJQ+Dahp/4yMjAx27NhR/oRrw4YNODk5VTs5nb+/P08//TSvvPIKhw8fxtvbm/vvv7/CkK0A//rXv3j++ef5z3/+Q0lJCWPHjuXVV19tsCd4UVFRfPfdd5w5c4a8vDx8fX3p378/TzzxRKUhbzMyMvjPf/5DcnIynp6eDB48mPnz51cakhZKZ3A/vxnX8uXLywuHskI3JSWFvXv3AvDCCy9U2kdVTbxEaspkr8usUSIiIi3UiBEjmDJlCn/961+NjmKoxx9/nK1bt1bZhKe2srOzGTJkCE8++WSFeQxEpHnRmFwiIiK/OHLkCHl5eZWeAkr9bN++ncDAwAaf8VpEmpaaKom0MDabDZvNdtF1mlOn0osx6r1WNwtwVcNENiUj8tVkht2LzYpcV2VDd15I2YACNRUeHn7Rzr9SN2PGjGHMmDFGxxCRelJTJZEWZtasWdX2AWjITp9GKuvEejHVdcSsrbi4uGrndfjjH/9YqU11U6qqbfT5GqON86JFi6odtnLOnDnlE181lLIOtxfy2/4LIiJSdyocRFqYxMREkpKSLrpOjx496jQSk6OJi4urcuz189VnNJuqFBYWVluMBAQE1Hvipvoo6xh5Ib6+vg1eOKalpREXF3fRdbp06YKnp2eDHvf48ePlI8xUxcXFpdpCSkREakaFg4iIiIiIVEudo0VEREREpFoqHEREREREpFotY2iVJmS327HZ1LrLKGazSd9/B6Nz4nh0ThyPzonj0TlxPDonxjGbTTUa9U6FQy3ZbHZSUy/cEU8aj8VixtfXg8zMXIqLLz4EpzQNnRPHo3PieHROHI/OiePROTFW27YeODlVXzioqZKIiIiIiFRLhYOIiIiIiFRLhYOIiIiIiFRLhYOIiIiIiFRLhYOIiIiIiFRLhYOIiIiIiFRLhYOIiIiIiFRLhYOIiIiIiFTLoSaAO3HiBEuWLGHDhg2cOnWKgoICQkNDmTRpErfffjvu7u7V7mPGjBls3bq1ytf+97//MWHChIaOLSIiIiLS4jlU4bBw4UI+/fRTxo4dy1VXXYWzszNbtmxh5syZLFu2jPnz5+Pm5lbtfnx9fXniiScqLY+MjGyM2CIiIiIiLZ5DFQ4TJ07kvvvuw8vLq3zZzTffTKdOnXjnnXdYuHAht956a7X7cXd355prrmnMqCIiIiIirYpD9XHo06dPhaKhzOTJkwE4dOhQjfdls9nIzs7GZrM1WD4RERERkdbKoZ44XEhiYiIAfn5+NV4/KiqK/Px8XF1dGTJkCA8++CB9+vRpzJgiIiIiIi2WwxcOJSUlvPXWW1gsFq6++upq1w8JCSEqKoru3bvj4uJCTEwMc+fO5eabb2b27NkMGzas3pksFod6UNOs2Ww2kpOTarSuk5OZ/Hw3srPzKSn59UmSv38AZrPOiRGcnMwV/ivG0zlxPDonjkfnxPHonDQPJrvdbjc6xMU8++yzfPbZZzz88MP8/ve/r9M+jh8/ztSpUwkMDOSHH36oVx673Y7JZKrXPuRXZ8+e5W+frsfq5Vun7fMy03jh1pG0b9++gZOJiIiIyPkc+onD66+/zmeffca0adO4//7767yfsLAwJk+ezKJFi4iNjaVz58513pfNZiczM7fO20tFGRm5WL18cfdpV699uLnlNGAqqSknJzNeXlYyM/MqPAUS4+icOB6dE8ejc+J4dE6M5eVlrdHTHoctHGbNmsU777zDtddey3PPPVfvu/whISEApKam1qtwACgu1ge6oRQX1/+BV3GxXefEYCUlNp0DB6Nz4nh0ThyPzonj0TlxbA7ZkOzNN9/kzTffZMqUKbz00ksN0n49NjYWAH9//3rvS0RERESktXG4wuHNN99k1qxZXH311bz88ssXLBqSkpI4duwYeXl55csyMjIoLCystO7evXtZvnw54eHhdOzYsdGyi4iIiIi0VA7VVOnTTz9l1qxZBAcHM2LECJYuXVrh9Xbt2jFixAgAXnvtNRYvXsycOXMYMmQIANu2bePpp59m0qRJhIaG4uLiwsGDB1m0aBEWi4Xnn3++yd+TiIiIiEhL4FCFw969ewGIj4/n8ccfr/T64MGDywuHqnTp0oVBgwaxbt06UlJSKCoqIiAggClTpnDffffVu2+DiIiIiEhr5fDDsTqakhIbqakawaehJCYm8vbG2DqPqpSbnsLvh3cmMDCwgZNJTVgsZnx9PUhLy1FnNgehc+J4dE4cj86J49E5MVbbth41GlXJ4fo4iIiIiIiI41HhICIiIiIi1VLhICIiIiIi1VLhICIiIiIi1VLhICIiIiIi1VLhICIiIiIi1VLhICIiIiIi1VLhICIiIiIi1VLhICIiIiIi1VLhICIiIiIi1VLhICIiIiIi1VLhICIiIiIi1VLhICIiIiIi1VLhICIiIiIi1bIYHUCkPux2GykpyXXe3t/fH7NZ9bOIiIhIdVQ4SLOWn5XOnO0ZWL1yar1tXmYaj06KIjAwsBGSiYiIiLQsKhyk2bN6+eLu087oGCIiIiItmtpoiIiIiIhItVQ4iIiIiIhItVQ4iIiIiIhItVQ4iIiIiIhItVQ4iIiIiIhItVQ4iIiIiIhItVQ4iIiIiIhItVQ4iIiIiIhItVQ4iIiIiIhItVQ4iIiIiIhItVQ4iIiIiIhItVQ4iIiIiIhItVQ4iIiIiIhItVQ4iIiIiIhItVQ4iIiIiIhItVQ4iIiIiIhItVQ4iIiIiIhItVQ4iIiIiIhItVQ4iIiIiIhItVQ4iIiIiIhItVQ4iIiIiIhItVQ4iIiIiIhItVQ4iIiIiIhItSxGBxCpjfzsDDITT2N2suAT3NnoOCIiIiKthgoHaRaKCvI4uGoxZw5sA5sNACcXVwK69qHbsIkGpxMRERFp+VQ4iMMrys9ly/xZZCfHA9AmIITigjzyMlI5vWc9Rfk5DLj2XsxmJ4OTioiIiLRcKhzEodlsJexe+hHZyfG4eHgRNeUufIO7YLfbObN/C3u++4Sko3uJWfkVkZffZHRcERERkRZLnaPFoR3fsoJzJw/j5OzKJVPvxze4CwAmk4kOkUMJH3kVmEzE7d1E/MEdBqcVERERablUOIjDyk1P4fjWFQBEXH4jXgEhldbx6xhOp6jRAMSsnE9+dkaTZhQRERFpLVQ4iMM6sHoxtuJi/Dp1p32PARdcr/OA0XgFhVJckM+RDd81YUIRERGR1kOFgzikzKQzJB/bB2YzvcbdgMlkuuC6JrOZ3mOvB+DM/q1kJJ5uqpgiIiIirYYKB3FIx7etBCCoe3882wZWu75PcGfa9xwAdjtHNy1v7HgiIiIirY4KB3E4uekpJBzeDUDYoAk13q7b8MlgMpF8bB9ZvwzdKiIiIiINQ4WDOJy4fZvBZsOvc88qO0RfiIdvAEHh/YBfn1iIiIiISMNQ4SAOxWYr4cz+LQB07Du81tuHDbkMgIRDuzTCkoiIiEgDcqgJ4E6cOMGSJUvYsGEDp06doqCggNDQUCZNmsTtt9+Ou7t7jfazb98+Xn/9dXbv3o3NZiMyMpIHH3yQQYMGNfI7kPpKOXGAguxMXNw9CQiLqPX2XgEd8AnpQvqZE8Tt20y3oRMbIaWIiIhI6+NQTxwWLlzIxx9/TIcOHXjggQf4y1/+QpcuXZg5cyY33XQT+fn51e4jOjqaW2+9lRMnTvDAAw/w6KOPkp6ezh133MHGjRub4F1IfZyJ2QpAcO9BmJ3qVteG9hsJwOnojdhsJQ2WTURERKQ1c6gnDhMnTuS+++7Dy8urfNnNN99Mp06deOedd1i4cCG33nrrRffxwgsvYDabmTdvHsHBwQBce+21XHnllTz77LMsX778okN7inGKCwtIPh4DQPueA+u8n8DwfjhbF1GQlU7KiQMEdI1sqIgiIiIirZZDFQ59+vSpcvnkyZN55513OHTo0EW3P336NLt372bq1KnlRQNAmzZtmDZtGm+++SZ79uyhf//+DRlbGkjyiRhsxUVYffzwCuhQ5/04WZwJ7nUJJ3euIf7gDhUO0uRsNhvJycn12oe/vz9ms0M9FBYRkVbOoQqHC0lMTATAz8/voutFR0cDEBUVVem1smXR0dEqHBxUwqFdAAR1j6r3U6GywiHpaDTFBflYXN0aIqJIjSQnJ/Pa8l1YvXzrtH1eZhqPTooiMLD6OUxERESaisMXDiUlJbz11ltYLBauvvrqi65bVmBU9cc2KCgIgISEhHpnslh0F7ChWCylBUJJcRHJJ0qbKQV171/v/XoFdsSjbQA5qUkkHNlDh8ghFzy+zmfdOTmZK/xXSlksJqxevrj7tKvXPury2dQ5cTw6J45H58Tx6Jw0Dw5fOPzzn/9k9+7dPPzww4SFhV103by8PABcXFwqvebq6gpQow7WF2M2m/D19ajXPuRX+fmlI2Wlnj6CrbgI1zY+9WqmVMZkMhHcexBH1n/H2YPbL1g4eHu763w2AC8vq9ERHErZ57o+6vvZ1DlxPDonjkfnxPHonDg2hy4cXn/9dT777DOmTZvG/fffX+36Vmvph62wsLDSa2UFg5tb/Zqs2Gx2MjNz67UP+VVGRun3sqxTdECX3g3Web19z4EcWf8d504dIT8rHbc2PlUe380tp0GO1xo5OZnx8rKSmZlHSYnN6DgOo+xzXd991OWzqXPieHROHI/OiePROTGWl5e1Rk97HLZwmDVrFu+88w7XXnstzz33XI0uJsuaKJU1WTpf2bKyJkv1UVysD3RDKS62Y7fbST6xHwD/OszdcCHu3n7lczokHNlN5wFjqjy+zmf9lZTY9H08T3GxvUH2UZ/vqc6J49E5cTw6J45H58SxOWRDsjfffJM333yTKVOm8NJLL9V4ZJGyUZl27dpV6bWyZRcauUmMk5ueTF5GKmaLhbYdwxt030Hh/QFIPBLdoPsVERERaW0crnB48803mTVrFldffTUvv/zyBYuGpKQkjh07Vt6vASA0NJS+ffuyfPlyzp49W748OzubBQsWEBoaqhGVHFBa3DEAfDt0w+Li2qD7DuhWWiimxZ+gMDerQfctIiIi0po4VFOlTz/9lFmzZhEcHMyIESNYunRphdfbtWvHiBEjAHjttddYvHgxc+bMYciQXzu+PvXUU8yYMYNbb72VGTNm4OzszJdffklycjLvvfeeJn9zQGnxsQD4hXZv8H27e/vRJqADWUlxJB3bR4c+wxr8GCIiIiKtgUMVDnv37gUgPj6exx9/vNLrgwcPLi8cLqRfv37MmzePmTNnMmvWLGw2G5GRkXz00UcVCgxxDDabjfSzsQD4NXAzpTJB4X3JSooj8eheFQ4iIiIideRQhcPLL7/Myy+/XO91+/bty4cfftiQ0aSRxMWdoqQwHxd3T9oEhDTKMQK79eXIhu9JOXlIk8GJiIiI1JHD9XGQ1uXQoYNAaf8Gs9mpUY7h4ReEu68/9pJikmMPNMoxRERERFo6FQ5iqCNHDgGN10wJSieDC+gaCVA+O7WIiIiI1I4KBzFMcXExR48eAWjwYVh/y79LbwBSThzAbtP40CIiIiK1pcJBDHP8+DEKCwuxuFpp0659ox7LNyQMJxdXCnOzyEyOa9RjiYiIiLREKhzEMIcOlfY38GnfGVMNJ/mrK7OTpXy41+QT6ucgIiIiUlsqHMQwhw+Xdoz2CuzYJMf7tbmS+jmIiIiI1JYKBzGE3W7nyJHDAHgHNW3hkH72JIV52U1yTBEREZGWQoWDGCIpKYnMzAycnJxo0y64SY7p1saHNv7BYLeTEnuwSY4pIiIi0lKocBBDlA3D2rFjKGanppuHsF3nXoD6OYiIiIjUlgoHMURZ4dClS1iTHre8n0PsAex2DcsqIiIiUlMqHMQQhw+XFQ5dm/S4PsFdsLi6UZSXQ1bK2SY9toiIiEhzpsJBmlxeXh6nTp0EICysaQsHs5NT+WRzaWeON+mxRURERJqzpmtcLvKL48ePYrfbaNu2HT4+vkBGkx7fL7QHSUf3kq7CQZpIQU4miUeiOXfqMDmpiRQX5uPk7IKnX3vadelFUPf+OLtajY4pIiJyUSocpMkdPXoEgPDw7oYc369T6XEzEk5RWFhoSAZpHXLTUzi6aRlnD+3CXlJS6fWc1CQSj+zh0JpvCBtyGZ0HjMHs5GRAUhERkeqpcJAmd+JE6Z3+rl27GXJ8D98AXNv4kJeewvHjx+jYsWnmkZDWo6CggGNbfiT+wA7sJcUAeAd1IqBbH7wCO+Di5kFRQS4ZCaeIj9lOTmoih9cuIenYXvpfeYex4UVERC5AhYM0uRMnjgFNP6JSGZPJhF9od+LSUzh0KIbRo8cYkkNapqNHD/P66//m9PHTmJ0s+HXqQfeRV+EdFFpp3XadehI2aAJnYrZycPVi0s+cYPPnrxM58Ragc5NnFxERuRgVDtKksrKySE5OAkoLh6wsY2Zw9gvtTlz0Rg4d0nwO0jDsdjvff/8tn302l8LCQlw9vOgz6Vb8wyIwmUwX3M5kNtMhcihtO3Rjx+J3yUlNYve3H5E6pDOBgYFN+A5EREQuTqMqSZMqe9oQFBSMu7uHYTn8Qkv7OZw+fYqsrCzDckjLkJ+fz6xZr/Ppp59gt9sYOHAQl9zwAAFdIy9aNJzP3acdg6c/iKdfEIW5Wbz99izy8nIbObmIiEjNqXCQJnX8uLHNlMq4eXrj7uuP3Q4xMfsMzSLNW0pKMv/4x5Ns3rwBs9mJO+64lzvuuKdOoyS5erRh4NT/w8Xdk7Nn45k9+x3sdnsjpBYREak9FQ7SpMoKh6aev6EqbTuUds7eu3ePwUmkuTp+/Bh///vjnD59Ei8vb5566lkuv3xSjZ8yVMXq1ZbIy2/GbDazefMGVq/+uQETi4iI1J0KB2lSZU2VHKFw8A0pfeqxb1+0wUmkOdqxYxvPPfd3MjLS6dixEy+88C969uzVIPv2CujA1VdfC8DcuR9x7lxKg+xXRESkPlQ4SJPJzMwovwDq3LmLwWnAu30nzGYzSUmJJCYmGh1HmpHly7/j1VdfobCwgL59+/PMM//Ez69dgx5j/PjLCQ/vQX5+Hh99NFtNlkRExHAqHKTJlDVTat8+GKvV3eA0YHF2Le9rsX+/njpI9Ww2Gx9//AFz5nwI2Bk79jL+/OcnGuXzbDabueee+zGbndi5czu7d+9q8GOIiIjUhgoHaTKxsScA6NLF+GZKZXr06AnAvn17DU4iji4vL4/XXnuFH3/8HoCbb57BPff8HxZL441q3bFjKJMmXQnA55/PxWazNdqxREREqqPCQZrMyZOxAHTq1NnQHOfr3r20cNi/f5+agsgFpaam8vzzT7Nz53YsFmcefPAxrr762np1gq6pa6+9Hg8PT+LiTrF27epGP56IiMiFqHCQJuOIhUPnzmG4uLiSlZXB6dOnjI4jDujUqZM8/fQTxMYep02b0pGThg4d3mTH9/T05Nprrwfgq68+p6CgoMmOLSIicj4VDtIk8vPzSUg4CzhW4WCxWMpHwtm/X82VpKI9e3bxzDN/IzU1hfbtg3nuuZfo3r1Hk+e4/PJJtGvnT1paKj//vKLJjy8iIgLQeI1zRc4TF3cKsOPl5Y23t4/RcSqIiOhDdPRu9u/fy+TJVxkdRxzEihU/MHv229jtdrp1C+feex8AqNEIXCkpyfU6tt1uq7SP0aPH88UX81i06Cv69o26YN8Ki8WEt7fxo5aJiEjLo8JBmoQjNlMq07t3JAAHDuynpKQEJycngxOJkWw2G198MY/FixeSmJFL+54DcB44hTl7koGaFQRp8SfwDa77xXt+Vjpztmdg9cr5NZc9mAybC8lxiTw3Zwntew6octu8zDReuNUdNzevOh9fRESkKiocpN5sNhvJyRe/oNq/fz9FRcW0betX4Y5tfe/MNoQuXcJwd/cgNzeHEyeO061buNGRxCCFhYW89dZ/2bp1EwBhg8fTa9wNte4EnZeZWu8sVi9f3H0qzg3RddhEDq3+mjMxWwkbPAGTWa1NRUSk6ahwkHpLTk7mteW7sHr5XnCdnTsPkJldwO4MZ85ujC1fXt87sw3BbDbTu3cE27dvZd++aBUOrVRGRjqvvvoKR48exsnJws03/44dxUFNMnJSTXXsM4zjm38kNy2ZxGN7CQrvZ3QkERFpRXS7ShpE2d3Rqv5ZvdqSl3kOs5OFdl16V3jNzdPb6OgARET0BUqHZZXW58yZOJ5++gmOHj2Mh4cnTz75NIMHDzU6ViUWFzc69hsBwMmdawxOIyIirY0KB2l0eVlplBQWYHKy4OEbYHScKkVElPZzOHToAEVFhQankaZ05MghnnnmbyQnJxEQEMgzz7xAr14RRse6oNB+I8FsJi3uGJnJZ4yOIyIirYgKB2l0Wb9c3Hj6BWJ20I7HISEd8Pb2obi4iCNHDhsdR5rIrl07+ec/nyEnJ5uwsG4899xLhIR0MDrWRbm18SGoW+kTslO71xmcRkREWhMVDtLospJKC4c2/iEGJ7kwk8lEREQfAPbt03wOrcG6dWv4z39eoqiokH79onjqqWfx8nKMpnPVCY26FID4mO0U5ecanEZERFoLFQ7S6LJS4gHwcuDCASAysqyfgwqHlu6nn37k7bf/i91uY+TI0Tz22OO4ubkZHavGfEPCaBMQgq24iDP7txodR0REWgmNqiSNrqwddhv/YIOTXFzv3qXt2o8dO0peXh5Wq9XgRAI1G+73Yvz9/TGfN2zpTz/9yAcfvAvApElXctttd1R4vTkwmUx07DOMmJ8WELd/C50GjHao0Z9ERKRlUuEgjaq4IJ+89HOAYzdVAggICMTfP4Dk5CQOHjxAVFTVE2xJ06rJcL8XkpeZxqOToggMDATg559XlBcNkydfzW233d5sL7jb9xzIwTVfk50cT2biabyDQo2OJCIiLZwKB2lUWefOAuDq6Y2L1cPgNNWLjOzLqlUr2b9/rwoHB1LVZGg1YbfbyicZ3LVrBx9++C52O4wbN4HLLptEUlLSBbd1hMkJL8bZzZ3A8H6cPbCDuH2bVTiIiEijU+EgjSo7pbRw8GzX3uAkNRMREcmqVSvZty/a6CjSAPKz0pmzPYOCnP1Efz8XW0kxwb0HkxA4iHc2nbzoto4wOWF1QiKGcPbADs4e3EHP0dfi5OxidCQREWnBVDhIo8o+lwCAp1+QwUlqpmxkpVOnYsnKyqJNmzYGJ5L6spUUEfPTAgDa9xxAvytmYKpBn4a8zNTGjlZvfh3DsXq3JS8jlcSj0QT3usToSCIi0oI1rx6B0uxk/9JUqU0zKRy8vX0ICekIQEyMZpFu7ooL8tn7w2cUF+ThE9KFvlf8rkZFQ3NhMpsJiRgCQNzezQanERGRlq7l/AUVh/TrE4fm0VQJIDKy9KmDhmVt3uw2G0c2fk9+ZhpW77YMuOZenCzORsdqcCERg8FkIvX0EXIzzhkdR0REWjAVDtJoivJzKcjOBJpPUyVAE8G1EMc2/0B6/AnMThaiptzdLDrn14XVqy1tO4YDkHBop8FpRESkJVPhII2mrJmSWxsfLK7NZ3KtXr16YzKZSUiIJzVVd3Cbo+TYAxzdtByA7qOuxiugg8GJGldwz9IRwOIP7DA4iYiItGQqHKTRlDdTaiYjKpXx8PCkS5cwAPbvVz+H5qYwL4d9P3wGQGB4P4K69zc2UBMIDO+HyclCdspZslMTjY4jIiItlAoHaTRZKc1rRKXzRUREAmhY1mYo5ucFFGRn4tE2kM4Dxhgdp0k4u7kTENYbgMQj+syKiEjjUOEgjaa5DcV6vsjIvkDpEwe73W5wGqmp+IM7SDi4E8xm+k6+DbOl9Yw43b7nQACSju3FZrMZnEZERFoiFQ7SaMonf2tGIyqV6d69JxaLhdTUFBISzhodR2qgICeTmJ++AqDbkMtb3UzK/mERWFzdKMjO4MiRI0bHERGRFkiFgzSKwrwcCnOzAPD0CzQ4Te25urrSrVt3QPM5NBcH13xDcX4eXoEdCRtyudFxmpyTxZnAbv0A2Lhxo8FpRESkJVLhII2i7GmDm1dbLC7NZ0Sl82lY1ubj3KkjnD2wHUwmIi6bjtnJyehIhmjfq3R0pW3btlFSUmJwGhERaWlUOEijaM79G8qUFQ4xMfvUZtyB2UqKy5sohfYbiXdg62qidL62HcOxuLmTlZWlJ2UiItLgVDhIoygrHNo0s6FYz9e1azdcXd3Iysrk9OmTRseRC4jdsYqc1ERc3NsQPuIKo+MYymx2wr9zTwA2b95kcBoREWlpHK5wePfdd3nwwQcZP348PXr0YNy4cbXex4wZM+jRo0eV/1auXNkIqeW3yiZ/a85PHJydnenZsxeg+RwcVUFOJsc2rwCgx+hrcHZzNziR8fzDIgDYunWLnpSJiEiDcrixCl977TV8fHzo3bs3WVlZdd6Pr68vTzzxRKXlkZGR9YknNdQSmipBaXOlPXt2sX//Pq644mqj48hvHN20nJKiAryDOhHc6xKj4zgEn+Au5Hp4kJGRwcGDMfTurd95IiLSMByucFi5ciUdO3YE4KqrriI3N7dO+3F3d+eaa65pyGhSQ4W5WRTmZgPg0bb5jah0vrJ+DgcO7KekpASnVtrp1hHlpCZxem9pc5wel07BZDIZnMgxmM1ODBw4kHXrNrBlyyYVDiIi0mAcrqlSWdHQEGw2G9nZ2Xpc38TKnjZYvdticXE1OE39dOrUGQ8PT/Lz8zh+/KjRceQ8h9d/CzYb/l0jaduxm9FxHMrgwYMB2LJlk37/iYhIg3G4Jw4NJTExkaioKPLz83F1dWXIkCE8+OCD9OnTp977tlgcrt4ylMVS8U5vVkpZMyXH7xhtsZiqOZ9mIiP7sGXLZg4c2E+vXr2aLFtz4ORkrvDfxvDbzxdAWvwJEo9Eg9lMj5FXNdqxm6uIiAg8PT3IzMzk6NFD9O4dYXSkVq0pfk6kdnROHI/OSfPQIguHkJAQoqKi6N69Oy4uLsTExDB37lxuvvlmZs+ezbBhw+q8b7PZhK+vRwOmbf7y8yt2SG1OHaO9vd2rPZ+DBw9kx46tHDlyQOf+Ary8rI22799+vgCOrF8KQIeIIXg245G7GovFYmHkyBGsXLmSPXu2M2LEYKMjCY37cyJ1o3PieHROHFuLLBxefvnlCl9ffvnlTJkyhalTp/LMM8/www8/1HnfNpudzMy69btoqTIyKn4/clKTgOZROGRk5OLmlnPRdcLCelBcbCM6eh+JiWm4uLg0UTrH5+RkxsvLSmZmHiUljdMk5refr9TTR0k9fRSTkxNdh01slGO2BAMGDGb58h9ZvXotN944A7NZd/GM0hQ/J1I7OieOR+fEWF5e1ho97WmRhUNVwsLCmDx5MosWLSI2NpbOnTvXeV/FxfpAn6+42F7h6+zURKB5dIwuLrZXez4DAtrj4+NDenoaMTEHiIysf3O3lqakxNZoPxe//Xwd3Vxa+HeIHIq1jW+jHLMliIiIxGp1Iy0tlZiYA+VDC4txGvPnROpG58Tx6Jw4tlZ1CyokJASA1NRUg5O0XEX5uRTmZALg0TbA4DQNw2QyERnZF4D9+/canKZ1SztzjNRThzE5ORE2eILRcRyas7MLAweWNlHaulWTwYmISP21micOALGxsQD4+/sbG6QFy0krbabk6umFs6tjt1O0222kpCTXaN3g4A4UFRWzfftWxowZD5R+jtT8o2kd3bQcgJCIwVi92hqcxvENGTKM9evXsGXLZm677Q59XkVEpF6adeGQlJREVlYWwcHBWK2lF6kZGRlYrdZK7dD37t3L8uXLCQ8Pb9AhX6Wisv4NHm0dv39DflY6c7ZnYPW6eB+H0nU9SM4uIHn/IWatPkRRfi6PTooiMNDxm2O1FGnxJzh38jAms5mwwZcZHadZ6Nu3H25uVtLSznHs2BHCw3sYHUlERJoxhyscvv76a+Lj44HSJkVFRUW89dZbAHh5eXHbbbeVr/vaa6+xePFi5syZw5AhQwDYtm0bTz/9NJMmTSI0NBQXFxcOHjzIokWLsFgsPP/8803/plqR8hmjm0kzJauXL+4+7apdz92nHZ5+QeSmp5CfldYsOn63NLHbfwYguPcg3L39DE7TPDg7u9C//wA2b97Atm1bVDiIiEi9OFzhsHDhQrZu3Vph2RtvvAGU9lE4v3CoSpcuXRg0aBDr1q0jJSWFoqIiAgICmDJlCvfdd1+9OkVL9cqaKjWHjtG15Rfandz0FM6dPqLCoYnlZpwj8Whp/5IuA8canKZ5GTJkGJs3b2Dr1s3cfPMMzbAtIiJ15nCFw9y5c2u87ssvv1xp6NWuXbuWFxrS9HLKR1RqHk8caqNtx3BOR28k9dRhOvUfZXScViUueiPY7fiHRWjehlrq27c/FoszSUmJnDoVS6dOXYyOJCIizZR6ykmDsZWUkJueAoBnM+jjUFttO4YDkJUcT2Fe9f0ipGFkZWWRcHg3AF0uGWdsmGbIarXSv38UAFu2bDY4jYiINGcqHKTB5KYnY7fZcHJ2xdXT2+g4Dc7Vow2e/sEApMefMDhN67Fu3SpsJcV4BYXi26Gr0XGapUGDhgKwdasKBxERqbtaN1WKj4+nbdu2uLm5NUYeacbOb6bUUttR+3UMJzs5njQVDk2ioKCANWtWA6V9G1rq56oh2e02kpKScHXNLZ88r0OHUGw2OydPxrJnz26Cgi7c3EvDDIuIyIXUunAYP348//rXv7j66qsByMvLY+bMmdxyyy106tSpwQNK85Gd2nI7RpfxC+3OyZ1r9MShiaxbt5qcnGzc2vgQ2L2f0XGahfysdN5YkYHVq+Ks2oVeIaSePsLMhSvoFHVpldvmZaZpmGEREbmgWt9WstvtFb7Oz89nzpw55UOoSutV9sTB06/lXnT4dugKZjN5GedIS9MM5I3JbrezfPl3AHSIHIrZ7GRwouajbJjh8/91iByC2clCatyxSq+V/fttsSEiInK+Bnke/dtiQlqnnFbwxMHZ1Yp3YOkEgocOHTA4TcsWE7OP+PgzuLi4ENRjgNFxmr2ArpFgNpOVFFc+iIGIiEhtqCGrNAi73U526i+Tv/m2vKFYz+cX2h2AmJj9Bidp2Vau/BGAQYOGYHFxNThN8+fi3oa2IaWdyxOPRBucRkREmiMVDtIgCnOzKCksALMZaw1mYm7O/Lv0AuDgwRhsNpvBaVqmtLQ0tm3bAsCoUWOMDdOCBIaX9hNJPLrH4CQiItIc1WkCuD179uDkVNreOCendDz7bdu2kZaWVuX6V1xxRR3jSXNR1vTB3dsPJ4uzwWkal3f7zlhc3cjNzeXo0SN0797D6EgtzurVP2GzlRAe3oMOHTrCqVijI7UIgeF9OfDzAtLjY8nPSsetjY/RkUREpBmpU+Hw6aef8umnnwK/9m946623Kg2VaLfbMZlMKhxagbLCoSX3byhjNjvhG9IVko6ye/dOFQ4NrKSkpLyZ0oQJEw1O07K4eXrjE9yF9PgTJB7dS6cozYAuIiI1V+vC4aWXXmqMHNLM5aYnA+DZCgoHKJ1FOjXpKHv27GL69JuNjtOi7Nq1g7S0c3h6tmHo0GGkplb9JFPqJjC8b2nhcGS3CgcREamVWhcO1113XWPkkGbu1ycOLbtjdBm/juGk7ljGiRPHyMhIx9vbx+hILcbKlT8AMGbMOJydXQxO0/IEhvfj0JpvSD1znMLcLFzc2xgdSUREmgl1jpYG0ZqaKgG4uHvSsWMoAHv27DI4TcuRmJhAdPRuAMaPv9zYMC2Uu7cfXoEdwGYj8eheo+OIiEgzUqc+DmV27drF6tWrOXHiBDk5OXh4eBAWFsbYsWPp10+zvLYW+fn5FORkYnaytJqmSgC9e0eQkBDP7t27uPTSsUbHaRF++qm0b0Pfvv0JDAwyOE3LFRjej8zEOBKP7KFj3+FGxxERkWaiToVDdnY2f/7zn1mzZk2Vk7+9++67jBs3jn//+9+4u7vXO6Q4tsTE0vkbXNzb4OzWes53RERffv55BXv37qGkpKR8pDGpm6KiQlav/hlQp+jGFhTenyPrv+PcqcMU5ee2qp9bERGpuzoVDg8//DDr16+nf//+TJ8+nZ49e+Lp6Ul2djYxMTEsWLCAn376iUcffZR33nmnoTOLgykrHFpL/4YynTt3wcPDk5ycbI4ePUKPHj2NjtSsbd68iezsLHx9/YiKGmh0nBbNo20Ann5BZJ9LIOn4fkJ6DzI6koiINAO17uOwceNG1q9fz2233cYXX3zB1KlT6d27N6GhofTu3ZsbbriBL774gltvvZU1a9awadOmxsgtDiQx8SzQekZUKmM2m+nTp7RJ3u7dOw1O0/yVdYoeP/4yPb1pAuWTwR3RZHAiIlIztS4cli5dSlBQEE888cRF13viiScIDAxk6dKldQ4nzcOvTxxaV+EA0L//AECFQ3VsNhuJiYkX/Ldz5w5iYvZTUmIjMrJvhddSUpKNjt8ilRUOKbEHKC7MNziNiIg0B7VuqrRv3z4mTJhQ7R1Bi8XChAkT2Lp1a53DSfOQkNA6myoB9OvXHzBx8uQJzp1Lwc+vndGRHFJycjKvLd+F1cu3ytcPr/uW5OwC/MMi+Gx/OpBe/lpa/Al8g7s0Sc7WpI1/MO4+7chNTyH5xAHa94gyOpKIiDi4Wj9xSExMJCwsrEbrhoWFlV9USstUUlJCcnISAJ5tW98oON7ePuUzR2/fvs3gNI7N6uWLu0+7Sv9crJ4knziA2clC2ODLKr3u5ultdPQWyWQyqbmSiIjUSq0Lh+zsbDw9PWu0roeHB7m5ubUOJc1HUlIiJSUlmC0W3Nr4GB3HEAMHlnYs3bFDhUNdxB/YRklRAR5tA2nbsZvRcVqVssIh+XgMJcVFBqcRERFHV+vCoaSkBJPJVKN1TSYTJSUltQ4lzUd8/BkA3L3bYTK3zvkEL7lkMAAxMfvIyck2OE3zYrfbObVnAwAd+w2v8e8WaRjegR1xbeNDSVEB504eMjqOiIg4uDoNx7pnz54ajXqyZ48ef7d05YWDT+tt29++fTDBwR2Ij49jz55dDB8+yuhIzUZ6/HGyU85itjgT0nuw0XFaHZPZTFB4X07uXEvC4d10GzbJ6EgiIuLA6lQ4fPrpp3z66afY7faL3iGs7nVp/n4tHPwNTmKsSy4ZxJIlcWzfvlWFQy2UPW1o33OAJiEzSGC3fpzcuZak4/sIGzzB6DgiIuLAal04vPTSS42RQ5opPXEodcklg1myZDG7d++iqKgQZ2cXoyM5vMLcLBIOlz6VDO030uA0rZdvSBgu7m0ozM0iPT4WUD8TERGpWq0Lh+uuuw6A1NRUTp8+ja+vL6GhoQ0eTByf3W5X4fCLsLBu+Pj4kp6eRkzMfvr109CW1YnbtwV7STFeQaF4B+l3iFFMZjOB3fpwOnojybEHAD11EBGRqtW6N6vNZuPpp59m5MiR3HTTTUycOJEbb7yR5GRN0tTaZGZmkJOTjckEVm8/o+MYymw2M2DAJQBs3665S6pjt9k4HV3aTCm03wiD08j5k8HZbDaD04iIiKOqdeEwb9485s+fT7t27bjsssvo3r07e/bs4e9//3tj5BMHVva0oW3bdjhZnA1OY7yy0ZV27tyui69qpJw8SF5GKhY3K+17DDA6TqvXtmM4zm7uFOXlcOzYUaPjiIiIg6p14fD111/TtWtXvv/+e/773//yzTffcMMNN7B27VoyMjIaI6M4qLLCISio9U38VpWIiEjc3KykpaVy/Lguvi6mrFN0SMQQnNQfxHBmJyf8u0YCsHv3DoPTiIiIo6p14XDixAmuu+66CpPA3XbbbdhsNmJjYxsymzi4ssIhMFCFA4Czswv9+5f2bdiyZbPBaRxXXkYqycf3AxDaV82UHEXQL82Vdu3aqSdmIiJSpVoXDnl5eQQEBFRYVvZ1Xl5ew6SSZkGFQ2VDh5ZeCG/evAG73W5wGsd0eu9GsNtpG9odj7YB1W8gTcKvUw8srm5kZmZw4MB+o+OIiIgDqtNUv7+dm6Hsa10otS4qHCrr338Abm5Wzp1L4ejRw0bHcTi2kmLi9pY+jVGnaMfiZHHGv0tvADZu3GBwGhERcUR1mgBu9erVJCQklH+dn5+PyWTi+++/Z9++fRXWNZlM3HPPPfVLKQ6noKCgfCStwMD2kHTO4ESOwcXFhQEDLmHjxnVs3ryR8PAeRkdyKIlHoinMzcLV04uArn2MjiO/EdCtL/Fx+9m6dRN33HGX5iMREZEK6lQ4fPfdd3z33XeVln/11VeVlqlwaJkSEuIBOx4enr/0d1HhUGbYsBG/FA6buPXW2zGb6/Rgr0U6tWc9AB36DMfs5GRwGvktn/adyPH2Jicnm+joPQwcOMjoSCIi4kBqXTjMmTOnMXJIM3PmTGkzpeDgkEpN11q7vn37YbVaSUs7x+HDh+jZs5fRkRxCdmoiaXHHwGymY59hRseRKphMpfORrF+/hg0b1qlwEBGRCmpdOAwePLgxckgzU9a/ITg4xOAkxrDbbaSkXHjSw549I9i6dTMrVizH17dtpdf9/f1b3ZOIM/tLJ8YL7NYHtzY+xoaRC7rkkiGsX7+GHTu2kZeXh9VqNTqSiIg4iDo1VRJp7YVDflY6c7ZnYPXKqfL1cy4dSM4uYMmq9aSEDMVk+rVIyMtM49FJUQQGBjZVXMPl5eWSdDQagE79RxmcRi4mNLQTQUHBJCTEs337VkaNGm10JBERcRCt65anNJjWXjgAWL18cfdpV+W/kIjBuHp4UVJYQG7GuQqvWb18jY7e5LZs2URJUSGefkH4duhmdBy5CJPJxPDhIwHYuHGdwWlERMSRqHCQWrPZbJw9Gw+07sLhYsxOFtr3HAhA/C9NdForu93O2rWrAQjtP1J9YpqBssIhOnoPmZkZBqcRERFHocJBau3cuRSKigpxcrIQENB6mtvUVkhEaX+gxKPRFBW03skR9+3bS1JSIk7OLgT3Vmfb5iA4OIQuXbpit9vYuHG90XFERMRBqHCQWitrphQU1B4nDal5QV6BHfH0C8JWXEzCoV1GxzHMihXLAAjq3h+Li5vBaaSmLr10LACrV/9scBIREXEUKhyk1tS/oWZMJhPBvzx1OBPTOpsrpaQks2PHdgCCe2tEtuZkxIhRWCwWTp2K5eTJE0bHERERB6DCQWpNhUPNBfe6BMxm0s+cICftwsO3tlQrVizHbrfRvXsPPHz9jY4jteDp6cnAgaXFnp46iIgIqHCQOlDhUHNunt6069QDgPiYbQanaVp5eXn89NMKAMaOnWBwGqmL0aNLmytt2LCOoqIig9OIiIjRVDhIralwqJ2QX5roxO3fgs1WYnCaprNmzc/k5uYQFBRMREQfo+NIHfTt2x9f37ZkZ2exa9cOo+OIiIjBVDhIreTkZJORkQ5AcHCwsWGaiYBufXBx96QgK53kY/uMjtMkbDYby5YtBeCKK65qdbNktxRms7l8Ajg1VxIREf01l1ope9rg69sWq9Xd4DTNg5PFmQ59hgFwak/rGNpy27YtJCcn4enZhlGjxhgdR+ph9OhxAOzZs4u0tDSD04iIiJFUOEitqJlS3XTsMxxMJs6dPExueorRcRrdd98tAeCyyybh6upqcBqpj/btgwkP74HdbmP9+jVGxxEREQOpcJBa+bVw6GBwkubF6t0W/7AIAM608E7Shw8f4ujRw1gszlx++SSj40gDGDNmPAA//7wSm81mcBoRETGKCgepFT1xqLvQ/iMBSDi8m4KCAoPTNJ6ypw0jR16Kt7ePsWGkQQwbNgKr1Upi4ln2799rdBwRETGICgepFRUOddcutAfuPu0oKcxn27YtRsdpFHFxp8vf2xVXXG1wGmkobm5ujBw5BoAVK34wNoyIiBhGhYPUWHFxMYmJiQCEhKhwqC2T2Uxo/1EA/Pzzjy2yycfXXy8E7AwaNJQOHToaHUca0GWXTQRgx45tpKaeMziNiIgYweEKh3fffZcHH3yQ8ePH06NHD8aNG1en/ezbt4+7776bgQMHEhUVxYwZM9i2rWW3LW9siYkJ2GwluLq64evb1ug4zVKHPkOxuLqRlJTE9u1bjY7ToM6ejWfTpg0AXHfd9QankYbWoUNHevbsjd1u4+efVxodR0REDOBwhcNrr73Gli1bCA0Nxdvbu077iI6O5tZbb+XEiRM88MADPProo6Snp3PHHXewcePGBk7cepzfTMlkMhmcpnmyuLgR/MuEcEuWLMZutxucqOF8/fVC7HYbAwZcQufOYUbHkUYwYULpU4dVq36ipKT1TGYoIiKlHK5wWLlyJVu2bOGjjz4iICCgTvt44YUXMJvNzJs3j7vvvpsZM2bw2Wef4efnx7PPPtuiLtaakvo3NIwOkUOwWCwcP36UmJiWMSFcYmIC69evBeC666YZnEYay6BBg2nTxpu0tHPs2KEnuCIirY3DFQ4dO9avXfTp06fZvXs3kyZNqjCzcZs2bZg2bRqxsbHs2bOnvjFbJRUODcPF6smwYaUjLC1cOL9FFLKLFi3AbrfRr18UXbt2MzqONBJnZxfGjZsAwLJl3xmcRkREmprF6AANLTo6GoCoqKhKr5Uti46Opn///nU+hsXicPVWk0hIiAdMdOzYocL3wGJRs6Xamjx5Mjt2bOHgwQMcOLCPvn37GR2pTpyczJw8eZK1a1cDJqZPv6nKnw99RpoPi8V00d9xkydfwXfffcOhQwc4efK4CsUacHIyV/ivGE/nxPHonDQPLa5wKBv1JzAwsNJrQUFBACQkJNR5/2azCV9fjzpv31zZ7XaSkhKwWMz06hVe4XuQn+9uYLLmqUuXDkyZcjXffPMNCxd+waWXDmu2/UZeffVDTCYYPXoUgwdXLthBn5HmxNvb/aK/43x9PRg3biw///wzK1cu45JLHm/CdM2bl5fV6AjyGzonjkfnxLG1uMIhLy8PABcXl0qvubq6ApCfn1/n/dtsdjIzc+u8fXOVnp5GRkYWZrMJd3dv0tJyyl/LyGh934/6ysjIZdKkq1m69DsOHDjEihWrGTRosNGxau3gwRi2bt0KmJg69cYKn4vz6TPSPNjtNo4ePVnt+erf/xK+/34ZK1as5NJLx9O2bekoa/7+AZjNulv4W05OZry8rGRm5lFS0vKGYW6OdE4cj86Jsby8rDV62tPiCgertbRSLSwsrPRaWcHg5uZWr2MUF7e+D/SpU6cBO+3aBWIyWSp8D4qLm38b/aZWXGzHw8OLiROvZMmSRcyd+wmRkf1wdnY2OlqN2e125sz5GIDx4y/D3z/ogj8b+ow0D/lZ6Xy0JQOrV3a16xZ4BpFx9iTPffAVXYdcTl5mGo9Oiqryaa+UKimxtcq/H45M58Tx6Jw4thZ3a6jsj1ZZk6XzlS0ra7IkNaeO0Y3jmmuuw8vLm4SEeH744Xuj49TKpk3rOXr0KFarlRtumG50HGkgVi9f3H3aVfsvfPgVmJ0sJB6NxsXdE6uXr9HRRUSkkbW4wqFPnz4A7Nq1q9JrZcvK1pGaO3MmDoD27YOrWVNqw2p15+abbwNg0aKvSEtLMzhRzeTl5TJv3icATJs2DW9vH2MDSZPz79Ibd19/ivPzOL1H8+OIiLQGzbpwSEpK4tixY+X9GgBCQ0Pp27cvy5cv5+zZs+XLs7OzWbBgAaGhofUaUam1iosrLRw6dgw1OEnLM2rUGMLCupGfn8fcuR8ZHadGFiz4kvT0NNq3b8/112uW6NbIZDYTNmg8ACd2rMJWUmxwIhERaWwO18fh66+/Jj4+HoDU1FSKiop46623APDy8uK2224rX/e1115j8eLFzJkzhyFDhpQvf+qpp5gxYwa33norM2bMwNnZmS+//JLk5GTee++9Zjt6jZHi40sLh5AQNVVqaGazmTvvvJenn36CzZs3MGzYcAYNGmp0rEpsNhvJycnExZ3m22+/wW63c+21Uzl37hwZGbkX7ceQkpLchEmlqQT3HsTRTcvJz0rn7KFdMEpDs4qItGQOVzgsXLjwl1FafvXGG28ApRet5xcOF9KvXz/mzZvHzJkzmTVrFjabjcjISD766KMKBYbUTE5ONmlpqQCEhHQwOE3L1LVrN66++lqWLFnEBx+8R48evfHy8jI6VgXJycn857ttxPz8FTlZ+fiHRfBTijs/fbu/2m3T4k/gG9ylCVJKUzI7WegyaDwHfl7I6T3rKSm5zuhIIiLSiByucJg7d26N13355Zd5+eWXq3ytb9++fPjhhw0Vq1U7c6a0Y7Svrx/u7q1vDoumcv3109ixYxtnzpzm/fff4ZFH/p/DPR1LPBpNXvo5XD296Tt5Bq4ebWq0XV5maiMnE6N0iBzKsc0/kJ+Vzvbt25gyRU8lRURaqmbdx0GaRlnH6A4d9LShMTk7u/D73/8JJycL27dv4fvvvzU6UgXHjx/j1O51AERcdmONiwZp2ZycXeg8cAwAP/zwPSUlJcYGEhGRRqPCQaoVF3cKgA4dOhqcpOULC+vKjBl3AvDZZ3M5cKD6ZkBNITMzgw8/fBfsdtr3uoSg8H5GRxIHEtpvFBY3d5KSElm/fo3RcUREpJGocJBqlT1xUP+GpnHZZRMZMeJS7HYbr732r/Lvv1FsNhuzZs0kPT0dq087IsZPMzSPOB6Lqxuh/UYApcMKFxUVGZxIREQagwoHqVbZUKwhIXri0BRMJhN33/1/hIV1Iycnm5deep7U1HOGZLHb7cyd+xH790fj4uJC5GU3YnGt38zr0jKFRAzGy8ub5OQkVq36yeg4IiLSCFQ4yEXl5eWSmpoC6IlDU3Jzc+Mvf3mSoKBgUlNT+Oc/nzFkSNMlSxaXz2h966234+Eb0OQZpHlwsrgwadIVACxe/BUFBQUGJxIRkYamwkEuqmxEJW9vHzw9PQ1O07p4eXnz+ONP4efXjoSEeP7xj781abOlH39czpdffgrAjBl3MnDgoCY7tjRPw4ePwt8/gIyMdH78cZnRcUREpIGpcJCLOnPmNAAdOmjGaCMEBATyzDMvEBwcQlraOZ5++gl27NjWqMe02+0sWvQVH388G4ApU65j8uSrGvWY0jJYLBauv346UPq0Kjc3x+BEIiLSkFQ4yEWpY7Tx/Pza8fTTzxMe3oO8vFxeffVlPv10DoWFhQ1+rIKCAt577y0WLPgCgKlTp3Hjjbc2+HGk5Ro5cjTBwR3Iycnmm28WGR1HREQakAoHuaiyjtGaw8FYXl7ePPXUs0ycWNqG/LvvvuEvf3mE3bt3YrfbG+QYJ0+e4O9/f5w1a34GTPzud3dxww03OdwkdOLYzGYzN998GwDLli0lKSnR4EQiItJQVDjIRZU1VdITB+M5Oztz++1389hjj+Pr60dSUgL/+tcLPPfc00RH78Zms9VpvxkZ6Xz00WyefPIvxMWdwsvLmyef/AeTJl3ZwO9AWosBAy4hIqIvxcXFfP75PKPjiIhIA7EYHUAcV35+PsnJSYCGYnUkAwcOonfvCBYs+JIVK37g0KEYXn45hoCAQEaOHE1U1EC6dAnDbL7wfYGiokIOHjzIunWr2bx5A8XFxQAMGTKc22+/Cx8f36Z5M9IimUwmbrvtdp588v+xZctGDh68gp49exkdS0RE6kmFg1xQfHzpiEpt2njj5eVlcBqB0snYkpNLh2W9/PIrGDx4GCtW/MCWLRs5c+YMX375GV9++RlWq5Xg4BACA4Pw8PDExcWFoqIibLYSEhLOEht7goKC/PL9du0azs0330bv3pFGvTVpYTp16syYMeNZtWoF8+Z9zHPPvXTRYlZERByfCge5oLKO0erf4DiSk5N5bfkurF7nPREIGUrnKQNIPh5DyslDpMUdpSQ7nVPJ6cD+8tVsJSUEervj7Fz6Y+/t7UP//gMYP/4yunXr3rRvRFqF6dNvYtOm9Rw/fpT169dy6aVjjI4kIiL1oMJBLujXoVjVTMmRWL18cfdpV2l5m3bBhA2egK2khOzUBLKS48nLTKUoLwdbcRHFRYVM6B1C9+496dChIx07huoOsDQYu91W5SSFY8dOYMmSxXzyyQeEhnbCanWvcnt/f399HkVEHJwKB7mguLjSwiE4WE8cGtKFLrBqoibbmZ2c8PIPwcs/pMLy3PQUrhjemcDAwDodW+Ri8rPSmbM9A6tXxbkbbNZwsp08SY5P5m//+5jw4VdU2jYvM41HJ0Xpsyki4uBUOMgFlc0araZKDetCF1g1kRZ/At/gLo2QSqT+LvQ0rM+kW9i+4G3OHtpFl0vG4RWg3ykiIs2RCgepUkFBAYmJCYCaKjWGC11gVScvM7UR0og0rnadehLUcwAJB3eyf+V8ht70MCY1SxIRaXb0m1uqVNpMyU6bNt54e/sYHUdEmrmeo6/FycWVjLMnidu32eg4IiJSByocpEqnT58CIDQ01OAkItISuHl6Ez6itH/D4XXfUpiXbXAiERGpLRUOUqXTp08C0LGjCgcRaRih/UfRJiCEovxcDq1dYnQcERGpJRUOUqVTp0qfOHTs2MngJCLSUpjNTvQePw1MJs7s20LKyUNGRxIRkVpQ4SBV0hMHEWkMvsFdCO03EoD9K76guLDA4EQiIlJTKhykkszMDDIzMwCTRlQSkQbXfdRVuHn5kpeRypGN3xsdR0REakiFg1RS1kwpICAQNzc3g9OISEtjcXEjYsJ0AE7uWktmUpzBiUREpCZUOEglZc2UNKKSiDQW/y69Ce59CdhsHFzzNcXFxUZHEhGRamgCOKmkbChWdYxuWex2GykpyXXevj7bilSl55ippMQeJDctmR9//J4777zX6EgiInIRKhykklOn1DG6JcrPSmfO9gysXjl12j4t/gS+wV0aOJW0Zi5WD3qNvZ5dSz7ghx+WMWHCRP3eERFxYCocpAKbzcaZM6XtjfUHvOWxevni7tOuTtvmZaY2cBoRCOoRhV+nHpSci+Xdd//HM8+8gMWiP00iIo5IfRykguTkJAoK8rFYnAkKam90HBFp4UwmE91HXoW7uzvHjx9lyZLFRkcSEZELUOEgFZQ1UwoJ6YCTk5PBaUSkNXD18GLatJsBWLToK2JjjxucSEREqqLCQSr4tWO0mimJSNO55JLBDBo0FJuthLfemkVRUaHRkURE5DdUOEgFZYVDaKhGVBKRpmMymbjrrntp08abuLhTLFz4ldGRRETkN1Q4SAUnT8YC0KmTRs8Rkabl7e3D3XffB8C3337NkSOHDE4kIiLn09AVApSOpnT69OnyJw7u7u4kJibWaFuN7y8iDWXw4KGMHDma9evX8Pbbb/LSS//B1dXV6FgiIoIKB/lFcnIyL89fQXJ2Aa4eXsyNPgecq9G2Gt9fRBrS7353F/v37yUhIZ758z9jxow7jY4kIiKoqZKcp7ggF7OTBZ+QMNx92tX4n5unt9HRRaQF8fT05N57fw/AsmVLiYnZZ3AiEREBPXGQ82SlnAXAyz/E4CQi0prY7bZKTR7btw9h0KBhbNy4jpkzX+WJJ57G3d29yu39/f0xm3UfTESksalwkHLZ5xIA8ApQ4SAiTSc/K5052zOweuVUWF4cMpgs8w6ST5/lr6+9Re9xN1TaNi8zjUcnRREYGNhUcUVEWi0VDgJAcXExOWlJmDDRJqCD0XFEpJWxevni7tOu0vIBU+5h85dvkHLiAOlnYwnudYkB6UREBNTHQX6RkHAWe0kJFjcrVq+2RscREQHAJ7gz3YZNAiDmp6/IzajZoA0iItLwVDgIAHFxpcOwevmHYDKZDE4jIvKrsMET8AnpQnFBPtHL5mKzlRgdSUSkVVLhIADExcUBqJmSiDgcs9mJvpNnYHF1I/3MCY5vXWl0JBGRVkmFgwAVnziIiDgad28/eo+fBsDRTctJj481NpCISCukwkGw2WzExZ0GwEtPHETEQQX3uoT2vQaCzcaeZXMoLsg3OpKISKuiwkFITk4mPz8fs5MFj7Ya0lBEHFfv8dNw82pLXvo59q34ArvdbnQkEZFWQ4WDcPLkCQDc2wZgdnIyOI2IyIU5u1rpf+XtmMxmEg7tIj5mm9GRRERaDRUOwvHjxwBo4xdkcBIRker5BHem+6VTADi6eTmnTsUaG0hEpJVQ4SAcO3YUgDbqGC0izUTnAWMI6NYHe0kJH3zwHrm5OdVvJCIi9aLCoZWz2WwcP15aOKhjtIg0FyaTiT4Tb8GtjQ/nzqXw7rtvqb+DiEgjU+HQyiUknCUvLxdnZ2fcff2NjiMiUmPObu70njAdJycntm3bzNKl3xgdSUSkRVPh0MqVNVPq0KEjZrM6RotI8+LlH8L1108H4IsvPmXPnl0GJxIRablUOLRyZYVD585dDE4iIlI3o0aNYezYCdjtNmbNep2zZ+ONjiQi0iI5ZOGwdOlSpk6dSt++fRkyZAiPPvooZ86cqdG2M2bMoEePHlX+W7lyZSMnb37K+jeEhnY2NoiISB2ZTCbuuONuwsN7kJubw6uvvkJeXq7RsUREWhyL0QF+a968eTz//PMMGDCAJ554grS0ND755BO2bdvGggULCAysfoIyX19fnnjiiUrLIyMjGyNys1VUVERsbOkcDp06dWb70TyDE4mI1I2zswsPP/z/eOqpvxAfH8d///s6f/7z4zhpbhoRkQbjUIVDWloar776KhEREcydOxeLpTTeqFGjmDZtGv/973954YUXqt2Pu7s711xzTWPHbfbi4k5TXFyEu7sH/v4BcPSk0ZFEROrM19eXRx/9K88993f27NnJhx/O5p57/g+TyWR0NJFasdlsJCcn13l7f39/zGaHbFQizZxDFQ4//fQTubm5zJgxo7xoAOjTpw+DBg1i2bJl/OMf/8DFxaXafdlsNnJzc3F3d9cPzwUcO3YEgLCwbvrDKiItQteu3fjTnx7h9df/zapVK/Dz82Pq1GlGxxKpleTkZF5bvgurl2+tt83LTOPRSVE1aqEhUlsOVThER0cDEBUVVem1qKgotm7dyvHjx+nZs+dF95OYmEhUVBT5+fm4uroyZMgQHnzwQfr06dMgOS2WllGInDhxDDDRvXs4FosKBxFpniwWU4Xfy0OHDuXuu+/l/fffY8GCLwkI8GfMmHGG5XNyMlf4rxjP0c+JxWLC6uWLu0+7Om/f3K5VHP2cSCmHKhySkpIACAoKqvRaWeWcmJh40cIhJCSEqKgounfvjouLCzExMcydO5ebb76Z2bNnM2zYsHplNJtN+Pp61GsfjuLUqRNYLGb69++Dt7e70XFEROrE29u90u/lG2+8ntzcTObPn8/777+Dv78vI0eONChhKS8vq6HHl8oc9Zzk55f+TS4pLiInNZHMpDhyUpPIz06nIDuDooJ87HYb2GyYnV1wsXrg7OaBu7cfTi6unIuw0rVrKM7Ozga/k9pz1HMipRyqcMjLK+2cW1VTJFdX1wrrXMjLL79c4evLL7+cKVOmMHXqVJ555hl++OGHemW02exkZjb/0Try8vI4cSIWm81OYGAHMjKa/3sSkdYpIyMXN7ecSsuvuWYaZ84ksGbNav75zxd47LG/csklg5o8n5OTGS8vK5mZeZSU2Jr8+FKZo54Tu93OyZOxrFmzit0/byEr5Sz2kuJa7cNWUsxT27/DanWnS5cwunfvTmRkHyIi+pRfSzkiRz0nrYWXl7VGT3scqnCwWkurzMLCQtzc3Cq8VlBQUGGd2ggLC2Py5MksWrSI2NhYOnfuXK+cxcXN/wN9+PBhbDYbvr5+tGnjQ2JiotGRRETqpLjYfsHfy/fe+wCFhUVs2rSe1177F48++lf69x/QxAlLlZTYWsTfj5bEUc7J2bPxbNiwjo0b15OQEE9RUTHp2QWYnSw4u7nTxj+ENv7tcWvji6unN86uVkxmJ0wmEyVFhRTm5VCYl01eegoZCaewlmRQVFTI4cMHOXz4IEuXLsFicaZnz9707x/FwIGDCAys3LrDETjKOZGqOVThEBAQAEBCQkKli/uyC9u6dvYJCQkBIDU1td6FQ0tw+PAhAHr06GFwEhGRxmM2m3nggQcpLi5m27bNvPrqK/zhDw8xdOhwo6NJK1dSUsL27Vv54YfvOXgwpnx52QX+abM/wb0H4e7jX6sBTHLSkpjW3RO73U5s7AmOHTvCgQP7SU1NZdeuHezatYOPPnqfjh1DiYoaSFTUwNKRFc+jUZnkQhyqcOjTpw9ffvklu3btqnRxv2vXLtzd3QkLC6vTvmNjY4HSHwaBw4cPAtCjRy+Dk4iINC4nJyf+9KdHeOutN9i8eSP//e9r5ORkM3785UZHkxbsQkOq5uRks379GtatW0N6ejpQOolhr169ueSSIfTt25/s7Cy+OpxTp87R+VnpzN2R8cuITEHQKYig0JF4paeQGneU1FOHSYs/QfKBI+w8cAQ++wLPdu0JCIskMLwvtpISjcokF+RQhcP48eN54YUXmDt3LldffXX5kKx79+5l27ZtTJ06tbz/Q1JSEllZWQQHB5c3X8rIyMBqtVbqI7F3716WL19OeHg4HTt2bNo35YBsNlv5E4fu3S8+QpWISEtgsVj44x8fwd3dg59/XsEHH7xLWloa118/XcNRS6P47ZCqhfk5xEVv5Mz+rZQUFQLgbPUguOdAgnsPwuThxY5i2LEzgbT4E/gGd6nzsasakcnD1x//LqU3Cwtzs0g8Gk3C4d2cO32U3LRkYnesInbXGnyCOrHDv5jLLrscZ+fqh7+X1sWhCoe2bdvyyCOP8OKLLzJjxgyuueYa0tLS+Pjjj2nXrh0PPfRQ+bqvvfYaixcvZs6cOQwZMgSAbdu28fTTTzNp0iRCQ0NxcXHh4MGDLFq0CIvFwvPPP2/UW3MocXGnycvLxdXVjdDQTkbHERFpEmazmbvv/j88PduwZMkiFi2az5kzcUydOr1G8wNVRU065GKsXr5YXFw5sX0Vp3avp6SotL+md/tOdB44lqDu/XGyVB75KC8ztVFzubi3oWPfEXTsO6K8iIiP2U7ameOknj7CRx+dYvHirxg+fBTjx1+mawUp51CFA8Dtt9+Or68vH330ES+++CJWq5Xhw4fz6KOPVvvYrEuXLgwaNIh169aRkpJCUVERAQEBTJkyhfvuu099G35R1kypW7fuODk5GZxGRKTpmEwmbrrpVgICAvnww/dYv34tP2zeRd/JM3Br41OrfWmiLbmYrKwsjm35kYRDu8sLhjYBHeg2bBIBXSMd5knX+UVETloSJ7avwjX5EDk52axYsYwVK5YREdGXK664in79olQot3IOVzgATJkyhSlTplx0nZdffrnS0Ktdu3bljTfeaMxoLYI6RotIazdu3ATat2/PK6+8QHJiCjuXvE+fy28mMLyf0dGkmcvISOe775bw3XffciY1C7OTBa/ADnQbNhn/sAiHKRiq4uEbQJdLxnJD+FWkpCSzceN69uzZxe7dO9m9eycBAYFMmDCRIUOGVXnj0WazYTKByVT74sJiMeHtXffmWdI0HLJwkMZ16NABQP0bRKR169Urgr/85W/89ZXXyDmXyK4lHxLafxQ9Rl9TZfMRkYvJyEjn22+/YeXKHygsLKCoqJg2/sH0uPQahy8Yzpeflc68nb90ru49ibCOQ4nbv4WzB3eQfPwU+9+bjfXLRXS5ZOwv7+vXIiEt/gRunj7l/TpqIy8zjRdudcfNzash3440MBUOrUxqairJyUmYTGbCw7sbHUdExFB+fu2Iuvou4vZtIXb7z5zavY7U00eIvPxmfII7Gx1PmoH09LTygqHol07PYWHdGDfuMtake+Lh2/xGczy/c7W7TzvaduxG73HXc3rvJo5vWUFBdgYHV3/NmZht9B53Pb4hXYHSvhlVdcyWlkOFQytz8OB+ADp2DMVqdTc4jYiI8cxOFnqOvga/0HD2Lv+M7HMJbP5iJp0GjKb7iCtx0sgyrdaFhlSF0oJhxYrlbNiwjuLi0tmdO3fuwuTJV9O7dwTnzqVgyqg8o3lzZXFxo8vAsXTsM4yTO9dwYvvPZCWdYcsX/yUkcgg9Lr14E3NpGVQ4tDL795cWDhERkQYnERFxLP5dejPyjic4uHoR8THbObljNUlHo+k19vpm1dREGs5vh1SF0qY8p3av4+zhXdhLSgDwCuxIpwGjce/QjbUZJtZuOlnvIVUdlcXFja5DJ9Kx30gOr/+WuOhNnNm3heTjMYRGjcLq1dboiNKIVDi0MgcOlBYOvXurcBAR+S0Xqwd9J8+gfY+B7Fv5JXkZqez8ejbtuvSi15ipeLQNqH4n0qKUNb3JTk3kxLafiI/Zht1mw4SJtp160HXoRPxCu1cqLBt7SFWjuVg9iLzsJkJ6D2b/ii/JPpfAwVWLyE1LJuKyG9VPqIVS4dCKpKaeIyEhHpPJTK9evY2OIyLisPzDejPqjic4tmUFsTtWkXLiAOtPvkSnAaPpOuRynN3U1LM1sNvtpJ05RszPC0k5EVO+3K9Td7oOmUjbjt0MTOcYfEPCGHbbnzm0dgnHt/xI3N5NZKcmMuCae3Dz9DY6njQwFQ6tyP79+wDo0iUMd3cPg9OIiDg2i4sbPUZdTYfIIRxYtZiUEzHEbl/FmX1bCBtyGf5ddAOmpSooKGDTpg18/fUC9hw6jtnJAiYTAV0j6TJofItsglQfThZneo+7HherB8e3riQz4RSbPnuNAdfcg3dgR6PjSQNS4dCKxMSUFg69ekUYnEREpGHY7TZSUqruvFoTNdnWwzeAS6b+H0nH93N47RKyzyVwaM03HN+6kktcbuaqq6ZoUqwW4sSJ46xatZING9aSl5dHUVExZoszof1G0mnA6GY5QlJT8g0JY+B19xHz8wJyziWy5Ys36H/VHQR0VfPolkKFQytSVjiof4OItBT5WenM2Z6B1atuo9fUpgNrQFgE7Tr3JD5mG0c2LiM3PYWPPnqPn3/+kSlTrqd376o7UFssJvLz3cnIyKW42F6+3N/fXwWHA0hLS2Pr1k2sXv0zJ0+eKF/u7x/AoEFDOWDppLvmtWD1asvQmx9hz3efkHLiALuWfFDab6jnAKOjSQNQ4dBKJCcnlc/f0LNnL6PjiIg0mPqMG1/bDqxmsxMdIofSvscA9q+cz5mYraQdOs7uf/8bn+AudBk0Du/A0BocN41HJ0URGBhYp9xSP+npaaxf/zMrV/5MTEwMUFrQWSwWLrlkCGPHTiAiIpLk5GSObow1NGtz5OxqZcC197J3+WecPbCdPcvmUlJcSIfIoUZHk3pS4dBK7N0bDUDXrt2wWq0GpxERad6cnF0IiRhMp6hLOXtoJyd3rSMz8TR7ln6Cf1gE4SOuwCugg9ExW4yLzadQE35+fpw6dZI9e3axe/dOjh07gtlsorjYBtjp1q07w4aNYOTI0bRp06bhgrdiZrMTfSfdisXZhdPRG9n3w+eYTGZCIgYbHU3qQYVDK7Fnzy4A+vbtb2wQEZEWxNnNnZ6jr6VT/0s5uuUHzuzfSvLx/SQf309g936ED78CT78go2M2e1XNp1Cdwvwc0uKOkXh0Lz5FqeTn5533qomePXswcOAQLrlkCP7+Gma3MZjMZnpPmI7ZYuHkzrXs/fFznFxcCQrvZ3Q0qSMVDq1AcXEx+/btAaB//yiD04iItDxW77b0ufxmwgaN5+im5Zw9uJPEw3tIPLqX4J4D6DZscp2bU0mp6pqk2WwlZCScIiX2ICmxB8hIOAV2OyUlxdjdzHh4eNK9e08iIvrQt28kXbp0LO93kpiYWOU+69PxXkqZTCZ6jplKcWEBZ/ZtYc93c7Bcey/tOvc0OprUgQqHVuDo0SPk5eXh6dmGsDCNOS0i0lg8fAPod8XvCBs0gaOblpF4JJr4mO2cPbiTDpFD6Tp0Im5tfIyO2WLkZ6WTEnugtFg4dYjiCk8VwNM/GHdvP3zadyGgawQ2Jwt7gb37MmHf/mr331Jnf25qJpOJyMtuoqSwgITDu9m15EOG3PQQXgEhRkeTWlLh0AqUNVPq06efRvAQEWkCbfyDiZpyNxkJpziy8XtSThzgdPRGzsRspWO/kbTvoae/dWG328lKiSfpSDQJR6PJTo6v8LrFzUq70B6069yTdp174dbGh3OnDmP1alunJz4tffbnpmQym+l7xQyKCnI5d/IwO75+j2G3PKpJ4poZFQ6tQHT0bgD69dMfKhGRpuQdFMolU+8nNe4YRzZ8R1rcMU7uWM3JnWvonH0FN910K15eXkbHdGh2u53Y2OMc27KStDPHyU07r/mQ2Yx3YEf8O/fCr3NPvINCMZudjAsrF2V2stD/qjvZ/Pnr5KQmsfOb9xky/U84ObsYHU1qSIVDC5eRkc6JE8cA6NtXnZFERIzQtkNXBk//E+dOHeLw+u9IP3OcFSuWs3HjOsaPv5wrr5xC27ZtjY7pUBITE1m3bjXr16/hzJkzJGcXYHayYLZYaNe5F4Hd+uIfFoGL1cPoqFILzm7uDLzu/9j02WtkJpwietk8+l91h9GxpIZUOLRwu3fvBKBTpy74+NR8NAoREWlYJpOJdp164hfag9N7NuByejsJCfEsW/YtK1YsY8yY8Vx11TUEBLTeuR1yc3PYvHkT69at4dChmPLlrq6uBASG0yFyKO269MLi4mZgSqkvd592DLjmbrZ+9RaJR/ZwbPMPBPceZHQsqQEVDi3ctm1bABg4UD+QIiKOwGQy0a5zT+6/eSIJCfF8/fVCDh8+yMqVP/DzzysZOfJSrrrqGjp0aB2zFZeUlLB37x7Wrl3N9u1bKS4u+uUVE5GRfbn00jGEhnbmwx1nNTJVC+Ib0pXIy25k7/JPObppOa6e3kCE0bGkGiocWrD8/Hyio0uHYR00aIjBaURE5Hwmk4n+/QfQr18UBw/GsGjRAvbvj2bt2lWsXbuKyMh+TJ58Ff369W+RA1ucPn2KtWtLmyJlZKSXLw8J6cill45hxIhRtG3rB3DB4VKleQuJGExGwklO7V7PgVULSbxhOJ06qc+PI1Ph0IJFR++muLiIgIBAQkM7GR1HRESqYDKZ6NUrgr/9LYKjRw/z7bffsH37Vvbt28O+fXsICgpm4sQrGDXqUtzdm3d7/szMDDZuXM/atauJjT1evtzTsw3Dho3k0kvHEBbWFZPJZGBKaUo9x1xHZtIZUk8f4Y033uCll17FycnZ6FhyASocWrCyZkqXXDJEv4RFRJqBbt2688gj/4+kpER++GEZq1evJCEhnk8+eZ/PPpvDkCHDGDt2PD179m42v9czMtLZtm0LW7ZsYs+e3djtdgCcnJyIiOjDkCHDiIjog8VSekmSlJRUaR+aiK3lKh1p6Q7Wffwip0+f5r333ub++//UbD7frY0KhxaqqKiInTu3A2qmJCLS3AQEBDJjxh3ccMONrF27ipUrf+TMmdOsX7+G9evXEBjYnmHDRjBkyFBCQzs71EWW3W7nzJk4oqN3s337Vg4ePADYKSoqJjEjF6+gjgSF9yegWyQlbh5szIGNW+Muuk9NxNayubXxIWLCdJLWf8m6dWsJCwtn4sTJRseSKqhwaKFiYvaRl5eLl5c34eHdjY4jIiJ1YLVamTjxCi6/fDJHjx5m1aqf2LRpA4mJZ/n66wV8/fUCgoKCGTx4KP37R9G1azjOzk3bzMNut5OQcJYjRw6zf/9e9u2LJi2t4sRpYWHd6NmzN9HF/rTr1KPWx9BEbC2fT/vOTLjlFj777AvmzfuYLl3C6N699p8VaVwqHFqoDRvWATB48NAW2alORKQ1MZlMhIf3IDy8BzNm3MmOHdvYsmUje/bsJiEhniVLFrFkySJcXd3o1SuCXr1606VLV8LCwhqsX4TNZuPs2bMkJydx9mw8CQnxnDwZy4kTx8nNza2wrsVioVu37vTuHUG/fgPw8/MjJSWZo4dzGiSLtEwTJ07kxImTbNq0kTfeeJWXXvo3Xl6aWdqRqHBogfLz89m+fSsAI0eONjiNiIg0JKvVysiRlzJy5KXk5eWxa9d2tm/fxv79e8nKymT37h3s3r2jfP2AgCDat29PQEAQgYGBeHl54+npiYeHB25u1grNnPLz88jLyyM3N5ecnBxSU8+RmnqOc+dSiIuLY9+xWEymyjejzE4WPNu1xzsolLYduuId1Amzk4VDwKFDWUCWmhtJtUwmE/ff/0diY2M5ezaeWbNm8sQTf9cNUAeiwqEF2rlzG/n5efj7B6iZkoiIA7LbbfXq8Ovv74/ZbMZqtTJ8+CiGDx+FzWbj1KlY9u6N5tixo5w4cYzk5CSSkhJISkr45bhQXFxcp2MWFxdhMplxtnrg6ReEp18QXgEd8GnfmTb+wZidLn5JoeZGUhNWq5VHHvkLf//74+zfH81XX33BjTfeYnQs+YUKhxZo3bq1QOnTBkfqMCciIqXys9KZsz0Dq1ftm+7kZpzj9kGhtGvnX+k1q9WDwYOHMXjwMACys7OIjz9DSkoKKSnJnD59kl2xSdjtNooL8igpLgK7HTuA3Y6TiysWZ1csLq44Obvi6tEGV09vXD28KczLpke3vviGhOlvizSqDh06cu+99/PmmzP55puFhId3Z8CAS4yOJahwaHEyMtKJjt4NwMiRlxobRkRELsjq5VunmZDzMlOZsz2uFkWHG9AB2nUgo9CHgVFd6nTcc6cO4+bpraJBmsTw4aM4dOgQK1Ys4623/ssLL/ybwMBAo2O1eiocWhCbzcb33y+lsLCQ0NBOmM1ONZ5tU2Nki4g0H/UpOkSai9tuu53jx49y7NgRZs78N88++yIuLi5Gx2rVVDi0IImJiXy0cAmFOQX4+vXg7Y2xNd5WndZERETEkTg7O/Pww3/mySf/HydPnuDjj9/nvvseMDpWq6bCoQU5fPgQhTmZOFs96DxwDBYXtxpvq7tQIiIi4mj8/Nrxxz8+wksvPcfq1T8RHt6DsWPHGx2r1dL4Vi3IunWrAQjpPbhWRYOIiIiIo+rTpy/Tp98MwEcfzSY29rjBiVovFQ4tRGrqufJO0aH9RhgbRkRERKQBTZlyHf37D6S4uIjXX/832dnZRkdqldRUqYX48cdl2O12vNt3wrNde6PjiIiIiNSY3W4jKSkJV9dciovtVa5zww03cfz4UeLj43nxxWf5wx8exmIpvZQtm9tEGpcKhxYgJyebH39cDkDHPsMNTiMiIiJSO/lZ6byxIgOrl+9F1/McdC1Hvnmfjbv2cvzfb9Lj0mvIz0rn0UlRGq61CahwaAF+/HE5+fl5tG8fjG8nzRQtIiIizU9Nhhl292nHgGvvZcfXs0k6uhef9p0I6h7VRAlFz3Saufz8fJYtWwrA5ZdPxmTSKRUREZGWy79Lb3qNmQrA4XVLSTq2z+BErYeuMpu5FSuWk52dRUBAkKZjFxERkVahU9QoOg24FIADqxYRE6PioSmocGjGMjMz+PrrhQBcd931ODk5GZxIREREpGn0HH0dQd37Y7eVMHv22xw6dNDoSC2eCodmbOHCr8jLy6VTpy6MGjXG6DgiIiIiTcZkNtP3ihm07RhOUVER//rXPzl27KjRsVo0FQ7N1Jkzcaxc+QMAt912u4YgExERkVbH7GQh4rLpdO3ajby8PF588RkOHjxgdKwWS1ebzZDNZuP999/FbrcxcOAgIiL6GB1JRERExBBOFhd+//sH6dmzN3l5ebz88vPs3RttdKwWSYVDM/TDD99z6FAMrq5uzJhxp9FxRERERAzl5ubGX//6FP36RVFYWMC///0C69atMTpWi6PCoZk5ezaeL774FIBbbplBQIAmOxERERFxdXXl0Uf/wuDBwyguLubtt//LF1/Mw2azGR2txVDh0IwUFBTwxhuvUlRUSEREH8aPv9zoSCIiIiIOw9nZhQcffJRrrrkegCVLFvPqq6+QmZlpcLKWQYVDM2G323n33f9x6lQsbdp4c//9f1SHaBEREZHfMJvN3HjjLfz+9w9isTiza9d2nnjiMfbt22t0tGZPV57NxMKF89m8eQNmsxOPPPJn/PwuPiW7iIiISGs2atRonnvuRYKDQ0hLS+XFF5/l448/IDc3x+hozZYKh2bgm28WsWjRfABuv/0uevbsbXAiEREREcfXuXMYL7zwb8aNuwyw8+OP3/PYYw+yYcM67Ha70fGaHRUODsxut7Nw4Xy+/LK0M/T06bdw2WWTDE4lIiIi0ny4urpyzz3388QT/yAoKJiMjHT+97+ZPPnk/2PHjm0qIGpBhYODKigoYNas11m48EsApk6dzrXXXm9wKhEREZHmqU+fvrzyyqtMn34Lbm5WTp48wauvvszf/vYX1qz5mcLCQqMjOjyL0QGkssOHD/Hee/8jPv4MZrMTd955j0ZQEhEREaknZ2cXrr32esaNu4zvv1/C8uXfExt7nHff/R+ffjqHkSMvZejQEXTrFq5BaKrgkIXD0qVL+fDDDzl69ChWq5URI0bw2GOPERISUqPt9+3bx+uvv87u3bux2WxERkby4IMPMmjQoEZOXj8pKcksXryAVat+Aux4e/vw4IOP0qtXhNHRRERERFoMLy8vbrrpNq64YgqrVq1k5cofOHcuheXLv2P58u/w9fVjyJCh9OnTj549e2O1Wo2O7BAcrnCYN28ezz//PAMGDOCJJ54gLS2NTz75hG3btrFgwQICAy8+4Vl0dDQzZszAz8+PBx54ABcXF+bPn88dd9zB7NmzGT58eBO9k5qx2+0cO3aEn39eyfr1ayguLgZg9Ohx3Hrr7Xh6ehqcUERERMRx2e02UlKS67z91Vdfy9VXX8vu3TvZtGkDO3ZsIy3tXHkRYTY70bVrN7p1605YWFe6dAkjKKh9q3wi4VCFQ1paGq+++ioRERHMnTsXi6U03qhRo5g2bRr//e9/eeGFFy66jxdeeAGz2cy8efMIDg4G4Nprr+XKK6/k2WefZfny5ZhMpkZ/LxeTnZ3NsWNH2Ls3mp07t5OQEF/+Wu/ekUybdjM9evQ0MKGIiIhI85Cflc6c7RlYvWo/zGpuxjluHxRKu3b+hIR05IYbbuKaa64nJmYf+/fv49ChA5w7l0JMzH5iYvaXb+fq6kpQUPvyIqJ9+/YEBranXbt2tGnj1WKLCocqHH766Sdyc3OZMWNGedEA0KdPHwYNGsSyZcv4xz/+gYuLS5Xbnz59mt27dzN16tTyogGgTZs2TJs2jTfffJM9e/bQv3//Rslvt9spLi4iNzeX7OxssrKyyM7O4ty5cyQlJZCUlMiZM2cqFAoAFoszQ4cOZ9y4y+jZs1ejZBMRERFpqaxevrj71H6Oq7zMVOZsj6ui6PCFzqMI6jwK76w00uNjyU6JJyslnuyUBGzZmZxKSuPkyVicnSteTpvNTnh7++Dr60vbtm3x8vLGw8MDDw9PPDw8cHf3wNPTE6vVirOzCy4uLri6upb/v7Ozs8MWHg5VOERHRwMQFRVV6bWoqCi2bt3K8ePH6dmz6rvx1W1ftk59Cgez2YTFUkxhYWH58F2//a+7uxPu7t4EBHhfcD9OTk5YLM64uJR+SBriKYinZwee6RCEqQ4fNltJGCaTuVlta+SxW9u2Rh67tW1r5LFb27ZGHru1bWvksbVt8zh2c9vWZivBVlyMq1Pp9V9JSQklJSXYbLZaZ6iokJKSQmy2sutCE7+9RKx4zVj5dSidPdvTs80FCxAnJ6ffrF+z61CHKhySkpIACAoKqvRaWd+GxMTECxYOiYmJFdY9X9k+ExIS6pXRZDLh6+tbr300HmfUd0dEREREGoNDPQfJy8sDqLIpkqura4V16rp9fn5+vXOKiIiIiLQ2DlU4lA11VdUEHAUFBRXWqe32ZQWDm5tbvXOKiIiIiLQ2DlU4BAQEAFU3J7pYM6Qy5zdnutD2VTWDEhERERGRi3OowqFPnz4A7Nq1q9Jru3btwt3dnbCwsDpvf/46IiIiIiJScw5VOIwfPx6r1crcuXPLJ0ID2Lt3L9u2bWPy5Mnl/ReSkpI4duxYhT4PoaGh9O3bl+XLl3P27Nny5dnZ2SxYsIDQ0NBGG4pVRERERKQlM9nLxhB1EJ988gkvvvgiAwYM4JprriEtLY2PP/4YZ2dnFi5cWN4c6fHHH2fx4sXMmTOHIUOGlG+/Z88eZsyYQbt27ZgxYwbOzs58+eWXHDt2jPfee4+RI0ca9dZERERERJothxqOFeD222/H19eXjz76iBdffBGr1crw4cN59NFHL9q/oUy/fv2YN28eM2fOZNasWdhsNiIjI/noo48qFBgiIiIiIlJzDvfEQUREREREHI9D9XEQERERERHHpMJBRERERESqpcJBRERERESqpcJBRERERESqpcJBRERERESqpcJBRERERESq5XDzOEjrsnTpUj788EOOHj2K1WplxIgRPPbYY4SEhNRo+3379vH666+ze/fu8jk7HnzwQQYNGtTIyVuuup6TjIwMvv76a9asWcOxY8dIS0ujffv2DB48mAceeID27ds30Ttomer7s3K+hx56iOXLl9OlSxeWL1/eCGlbh/qek5KSEubPn8+iRYs4evQoAB06dGDSpEn84Q9/aMzoLVZ9zondbuebb77h888/JzY2lqKiIkJCQrjyyiuZMWMGHh4eTfAOWpZ3332X/fv3s3//fuLi4ggJCeHnn3+u9X70t95xaB4HMcy8efN4/vnnGTBgAFOmTCEtLY1PPvkEFxcXFixYUO2Ef9HR0cyYMQM/Pz9uvfVWXFxcmD9/PsePH2f27NkMHz68id5Jy1Gfc7J27Vr+7//+j6FDhzJs2DB8fX05cuQIX375Jc7OznzxxRd069atCd9Ny1Hfn5XzrV69mt///ve4uLjQvn17FQ51VN9zUlRUxJ/+9CfWrVvHFVdcwYABAzCbzZw5c4bU1FT++c9/NtE7aTnqe05effVV3nvvPYYOHcpll12G2Wxm48aNrFixgkGDBjFv3rwmeictR48ePfDx8aF3797s378fT0/PWhcO+lvvYOwiBkhNTbX379/fft1119mLiorKl0dHR9t79Ohhf/LJJ6vdx/Tp0+39+/e3nzlzpnxZZmamfdSoUfbLL7/cbrPZGiV7S1Xfc3L69Gl7bGxspeUbNmywd+/e3f6nP/2pwTO3Bg3xs1ImOzvbPmbMGPtzzz1nHzt2rH3ixImNEbnFa4hz8sYbb9h79uxpX7t2bWNGbTXqe06Kiors/fr1s1933XX2kpKSCq/9/ve/t3fv3t1+9OjRRsnekp06dar8/6+88kr72LFja70P/a13LOrjIIb46aefyM3NZcaMGVgsv7aY69OnD4MGDWLZsmUUFhZecPv/3969hUTx/nEc/2h/TSMjrSys3LIbi7LsSJaUSWRbYGRCBzXqposMOkAn6CqwggqtjChKLSOEpBKSAglKbyrC04UdsKMdlKiLPBSu7e8i1r+22lizuuvu+wV7M8/M8n34MvvMd595Zt69e6eqqiolJSUpIiKic3tISIhSU1P1+vVrVVdX92sfvI3ZnEyYMEEWi8Vpe1xcnEaOHKnnz5/3S9zezmxeusrOzlZ7e7t27drVX+H6BLM5aW1tVUFBgRISEhQfHy+73a7m5uaBCN1rmc2JzWbT9+/fNXr0aPn7d780Cg8PlyQFBwf3T/BebOLEiaaOZ6z3PBQOcIuamhpJUmxsrFNbbGysWlpa9PLly38+vus+6BuzOenNt2/f1NLSolGjRpmO0Re5Ki81NTUqLCzUwYMHNXz4cJfH6UvM5uTJkydqbm5WTEyMjh07prlz52rOnDmaP3++Dh8+rLa2tn6L3VuZzUlQUJBiY2NVXl6uCxcu6M2bN2poaOhcg5KamtrtwhUDg7He87A4Gm7R1NQkSRo3bpxTm+M+1MbGRkVHR/d4fGNjY7d9u3J856dPn1wSq68wm5PenD17Vu3t7VqzZo3pGH2RK/Jis9l06NAhxcXFyWq19k+gPsRsThwXsAUFBRoyZIh27typ8PBwlZWVqbCwUPX19crLy5Ofn18/9cD7uOI8OXHihPbt26fjx4/r+PHjkiR/f39lZmayWN1NGOs9D4UD3MLxj1pgYKBT29ChQ7vt86/Hf//+3XScvsRsTnpSWlqqvLw8LVq0SCkpKeaD9EGuyMulS5f06tUrnT592vUB+iCzOWlpaZH060lkJSUlnQ8NWLFihSSppKREFRUVio+Pd2nc3swV50lQUJAmT56siIgIxcfHy9/fX2VlZTp16pR+/vypHTt2uD5w/BFjvefhViW4heNe0Z7uOf3x40e3ff72eMePSFBQkOk4fYnZnPzu/v372rt3r6ZNm6acnByn+4bRN2bz8vbtW+Xm5mrbtm2KjIzsnyB9jNmcOH6bYmJinJ405iiwHz586JJYfYXZnLS1tWnDhg1qbm7WsWPHtHr1almtVp08eVJr165Vbm6u6urq+id49Iqx3vMwksMtHIvNeppi/NPUpEPXqefeju9pyhq9M5uTrh48eKDMzExFRUXp4sWLCgkJcV2gPsZsXo4ePaoRI0bIarWqoaGh82Oz2WSz2dTQ0KDPnz/3T/BeymxOHL9Nju/pasyYMZJ+zUag78zm5O7du3r9+rWSkpKc2qxWq+x2ux49euSiaNFXjPWeh8IBbjFjxgxJUmVlpVNbZWWlhg0bpqioqH8+vus+6BuzOXEoLy9XZmamJk2apPz8fIWGhro8Vl9iNi/v379XU1OTkpKSlJiY2PlpbGzUu3fvlJiYqH379vVb/N7IbE5iYmIkSR8/fnRqc2zjYQJ/x2xOHBehNpvNqc2xraOjwxWh4i8w1nseCge4RWJiooKDg3XlypVuP9S1tbV6/PixVq5c2XlPY1NTk+rr67vdnxoZGamYmBjduXOn2+Db3Nys69evKzIyUrNmzRqw/ngDszmRpIqKCm3fvl0Wi0UFBQUKCwsb0D54I7N5OXDggHJzc50+YWFhGjt2rHJzc5WZmTng/RrMzOZkwoQJmjdvnmpra1VVVdW53W636+rVq5KkJUuWDExnvITZnEyZMkWSdOPGDafvLi4ulvT/gg/9g7F+cODN0XCbgoICZWVlafbs2UpOTtbXr1+Vn5+vgIAAFRcXd05R7t+/Xzdu3NDly5e1YMGCzuOrq6uVnp6u0aNHKz09XQEBASoqKlJ9fb3Onz+vxYsXu6trg5aZnNTW1mrTpk2y2+3as2dPjzMNycnJA9ofb2H2XOnJsmXLFBgYyJuj/5HZnDx9+lQbN26Un5+f0tLSFB4ernv37qmiokIpKSnKyspyV9cGLTM56ejo0Pr161VTU6O5c+dq+fLl8vPzU1lZmR49eqSEhASdO3fOnd0blG7evKkPHz5I+vVm7/b2dm3ZskWSNGLECKWlpXXuy1g/OPBUJbjN5s2bFRoaqry8PGVlZSk4OFhxcXHavXt3n+6lnzlzpgoLC5Wdna3Tp0/r58+fmj59uvLy8gwvmtAzMzl58eJF5yLEI0eO9LgPhcO/MXuuwPXM5iQ6OlpFRUXKycnRtWvX1NraKovFogMHDigjI2MAeuB9zORkyJAhunz5sgoLC3X79m2dOXNGP378kMVi0a5du7R169YB6oV3KS4udlobkpOTI0kaP358t8KhN4z1noUZBwAAAACGWOMAAAAAwBCFAwAAAABDFA4AAAAADFE4AAAAADBE4QAAAADAEIUDAAAAAEMUDgAAAAAMUTgAAAAAMEThAAAAAMAQhQMAAAAAQ/9zdwAAAEjSw4cPlZGR8cd9nj17NkDRAAB+R+EAAPAoVqtVS5cudXcYAIDfUDgAADzK1KlTlZyc7O4wAAC/YY0DAAAAAEPMOAAAPEpbW5u+fPnitD0wMFDDhw93Q0QAAEnys9vtdncHAQCA0eLohQsXKj8/f+ACAgB0w4wDAMCjrFu3TqtWrXLaPnLkyIEPBgDQicIBAOBRLBaL4uLi3B0GAOA3LI4GAAAAYIjCAQAAAIAhCgcAAAAAhljjAADwKHV1dbp161aPbYmJiTySFQDchMIBAOBRSktLVVpa2msbhQMAuAfvcQAAAABgiDUOAAAAAAxROAAAAAAwROEAAAAAwBCFAwAAAABDFA4AAAAADFE4AAAAADBE4QAAAADAEIUDAAAAAEMUDgAAAAAMUTgAAAAAMEThAAAAAMAQhQMAAAAAQ/8Bib9ixPgl0QIAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 900x450 with 1 Axes>"
       ]
@@ -557,7 +546,7 @@
     {
      "data": {
       "text/plain": [
-       "[array([ 372,  579, 1926, ..., 2553, 1182,  316], dtype=uint16)]"
+       "[array([ 850, 1656, 2683, ..., 2304, 1805, 2358], dtype=uint16)]"
       ]
      },
      "execution_count": 23,
@@ -594,7 +583,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -669,7 +658,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -772,7 +761,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.0"
+   "version": "3.10.20"
   }
  },
  "nbformat": 4,
diff --git a/tests/conftest.py b/tests/conftest.py
index e393b1b..18c8e44 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -44,7 +44,7 @@ def _alex_process(d):
 
 
 def load_dataset_1ch(process=True):
-    fname = fetch_if("0023uLRpitc_NTP_20dT_0.5GndCl.hdf5", 'doi:10.6084/m9.figshare.1456362.v15')
+    fname = fetch_if("0023uLRpitc_NTP_20dT_0.5GndCl.hdf5", 'doi:10.5281/zenodo.20038738')
     d = loader.photon_hdf5(fname)
     if process:
         _alex_process(d)
@@ -60,10 +60,10 @@ def load_dataset_1ch_nsalex(process=True):
 
 @pytest.fixture
 def dataset_1ch_file():
-    return fetch_if("0023uLRpitc_NTP_20dT_0.5GndCl.hdf5", 'doi:10.6084/m9.figshare.1456362.v15')
+    return fetch_if("0023uLRpitc_NTP_20dT_0.5GndCl.hdf5", 'doi:10.5281/zenodo.20038738')
 
 def load_dataset_8ch():
-    fname = fetch_if("12d_New_30p_320mW_steer_3.hdf5", 'doi:10.6084/m9.figshare.1019906.v26')
+    fname = fetch_if("12d_New_30p_320mW_steer_3.hdf5", 'doi:10.5281/zenodo.20038738')
     d = loader.photon_hdf5(fname)
     d.calc_bg(bg.exp_fit, time_s=30, tail_min_us=300)
     d.burst_search(L=10, m=10, F=7)
@@ -71,10 +71,10 @@ def load_dataset_8ch():
 
 @pytest.fixture
 def fake_pax_file():
-    return fetch_if("0023uLRpitc_NTP_20dT_0.5GndCl.hdf5", 'doi:10.6084/m9.figshare.1456362.v15')
+    return fetch_if("0023uLRpitc_NTP_20dT_0.5GndCl.hdf5", 'doi:10.5281/zenodo.20038738')
 
 def load_fake_pax():
-    fname = fetch_if("0023uLRpitc_NTP_20dT_0.5GndCl.hdf5", 'doi:10.6084/m9.figshare.1456362.v15')
+    fname = fetch_if("0023uLRpitc_NTP_20dT_0.5GndCl.hdf5", 'doi:10.5281/zenodo.20038738')
     d = loader.photon_hdf5(fname)
     d.add(ALEX=False, meas_type='PAX')
     loader.alex_apply_period(d)
diff --git a/tests/fetch_files.py b/tests/fetch_files.py
index 2639fba..e68b344 100644
--- a/tests/fetch_files.py
+++ b/tests/fetch_files.py
@@ -10,13 +10,10 @@
 # data subdir in the notebook folder
 DATASETS_DIR = u'data/'
 
-alex1c = pooch.create(path=DATASETS_DIR, base_url='doi:10.6084/m9.figshare.1019906.v26')
+alex1c = pooch.create(path=DATASETS_DIR, base_url='doi:10.5281/zenodo.20038738')
 alex1c.load_registry_from_doi()
 _ = alex1c.fetch("12d_New_30p_320mW_steer_3.hdf5")
-
-phdf5 = pooch.create(path=DATASETS_DIR, base_url='doi:10.6084/m9.figshare.1456362.v15')
-phdf5.load_registry_from_doi()
-_ = phdf5.fetch("0023uLRpitc_NTP_20dT_0.5GndCl.hdf5")
+_ = alex1c.fetch("0023uLRpitc_NTP_20dT_0.5GndCl.hdf5")
 # _ = phdf5.fetch("dsdna_d7_d17_50_50_1.hdf5")
 
 mphmm = pooch.create(path=DATASETS_DIR, base_url='doi:10.5281/zenodo.5902313')

From 39c69b7f55cc917b89d7a08095f6234ec4ed2358 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Fri, 4 Sep 2026 08:32:58 +0200
Subject: [PATCH 132/142] Update tests.yml

---
 .github/workflows/tests.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index b360324..70b829f 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -43,7 +43,7 @@ jobs:
         id: cache-action
         uses: actions/cache/restore@v6
         with:
-          path: ./data
+          path: ./tests/data
           key: testfiles
           enableCrossOsArchive: true
       - name: Install Conda

From 2ea0a6cb0761c17c224ce5bc1c76f560789e9869 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Fri, 4 Sep 2026 10:58:42 +0200
Subject: [PATCH 133/142] Fixes for numpy 2.0 update

---
 fretbursts/burst_plot.py                      |  17 +-
 fretbursts/phtools/burstsearch.py             |   8 +-
 .../Example - Burst Variance Analysis.ipynb   | 151 ++++-
 ...le - Customize the us-ALEX histogram.ipynb | 593 ++++++++++++++----
 tests/test_exp_fitting.py                     |   8 +-
 5 files changed, 636 insertions(+), 141 deletions(-)

diff --git a/fretbursts/burst_plot.py b/fretbursts/burst_plot.py
index 8fce946..c730fb4 100644
--- a/fretbursts/burst_plot.py
+++ b/fretbursts/burst_plot.py
@@ -63,7 +63,8 @@
 from .scroll_gui import ScrollingToolQT
 from . import gui_selection as gs
 
-
+# so compatible with old and new version of numpy
+trapz = np.trapezoid if hasattr(np, 'trapezoid') else np.trapz
 ##
 # Globals
 #
@@ -295,8 +296,8 @@ def _burst_info(d, ich, burst_index):
     burst = d.mburst[ich][burst_index]
     params = dict(
         b_index=burst_index,
-        start_ms=float(burst.start) * d.clk_p * 1e3,
-        width_ms=float(burst.width) * d.clk_p * 1e3,
+        start_ms=float(burst.start[0]) * d.clk_p * 1e3,
+        width_ms=float(burst.width[0]) * d.clk_p * 1e3,
         nt=d.nt[ich][burst_index],
         nd=d.nd[ich][burst_index],
         na=d.na[ich][burst_index],
@@ -1098,7 +1099,7 @@ def _fitted_E_plot(d, i=0, F=1, no_E=False, ax=None, show_model=True,
             ax2.fill_between(x, scale*y, lw=lw, alpha=alpha, edgecolor=color,
                              facecolor=fillcolor, zorder=10)
         if verbose:
-            print('Fit Integral:', np.trapz(scale*y, x))
+            print('Fit Integral:', trapz(scale*y, x))
 
     ax2.axvline(d.E_fit[i], lw=3, color=red, ls='--', alpha=0.6)
     xtext = 0.6 if d.E_fit[i] < 0.6 else 0.2
@@ -1779,17 +1780,17 @@ def hist_ph_delays(
 
     efun = lambda t, r: np.exp(-r*t)*r
     try:
-        re = bg.exp_fit(ph, tail_min_us=efit_tail_min_us)[0]
+        re = bg.exp_fit(ph, tail_min_us=efit_tail_min_us)[0][0]
         re_do = True
     except:
         re_do = False
     try:
-        rg = bg.exp_hist_fit(ph, tail_min_us=efit_tail_min_us, binw=hfit_bin_ms*1e-3)[0]
+        rg = bg.exp_hist_fit(ph, tail_min_us=efit_tail_min_us, binw=hfit_bin_ms*1e-3)[0][0]
         rg_do = True
     except:
         rg_do = False
     try:
-        rc = bg.exp_cdf_fit(ph, tail_min_us=efit_tail_min_us)[0]
+        rc = bg.exp_cdf_fit(ph, tail_min_us=efit_tail_min_us)[0][0]
         rc_do = True
     except:
         rc_do = False
@@ -1851,7 +1852,7 @@ def hist_mdelays(d, i=0, m=10, bins_s=(0, 10, 0.02), period=0,
     max_delay_th_F = m/rate_ch_kcps/d.F
 
     burst_domain = bin_x < max_delay_th_F
-    burst_integral = np.trapz(x=bin_x[burst_domain],
+    burst_integral = trapz(x=bin_x[burst_domain],
                               y=mdelays_hist_y[burst_domain])
 
     ax.set_title("I = %.1f %%" % (burst_integral*100), fontsize='small')
diff --git a/fretbursts/phtools/burstsearch.py b/fretbursts/phtools/burstsearch.py
index 222a8e9..aed08f2 100644
--- a/fretbursts/phtools/burstsearch.py
+++ b/fretbursts/phtools/burstsearch.py
@@ -305,8 +305,12 @@ def from_list(cls, bursts_list):
         """
         bursts = cls.empty(len(bursts_list))
         for i, burst in enumerate(bursts_list):
-            bursts.istart[i], bursts.istop[i] = burst.istart[0], burst.istop[0]
-            bursts.start[i], bursts.stop[i] = burst.start[0], burst.stop[0]
+            if isinstance(burst, cls):
+                bursts.istart[i], bursts.istop[i] = burst.istart[0], burst.istop[0]
+                bursts.start[i], bursts.stop[i] = burst.start[0], burst.stop[0]
+            else:
+                bursts.istart[i], bursts.istop[i] = burst.istart, burst.istop
+                bursts.start[i], bursts.stop[i] = burst.start , burst.stop
         return bursts
 
     @classmethod
diff --git a/notebooks/Example - Burst Variance Analysis.ipynb b/notebooks/Example - Burst Variance Analysis.ipynb
index 4b185eb..cef4bea 100644
--- a/notebooks/Example - Burst Variance Analysis.ipynb	
+++ b/notebooks/Example - Burst Variance Analysis.ipynb	
@@ -21,9 +21,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " - Optimized (cython) burst search loaded.\n",
+      "--------------------------------------------------------------\n",
+      " You are running FRETBursts (version 0.7.post212+g2db78d238.d20260904).\n",
+      "\n",
+      " If you use this software please cite the following paper:\n",
+      "\n",
+      "   FRETBursts: An Open Source Toolkit for Analysis of Freely-Diffusing Single-Molecule FRET\n",
+      "   Ingargiola et al. (2016). http://dx.doi.org/10.1371/journal.pone.0160716 \n",
+      "\n",
+      "--------------------------------------------------------------\n"
+     ]
+    }
+   ],
    "source": [
     "from fretbursts import *\n",
     "sns = init_notebook(apionly=True)"
@@ -31,7 +48,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,9 +68,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "# Total photons (after ALEX selection):     2,259,522\n",
+      "#  D  photons in D+A excitation periods:      721,537\n",
+      "#  A  photons in D+A excitation periods:    1,537,985\n",
+      "# D+A photons in  D  excitation period:     1,434,842\n",
+      "# D+A photons in  A  excitation period:       824,680\n",
+      "\n",
+      " - Calculating BG rates ... Channel 0\n",
+      "[DONE]\n"
+     ]
+    }
+   ],
    "source": [
     "full_fname = \"../tests/data/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\"\n",
     "\n",
@@ -64,9 +96,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " - Performing burst search (verbose=False) ... - Recomputing background limits for Dex ... [DONE]\n",
+      " - Recomputing background limits for all ... [DONE]\n",
+      " - Fixing  burst data to refer to ph_times_m ... [DONE]\n",
+      "[DONE]\n",
+      " - Calculating burst periods ...[DONE]\n"
+     ]
+    }
+   ],
    "source": [
     "d.burst_search(m=10, computefret=False, ph_sel=Ph_sel(Dex='DAem'))\n",
     "d.calc_fret(count_ph=True, corrections=False)"
@@ -74,7 +118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -85,9 +129,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'matplotlib.figure.Figure'>\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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gsqnwAL5N165d9VQcVVZHTQAAeJL43WslMyXB0BbZob9UNjExMfLoo4/KCy+8YG9Tg9s/99xz8uGHH4rVajW1fwAAAAAAmIlPxQAAVDA1cOuZDQsNbVVimop/VO1KeS+GDBki/fsbH16o0jrff/+9aX0CAAAAAKDSBfBfe+01GTx4sJ6WL19eYfsCAOBKkg7tlLSzsYa2qu0vlcpK1f4fP358vpI5KgN/z549pvULAAAAAIBKFcBft26dzJkzR08nTpyosH0BAHAlZzb+YVj2i4iWoDrNpTILCQnRte9VMN8mMzNTnnzySUlNTTW1bwAAAAAAmIUSOgAAVKDUuOOSeHinoa1qu75ioda7dOjQQW6++WbDtTlw4IC89957fI8CAAAAAColtwngZ2dn2+e9vU0bexcAAIdm33v7V5HQ5p25qv/vrrvukubNjW8jTJs2TZYuXco1AgAAAABUOm4TwI+Li7PPBwcHm9oXAADKIjP5gsTvWmNoC2/TW6zevlzQ/+fj4yMvvfSS+Pv7G67JCy+8IGfOnOE6AQAAAAAqFbcI4B8+fFjWrPk34FG/fn1T+wMAQFmc27pUcrKz7MsWL2+JaNuLi5lH3bp1Zdy4ccZrd+6cPP/885KTk8P1AgAAAABUGk6rRfPpp5/Khg0bDG0bN240rF+4cGGRx8jKypJTp07JokWLJC0tTbfFxMQQwAcAuJ2crCw5t3WZoS20aSfxDgyRyiInK1N27NhRom1r164tTZs2NfwtsWDBAnn11VflkUceEV9f3loAAAAAAHg+pwXw58+fLzNmzCh0vfoQXhaPPfaYWCyWcvQMAICKl7B/s2QkxRvaqrbrU6luRWrcMXl+7jHx35BYou2zgtvJseQNkpWaZG974e2PpEWLFjJ8+HAn9hQAAAAAANfgNqPBVqtWTQfv7733XrO7AgBAqZ3dvNiwHFijvvhH1a50V9I/uq4ExTQr8fZeQ/3l0MwP7cvZ6Sny8ccfy+WXX04WPgAAAADA4zktgH/33XfLoEGDDG2ffPKJrF+/Xs+PHTtWOnToUOxAdkFBQdKoUSNp2bKleHu7zfMGAADsUs8cl6Rje/MNXoviBdVtIVXb9ZUzm/40jI3z4Ycf5quTDwAAAACAp3FaRLxfv3752n7//Xd7AF+tHzlypLNODwCAyzi3Zalh2TsgSEIaXWRaf9xNte7DJOnobl2Cx+b777+Xiy++WLp27Wpq3wAAAAAAcKYKTWkfPHiwHpROady4cUWeGgAAU2Slp8r5nasMbWEtu4vV24c7UkLqWtUaNEb2//Caof3ZZ5+VKVOmSFhYGNcSAAAAAOCRKjSAf8stt4g7ysrKkpSUFPuyKuvjqONarVaHDsrrjGMCAMoufudqyc5Isy+r/z9HtO7JJS0l/6o1JbrnCDk2/2t725kzZ+TFF1+UCRMm8HsPAAAAAOCRrOLC1AdzNVUkVVd3zpw58tprr8no0aOlbdu2EhgYKMHBwfYpLi6uzMefNWuWLh1Us2ZNPfien5+f1K1bV2699VZZvny5yxwTAFB+OTk5cnaLcfDaoPqtxSckgstbBmrcgIAaDQxtixcvlp9//pnrCQAAAADwSBUewD927Jjs2rVLT4mJiQVu8/nnn0v9+vUlMjJSTzExMfLee+/pQIiztWnTRpf6GT9+vEyePFm2bNki6enp5T7uuXPnZMiQITJs2DCZMWOGnDhxQrKzsyUjI0M/NPjyyy+lR48ecs8990hmZqZpxwQAOE7ysb8l7WysoS2iTS8ucRmptxciO18mISEhhva33npLDh48yHUFAAAAAHicCg/g9+7dW5o3b66nkydP5lv/7rvvyu233274IH706FF58MEH5YEHHhAzlLccTWpqqgwdOlRmz55taPfy8sp37I8//ljuuOMOU44JAHCsc9uMg9f6hlWTKjHNuMzloAYAvu222wxtaWlp8tRTT+kH2AAAAAAAeJIKDeDv379f9u3bp+ebNGkiDRs2NKw/deqUPPnkk/ZlVQpG1XO3+eCDD+SPP/5wah9V6ZnLL79cHnvsMfn2229l48aNeioPVZ932bJl9mV1/LVr1+ogvKqt/+eff0rnzp3t67/66iv54YcfKvyYAADHyUpLkQt/bzK0hbfqIZZcv9dQNhdddJFcffXVhjb1Zt8nn3zCJQUAAAAAeJQKHcQ2d/C9b9+++dZ/8cUXkpycbM8aV5n4qkzMVVddJUuX/pPFqAaq69evn9P6uHnz5nxtKihQVrGxsfqtAhsVcJgyZYo9S97b21v69OkjS5Ys0f+uXLlSt6sHGWpbtb4ijgkAcKzEQ9slJ+vf8mUqcB/WvAuXuZzUNd2xY4d0795d5s+fL8ePH7evU387qPI6LVq0KNGx1Dg3auwYAAAAAABcVYVGclUpHJvatWvnWz9z5kz972WXXSZjx47V81FRUfLRRx/p2vTKwoULdZa5v7+/uAOV9W57KKEGw1XBhYJK8qi3DVTt/5YtW+pa/wcOHJC//vpL+vfvXyHHBAA4jvp/bsK+LYa24IbtxDswmMtcTqlxx+T5ucfEPzpR0mp1l+Pbv1VRffv6e599Q2oNGiNefgFFHyf2kEx5RKRTp07cEwAAAACAy6rQ9/jj4uLs89WrVzesS0pKkvXr1+v5vK/Ft27dWho1aqTn1WCsKvPOXfz888/2+euuu06qVq1a6LZqXIBLLrnEvqwGpq2oYwIAHEeVi8uIP52vfA4cwz+6rgTFNJOqbXpLjT7XitU3wD6pDP0Lu9dJldpN9TaFTeoYAAAAAAC4ugoN4J89e9Y+r15xz1u6RgXnFVX2Ja+YmBhDrXx3kJ2drevS2wwYMKDYfQYOHGifX7VqVYUcEwDgWIsWLTIs+4ZUlSq1m3CZnaDqRX2lSkxTQ9uFvzdK/E5+3wEAAAAA3F+FBvBz15lNTEw0rLNl31erVk3q1auXb9+goCD7fHp6urgDNWivKvdj0759+2L3yb3N7t27JSsry+nHBAA4jnqjbPXq1Ya2sJYXM3itk6ixBWoNuEm8/AIN7ScWTZX088a3IAAAAAAAcDcVWgM/OjraPr9nzx7DOtsgtYXVoj1z5ox9XtXFdweq5ryN1WqVOnXqFLtP/fr17fMqUH/ixAnDeAHOOGZpxi4oiDqeTUpKip7g+nI/CMo9D/fFPXUNs2bNMtwLNUaJCuDDeXyCwqXmpaPlyJxP7W3ZGWly9Pcvpf7VD4vFy6vA/dR9qqjfWfx8ehbup2fhfrovPncAAIDKoEID+O3atTPUcX/ppZfE29tbLly4IL///rtu79mzZ4FlY7Zv367nvby8pFWrVuIOEhISDG8Q5H4DoTDh4eGFHsNZxyxO7vJFxVmyZIlERkaW6vgwn7pv8CzcU/P873//01n4Iv8MLh5Uv7X4VAk1sUeVQ0ijdhLe4mI5t2OFvS3l5EE5veY3qdZtcIH7qDclcpf3qyj8fHoW7qdn4X66l9xjrAEAAHiqCi2hM2jQIPHz87Nn4I8cOVK++uorGTJkiA4qqyzFq666Kt9+69atk/j4eD3frVs3CQ4OFneQu0xQQEBAifapUqVKocdw1jEBAI5x7NixfG8thbfqzuWtINV7jxTfUONbenFrf5Pk4/u4BwAAAAAAt1ShGfgqE/yBBx6QN954Qy//8ssverJRAf1GjRrl208F+W3GjBkjnky9bZCbeqhh9jGPHDlSbAmdzp076/levXqVqjwPzH1d3JZlpu6bv78/t8PNcU/NN2HCBD1Ie3JyssSlJ4tPUJgE1W1pdrcqDS9ff6l92a1yYMqbkpPzz+++nJwcOTbvK2lw/RPi5Wd88N2lSxfp2LFjhfSNn0/Pwv30LNxP91VcqU8AAABPUKEBfEWVzTl06JBMmTIlX3mdiRMn5tv+5MmT8u233+r5mjVryvXXXy/uIvebAiqYUxJ5t8v7toEzjlmc0gTk1VsBJX0zAK5DBe+5b56Fe2pOHd6FCxfq8UlsD0rDWjB4bUULqF5XorpeIadW/mpvS79wRmIXTZFaA29xiZ8Tfj49C/fTs3A/3Qt/vwIAgMqgwgP4Pj4+8uOPP8qDDz4oixcv1gGPNm3ayNChQ3U9/LzU4LYDBw7U89dcc41bZQmrLMzcZWvS0tLsJYQKk3uw3rzHcNYxAQDlp4L3/9S+t1GD13bj0pogsuNASTy0w1A65/yuNRJUt4WENvvnjTEAAAAAANxBhQfwbbp27aqn4qiyOmpyRw0bNrTPq1f4Dxw4IM2aNStyH7WNTWBgoERHRzv9mACA8lODs+cWUKO++IZU5dKawGK16mz7/ZNflqz0VHv7ib9+lIAaDcQ3lMHWAQAAAADuoUIHsT1//ryul378+HGdee/p6tataxhAVg3GW5w1a9bY55s3b56vXr0zjgkAKJ/9+/fLli1bDG3BDdpwWU2kHp7U6Gssu6eC+cfmfy05ecaGAQAAAADAVVVoAP/222/Xdexr1aolc+bMEU+nAuXduv1bPuG3334rdp/ff//dPt+9e/cKOSYAoHxmzpxpWA4NDZXAWvkHZUfFCm3aUcLylMxRZXXi1s3jVgAAAAAA3IK1ouvfVzYjRoywz0+fPl2OHTtW6LYqm37lypX25auuuqrCjgkAKJv09HSZPXu2oa1Hjx5isXpxSV1AdJ9R+UoZnV41W9LOnDCtTwAAAAAAuGQAPyoqyj6fkZEhlcGoUaN0JqYtyDNmzBj9b17x8fF6nU3Lli11AKiijgkAKJtFixbJhQsXDG2XXHIJl9NFePkGSK2BYwzl49QYMqdW/iqpqf/WxwcAAAAAQCp7AL9Ro3/LCaha+K5IPVhITEw0TMnJyYZtkpKS8m2TmZlZ4PEiIiJk/Pjx9uUFCxZIr169dLZmbGysHDlyRH744Qfp3LmzbNu2zb7d66+/LlartcKOCQBwzOC1HTt2lOrVq3M5XUhgzQYS2ekyQ1tm4jn56quvdDAfAAAAAABXVaHR3GHDhtnn581zzfqzb731lgQHBxumDh06GLapV69evm1UEKAwjzzyiFxxxRX25dWrV8uQIUOkRo0aUqdOHbn++utlz549hW5fUccEAJTO0aNHZe3atYa24cOHcxldUFSXyyQwur6hbcWKFfLrr7+a1icAAAAAAFwqgF+3bl255ZZb9Pz8+fN15nhl4OXlJVOnTpVbb721yO28vb3lueeekzfeeMOUYwIAyjd4bUhIiPTt25fL6ILUmAS1Bo0RL19/Q7t6O23//v2m9QsAAAAAgKJ4SwV7//33dWa4ynpTA6p+8MEHMnr0aB2QdgW+vr5SpUoVhw/QGxgYKJ9//rnceeed8s033+iv//jx4zrAXqtWLenXr5/cfPPN0qxZsxKf0xnHBACUjCqdNmvWLEObetNJ/R6Ba/INjZSal46WI3Mn2dvS0tLkscce079HAwICTO0fAAAAAACmBvAnT54sW7dulS5dusjmzZslISFBZ+Q/8cQTenDVmjVrip+fX7HHUQH/Vq1aOaWP48aN05OzqK9dTa5+TABA0ZYuXSpnz541tF155ZVcNhcX0ri9RLTpJXHr/i3ld+DAAf2m2rPPPmtq3wAAAAAAMDWArwb6mzFjRr52lTWuysGUlBog0FkBfAAAyjJ4bZs2baRBgwZcPDdQvedVcuHvjWpYenubqoWv/r5gvBgAAAAAQKWtgQ8AgCeIjY2VlStXGtoYvNZ9WL19pFr3YeLvb6yH/+qrr+psfAAAAAAAKmUGvqrH3rVr13IfR2U5AgBgFlX7Picnx76sxk7p378/N8SN+ARH6IHgv/76a3tbamqqPP7447otb3AfAAAAAACPD+APGTJETwAAuKvs7GyZOXOmoW3QoEEMgOqGVFKBGo/np59+srft27dP18N/+umnxWKxmNo/AAAAAAAooQMAQCmsWLFCTp06ZWgbMWIE19BNPfzww9K4ceN8b1jkfUgDAAAAAIAZCOADAFCOwWubN28uTZs25Rq6KT8/P3nttdfyvUGhsvB37NhhWr8AAAAAAFAI4AMAUEKnT5+WpUuXGtquvPJKrp+bq1u3rjzzzDOGtoyMDHn00Ufl/PnzpvULAAAAAAAC+AAAlNCvv/6qa+DbqKxtVf8e7k8NQnzDDTcY2k6ePClPPPGE4Z4DAAAAAOCxg9iqerK7du0q93FUtiPlCgAAZg9eO3DgQAkMDORGeIj//Oc/snPnTtmwYYO9bc2aNfLJJ5/IPffcY2rfAAAAAACVU4UG8L/77juZMWNGuY/TqFEjAvgAgAqlArnHjx83tFE+x7N4e3vLq6++qjPx4+Li7O1ffPGFtGrVSnr16mVq/wAAAAAAlQ8ldAAAKIGffvrJsNykSRNp0aIF187DVK1aVV5//XXx8vIytD/99NNy8OBB0/oFAAAAAKicKjQD/5prrtEZbMXJzMzUWY4LFiyQo0eP6raLLrpIhg4dqucJmAAAKtLZs2dl8eLFhrbhw4eLxWLhRnigtm3bykMPPSQTJkywtyUlJcm4cePkq6++kpCQEFP7BwAAAACoPCo8gF8aGRkZOgvumWeekU2bNslNN90kDz74oNP6BwBAYYPXZmVl2Zf9/Pzksssu42J5sFGjRsnWrVtl3rx59rbDhw/Lk08+Ke+9955YrbzECAAAAADwsAB+afn4+MhTTz2lMx/feecdefjhh6VTp07SvXt3s7sGAPBQ6enpsnnzZsPgtV9++aXOwLZRb4UVNSj7jh07nN5POJd6u8JWNmf37t329pUrV8r7779PQgEAAAAAoEK4dADfRn2A/uijj3RQ5YknnshXxgAAAEdRwftRE6aJf3RdvZxy8pDE7txv2CYtOUqWf7em0GPE71gloS26clPcnL+/v7z99tty44036mQCm++++04aNWokgwcPNrV/AAAAAADP5xYB/PDwcGnXrp2sWbNGli5dKidOnJAaNWqY3S0AgIdSwfugmGZ6/vy25WL1DbCv84uIlqrt+hZZ/14F/eHacrIyS/ymxC233CKvvvqqHqPHZvz48ZKQkCBXXXWV+Pr6OrGnAAAAAIDKzC0C+Ep0dLT+NycnR/7++28C+AAAp8tMTpALf28ytIW36sHgtR4gNe6YPD/3mPhvSCzR9gn1Lpa4Nb8Z2m576AmJiIiQgQMHOqmXAAAAAIDKzm0C+BcuXLDPnz9/3tS+AAAqh/M7V0tO9r+D11q8vCWseRdT+wTnvGlRHLWdRSxyZtNf9rbs9BRdYqdnz54SGBjIrQEAAAAAOJxV3IB6RX3dunX25apVq5raHwCA51NvfJ3btszQFtLoIvHyr2Jan2Cu6j1HSJWYpoa2w4cP6/F51GDHAAAAAABUugC+qjd73333SWLiP6+4qzqzqh4+AADOlHRkt6SfP5WvfA4qL4vVS2Iuv118w6oZ2pctWyZvvfWWaf0CAAAAAHiuCi2hs3btWjl27FiJtk1NTZW9e/fKDz/8IDt37rS3X3fddbymDgBwunNbFhuW1eC1gbUaceUrOfUGRp1h98iBKW/qEjo2U6ZMkZiYGLn22mtN7R8AAAAAwLNUaAD/9ddflxkzZpR5//r16+tjAADg7MFrE/ZvMbRFtOnF4LXQ/MKqSZ3Bd8n+H18zXBGVhV+zZk3p1asXVwoAAAAAUDlK6CgWi0WGDx8uy5cvl+rVq5vdHQCAh0vYt0nXwLex+vhKKIPXIhf1NkZkl8sN10R9z6h6+LnfHAQAAAAAwG0y8Hv06CHe3iU7pZ+fn4SFhUnLli3l0ksvlQYNGji9fwAAqLFXEvZtNlyI0Gadxcs3gIsDg6C6LWREq3D5/fffDSUAH3roIfn6669JOgAAAAAAuFcA/8EHH6zI0wEAUGrr16+XrNQkseYK2Ee0piQKCjZ06FCdnDB79mx7W1xcnNx///0yadIkCQ4O5tIBAAAAANwjgA8AgKv7448/DMuBNRuKf1Rt0/oD15WTlanL5QwcOFC2bNliKJ2jlm+66SZ59NFHxcfHR2fm7927V6+LiIgQf39/w7Hatm0rvr6+Ff41AAAAAABcGwF8AAD+3/79+2XXrl2G6xHRpjfXBwVKjTsmz889Jv4bEiWr2sVyYssByUg4Y19/cMV6WXr/cxJ18RCxWNSwQ/+81fHFEeMAyamxh2TKIyKdOnXiSgMAAAAADAjgAwDw/6ZNm2b8JRkQLMGN2nF9UCj/6LoSFNNMzwdExciBqW9KZnKCfX3KyUOSuH+bRPe+WiwWC1cSAAAAAFAqKh0MAIBKLzk5WebMmWO4DuGtLharF8+6UTK+oZFSd/h/xOrjZ2g/u3mRnFm/gMsIAAAAAHDvAP7p06fltddek0GDBulasN27d5fbbrtN5s+fb3bXAAAeTgXvVRDfRmVLh7fqaWqf4H7UeAkxg+8Si9X4J9bJ5TPl/M7VpvULAAAAAOCenJ5WqAZ0mzRpkqxdu1bOnTsnVatWla5du8pdd90l9evXNwRObrzxRr1NbitWrJAvvvhChg8fLt9//70EBPxTPxYAAEfJzs6WH3/80dAWVL+1+IREcJFRakF1mkmt/jfJ0XlfGdqPL/hWvKuESFCd5lxVAAAAAID5GfgfffSRzqR/++23ZenSpbJt2zZZvHixvP7669KiRQv55Zdf9HYquD9ixIh8wfvcZs6cKTfccIMzuwsAqKRWrVolhw4dMrRFtL3EtP7A/YU26yzVe1xpaMvJyZYjsz+VlFOHTesXAAAAAMC9OC2ArzLq77vvPsnIyChwfWpqqowePVpiY2Pl/vvvl/T0dN1erVo1ueaaa+SOO+6QSy+9VHx8fOz7/PzzzzJ79mxndRkAUElNnjzZsOwTGiVVYpqa1h94hqrtL5Wq7foa2rIz0uTwzI8kPT7OtH4BAAAAANyH00rojBs3zj5vtVrlqquuks6dO+v6wioQv2nTJklMTJTx48frzEdFZdh/9tlnhjI5qgSPqol/+PA/2WoTJ06UwYMHO6vbAIBKZv/+/bJ6tbE2eWiTjroGPlAe6nuoes8RkpkcL/F71tvbM1MS5NDP70v9qx8W7yqhXGQAAAAAQMVm4K9cuVL27NljX546daqeHnnkEXnmmWdk/fr1Oste+eabb/S/NWvWzBe8V5o3by4ff/yxffmPP/4oNKsfAIDSUuOr5BYcHCxB9VpwIeEQajDbmv1vkiq1GhvaVQb+oZkfSlbqvwMnAwAAAABQIQF8NfCsTZ8+fXT2veGkVqu8++67+l81cKCiauAXNkCtysCPjIzU86rUzq5du5zRbQBAJaPGXpk7d66hrW/fvmLxcvoY76hErN4+EjNkrPhH1jK0p8Ydk8OzJkp2JokJAAAAAIAKDOD//fff9vkBAwYUuE2NGjWkVatW9uXWrVsXejwV6G/ZsqV9+dSpUw7rKwCg8lIl3WxjsCje3t7Sr18/U/sEz+TlFyB1ht8nviFVDe3JJ/bLqeW/SGZmpml9AwAAAAC4LqekGMbHx9vnY2JiCt1OrduyZYueDwsLK/KY4eHh9vkLFy44pJ8AgMpLlWNT5d1yGzhwYLG/j4Cy8qkSKnVHPCAHpk6QzOR//5ZJObFPlxHs0qWLTlqAZ1IPCzdv3lzu47Rt21Z8fX0d0icAAAAAlTSAnzub0c/Pr9Dt/P397fPFfWD18vKyz2dlZZW7jwCAym3+/PkSFxdnaLv++ut5SAyn8g2NlLpX/kcOTn9bstJSDOMHTZgwQR599FEGUPZQKng/asI08Y+uW+ZjpMYekimPiHTq1MmhfQMAAADguijyCwCodNT4K19//bWhrX379tK0aVNZu3ataf1C5aBq4dcZeq8c+vk9Q/179UZIaGio3HXXXab2D86jgvdBMc24xAAAAABKjPe0AQCVzrJly2T//v2GttGjR5vWH1Q+gTUbSMwVd4klzxuIqpTOjz/+aFq/AAAAAACuhQA+AKBSycnJka+++srQ1qBBA+nRo4dpfULlFFSvhdQacIuIWAztqpTOr7/+alq/AAAAAACVqIROamqqJCYmFrgudy37orbLuy0AAGW1adMm+wDqNrfccguDh8IUoU07StWOA0T2LzO0v/DCC+Lt7S2XXXYZdwYAAAAAKjGnB/BvvPFGh24HAEB55M2+j46OlgEDBnBRYZqQRu3kqvbR8ttvvxneFHn22WfFy8uL708AAAAAqMQooQMAqDT27Nkjy5cvz/cAWWU6A2YaMmSI3HTTTfkGW37qqafkzz//NK1fAAAAAABzEcAHAFQaX3/9tWE5LCxMhg0bZlp/ABuLxSL/+c9/5Prrr88XxB8/frwsXryYiwUAAAAAlZBTUg5fffVVeeSRR8RZmjRp4rRjAwA807Fjx2TBggWGtuuuu078/f1N6xOQN4j/0EMP6XF/pkyZYm9Xy4899pge3JbBlgEAAACgcnFKAL9x48Z6AgDAVXz++ec6m9kmMDBQrr76alP7BBQUxFdJEJmZmTJjxgx7u1pW7a+88or07duXCwcAAAAAlQQldAAAHu/IkSMye/ZsQ9uIESMkJCTEtD4BRQXxVcb98OHDDe0qiP/444/n+14GAAAAAHguAvgAgEqXfa/K5uQdMBRwJVarVZ544gkZPHiwoV19Hz/33HMydepU0/oGAAAAAKg4BPABAB7t8OHDMnfuXEPbNddcIxEREab1CShpEP+ZZ57Rb4vk9cYbb8ikSZMkJyeHiwkAAAAAHswpNfABAHAVKsiZO/s+ICBAbrzxRlP7BOSWk5UpO3bsKPSiXHrppXL27FmZM2eOof2tt96SdevWyc033yze3t7Stm1b8fX15eICAAAAgAchgA8A8FgHDx6U33//3dA2atQoCQ8PN61PQF6pccfk+bnHxH9DYqEXJyennsTX6iDnti4xtB/8ea5MXbFDQhq1l2njRTp16sQFdjHp6ekSGxsru3fvluQT+yU7LUUsVi+x+PiKt3+Q+IRGipevv9ndBAAAAOCiCOADADzWxIkTDdn3gYGBZN/DJflH15WgmGZFbhNcp7kE1qgvJxYZ69+nnTspZ7cukdjYfk7uJYqTmpoqmzdvlu3bt+u3Knbu3CknT57U65KSkuTk2SSx+gbk2887IEj8q9WRwJoNpUpMUwmoXk8sVipdAgAAACCADwDwUFu2bJE///zT0HbttddKaGioaX0Cyiui7SXiXSVMjv3+pWRnZdjbMy7EybPPPishISHSu3dvLnQFOnbsmKxZs0aWL18uGzdu1Bn3pZWZkiiJh3boSVb+Kj5BYfqtirBWF4t/1ZpO6TcAAAAA90AGPgDA46iBPd977z1Dmwrc33TTTab1CXCUkEbtxGfkQ3J41seSmZJgb09JSZGHH35YbrnlFhk7dqyuiw/nOH36tCxevFg2bdok58+f1wMOO1JG4nk5s+lPPamM/Mj2/aVK3eYOPQcAAAAA98AnOwCAR1BZr6p0hbJhwwZZsWKFYf2wYcN0OYviFDWYKOAqAqLrSf1Rj8qR2f/TNfRz++qrr3RG+Isvvih169Y1rY+eRpXjUln206ZN0//Gx8frdvXWQ0n4+/uLV4BVfIIj9MDFORlpkpmaVOx+SUd266lKrUYS1LBtub8OAAAAAO6FAD4AwCOo4P2oCdPEr1qMHPvtc8lI+Dcw5l0lVL4/GSI/fLem2OPE71gloS26Orm3QPn5hkZK/WselRN/fi9ntyzO9yDq+uuvlwceeEBGjhzp8Axxd3ywV1ZnzpyRpUuXypIlSyQuLs7+lk9aWpqeT05OFovFYt9eXev69etLgwYN9L8xMTESFRWlB9V+fUOiYayD7MwMybhwRlJPH5XkE/sk6fAuPaZBQZKO/S0JB7bKp4GnpXHjxhIWFlaurwsAAACAeyCADwDwqIFA08+dkqy0FMNAkTX7jZbgeq1KdIyUk4ec2EPAsaw+vlJzwM1i8fYVryOrDetUgPmNN96QuXPnyvjx46Vp06aV9sGe+n9DaegA/ZnjkvD3RonfvU4sVi99rY3+CdrHpSfr/98E1m4sgTUa6AFo9/v6yX618qCaVED+ZIEPB63ePuIXEa2n0KYddVvqmeMSv2utnN+2rMAM/WXLlslVV10l//3vf2XgwIGlvSQAAAAA3AwBfACAx1CB+1MrZhnaAqrVkZDG7U3rE+BsKvs7uGEbuaFXI1m4cKEcP37csH716tUyYsQIufTSS2X48OESFBRU5PHatm0rvr55g9XuSwXvc2e9FyU7I00H7NUbDSorXrEF73M/FFSsPn4S0rCdhDTtqI9v8fJyyMNBNWitf/dhEtXlconftUZOr5krGQnnDNuo8j1PPvmkLuXz+OOPS2BgYImODQAAAMD9EMAHAHiMc1uWSFZasqGteo8rxVJJy4eg8lB18D87LeLbbIicS1skF/ZuyLfNpz/+LJ9NnyOhzTpLaNNOBWSUi6TGHpIpj4h06tRJKhNVtkb9/+P8jpWSlZ5a5Lb+UbUlok3vQq+ho6js/PBW3fX9Ord1iZxeNUey01MM26i3K7Zu3SpvvfWWLtkDAAAAwPMQwAcAeIQDBw5Iwr7NYvX1t7eFNL5IqsRUvrIhqJxsmeYh9VtL4uFdcuKvHyX9/Kl8213Ys16SD++SiHaXSHib3uIdUHRGvqfKyc6ShP1bdba9GiS2KCoLP6xZZwlv21uXyMld897ZVCC/6kX9JKRxBzky+xORpH/eDLA5cuSI3HLLLfLyyy9Lz549K6xfAAAAADwwgK+yg1auXKnnH3nkEenalUECAQDll52dLd98840KyRmCXtE9R3J5USkF1WkmDW94Us5sWCin1/wmOVmZhvWqtvqpVXMkbu18CW3RRSJa99KZ5ZVBRuI5ObdtuZzfvkIyEs8Xua1vSFUJrNlQItr1kVCTS3H5BIVJte7DZWzDLJkxY4YkJf1bH18NpDtu3Di577775KabbqrQBwwAAAAAPCiAr4L36gOHcu2111bkqQEAHmz27Nmyb98+Q1tk58vEJzjctD4BZlMPsaI6X6ZLvZxeNVvO71qTb5vsrAw5t3WZnvyjYiSgel1JTGwhniYnO1uSDu+Ss1uXSuKBLXqQ2qIE1WspEW16SVDdlhK3YYF4+btOjflu3brJyJEjde37HTt22NvV1/TBBx9IbGysPProo2KldBgAAADgEVyqhI76sLFgwQI9P2HCBD3YGgAARTlz5oy8++67hjbfsGq65AQAEd/QSKk18Bap2qG/HuQ54cDWAi9L6ukjknxsj9x//xrp06eP9O3bV3r37i3h4eFu/f+H8ztWyYmFkyX9wpkit/XyC9Q158Nb9RDfsChxZTVr1pTPPvtMXnrpJfntt98M66ZNm6az85955hnx9napP/UBAAAAlIHD/6rfu3ev1KpVSwIDA8tUv3jz5s16/vz5ol9pBgBAZZy+9tprcuHCBcPFiO59tc4+BvAv/8haUmfo3ZJ6+qgurRO/e53k5GTnu0SZmZmyYsUKPb3yyivSpk0bXfawS5cu0qJFC/Hy8nLpy5qYmCh//vmnHuB1yZIlcu5Molh9AwrdXr11oAalDWnSXqzezhuU1tH8/PzkhRdekEaNGsmHH35oeKtAfe3qOqj/P/r6us/XBAAAAKACAvjjx4+XWbNmSfv27fVAWj169NBT1apVHX0qAEAlt3DhQvnrr78MbaFNOkhwvZam9QlwdarWvcrIr9Z9mJzbskTO71xdaC14Nb7Epk2b9PTJJ59IUFCQdOzYUf+dpwL7TZs2FR8f8x+WqcSPpUuXyuLFi/WDh/T0dN1eWKkcq4+vhDbtLOGte0hAtTriLtRYBrnL5ijqoYoaxFbdH/XwxUZl5h8/flweeOCBAjPx27ZtS3AfAAAAcANOea82IyNDVq9erSdVCkcNpNW8eXOJj493xukAAJXQuXPn5PXXXze0Wf0CJbr3Nab1CXAnPkHhUu3iYRLVdYgkHt4p53eskPidq4vcR2V1L1q0SE+Kyu5u1qyZDua3atVKmjRpIrVr13Z6/XUVoN++fbts2LBBVq1apd/gVA8biuNftaaEt+kloc06iVcRWfmuKjXumDw/95j4b0jMty65+QA5tWym5GRl2NsO/rVCFuyN0/fZkuuepMYekimPiHTq1KnC+g4AAADARQL4999/vy6ho7KgbB+mVPZT3myhMWPGyFdffaXr3KtJfegDAKAk1O8VFbzPW26tavtLxTswmIsIlIIK7Kq3VtSkstLvbu6tM7eXL18uKSkpxQbSt2zZoicbFdQPCwuTmJgYqVGjhkRFRdknlcFfGipLXJ3j6NGj8vfff8uePXtk165dsm3bNnuWfXGsPn4S3vJiCWtxsQTUqK8TS9yZf3RdCYpplq9dtQXVaiKHZ02UrPRUe3vqqSMSv3OV1BpwiyGIDwAAAKCSBvB79eqlJ0XVJF62bJmuP6oC+mvXrtXZ+bYMrjlz5uhJiY6ONmROlSSLCgBQOalSbap8Tm6qrMe68PxBLQAlZ/X1k27dOuvM7LS0NFm3bp1+o1Jlue/fv7/Eb8es37VfrOu35T++t59Y/QPFy9dfrH4BOrj+T1DZIqIC6znZkp2RLtkZaZIRf0aaRgWWKeCu6vR3795dGjZsKB/uzJTgepUjUSSwViOpM/w+OfTzB/oa2qjxDqw+/lKj73Vu/wADAAAAqGycUkLHJiQkRC6//HI9KSNHjpQZM2bo+W7dusmRI0d0RpUSGxtr2Hf06NHyzjvv6Dr6alIfwiIiIpzZXQCAG1ADnr/xxhv5ft/cdNNNsn72HtP6BXgaNUiq+vtLTUpcXJysWbNG1q9fL1u3bi0yoK9qzBc2cGx2eqqepJC6+/9ulyLJySJVqlQpUX/9/f31YLu9e/fWySShoaE6ecSyZ41UJoE1GkidoffI4ZkfSnaucjrnti0Tn+Bwiep8man9AwAAAOBCAfyijBs3Tgf01WvQKovSNiUlJen1KlNfZXup6c0339TZQi1bttTBfLVvo0aNzOo6AMAkKiP4iSee0P/m9vTTT5e6NAeA0omMjDQkZiQkJOhSNiqYr0roqPI2Z8+erbDLqv42bNy4sXTo0EG/MdClSxf90AEiVWo3lpghd8nhWR9LTnaW/ZKcWvmr+ASFiXdwOJcJAAAAcBOmBfBt1MBnarrvvvsMGfp9+/bVHwLVB0JbHX31IVFNah0BfACofNSbWXv37jW0qd8dffr00Zm2ACpOcHCwfqNSTTbqbzdV4urJHxeJ5IhkxJ+W9AtnJDPxvP5brjzUGEsqYK8GylV/O7Zr106/fYOCBdVtIbUvu1WOzPnM0H584WSJ6nqFiHTm0gEAAACVMYB/22236YHPbKVvOnfuXKZsqLvvvlsHZVQd/RUrVuga+qqevnp1GwBQ+aig4PTp0w1t6mHuQw89ZFqfABipcofqjcnQpkmGgVazszIlM+GcZCSek6zUJMlKSfrn3/QUNSq13iYnR41/ZLHXx0+/ECePjegq/fr1k+rVq+vBcVE6IY0ukujeV0vs4mn2NnWdTy3/RY7f0JPLCQAAAFTGAH58fLz8/vvvelJU8F691qyC+YcPHy718VRm1aBBg/SkpKen6wkAUHls3rxZXnnlFUOb+v3y6quvUjIDcANWL2/xDYvSU0klHtmlHwbExMQ4tW+ermq7PpKRcE7ObPh34O/szDR599139dtLvMUAAAAAuDarow+oMu5zf9BSdYpV5rwKsuQub/D+++/LxIkT5e+//y7V8VX2FXWOAaDyOHnypDz66KOSmZlpaFe18OvXr29avwDAXVTvPlxCm3QwtMXGxsr48eMlK+vfGvkAAAAAKkEA/7///a/OtD948KB89913MnbsWGnVqpUeaCw3VRLn3nvv1bVMGzRoIHfddZfs27fP0d0BALix5ORkefjhh/MNjHnDDTfIFVeoGs4AgOJYrFap2f9GCahWx9C+evVqee+997iAAAAAQGUK4NvUrVtXB1g+/vhj2bp1qw6+qMFnbXLXxT9w4IB8+umnsmnTJnvbW2+9Ja+99prO3ldZ/ACAykWVS3vkkUdk165dhvauXbvK/fffb1q/AMAdWb19JWbIXeIdEGxo//777/UYIwAAAAAqSQ38woSFhUl4eLh9+dtvv5WGDRvKggULZP78+bJ8+XJDoH7VqlV6yltHX029e/eWwMDAiuo6AKCCZWdny3PPPZdv4PI6deroWvheXl7cE8AJcrIyZceOHeU6Rnn3h/P4BIVLzOC7ZP8PxjFFVNJMkyZNpFmzfwceBgAAAFDJAvh5qZI67du319Njjz2myyQMHTpU/vjjD70+NDRUD4ibu46+rZb+tGnTZOTIkWZ1HQDgRDk5OTJhwgT9cDfvg+B33nmHARcBJ0qNOybPzz0m/hsSy3yM+B2rJLRF13L3hYcJzhFYs4FU7TRQZO9iwxtPqgymKn/JoLYAAACAazEtgJ+XyqhXwRmbSZMm6TIJqla+bdq+fbsO7AAAPJP6f7wK0k+dOjXf7wg1+LkqzwbAufyj60pQTNkzsVNOHvK4hwmeJrh+axnQwF8nx9gcP35cnn32WV3G0mp1WpVNAAAAAO4awC9I7dq15brrrtOTcu7cOf1Bo2nTpmZ3DQDgpMz7KVOmGNq9vb11e4sWLbjmQCXjKg8TPNGoUaPkwoULsmXLFnubSpj5+uuvZcyYMab2DQAAAMC/3Cq9RtXQHzJkiK7RCQDwrJr3b7zxRr7gvcoCffHFF6Vz586m9Q0APJF6OKpq3+ceo0r5+OOPZd26dab1CwAAAICJAfxvvvlGZ9GradiwYRV5agCAi1K1l59++mk9vklBwfv+/fub1jcA8GTVqlWTl19+2VAyRz1QfeKJJ+TUqVOm9g0AAACACQF8W517Nfn4+ORbrzKAqlevrid/f/+K7BoAwARJSUny4IMPyrx58wztKpikgkoDBw7kvgCAE6k3nMaOHWtoO3v2rA7iZ2Vlce0BAAAAk7lUCZ3PPvtMYmNj9TR48GCzuwMAcCKV3XnnnXfKmjVrDO1eXl66rAOZ9wBQMW655Rbp0aOHoW3Tpk0yceJEbgEAAABgMpcK4AMAKoft27fLTTfdJLt37za0BwQEyLvvvit9+/Y1rW8AUNmot55eeOEFqVmzpqFdDWirBrYFAAAAYB5vE88NAPCQGvabN28u8fbLly+XL774QjIyMgztKnD0wQcfSIsWLZzQSwBAUUJCQvTbT7feeqtkZmba25999lmZPHmy1KhRgwsIAAAAmIAAPgCgXFTwftSEaeIfXbfI7XJysuXc5iUSv2t1vnVWbz95882HCN4DQAXIycqUHTt2FLhu6NCh8u233xrGKrn99tvlqaeeEm9v40eHtm3biq+vr9P7CwAAAFRmBPABAOWmgvdBMc0KXZ+ZFC9Hf/9Sko7uEatvgGFdldpNJLxtbz2AOQDA+VLjjsnzc4+J/4bEfOtyckLkdJUYSTqyy9528OxWWfffN6Vq+37/HiP2kEx5RKRTp07cMgAAAMCJCOADAJxKBe2P/va5ZCYn5FsX0ba3RPccKYlHdhaaDVpS5d0fACqToh68BkbXk/3fvybp8aftbYkHt0t4y4slpNFFFdhLAAAAAATwAQBOkZOdLXHr5snpVbMlJyfHsM5isUqNPtdKeOsexWaDllT8jlUS2qJrufsNAJWdl2+AxFxxh+yf8oYut2NzfMG34hdZS/zCqpnaPwAAAKAyIYAPAHC4jKR4OT7/G0k8vDPfOp+gMKl92e0SWLNBqcrwFCfl5KEy7wsAMPKPqi01el8jx//83t6WlZ4qR+d8JvVH/ZfLBQAAAFQQAvgAAIe6sG+THF84WbJSk/KtC6rbQmoNvEW8A4K46gDg4sJadZfk43/L+V1r7G3qjanYxdMkpEl7U/sGAAAAVBYE8AEADqEyM08umS7ntq/It85isUhUtyES2WGAWKxWrjgAuAH1/+7oPtdKyqnDknY21t5+btsysfr6iUhnU/sHAAAAVAZEUQAA5ZZ65rjs//7VAoP33oEhUnfEAxLVaRDBewBwM16+/lL78tvF6u1jaI9bN1+OHTtmWr8AAACAyoIAPgCgzNLT02X69OlyYuFkSY8/nW99SMN20nD0U1KldhOuMgC4Kf+qNaVG3+sNbTmZ6fLhhx9KSkqKaf0CAAAAKgMC+ACAMtm2bZuMHj1aZs2aJZKTbfzl4uMnNS8dLbWvuIN69wDgAcKad5Hwlhcb2lQG/muvvSY5OTmm9QsAAADwdNTABwCUSlpamnzyyScyefJkyc42Bu6VwOj6eqBa37AoriwAeJDoS66RlJOH9EC2NnPmzJEOHTrI0KFDTe0bAAAA4KnIwAcAlNiGDRvkuuuuk2+//TZf8N5isUq1rldIvavHEbwHAA9k9fb9px6+jxrA9l8qC3/v3r2m9QsAAADwZATwAQDFOnfunDz33HNy5513yuHDh/Ot9w2PlgbXPS5RXa4Qi9WLKwoAHsovvLoukZZ3PJTHHntMEhISTOsXAAAA4KkI4AMACqWy7H/66Se56qqrZPbs2fnW+/j4yNVXXy01+98o/lG1uZIAUAmENukgIY3bG9rUw90nnnhCsrKyTOsXAAAA4IkI4AMACrRnzx657bbb5JVXXpELFy7kW9+6dWv5/vvvZciQIWKx8usEACqTiHZ9pH79+oa2lStXyrvvvmtanwAAAABPRMQFAJCvXM6rr74qo0ePlq1bt+a7OoGBgfLwww/L559/ni94AwCoHCxe3vKf//xHIiIiDO0//PCD/Pzzz6b1CwAAAPA0BPABAPYaxmpw2uHDh8uMGTPyDVKrDBgwQK9TA9layboHgEotMjJSJkyYoMup5R3UVg16DgAAAKD8COADQCWXk5Mjf/31l1xzzTXy3nvvSVJSUr5t6tSpIx999JEupxMVFWVKPwEArqdNmzby1FNPGdpUHfxHHnlEDhw4YFq/AAAAAE/hbXYHAADm2blzp65XvH79+gLX+/n5yZgxY+Smm24SX1/fCu8fAMD1XXHFFbJv3z755ptv7G1q7BRVYueLL76QatWqmdo/AAAAwJ0RwAcANy13s3nzZkNbamqq7N27V8+rmsT+/v6F7n/ixAldCmfbtm1isVgK3Obyyy+X++67j8ALAKBY6vfFwYMHZcmSJfa22NhYHcSfNGmSBAcHcxUBAACAMiCADwBuSAXvR02YJv7RdfOsCdD//eLIlgL3y0xOkPPblkvCga2SnZ4qDaqHS5UqVfKVQ1CD1LZs2dJp/QcAeBY1LsrLL78sd911l+zYscPerjLzx40bJx988EGRD5YBAAAAFIwAPgC4KRW8D4ppVqJtM1MSJW7dfDm7eZHkZGWK1cdPJMc4SG10dLTcf//90r9//0Kz8gEAKExAQIAeS+W2226Tw4cP29s3btyog/jvvPOOLs0GAAAAoOQI4AOAB8tKT5Wzm/6SM+sX6HmDnH/K7qgMfFW/eMCAAbrO/bp160p1jtyZlgCAyi08PFw+/PBDPX7KmTNn7O1r1qzRA9u+9dZbjKkCAAAAlAIBfADwQNlZmXJ+2zI5vfo3yUxJKHCbnOwsSQhvIr4dB8m08wEybeqmMp0rfscqCW3RtZw9BgB4ipo1a+qSOWPHjtWD2dqsXLlSB/HffPNNMvEBAACAEiKADwAeJCc7W+L3rJPTK3+V9Av/Zj7mZrFYJbx1D7H4+klQneYlLsNTmJSTh8q1PwDA8zRp0kQ++ugjufvuuyUxMdHevmLFCj2w7dtvvy1BQUGm9hEAAABwB1azOwAAcFTgfr3sm/ySHJv3VaHB+9CmHaXRTc9KjT7XipdfIJceAOA0zZs3l4kTJ+YbLH3Dhg1y5513SlxcHFcfAAAAKAYBfABwYzk5OXJh32bZ/8OrcvS3zyXtbGyB2wXVaykNr39Cag+6VXzDoiq8nwCAyqlFixY6Ez9vEH/Pnj1y6623yoEDB0zrGwAAAOAOKKEDAG4auE8+sV9OLZspKacOF7pdYI36Uu3i4VKlduMK7R8AwLPlZGWWahBzVTZnwoQJEh8fb2/bu3evjBgxQrf369fPST0FAAA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OPXv2uB334osvdo47ZMiQbi239fH7008/7XD8hoYGx4QJE5zjn3nmmd1a/vHGHF9dt/fhw4d9voylS5e2WMYLL7zgdtz777/fOV5ISIhj//79Pl8fBIfVq1e32G8eeught+P+97//bTGuOWdE98TGxjq35xtvvOHxdEVFRY6wsDDntLfddpvbcT/66KMW7fbSSy/12jz7wnn0eeed5/w7ExMTHV988UVAnZf761wfAFwRwAeCgAkS9evXz3kiMGzYMMe2bdvajPfHP/6xxYneXXfd5XaexcXFjnvvvdc5mKBuby0bDsfChQud2yomJsbx+uuvt9ks77//vnUS2zzeggULOtx0f/rTn5ztuWLFinbHMQGE4cOHtwhOHjx4kCbpJrO/u+7/99xzT5txdu/e7Rg1apRzHPO5Mp8vd5599llne7788svdWr8TTjjBuVwzP3Tsgw8+aNGel156qaO6urrFOJWVlS1+cJoA3fr1693O8/PPP3e2p7kw6k5+fr51TGie7/XXX++oqalpM15eXp5jxowZLS6wmh+NaP9CR3x8vHNbTZo0ydrOrsyFkp///Oct2v3hhx92uzn37dvX4jv06NGj7X7Puv6Iz8rKcnzyySftzq+kpMRx0UUXtVh+R/sJfBvAb2xsbNGemzdvbnc8s59Mnz7duYzU1NR2L8yYwL5r21933XU02XHOnMM17w8moPzWW2+1Gefdd99tcawy54rwXwDfuOmmm1p8z/7nP/9pM87atWtb/FYyF2Kbmpp6dZ7BypzfuH42TJDc3W8Yf5+X98Q8AaArbOYff98FAKBz5lbtK664okVHSBdccIFVT72yslJLly7V2rVrne/n5uZqzZo1Vhmc9pjSDK71/D7//HOdeOKJvbJsSFu3btX06dOt7ddcx/r000+3OsQzj5cvX663335bdrvdej82NtZ6rXUnl66SkpKct+Lee++9+sEPftDifdN5oymV03wrqampasbprGPiZldddRUlOtwwnRKb8ieuHVuZz5e55dbU8NyyZYtVSsG1nvqSJUt0+eWXu93eZlpz+7Bx8cUXW6VavGU+2+Y2Ynf7BtoyxzzXEhmmPM1FF11k/Z+fn6/nnnvOKkXV7NZbb9Wf/vQnt5vyrrvu0v/7f//PepyZmamDBw922EGxaxtlZGTo3HPPtTq/M5/jDRs2WH0ZmJruzUyfBr/73e9oSjceeOABfec733E+N8dU057m+8r0B/Laa69p27ZtzvdPOOEEffrppy36gXH1zjvv6IwzznA+N8fV9urc/+xnP2vTEa35PM6aNUtpaWnWMWHz5s1WKQXT6V2zYcOGWaV8zPct2jLfh64l+wzzeXT9DNxxxx1tzkNMh5Gu5z6ufYpER0c7nz/66KNavHhxu5venC/NmTPH+iw2f5eaDoqnTZtmvWb6lXFdN9PO5hyJzuGPb+bzbI4rzZ9zc643d+5c67zP/Bw339HmuNL809yU8TBlDk3fR/CM6e/Htexks9tuu80qaWXccMMNbToRnjRpklU+rz2m49KJEyc6OzBtPoabMpTm+Gw+2+b7uLkcpinBZ9rRHCPc6Yl5BqtFixZZ51PNTBkdcz7tCfO7acqUKb12Xt4T8wSALulSuB+AX/3qV79qceXf3WCy/Hbt2tXhvDZs2NBiGpMd2lvLxjEmC8g1Q8/dEBER4Xj11Vc73Wyu2frtZVlXVFR41Ibuhs72keOd2e9dS9V0NJjPU2dM+Yzm8U1Zj+4gA7/rTMb9ySef7FF7nn322Vb5k46YckXN42dmZna6/G9/+9sefzavvvrqPpmZ52u33HKLR9vT3KVkyhx05O23324xjbuyPGa/cC3L48lgMva2bt3aQ1uhb7j77ru9+h5zV7rEZIG6jvfoo492uHzzvsmc7Wx5JvP3s88+66GtgGBjzuXMOZ0n531dzRRH2+Oyp8PXv/71DjffypUrHQkJCZ3Ox9yJ19GdWz09z2Bk7gT2ps3M8Ic//KFXz8t7ap4A4Ck6sQWCiMnefPnll60szPaYTEFzlX/9+vVuxwnGZfdVJmNj1apVOvnkk92OYzJyTAbWOeec06vrhq4z+/26deuszO3mTlBbGzJkiF566SVnJjYCl8nGfe+996y2cteZqLnr5Z577rGOje7a3Fv/93//Z3Vk2F5Wt2uW9r///W+r0zeTBYyO/fnPf7a2Vb9+/dp9PyIiQl/72tes47K568EXzH5h7p55+OGH22R9tmYy+G655RbrDrnOxoV/mex8k2lvMnfbY7KrTUav+U4wGdaAYc7lzDldRx1zmvfMMci100z419SpU63fN6YTWfPZbs+4ceP0wQcf6Prrr/fbPNHz5+Wc6wPwJ0roAEHI3F77xRdfaPXq1VYZBhNoGjhwoObPn+9x0MFMd9999zmff+tb37Lm0RvLRls7duzQhx9+qAMHDljPTYBp9uzZXQrimJPP5ts2TbmN1rfamtuHTTkHb3m6j+BYKQdT/iYvL8+6XT4rK8u6zbe5RJInTMDPlFoyJkyYoGuuucbrTWtKu+zbt8/tvoGOmc+VKRW2c+dOlZSUKDU11fpsmgCdpyVO3n33XatMipGQkKBf/OIXHm92U1rBBHTMMdfsP6YEjynFYG7lJnDfdaY0mSlxYoInhw4dsgLngwYNsgJm5qKMp8fsv//97y1KNCQnJ3c6nQnOm7Y0x/qKigpr/zH7k7kNf8aMGZSe89D7779vlT3qqksvvdTazq2ZUhU/+clPWpRxcFeaoTVT0uqzzz5TQUGBFSQypXJMaYeOLr4BpgxH83mfOa6b8z7z3WzKesE7rY/LnjLnZqZUoSdMCT1T4tK0mzmvNmX1TLKNp2VfemuewcKUDCwsLPRq2vPPP1+nnHJKr52X98Y8AaAjBPABAAAAAAAAAAhA3G8NAAAAAAAAAEAAIoAPAAAAAAAAAEAAIoAPAAAAAAAAAEAAIoAPAAAAAAAAAEAAIoAPAAAAAAAAAEAAIoAPAAAAAAAAAEAAIoAPAAAAAAAAAEAAIoAPAAAAAAAAAEAAIoAPAAAAAAAAAEAAIoAPAAAAAAAAAEAAIoAPAAAAAAAAAEAAIoAPAAAAAAAAAEAAIoAPAAAAAAAAAEAAIoAPAAAAAAAAAEAAIoAPAAAAAAAAAEAAIoAPAAAAAAAAAEAAIoAPAAAAAAAAAEAAIoAPAAAAAAAAAEAAIoAPAAAAAAB6zPjx42Wz2XTiiSe2ee+OO+6w3jPD4cOHaQUAAFohgA8AAHrc3/72N+eP864OkyZNooUAAAhSZWVl2rx5s/X4lFNOafP+Rx99ZP0/cuRIpaen+2y5L774YqfnGFFRUcrKytKcOXN02223aceOHT5bPgAAvkIAHwAAAAAA9IhPP/1Udru93QB+Q0ODvvjiC+vxySef3OstUFdXp6KiIusiwm9+8xuNHj1a3//+99XU1KTjyV133eW8qHH77bf7e3UAAK0QwAcAAAAAAD3ik08+sf43weFZs2a1eG/VqlWqrq52m53f20zg/v7779fNN9/s71UBAMCJAD4AAOhVw4YNk8Ph8HhYu3YtLQQAQJAH8MeMGaPU1NR2y+f0dAZ+bGxsu+cY5uKBKe9z7733tli3f/zjH1q2bFmPrQ8AAP+/vTsNtarq4wC83roWqZWNaoMoSmqjFiWN3mhQU1LT7Db4KZsooonCDxUhhM1FRUYGpYiFhEmlqVkfTD+UYTaQmqJiYWqDGZaZ9b6sBWdz7r3nTnruOfvtPg9s3Oeu5VnrnA1y/e21/6stBPgAAABA2e3ZsyesWLGixfr3PXv2TDf4K+2QQw5JZXPuu+++sHTp0vS6YM6cORWfDwCUIsAHAAAAyi6G97t37y4Z4Me6+LE+frXq3zcUg/wrr7wye7127dqqzgcACgT4AAAAwD5bvXp1tglq8VFc8/7666+v13bggQeGHTt2ZKvdG/7dO+64o+JXpFevXvWeHmhKbW1tNs/CJrzNufXWW7P+r732Wsk+8SmAQp9nn302/Wz79u3hscceC+edd156SqGmpia1NxRvkkyfPj3dgOjTp08qGXTQQQeF4447Lpx++ulh1KhR6T3jdSpWV1eX3u/BBx/MfvbII4+UvJaTJk0q27gAtE1NG/sDAAAA/Ots3rw5Oz/xxBOrOpeFCxeGiRMnphC/oVi/vxDkr1y5MowdOzZs2rSpUb8tW7ak48svvwzvvfdeuPvuu8Mvv/wSunXrtt/zq9a4AB2RFfgAAABAhxZL5sybNy97PWzYsKrNpRCOlwrvC+WHohiKjxgxomSI3p6qNS5ARyXABwAAAPbZgAED0qrw4uO7777L2h9++OFG7YMGDUpt55xzTqO2eLzwwgvtfkViCZhY3uWZZ55Jdfj/+OOP9PNYsiYG6NUyY8aMNJdYlz+exycD/vrrr+y7ieWHolmzZoWtW7em87gBbyx/E8P/nTt3hr1794Yff/wxfP3112HBggXhnnvuSdepuATPG2+8kd5vypQpzV6reMRSOQX7Oy4AbaOEDgBQUevXr2/Tf+Lmzp0bxowZ065zAgDKa9GiRdn5ZZddVq8tBryrVq1K55deemm7f/W7du1q1e8enTp1Ctdcc014+eWXs5C8WoYOHRrmz58fOnfu3GSfGJIXPPXUU+G2226r137UUUel4+STTw7Dhw9PfcqhWuMCdFQCfAAAAKCsFi9enP487LDDwpAhQ+q1LVmyJK3qrlSA3xpx89W4Wvyuu+5qNjSvhLhZbVx539I8Dj744Oy8e/fuFZhZdccF6KiU0AEAAADKJobzH3zwQTq/+OKLUyBdrNAWy6/EcjV5sGfPnjB16tTQu3fv8Pzzz1d1LvGmRq9evVrVryDefIgla2IJm/ZWrXEBOioBPgBQUX379i1ZW7WpQ/kcAPj/8vnnn2cbsF5++eWN2uMK/OjCCy+st5q7vXTp0qXk7xixrvwPP/wQFi5cGOrq6rK6+HfeeWd4+umnQ7WceeaZreo3atSocPXVV6fzuKHsFVdcEY488shUsuiBBx4Ic+bMCdu2bSv7/Ko1LkBHJcAHAAAA9kmsLd/wKA6gb7/99kbtGzZsyOrkN2yLNdMrJT4ZEEvAxJsMs2fPDo8//njWNnny5LR5bDXEMLy13nzzzTBt2rRw0kknpde//fZbesIhfpYJEyaEHj16hIsuuii89dZbZZ1jtcYF6IgE+AAAAECHd++996bguVBSZ+bMmVX5TtqygW686XHLLbeENWvWhK+++iptwDtp0qRw2mmnpbb4pMHSpUvD+PHjw9ixY9NTB+VQrXEBOiIBPgAAANDhHXDAAWHw4MHZ9/DZZ5+V/E5iQF1Q2Iy3Ob///ntFvttTTjkl3HzzzeGVV14JX3zxRSoP9Nxzz2Ur+t9+++3w6KOP/mvGBegoBPgAAADAPmlYV/7XX3/NVpDff//9jdpra2tTW//+/UvWpX///fereiWKA/lYFqaUzp07Z+fx87Zk7dq1oRqOPfbYVM+/+EmC119/vdkbEpUcF4DWEeADAAAAZbF8+fLw999/Z5vUFotlVD755JOSbXkQ571y5crs9THHHNNkQN3SKv2C1atXh08//TRU0wUXXJCdb9y4Mfzzzz/12os3Eo6lgyo1LgCtI8AHAAAAyiLWPS+s6j7//PPrtcWwu1BOJo8B/hNPPBG2bt2avT733HNL9jvrrLOy81j7valV+Dt27Ag33HBDuwXXN910U5g7d26LZXzi5rIFXbt2TaWCih1xxBHZ+bp16yo2LgCt419PAAAAoKwBfqyLXhwMRx9//HF2nocAf+/evWHbtm1h0aJFoa6uLkyePDlr69atW/pZKWPGjMnKBG3evDkMHTo0zJ8/P+zcuTM9ZRBXm0+bNi0MGjSoxRX6++Obb74JV111Vejbt2946KGHwkcffZQ+T5zDTz/9lMaOnyneRCi45JJLGr3Pqaeemp2/8847Yfbs2eHnn39uMqAv17gAtE5NK/sBAJTF+vXr21xrdcuWLaFHjx6uAADk2J9//pmVyCkun9Iw3D/++ONDnz59KjavXbt2tel3jxjOx5X1Rx99dMn2E044IW3a+tJLL6XXq1atCiNHjizZt3fv3inIjxu5tpcNGzaEKVOmpKM5sXZ/qc1kzz777HRNvv/++7B79+5w3XXXNepz4403hunTp5d1XABaxwp8AAAAYL/FWu8xxC+1wj6u5l62bFnJtjyJQfa8efPChAkTmu335JNPhhEjRjTb54wzzgiLFy8O3bt3D+3h1VdfTU8JFJ4GaE68YfLhhx+GgQMHNmqLpW1efPHF0KlTp4qOC0DrCPABAACA/VZYYV9qBX4suxLLq+QpwI8B9OGHHx4GDBgQrr322jBr1qzw7bffNrmavuGq8nfffTf9nWHDhqUNb2MAHsP62traVEInPo3Qr1+/dpt///79U7mbTZs2halTp6YyNXH8mpqa0KVLl1TiZty4cWHGjBnp+x8yZEiT7zV69OiwYsWK9GRBLH906KGHNvnUQjnHBaBl//lvS7uOAAAAAAAAFWcFPgAAAAAA5JAAHwAAAAAAckiADwAAAAAAOSTABwAAAACAHBLgAwAAAABADgnwAQAAAAAghwT4AAAAAACQQwJ8AAAAAADIIQE+AAAAAADkkAAfAAAAAABySIAPAAAAAAA5JMAHAAAAAIAcEuADAAAAAEAOCfABAAAAACCHBPgAAAAAAJBDAnwAAAAAAMghAT4AAAAAAOSQAB8AAAAAAHJIgA8AAAAAADkkwAcAAAAAgBwS4AMAAAAAQA4J8AEAAAAAIIcE+AAAAAAAkEMCfAAAAAAAyCEBPgAAAAAA5JAAHwAAAAAAckiADwAAAAAAIX/+B7n4RgVzcYNfAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 700x700 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 607,
+       "width": 760
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "dx=ds_FRET\n",
     "alex_jointplot(dx);"
@@ -109,7 +176,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -165,9 +232,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING: reduced indexes did not inlcude origninal start and stop times, new bursts are shorter. This is expected if burst search was performed with differen ph_sel than the given times.\n"
+     ]
+    }
+   ],
    "source": [
     "ph_d = ds_FRET.get_ph_times(ph_sel=Ph_sel(Dex='DAem'))\n",
     "bursts = ds_FRET.mburst[0]\n",
@@ -176,7 +251,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -194,7 +269,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -211,9 +286,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 450x450 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 455
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.figure(figsize=(4.5, 4.5))\n",
     "x = np.arange(0,1.01,0.01)\n",
@@ -226,16 +317,32 @@
     "plt.xlabel('E', fontsize=16)\n",
     "plt.ylabel(r'$\\sigma_i$', fontsize=16);\n",
     "plt.text(0.05, 0.95, 'BVA', va='top', fontsize=22, transform=plt.gca().transAxes)\n",
-    "plt.text(0.95, 0.95, '# Bursts: %d' % ds_FRET.num_bursts, \n",
+    "plt.text(0.95, 0.95, '# Bursts: %d' % ds_FRET.num_bursts[0], \n",
     "         va='top', ha='right', transform=plt.gca().transAxes)\n",
     "plt.savefig('BVA.png', bbox_inches='tight', dpi=200, transparent=False)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 611,
+       "width": 621
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "x, y = ds_FRET.E[0], np.asarray(E_sub_std, dtype=np.float64)\n",
     "hist_kws = dict(edgecolor='k', linewidth=0.2,\n",
@@ -255,7 +362,7 @@
     "g.ax_joint.set_xlabel('E', fontsize=16)\n",
     "g.ax_joint.set_ylabel(r'$\\sigma_i$', fontsize=16);\n",
     "g.ax_joint.text(0.05, 0.95, 'BVA', va='top', fontsize=22, transform=g.ax_joint.transAxes)\n",
-    "g.ax_joint.text(0.95, 0.95, '# Bursts: %d' % ds_FRET.num_bursts, \n",
+    "g.ax_joint.text(0.95, 0.95, '# Bursts: %d' % ds_FRET.num_bursts[0], \n",
     "         va='top', ha='right', transform=g.ax_joint.transAxes)\n",
     "plt.savefig('BVA_joint.png', bbox_inches='tight', dpi=200, transparent=False)"
    ]
@@ -278,7 +385,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.14.7"
   },
   "toc": {
    "colors": {
diff --git a/notebooks/Example - Customize the us-ALEX histogram.ipynb b/notebooks/Example - Customize the us-ALEX histogram.ipynb
index 15a0560..44e0383 100644
--- a/notebooks/Example - Customize the us-ALEX histogram.ipynb	
+++ b/notebooks/Example - Customize the us-ALEX histogram.ipynb	
@@ -20,18 +20,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " - Optimized (cython) burst search loaded.\n",
+      "--------------------------------------------------------------\n",
+      " You are running FRETBursts (version 0.7.post212+g2db78d238.d20260904).\n",
+      "\n",
+      " If you use this software please cite the following paper:\n",
+      "\n",
+      "   FRETBursts: An Open Source Toolkit for Analysis of Freely-Diffusing Single-Molecule FRET\n",
+      "   Ingargiola et al. (2016). http://dx.doi.org/10.1371/journal.pone.0160716 \n",
+      "\n",
+      "--------------------------------------------------------------\n"
+     ]
+    }
+   ],
    "source": [
     "from fretbursts import *"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "seaborn version:  0.13.2\n"
+     ]
+    }
+   ],
    "source": [
     "sns = init_notebook(apionly=True)\n",
     "print('seaborn version: ', sns.__version__)"
@@ -39,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -59,9 +84,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "# Total photons (after ALEX selection):     2,259,522\n",
+      "#  D  photons in D+A excitation periods:      721,537\n",
+      "#  A  photons in D+A excitation periods:    1,537,985\n",
+      "# D+A photons in  D  excitation period:     1,434,842\n",
+      "# D+A photons in  A  excitation period:       824,680\n",
+      "\n",
+      " - Calculating BG rates ... get bg th arrays\n",
+      "Channel 0\n",
+      "[DONE]\n",
+      " - Performing burst search (verbose=False) ...[DONE]\n",
+      " - Calculating burst periods ...[DONE]\n",
+      " - Counting D and A ph and calculating FRET ... \n",
+      "   - Applying background correction.\n",
+      "   [DONE Counting D/A]\n"
+     ]
+    }
+   ],
    "source": [
     "full_fname = \"../tests/data/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\"\n",
     "\n",
@@ -84,9 +130,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'matplotlib.figure.Figure'>\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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bO/XPf/5TnC6nNJg//OEPLtuaYiFpUqu7kYuWo3VduW5rNCmOjqvlyHpERIRIk6HR/oGEHgv640jFVHdjN7szyvzq+7vgsDagcOPX4mt+Ey6GOqDrK19Sm9ONN96Il156CStWrBATli+44ALMmNHYItObx9kS/Wz85cNHUZ15VJw5iJh/l0uhPlDR80PpTpdddplIZqKfrYqKCnz55ZfiOWtqn+uq3n4+z6XBcqwD+ThbtnMyxthZL9ap53Tq1Kk4dOhQm5UJKRGGJnbK5d26a9HKQOkY11xzjTht3nphmKZJrcXFxW2KZop5pNPt3blua9ROQZfW6A/IQPsj0hvH1vR9yvavh7W2CkqDHwKmXNatFTRLS0vF/VHBTs81jbDTapr/+Mc/xPyJ1ito9sZzSJMs//3vf4szCFK5AuGX3gn9EPdrFQw0LR/v6dOn4+OPPxZvwmnuC00ip9VOaZL5mbj/gW6wHOtAPM6BdjyMsT4+wZRG1cPDw922kVCR7q7I7Yy33npLJLcsWrRIjLo1TTBtPTmUtI6GpD55+uPftL2r12Xnns1cg9J9jae7A6dfIVYo7e4KmvPf2orL39mODV6Tka8Jx/H8Clx7z58w6/G3en3SJrWMUeoL5alTDze1+hiGnZ6HMdhQSwwV7OPHjxePzUMPPSR+p/DEU8YYY+wMFeuUBEMjlu2NTHfVRx99hPvvv19MDvzqq688jszPmzdPjJS+//77Ll+nz2kU/pZbbunWddm5V35wo2iDoTxuw/DxZ2QFTZUxEFFXPgBdVIIYdS/a9h2ys7PRW+h70+ts79690Gg0CJt3KwzDxmGw8/b2xuuvvy7WUaCkmBdffBH/93//J85AMMYYY+y0bvWq0Klsaj2gvmPqRe2pn376CXfeeSeGDBkiJp2tX7/eZfu4ceNEAk1TTjoV21deeSVuuukmsZAMLaREfe40yZXeRDTpynXZuUU9y1XJuykvBIHnXdmp9JfOojaTiMvvRdY3/w91Beli0ulnn30mFsM6lyjSlF5nNHocGBiIxx9/HA+tKzyn+9DX2wKeeuopMdL+8ssvi/kGNDmYni+Ks2ytdTsTY4wxNhh0q1inCZs0MYwKa+otnzhxoiiEWvaXU2KKr69vp+6P2lLmzJkjPqbRttaoh72pWCc0mZVaW9599118+OGHIg3kxx9/FJM/W+vKddm5QwtoOe12EVOojYg7KxnflLSS/uXzIoWF3gxSQXgumM1mMffim2++EZ+PGTNGjBxTHCkGWbFOZzc6akNKTExEVezFKN39M1J/24vVV90kcvZpBdsmVlMZ1jx2xRkZHGCMMcYGfLFOhdaSJUvajdhavHixaGfpDCr66dIVCQkJnS6+unJddvbV1NSIN0zEf9K8NpOIz+QqmuGX3I7I1B9EwUiLENFZlrOF2jnoLNEbb7zRXKDS96PXNhXqvd073xua5hRQq5InlC3vHTcR3rETkP3TW2goK0Derx8i5ILrYRx1ev0GxhhjbDDqds76//73v3avExsb2919YgMctTvQ6LPSJxD6IWe3FUmuNYhRbbpQCxQtykPtFNdee+0ZfZOQn58vVuj8+LufIZHJIfcyIHjmNfjeMQzfv79rQC541NU5BZ40ZcsrfQIw5LpHkb/mE5Gak7/hC/GYBV943TncW8YYY2wAFOvDhw8XF8a6ym63i+QP4jt6xhntVfeEYjtptJtWzaQR/RdeeAEHDx7EI4880ulWLU8oVYjaq2hEnUbPJXIF/MdfjICp8yFTuS7uNBAXPDob7Uvh8+9C6Z5fULLzJ1Qm7UJd/kkxrwHgUXbGGGODT/fC0Bnrps2bN4ssdEoDcQ4dc84eR0pioSKd4gG3bdsmVqKlKEXqZaeo0I5WpaUWl6Y2FvqY5kHQ4ku0KFhT5CCdTbLMmAF9dOI5OaaBis54BEy+FNqw4chb8zEspjLkrHoH74WZ8eijj57VFYRbPs/t4cmujDHG+nSxTgsKHTlypMN4x5EjR3Z3v9gARUvNk0suuQTLzPJzOsGRVkekBKD58+eLTP+TJ0+KhYpo1P2KK67AhRdeKLZTipDrfThFSsnCZz5AQ3khanOSYKurbt6uDY+F79gLcchug5fx9ERo1jNeYcMwdOlfULjpa1Qc245vv/0Wu3btEisLz549+6w8vPRamfvi9+322PNkV8YYY+dSt6qljRs3Nk8w9aQrE0zZ4JCWloZ9+/aJ+D1aen7ZN8m9MMFRC2SUwxl/DUyy/ag4vBkNhXmo/eILLF++XIzq0sq3Pj4+IlqQJsNSWhFFlRaX1Ih+dCL30sN7xGQYE8+DOiBcfK02J+WsH89gQ61ElE2vCY1BUMF2cVbmz3/+sxgIoNVPz8ZCSh312DPGGGN9vlinEcjWKTANDQ1ipPLTTz8Vy743RTEy1npU/YILLjiVeX72i/X2iq+AyZfAf+JcVB7fgbkB1WICKsU80voBdGmJivjGCbGjxMUrfLjIc2fnhi4yHm/9/WasW7dOrH5KZzoeeOABkcdOb654NWLGGGMDVbeKdco8b5l73tI999yDBQsWnNWIPNa3uev7pfQXivykVhR6s9dXYgxpgivlvP/h3vNF0UeLNWVmZooRdVpNU6fTiUmoMpkMV3+0l0dcewm1M5WXl4sWJlqUjSYp06TelJQUMe+AsuyXLl3a4UJn3GvOGGOsvznjTcPU70tLiL/99tt47rnnzvTds37AXd9vVcpeFOWUimL3z9uqYC440OdiDGn0nApzdwkxrUfa2bnVtp0pEdZZYWg4tAlpKXuR8vMGrFizRbzx8hs3G5rg6Db3wb3mjDHG+qOzMsOvrq6uz4ycst7RuvWkJuOI6Pf2HXMBVD4BsFVX8FPDevSaoo+1YcNgOf8q5K/7HLXZx2EuykLuLx+Ioj1w6oI+94bwbKHefZpbQfNCKACA2rno7BCpra0V7UJ0ViEuLk6cLWqNzzgwxtggKNbpjwVNwqNIvOeffx7PPPPMmbpr1s9RgkpdQYYYufaOn9Lbu8MGGKXBDwGTLhFxj1XJe1B5bDtqc5KRkZMMXXQiAqcthCYwAgMJFeAUH0pRqMeOHUNGRoYYJGmN2s4yymqbJ0ZDIoHaLwz6YWNhGD5e5NrzGQfGGBuAxTqlvLSXBkOxapTUwBihCZxEFz0SCq13n3tQWsY7esJnivo+hd6I0NlLxaRhWlSJXnc1mcfExTB8nEju6a9zP0TRnZEh2gt3794tJkPTnIrWhTidbVDofSHX6CBVamCpKoUxxAcOawMaKopgqSxGQ0UhGvb8gvID6+E37iIYYif27oEyxhg788V6QkICHn/8cZevURwfZauPGzcOM2bM6M7dsgHIabej8vhO8bFP4jT0j37otmjZ+8HSUtHfUcEaOudG+E2cK1ZBrUreC1PqAfHvy6oskdNOk4n7OirUL37+W1gqS8QZg9pc12hQqUIp4kP1Q0dDExQl0opaJxRRnKjC4NvcPmStqUB12iGUH94izniV7PkVZQc3Ydt4Na677rpzenyMMcbOYrFOiQsdpS4wRmqyjsFmroZco4c+elSffVA6yta2msrP6f6wnlP5BCL8ktvhP3Eeinf8CFPqfhE5u2XLFkybNk0shDV16tSzuiJqd9FI+urVq5H7y4ew19eKr9EIulfIEOhjxsEQMwbW6gqXQrwzFDqjmDdiHD0L1emHULR1JRpK8/D000+LBafuu+8+sdqvO9zXzhhjvePsLyHJBjVaeZJQr7pEJuvt3WGDkNo/DJGX34uq1AMY13BUZLRv27ZNXCiSMzExEUOHDkVYWJhoLTGlpkPpEyCKY4lcAalMKUax6Q0dLdLUmbap7ha2NpsNP/74Iz744ANkZ2fDVlsDhc4HPgnT4JM4HaoWK+RSsd5dNH/EEDNWtKbl/fwhMk7swJtfrsQHv+5E6MU3i+/ZEve1M8ZYPy/WTSYTVq5cKVYXnDRpEmbNmnUm7pb1c7baKpECQ3wSpvb27rBBjiaZ/vPeJbDb7fjhhx/EZPj8/HzR/02XphHtwhYr1bZGveAyjR5X7I6EPmYsVH6hovDtamHbei0CKtJpwSdaOIzSXAiN+AdMmYWAKZdCKlfibJCKhKZZYsJp0ZYVosc9d/W7iFx4v2itYYwx1s+KdVqhlCaO7t+/HzExMfjf//4nFkc6//zzUVhY2Hy9+++/H6+//vrZ2F/Wj1Qm7RYpQV7BQ6D26/s9wmxwiIyMxO9//3txaSrWaRSbPqZCOW9vOiQSKZx2Gxw2C5w2KxyWetjqa2Ez14iCtr4kB5UndkHpHQDv2AnwHjEJKt+QLq9FINN6i/acikObRT85kam1ooCu9Q6AMSLurBXqLVF7zZDrH0P2D2+goawAmd/8P0RecT+0YcPP+vdmjDF2Bov1G2+8UcSETZgwAenp6Vi8eDEmTpyIqKgosQASjQT9+uuvePPNN3HXXXdh7NixXbl7NoBQkd6UAkOn8Bnri2iiacvJprT41Y63trrtA7dbzLBUFKPiyFaRqmIuyoSlqgQle34RF3VAOLzjJkMTMqTD70ursVZnHBUTR211puZ+cv9Jc2EcOUO03dDk0HMdgTnkukeR8+NbYjJr9srXEHH5fdBFjjin+8EYY6ybxXpSUhIOHTok+j1pVJ16Oy+66CJs375dLMTRtNAGJQrQYhzUd8nF+uBVX5wt0iYoncIQN6G3d4exHpMpNaI1xGFpEBM7ZRodatIPi9V5azKPo74kV1xoRP6PuevEGcfhw4cjICBA9K9XVFSIQY69e/eKyZylxdWi3Yb6w/0mXCyiJalI702Uu04j6jk/vYOarONipD1ywb1QGgN7db8YY2ww63SxTn9k6I8PFepEqVSKrPVVq1a1WRGPivg9e/ac+b1l/YYpZZ/4t3HhFffpEoz1Z1TYUnQiXag9htpZqpJ2oTY3FcnJyeJ3ZntnnjSBkfAdd5G4PfWO9xXUdhOx4B7k/vyBSIzJ+elthFx8I4Dze3vXGGNsUJJ3ZRIpLVndEn1uMBjaXNfb27t5qWs2+JjNZlSnN07Y80mY3tu7w9hZRxNPfUfPFJfavJN4eJwGOTk5ohe+rKxMXEev1yMiIkLE3lLr4F0/pHcpdvFcojNi4Zfdgdyf3kF15lHk//oxUpZOaXfSLGOMsbOj7wznsAGDWqNoYp7KNxheYcN6e3cYO6eorWX27PPbLWypNx7wPPLeF9Bof/j8O5G98nXUZJ/Ak08+iU8++UTEXDLGGOujxTotd/3RRx81f059l62/1vR1NnjRJOOmiaWtY+0YY/2HaIlZeB8yvnoR1dWVIunrvffeQ3h4eG/vGmOMDRpdKtZ3794tLu6+3holxbDBJzc3V0xCJj7xnK3O2EDozQ+ddzOCDn0tIidvv/12/Pvf/4a/v2sLD69wyhhjvVysz5w5UyS8dBb3Ng5OTa8Rr7DhUOiNvb07jLEzwGmxICt6DgqzVyD1aBpmLbkXEfPvEpnwhFc4ZYyxPlCst84jZszdqoxNxTotFMMYGzgotnLIdY8gc/lLsFaXo2DDl4i6+iFOe2KMsbOMJ5iyM4bmKhQXF4soT2dUAj+ybFByOh2iXaQ9HW3vq2jhpKirHhQFu7k4Gzk/vInIK3/f27vFGGMDGhfr7IxpGlW/4IILsErKLy02ONmqK3HLp7uhMLhG3bZUl58Gr9DGNSv6G0p5irzqAWR98/9ETGXOqncRPOPq3t4txhgbsLiiYmcE5fBv3LhRfDxv3jysWts/Rw7ZwHUuR7ypUG8vQ91qKkd/Rgs6RS68H1nf/Q81mcdQ6HDAcd/M3t4txhgbkLhYZ2fEL7/8AqvVitjY2MZVbrlYZ33MQB/xPtdoDYXwBXcj58c3UZ1xBK+99hqef/55jmtljLEzTHqm75ANPrR0+ooVK8THCxcu7O3dYazDEW9PF4WOE4y6Qh+diLB5twESiXjD/sorr4jfB4wxxs4cLtZZjx08eBDp6elQq9WYP38+P6KMDSKU/BR0/lXi488++4wLdsYY6yvF+vLly3HzzTc3f06jKRTdxwafplH1Sy65BHq9vrd3hzF2jnnHThSrmzYV7C+//DKPsDPGWG8U65WVlaivrxcf2+12WCyW5m1ff/01li5deqb2i/UT5eXlWL9+vfj4mmuu6e3dYYz1kgULFuAvf/mL+PiLL77Af/7zHy7YGWPsXBfr1JPo5+cnfin/8MMPPJLO8P3338NmsyExMREjRozgR4SxQWzRokV48sknxSRTGsB58cUX+e8EY4ydyzQYKtI/+OADrFq1Cj///DNKS0sxdOhQnHfeeT3dD9YPUdvTt99+Kz6+9tpre3t3GGN9wBVXXAGpVIpnn31WtEvW1tbiqaeeglzO4WOMMdYdXfrtSStTLl68WFy++uorEdVFEwp37NghLmVlZUhNTcWll14qircxY8Z0a6dY/7B161aRS20wGHDxxRf39u4wxvqIyy+/XBTnTz/9NFavXi1aKF944QVoNJre3jXGGBvYbTApKSmiQKN+dRIeHo4nnnhCtMT873//w7Rp0xoXxFm1SuTtsoE7op6Xl4e3335bZKvTiqV0loW+Rpf+upQ6Y6xnC041/Q6gy+jRo8XfB2qJ2bx5M2677TZRtDPGGDuLI+vHjh0TPYlGoxHR0dHw9vZGTk4OIiIixHb691//+pe4NE1EZQMP/VGe+fibKNi4ExKpFF9XBuPbt7Y2b+eFZRgbXNpbcMo87lrkr/kEKRt3oPamm/D+++8jMDCwV/aTMcYGfLF+1VVXITs7W0w0pXiuTZs2ITIyUlzoly8V71VVVeJfytxmAxetWCiRyeETPwXaVis+9vel1Blj3V9wqjX6msovBJnLXxJ/P2655RYR7cgT0hlj7CzlrNPo+V133YX77rtPFO/UFkP5ugqFQkT4+fv7Y8aMGaKnnQ3ckfWazGPiY7/xc3p7dxhjfZzaLxQRC+4VAzslJSW48847sWXLlt7eLcYYG/grmCqVSpEE8/jjj+PBBx8UKQAU5Td27FixqiUbmL777jtqUoUuKgFq/7De3h3GWD+g0Bvx0ksvYcqUKaJN8uGHHxZ57LSgHmOMMc+6naUVFhaG8ePHu3yNWl8uu+wycWEDU3FxsWiDIv4TOAGGMdb5SagmkwmPPfYY3nzzTRH/SwkxR44cEWdqZTKZmLDOK2EzxtgZKtap1YUuTaKiojB9+vTu3h3rJyhnnxZB0gQPgTYirrd3hzHWDyehOuVjUTmkBiW7f8YrHy/Du+sOIOTC6+FoqMPDY3i+E2OMnbE2mJYotpFaYdjA7lVfuXKl+Jh71Rlj3Z2EqvIJQND5VyFy4f2QKTUwF2Yg7+f3uSWGMcbOZrHOBr53331XjKrTnASvkCG9vTuMsX7OMGwsoq97GAqdDxoqi5H9/es4efJkb+8WY4z1KVyss05JSkrCjz/+KD6+6aab+FFjjJ0RmsBIDF3yZzEA4LA2iCSxzz//nEfZGWPsFC7WWYcorYFSHOjfSy65BPHx8fyoMcbOGLmXAVGL/gDv2AligimtiP33v/8dFouFH2XG2KDHxTrr0Lp163DgwAGoVCo88MAD/Igxxs44qVyBgPOuFMEF1G5HMcA33HADDh8+jLy8vOYLp8UwxgabbqfBsMGBotb+85//iI9p5cGgoCDxB5Mxxs40e00V9vtOg3XCCBRs+BKp2/bj16tuRujFN0IdEAGrqQxrHrtCRAczxthgwSPrrF20LHhZWRmio6Nx66238qPFGDvriTE+CVMRc+PfoA4Ih91iRu4vH6Im+wTkel9+9Bljgw4X68yj7du3i0mlEokETz31lFixljHGzgWlTwCGLH4M+qGj4bTbxEh74aavYTab+QlgjA0qXKwzt0pKSsQEL7J48WKMHj2aHynG2DklU6oRMf9ukclOgwbV6YfFvJmUlBR+JhhjgwYX68wFTd7Kzs7GQw89hOLiYoSHh2PRokUuE7xocSTGGDsXJFIp/CdcjOhrHoZc6438/HzRkvfxxx/Dbrfzk8AYG/B4gilzQX8IJy19SIxgSeVKWGddjEUf7nG5Tl1+GrxCY/iRY4ydM16hQxF15QOYUrUD+/fvF/GO69evFy16w4YN42eCMTZg8cg6c/HNN9+IQl0ikyP80tuhixwhlgdveVHojPyoMcbOOZnaSxTnTz/9NPR6PY4fP46lS5fixRdfRGVlJT8jjLEBiYt11owmk37wwQfi46DzF8EwfDw/OoyxPoV61xcsWIBly5bhwgsvFK179PGVV14pfn9VV1f39i4yxtgZxcU6E3777Tf885//FB8bE8+D3/jZ/MgwxvqsgIAA/Pvf/8Zbb72F2NhY1NTU4I033sD8+fPx6quvipY+xhgbCLhYH+Roghb9YVu5cqX4/KqrroL/lMvE6BVjjPV1EydOxGeffYZnn30WQ4cORV1dHT755BMsXLgQ9913H1avXi0Wd2OMsf6KJ5gOYpT28uSTT2Lv3r3i87vuukucXl759rbe3jXGGGvD6XR4TKMaM2YMXnrpJezatQs//PADDh8+LNaKoItUKsWoUaMwefJkcT1a5I1WQaWvM8ZYXzdoinXqa6SVOL29vQf94j70WHz33XdiRL22thZeXl6i3/OOO+4QBTxjjPVFtupK3PLpbrHKqSeUVqUwTgUumA5T6n5UZx6BpaIYyUXbsGJd40CERCrD9fNmYMqUKRg+fLi4hIaGcvHOGOuT5IOhzYNGj998803IZDJRnF5xxRV47bXX4O/vj8GEHouNGzfi7bffRkZGhvjayJEj8de//lWkKjDGWF9HhTqlUnliNZVDYfAV19GGD0cIFsNSWYLqjMOoyTqOuvx02OtrxRnFQ4cONd9Oo9EgIiICwcHBCAkJERdfX1/4+PiIQZ6mi1arFUU9DXp0Zs0Juh8ewWeM9cSAL9YffvhhvPvuu6JvcdasWcjJycG8efPEJKQdO3YM+F+iTqdTFOZr1qwRfelNf1zoD84NN9wg+jqpiKezDrTgUWlpaW/vMmOMnVFKnwD4jZstLk6HHab0w7g2vB7l5eXi9yMtBEd97ceOHROXpt+dpPX8HfpcrVaLwZ+jxWbItN6QKlRitVX6V+UXCp+EqeK6VlMZ1jx2hWi5YYyx7hrQxToV5jSC/sgjj4hCndDIycsvv4xLLrkEy5cvx+LFizFQ0EhPRUWF+MOTnJyMpKQk7NmzB0VFRWK71WrFyfJ6GEfNgGPk+Xi3UIN339l+6tZavPr+Ll7wiDE2oFELjFylxZclXlAYhgMjp8CZ4IClqhS2mgpY6VJdAXNhJuB0wGm3w95ghr2+Dk679dS9VMNhqYdUqYatvtbl/vUOG5TeC3rl2BhjA9OALtZXrVolRo1pFL2liy66SJzy/P7777tcrB89ehQ6nc6lQC4pKWkz8kKXplH7oKAgMQrT9PWW24jNZoPFYhGXlh9TcU0X+ri+vl6MipvN5jYX2kYj4zQqTt+nNaVSKRITpk6diuePOqH2DW73FDJjjA22dhqVMdBle21OSnM7TROHzQqHxSyK99rsJFGsS5UqOBrqYbeYRQHfXosOY4x1x4Au1pv6sFsvRa1QKEQaQNPpzq64++67XQpiKqYzymrFip/u0EjMED+d+J6eeDrd2lJH36fpew3114seSYowi4mJwYgRI0QKgkqlEsW+9ERaJ4+UMcZYS1K5QlzkXgbYaqraFPOMMXY2DOhinVpCiMFgaLONvtbeohkNDQ3i0qSqqkr8m1Fth6RFm7ujoUH84pbIlVR1U8ks/k//of/RRKbMSgskMsepbY2Fudh+6mOnzQapQilGaCQSGSRSOSCTitO1TRe7rQ7ayEgodD6Q0B8MhbrxDwfdTq6CVKOFrbYK9QoVcgy+yAGwmeaQZqQCP6c2HlNxLtSBEbA31Hk87vryArG9J9c5E/fB+8KPP7/m+Oesv/9uoTOV1BdPPe5NK6s2/d5njLHOGtDFelOribtfjtS+4q5lpMlzzz2HZ555ps3X67KTzvBeMsYYG6hGv+36ORXtlCrDGGOdNaCL9cDAxh5E6udu2WdOaLSDlqv25IknnsCf/vQnl+KebuPn5zfgVvekFASaeEsTct2dhRgo+DgHlsHyfA6mYx3Ix0mDRlSoU547Y4x1xYAu1seOHSv+pd70qKio5q/X1NQgKysLs2fP9nhb6vGmS0uUtzuQ0R/HgfYH0h0+zoFlsDyfg+lYB+px8og6Y6w7BnTIOKXA0Ij6119/7fL1ZcuWidSVJUuW9Nq+McYYY4wxNqhH1mkk/MUXX8Tvfvc7JCQk4Oqrr8bhw4fx6KOPYunSpbjgggt6excZY4wxxhgbnMU6ue+++0SU4euvv4533nkH/v7++Otf/4oHH3ywt3etz6B2n7///e9t2n4GGj7OgWWwPJ+D6VgHy3EyxlhXSJycI8UYY4wxxlifNKB71hljjDHGGOvPuFhnjDHGGGOsj+JinTHGGGOMsT6Ki3XGGGOMMcb6KC7WGWOMMcYY66O4WGeMMcYYY6yP4mKdMcYYY4yxPoqLdcYYY4wxxvooLtYZY4wxxhjro7hYZ4wxxhhjrI+S9/YOMMYYY4wx1teVlpZi+fLlOHLkCKxWK+Li4nDLLbcgICCgzXWXLl0Ki8XS5usXXnghfve733Xp+0qcTqezR3vOGGOMMcbYAPb555/j9ttvx6hRo0SBrlKp8MUXX2D//v345JNPcOWVV7pcX61Wi8L8jjvucPl6dHQ0Jk6c2KXvzcU6Y4wxxhhj7XjxxRfFaPoTTzwBqbSxi9zhcGDGjBk4duwYCgoKoNFoXIr1O++8E6+99hp6ittgGGOMMcYYa8c999wDb29vl69R0X7ZZZdh+/btOHz4MKZMmYKzgYt1xhhjjDHG2tG6UG+SnJws/vXx8WmzbceOHaJlhkbgY2NjRR97TEwMuorbYDqJHuj8/Hzo9XpIJJIuP9CMMcYGL5oeVl1djdDQ0OZT6GcS/40aeM72a6Y/q6+vdzt5szuPceuajnrR6dIZBw8exKRJkzB27Fjs2bPHZVtwcDCuueYajB8/HhUVFfjss89w/PhxvPXWW7jtttu6vKOsE3JycmgiLl/4MeDXAL8G+DXAr4Fuvwbob8nZwH+jBu7P5dl6zfRXZrPZ6Q3lGXlsdTpdm6/9/e9/79R+FBcXO2NiYpwajcZ58OBBt9tbstlszosuusipUCicaWlpXTpmboPpJBpRJzk5OTAYDF17R8QYY2xQM5lMiIiIaP5bcqY13W9GRgZ8fX0xkNEkvzVr1mDu3LlQKBQYqM72a6a/slgsqIIF/8F0aHrQzW2GDY/UbG9T13VmVL2qqgrz5s0Tt/3uu+8wZsyYNtdpHecok8nw6KOPYsOGDfjxxx/x0EMPdXpfuVjvpKbTJPSEcrHOGGOsO85WG2XT/VJhN9D/RlGx7uXlJY5zIBfrTbj11j2tVAGNpPtlrNQpARxdr+uoNemSSy7B0aNHsWLFCjHBtLOoNYaUl5d3aV+5WGeMMcYYY/2KVAZIe/DeV0pNL46u3aaurg4LFizAvn37sGzZMixcuLBLt6dMdjJs2LAu3Y5nLDDGGGOMMdaOhoYGsfARJbxQod56EaSW1q1bhwMHDrh8jUbi//a3vyEyMhKLFi1CV/DIOmOMMcYY61ckUgmkPWgrk1AbTBc8//zzWLt2LaKiokSyC11aevzxx0UyDKG2mgceeABFRUViFJ3aXmhUfdq0afjggw+g1Wq79L25WGeMMcYYY/2KTAbIetAGI6M2mC6g0fDExESP28PCwpo/njx5MrZu3YqsrCwkJSWJiMj4+HhR6HcHF+uMMcYYY4y1Y9SoUeLSFVScd7dAb4mLdcYYY4wx1q9Ie9gGI9Jg+gku1hljjDHGWL8ilZ6BNJh+gtNgGGOMMcYY66N4ZJ0xxhhjjPUrUhm3wTDGGGOMMdYnSSWNrTDdvn0XF0TqTdwGwxhjjDHGWB/FbTCMMcYYY6z/tcFIe5AG04MkmXONR9YZY4wxxhjro3hknTHGGGOM9SsyaeOl27dH/8HFOmOM9ZK1a9fi7bffbv5cIpHA19cXw4cPxy233IKAgIBee26WLVsmLl9++SUUCsVZ/V7Hjh3D3//+d7fb/vjHP+K8885r/vzFF1/E7t27Pd7X3/72N4wdO7b58/r6enz22WfYsWMHrFYrxowZg9tvvx1Go7HNbbtyXcZYH1gUSdqDNhj0nzYYLtYZY6wHqqurRUFHRSIVd3v37sW///1vfPLJJ1CpVO3eNi0tDd988w2eeuopsYy10+lEUVERPvjgAzz99NNYvXo1Zs6c2SvPz/Hjx8W+2e32s16sl5SUiO/15z//GRMmTHDZ1nqp7hkzZmDo0KFt7uP3v/89SktL8eqrrzZ/raysDBdeeCGKi4vx8MMPQ6/X47333sN///tfbN68GcOGDevWdRlj7FziYp0xxnpg3bp1+P7770WBTb766itkZmZ2WKi3LkDnzJnT/PlNN92EsLAwUbBv2LBh0Dw/NIK+YMGCdq8zbdq0Nl87dOiQeJNzxRVXIDQ0tPnrTzzxhHjTceTIEcTHx4uv3XrrrRgxYoR4g7Vly5ZuXZcx1vsksp5FN/aj+aU8wZQxxnril19+EcU2jcQSGg2/9NJLe3Sf3t7eCA4OFqO9LdGo8T333NPm+ps2bcI111yD/Pz85q99/vnn4mtNbyjuu+8+3HXXXeJzGsH/6aef8Ic//EG8MXjyySexf//+5tu+/vrrogWGLF26VNwPXehNSGdu33TGgW7z0ksv4Wx7//33xb933323y9fpTdTEiRObi2+iVquxePFi/Pbbb6I47851GWO9Tyo93QrTvQv6jX60q4wx1jc8+uijzQXsihUrkJubKz5etGgRTpw4gTVr1ojPH3jggW7dP/VkU2F8wQUXtPn6qlWr2lyfrkttJCaTqflrNEJMX6MCn9o5xo8fj5qaGrHtd7/7HW688UbRi33JJZfAy8tLFPPUr02mTJmCkSNHio+vvfZaXH/99eLS1Lvd0e1JQ0OD+P7U/91ZtJ/05uDOO+/EW2+95XI8ntD3oTcmERERYl9aqqiogJ+fX5vbNH1t+/bt3bouY4ydS9wGwxhjXTRv3jxUVlYiNTVVFKT/93//JyaDrly5Uvz7pz/9SVxPp9N16v6effZZUZwS6pnes2ePGLWm++0p2kdqzSE0Ck7FLRXF//jHP/D44483X4/6xel7ExphTkhIEB9fddVVYoS5SWduTwwGA5YvX47w8PBO7SdNqh09ejRiYmKQnp4uRuvpe9CZC+rn94Qe8/Lycjz44IOQthoqo373lJSUNrdJTk4W/9KbrO5cty+jsx6MDZ6RdXT/9ug/uFhnjLEuauovp3YRGnm99957RZILFe7nn39+c/tJZ9HExqaClApPSoShHngaWe+oh7sjd9xxR/PHVHTbbDbI5XJs3LhRjGA3jRzT/gcFBXV4fzKZrFO3VyqVnX4cxo0bJxJhWk5kpZYWKt6XLFmCo0ePttsCQ/vU8jib0P7Rm4h33nmnuUXmwIED+OKLL5rfeHTnun0ZTbL19/fv7d1g7KyTUiuLbHCkwfSnNxaMMdanbN26VUyKpEK1qVVi+vTpXb4f6nlvaquhQpFGi2kiJRWqlJTSEzRS3RIV2m+++abYd+qLp/3/61//KgrTzujp7T316LdOnKEJtnQmgIr4ptHt1rKzs7F+/XoxR8DdCP4jjzwiWnbuv/9+cbaA3hTRhQpz0rLtpSvX7cvo8WKMDSxcrDPGWBdQe0ZTYU0tGklJSc396lQ8fvfdd+LzpsmcPRm9px7zlr3SlDBjsVjaXJdacjxpmvjaEmW4FxQUiH57enPx7bffisjE//znP53at57evrPozUDT2QZ3PvzwQzgcjjYTS5vQiPtrr70mWliopeaxxx4TbUFUhJOWMZFduW5fRi1UjA2mRZFkPbj0F9wGwxhjXTB79myRp04jmNT2QjncVBDTxzQaTIv4kJZ93t1BaSqt0SRKSogxm83QaDTNX6ds966ifaaoQ7q88MILYoT8lVdeESPMTSPohIrh7tz+TKCEGTpr4S5XnXqzP/roI/GYX3bZZR0W/S0TeijphnrU3WXYd+W6fRFlwtfV1YlJv4wNZFIZt8Ewxhhzg/rIaeScikjK4L7tttvE51REz58/v3nUvSe95jRRkwpRSltpmQhDRSQVzy1XPaU2kIMHD3b6vmlE/OOPPxYrdDahPnbqyW7Z60xvDEhWVla3bt+V6EZaQIpy0luihBcauad0GHe99HTclIJDGeg0Ku4O7Tv11rfucac3VtTK0/J2XbluX0Zv5Fqm8jDG+j8eWWeMsW74+eefm6MCaSSTFs2hfufuaJkGQxGC1MpA6Sg0uZH6uZtQpCLFQVLaDBWytLrokCFD8NBDD3lsBWlNq9WK0VeaTEn97D4+PqLfnN4YfPrpp83Xo7aeZ555BhdffLHo4aaRdmpzocmvnbl9U3RjZ1CxT337NBpMI+W0sislwtCk0ZYrkrYupCn9pamn3NOx0qg/9aLTvlLaCz1X1LbTOgu/K9ft6+iNHp0Baj1fgbGBRNrTNJh+FJwkcXLOU6dQ3i/90ayqqhKRZIyxwY160ym7nFokqMCmkV5qx+hK+wEVpK0XE6LfL9HR0YiNjfV4O5pwScUkXY9SZGhUmAp8ipRs6lGnBJWmfnp36HcZXYd+t0VGRiIxMbHNdag/fteuXWKSK43oU+He9Oaho9vTbX/44Qcx8XPq1KkdPhb0xoMy6jMyMkTk5dixY5tz3d2hRZloUiodc0eo95wer8DAQNF73jrisbvX7Ut/Q5run9680JsnetwpYnMgJsPQWR1afIx+3lpPTB5IuO5o/3FZEz8XWln3n/9auxVzT6zpF3UdF+udxD80jDHG+nqxfvLkSTFvglazpTeSNI+gs1n3/QUX64Ob6dRrfV3ivB4X63OO/dovivV+NBeWMcYYY+2htiTqsaczA3TG5eabbxZtS4wNNBIZTTLt/oVu319wsc4YY4wNINT3T5OAaRVaGoV8+OGH8be//c1lhVnGWP/BxTpjjDE2wAQEBIiedcrEp757WhPgqquuwhtvvCFO+zM2IFYwlfbs0l9wsc4YY4wNQEqlUqQHUToMrQ1ACT0ffPCBiBilSE3qa2esv5LKen7pL7hYH+SKCqpRUtR28ZUzxWZzIOmYa35ySxRGdOJIIRx29wuvMMYY6xlqh6FR9hdffFGkDNXX1+PLL7/EwoULRdzomjVr3K6MyxjrGzhnfZBqaLDhx+VH8Mv3xwGJBJddlYgFixKhVJ25l8SRA/n4/L09KMgzIX5UEG68azLCI32at2ecLMOn7+xGWkopIqONuOnuyYhNCDxj358xxlgjWsTroosuwoUXXoidO3eKRacolnP37t3iQmkYc+bMEReKJG1awZaxvkomkUDWg1YWmaP/tMFwdOMgjG7ctTUTX320D+WldS5f9w/QYsntEzFxWmSP7p9G6r94fy/27851+bpMJsHsS+Nw8YIR+Ombo9iyPg1Oh+uqBNNmRmPxrRNg9OWlshljA8e5im48fPiwWLjKnZCQEJfceGqD+fHHH0UefssVZGn/aOVcWliJFsRSqVToSzi6cXAznXqtb598KXTy7kc31tismL77535R1/Fb50GmxtSAN/7zm9ttpSW1+N8Lm/HWF4uh8VJ2+3t8/fH+NoU6sdudWPNTErIzKjy2xuzYkgmVWoHb7u94ERXGGGOuln66FzJV28EOq6kMax67QiTFNAkNDcU999yDu+66SyyqRQt7bdy4USzyRQU8XajvnQp2Wthq+vTpIrudRukZY+cOF+uDjBMdr6/r7OESvB3dvqN94EV1GWOse5QGP8jU2i7dhkbbp0yZIi6PP/44Dh48KAr3TZs2ibjH7du3i8t///tfBAcHY9q0aeJC7TKU685Yb5DKJOLS7ds7+8+bTi7WGWOMMSbIZDJMmDBBXB599FGkp6djx44d4nLgwAEUFhbiu+++ExcyZMgQjB07VlzGjRsnWm145J2dC1Jp46Unt+8vuFhnjDHGWBtUdMfExIjLjTfeKFJk9u/fLwp3mpxKhXxGRoa4NBXvlO/eVLjTv8OGDXPpk2eMdR0X64MM/dKkhQAcrSZ2Nm+XSWBtJ0aRWlScsEIq8dzTrpFZ290HpcPW7nZFB9ut1XVQ6D1PQHXU10OiVELi4Q+E02kX/0o8rDVMx2h12KCUeZ64YrZaoVF0f2KL2WyFRuP59nX1Vnipu3//FotdPM9yuXRAJxop5FJIZQP3GBnrS9RqtehbpwuhiXmHDh0SbTN0OX78OEpKSrB27VpxIVqtVmS8N42+JyYm9rkJq6x/kkid4tKT2/cXXKwPMlqdEs/+dz4+e3dPm0meAaOCkOOrxm3v7cID8+JwYWKwy/Z6WymKzTtgcVTCXz0R3soRLqc7HWUFsH7/FpaW7sfE8y/AW7sCYLae3m5UOXB+dRpqX96N+fPOwxZFFKptp7dr5E5cYM9Fw2OfY/POGZj073vgFep/+v6tNhx/9VscfPZTBExLwNRXfg/vuAiXIrt63QaUf/gx5P5+8L/vHqjjR7gcg7M+E6hqnGDr9D4fEvUQl+0FdaVYm7sX1dZazAwZi5HGoS7HmF9djQ8OH0JKeTmujhuB+cOGQd6FUaO6Wgu+++oQNvycgmmzhuC6m8bB4KNp3m612vHdmlQs/zkZo0cE4M7rRiMkUIeu2LsjG19+sFfEcN541yQkjgnBQLNjS4aYyKzTq3DTXZMQlxjU27vE2KBDiRwzZ84UF0KLLh07dqy5eKdCvra2trnnnVAkJOW+NxXvdOnrSRyM9TaObhyE0Y1Ndv6Wga8+2g+rQgrn6CAcarU40sShfvjjZSMQ4SdHSf1eVFmSxfTQJiqZH4I006F2+sC6/ivYNi4HbKcX1nB4B2GNfQZ+PKzGBepyyH7dCGtlzenbB/jAPOcC/Fbng2naahg2bkJ9QVnzdrlOgzF/vQGJf7wGRVsOY+dDr6HqRHbzdqlCjoQHF2HsUzfBUZSPkjfeRsOJpNMHIJFAP/tC+N5+K2R6aWOR3pDp+iCoogDvGahzqrGl4ACOVKS7bA718secMHpj4o1vkpKwKu0kbI7TZx5CdTrcPnoMxgS1XyzSG4mtG9Kx/NP9qKqsb/66l5cCVy0Zg9mXxWH/sSK8+/VhFJbUNm+nkeOr5g7HtZfGQdVBBn5+bpV4E3bsUIHL1ydOjcSS2yfAv4tFf1+Uk1nh9o3m1BnRuP42jvxkfde5im4c/4/v3U4wtVSVYtW957ukwZxtDocDJ0+eFIU79bvTpbS0tM31hg4d6tL3TpNY2+t75+jGwc106rW+d1bPoxsnbu4f0Y1crA/iYp2YzRYseGkz6q3uW1/kMgnef7AKdlR5vI+Ib04Cx/d73L4/dyxSl+/zuD147iQUrtnjcXvInPEoWOf5/iNmxmGINo/+Mrg/hmB/RP5zMoVHergHGd6uDIbJ6po730QCCSorg5FSXulxHx6bOhWTQkI9bqdc+eWfHvC4fcS0COxIK/e4feLIIDz14Hket5sqzfjDnd/CbnP/GCiVMrz5xfX9ui2G8vsfu+97jy1cdNbojc8Wn/P9YqwvFesjH/3AY3TjxzdNFhNAewsNWlCeO42+N11ycnLaXM/Pzw8jR47EqFGjxL8REREuxTsV6xs2bBCLPCl60I54trXOte+qgVp39JTp1OOy74JLelysT9j0S794fLkNZpBTqhQeC3ViszvhcNqoYvXI2WBubzOcDe0vY+2sb2h3u63G3O52u9kMaNrps7c0tFOoi3uApZ0+eerSr7e1d3ugoYPt9fXt9/Gb69vv0zc3tH//NpvDY6He1MPe3yMxrRaHx0Kd1HfwGDI2GFhNFXCoTp+9O02CO1amQiJJQ+9TAcrxwLjxsI+oQX1xNsxFmTAXZqKhNA+pJTXYmZQFrFglrk1vPjQhQ6AJihb/qnxp5F2LV9/fhb7KXa49Y93FxTpjjDE2QGjDh3U5Z703Kb394RUcDaCx791htcBcmIHavFTU5Z2EuSADDpsFtTnJ4kJkSjW8woZDF5UAbVQ8lN4BHBc5CEmkjZee3L6/4GKdMcYYY32CVKGENiJOXIjDbkN9UTbq8lIbC/j8NNgt9ajOOCIuTQtBaSPjRfGui4qHVMFpM4OBROqElNNg+gbqTVu3bh2WLVsmJqnQCmtvvfVWl+5j69ateOONN5CWliYyYK+//nqRGcsYY4yxvksqk8MrdKi4+E+aB6fDjvqSXNRkn0Bt1gnUFaTBYiqD5ehWVBzdColMDl1UIgzDxkI/ZBRkas8xv4z1F33+JADNDn/11VdFNBTNKqdLV3z99deYNWuW6Bt75ZVXsHDhQtx333148MEHMdBRcsbzT67Bx2/tQm1N277w4rI6vPj2LkwwesFf13YkwqCR45pZGhSadZBJ2k6+kEAGrxMyVKXZgSDXCERBKkOVfiQq0ivhP9k1QrFJ4PRE1BWUIej8kW5z0f3GD4elqhbBs8ZAqmw7kcQ7PhL1FhlqA0dAojkdgdhEFhCAvCo/JH9thhPqNtvpaykrGhD6QSq8JW1vr5TKcUFIJO4eq0WMT9vHgGIbL4wyQqo4gLL6vLb373Tip/25+LHYhOgx1GfZ9jEYOtwP9cU1GDPECJm07RVGxPjizsWj4Ul+ThXefXU7YuMDoNO3zb83+nnhvofPh0LhPle+vwgO1YsoSi9t22P0D9Tid4/M6JX9YoydOxKpDJqgKARMugTR1/wRcff8B5GX3wffMbPECLvTbkN1+iHkrfkYye/9GXm/foTa3NR+P2eHtUV/T5taYbp1aW+yXR/T59Ngqqurodfrxcc+Pj6YOHGiGGnvjJqaGkRFReHCCy/EihUrmr/+2muv4YEHHhArsE2eTCkhA2tWdm2NBd9+cRAbfklpnpCnN6hw9Q1jMevi4bDZHfjmlxRxsVgbJy4qlDJEjAzEweIacZtZo3WIHZEHqaJxcii9pkf6+kIjK4YDVmgq9XD8dwPsqfnN39dnaiw0jlygpgI2vxjs+rEWubtOF7B+E2LRUG5CTUYhDLHhkKkUqDiS4VJ404z/yuNZ8AoPgFeoH0p3n45i1EYFQR3gg7K9yVD5GeAdH4XibUepIm48xnAjxs6PhiQrBRKVEnXGoTjw1WHYTk3u1ATpMfuzy+EX3/jGpSxJjfU3/QhzganxGL1U0L1+LXLGaWB12pHoE4xZIXLoFI0TN+lHZUOWBl8cL4PJYkGivzemRFRBozwdeemnjsAwwyRo5HqcyKvCf1edwPG800k6iToVfLJNKMqpEgUm5YRntkiB8Q0zAEFapOWaYDSocOvVo3DBVNckhCbmOgtWfnUYa1cnN08u9dIqEB5lxMmkErEo0rwr4nHFtaOg6sECS32NqaoeKz47gC3r00S05WWLEjF/0UiReMNYX9Xb0Y2DAf2OpgmqppMHxKWhvLB5G01K9Z84D95xE0XBf7adiajM/lR3nEumU4/LoXlzoe9BGlC11Yoxv67pF49vny/WW+pqsb58+XJcd911WLlyJa644ormr9MT4+vri/vvvx//+9//BtQPTXFhNZ59/GdUV7lPWBmWEABKKi8qdR9T6BugxaxrnFDqTuedt6SVKzF+czasX29yu13qpYY5NAa7XncfxSiRyxA2dyJyf9kNeEj2CLtkMgo2HoCjwX2CSsiF41C6PxXWqtOZ7S1FXxyPitRiVGW6P4awi4dDKpch5+cWmewtKEeHY+bGuxDh4/7711qk+C3fDrWqbeQYkUKGjPTReHddQdP7iFbbnbhM74XUHdmw290/BuMvisFdd0/yuIppeVkdnn5kNaoq3CflhEV648E/X4Dg0L77Wu2pjJNl4s1OQFD/z49nAx8X6+cWlTaUMkOtMVXJe+GwNv5NVPuHIXTOjWJ0/mziYv3sMQ3CYn1ATzDdt68x23v0aNcWAnqSIyMjsXfvXgw01aZ6j4U6ycsxoUjj+WkvL6mFl94Gm4e3cLU2Cxy7XRcOaslRV4/co6cX9WnNabPDSlGM7UTw0XZPhTppqKz2WKiT4uMlqMtzX6iTvLWp7Z7/shzORWg7bY5apQMhhnpUeHiYHbAjs6TabaHeuF2CGrvDY6Eu1DR4LNRJbXWDx0KdlBTVDOhCnQwZ5tfbu8AY66PobCQV5HQJmrEI5Yc2o2zfWtSX5iFj+UsIv+Q2GIaN6+3dZD0g4TSYgaGwsPEUmL//6SXrm9BEU1qcwRNaNpkuLd/JMcYYY32Bp79RNNlS1tD+2hSDkXfsRGgjRqBo67eozU5Czqp3EXXVgyI68mzlrFNABl26fR89uO1gIJU2Xnpy+/5iQI+s2+2N/dgyWdv+NPpaez8Izz33HJ555pmzun+MMcZYd3j6G+V5USRGAiZfBltNFepLclB5bDuMI8/v8IGx1lTgngQVjEZjFx5ENQ4cOIBDhw51+4Gvq3PfrsoGnwFdrFO7S9MkVS8v174G+hr1wHvyxBNP4E9/+pPLqAUtecwYY4z1Nk9/o/rboki9Qe0fCktVCRSUzx4R26n+86uvnnLOVyPlM/rtk0ic4tJdPbntudaPTgJ03YgRjXGBlK/eesQ9MzOzebs7KpVKTDhoeekPqE954rRIt9vkCimio30wLsxbTHJsjVIDx4d5o3KrD2B339MdrvVF7V2zAL/GN0Ktec+fiCkvTIbv6BC3230TI2EwKuAzzP12SoGRKmTwSaQV7dqiFBiljw5+E93/gpV5qeAdG47A80a63S5RyRH72fWI+/x6SDVuesIlEgx9eSEy6yywOdw/BjVWKbwVEiilbSMESXm5P0x1CgQa3C/MEaZTUuYjAqLcv1k0eKthszlw7FCB2+2UArNl/UnExLk/fatQSHHJwgT0xN6d2fjig72oqfY8/4H1fwf35uLz9/aIhB3Wv/TXv1G9yelwoGTXKpjSGke7KYu9sxQKRa9cGBvwI+uXXHKJmGSyZs0aTJ8+3WWRpNraWsyfPx8DjVanwgOPzxKF3mfv7kF+blVzlndFuRknjjT26Y8I90a9twrpJY2TQYcFaiGvqEfGgXxkHAD8d2gx5ioNZDGNsYJGpRb+GhnqbMUoCQIULy6A/64KyN79mSKFoBoWgqAHZkKuq6S8HVy+egbytjqw6Y4fYKu1QO2rQ/i0Yag+eAJVuwqgUsgRM38csrckwVpthlStRODkESjZnYTC3BJRNFPBbUrOQX1plXgnEXTeKJQfTkPhxoNin/wnxsFcXIna7MZjCpiWgJrMQhSc2k4Fv9PuQFUS5d8AYQ/OgP7hRDTIGk8txibdger/HkfeK1sa7+/qMQh8YTrM6lrkmHNQ1KBBjCEEQRqb2G5xSFFsrkatrXEuRIBaAScCUFBXIt76NNRrsHV/ODYeqYYTFVDIpBgXbcTx3Co02BzQyKUYZ9AgLbkUR2wO8eYocUo4So8Xo77aAqlMguEjApCVXo7D+/PFhd54Lb19IvwCtCLdYOuGdCz/dD+qKhuLq8hoIxoabCgqaIyPHDMxDDfcMRFBId37w12QVyVeN0cPNr5R2LYxHdfc2Bj5SVGQbGAoKjDh8/f34tDexmjVrRvTsWjJGMy+NBZS2YAew2GDFC2kVLhlBWpzU8TngdMuP+uJMOzskgyiCaYDKrqRVjeNi4vDJ5980vy1W265Bd9//z22bNkiUmEqKipw8cUXi2KdesmUSvejo/01urElGp399fvj2L09yyXLuwkFogxJCKSVgZB+vNjtfcSN9cPsu+WoRyGcbkbjdTYdYnOqoRtSDwkai9qW7A4tDj6Xg/KNR2CtPJ1H3kTp7wNZVDRKDmagLq+0zXaFQYuAKSNQk1UEU0pum+1SlQKB54+CpbQK5YfS2h6jVIqgC0bB7+VpqPNz3//nVa6Fs8oG8xDKlG97jN5KP4R7eaHCkgUnGudBtCSXGLAzKRifrLeipqHtY+CvVyHOR4PCpFJUmtqOVOu8FBgZqEN5VqVIcWmN8sMXXDMSh/bliUK/zWMglSB+VDDmLhiBsZPC0V0rvz6MH5Yfac5sbylqqK94E8gxif3fqm+P4bsvD8Jqbfs8R0Qb8fvHZg74JKHewNGNvaOhvACle9eiKmmXGPCgFU5DZl0H46iOe9XPZAxjd/THuuNcPi4nrpzT4+jG+JXr+sXj2+dH1h999FGsXbu2uc+cFjKiVU3JQw89hNtuu635ulR8t1485u233xankqiQpwWScnNzxUJI33zzTacL9f5KLpdiwrRILPv0gNvtzqYivZ23a8kHyzALareFOqmR10A9XAOJzX2UokxaC6XS7rZQJ5bSSqhDrG4LdWI11cJaXee2UCcU8ViXWwxTcq7H057lSZnQ+I3xcIRAnW8t5H7K5sWVWquylMFbWe+2UCc2pwnHMsNQ4yGBobS6AbEKudtCndTUWWG22t0W6sRisWPP9izkZNJZi7ZoESs6g9KTQp3s/C3DbaFOaLQ/L7uSi/UBYNfWTLeFetOqx/Rcc7HO+jP6vV+TfQIVhzejOuNo89cNw8ch6LyrzloCDGODtlj/3e9+hxtuuMHtttDQUJfPd+/eDU2rJefVajXee+89vPzyy6JQ9/PzQ2Bg4FndZ8YYY4ydWxRbWXl8h0h5sdacHtwwxIyB34SL4RUylJ+SAUQyiNpg+nyxHh3tfqKhO60XP2pJr9cjPj7+DO0VY4wxxnqbrc4E08mDMKXsRW3eyeavy1Re8B4xCb5jLoDKGNSr+8jOZs66s0e37y/6fLHOGGOMsc4ZDIsi2RvqUJN5HNXph1FXkObSwkij54a4SdBFJ0Iqkzf3nPd0gSPGehMX6wOcTqdCaIQ38nMaU2Fai4jyEb/ncrPc90MHhhlgqVdDonY/OVMh9UKxXYMwlLjfAYkKPmMjkPfdPjgsbSdfUgqMX5QGBXqNSIVpc3OZFIFj/FCd4YP6Ivf7GDBtCJx2O6pPuo86DJgYA53CBzVW97f3knlDJlWg2ur+F7pSqkWDXQupxP3tpVDDz08CmVQCu6Ptu3yNUgatTAKVUoYGi5sJqjIJtGoFtDolamtokqsrmoYxfEQgzLVWlJ5K72ktMEiH7MwKkQ7TXbHxgSjIdb9Sr9HPCwHBOo+3pf3evysHU2ZEiwmxrO+KTQgUfenuePuouV+9n+vriyLRAkNf3DcXISHu43vbWyBox44d2Lx5M/bv3w9nRQVCDQZI/LUYPnw4Zs6ciRkzZpy1Nteu7i87+ySDqA2mX6XB9Kb+PCvbbndg7U9JIu3DXNe4aqu3UYOAQC1OnkoXGTYiAMUF1c15yxovBXzHBmNrXQPsTmDhFBXOH1cGq7NxEqQUMjQ4ArAmtwJmuwNTfLS41q8WKkdTESABvKIBvRYSqRM1abU4/vctKNl4uHm/gs6LQ8zQYqhsZaiXeOPIiVCk/3KseZQk6LxYTLnfD8aQGljqFTj0jQInPt4Lp62x4DWOjsL4ly+E/ww97A0OpL6SjWPPrYWttvEYtFGBGP/vOQi90k+kAJTV65FTWwqro3Gip1yihFbhgypLYxKOXuEHi92MBkfjGxOpRAanMwD7SstgczoR561HopEej6Y3PhLUWEPwTUYNTFYbdDZvlKT6ICnn9ETRcUF62PcXoK60DgZ/L3gNMeJEizdGwyK84SyoRnl+Nbx0CkREGpGaVCImjZIhw/xw092TEBMbAEuDDT99ewyrvzsG66minyIdKZc942SZSIW56JJYLFo6VhT+3dE68pMmKc9bGI+F146C2k0uPT2uW9adxPLPDqC6qkFMQF16x0SMn8wLiPVlSceK8Ok7u5vfpMtkEsyZPwJXXT8aGq+BPfG+t3AaTNeTVcxmM3777TcRMrFt2zZYLJbm3ztarRY333yziGgOD+/Z5Pq+qj/XHeficTl53Wzold0fc6622DBs2fp+8fhysT6IfmgqK8xY9sl+lBbXIPNkGRoaXEd5VWo5omN80aCQYb8cKLe6blcrgHvmKxEWbMZvRQ3IrXUdCZfAiaWh3pikb4DM4AeJou37wKI1Rch4eycih1ngYz/dX9ikzBGN4/vlGHZ5IIZMbDuSXVmkw76PaxB8yTAMuSdMZJO3ZM614fBfDkA/zIjYR2Mg17hutzlkKKhToc5mR42lEna4jmRLIIVBGYAGuwNHys2osLiOUNG9nR9khFFlw9o8B5Kr2p5x8KkNRXG6AvqcGpQeaxuJGRrnD4tOCU2DHTlHGzPiWwoO1UOnV2PG7BjMnDOsTb55SVE1vvxwn1iwKC25BDab6+Os91bh2hvHiWz0nry5Sz5ejMU3j0dwmPvXO43OfvTmTqSntj1FPHp8KG66ezICg/Xd2gd29jnsDqz/JUVk6l938ziERXhe0Zn1HBfrnSvWqSCnEfRff/1VRC7X19e7zGGbO3cuLrzwQhw/fhyXXXbZgF44aCDUHWeDiYt1NtB/aKpN9fj9zcvbvU7+pBDUuWnXaHLxAitKLe7bJcg/JgyDUeW+XYNYftoP26ZfPW6XX3A+5Hr3p+mJPWwkrEPai95S0xIY7WwHdhe7bwtqklzljSqL52OQwBdZNRUet4ft8kPSFvfZ9SQuIVAUw56MnxyOh/5yocftFLH3tz/85HG7UiXDu18vxdn09v/bhu2b0j1uv3LxaFy1xHNkJmODCRfrnot1WlV8z549YgHDDRs2oKbm9NlJut68efPE+ijDhg0T8cxWqxWrV6/mYn2Q/yylXd/zkfWYr/rHyDr3rDPGGGPsnKJWliNHjuDnn38WCx3SgoVNqO+cinMq0inFrfX6KYwRarGlS3f15LbnGhfrjDHGGDsnrLVV+Prrr7F161ZkZ2e7rFA+Z84c0eZCCx9K+1OuHmNnGRfrjDHGGDtrnA67iFmsOLoN1RlH8LG/VvSa06KFs2fPFpNEaWVxmYyTpFjX0lykgyQNhov1QYbSPby8FKg7lQrTGqWIGLVK1Fnc5/Qq5BLI7J7TIui1X2VxwqjyvA8mH194tbOPZr03dKgQE1bdqYQaaifQam5pM0mdFU6l51e31SGHXKKCzdmYCtOazS6H3UoH4L5nXWIH5DUywNPfFdo3ZfuTnryN1Ffvmd67/e1qjVxEJFo8zC3QiRjIBmh17p8Ic50FZrMNvn7tPRPtoxSanmxng0NBXhWCgvWQyvrRX8Z+rC/lrNvMNahK3oOqE7vE4kXEabchISEBS5YsESPpXl7d/x3E2GDBxfogQ7FsL7xxBb7+5AC2bUxrXktCIpVg5uwYXHvTOMhUcny4OQ3LdmbBRrmNp4yMMaBc0oDNhxswKToKZq882CSns9OH6L2hV9ixLOMkRhqDMStEDq3c0by9skGOvaW1KApWIvTu+zDylzXQZqc1b7eEhSP3ystQqq6FQToTMUVZ0FdkNm+vl+uwNWASDtXVwpgmwcwQLcK1pyciwQ4oU4ohSd4NKLWwj5oCW8TpPwR0rKX1OuTUlMAJJ7yVgc2xjU2yisPx4xEJTPU2nDcsGl4+OXBKTxfE2nwjslZZUJxfjLjzAlA2wYR6xek3Pj513qjeo8LWzEqMmBQGc2YlTC2y0VtGG150SaGISczNrmyT5EIpMO0JCNLjudcW4osP9mLfzpzmr1M6zvARASKp5fH7v8fVN4wVqTBNiTLUJ7ptY7pIBTKbrbj8mlG49MoEKBRdH9FactsERA014uuP9oukoSYhYQbccOckjBoX2uX7ZAOHqdLc/HsmIsoo0oEo453175z1zuSkZ2Rk4JtvvhFpLr42G3y1gHfo6YmiEydO5DYX1mOSQdSzztGNgywNpqWTySWiWKRCjv6QUqZ3S1mltfh/q08gt6oOxmA5UspcE2B8vZQYGS2DQ1OG4QYtiupdFxVSSRU4LygEI40OHKtwIrkqXxTJTaQSKeIqHRi6+keUz5+H3BAFHFRxN5MgBHpEZR3ACUM8tjrlMNtdoxaHGQJwXpANxtJKyA7tAepapcgEDIV1TCJMGgOyampRa3M9Bo1MD6lEjoIqCdYcM+JoYY3rzbUqTB7uhNJRgdoNXkje63qMWr0S0XONKI4yQZPkjyP7ytFyXSRaCCkh1IDi48W45PJ4XHpVosuiQRSTuP7nZHz/9RFMmzUEV10/pssZ6UcP5uOz9/ZAqZSjrtaCkiLXY6A4zpvumgy5QopP39kjnveWKF7xhjsmYuyk7mUVU9H/w9eHsXn9SSxYNBJzL48XZ3DY4ESv6XWrkrHyq0NtzuDRa/z6W8bDx3fwjaYOlDSY9qIXk5KS8N5772HTpk3NX6NR9Ouvv16MoiuVZybDn9NgBjfTqdd61q0XwtCDNBiTxYaojzb2i7qOi/VBXKwTWnyHJtq3N9t+yeebUeBmddEmN86kTHbPyzGP9AlAhcXDCqcAYvT+qHcUetxucwRif5nnKMcRTgnmJ3uOgrSpdNgxeTLa8/KacFTVu28NItOSrcjPcL+CKQmZHI7jmZ6jHG9YGI/FC+LbzbzuSZtAWUkN/nTXdx6309Mrl8tgbZWd39Lzry9ESJh3t/ehp8fABobNa1Pxwes7PW5PGB2Mx5+9GIPNQC7WU1JS8MYbb4hJo4T+nlx00UW46aabMHLkyDO+D1ysD26mQViscxvMINd6wR13HB0sctvhIrgdfAtnj1O5HD36/p07RvTwMWh/J3pa5HYUbUa719E+Ojt4GDvChTpreq21/1rsP6eeWftKSkrw5ptv4scffxTPKyW4UJrL7bffjqFDh/LDx84qiYQGG3vQBtOD255rXKwzxhhjrNMcDge++OILUaibzY1nXalIv/feexEZGcmPJDsnpD1Mg+lP6aBcrDPGGGOsUxoqivDwww8jPb1x9eLRo0fjj3/8I0aNGsWPIBvwSktLsXz5crGgF7VjxcXF4ZZbbkFAQIDb69PPyYcffigmXdN1Fi9ejKlTp3b5+/aj9xWst0g7Wj2uo+09PNPk7OnL2HkmjrGDzT1cYY/6vXuyndJ82t3ewbwEwu0J7Ezo+NcFr0bZH9Hvh/JDm5C98jUkJydDq9XiL3/5i5hQyoU66800GEkPLl3x+eefi7ka77//vlhZd9KkSfjpp58QExODlStXtrn+hg0bxJyNEydOiLUEaB2BGTNm4JVXXunysfLIOms3H5nSYgIq6+AzxRcnqlxTRnw1SoQavLDhYA0uGh2ECnuRy3alVIFAjRFppgoM9Q5EVUNjZGLLNBiJPRDv76vBwrgw+GgK26TBNDSE4MPtTkwYGg6Vvhj1bdJg/DDa3wSzzwXQHDkC1LlOdG3wHYJfA4bDXu+NMK0VDXbXNBiFxID1J3ygU0oRoFXjZFm1y3Z/LxX8oEZqQB0StUpkHXWNeqTkFsOYIByx2pAYH4CUpJI2aTDRsf748HAuarVy3HDeEKgUrdJgVifj+2VHMG1mNK5aMrZNGsz2zRS1eEDE3l1/6wS32ehGXy889swc8Xzl51a1TYO5e7JIaPn03T04meQ62dcvQCsy0f/fvzZh6e0TMG5yRJv7Z6yzzr8oBg31Nnz3Zds0mOmzhmDxLeP5wexn7JZ6FKz/HFUp+8QCR1Sk/Otf/0JgIEdxst4jkfZsYaOu3jYvLw9PPfUUnnjiiebo0TvvvFMU4LfeeisKCgqg0WjE1xsaGsSI+3nnnYcVK1aIr914441i+yOPPILLLrsMw4cP7/y+Onk4bVCnwXiK4vv+68NY81MS7LbTI7rB54UgI0iGinoLEoJ8kFxShfoW2ydEqREbVY0qqwlhXgEobzDBbD+98JC/SodAjQJVlgpoZf74LdOO7KrTecARBiWuHCGDQ1IMOYz48ZAOu7JOF+dGLxkuGSuDyVkIo0qLqUFSBGlaRCnaAcNJM+Sp++FQaHEwfDw2yk+/H6XxvPF+/lDKKFnGieySEHywowEtouSREOSNoup6VDdYMcLHG0eSq2Cxnr7CGJ0KXumVKC2qQfTYEOyWOlHVImFlqLca3jU2ZOdWYfgwP6Q77ShuUbCEGjV48JIRmDEiECeOuM9Zv+bGcZg1ZxhyMitEcZ1y/PQbBJVajoXXjsIlC+Mhd5ONbrM5sPanJKz8+jAUCqm4L8psd8lZ39RY/NPCSBTXmZZcAputxTFOCBNZ8MGhA/t1zs5+zvqyTw9g64Y0REQbRXzoYM5ZP1dpMCMf/QAy1ZmLxrTWViHvlw9hqSwWZ0WMo2Zg53v/QHh496Jez8g+Wa1YvXq1KHhoJdSBajDVHd15XPLvmdXjNJjQtzd3+vGl69H3be2f//wn/va3v2Hnzp2YMmWK+Nr333+PK6+8UrTMXHPNNS4FP/3s0Fkpul1ncbHe2Sd1EP3QPHbfShQVuI4wN5GpZVAuikJSufvtVBPeMlOBIovnKEYfSTjWpnmOepwWHIgfD1XD6aH35NpxGlwzOhNSifvWEFuNGivyHPAUNqmTK7H1kA/yTe5jDFUyKYLsWqQVuJ5JaEIrp44P1GNPUbXnY4gyYkeW5yjHK0K8ceyXVI/bx0wMw5H9+SJa052EUcF4/B+e4+9okSIq1ttbwfSZR39GQZ7rmYYmNAr/zEuXITzK6PF7MNYZvILpuS3W4+55CTJV4+heTxc9ys7OFkUI9ekajUb89a9/FbnptK1pZLE3cLE+uJmaivX7LoBB1YNivcGG0Dc39fhn8uabb8ann34q1hmgHnZCxfhzzz0netaHDBnicn36+aGfo/Xr13f6e3AbDGv7Am4x2t2avd6Ounayuqm2tNg9byeV9e33ieWbJB4LdbEPNofHQp00qGQwtxPnWGOzoMBDoS5ub3eg1mzz/P2dgKmDHvG6DuLpqtp5jEl1Vb3HQr2j54j4GDUdrmRLCyh5QiP0rVsYGOuOnmT3s67Thg/rUc465ahTMUGn8alQp0Jm2LBheO2119pdtZSxc00ilXQ4X6uj2zcV/y2pVCpx6YyDBw/iyy+/FKvyNhXqJDc3V/wbFBTU5jb0NRph7wqeYMoYY4yxZkVFRfj9738vCnWaSEcT6rhQZwNVRESEGKlvutCIeGfXGaAWF2rFoonWLVksjYNh8hatuE3o+vX17Q+4tcYj64wxxhgTHNYGMYmuuLhYLGxEI+ru+nQZ63UyaeOlJ7cHkJOT49IG05lRdXojO2/ePHHb7777DmPGjHHZrtPpxL91dXVQKl1DI2pra6HX67u0qzyyzhhjjDExAb3ot29FAUJJL1yos8HAYDC4XDoq1qurq0UU49GjR8UEUpro3FpT0ktWVlabBcXo56srSTCEi3XmkgLz5Yf7RHKDTq9yO+lw/tWJuGtqLEINbXuiJU4nptk0KPjaBk25h3eNlSE4eUSJUKmf280hah/U1EqQGOR+JCfSqEahBdiWHQmHs22vWkWtFh/tDIO5PBoSR9u0FJlVCcu+MEyyamGQtt2ulEkwfbgOsSOBIF+F28mlU/x18KlqQJRe7XYfx/vroCyuQ5yHvvHROhVkpXUYOtz9YxAQ6Y1qbzWixoc2zthtxdffCxovBX5cTosytD8/oD3X3zYB3m72USaTiLSZqCHuJ5eKRJmN6fjPs+uRltIijacLUjLK8fQr27BhRxbnuzPWR1Qe247qjCNi8ujzzz/P0Yysb5NKen7pIhopX7BgAfbt24dly5Zh4cKFbq83e/Zs8e+mTZtcvr5nzx7U1NTg4os9B0S4w20wzCXOr6qiMUOFisG4hECkJhXD4QBGjw/FDXdOao7zmxwVgK8PZeKLA+mot9kRK1XB93gN8jOzxXbJMQlGzgiA7DwTrCoLpA0GpB3zxeFMSlixAtnA6CEhsBgrYXKYoZeroXcacSiLMsIbe71i/PRosNqQazJDq5RhTJQG6dUlSK10IrUS+C0nBIsTrBhiLIHVLsMvx6Ox7FAD6m2NKS2BuiBMGuaEU1MAOCWQZwVj3+Y6VNdQdCOg0yowPNEXB2wmOCDB2AgdzNJqpJoaoxI1Q2SYMdwfu/bXivjGEUYvKMrNOHmkMU9eJpXgvBEBOFRbjxqLXRTv/g12ZJ7aTqbF+iHFZkeZ2YpglRyJdXZkbctBVosM9NrqBpQU10KjV8IvIRDHsirhOBXnGDEyCNpaC4rSykW6S0xcANKSS1FeWofUpBL8tiFNxCyOndj1GLXps4aKTPWWMZ2JY4Jx452TERrh/s1SVno5Pnlnd3NW+9ED+ZhxUQyuvWkcDD4dJ1BUmurxyXfHsH47FenA/mNF+GVLBu5ZMhYxkT7808hYL7HVVqFo67fiY8qMppVJGevLJDK69GCCadvxunbRpGuKY9yxY4co1OljT2jCKRXsL730EpYsWSLe+FKK0ZNPPikWVqIEmS7tK+esD+7oRirU/++JX9sslNMkKESPRTeMwdTzXaOHmhRVm/HZx/twZH2GKL5aowV+Iq6IwK8namBtGWh+ikouxbRxPthfVAmzm1FiWll0arQRRbYSmCynM9tbOj/MD7tPOpFncj9hY3SQDs6jZmTluI9aDAvVw3uGAmmmxiK+NX+1F4zpOhw95LogUhODTokhw3xx9HAR7G4SXNQqGUbH+CFrRzYsDW2PUSaXIm5iKA4V1qDGTQILLfg4YagvylPKUFFW53YfpsyIxv0Pz0B30UJKhXkmjJ/ieUEkGsn/5stDcLo5Ri8vBR748ywkjPacFnEoqRjPv7kLtea2x0gDHEsXJuC6+SO6fQyM9WV9PWe9cMs3MKXug9Lgh8NrViAyMhJ9FUc3Dm6mU6/1wofnwKDqfs6+qcGK4JfWdfpn8plnnsHTTz+NqKgoUYy39vjjj4sFw1pOQL366qtx7NgxTJs2DcePHxdrFXzzzTcYO3Zsl/aVR9YHOSq8PBXqhPLWY4YHeNwepNfAVmR2W6iT2hoLCkvgtlAnDTYHquucbgt14nA6YbZbPBbqJKXUijyT56jFtBIzZB4KdZKXXw2z2fMPfGl9HaRlnt+Cm2osqDdZ3BbqpL7BDkdlvdtCndCIdm2D3W2hTuixray2eCzUScuFk7ojNNxbXNqTmlzitlAnFPOYm1XZbrGenWdyW6gTutukdrL3GWOdYzVVwKGq73SWOsnPz8ddP6YgKECHl176V68ueMRYp0m718ricvsuWLRoERITEz1upxHzlgICArBlyxbR256RkQF/f39MnjwZMlkXh/S5WGeMMcYGV856U5Z6U3FBC7pQxNz06dMxZ86cc7SnjPWQTNJ46cntu2DUqFHi0lUjR44Ul57gCaaMMcbYIEV9uL/88ov4+MYbb+zt3WGMucFtMIwxxtggtW3bNpFOQSPt7vpwGeurJJIermBKE8L6CR5ZH+TohT5tlvvJo2TMhDDoDZ4zR9NSSqBSy6FUue/B8g/SQecEDBr37wt9dUrI7VL4e7n/HlqFHAqLEsFejQsMtKaQSmFUeWGYn/uoSPo5jjXoEOchhpAMj/VFmJcPPP3YRuq94ROuFWks7kSFGTB9fCi0Xu773n191LAa1W5jEomXTgmnjxpBAe5PXSsVMuiNaoRFuU9LkdJzONPzc0h2bMnAlvUnexSTOHFqJNRq989jYLAew+MD2719/DA/BHs4Ro1KjmnjXfv92MDT0GATE5Xp9wbrG3bu3Cn+vfDCC0VkI2P9blEkWQ8u/QSPrA9y9M7y3j+ej4vmxeLTd3YjO7Oiufi6gSIBJ7mfaGSqNOPrTw5g28Y0MQHS26gW+ewUK0iogA+LD8ShgirYDhXC4KXAsPgAHCowwe50Qi6VYGSED5Lyq7ArqQxKhQSj44xIqqyCxU5BikBigBHp6bXYll0hfqamjAtCsbQcddbGSYpDDEbkFANbC0zi84QgbxRV16OsrnEy6lBvLZRZDUjbV9D4eYQ36uptKCypFZ8HB2ohG+qFfZZaIAUY4u8Ho48VOdWN9+ejUkMvMWDfyca4yeDxBkSVS3EytTE1horzpZfH47ILY0SM40XTovDJd0exbltjLCEV90PjArC/sg6p5bXwGmLAtGG+yDtQAJvNId4oRY0Jxh4ZcLi8FnIlMGlUEHJSy2Cub5wwGzfUFyVltTiQXAoJnBg9LgQV6eWormo8xhGJQbjxrknisXeHns/P3t2N5GONE1A3/ZqKm+6ejCHD3Ge8t2fmnGEYNT4UX3+8Hzs2Z4iv0Zu0y68ZhUuvTIBC0f6kmWFRRrz+9Bx8tyYVy39ORoOlccLtrCkRuPXqkfDrRPQj6792bc3EVx/tE7Gjki+A8y6MweKbOxf5yc6evXv3in9p4htjrG/i6MZBHt3YksPuwMZfU1Fba2m3+Nr4awqWfbxfJIC0FjnECI1BjeTqelTUNOaltxQUZoAmwoD8qnoUVJrbbvdVITLCC2WlFqTnNRbVLfnoZBg/Tg266ZE8KqJdqWRSJAR6Q1Jixck9xYCz7Uh7wjB/WL2lOOCoQesMGSqIJ0TroVZKsC+jFmaro833SFBqMUKpxS1XJMLbzeJRtODPV7+k4HC1GQW1bR+DSI0CCVYn0lRSpLrZ7qdRIFGthLm6Aek5lDvvyosy54P0uPCCIZg6w/2IusPhxOfv7cGGX1LExy7HKJVg5uwY3Hz3ZMg7KLDbS5+h0foFVGR7GC1vT0l5HVb8nIyZkyOQONy/W/vA+oeyklq8++o2nGix/kDLyM/rbhmPC+fFYqA7V9GN4//xfacmmK6693z4+flhxowZ4ozbmjVr4Ovri/6AoxsHN9Op13rxU5fBoO5BdGO9FYHPru4XdV3/OQfAzjqpTIrZl8Vh4bWj2h0lpQLQXaFOsjMqUKmUui3USVGeCRKr3W2hLraXN6Chwum2UCeVNXbkZUvcFuqkwe5AXkktTu5uW6gTqluPppZir5tCnTghwd7MGqTmW90W6uS4pRZTZka4LdRJ7BBfeEUY3BbqJNtsRWGY3m2hTmgBpSop3BbqpM5iR77d4bFQJ1aLDetWJ7cp1MUxOpzYvPYkajw8R50RmxCIW+6d0q1CnQT4euG+G8ZxoT4IpKeWui3UCf0eod8nrHdQnBwV6j4+Pv2mUGesTRpMTy79BLfBMMYYYwOExVQGWYP7wZAmVlMZCgoKRLFOo9RGoxF5eXlikin3rTPW93CxzhhjjA2iRZGo4e+OlakwJe9FUUkN8tW1mPvi91jz2BVtFnZhrM+Snduc9d7ExTpjjDE2iBZFaiaVQiKTQ+kdAIWh65POGWPnBvessy4Li3QfIUgUXgoofNQet8tlEvhaHB7f0EokTgSHOaBWeX7H6+uQQi/z/D4zQK6A0dtz3GTEMA2iAzxvN6qUMCo8b/eSy1BU7nnkymK1Q262QequaV5McnVCq5ehvbmdBoUMGg9xmIT65SuqPO9DZmktDBGeJ8wY/TQozHPfE09M1Q04ktz9eD273YGDe3PFv92VdLQI1aaORghZX+frr4Vao+jW7xN2djntjTN3pPLuT9JjrLdIpJIeX/oLHllnXUZRjxOmRODLD/eJpIcmwdMicFzixIniasSOCYKsuA7FBdXN20cE62AvqkX6+nRMDTegKsobR2saIwhJQowKEfG1KLZkYOocLzQUGLFj/+liLUynRKgNSNldAINWidhEHxywVaMx6BEwyhUYVqNAyoESkU0+crg/ktLLYLM3Fs1anQzjFuqQpyyBHpWYFxGMbcetqKlvLCjlEgnidT44nlGNYqsJo6ONKHDWotLSOBGTvkuCwYiMnFq88N1xbDpShD9cGo9I/9OjWDsP5uP9ZYdRVFqHUaEG1BmUSK063T86LFwHh86B3WUlCB6qgc6mwImM049RpEGNwHo7jh8oEAV5dIQ3TpxsjIoUx+itQqCfFoeTS3Dfk2uweEE8Lr8oBnJ54/tuk9mKd9en4vt9uZAGaDAhzh9V27JhNTf+URZxkrH+yEgtw/NPrsWM2cNw7U3jYPBufINldzjx86Z0fPHDcdTUWTFlTAjuWDwawS2OsSPHDxfg03f3ID+nCqER3rjprklIGB3S6duXFFXji/f3Yv/uXGh1SixaOhYXzRsuJkCz/icm1h8vvL5QRH5uPxX5SWhy8pLbJmDS9Khe3b9BzWFvHmFnrN+RDZ42GI5u7KTBEN3YnQVOflpxFDsOF6A0XI/UUtcEF8oeHxtsgDW7EoE2JzJPZbC3FD06CFkBCsRNciLfXthmLDpE6Ye0Qyr418pxMrlU5JO3FB6qhzVKBR+rDNlHT+eTNwnw1cDHoIJ3LFAXUYEa2+k3B0QnV0FRH4DCPDkqiqwoqKxvE5MYN0SPGocVtiogo9j1GBUyCa6bGoW58UH47Ntj2H/MNfWCFkiLjQtAntwBY6gKx0or2zwGI/y8UV1sRZhVitSkElEwt150iYLb9ToVUjMrmvPJm0SE6HHndaORbbbgnfWpqGyV1OOnVWKEA9CVm1FeZkZFWZ3rMWqVWLRkDIKH+eLdZUeQmes64q5USHHV3Fhcc2kcVErPo/30xu3LD/diz/bsNtsmTY/EktsmtpseY6HX0zdHsXrlcVhbHWNktFHkycclBnm8Pev7Uk4Ui6z1xNEhWHDNSKhUg2e8qC9FNzYp2rYSpXvXwG/sRfAde4GIc+wPPesc3Ti4mU691kteWNjj6MaAx3/oF3Xd4PlNyc44+kN79Q1jsdlqRWrG6dHfJlR07suvwhylHJnHGhcmai3zcBFG3R6MTLv7losCSxmiAwOx96e2909y86sRZnciuch9lGNJuRl1VgsaQmrhLqtRFO/yXORkB6H61Ohz65jEA8mViA7QIrPFWYQmVrsTn2/LRNqhQqSmlrXZTosjJSeVYNgFIdjvplAnSWVVGK/SIelQ48JFrWXlmRAd5o2jKW3f7JCcgmq89PE+ZGjcF9JltRZsozcF6RVt3uyIY6y14LP39kAVH4DCVoU8sVgd+HpVEmKHGDGpnRHyn78/7rZQJ/R1H6MGN97leeGV40cK8f2yIx4Xd/rwzZ14/rUrPN6e9X2x8YF46oVLe3s3WJOmFY37zwAjY6dJpD07K0S37ye4WGeMMcYGIZpcSpx21zNZjPUHEplEXHpy+/6i/7ytYIwxxtgZI5Urxb8OW/cXSGOMnX39amS9uroaWq2224s21NbWwsvLCxJqJGaMMcYG4aJITezWBpEIY6upFAslMdavSCWNl57cvp/o8yPrDocDzz77LAIDA8Xqanq9HrfeeisqKio6dfuUlBRcf/31YjIC3QcV6zNnzsT69evP+r4PBrRUtaKDF7xa3f57Qpmj/ZehvIMUEIVC1u7PnFIhh1zieWKkXCKFpp2Jk3TfSnkH+9DRPnbQFCpXdHSMUjFZ1RO1UgZ5Ow+CSi6Fqp3nQSqVQNHOY0Ds1g5iGDt4Hag6mAjU0WRDdQ8mEjE2mBZFsprK273U5afhvSti8PTlYzE8QIf5cb5iQST6G8tYv0uDkfXg0k/0+WL98ccfx3PPPYdPP/0UNTU1OHz4MLZv347LL79cFPLtoRm+VJgfOHAAO3fuFCPrOTk5YmnlefPmYffu3efsOAaitKwKPP7CZmTuysOkEEObgjbMV4MXlo7D44/MFEkelDrSkt6gwvARATj4cT7CUv1gkLvmsyulcgRawvDrfiuCEwMR1CpJhDLbRyQG4oTUAX2cHyLDXWdzU3Ebl+iPqhgVSnP9Eab2b3MMicZQ/HXcfHxw9zQsGBfWpiAeGqRDdIwXCmQ1GD/cKNJfXI7RR41pkKJoezbGRvlA06rgpCSaEUN9kbQ5H+PlOvi0Kji1SjlGhxixx2xCxNQABPh7uWxXyKWISwzEQZsNxhH+Iv2mdX08aogR8qwqjCmrR0Kgrs0xjgnSIz6vBjK5FMNGtH0MwmJ8YZ8ShpNKCUYkBIoUn5ZCA7RI8Fbjvec3YcXnB0QKUEuFlWb85asDeDurFCHTI0TcYks6vQq33jdFTEZuT/yoYDzxz7kIj3LN3ZbJJJi7YAQee2ZOu7dnjDUuiqSNiG334hUaIwrzuLg4KBQK1NfXixSY7p61ZowN4ujGvLw8REdH4w9/+AP+/e9/N3991apVWLBgAVasWIGrr77a4+1//PFHLFy4EG+99Rbuueee5q8nJSUhPj5evBF4/vnnO7UvHN3Y4rGoacCn3x3D2q2ZaJkyaPT3girSG2mltbh5xhAsOW+ISwFvqqrHis8OYNvGdMTE+SM7vRzmFgksKp0McTcFIEdfhiCZP3bvlKC4/PR2uqtJvjrkpJYhJFiPAqUEudWnoxglcGKCvx7lmZXQG5RoiFAizeJ6OnjCEA28fCvFKPTVQyZgpK9rTNnx3Er8d/UJFFSaERXuheNVlS5JCUEaNXQWFbKLazFOp0LJb9lwtEhY0Rk1MAz3Q0puFeKG+uJkZgXqW8QQqr3kCJ8SgCPVJowI9EZ2ZS2q6k9HLSokEoyDFhlHyhAe7o0cqRMFtaf7SWmhpYn+ehSnV8DPWwVlRT1KslxTZgInhuKkmhZdkiKSth92jZOkYthmdcBstkKREICdNa5xlVF6Nfwb7CgprsVwfy2yD+TD2eKJ9vX3EjGMYyaH4/NtGfhsawYaWoy6eytkmOyQIOdwIWZdPEzkpFPB3lkOuwPrf07Bt18eQtRQI268cxLCo4ydvj1jfVFfim60VJWKmMa6ujosWbIEPj4+WLduHfoLjm4c3EynXutlr10DQzsLrnV4P2Yr/H6/gqMbe4qKcpvNJgrzli6++GKo1Wp899137RbrQUGNmcz0C6klGmEn1BbDuu7Z/21HSkbbNqSK0jqgtA5P3zMZMyaEt9lOC+/c/rtpYuR19XfH22xvqLHj8JuFCL8oEj+VtV25kmriHaU1SEjwxx5abMk1Mh1OSLC3tAZhkTrkqergaJXVTfZlmKHIVmHV7XOgdLMKakK4D969aypu+Wprm0KdFJnrUYR6zKiXIHtPZpvb11SYUbM7F4nTInDQTdRifZ0NJzcWYNzMEOwubBvlaHU6sRs1SJgWiN0ppjbbaQGo3aU1iA3VoWp7Dh10G8V78xGoV8BitqPETVRjblalOINQNTUMxa0KdZJVXY8sALOUMmTty2uzvby0Dq//ewv8LhuOXW5WQa2y2rEWwJ33TcLNs2PRVbT40cULRuD82THQ9OAXMWOsfU1/IysrK2E2m6HRaPghY/2HTNp46cnt+4k+vafHjh0T/8bGuv7BVyqVYsT96NGj7d5+8uTJuO2228ToORX21AKzbds23HvvvRg/fjzuuuuus7r/A5XVTQHYkrTD0N72t1s7CCawdXAuiAreplVN3W630yi95/5smoBs7+CEk7OD/u3WCxt1dbutg4dQrMrazl3YG+wuI/6t0eF1lP/g7OB57uh14OjhKXUu1Bk7u2iE38/PT3ycmprKDzdjfVSfToNpmkTq7pQhnQIpKHC/0E5L//3vf0WKzKJFi8SoAY0ejBw5El9//bWYrOpJQ0ODuLQ87cIYY4z1BT39G0WtJHQZPnw4SktLxeAXtYf2B7TfLf8dqAb68fWY7NQk057cvp/o08V6U8Siu7Z6mlza0WQYKvYnTZokivQjR46IIp1+KT3wwAOYMmWKSISh0Xd3aFLrM888c4aOhDHGGDtzevo3asOGDWJU3W63i55dOvtM0cj9ydq11HA3cLVu4WWDV58u1v39G5MrysvLodPp2hTiTds9efvtt5GWloZNmzaJQr3pPj/44APRq/f0009j9erVbm/7xBNP4E9/+pPLqEVERMQZOKr+r2dNLo0pLe3fvqMrtL+9MzH6Pd7HDo/hLG/vcP86fhA6epzP9jEyxrrP09+ozuSsU6Z6QsJ4BAcHQy6Xi2Q0Gsi65JJL+kUiDI04U6FO89cozWag4jP6Hf+dk/QgK70/rbnTp4v1sWMbo96OHz+OyMhIlwmimZmZIm+9PRkZGeJfOs3XEo20U0xVenq6x9uqVCpxYW09dOsEvP3lIRw/6bqIhkopw7WXxWHS6GC3D5vd7sDaVUnYtTUTsfEBSEkqadN3PW3WEFy5dAyGHinEVzsyYWnVFx0frYdZZcUEfx+cSKlGXYPrJNKEGBUi4+ugkCixNxkorXaNGQz1USE8APjXju24bdRohLZqhSqrbsAba1OAcimivHXIqqlx2a6RSxEb4I0MXT1CvYNRtLPQZTvFI4aPD0G+qR7xMb44kVbueoASYOioIGSUWjEq2BfHqivgaHXmaLxTDclvhZgT4Y3tNhvqqD+9hTijBuqKBvhMCUdFUgnqqlxn2oZGeOOmuybBXGfFlx/uQ0mR6zEY/LzgNdQIg8UBH4MaGSbXSaY6tQwxQQbx9TCtEiVHXNNkKI99/lWJmDQnBm9vTMOGY60eA6kEV0wMxzVTo3C2ZKWX47N398DHV4Mlt02Ar/+5HREUr+WfkkRqzfyrEzFrzrAz/os/LaUUn727GyHh3rju5vHwMZ7ZyX/mOotI3Dl+qBDX3zYBo8aFntH7Z2eXp79RlLPuULWdOO5Kgnt+yoBEkgVLZQm8lUrRLkp/a8eNG4f+ggr1gVysD+RjOyNkPcxK70c56326WJ8/f75YxGjZsmXiHX+Tb775RqTELF682OX6ZWVlYpSA+tnJsGHDxL979+4VEY5NaASBiv3Zs2efs2MZSIZE+OD5x2Zh065sfPTNUZRX1uP8iWG47ZpRCPB1zQlvcvxwAT57bw/yshvTQ0qLaxEa7i1Gb/NyqhA5xIib7pqM2ITGhJ57gvSYPy4U/+/nJOxILUWQrwqBoSoklVWJFJg81MEnVInhSh0Op1TCqJdj+nlS5NvzUXRq5mTUUDnGOIOw+agZcqkU44d4IaOmFBkmB2ACHi5Zj/kxw3D1iBFQSKRYsSsbH2xKQ+2pHHFJMTB6qBG5thqYrFaMDPZBblUdDhU0zqXI9QESFw+FdHsJTDnVCIsPQLETOJjZmPJSXGZGVJgBNpsDeUU1CIn0Rq23CntKGovnwqp6kUWv95cjrdqECKkC0RkWZJ/IaXyMCmoQ76uBIr4xXtFPLUesQoGU440pM7mUqeyjwfBh/iJekRafuvL60bh4/gjITs1yHzU+DKu/O4ZV3xwVk1rDx4XiRIEJDafiHmkxpOlx/jhS14Aaix2jI32QUVKLQ1mNx5gtBcbMjQEOFaK2qBbjp0Rg6e0TERDUeKbrH9eNwZUZ4Xh5dRIyimvE7f80Px7Dg898NB2pqW7AN58fxKY1qXCcmqR7aF8eLr96JC69MgFyxdlvQjx2qEC8UcjPbXwtf/j6TrE/9PqNiW3/bF9nUMTp8k/247cNaWIicHpqGfbvysGVi0eLlJym57a7qK1w64Z0LP90P6oqG4u6/zyzHuMnh2PpHfTcep7Lw/pHznpH0Y2tTZ8+HZs3b8ZPP/3Ur4p1xgaLPp2zTl555RVxqo8milJMIy2KRCPqs2bNwvLly12uS3GONBpPCyA1tc/Q59Tf/uqrr2LixInIzs7Gn//8Z1HAb9y4EdOmTevUfnDOunvmehuy800iU9yTn745iuWfHnC7jYr1eZfHY/Et40VknzsrD2bjjT1JsNjdp4+MC9NDHpAPs939ZBx/pQF5JVJUNrgfbfJVq2HNNiI53/0ELR0VySO0OFLUNmqRKGVSTG3wwsHDxW6301m6hEnh2JJT4THA5cIQPQrWZYjC3p2h40NxvLRWPN7uDIv0xt9/Px3ePu5HX0uLa/DXV7ahoKTW/TFqFdCO8MdxD4+BWiHFny8cjovPi3a73WZ34EhOJcZFe34d9FRFeR3+9tBPomB3JyTcgOdfuwJn0/fLDuPbLw55fC3fev9UXHCx65m8rigqqMbTj6xGXYts/ZaGDPfD0/++DD3x2otbsGc7hXO2RWdNHnt6TvObZjYwc9ZbZ64/f543/va3v4nBsV9//bXPRzhyzvrgZjr1Wi//aAkMXsru30+dBb63fsk562fCQw89JPrLX3/9dbz44oui55wWSXrkkUfaXJe20eIOTXx9fXHw4EH85z//EbelRZZoQg2NHFDfeutISNZ1GrW83UKdFBdWe9xGbxWpdcRToU589EqPhTqptVug9FCoE5PVjMoGzyOu5fX1qK7wPJGnpt4GU4uFi1qjfatzk+nehAaA6+z29pIWYa22eizUibm6wWOhTipMDR4LdeIfqENJhec+1ppaK6x1no+x3uqAV6vVVVuSy6RntVAn5lqrx0KdFBe6tvucDa1bilq/lkvb2d4ZtTUNHgv1jn6WOqu9+7Ba7OJNERtcaE4X9btTvDGtb3LNNdf09i4x1iGJTCIu3dWT255rfboNpsn1118vLh3JzaXGAFdUsP/rX/86S3vGGGOM9W8034JWMqVBrU8++QRXXXUVZLJ+lGvH2ADX96d9M8YYY+ysonldRqMR+fn5WLduHT/arO+TSnt+6Sf6z54yxhhj7KygOV80uk7effddkb/OWJ8mlfT80k9wsc7OuvMvihGT/9wJivDGwQYLjua4n7xZUlSNQ6tOIsHbfUKFt1oBrVSDcFWQ21xvpVQOIwIw3BAImZtoPfpagp8/Rg73gkbZ9rQv3WJqvB6JkYCP2n2M1tgwPUaMsyPI333PeGSoHvI6G6L93Pd8h/pqIB0iQVR847LfrRl81JDE6BDrYbuXRo7AOB98cyRTJL60RvGXH29OQ0x8AJRu0lLEBNhhfohUyqBXu++MSwjzxprDBSisdN/3nnysCG+/vBW5p1JkWksrqsaz3xzGQQ/bO8MvUIsL5g53m6urVMowfEQAflh+BFZr2yKDHpflO7Pwv1+SUNPO/IP2HD2Yj/KyuuYknNaihvpi0vSexVWGhBlw3oVD3Wbcq9RyREQZ8fPK4+3Ob/CEsgR+W58GrU4Jnd59LO3YiWE8uXQQu+6668TEPUpL+/HHH3t7dxhj/SUNpq/gNJieoeKCcqlXfn0Y9WYrvHRK+IwOxtZaMxy0PI8EuGRMKO6/OBa+OhUsDTaRIrN65XEx6Y0ETwtBVqgchbX1kEokGBXsg5OlJtSeKs4SwlQIDqlDcUNjpF64KhD7U4BiU+PEzDCjCqH+QKapsWAc4uODWosFxadWidNJ1fCqMuJgWmMiytAgL4wZWweHtjEqUepQoKosHNvTq0XxF6JX44J4O3T6xpxxp12KsiPB2LrZhAaLHQadEmFBuuasdcmpjPWkmnqYzDZ4KWUYPUKLXGkhHKemn46oNqB6SxUqSuvExNvIySHY421F7akf01ilF6SZ9cgvrBH3F5fgjxQvCyrtjcc41FeHB89PwNjQxsme25KL8covScgrbyyyA70UGCqRIfVURn5UqAF2hwO5pyZn0vMSMMIfB/OrxMTYUKNGpOGkFDROSlQrZLjx/CFYel40VAqZKF6//mgfdv6WKbbLZBJcdGkcrrp+jCgKq81WvLfxJL7bk9P8RuLiUcH43dw4BBjU3XotZZwsE9GJJ5NLxOcxcf4oK6lFZdMxButxwx0TMXZSuPj8QGY5Xl59AmmnJn/66pS4d04sLhsb2qlsdErS+fKDfdi7M7vxdSCTIHZEADLSytFQbxOF7zU3jsWsi4eLKMwz4WRSCT55Z7fIkyfDRgSIiaGmU1GL9Ob3xjsnYeTY0E4/Zp++uxtpyY2vZY2XQsSlpp4oERGYQSH0mE3CmIlhZ2T/We+lwYx89APIVJ4ng7tbIOnjmyYjJCREfL5y5Uq88847Yr7Xe++9J0bcCW3vKwsmcRrM4GY69Vqv+PqWHqfBGBd/3C/SYLhY7yQu1s+Mygozvlh+GD8VV6PKzQioViXHnRMisHPFUVGAtSZXShG8cAgypVbkmdqO8kolTsxK0KLM5MDhHPdRjWMidNBqrUitcD/KGywzIsxLAal/Nv2EtN0HuzekDb7w98uCRNJ2hNNRp0HurkAcOlqOOjcJLlQoDZkchCJtKWocbfdR6ZRiZIkPjkscyHG0TQahP5eTlQaUwIo0i/uR7ouGBcNUaBcZ9e4k+nrBz+rEsRT324PCDdBFeWNfZjncBfFQEb8k2g+/fnUY9W6OUe+twqwbx+KjA3modJNuQmcxHlmQIN6gdQeNMWxZdxLrVicjO8P98zhmQhhM8X5Ye8R10aYmCeHe+PfS8fDRev5lv/HXFHzx/l5Y3KT9GLzVmDgtElffMNbjSHVPUBG9/udkbF6TipxTufitTZwaiQf+PKvd+/n8/T1YuyoZTjdnXYKC9WIhsgXXjITiHGTUD2bnqliPu+clyFRdi16U630gkTQW4g67DVkrXoa1pgL+E+bCd+wFoqBf89gVYjHBvoCL9cHN1FSsL7+t58X6tR/2i2K9X6TBsIFDrMIY7YOqPPfFBy1ItP9QgdtCndgsDlgya5Dn774NwOGU4EimHaW1niP+DuXUIDDIczxdob0CCaFSVFvdn3SyyaqQECpDlcX9Pki9zIDC5rZQJ7SyqFxpdluoE4vEgZIoKXIy3W+n75qjdv9mpcm29GLU5Hk+aXasvA5DajxHQRblmtDgq3ZbqJP8CjMOlee7LdRJdVUDdp0odluoE7PFjt0nS7tdrNOIOI2cf/B645oK7tBiSbkSz323x3OrUFrd0G6xfnh/vttCvWnxooBg3Vkp1AmN0o8eHyrOInjSNNrfngO7c90W6qSosFq0vXChPrgXRWoteObVyP31I1Qe3w7/yZdAYXDfgscYOzf6xjktxhhjjPUJhtiJUAeEw26pR+nuX3p7dxhzT9LDyaWdaIPsK7hYZ4wxxlgziVSKoPOvEh+XH94Ea3Xj3AnGWO/gYp0xxhhjLnSR8dBGxMHpcKB031p+dFjfI+Wcdca6hZIzaFKczc3kUVJVZ4GJ0mDcxCQStUwKtUYOLfW2u0FpI2NiAxHu7TntINquwlCl5wlWsf56hHt57sEMUhtQW+Urlo93x6jUQiNrp0/ZoYY8XA6Vxv17YYrgc1g10Ejc90pTnKRRK0OQwf12CZwYN1SKhBDP+zB2iArjEjynrcSE6xA0wtvj9mB/L4TIZPC0GnOgSiYmA8sV7o9RQ/GAEin0HiYtKmUSqGqtKKtw33fvsDvEBNL8nMZkH3cOJJUgKKYx9cadgFGBiAl0H/lJog1qJCUVi8mqniSMCoZc7v4Y9QYVhsR4fh011FuxdlUSqjzEXXaE9utQShkChxg9Xic6xhdHDuS3ez8jx3meFxAUqvcYRclY0+h6ddohpKWl8QPC+hbp4CnWOQ2mkzgNpn00CW/Vt0ex+ttj4mMqAm64/XQUHMX2fb83B+9uOCmKdaNWiXBfLxxpka8+3l+L6hwTyivroVHJEResR86BfDhOZUrHjwrCjXdNRnikD6x2B5YdzsSn+9JQb2t8YxClVMOQb0dWdlVjrGFiAJI1Dag6FWvor1UhQKvGieLGAjAh2ACbwoQKS+NkUy+ZEr5SPxzKa4wtjA3QIi66GnZ5Y9qIQipDrMEPJmspHE4HdAodFBIFKiyN251OCarrQrAjpxpmmw1GlRq+JV44vu3UKWSKbkwMQlq9BeW1FhGJmBinQa6kCM5T0Y1DfXxQcypOUiGVIlrnj30ZtWiwNW4fE65C4tA6VForKfASBkkwfjloR0XdqcfAT4G4oTbk1zdGM4YpArFvtwQFJY2PgVGvQFS0F46XVorvONxHD+fJOhRnN0YzalQyDI0yIjmtDDa7E6HBOlh81Ug+VVQrJMD5aiWKDxSiocEGX38vMWk4PbXx+1EfYNTYEKRVmFFda4FepxTF5t7SahHRScb6aWHOr0ZpuRlqlQzXXjYCV148HIpTRXHK8WIRM0gpLxRfefFlcbjy+tHQnJr1n51vwjtfHcLhpBLxPCdEGVGVWobaU/uoDdICY4JxqKjxmEaEGlBZZ0HhqdhDPUVmeqmQklwqEldio424Z+kYDI92X/gX5pnw2ft7cGR/fvPEzwsvicXVSyme0v0bpp2/ZeDrj/aLaEtK/6H9v3j+CMhknfvjkJZdiXe+PIQTaWWitTIx2ojyEyWoMzVOnPb184KP7+nHffyUCCy9faLHwls8pu/sRnZm42tVrZZj4XWjMO/yeMg5BWZApcGM/8f3PZ5g2lLuzx+g8sROXDd3Bj766CP0BZwGM7iZmtJgVt4NQzsBAR3eT60Fxivf6RdpMFysdxIX62g3dYMKgZJTOdatF1mZdmUC3votHamFjcVTS0MDddBJJVCW1yPDTTwdjfCGqxSYNzsGU86PbrO9pKYeb29Pgim5GinHGouvlrw0CkSMNMISqBBFuqVVvIlSJsWYcD0gceBEvhk1Ftd0EyqUpg7RY1xMLaSSGtTb246S+qn8UF4nw64cB/Jr2qbYRHl5w3FSgTKnDCdL2j5Gkf5eiBjmgEztwMnKtjGEvmoNfKR6xIQ2oNLRNoZQLVOiwRQIq8yJInsh7E7XY6Q3Gb4NwSgpViO12gRzq7MeNJI/2mCAKteCvMIaVFW3TdKJjfWHg+rM5DKUuXmeaYQXajlKZVLkuHmew0P1kPhroKi2It1N1GJooA63XJmAQ79lYsfmjDbbvY0aXHvzOCQX12LVxrQ2iz+JN3dBOpgNSuw3mVFvbfUYyCQYGe4DRZ0VxWkV4o1ES1T0zzkvCrdfMxpaL/eLX+3flYNtm9JxxeLRiIw2eizsP3xzJ5KOFrU9xghv3HrvFMQlBsETc70NH35zBGu2ZIg3jC1pNQrE+ntBXW9HWkppm8WfFEoZ5l+ViCuuGwWpmzcFdLZi46+pSEstxTU3jhMFPzt3+muxbqkqReqHf8MwPy+xsunkyZPR27hYH9xMTcX6D/f0vFhf+Ha/KNY5upH12C8rj7st1MnBvXnI9FUj9dQoZ2vpxTWY5q9Diocc6cLSOgwdH+a2UCcBOjWWxEXhjys2ut1eZ7Yi61g5iu3uf6CpeN+TVQW9St6mUCdUMNEiSJNibKhzU6iTsoYyZJaHIr/Gfd53Vl0VIgJCcPJY42JLrWWX1iE0XIeM+rYFHimvNyMkUIZKR+MiQK3V2y1QGUqRVeU+rtLqsKNIkYfkSj9Y3UT42Z1OHKiqwvBKidtCnaSklGKsjwZZHp7nzLRyREwJR46HzPPc/GokapU45mF7fnENVv5wAoWH3WeiV1WY8e2KI0hrVYQ3MTfYcCSvCsVW9wWo1e7EgawKDKuztynUCXXCrN2ahQumRGJUXIDb+6DRa7q0Z/f2LLeFOqGWHmoRa69Yzy004Rc3b1ZIrdmKkyW1kGe7bw2ixcNo0bG5l8eLBalaowJ+9mVxmI24do+B9W8WUxlkDd1rvfJEN2QUrGVJeO655/Dqq6+KvPW+skASG8SklOrSg9fhGVrE7lzgYp0xxhgbIKymCjhU7tdo6C5jwnnI/iUJmbsO47z7/4kdbz3ZZxZIYmww4GKdMcYYGyDOxKJI7gRVX4nC375BxbHtqKvzvKgcY+eMtIeTRPvR2aH+s6eMMcYY6xXGMbOg9AmEvb4WX331FT8LrPdJe7goUj9qg+FinfWY0d/zRDVKzgjy9hwhSAKMmnYXEpMppbB5WveeerrrrCJVxBM/bzWMGs+TULxVCvioPG9Xy2WA03NMIqWyqJyevz8dmpdX+z9qWpUU0nYeBJ1CAbnE8/fQylTwkns+Br1CBT+t52PwUsigN7ifWEkoqUXlYeIlkdAk4Xa2i/tQK9r93aj2UniMgiS+Rg281J5PBvoZNfDTez5Gg0YBPx/Pr0WlQobqmrb97C0naGacPJV644HR16v917JcCns7r2WtRtnua9mgV0LXzjHqDCqUV3W/X9lmcyArvbzdOMn01NJu3z/rv6QyOYJnLBIfr1y5Ejk5Ob29S4wNGpwG00mcBoN2/4Bv3ZCO5Z/uR9WpeDwSGx+Im+6ehMghvtieUoJXfk5CbnmdSxLMHy4bgQlD/JCUVibi+E62mGjq66OGPsKA/aW1iPLXiutOjvE//ZzUWfDOhpP4YV8ufFRyxMplSEkpcym+rrk0FovmxcLicODDPSex8nh2c5IIFcfjtTqY1uTCZrEjYG4kDtrr0NCimEoM8kGByYzK+npcM8EL/sZiWB2nCzqtwxdbfwJSks2In2ZEZZAZ5Q2nH4MwvQ5Ggx0l1gp4NwQg/bgK2SWni6lgHxWmTLAD3oVQwYD8cg3SK09PRDUolbhoqBqhhjxA4oVisx5ZNaePUSGVI0jji/zaEsglCshlPkiuLG2OgpRJpBjv74sIbWNSzqHMKPySVCeiLwnVleMjDeL7W5wNCMgLxvHt1agzn55sOzzKB5XVDSgpN2N0qAFVmRUwtXieQ4cYURrvhVSHBQlKLaxptSgqOf08B/prIQvywtHyOsR4q6GvtiIn7/Qx+hhUCPbXIim9HEHeKoRIpMhKOV0QenkpcNWSMWJyJM3e//ibo9i4M7s5B5/eSFw1dziuvTQOtNcfbkrD8l1ZInqy8XkGLp8QjntmD4eXUo4f1qVi2apkMSm1+bU6xCgy38ur6jF7ehRuvioRPobThX3SsSJ89u4e5GRWYOykcNxwx0QEBrvPcE9NopjEPS5Fr9FXA19/rUhxCQ33xo13TULimBC3ty+tqMOHK47itz25zV9TKWUYHm0UPydquRTxRi+kHy1qTj+i9QeiRwbhRLkZVodDvOavviRO3K6zjh7Mx2fv7UFBrgmTpkdhyW0T4Bdwup2C3qhQpGZacqlLjCob2GkwrX/XZyz7N8IshZg+fTreeOMNSHphyXZOgxncTE1pMGsegKGdQagO76e2Aca5/+sXaTBcrHcSF+sdq6u14LuvDmH/zhwRDTdt1hCX7RabA19uz8S3u7Nxw3lDsGhyBOQtIuao8FizNRNf/XQCAWEG7K8yo77VKOTM+ED8fm4c9lBxvyEVVXVWl+0jjF6Ql5kxJMyA268djcBW8XTp5dV4desJ1NZYoDtUifJk13QSfagO0hmBKIcdKpkUaeWu6Sf+WhmunSSDQlGFnIN6rP/VNeFFqZIifo4vChXViPbToMBaRD9lzdslTgkMVeE4ctyG8fFKeIVlA1LXY1Q7gnC8yIIEfw0Sggogl7keo8Phh7QaOzQyNSobalBnd51MppMbUGWRiqJupNEMhdQ1iafWbMDGpECU1toRFlaHKriOpGpsaqhOGFGY1QCDTunyBkpsV8gwMkCL0uxKaCb4Y5fUDGeLP9g09j1OqkNRShUCo3ywp7wGp6LyGx8DODHRX4/KnCoR2XgyswL1FtcYwvgQPawF1WJRoutuGgeDj+siV01v7ny91bjjutEICXTNF88qqcHLPyeJ+/3jZfGIC3X9RVxWacZHK44iOaNcxHumZ1e2iUlcsjAe00cFY9mnB7Drt8w2MYmXXpGABdeMhErVdrSfXsub1qTi+2WHERxqQFpyCaytkmwmTosU2egtC+KWjiaX4O2vDkGtlKGkvA7lrdJ+Iny94NNgE8VSuUKK3FYLTNFr//ZrRmH6hPYnAlKS05cf7sW+na4jpUqVDAuuHokZs2Ow8qvD2LI+Dc4WaUL0BmH2pXHijZRXD+LTBouBUKyTmpxkaLd/KAr3p556CgsXLsS5xsX64GbiYp119OLoD+/Aehv9Em9vtKWj7d/vycaLP53wuD02xICUAvcxiCTER40Vf5zV7j7euugzl8KjtYZF4Siscx9jSMZVKZGW5j5ukpx3gwE58mKP28f6+qPc4j6KkcR7G6FXet7ucOixt8xzu4NSKsNYP8/7T37J0aHG5vk60k0ByMxyH7lJoi8KxuEWZwFaG6kz4kCq58dogr8WmUc8P0ZxQ434958v7PbrqDMeeGYdslqM8rc23tcLGUmen4frb52AS69M8Lh93eokMcruyahxoXjk77M9bs8vqsa9T3pe6p0Ov50FWIV3/jVPnLnw5MW/r8WxQ+4jM0lsQqBYVMmTy65KxOJbxre/E2zAFOuUu36bfyE+/fRT6PV6rFixAn5+nlfyPRu4WB/cTE3F+rqHej6yPueVflHXcRoMO+M6KqA6LLA62t5RdXJqpcwe3UcH+0Crlfbg5p14DDq6fUc37+gx6vg+OnEQHX6Pdm/e4fYevo7OgKZ2ou5u7+gY6A1H+3fQ0e3bv3njldDz+zird8D6es56S1ZTGaYumIotW7bg5MmTePzxx/H00083/zyGhIRwBjs7JyQSSY/+DvRGC1d3cbHOGGOMDRBnI2fdlQR3/5gOS/BMZO8+gtTVG7G2XAtj4nRRyK957ArOYGfsDONinTHGGBsgzlbOemsqn0CEXLAYBZuWoWz/OngPHw+F4dy2w7BBTtLDnHW6fT/Rf/aU9RsWq+tkwa5qOenUHVkHp67kHZzZsjsckMrb/x4d/fxLOwjZoCjD9sg7bPWR9uz0nfP/s3ce4JEUZ/p/O07OSTmvpM2ZZZclZwwGDBiw4ZzT3/nO9tnn8/l8Pp/PdzjenXMCY8BgwIDJmCWzbM5ZOUuTc+ru/1M9kkaj6ZYWtAur3f7xzMNqakJXdc3MW9Xf937UjKE6JHKBniVEY7Y+0vTM4Q8zWVGeiHCpfE6YMYyEJHkSK8K5zCViPToXiFXjLAcwY7M0g80jgWUoMPTcXmO2YyCJpDMyf64ka5xgHMvOh6VxKSQhj/6nfgsxN3OejIbGSSmKRM/hNk+YP0eqccpDrO/++1db8IEvPYG/PHt0Rj/pmbhqRRX+8d2LYZ/mMEFDwjq3GclDfqwz6lCp4ECxzqyHb+8YvvO1pxX9ovePjeHLm56H6cNu1Kwv2kBO4Flsg/eTlTA7k1hVV+owQnCbdFjoteGIS8CCDb4yUW5zstj4d1YMMX60WL1gp63cDQyN6+pdaLQM4xyfCya21JucvFqNyYuOWBSjKR8glbqgEKycBxydxXkVVlQYynfQbLwTwawBr486kMo7y9rTgh1bxtygKBr15vJ2EyyQ/PXo8lJoOdtbJsacHg4bP2ABU9OPS5YbwE0T9UaWwRKTE/u7IlhR74CBLx0DYqW+3m3G0JEAFjY7YTOXn8eFLU70j8Twbz95FYMjpY48hB1v9OGrn3kU3/7HpxR9v48cHMU3/+Fx/PPnH8PenYNl7QN9YXzvG89C6AxiWXV5YpHDpkdbkxMH41nUr6kGPU2wGsw8atfV4nfPHcMfHt6PzBQbyKmcd0kLPvipdWXe6GTekNfd4U/iP362GSP+RNlz33ilG3d841ksNfGy88t0lrV78KNvXIwffeMiLG0tn8u1LqP83P/+xrPY8mpPWftgKImv3rsTm3gKvnPrywqEWGw6fOjTZ+OLX78Q1753qeyAM90tpn2xD889cRh3/2qL7LCkcWZBFtRVl94OzmRDJjiM4Zf+PHsehoaGxptGs248TjQ3GHVyeRGPPHsU9z9xCOlMcVe9ttKCj928HCsWed/8zAQQS+Xw603H8PDWPrTY9OCDaQwOF4Ubx9FoanNjeziJSp5FQyCN/qOBkl3RCy5bgBvevwIZVsCde/fi9YGBkvdo0zkQeSKIXCKPiut8OCiFIE75sam32DEapDEUzmCRz45Do5ESH/ZaqxGW4TwGj4ax9ko7wt4AUkLRatHOG2HjDbI3+rk+J2rMUeTEYvIXS+mQEhzYFfDDo3cgK+YQzk7pI82i1eaAkRmBkTWDphgk8qGSBEaG9mKHPwIKPHKiEZ2x0sI9SxwuLLCGZQvJzqgTe4LEh71IncmJeC6DSCYDY6oa27riyE1xymmwmaDvzWKkO4KzrrEj4PAjLRbFqYM3IRm0Y3tnCottdvT0pxBKFIWbw8SjxmnE3r4wlrpMyA/FMRoo+rAb9Cya6mw4eDSA6nHv8r6hogsNy9K49pIW3PyudoT9Sdz9m63Yu6MowMnm+LkXt+Cm21fKhYv+dOcOvPZiV8kYrDqrBu/7yBpZND9072787YnDEMZ92Ak1jQ6E9SyGwim0N7twtDuEzBQ7yUqPCc68iMGjAdSvqERHKIXYlD66HQZ8+Kal2LimBkok4hk8+Mdd2PT0UXibnYjqGAyMJkpqAtxwBfFGb8XoYFT2Oz+4d6RkLjcRH/VQEnojr/hexJf9d3/ei0wqhza7Hl37Rid92AnEBvO2j6+Fu8KCu17uxD2vdst2qpPn2WlExUgCwUN+XHxlK66/dQVMUxZSYyMx/PE327BzSz9a2tzwjyYQnmIXScQ9sWw9/5KWeZW49XZxurjBKJEc7ET3gz+AmMviO1/+DL74xS+e1PfT3GDObCbmevilL8NqnoMbTDwD+3n/PS/cYDSxfpxoYl2d7/5sM15X2L2c4CsfP0tVxBwPz2/pxY9+vU21vaXaivCe4RLxNRVHhQn+qxhkBOXwHB3DwMLx8KdTqqESNtGLw6PqNobvWq1DT3ZItf3aei84unShMBVBrML2gLo93gKrEw4dsRBU7iMFC/7SnUNOUusjC45iVK0aSfGk+GA1OhR2eAuvD1y5hkVvRv0YLaNNeGlXRLV9Y6UVB3eoj9GiFhcOdgRUzUVaSEGmfSOqoS1mK498TkI6VepLPwHZGXZ7TXLRHyWIIPasqcah7lLv/aksb3Nj92H1Cp4fvGGJXJBIjU2vdOOHd+1QbV9UZ8fozkHVuUyO/z/+592K3u6EdCaPr336EQT9xcXQ9JAW7rJm7BtUt6v812sW49IZPq/337UDjz+0X7WdWFkSS0uNM0esE4J7X8bgs39Aq9ciF0siRZNOFppYP7OJToj1V/5x7mJ94/fmhVjXwmA05kxCRRxNEJ9WuOjNws8SV5ZN51TFDSGZzKoKdQJpSwnKYQwEQZKQmSX2WaBmjtOnMHO7IB1Pu3ofc2JeVagTMkK+ZMe//PVFpKYV7ZkKeec8Jc56hWUm8jO8/kSuw0xX0JPJ3Iwx6MlETlWoy8eXFZBKqLeTXejZ8i2y+Zkv8SdmmevMLBVFSWGxmeZyKpVTFeoEvY5FeoZjIK8dT6vPdQKtn5vvAOmDxpmHc+m5sLWtlcNgvv71r6Onpzz0SkND462hiXUNDQ0NDQ2NOeNZfw0WLlyIWCyGz33ucwgGy/OGNDROGDQ199s8QbNu1NDQ0NDQOE042UWRZkJIRPDxj38c3/ve99Dd3Y1PfvKT+O53vwu9Xq8VS9I48dBEcM9hz1kT6xoaGhoaGhqnX1GkmaDwxedHkGt7N/oO/wxHX9mOF277AtxrL8ez/3i9VixJQ+Mtou2sa8yZ265bjF/cswsdveGyRStx1nj2lW743EasXOQre+72zb144uEDuOSqNqw/v7Gs/ciBUTz/4D4sa3RgX0+4xN1iwqWDM7BoXlmJ3v2jclzyVOwuA7g1bthNHEazQSRypfG8Jo5DndWKvCgikEohmC79keNpBvVmFwIhCjU2I/ojyTIv8SUVdvQNZlBZ7cBwujw5cbHDBZ5Ow8S5EcuVJyeaOSeyTBYtVjc6ooGyEvYO3oqBBA07XwGOHoWE0rhtnjYhljViqYPD0WgY6Wmx6SZWh6VOi5wkuj8URzRX2kcdzWJjhQ2cJ4X7tuvRHyltZ2kKi5x29B3KoLLVjtFs6Xkmr9tocSNaGcWSuAX7usutFhfVWZB3pbAIThzcFyyLTW+qt2DZBRlUdjrx+msh5KfFbfs8RjDNJtRV6TGyeRiZaXHXVpse3kqL7AxDEkjjsdJEWoORQ12jQ54fxIZxegImcRa64rpFaFtdjd88sBedfZGyuby4wYH8WBzNNTZ09Jcn0p69sgpXnFc+hyc4tH8Emx7arzqX66ut+PCNS9G1dxhPPLQf2elz2WmEud6Gn9+zC7dduwjmadalsWwW9x7YD8/lbkReCiMcKN1d5XkGTQtcCAXTyNsN6A+nyhKpV1Ra8eQTh1CpY7FoQbkd5OsvdmHfriH5dTqnOC9N9qHJie6OAF545qhsWzlXn3qNU7co0mwFk+qv/yy6H/oxEv1HwJkdAK5/R49J4zSEnqNX+jzyWdfcYI4TzQ1mZojweOblLvzhLwdkS7u6Kot8X/8Uq8X1K6vwkfcug9dlxNBABH/89bYSD+y2xV7c9rGzUNfgQDiYxH137pDFwQTuWhsEtxGdA1EY9Swaa204eCyACc3jtvCo41h0HRoDy9GoWVeFNyw5pKWCuLUaGCyp1eNYhLiqAG0uF/qi0UkBTxJZW5xOHA0GkRNFNNmc6BoSMBortBPZsbTSgY5ADIlsHs0uCzJ5oUTAb2gzQDIFEMulUWGwYJWbgpENlAjzvJhFWoiDp43QMcYSAS9KdvQlGIykItAxPIysDXsCRavFGpMB51YQi0Y/aDCQKA92jAWRG++jjtaBoqw4GvHLto5LnG5Y2BBETAh4BinBjb1Bv5xUuszpwjpPAHq20AdBpPFaRzMe3JNFKidggdOK8Egeg/6CsKMoCeevMyDv9CORz8BnsMoLlqFkUbzakj4c2sdiOJxGhV2PuiYK/WKxj9WCBdn9EvoH4rBZeGy82ABTy7AstOUxiFiw7wUTDh6JQK9j0LDUjR1iDBPy3EOzWDQoonvXiFx0aEGbG10dwUkBLwvzBgeOHhqTk91a2j0lAp7MjZZWtyw2iSBesaYa7/vIWvgqC7aRZN4+/XIX7h6fyw2VFugiaYx2FxcptUt9GMjkEQinUVNhwcduWaa4GCUEA0n86ffbsfnl7uJcrrMh7zSiazAKk5HD+65ZiKsubJ4scOQfjeOe327D9s194DgGdUu82DsSn0x0tpp5eZF82cYGeWI+192F+w4ckAW7PA8kGu39ZnS9PCIn9ja3uhHwJxAOFs4jWbB4NtZjVyqLRCaPVq8ZzFgSo4NFx6Pz19XiQzcsgdNuQG93SPZSP7y/6AbU0OyUvdXHRuJwuIxwuozoOFI8z40tLtz+8bVobvUojsuZxunuBqNE5PA2uVgSKZr0/W98CZ/61KdOyOtqbjBnNtEJN5gtX4fVrH/rrxNPw37Wd+aFG4wm1o8TTawfH0TckF32l7b2K7YTP+lr19Vi06MHISg4e5CdOLLL/tLfOlSdPVrW1eLQaLzE53oqi+tt6PEAg6Ly8xvdBthtGfTHy3eACS6DAUbRih29ylaNVh2HVrcV2wbKdxYJRp7GB87h4TX3yuK2HApOXRVCmWFICi4xZNc5KdTg5aEQ4nnlPm6scCGeTSCYVY5NtXI22HkRgqTcBwZm1FoouPXKVoyxlAl/erUJr+0NK7++icaVl3I4klS27KQlGq5EAw4lh5GfdiWg0Elgg84DT+MQKE75PMW7q/FMt4CAiovNckoPbnsQY1P8yqdCxDcp3NM3RWRPxe40yBaD61V2w4mt1+9+vx07/tahaMTD6RhccMMS3PKeJbIXvBL7dw/hJ999AWkVB5ZlFzTiox9ZC9u0okkTbN3ci1/+eR9GosphDS31drDrJXRFlC0zPXkdKnfQOHZQ2W7S6DTAtroKB3YPK7YTD/x3razGpscOll0JmBD9S1dU4cCeIeQU3H7IAuz6W5fj2vcuw5nOmSjWCf6tT2P45QdlS8ef/OQnOO+88+b8mppYP7OJToj1bf88d7G+5t/nhVifP9cANOYFFhOP/AyVS4k1XndnUFGoE4gg6OoIzGjBlwyXFqSZzmA6pyrUCV3+FAZUhDqBhMMMhtSfH83k5Jvq8WVF2LmcilAnSBCkvKJQnxA4gpRRFeqEjmhGVagTUkJMVagTBMThNah7bVsMpOCN+vtHEyIS08I0piJSIhhbSlmoEyjAUJNVFeoE1ptWFeqEPuRUhTphZCgm39Qgu8zVdXbVdtm/N5ZVdczMZQQ4eUZVqBOGB6KqQp0ghtOqQp1Q3eRUFeqEYz1hVaFOGGMzqp7rhGQwhcy0cJmppNJ5dHcGFIW6fPyChHQ6pyjUJxaevZ3qvvUapz+uNZdNWjp+7Wtfw4EDB97pQ9I43cJg6Dnc5gnz50g1NDQ0NDQ05hWkmq13w7VYvXo1MpkMvvCFL2BwUL2InoaGRjmaWNfQ0NDQ0NA4aVA0jX/6p39Ca2ur7L3++c9/Xg5l0NCY48TCnHbVyfPnCfPnSDU0NDQ0NDTmJQaDAT/60Y/g9XrR1dWFL33pS8iOJ0RraLwl6DOnKJIm1jVOOOeurZXdKpSo9pmx4dwGuL3KCVBWu162mKuoKjhzTEdv5WDcYETzMuVkEBI/7Kq1o91qU2ynIWFDXo8lfhvJrlZ8TA3lgY82gVP5IK9t0GPNgixsBuWPz9IqHhRLg4ZB+RgoI6JZFiyl3EdB4hFI6VBlVO6DiWVwc2MFNvqqFNtJQfsLqxxY7nIWMjmVjtHphI1zglL5CjBzbnzysiycZuUxaKkzQMhzMNDKyT1evRG1Dgk1VuV2i8TDv4ODEFSOGRdFFsNBG1rdymPEAVhAGVG3vAJqtC70yI4kamGJK9bWwOVWT8Q7emgUkijBpDKXfYssGGkII5hOqLrAEHeUCZeZ6egsOkRqrdjVrVzlMScK2JHoxIoLrKq5DQvW+9Bg8KnG1S+tsKH2ehs4vfIguJd4wVSaYDCQES2nymuGYNPD7FSey+RzvOH8JlTWKB+jxarDunMblA9O46QVRcpG/KfULRcNYGiIJCHn5Lh1lmXxxhtvyFVOe3t7MTAwIN9EUT3fSUPjTEZzgzlONDeYN0c8kcXdjxzAUy91yclpxFXi5ne1490Xt8iCOpvJ4/GH9uPxh/fL3tcMQ6Gl3St7NBMLPuIKs6Ddg76eEJKJnJyQ2Hh1Jfp8aURzhd2YBQYnBl9NIjhSSMCrb3NjEBKGxxPyWqssSBvyGEoWEuzaKR1s+2MY7i0k5FXUWyGuM6DbUEg2dTJm5EfMONRfSEr0Eu/uCh6Ho4XHV9pYXLRUQkgoOKjoGR3SUQ+e3JOEBAoOA42bz2IhssTlRQJLsagyOSCIIwAlgpIYUJQXg8kgBEkAkfOVJhcojEFCHpJEIZCpwJaxKJL57KR/+Wgqhni+YDt4fX01LqhiwVCFMUjmjXisZxQ9icIl5WVOG1a7MwA1cYnZjoNBBn2JQp+qjGYsdhKhVxCINMxICzziuYKdJbGTtPIWiBh3D5EMeG63D//7ZOH97GYGy5br0JEgxwwYWBbtXguG8oU+E/tLYgcZyBSsIRmKhpX24qWuGNJ5EbQEtMTdOLYtLCcvkvXQWWc5UbfOD0pX6GM4XI2/HRQRTBbec6HXhrFEGv5EoX2RzoTs0QRGxxMnG6us4IJJjI2f16rawiJncNwrveAKw6Kvu5Do6Kuy4P0fWYvlq6sV5244lMKfZNvQTjlBkoh1koh65OCoLIoNNhatt7vRYwzIfeRoBpdVL8IlNYvkf+dzAp585AAe+/O+krlM7A9TycJc9m2owz5BQGQ8kfqSJRX4zOVt8IwvbvYE+vFg13YEMoWFQBXrxMCLEvqOFZJBa1rtiFex6I0WxqDeYYTVmsdwuuB8U201wOsABlOFPjtYI3Q7dTj6VOG8mslieUUFdo0U5oXdyKHFqEPXvhG5jyYDi4YaGw52BOXPL7HQbK+wom/nIMS8KC+or3rPYrzr+sXy2ObzIp557CAeuX+vnBxO+nzhFa244X3LYSKJuhpvmxtM2ye+D0anvLh6J2EtdlDjYQfJwQ4MPPN7SIIA64LV8J37HuRjQTzzlWuPq3CS5gZzZhOdcIPZ+21YLXNwg4mlYV/6jXnhBqOJ9eNEE+tvja6+MDZt7sN1l7bIfs3TIR7ND/5xp+yJ7Vdw9jBbeNQuc2F0qYS+VLmzh45h0JRzYWiUxoHh8nbiW728yQZnVxQ9O4fLivEQmtd4EW6xYfvhKPIKrhftNVYsW56DqB9CTip39rBzNiBtg8M2gqxYWoyHYGJN8BpMGEnFkcyXO3MQ0W/jbXhjLI2hZHkf9AyLJQ4PbmoywjTuhz4VIvIHkuQLKwAdq2zFmBV8ECUKenZEcbedhhuiXCONiPZyl5dM1oU7N7twJBtEclphKUKl2YRFlRwEKoxEvtxdxMQakBpx4eimNIZGys8z8Ro/9wor9iRpHFKYBzqGxmK3HTiWxLGj5TvRRBwuqbdDH83gmOyvXj4GzW1uWaATgcly5PpDOS8+exT3/m57QVRP72O1FZ6zTBhrj0wunqbi0plwMbMQf/3VAYxM8SufwGzRoardjUMWDp2B8vNo4Bl89OJGBC29OBAeKmsnC5/qjAf9o0bsCZU7wJDF3bJqG2qrU+hNj0BUGIRa2onILjPeCKSRzpWf5ya3CXUC0N0TVnRc8rmMWF5pwc03L4fHZy5rJ/URnnzkIM65oBF1jeTKjsaZbt2oRrRjN/of/6XsEuNaeREcy87HE586VxPrb+Ocmfdifd935i7Wl3z9TY/vkSNHcP/992P37t1YsWIFvv71rys+7n3ve59iqNeFF16IT3/602/qWLUKphonlcZau3xTg/zg19Q78PpLxYIxU4nHskiZRPSllK0WM4IAP5vGgWFlG0FBlHCkK4qKHco+0oSObaMYYVhFoU441B/FWetziAjKFnzhXASLXRzC2XIBR0jkE0jkTYpCnZAWMkilJEWhXmjPw8QKikKdQCwiK43EylFZqBN4hoh0squlYsEHUmTJDFHFTlLHBwCzCckRZSvFoXgCyygrogpCnUAEPB0WFYW63J7M4dBR4BCn3J4RRIwEE4gdVbYBJLu/B3pCsAyrWzl2HPbj0186T1WoE157oUtRqBOGBqJwNxgUhTqB7IJvPtCtKNQJpChThIaiUCeksgKeO9IHfXO5UCeQXfwhPoQ9Krai5MzuHoiA88QUhTqhTwwiLJkUhTqh05+A16RXtUYdCSRRcV6jolCfqLJ664dWK7ZpaEzF2rwcVZfcjoFn70Jg5/OyaAfO1QZJ45TmvPPOw8jICG666SY89thjiMXU7YEfeughWZh/5CMfKbm/oeHNhwZqYl1DQ0NDQ0Pjbce+6GwImQSGX3oQge3P4vHHV+HjH/+4diY0jg9qjo4ub+G5v/jFL7Bw4UL53yRhejaam5tx4403Yq5oCaYaGhoaGhoa7wiulRfDs/Zy+d8//elP8cwzz2hnQuOULYq0cFyov93Mm531PXv2oKOjAx6PB2effbacTf5myOfz2Lp1K8bGxtDe3i77vWpoaGhoaGi8s3jWvxuZsB9SYD/+5V/+BRaLBevXr9dOi8a85/XXX8cHPvAB2emI6E4Sx05220+7nXW/3y/HCF100UW488478eEPfxgtLS3Yvn37cb8GSQSoqanBJz7xCfz+97/He9/7XrzrXe+Skwo03lmIcwaJ5SXuEkpQNIUmnxNmTtk+j+DgdPDMULK9ym2Ct0o9ecRTaUGNTd09wW3RgRHVE7Z0DAdR4kHJKX7lMBQDO8+DVrnkRp5nYDg5kVQNnmEhScr2egRJ0skx52owlA0MpZ47QMEEETMlpelg4jiVHhYSfSVRvY/kfp2FhU6ncp4poMFHw65X76PHoYNbxUKQUFlpga9G2e5ywmawV8UmkeCPZcDN8PoGIwcqqwetMgosRYMy6cDNMJctOg5mvfp59ll52Hj1Y3CxFlRZjKrt1TYjHJyyVSTByhngVLGiJFj0rJzsq2Y/zHMMgrk8cvmTZ7F3cO8whgffesGckaEY9u9WjvvXOJWrnF4j/9aTjbUvf/nL8gadhsYsEweToTBv6UZNJqxOvZFKuycCu90uLzrPP/98ORGVxLAvWrQIv/vd704/N5grrrgC+/fvl8U5KaZAPsjXXnstduzYIWfkkhX4TDzwwAPySuYPf/gDbrnllsn7n3rqKXnwKirUfZqnomVln3h2bx/AH3+zVU7IczgNcLpNsi/1BE0LXLj942ehaYEbsUwG9x44gOe6uyZTJD16Iwy9HI5uj8gCqXapF7tG4sgJBSHhMPGocRqwty8ChgI2GnQI7h6eTCDUGzhUtXuwazACQQKaFnvRkckhOJ5cxzE0ljbZcCQZRkYUsaFZj9qqCGL5+KTI9hrcOBSKIpbPosliRbMViOeKi8BqkxtLHCKMXAaJnA77ghQGk4HJditvRyAtYDQdg47WgaKsOBrxT/bRZzBjtZuGkQ2Ap3kscTTCwqdBjT9CAi/bPCYFkqBLgaW8EKSAbAVZOEYeOtoHmiokwYgwIyOQ109PurJn8h50Rf0QIKLK6IRTn4KEiURNCiPxSvzvtjQCKQF1FgvykoTBeDHht9XpxGgigXAmg3q7AQsrgEC2KIpdvBMHh4GecAo2SQdnnwmH9hfbG2vNuO7aFKrqxpDO6fDMgUY8eSgxmSTpM+tR7abQlwqAlSg0hV04vD2E7HiSJPH0dzc6sM0fk2X0RpMB4T3DSMYL51GnZ9HQ7MKxQ6MQBAlrN9Th1g+tgctTWJzkBRF/er0Hv3+pA8mMgKVmHSxdEYyOC0byfV6/vAI7OQqBrIDWBh2al6QwnC0mu1bxbuzdzqJ3KIcqPYf2WA69B4oJv1X1diRMHLr9SZgsPFytbuweimAip7naqcfaVTlI1hF5cWdk3Tgc9iMvFeayhdVDGnPh5a1pMDSwvN2OjngUyfExMPEsmp0W7BsJya95TpsRotGPWD49uZDwCBXY9HIOqYyI9kqrbBs5FC4kBBNxvqLSirHDAXncase94fuGislTLS1OdEPESCKHWpcRn7+yHesXeHCi8I/Gce9vt2Pb5l7Z3vXyaxfi2puWQjfDAm4qmUwej/15L576ywHkciJWnVWD931kDTy+mX8jTjc3mCVf/i0YnfqC7lSFeLH/5tZV+OUvfyn/3ptMJnzve99DU1PT5GMqKytB07Rm3XiGE51wgzn0X7Ba3rpNaTSWgr39K2X3f/Ob38S//uu/zvp8s9mMjRs3ynpSCRLJQaJBJhAEAZdddhlefvllHDp0qGRuz2uxvmvXLqxcuRLf//738fd///eT9+/cuROrVq3CT37yE3z2s59VfT5ZHdXV1eHd7343fvWrX83pWDSxfuIYG4nh7l9vw66t/WVt9U0OeZfl4ivbcO7FzfK/p9IZDuGuPXshjAH7nw8gnyudvk6PCbpaK1ieweGhCFLZ0h1AJ8dgVR5g8iKOxTMIT3P+0OlYVC3xIMkAETaD0fSEqC1AjESuXqGHxZKEPyOiN166A0hE9FleD9y6PBY5eHjGPdynMpKy4FAojVSexbFYcXEygYWzIZaj0WBm4dEPy24vJX3QOdBu88ne7ZFcB/mZm3YMejCUBTRFCi/lQVGlLjaSRCMv6ZEVsuiOJcusFsnOcZOVfMHk8ce9LLYNlzu8LHS5kMzlZb/AHoUrVGfVWOEwZxCK67Clv3yXtFawAcckrFstYdnqXtDEgH0KoxEXHtztAcfR6M+MTorWCRySHvZeg3y1YU8yjXi21N3EztFYI1DgUnmMDMcQDZeeR17H4OoblsC3vAI/eeYIevylLjLy4s6oAzWaxIjXgIMK7ijnrjXAXJFAsNeEN3aXj9FKsw62oQRomw57BiKyF/9UKmqtENwGNDRI0FX2yedzKgbGhJxoQDqqw6ZXskimSsfIbuHQ2GACxUroDScQSZfOA5OOwsbF5IpTFnt2FBYSU2FpYEmtA6l0DmwgjeFxX/qpLGxxIZXJI23VYX+w3GnnnDYPvnBlO6ocxjldXfsrqbnw4D5kp51Hp8uIWz60Gus2zuyesPW1HtlyMzBWeoxkEX/VdYtw9Y1LVa/evV2c6T7rx+vFLgl59D/5W6RHe8EYzKi9+hPgrS5ZzE/4sGs+62c2k2L98B1zF+ttX0JfX1/JZ1Kn08m3uYp1Jchjr7zySjk59fOf//zpEbP+3HPPyf+/4IILSu4nAp5cXiCJKDOJdTIoo6OjctjL8PCwHLNOVuWrV68+7h11jRPP808dVRTqhJ7OEM6/tAXnXdKi2N5kd+A6dzv+5e5XFNuDYwnYRQkdvPJ1/GBOAJlV7rGEohc32Z3r2j4Ew7lkt7hU4BHIRubD29O4bC2DwfECNFMhguyNUT++tKwaHoOyzaDPEMNw0oq9IeVL9bFcBAvtDlg45fZgJoQD4RyqzcqhAmTXPC/lYWKUdxQpSgRHJbEvmEFOLO+jCAnHoqN46kgVOkLKtlQHAwG0OZ04HFQOKyECfZHLhQOB4lWEqfQxEbz3ehOW+foU2722AK5fSeN/tylbDIaoNLKNEo7uUP4KC+dE+Twv6goimyl/DXLfQ/fsRmYwjL5QudAmV1peTGSwqNWJgwPK4XIvb01hRb0Lu3qUz/POeAbrGu04tpfYZpbPx+G+KNbUS9BVKVtupoQE6AyNx59VbEY4lsPOvSFwlcrtiYyEp3ek4UgaMBopP88kkoUc+xqeQ5eCUCccPBZA/epK7FcJS3n18Bjq3SZ8+rI2vFUG+iJ4+N7dqlVgf/t/m2cV63f+/A3EouWXrknBNVKoadnqarS0nbirAKcyppqWeeGzPhONN34R3X/+IdKBQQw9dzca3vslcFbXO31YGqepG4zVan3bfOwntGdQ5bdzXor1Y8eOyf8nu+PTIfdNtKtBQmUIJATmkUcewbp16+QBIkb2ZEXz3//932U7t1N35afGLZGVnIaGhoaGxqnA6fwbRRYbddd/Ft0PfB/ZiB89D/8E1Zd9QN5Rn7gRJv5/unK69+9MZMe4LiW5l6eNWI+Px8WS2LXpkPsCKrt2E0ysXIhQf+ONN2QXGMIdd9whJ7AQY/rPfOYzis/97ne/i29961snoBcaGhoaGhonltP9N4oz2VB//efQdf8dyASGMPDMXXiqKSPHrU/w7LMql51OE5JJ5QJqGuO8RfvFSeby3OOIDHG5XHIkyAT79u3DP//zP8ubze95z3tOH7HO8wXXAlKu1WAojUsi9+n1+uN6PnGQmRDqBBL//oMf/ECOY1cT61/72tdK4uTJrkVtbe2c+qOhoaGhoXEiOBN+o3ibWxbs3X/+gRzD/tprr+HnP//5pFC/9NJLwXHHl4A8HzmdrpacLkWR/uu//gtbtmyR/51Op+XcyomiR8Si8ZprrpH/TcJqSJg2qXZKdtHJ5jHZVSfuML/97W8VN6HnrVgndosE0lmSTDAVEoM+m1flxBfXdE91Ere+YMECOVFVjeNNMNB48xhNM3+5SsTuYgayubwcAayWGW2w0OBF8jjlR5h5BkY9i0SqNPFyAuJEoZ9hxU1TEjh65o+OQph0CSZWzQSxgCQRq8ZJZ6kyeJECJGJbpdxHChwkiQFFqR0IAz3NK8asT+A1UuhQDseWqTRzODxD2J2Om7mPmdzMYyiBAU0Jk44p0zHraRj1NJJpZRtBo46B0cgjmymPSSfQDDWD2SUZewk6ldyHCfSz5DbN9g1CcfSM51HPM9DzFNJZ5XaLkQXDS0hMS8ycgJhtkj6OzjCXWePMiZem2cxUZvm9I0mfTrdRNeSQJHUzLA1BxQ5ytu+LwmN4xZh1AsNQ0M9glzlfOVN+o/TuKtS9+/+h64HvyyGs3/72tyedOohQP53F+unct/nKueeeO+niMtVhkNDWVszdOeuss/DKK6+gp6dHdn4hXi6koFJ9ff1bet9T+huMFD8iEBunqYKbCPWBgQHcdtttMz5/w4YN8v+VwmWIfzu5RKHx9nPNjUvhrbDgvt9tlxPIJjDZ9LC1ufHozgF0/+BlfOyW5aib4o+eTOfwp78ewmN/O4aqCrP8I9w7UEyAJHWyFl3sQg8XRhvHg41YsPNgosSpZY3bAn9XSF4QLGpx4sCxUrVZ3+bGICQMdGexpMGBQ/GwbFU4weJqPXyVCfSn/GixetEXDyAjFoVSrcmEhXYe28b64U97sdCRhZ4pxh0S95KcRKPS1I3bFrixaSCPgSmXOnmag9dgx+GIHx69C/Vm4uZSTP5jJAqLBR724EEgZEbSW4GUofRSacG+MSwnKOoZF2hqmkMG5QVLS1jlFuDPeHEg1AFpytLHo7fDZ8xjsXMA59dX4n+3JhHPFYXU2ZU2fHKVBUYugWsX1OJ7m0cwPMUtxWPSod7DoS/Zj7UNbnSP5TCWKAopM8ehxmrFo0cDOBSox1UtMbhMxfOQF1nsC1Xi9ZEQFtaYkE3pcdQfK3FqOa/Rjiztx8JqFmO9Ljz7Rumi47JllXLSo14C/nzPLrzwzFFIU1R/Q7MTiXgWkdf7cdHKSrySySE7tb3SCM5O4UAkjFVL7ThyLIZ4qnieq706NLRK6Ir349yNLhzaL2AsVDzPbp7BiqyEnuc7sbzZhTGawuC4TSJBb2LQdpED+4UAqgJuNLjzSCNU4shTbfJgOBXEDdfpMNDhwAvb0yULiXUrLAixITl31cXYsa07VuI4swp6SDuCSESDuHBlJV5OZ+Sk0glaaswQzSJ2x5JYdmEl+jaPITNlAVvbYkD1+RSG8l24usmH11+XEAgXx8Bt06G6To8HjnWhPxfHZzYslH3eJ0jEM3IS7/NPHZHtV2/72Fo0tpR/51ZUW/HvP7oad/9qa4k/Ok1TuPDyBbjh/SswG9/876vw8H278bcnDkOcch4XLvXhto+dhZo69RoDGqc+xqomVF1yG5jdD8k76kajEUuXLn2nD0vjDNxZX/8mi3URcf5WBfq8sW4koS5kB7yxsRGbNm2a3JkhHpjf+c535PifqeEtxFPd4XDgkksumbyPWDySJByyIp+oekouYZBk0y984Qv44Q9/eFzHolk3nngy6RwefWAfnnn8ECqX+HBkNI5kuigWGJrC1Rc149ZrFuKN3UO488F9CE5xtSDTob3JhcGRGLytBqTrcxhNlwrXBrMDvUcBXZqGJZZD30DpZcUJP+mEIIKrtuDAcKn7SaXDAIeHRVBKYG07jYHMSMmOvoUUZNJbMJQMYYPPiXTeDxFFRcTRHBbZ3ai3kNc1IiMOT3qgF6ARSvvwVF8ILoMLwXQUKaEobImXe7PVBacuiAaJRW2sH/QUH3eCaKlD1K2DxJpk+z9BKm1nKSc4WgeW4qCjLaCmiXdR0qMzGpNdZhqsJlDUWOmJkox4qdeBv3Vl8JWzK1Bvi5d4m5CrAK8NcPj5zmEsrbFiKDOG3JQFDEczqNR5sLsvgmaHE73RKBJTEqdoisL5dTZsrBvAaNqJF4ayCGdLd8PrjG50jGRRbdXBZ08hkis9Tw7Ghdff0EHI6vD3Vy3E8npHSXtPZxB3/XILAmNxOJxGdB4NlFl+iq1OHJUENDWZcCAQLtnRt+o41OrMONwTwdqVRvRk/MiJxfOsZxhUM25s25nAOo6Hf9cw0qliH8nOceNiL/YFEmg624FRRxzR7NTzDCzzOWAyBeE0GJAWsohkS8+Tg3bj5dc50CwDe00OA4nSuVxjtiIa5ZAN5NHQlUXvwVJbUHeFGZlmB7opEfUNRuz3l7oZOQ086rM8Rg4FseIaE/r50Umv+0IfOZgTXrz8RgaLW2w4Eo0inZ9ynhkaNy9vwPuWN2HLi1348907S3a7SVGo8y9pwU23rYTZqrwjvO31Xtz7221yzYXbP74WdY1OvBn6ukOy6B8dicle+medM/cfyflm3bjq24/MezcYJUii6VcXS7KVM6kGSX7Df/zjH5/Wu8+a7ph5XMJdP527dWPj/ztpn8kTySkt1gkvvviiXG2UVDAlAfmkqtn//M//yMUSpsbrEUgMOyl0tHnz5sn7SEGlCy+8UI4Zuv322+W4IRKvTv4mCQCzFVWaQPvQnDz+/ORh3PXwftX2RQtcODBNXE2lrtWCkWZli0GCieIQfjKnGk5BRH++yV7muT6VK64WMJItt2qc4LJqN2K5aSJ3Cht8bpj5QdX27lglNg2q93GlwYBLMuoV/US9A8Hamb5sKLj5Rtm2UY1gNlQm9Kfi4BaDpcu9tid4sIvGpqEB1fZqrhI7BtTjapb4bBiTiM2hMpVGMzha/TyzFIPPL7lZXuQpQb7qvvSJh+EfVe+D4eZGHIqUe+NPcE6rCUci6ud5xbANR55WP89NV1Rin089DrXdbYbFqv76PKXHax0TJbHKIT33PJSXqwKrwby3AZ1R9TG4cg2Hvoz6ebCmGvDaMfU+bJSM6Hu6R7V9yYpKfPlfixsq08nlBHCkoMEcOBGvcaLRiiLNDeKzfuftZ8nhq0QDkPH86le/OhkjPLVo0umCpjtmHpfwGSTWT+kwGAIp00oE9+9//3t5d93tdstxQGRVPR3ipz79csPixYtx8OBBubwr2VEnSadkNX7zzTef1ivy04l8fub15NTdPyUEUVQV6gTydGGmB8jx07OtaWdpV4lJnnoMMz9g5vLukiTMfnyzHANxWJ+J6cWZyt9htvM087sLs/ZxltcnUe4qQp1ArsyJxEB9xmOYYx+F2Y9xxvZZxoCMEYnlVz8+UiVvtteY7Rjndh7UYs8n22c5vhMhsk81of52kouGIOrUc1HmLxQ+8pejcpXnUM06BHY9j7//9h2449VBmBsWlxRN0jhToOcWBjNbws0pxCkv1glEgJPQl9m46667FO8nself+tKXTsKRaWhoaGhonDqcDkWRZqPiwpshiXmE9r+G4ZcfRIOvTiuadCZCn7rWjSea+XOkGhoaGhoaGmc85CpZ5UW3wtK4FJKQR//jv0I+pR6+pqEx39HEusZJZ65pEWr2hcfdrlDq/c2+xpyZY2bIcfQApzqzHeHxnKe5vP7xPGjOxzDbRJp1HlBzP75Z+zhb+xzn0qxPn8229JROo9I4RaBoBtVXfBA6hw+5eBhDz98LQZgtHFDjdIKiaFAUM4fb/JHA8+dINeYd6Uwedz28Dx/+xyfx1EtdJZZqE7GrTz1yAC/evxfLGx3g2NLpSHTPwmYXRv0JLF7glr3Rp9PU6IAfPGpEH2x8ucNEjdECW7cBzfU2+NxFW7kJvG4jqhZ74TTr0Ogpv3TsMPFY3m7DsS49qvTusnY9w2Ktx4OsEIdH7ylrp0FDjFfha3ex6BmuVfjIUbLVYr0lhLO9bkX/9gVWL9bUe4GqtQBT3gcYKsCYHLBkbKBR3gcGZlj9FDDQD0ksHyNR4vG3AQb3HuOQE8qt9SjwSOZ82BXoR1oof39JorFzwIqHNguo5itk28HSMSCONh5EEcKKSid4pjyeuNXpxHA0gxq+AgamPJekUu9ER5cV2UgNTGx5QpGNtaLvYCU+9/ut6Bwt32ELZ2J4qOtFNL2fQ31ruY0fcSepOKcOgbEcFrpKazoQDByDtQ1WjKZiaHe5yuQmQ1FYYPBiSwKoX1ste/VPn8sL2j0IbYtgadIOw7gz1VTaXHa4bSlUG90wsuXnyamzIQ89llWb4TWWnweP0Qgf64F4vg+VDeV9tNr1qF1ZCfpYCu2O8sR6E8/i3DYTokIMTRZ3WR9ZikalWIX9e9NY5nbIfZ4KSRdY32SFe30Aq9/tg07h89rQ7kFHKof//cMORBWSYA/vH8G/fPFx/Pi7L2BsRNsp1ZgZhjeg9upPgOZ0SA13yblpGhqnI6e8G8ypgpaV/eZ4eWs/fvfnvfCHihZ8LfV2fPyW5Whvdsl+ynf/eisG+4ruI3afGVytDUd6w6iuMIOlafQMFl0nrGYe1T4zDnUG4XIYoK+yYHeg6GphNNBYs8KArvQYjCyHipgZ+18ITiZvklpL7c1udPQWHEnqWt3YFoxjioU4ltXZ0etPIJ7OY2mzHR0p4ldefMDaZgN09rBsq7fQ7oadj0JCUXTYeJu8MxjNRWGQ3Hj8JQ6H+oouM2ubOfz9uxMw6P1gKLucWSqgOAapnAE7AnYciQTg1JlxcZULjZYpPu0kgXE0AAQOAKwRsFYBVNFhhSQfpqy1SLJBUKBhTBigH9wPajJpkAJq1gE2IuoFdMYM+OWhbiSmWPBdVevAWi8Z9xQE0YuuWAQZoeijXmOsRKPVAoZKw5+w4JvPxrB/pDgGLT4dGurSGE6HUG20yzaOo+mii4uFMYLJWnE4EEalyQwdQ6N7SqU+i47FwkojepNjsHJ6CDEnNh8tziMjT+PqlTxSzBBYikXG78NjLyYhkuJC45afN5xVh49c2CwXNdo8uh9bRw8iPyUJ13TMhcN/jSEWyaBuZSW2QER0SmLkwgYLEnwOQ7EUllXZEKdCiOWLSXvVZrNsOdkXi6HebMfAMRq9w8UxqDNwaAln0XtoDJU1VtmhYqC36CZkrjDAdbULh3IhuI0GtPsYpKdYZnIUA5/RhcGkHzqaA89YsS84NumjzoCGj63AwZHC3KkxurG1Mzbpo05JEs4WjIhuGUEqmUPjEh/2+RNITSme1LjcjSGriLFEGqvqrIBtGBkU+1hlsEOAiJFUFFWcG3t3sOgdKs7Fao8BNi+Lo8EoFnhMaK+PQ+CKfvl8Uo/0JiMObvHDU2EG5THj8EhxHpiNHN737kW48oImREIp/On327H55e7iGPAM3nX9YrzrPYvB6+ZFetWMaNaNJ3Fsj+1E76M/wwKPGXfccYdc2fR0QNMds4xL329htRrnML5J2Go/PC/cYDSxfpxoH5rj51f37cZjz3coTzgKuGxJBbb8Tbmd0LKhDtuOBVQjBtoWerAtlkJWxXmjvcaAxKEwkjHlCqV2iw5RjwFjU3ywp2LSMahtNKAjomwTyLMUPno+ixyGVfsQHmzAPc+r7wze8Xc6tFR3qbancg2oNhUKACkhJUQgcgRQqVAqMBZQ4THQWZVj4Mz4hbgY+0LKVopGhsZtC6wIZtRsBCkMjbbjF5vVS5hes8aIrky/ansdX41t/TOcZ6cT249lkZq6mpra7tVjqDuPQER5DJxmHtdcHUM4q3we6RyDrs3V2DVW6s0/AcsAG9ebcSSubmPYxlVj01Z1S88rrAYce6VHNfxl6fWVkFYQEa7cRyfvwJFIHElBeS6bGD06jtrgTyjPZQtFo3kQ6BxSngccT2P1rTYMoliMaDqeWAOeeV3dMvO6C60Q7Z2qoWTGrgo8/1SkpCDTVJa3uDC6YxDpKTUWpkIWO//5v9divqOJ9ZPLwHN3wz20QxZdd95552SVyfmMpjtmGZf+389drNd8cF6IdS0MRuOEE5hSqXE6ZJd7atVSJXLp/IyhvcmcoCrUCeGgoCrU5fZYBn4VoU5IZATEBfX2bF4CR6m3E4ZI2MkMxFIz29e59KKqUJfhGVWhTmCEhLpQJ+Ti6Imrn4ekICKeUx9Doj4PjM48BmkVkT15CNLM5zmSFFWFOmE4JKgKdUIwnkU8p95HkRMQmuEIyMWGPGbuYzw+cx/JOM7USTGRURXqBFIgS02oy++fJ1c31I8xJomIZtTHKJcVAVbdk50QVrfel2HZzIw5H3m9qCrUCcFQSlWoE0IB9e8TDY0J3Ksvk+uspFIp2f0tHtfCqDROH+b/tUUNDQ0NDQ0NmWw0ACZz5i1w8vEQPvjBD8rVzTs6OuSiiT/96U8nK5drnIZQc/RZn0cJptos1tDQ0NDQOE04fYsizQaFL/xtGJmWK9F3+Bc48sjTaGn5Ab7yla+80wemcbKgzxyfdU2sa2hoaGhonCacCUWRZkJn96Lqktsx8PTv8cc//lGudk4qoWtozGfmz7JCY95w9UUtqK0st4YjLF/oxdXXLkJLe7nNIaGy2go2mcMilee7nQaYQGGt2wxKIRjYZuRQ4TGiaWUlmGn2eZMWfFVWXMxzMLGUclLhWjOaqxg4jOVrWeKysYqzYNfTeogxs+IxGlknWhbGsWxBuf0eoa3BgNcCFI4EvIrtLGUDTbHIi+UWhYTsWAbd33kNI0/lMMXgZBJJoCH2ZSEmrJAkhbLrFAtUrcHnWhzw6PjyZgCXVrugYzjwtPIxELvJtS0JLK0qfz7holYdzq9PYbmz3AaRsNRhxUeXsLh1kVOxvcKsw8oGAe9aYQBNlZ9nt5nB+88BPnq1Dnq+/Dwa9TTed5UOlUYXeNLf6X2UKHD5SjS2A257uVUkYUmNHbEBPZy08nmuop1IJShFy09CvduEkE0H76pKxfbahSYsvJDFAqvyZ4FP65B+2oi6Q27QYnkfSb8qmCpsWGCGgSuf62R6L690wLrKDrNdYS5SQNNSH4aO2GAUlPvoECuQ5Sg0VCr3ccNiHVqqMqg1ORTbmbQVnSNmNC5Wnut2qw4WhwENa6pBEe/HaRhNPG66faXiczU01HAs3gD7ovXyv7/xjW/IYTEap3EYDDWH2zxBc4M5TrSs7DdHPi/ir5s6cN9jB5FM5+F1GfHhG5diw+pquZ3YG776Qifuv2unbNtmIraMdXYcPTQGadyPvX6BS/ZbGY6kwXM0WhudONwZRG48W6222oq4lcOxcFr2eCa2i0eH40hkCslqlXY9KvJA79GA/PeyKisi3SFEw+lJ32n9Yi9eixf+XtZqBO1KYjRVsIMkXthVeie2dsVB8gQbeQOMAzn09RVsBokv/IaNdlSsGAG4PHS0CWlRh8FkcLK4jFX04YlNEvwRAU4bi8VLOHQm/JPLjNUVdlzfHoDTEAcFDkaWjA95/cIjaNjA0zrQVAZiTsTIPR0Y+u1zEJOFY9Y3VaPuE0thWSgWismEOEjH9gGZcfcOgwNUbSModtz1xdkGWC2gUIhplcBhe8yCu7pGIUKSRfRSZx5ZsZBVSIOBhXcjmi04lrCUBfG8HoF0aLKPdL4Cf9qSRyglot7J4ANni6DZolMOS3mwaTCFkXQGDp7Dh9u88BnDk31M5mz4xc4sdo0kwDMUzmuyIiGOTVot2jgbDnebsaM3LSfd3rTaAKdjFDmxYCepp004eNiBJ98ojMll6/Rw1wWQEAp9NDA6OPVWDCQKzjZ6uNAxSmEwXjjPeoZBNePG5h1xkJzaWpcRPMugY9xmkKUprGqzYkw/ioyUh402gg3asLe7MA+IxFxSa0dfIIFwMgergUWjx4y9fWFMlBZY4jWDP+hHtD8Kg4XBxZ9wI+8LQBxPLrVyNoSyEoZTUTlvWL/fg31PBpFOFpJHPVVmOC7TYcgTloetkvehy59GJFNIDrXpdLBRNmzvKSTVtbqtiGZyGI4VxsDEsWhnjeh4fUSud1BZZ0PSpkOnvzAGHENj3VIT0p4+CJQAEyyI+Z04MFQYA/L5Wuxy4FhnHNFEHtUuBjddCmTZolOOnXehP5FEJJcCJXAI99TglT1x5McHodVrBjOWxOhgDAxDob3JhWM9IWTG7SSrPCbYsyKGjvpl4X7exc248baVsNr0OB3Q3GDeXjKhESzrfRIHDx6Ez+fDb37zG1RUVGA+oemOWcZl5L65u8H4bpkXbjCaWD9OtA/NWyMUSeP1nQO4eEMDdMTBZBqpZBZ/vnsXXn+pC4l40ct7AlJcpnltDY74EwhGyuMw5cJJS33oEkT0B5WdP5ZVWuE8HEB/l7JNYVWTA7jSgmPxgqifjs9ogq3HjP3bRybF11Scdj2uut2APmkYglRue6GjeWRD1dgb8SOVL3e9IEWCPr3aiRU+IrSUnDkoSMNGdH7uz8j0Klvsua9eg9oNWSDUp9gOVyPos84GRSlb8GVhxrYEg6yo/Po62ghRcqI3TkR9eR/JgkIneGA390NEeR/JlQKyEFloT4Kisop93DfmxLagH/G88nn0sBWwG5JICkVP9qkYJBcCWQkhQdlO0qWzYThkxq5h5XaPwQh9wIVtR0MQFMpPECvIxU1mvHEgjKyCvYlRx2B5nUMW6cSnfzpEEF/TbkDDqlGkpfIEQLLwMWV8ePXnMYz0K5+nBes8CK9j0RNRHoM6iw3pmAn7R5TtJGssBngzemzpCSua1HisOixbpsfrXRHkFCa7hWdxRasR7oo+5KXyPjIUg2ysGg88l5fdeMrbKZxdZUWwK4xRFVeoFS0ufPD6xWhaUF6EbD6jifW3l2zEj3tuXSrvrPf09KCurg6//vWv4XQqX807FdF0xyzjMnLmiPX5cw1AY17isOlx1QWkOA2jLLCMPBwuo6JQn9ihTyYyikKdQDRVIJhSFeoEUtFSTagTBjtDGEorix/CSDKB8DApuqPcHgynEcvlFIU6IUN2gA0ZRaFOyAoC4sRCT1GoEySkB8dUhTohsa9TXagTAl0AVSwgNR0ecbAqQr7QhyQS+ayiUJf7IGZQYUspCnWCKOVRZRRVhDpBgsWQVRXq8mswMVWhTkgjqCrUCYFMBD1h9dcfSyURimcVhTqBiM9IWFQU6oRkRpCLDikJdUJOECE6BEWhTpAgIRZKqAp1Qv+xiKpQJ/TGIugOqVvW9cdSGE7mVN0kx6IZjEWgKNQJsWwedkdGUagTBElAMCwpCvVCu4RgKqcq1AkDweRpJ9Q13hmIoCOOMGRHvbe3F5/+9KcRDKp/R2jMM6gzJwxm/hyphoaGhoaGhsabgITAEMFOdtSPHj2Kj3zkIxgcHNTG8HRyg6HncJsnaG4wGhoaGhoapwlnqs/6dHLRAIaGClcjGYbBr371K3z2s59FX18fPvzhD+OOO+7AkiVL3unD1NA4LjSxrqGhoaGhcZpw5vqsT4fCR/5yFBTVIQv3Z75yrRyzTgQ7cYf52Mc+hq9//eu4+uqr3+kD1XirUNQciyLNXGn8jBLruVxOvuRUVVUFjlO2SNM4syE2jjaHQXaFmQ6vY2Aw87KbjFKcK0NTcLmMaJREdI0px2S3uUxwtLjQfUw5gZS4zkisCYfzAcVY3hqjBRa7HoODxBWm/BHk2DJjOnCVLHIKsbxGVgcbR8HC84hly2N59SyLpEBBkgygKKUdMRr6KhcMrXVIHektb6YoWM9aBHgywNgxxT5mK9pBUTboJeXYfQlmuJBHUvY2Ke+jgbHAxLIIZhg5Lrmsj4weLopCluKRl5QSC3mEMxTsvA6glGLzaUgSCwdvRigbV0y+ZCVyDBwSeeXkSQPjgpsH/Fm/YruLdyBlMSOcTiue50qTGZyDBzdCISeUP8Jn18HjE2EcZpAcdzCZbhvq9gpw+HmEEgrnmWNgMjIwsiYk8wnFPvJ2E6qbgIHOSHk7cTxabsOYh8M+EliuwAqvA5RNj2eOKLe3uE1o0LPoD1CKc7naqUeLV0JXmEZaITbfoecRGWThbuSRUzjPLMUBAgevVcRotPw88yyN9Qt92B3Joru//BiJ60yty4SDe4excOn8cu44VTjTfdZnwuv14ne/+x3+5V/+BS+88AL+9V//FTt37sQ//MM/wGh864mKGhrzwg1m3759eOWVV7B+/XosX7588n5ymemf//mfkclk4Ha75Q/JfF3FalnZJ5dUKodH/rQHz/z1EIRxkdC0yIvudA6BeHbS6q2jN4R0piCUGuvtGNUz6I2OWy/W2dHjTyAybndX7zSiciQJ/4HRwuNbXIiEUwj6C6Lf6TZCX23F/nF7uobFFlBtEgZTBbFo5nhUxszY/0JQTmStcBthNPLo7C2IRZI021LvwKHOAARBQkWVDuvezSHAFCwCaVBosHiQF4MQiIiXeAyHPdgyGIIoSbIsXupzwGQKIo80dDSDd9VVospERIxYtG7MJEGLSUiChLEnghj4zUsQIoVjNC5qRN3n1sLcVvDRlkYzEPdvA1IFUS6avehrWYLecdv6Gp0bTToKtFQQixJ0ABGWY/vkv+PGChw1VyAqFIQUQ3Fw6+2gMASKkiBIJgwl7BhOFZK0aNBoN9mxBL1gkUOO0qOTrcRwjiyMyFcLBRPrRW88hrSQhY5msb6iAg5dsY9ZwY7NI2EEMgn5KUbOi554CFmxcB6dvB3HRhl0jCcRX9xkg9scRkYsnHcjY8GRMQO2DRbO48oqCxyWBGK5Qh9NrAGplA1v9BX6VG0h1pUk2bLweBPHwcs7sK0rLicR+wx6mLM6HOovJHLqWBpnLTUi5e6XbQ31ggG5fh+2H47JPSQOJ2sXmSFWDiBH58CKHNiRamzZX1zcndVqQXXbKEQuIT++1eZBIjc+L4jLCufEwXAWfQkyBhJquz3ofTqEeKwgiGsbrbj8NgHe5sK4dwar8NhRCmPJwuLOZzTgfUsMWOErzM0Dw3b8/HUax8YXuDY9h0sWsnA4+mXRn03Y8MYOB/aOWz0aeAaXrdGjqqEPFC1CyJmwr9uDLb1F+8plFiv63hhDJkmSTFlcc4sROfOonBhL4HI+3P8cMBQQwLMUFlfbsX8gMpmQu36BG1+4sh01LpM8Lk++0Il7Hj2A+MTntdICfTSD0fFk8LPOqcetH1oNp/v0EJ6aG8w76wzz+Cc3orq6YB0siqK8y05CY4gEqq2txXe+8x0sWrQIpxKa7phlXIIPw2p9698P0WgCNuf188IN5oSI9SuvvBLhcBjPP/88DIZCERWyar3wwgvR2tqKtWvXymLe7/ejs7NTXt3ON7QPzdvDYH8ED/xxF3rjGRwdiSsWUamusCCuo7Fj3CN6KmYdgwUVVhhDaYy+2jvp2T4By9FoafUgx9PYMxJDNl/aLvtJX+gCY6FwdFMYiWj5TnlbowMcx2BgJC5bU05nxToz2s+hoOOTSAvlfRDzVvSFLTAbk0ijfKe70mDCVXVO2KQc2Hy5c0E+KmLgrh4YF9jhvswOatqlPCLqxa4ggqBw0KOHSJfv4C4yVcFNxPDQLkAo3wEdsbfDb7SBY4joLu9jWvAinuaxhBqDFeU7pDHGjU7ahuFUDoEJz/cpePVmrPLY0R3L4FC4sLiZCkuRxYcNAxEar/eWv76ZZ3BVqxnJLPDU0QimnUZwNIVzG21gaQkvdcUUd4nbXS65gNSe3jSiqfKd8laLFUaaBlc9ghRTPtdsWRcSQ1YYqkKIceW7/aacDbkhJ6rqYhDN5X20cnrUma0YTorYESi/6qPPcfDtsWFBQx7tG4dA0aWdzAsMtg3WQc9wuLw5BJ4pbRdECs8c9uDAGNBYNQCKKd8JD49UYXBYj7b2YTC68itXqYQHB7ssCO+NYay/fC4vWmLEuktZvLqXxev7y+eRx6JDg9eMm9bV4Zy28u/9aCyDux7ej9GuIHp2DZVd1NHpWVz73mV413sWY76jifVTR6xPsGPHDtnacWRkRI5rJ7HsH/rQh8DzysXe3m403THLuIQembtYd1w7L8T6nFNhR0dHZZF+zz33TAp1wi9+8QssXboUe/fuxd133409e/bI9kkPPvjgXN9S4zSmqsaGtvV1ikKdEI5mEBVFRaFOiGcEjAzHMPJyT5lQJ+RzIg7tH8H2wWiZUCeQp+x9PoCBl5OKQp1wuCuEVDqvKNQJu96Ig09RikKdQLNRtFdkFYU6YSiVwJFAWlGoE1grjfrPLoHnckeZUCdQpHJQswf7feVCnUB2QvcnBiAN7VMU6gRf+BAsLBlj5T7qmVGs0ScVhTrBIvhB5SRFoU4YTcexbSytKNQJeSmDTC6nKNQJ8ayAJ4+k8Ncj5UKdQKwHn+8I44XOuKJQJxwKBNA9LCkKdcKRWBS+1piiUCdE+AAaliYUhTohwUWwaGVcUagTork0euN5RaFOSHM5UOfHsfC8wTKhTmAZAWfXduGa1mCZUCcwtIQrF45iaeOIolAn2H2DOOeskKJQJxhMY3AFM4pCnXBgXxIvvmBUFOqEsVgGRp5RFOoEq0WHazY2oGdnuVAnZNJ5PPrAXsXnamjMlVWrVuHee+/FpZdeCkEQ5J32W265BVu2bNEGV+P0EuukOtg555yDxsbGkvs3bdqET37yk5MrVLJq+eAHPyiLdw0NDQ0NDQ2NdxqiTf7jP/4D//mf/ymH6xI/9v/3//6fvOOuebKf4lCaz/qbuhwxdUedQC4pkRsR8VOpqalBPK5esENDQ0NDQ0ND4+2EXKG85JJL8Oc//xnvfe975b+ffPJJ3HDDDXjooYfkGHeNUxDqBNzOlJ11kphB4r6yU1wunn76aeh0OrS3t5c8lvibElcYDQ0NDQ0NDY1TCbPZjK985Su488470dbWhlgsJu+6f/SjH8WxY8pOWxoa88K6cfHixXKoC0nKIPZHJNH0m9/8Ji677DJZsE+Qz+fl2HXia6qhMROJZMH9hTisKGHhWRizOSRzyrsdHp4FbdUhpmAdR7DWWsA49fAPKcfpmq06mJwGjI27j5S9v5nBgjYKHQouigTiUEpxDGbCyHDySlltv8Yh5iCJlGKsMiFHc2BEFrRK2ffcUA4GQULKrrx1QI+KCA1RcNYov7+YYcB0JoHS6LZJKBLmPZogOaAqUHAbzaAS4Um3kOl49AxCGUouQa9ElYmBgzwmrRxTvsjNYSguoTdacBOZTpOdh4ljsHdMuUCMkeFhNnBQcBCUseootNlpDAwrt5NkZNKHjijpofI46xlWTuhVG4MKA4vOKIOkoNxHj15P0ixJ9LZieyJnQTCtg1Ov3E6eW2lgcSymPJf1FIVKCCrZEwWnm7YWCZvfUG4nDkHuSoAZJQmtyo8xiRIyWUF2T1JiNJyCwcIjNe58Mx1f5biVkcZxoRVFmrlA0vFAkhe/+93v4tFHH5V1y/bt23HTTTfh+uuvx/ve976yaII3S2VlJeh5VD3zVEWSJPk2l+fPF06IG8x9990nT+CJlzKZTHjppZfk5I0J/u3f/k2e/MPDw/IHYb6hZWWffIjv8i/v2419R/zwuY0wmzh09BSVlNOmg9tpxJGuEGxWHRz1dmz3F8OqbCyNtSKFnt3D0OtZ1DU4cPTQKCauYLIGBu7L67AjVxAuS40W9G4eRXbcCpKmKTQu82F/OIVUNo/llTb4D/uRjBdFxHXXWdG6Moi8lAKd9uDB+yR0dReF0uoNFvjWJBEV4vAZ7DCzAtJCMcnSylnlS6yRbARm1oJEnkV/siiV6jgOV/MxmFJ9AGcFXI2g9EVBLlIc4pwOGWmYuHLDBA/0ubAsCOX2lITgw72I/PVVUAwNvPsc9F/sgjhR4oDo2rv86PvvTRBSGbR/aB1WfEgHnbkwBuQzHDtgQuiR3RDCUejOXYnMrXXIO4rnydEvwrZ5BxAZBVXbCnZlPSjTFLFpqAFsPlBUGnnRgG3+KDqixSRLr8GIZS4GNBUgxo7ojplxOFwcAwunw0KHDqI0QhzmsW/Ui3v3E5vEQh9dBgafWaOHz0x+fBkc9lfgp9tjmFi76RkKH1lhRJV1UBbJgUQVfrs7jUhm/AES0GD04dBQCvFMHosr7OgLJxBJF0X/h8/S4YqFo6Ao4ujjxgMdeXTGiqJ/kd2OKmMeiXwMZs6GjijQGStYPRKqTWZ49QwCmTBsvBl6msNIuthHl86AxQ4d0oIfHKXHYNKGF4eLCcV2nsel1WZIGAVDsSX2mYS8yGJfqAqvjxSec2GVF+dXxsEzE4qZAk9VgqFJgrCASMaIF4f6EM8X5+oaTocFqW5Q+TiSxga8DAMCYnGuNVhs8BkSEKQYxKwLf7pfhx17ignHzUtNcK7LYyQXgZu3YuyYBbsOFdurzTyq80BHV0iuRfDhG5diw+qiGwdJ0P79g/vwwhu9MBk4tLiMhUTT8cRwvYHDdTcvw6VXt4Nl57+webvcYNo+8X0wurmJydMR1kKcs978PMolIhjb/FfEu/fLf3NmO7wbroWptu0tHcdEgabpzjRKaLpj5nEJhf8yZzcYh/26eeEGc0LEOmHbtm145JFH5MtIN954I5qbmyfbQqEQfvjDH6K+vh4f+chHMB/RPjQnj3gyi3sePYgnXuiEOM3BpbXBgWg8MynSs7nSHcj6WhsiRhZVAhDZM1wirAneSgsMehbZWiOOuSn4k6U7kC6jDnUpFplQHkE9g75phZnMehaLrAaYpQguvzaPPFPq0EKDQXjAh7+9kMeyS2kEqNKCPEREN1jcYJCEmTPCn/aX7bI6eDdGkklcymdQlToKanrRIWM1JKcbaYMJSRBf69KdZBY2mEU90i8NIvCHlyAES/dJGZ8HyfetRyAiYuQbryJ2ZKCkXe+xYdWX16BuBYvAo33IHO0u7YNBD/6GDRA2WOHd0QW6+0Dp8dEM6CVrwSyuAOVpBdhUyT4z6W08Z8LmkSG02nUwciNl1xREyY09gTwqDHromBFImHbFQHLgsSMGLPawWFNFxrj0PEqSBU8cK/inb6gNQkSpgwsNA3YNe/ByF4twlEVvqHSn2cSzaHZZYOTi+PTGJPT89MJKNIYSPjzVl0G7XY9Yrvw8mzgP9gQTqDObMJoaKzvPlUYXUvk02u0GQBqDiNLzzNMObBsjIlkHh454sJfOZSNrgY2n0Rfn8MJQFuFs6Vx18AZcU2/DChcLjuZATXPyIUWneuI0+gNDOCsfAJcu3WmUKBYj5gXYQ3NotrEQpEJ9gqm9DI1W4Z6HJNRs5NFHkblYSjXrxZ6tNGoFHscO+5Gf5sSzfKEXH7lpCXYdHMN9jx1EMl16nmt8ZliSOTTV2nHzB1bB7jh9RKdm3Ti/iXXuxdAL9yEXK3y/2lpXo+K8G8GabCfERlIJTXfMPC4hTaxraB+at48HnzqMOx8q7Foo0dbkxOFOZRtDQr3biCgxk1ZBZ+Fw5Bz7jMdgCPOIppTDKQjf+3gGeUotWAAYS1egK6ZcOZOw2O5FODtd/BRZx+nQHCsUJlIi56hHxK3aDASA6EcfmuEBwCsv5CBm1Pt44d81QRhRifkgYSnvXQg6qB63yd32SdBe9SrFKYFGPN+p2i5JLoQy6mOkoy0w8ckZnk8hlFEOC5rgnx9rxlhCLWQEuPv9Oeg49fN4JFSF7rh6u4H14lhUvQ9LHC4YWbJYUcbI2JAcL0ilhCDxeLh75v2V/1lfA0qOU1JGOnYISKsfw2DtcsTJhFJhZ6AOrwyrj4FvuAJbn1a2syQ01drQOV6gSgmyC//r716B0w1NrM9/xFwGo5v/iuDO5+WrkOQKhvec6+BYfA6o4wxr0cT6ifssBcNzL4rktM+Pokjz/9qixrxntms7s7fP/IDjuXQ0+wUm6SQfwxwvcB3PBTLpJJ+IWV9+tjF6O+IH53ieT/r7n9ynH9+LzDYP5vjuc/w8a2i8U9CcDhXn3oDGW74Kg7cOQiaFoefvRc9f/ge5uPpmjsbJQZLEOd/mC5pY19DQ0NDQ0NA4TgzeWjTe/BU5DIZmOCT6DqPj7u8gcmS7NoYaJwVNrGtoaGhoaGhovAlI2Itr5UVoev8/je+yJ9H/5G8w8OwfIObVww01ThzSCfhvvqCJdY2TTiqpbMk2ySyXvZlZDEZZhgE1Q3EDjmXAEJ89tdenKHCzuU1IM7fTs7gM0DMdIIFi5vZRZVliZzNjO6NXjyeXB5DnZjmE2Y6RmlMfKMz8+tKsX1fk+TM9hgJHHHJmeo9ZzjNx8pn5BWY7xtlef+Z2hqJnnEuFNvXXkE7AXJvto8KyM48RO8s5IM4v4hxCYUiSenqG/JPj+k7S0DhOdA4fGt/7ZXjWXiF/P4QPvI7uP/9AC4vROKFoYl3jpBEMJPHT77+Mz37gAdx/146yH9BEPIM//HILHv/tdqyoscFq4kvaeY7GsnVu9LWE0X6xAx5XqTMEsVpc1uhAuieE6jo7KqvLE0TWbqjHt//rKvzmxnOwqtpV1t5qM2PpsSzqjoawwlfu51znNGKZnsev/0dENlRZJkh52oh4zoOeWAC1Ji/YaUJIz/BotXoQSo/Ao/fILh1TYSkWLVY38tYIQt52SNz0Y6AB12JwFbWwcc1gqPI+0qMmJP/tZbCVleAbG8radQtawDqdWHOJE9XnLixrd61twWWvfBiu/7wE5svXl4l+rsIL14YGSIcPAN728tWTyQ72ihtAecj9JJG3dAwkiYE/zWPbWDdiWS8o6Mv6aGDq4dGZUWNsBUdPHwMK8UwV7nhdh3v2ViIvlicLJ7Je/HiLA3fv8UEQPWXtgBPPD7pRVR/Cqjpr2bKizm7A+8+mscWfQCpfobDwMOJwqAKbR/2w8V7ZTrFkjGgesUgNfvZcFlSyRj7vUyHzwqP34tmBEAYSXnC0sayPRtaHnlgaOcEFPVN+nnWMB0ejLNpsRtSZy8eg1WLHV2slINAFSSh3UZEkPRCJAqTJ2lTWR5GzYZ9lKV7xp5AXq8oWTwylg42vwMaKQVxT54KBKZ3LOprFxgo3zl7Tj1tvNcNp15WOAUNh8QI3+oajcpJphac0MYysM5aucMB6SQL/tfspdE6x+zxeOo6M4d++8iS+/Mm/4KXnjpXFvw8NRHDHt/6GL3z4QTz+0D7kp7lLaWi8FSiGgXfDu1F33WfB6IxIjfSg897/RHqsXxvQk+6zLs7hNn921k+YdePpjmahdPyQH8AnHzmAx/68D5kp1mwOl1G2Y1u3sQEv/e0Y/nz3TsQiRWcOvYmHZ4kX+3rCaFlgR6wxhSBVtJ9jJQpNYRcObw+h2mMCF0xirHeKqwQFtC70YqA3DLvTgNs+uhaLlhGBXeSFjmH87PVDcsGl5oCE4ZdKbQzdS7wYcBsQSuVky8bOvSOQpthJLltmwJXX5iEwEYDyYPtYELkpSSpm1gALZ8RwKihbNgpiGIJUXKTwNA8bb5MtHCuNTvgMQTB00eGEFmlUJ3XQhztBGauAynpQ+qJoIp/WlEAhKfSDyuqQ+d0+pJ7aUdIH3cJ25IeGQXEcaIcD2SNHStoTjkYc2RJALpbC8n+/DPUfJJ7oReGWP5JG5BevI9c7AOuKZhhi+0BN6QPlrgBT7YMU6AGz8lww6xeCKil4Q0ScAZIURVowYl+oF4l8sY8cxaLJ5gRLj4Kn3TAxFrC0UNLHYEZCINMrWzI+etiILUNFm0JSYuhDy61YUzUGSeLwxDErHj9WatV4dYtJtnAEJeJw2Imn+kuTv2ySG/0DevgTWVzSroPH1VdSgKrCYMJSFw2KCiGY9uHp/hDSU6r+WDk96kxmhLMBsIIPD2wTMRYv9sFhZHDFCgZRaRgevRNdsRQCmWIfdDSNy2sc0DNj0LNW+FMSQtlizQByNuotLohSCCylw2BSh45YqcOKjXOjN5GUd1yu99BYyfeVtMNYD1jJAlUAyOcwTCw3p3zdS2YgmYaU8cuWjS/nBOSmtBMryFY7sXAcg5nzgaPDoKjiPEjmdHhxyI2tYwEscjixwBoBSxf7mM+xOPh6FTa9FEFdlRXJZBbD/uI8IBvs7S0udPZG4Hbr4VonIGgOlYzBWk8DrmtYCSs/s4VjNJzCn+7aiVc3dZRcqGta4MLtHz8LVTU2PPKnPXj6r4cgTLGTrKiy4raPrcXSlVWnjRvMki//VhaMGu8MuWgQg8/9AZnQCGhej+rLPyiHyRTbNZ/1EzXXR4P3w2p963M9Gk3C63zvvHCD0cT6caKJ9ePnlz9+Fa9uUrfoW7a6Cnu2D6q2t1xTjT1Odeu31qQV/Q+oV6OrrLHiOz++BozK5fZ0TsBnP3A/skllmz+aocAv9mJouCiepnPT54wYEtTt7ZbYvQjNYNW42OGGgZ0mrqbgYWvhnHaloaQPgxkMfOJn6mUjLRYgnQZyyuEAIs3A8/sPgXcor9XlNfzPXwI1XghECd0XvwCmWl1E9cdJ5Uz1ebDAWo9qk/peQWeYx1dfGFatDlpt1sGfyiKjUumWFEeqrxAQzqmEPEgUzvfZkFWt3wmYWDeORNVtCsVoA57aXyx6NZ3LlxkxIKnvrq12OcEz6vPIzJpxNBpXrXTLUAw+4wqDp9QsK2mAswE59T6+mm9AT0bdSvH8Shd0rPrnbSRZBX9a3fJz6HAV7r1XfYxa2i3InuUnv0aK7WRh9B9nvQcz8Q8fewj+sdIF2wRkHdpIFgVH1cf581+7AKvW1eJkohVFOnMQsmmMvPSAvMO+sNaL73//+2hra3vTFUw13THzuIyeQWJ9lmhgDY03z9Td9LfSLkzZ4VRsV5UuBXJZQVWoE/Qcg1xK/RhEQYKQnfnyuJpAnERFeExAz+CDTZCYmeOKKeKXribU5QPMqAp1+f1FAbyNfPyVH0N22qmSfVaFx+hLwxymk5NmPs+FvVP1dyARCmpCnRDNSjOeh7QgISHMMM6UBI7NIzvDYSZnOc+zhT7LxzfDb3JGlFByUWIaWVGccbYLUn4GoU4QAWHm+O30bLkGs8xl3WTVVJWn0zO358lnYYb3yE6pqqpGZgZ/fbLunOt30nzCVNMCRv/Wvac1TgzmujZ0P/gjiLkRuXr7b3/7W9TWntwF4ZmGBFG+zeX58wUtZl1DQ0NDQ0ND40SKK06Hqktuk6u5kyruX/nKV5AhmygaJwxJ81nX0NDQ0NDQ0NCYi2D/1re+BafTiaNHj+LHP/6xNpgabwltZ11DQ0NDQ0ND4yRAhDoR7IQHHngABw8e1Mb5BCFpPusaGm+dG29bgSUrSl1Yim4tHkRCKbS0uRWf29TqQmp7HMtGbWAUwlgraAcGIkZ4Lm4CO8UlZYKaOjs++tkNqscWGEvgf//rJTQtcMPhLE+ONFh41KyrgdnEo9JbHvfJ8QwWnF+J146YwWSVLAKBGpMXg8k0jGy5VSSh1uREKp+BKFVCksrjhYk9noEDcqJesV1KCeClYVR//QowznK7ScrpQc7ZiHzNQsBabvEnGa0IWduw5wMvINmhEHSdB/h9w+CbrWBaFpS30wzopqXI/P5PEA6MKNtphVKoH96PVtGg6KPv1jmREcdk1xdlPyojWp0c/uO8Wth05ak1Szw6fOs8Cd+5wIAqc7k/vNvI48p2M5Y7bfDoyxOQrByLD7XascSZh89gLmtnKRoXVTXjpkY7zvIQK8dSyFlZ6XLhisWjuHmVcuz+VUsMWFEdxUafR3FXZKHNjiqjAK/eC1rhEXrGgpSgQ43JA900y09ClcGMTy1sBbxrAK78PKegxzOZFvwp5sGY5ChrFykWflMdGm1p1JgU5hGARosbg4k0kjnlub5zqBo/20ajP1ytOFdHQpV4dsSABedVguXK+1jVZEWy0gxTvAGc7CJUiiVjAv2aGz/4zVYEw0Wnmel84osbZWeX6egNnOxA9aFPr5edYZQ83a+6fjFWnlWj+toaGnNl/fr1uOKKK+TvRm13/cQhnUFhMJobzHGiZWW/ebZv7sU9v90G/2gC1XU2uVjJUH90sr22wS4ngw4PxuDxmWA06dDTGZxs99ZYQK83otMYh5nWwxBxYHdH8fkeiw6tWQkjW/phNHK4/n3LcfGVbYrJpbmcgCce3o+/PrgP2Uwh6ZDnGTS1unHs8BjygoT6lVU4FkggnsxN+rgvbHaiqz+CZCqPplVuDBgFjCWLcYdr6iyoqByFSCfh0duRFwWEskXniwqDA0aWOJYk4ODNcOuJ7WLRmcPEWmHliVoNQkeb4TEQG8WiMwcFAzjaCpZKQ8pLwMgoEDo8mZgpEZ/3fRTG7nwZYDlIta2I7DwCKTveB4MetuXNkLoPyqI57W3DwGvHkI8VLPQojkXdh89H3eebwZppsH0JMHvfAFITx0BBMjciu+MAEA6Bql4ApGKQgkX3D3rhSvDXngfabZQXEhjsAFJF95C83odjJidGkIaJMcJl4JETix7aPG2FV++DiRMnbR+B4nkWJQ7PdNH4zZ5h2HUMPrNGjyoref+JL1oO+0d9+OVOMu4Uzm20IU2NITeemEgKCbl0buwJBpATBbyrzolmSwAiJmxBiciswIFgAkkhh2XOKix26EBTxfMcy5nwSPcQBlNxNJqtqDELyAjF88TAhYd3GLCjP4dFlTwuXZJFUiy6j5hYCwYSLA5GwnDrdFjlNiOWK7rMGBgDjJwRgXQADMWBoR3ojo1NVtgjvu0uvQ0DiTHoaAbXNTSgzpQBRYnFYk5ZGgjuhygJ2C024JVIEmmhsBijQWGFzYmNXC90VBYxfRXGmDTy0hTbUPhwMJRBLJ9BhcEOE5dBWijOZTvvhNeYAUfHMBhz4IGDZhwOFM9Tvc2M8+uzsBgCSKYt2HTQh619xef7THr4IhS69wRgsvLwrXVhTziCCWdUu4HD0gYWIWYArMjC3uXF3jfCyI1bLRr0LG5+VzvefXGLLLKVLGOffuwQHr1/j5xwuv78Jtz8dythdxYWa0Qovfx8Bx64ayeikTSWrqrCbR9ZiwqF+gzz2Q1m1bcf0RJMTxGyET8e/+RGVFdXY3h4GNdeey0EQcAf/vAHLFxYXvNiOprumHlcBv1/mLMbTJX79nnhBqOJ9eNE+9C8NbJZAXf/ast4gZLydiKIl6ysxP7dwyX+x1NpvrQeL4WzSKk4tJxd78A337MUVrtB9Ri+/rnHMDqsbB/ndBsh1tjQNVgUHlOxmHl417uxM6BsJ6lnGbzvHA7D2VLP9sk+gsI6bwUE2cJP2fWiyVIHt4Ecn/IYsFkLuO5tgKC8u5hNunDoX7cjN1Zc7EyFr/IiFAHiR5VtBHU+B867YxkY/yHFdrB65MMuCAe2qbRz0N1+M2ipS6WPFPy+FQjxQUjE91sBj64FTj0RYcrt0YwBGZDXL3rvTyUvWPGXHgaxnLKFn4k14JJqcjVGudgODR5VxnaYOOXnE0F8ICxhLN2j2E76mM3WIZAbgpqHi0tXiXB2THZxUcKp8+BYNIqUoJyIVmVw4NYWO5gpC4nSY+Txx2MhDE4utkoxMjpcU6tDWhpR6QGLVL4aoayytSoFGoF4Hf50IKhYZZQse87yVeCZ/XFkVdyKVnkcOBKJIa5iw7PIZUFyawTBsPJ5rqmw4P++dYlqNdlQMClfQWtpU74akExk0d0RKKvBcLLRfNbPPIin+p23nyVbNRK+973v4cUXX8Q111yDT33qU2WPn27pqOkOZaLjYn3Af9ecxXq1++/mhVjXrBs1Tipk95qgVnqL7LYT2zQ1oU6IR/KqQp0QzOZVhfrEjpuaUJef708iZ1b3NI/Fs+BmsEFM58kuq2qzbC4lyRaJ6vZ0lCxQ1cdAIgJURajL5GOqQp2QHRxFwq/++pmREKiYup848mlIaXXfeeRzQCoK6NX6KIFBRlWoE0SQXeDyUIgJjFwWmZyygCNQdByxnPpXWiKfAk1Rkzu5Su+vZ9XHiOxiMzPaJErQ81mIuRleAzlVoVz2ngAAAMccSURBVE5I5jOqQl1uF5JgKPW5TlFZBDLJGZ6fkUdZDQl5SDM8glidDcdFRaFeaAdGopKqUCdExbyqUCeMRdLIqAh1Qv9wTD6HjIrjpMNplG9qGE382y7U305y0RBEnfr4abydUPjIX46Cojrkv+JhJwbH4vjJn57AX6mlJY98M8WSNM4860ZNrGtoaGhoaJwmaD7rpy629rUY2nQvhHRCdophjeW5IhoaSmhuMBoaGhoaGhoaJxmG14OzFJK9s2HlcDyN40ciZgZzvM0X5sXO+sjICP74xz+io6MDHo8HN954I5YsWfKmXyedTuNLX/qS/P/PfOYzWLFixUk5Xg0NDQ0NDQ2N6TC8ATmEIOa1Akkap9HO+tatW+Ws6ccff1z+/+DgIFatWoVf/epXb/q1vvrVr+JnP/sZfvOb36C7uxtnOiSW+/GH9uPJvxyAMFPp+hnYuaUPd/96q+yuoMb68xrh8ZXb4xGqamy44LIFqGsst5YjOFxGXLmxEasbnYrtFj2L69fWqb53Nifg0ec70LysQk5mnQ5FU1iwoQJNy23gFazlCAtaXLDTPMy88tp2WaUJzVYJZk45ltitt8DGizAwypc8dYwRLC2CgtolUQ59ghEpo0o/KQ5iXSPs15xFgpbL2xkG3mtWoemW1aBV+lB17RqgtkVOJFXE1Qi6wQdY7MqHUNVQ2KXgbMrP5+0wgoKOVn4+Az10/SOQMsox65LEgBoKgU+pjRENU8KI1TqTnNCrRJvNDZ6yyUmUSmSyPmw6xkCUlNtTeSM6ozQ4Wjkemlgt6hkJVk75GInji9cgwa1XHgOe5tBs5dFiVbb8ZCgGNs6OsbRJPftBMGON0wWWKrc1JSywumBkjHIyrRI0XAhkWHC08lw2sFY0OfOoMiuPgUtPrDppNDqVP+9WHQeTyKHdZVNN1q6yWNDa5lacyixD4dpLW8AofJYJZA6+sqkD9/9hB1Ip9TwTDY13CjFXEOk0o56fo/HmYtalOdzmC6e0G0w+n8eiRYvgdrvxyiuvTGZJE9H9wx/+UC4u0NTUdFyv9fzzz+Oyyy7DJz7xCfz0pz/Fww8/jOuuu+64j+V0y8reu3MQd/9qK4bHHVCIaL7tY2uxePnxJV6R593zm23YvX1gMmnrPbcS68RW0ArWicSRhVgnPk6sE7MCDEYO1928DJe8q122YRMFEZuePooH792FRCwr33f5tQtx7U1LodMXvtSe3z+M/336MEYiafmH/F0rq/HJS1rhMCkLj827BvGb+/dgxF9IuKtzGmHL5NHfVbBOrGlxILGWxQBfaHdIeli7DThyqNBe4TVB8hhxIFhot5lYNDeZsT8QkhPcPCYet6wSsKS64A4iiCz6EpXYFwpCkEToGQ7LnVb4DAOgKHLJjYKESvjTYQhSDjQYVBjdsOlGQI9b8OnpGjA0ERkkyY9CLGvHayOjSOYLSX+LWR2WZgZA5wrHKFgbEHNyyHOF9vwhCaEf7kZyP3FNASwrWlBztRkGe+E8J0JmHLw3jrFXCq4v1kX1WPSPy+BcPG4FmWchEbu9/v2FdEGjE3BVgMoWrBglkYMwpEN+13ZAFACTFeyqpWBsoYK4IiLR1w4qP0qyYkkJP8DaANBh8m0ji8yosQZ+KgZBIj9aFOwJE5zbXgKdHndhWXoB6LYFoOhcQZTGKYivPg5ERuXmXOMSJJc0QKALCa9c3gbT0SNgIgU7Sb+pBs9bWtGbKSQV+wxWLHXkYOIKCbgMscNkLEgLhdcji6Qn93vw8N7CGFZYOHzncjuanFG5T8Q68tURCX/q7JPbdQyNK2oc0NFjECHIVotmzoFItvB65DyztA/DyQiyYg40RaPB7IRdNwR6PEE1ma9AVzSLlJACBQr1FhcaLX5wTOEYRlNubB4REcgU+lhpdCGeSyKWKyQZt9uduLzaCR0znkwqGYF0BBi3kwzn9Xg+yOFYvGAf6dKZsd5HwWMo/C1JOmQFKxL5wpjRlBF9cTu2+QvzithDrnI7IEqj8g8aS/EwsXZEcoU+iiKNnlAVNnUnkMrnwdMM6swubO9MICsU5tKSCjv6I0mEU1k5sXexy44jx2KIE3tP4jPfaEGczWE4XujTIrcdA71p+CMFMbPAboAxkkX/+HfU8oVefPyWZaitVP7+Jbavd/1yC44dKoQX2B0GvPcDq3DOBcf3G3Gy0awbNcR8Dod+9veQRAELPvhv4G1uRZvHt2vOzFei4+PSM/brObvB1Hs+Oi/G95QW68888wwuv/xy3HXXXbj99tsn7+/v70dtbS2+/vWv49///d9nfR1yIpYuXYrrr78ea9eulV/rTBXrxG/8p//9EnZsUbbwW7O+Dp/6h3MVfYwnIF7lD9+7G3kFB5faBgc+85XzFAuUEPyjcbz0tw5cdEWr/GM6nXg0g2cfP4T15zcqvkY6K+D+zT1Y2+zCwmrl3blUOo/v/eIN7NivbE+3rNoKtpbCfoeyvV1DzgbKb8DmUSIoFdorjVi/lMb5bcfAs+W7dxnBgpGUE1XGEbC00hUHUuzIC5suBJYud+6gwIGl6rDbH0V/svwYWVA4j+dgtglI68sdYshHOvtkGoZIEI5GZYeXkaMOZAQr6q7mQCmcainJQxpLANkBUAruJWLeDDHEg3HFZFFd3kU7KF8LwKcBqnwMBEqHEOeFed928CMKVoicDtT66yD1HwE6d5YfH0Uju/YC0FIOfO8+xT4edq2E3+2FR08WS+XtOtqJI6M2/OCFHNIK5iTnN5nw/rV63Hm0GxEFN6Bqox6XVFuQEQLIywuPUsjuNUt7YOMDYOlyNyKygx/PVcOtT8DMhxTaKRwJV6EzKmAkrez0c1l1HVZYiMe6stViZ9qGERFosg6CpsonsyTZMJw0YdNQEBmFq2tevRHLnGYk8n4IUrlLTC5vwJ7BKrzRkcForHyMjByDxV47+vtS6B0pn6scA6xa5EQoksWR3nK3IbK8Xee14oaz67FhtbpLBrm699wThyEpWP0sWOjB5/7x/Bkdo94ONLGukeg/iu4HfwjWYEHrx/6zxH5UE+tv/rPUcwaJ9VM6DObVV1+V/08E9lRqampQVVU12T4bn/3sZ+Vd+f/4j//AmU46lVMV6oRtr/cim5nJng5445VuRaFO6OsOyTc13F6zvAOvJNQJZqsO19+6XFXs63kGf3dek6pQJ0TiGVWhTtgzEMUBFaFO6OYi6MgQiz2V9qEkLlgQUBTqBB0TQ5strSLUCWk49TlFoU4gNo8jqZiiUCcQc70DLKco1AnkB0B3uVFVqBN8C0Kov9aiKNTl1zBmQfF5RaFOoNk42GarslAnpMOQiLeeglAnMFIGrpFhZaFOyGUg7XpFUajLxyeJ0O3ZpirUCW2BnfAZhhWFOiEjBnHPDlZRqBNe7Ezgwc6wolAnDCTTiOVERaE+YQXp0mcUhTqB7LJXmZSFeqFdQo05oirUCc8M9KoKdUKTPoJWW1hRqBNIAa79IWI9qvx5Hk2TwkqColAncGwKOklUFOqEZE5ALJZXFOqEHKmhNZhWFOoECRT2RJIzCnXC5pe6FIU64ejBMYyOzGA7qqHxNhE5vFX+v7l+oWqdAI3jRzqDKpie0gmmZAedMFFQYCoVFRWT7TPx4IMPytXCnnrqKZhM5eXj1chkMvJt6kpOQ0NDQ0PjVEDtNyobDYDJzFCTQeMdIZ+KI7z/VUhCHqb6RfJO+nSf9VwuJ98m75uhvocG5DDJiSrPb4VTNqxkvol14tpC4LjyRAydTodUauYvJFLe95Of/CQ+8IEPyOE0b4bvfve7+Na3vvUmj1hDQ0NDQ+Pko/YbpRVFOjXxb3sG+WQMSxoq8an1XlDU9ErJeuzcuRO7d++evCeZVC9wpnFmcUqLdbO54ChARLnRWBqXRCbxRLsaH/vYx8AwDH7wgx+86ff+2te+hr//+78v2bUgcfIaGhoaGhrvNGq/UVpRpFOP5GAnEn0HQfN6fPOb38QVV1xxXM/TrujPgjTHUJZ5FAZzSsesNzc3y//v7e0tayP3TbSrsWnTJvh8PnzlK1/BRz/6UflGbBsJ//d//yf/HY8rxzKSnXuScDD1djpA4jcra9T70rjABY5Xtn2boG2RV7XN5jDAV6lelS2VzOKl546pxsUTO8mX/9aBeOyte9CajRzqq9X7WNNmR6Ve2SqS4BVNqBt3oFHC5+RwOGCREwCVYCk97Dqn7PaiDC07x0iS2ntQcOvNsKtYQRJMrBE5UX2c9UFA4opOA9MRs1YIfTOMMWsDvA3ysSg+32BFurIKEq3cR5HTI2q0QKTU+wi7EzDZVWOVM80LILirVA8x29iOvLtetT3tbgRPq59njjLjslb1L2uPhYWJ41RGALBxLKpNDBiVwH/imLJvxIRMXqfYTlxj+hImJHPqCVI23o4mi/pctnJ2dKTU24m7jz6p/nmWJAOarYxqHw0MB0HkZDtGJUSJgd4MmPXK7cRhsa5WhMehfgzVZBzNyo5OhEV6Hp1H1fMvCK0zfCd5KyxwON96Etqpyun6G3W6IWTTGHj6d3Liv6V5uWw9TaIFjvemoY50Av6bL5zSO+sXXnih/P+XX34ZK1eunLx/7969CIVCuOiii2Z8PhHkSjFfL7zwAtra2uSiSGfKh2F0OIZ7frsNO7f0y97i5MdtoDeMRLyQOGax6XDTbStx3iUtsya+3Paxs7BmQ71s/TiRTMqwNC59Vzuuu2UZDIbyMSVfVK9u6sT9f9iJSCiFv/xpD2790Gqs3VAUW7u3DeCPv9mKkaEYzBYdbnj/CtmDXckffSbMRh4/+sbFeGJTB+557CASycIcsDr1cKx0YG8oAvQAS6sqkaBCiOUL4VYGsKgZtePgzgAEMYE1DXaMcDT64gVRy3MU1q0yoS/vx32HBDQO1eJdC1KosExUoqNQYVgAK0eBorKwWNtk671AprjY1DMe5MUEork+MJQeZs4DmhqdHHOe8oBndJAQwoU1PEZTTmweHoIw7gfr4M1wG2gk80M4GqbhM1bAoRsDTRXs8JgkDcuBYdADBwpeGr42gI6CEgshY5LII9/HQNi7Rd5VoNuWgjuvGbR1XLRSLGAmVncx0AYR0qpLIfUcAwKdheeDRrptLVIOARISSK9dB2N/CPzAwck+xuqXw+/gkccoApwZbtEAa2pKEiTnAcQMKGEE1OrFkCIMpD2vTO5y5GsXILm8HXkmBtQthCG4DPrNL4DKFc6T4KlGcs1q5HgSo2uCvqYehoM7QI/bHOb1VgRaliPKhgDRL495TkhAwMQlZRYGxo2UMIrVDR34aa0bv3/DjC29hQUkMUI6f4kBYWoER2J5ePUWsDSNwfGEX+JQcn2jC0udJPGzDy69EaGMCQOJgi0iIRD34fkuCaPJICy8A+9q0WGRt3cy0TOec2FvkMVoehQcRWwSq9BmHwFDF84jS9mgZ+yQEMVVdRQC6Ro81jOCeL4wl3WMHpAs2BfyY18IWOvy4DpfEja28HmWBAogSZ/hAzBCgt5Ug5jHiBxXmMuSRBaMbsRyY7DxfbiuwY69xOoxWhhDMhtbbR5khQh6E8MwsRa49RzyUlE0R7NebBlLI5ztx5KFerApF149lJQXWoRltXo4fTEMZnrQvI7FylQFnn81hfz4Or3BqoMzKeDoziHZfeqcNjd2RFNIjSevV+pZLIrn0fNSD/7t5R6ce3ELbrp9Jay28loAn/vqBdixpU+2kx0bTybldQyuuXEprrxuEThiO6Oh8TYjiSIGnvqtnEfAWZzwbrhWOwcap591I+G8886TE0m3b98Oh8MBURRx0003yYL72LFj8n0TfOpTn0JdXZ18eVCNu++++4yybiQ71Y8+sBdP/OUActmCEJjAZOZRU2eXCxJdf+sK+e83A/FG/9tTR7B/9xDee/sqVNUqO7T0dodw58/ewLHD5eWVFy2rwLtvXIqnHj2AXdsKnu1TqW9y4gOfPAvNrR68FSKxDO58aC96qCwO5BJIEfuJKRg4BkuqTeCCWfRujyMay5YVYWlu8yDjA3L2OPzp0hhCIkvW19hxfWsWdRY7aKo8jyIvGjGcGkNezCEjFgXdBDrGARNHfKzJTnm4rF2SjCDW70SwJ4l/+bTdAJ7Wo8pgQkVPH7gjO8hWTukLkB16XzOEsSTy2/cAyWnuJCwHdv05YDcuBqWnQUmlDi7yu2WNyIaHkKiyQaDKr0bxOTvYwWEEfG6kUO5iY6Ad8GYF6MjViJyCUw9lRX4wiFR9NTLmRFkfackAY2cYgtOOlIMIztIdcQo8jEEgJVIImnMQUbpIJ4WQiGiXJAE5iVhyTj9PFAKxGvz1sB6sLYJgtryPjRY3PLo8LqvNgCG+8WW4cDhI4/lOA/aOlY9Bo82Mdy3IICRwOBIpnwd23ogNPh5NshVZtGwMJInHoZAO2/wijkRCyIilV6f0DIsrKly4yBABFTgGCNPiXSkaOUcLwnYzYvkE8tL0mFlAkCpwIMSAp/OI5cqT6j16p1wTYE+QRmes3MXGp7Mj5DfD485iIFPwZJ+Kkzcj3mUHM0jh6CE/xGkOLg6bHrY6K2zxHIZ2DpV9Z5F6DtffugyXXb0QSpAaDk8+vB+D/RG89+9WweU5flOBk41m3XhmQaTV8IsPILj7BVAMi4YbvyhbNk73Uj8ddcfJJjo+Lh0j/weL9a1bssaiKTT7Pj0vxveUDoMh3HfffbDb7ViyZAluu+02+RISsWz8y1/+UiLUCb/73e/wyCOPvGPHeiriH0vgkfv3lv3oEciu+pFDY/JO+ZsV6gRS/Ijspn/hny5UFeqE5x4/pCjUCQf2DOPhP+1WFOoThU5IhdW3is2iw4dvWY7tyWiZUCeQ+7Z2R9G9JVYm1Al5QcLhA6OgnakyoU4gUuO1/jBMrEtRqBOIRSPZCVUS6oSMEAIFslsYVrHXS6LWTArpEJFbvrbOimn4w0FwBzeXC3UCKaTTvw/5194oF+pyJ3PIv/wCKK5cqMvvT258EumaCkWhLh8DF0a4plZRqBNSYghZhlcW6gQpCrG5FhlzXLGPIpVCstmFlIOMcXnoioQs4o4c/OZkmVAvtOeREoZUhHrhES5LH9zeuKJQJ3TF/DinEipCnRBAX9ioKNTl50fi2DZiVBTqhHA2iS759EQUx4BcramzJLE3NFYm1AlpIY+/DIwA/sPlQl3uoggueATJfFpRqBMYahhOHaUo1Alj6SB64ryiUCeMZMKorcooCnUCGVurJYXDB8bKhDohFEkj3h1G7xv9it9ZyUQWf/z1NnmjQAmeZ3DtzcvkWhGnklDXOPOE+tjrj8pCnVBz+QdhrGh8pw/rtBxnaY63+cIpHQZDIH7q27Ztw+uvv47Ozk68//3vx/nnn1+WcEr4+c9/DqdTuSz9BOvXr8evfvWrkrAaDQ0NDQ0NDY0TJdTHtj4t/11x/k2wLlilDazG6S3WCaSg0TnnnCPfZuKDH/zgrK9FklJnS0zV0NDQ0NDQ0HizMeojLz+EwK7n5b8rzrsBrhWF3DuNE48EUb7N5fnzhXkh1jU0NDQ0NDRmRyuK9M4g5nMYfvF+xLv3y3971l0FS9PykuJHpPCRhsZbQRPrpzk6PSvfMio11S0WHWLRNCzWcoeFE4XVPvNrswruMSXPV3B/mO50Y3ca5XhVxdenaVh0HGIZ5WpwJp6FyUQhPu4aMx2eo2Fg1D8qJKY7nZ85/SMvzvxRywv0jJ9GmmJBgVbdCWAYHcDqgXFnmzJ4EyhzFpJKvDQ4Yi+ofgDE4YPKU1B1oyTPlmbuY1rioG42Oe5EI4+mchyhCDJPyAEIyk+HDjTFQJSULSkp+fmkn2rF1GgY2JnP82zn0crPPA94hkZa9pNR7iNJhC30T/k8szQn2ymmhJy61SJtBCsqj4FEsRClmfJTKOiYmfugZ2Z2VmFA5qp6dUAdz8qJ2yQfRAmjgUN2hu8ss4VHNJKWP/Ma5WhFkU4+uXgI93zqssnq6sSd7t///d9BJXrAVtrxD//wD3K4rhJKFdk13hrSHOPO51PM+invBnOqMJ+zsoP+BO77/Xa88UrP5H0MQ6Gl3YPezqCcKHr9rctx8RWt8r9PBlte7cG9v9uGoL+Y+ObympBb4MSOeAbLzDqYusIYGywmQNrsetnR4ZwLmxTtJFOpHP5y3x48+/ghOF1G2Qpy9dl1iu8fTmXxqzeO4MnD/ZjIayOvuNjtQGdXAtm0gJVWAzoO+5Eft44jtLY5EG9MI0Sl0OZyozsSRmrCew5AndWKvChiKB7HR5dX4pIGATRVFFJ5UY+Xh5PY6h/GUqcNC6wJCFKxjzRlRH/Cjq1jISyyO3BeJQ+WnprgyCGd12EsTc6dGYm8CdFsUXATL/da3oHabB/onASpPw70k52diU7SoBpWgWrxEasP5F7uRP71l4iVz+RrMEtWgd1YC9pEF2wbqQSoKYJYEkwQd2+DNNKJ7KrzkKwh1pLFRFYGVpiTeXDpMcT1lRhlsiUJjDRlwp6ADZuGwjjXacJ1jjh4cUqCIq1H3lSNnD4NEXrkxBzy0tRFBYuemAf3HgvBa9DhlmYdjNzUBEYKqbwPW0ejhXPqJMXShkvkYlaowMPdaURzedzQaINLNwxpSh8ZyoOjEQHBdBoCPNgbDJcI4kqjFU4dcUOM4ByfE632SInop2EGRekgSAEcC3rxp/0MBuLFuW7T6XB2rQ4G/Qj0jAWxHIPRdDFRladZtNkcMDDDsPImtNicYOmpiao02JwJbPgY4iKLx6LV2BwMTvaQAoU6owcHh5KgRQFfbsxigXQElFTsY0Rfj99FTOjNCrJVY6VxFNKUZFw944SBJa8YxViqAofCCaSFoug3c2ZwFIdQliREO3E4kkUgUzzPVs4AKebC60eTWFilQ2VVEqOZSMlCwpzz4cUDSVSwOnjHgM7O4jzgOQaLqq0Y2DUEo5GT/dGPHSomphML1wXtHvT1hORTe90ty3HJu9rAnKTvrBON5gZz+kB2yydcXfbs2SPXcvH7/XKhxu9///tYvXo1znTdcTKJjo/L4aEfztkNpq3yi/NifDWxfgZ9aA7uHcbdv94q/+ilUzmMDpe6XtQ2OHDbx9aifbHvpLx/JpPHY3/ei+efPALXEi9eyeSQneIIwVDARoMOkX2jskC/nni2G3llz/YXOnH/XQXP9qksXVmF9390DSqrld1pDo1G8ONXDiCZyUOMUugcLHXFqDLpUCNIiMczsC/n0c2VOn9YeB7VZguGE3H4TGYcDpbuVDt0LP7x7Eo02dM4FmXwaE83iCP5BCxF4bwKB2y8H4m8Cy8NR5EWSneKL62uRKstDVEyYCw1gNx0hxbJi2A2CzOtR7MQgEEsdXiRkjykY90Ay4NevBCUuVTMiP4csk9thRSLgLt4FZjpGz2MBTBVAVIKUs8IpP2vlD7faEX67HORseRhzJmhj/XJe8WT7WAQMlUjJIUxlvbgoe4I0lPcO8i+7O3VVqwigtngQ8YoAXTpTrIomZARgojlzLjvWAb9ydIx2Fhhw0VV5Nzz2BsQMZwqPY8+gxmNFgGCJODVYT22B0rHqM6kx9X1LHg6gbG0FZ3R0vNMUTyiWTu641E0WiwIZ8Ywdb1IdpevqLHDqQ+AoZzIS6VOPYJI49W+Gvz1WBLLfBZ4rCOgqOIij2yRmDk3BpJxVBjMcOpCoKjS3fAakw9VJgq0yIGLDILOlR5jd96DB4ImZAUKkRiL7lDpGCyzMfhctR9GKY4n8zV4JlL6+j69Dtc16GBgI7BwVtB0aeEhUgypO+ZBXzwKG2/DWHq6oxOFnOjFgVAYdrjx8v4sUrnieSSe8hvbjcjq/bDTDrxxUEAoWTrXl/ImxA/H4bHqkO4OIzJW2ofaejtyOVEu1JbN5jEyZTFPqK6z4faPnYWFSytwqqOJ9dNLrP/1E+dg8+bNuOOOO5DP59HU1CT/m9hHnyhOB91xMohqYl3jdP/QRCMpfPYDf57xMT+/9xbFwkYnip8+fRh/fK1btf3dq6rxj9cuUW0/sGcI3/uX51TbPT4z7vjF9artROyf/2/PQMX9TWbZ2SL8abVwCaDJZkdnRM3CD7iqzYoxNZtCIkJMHvQllO0sCStddtSZh1TbTaIZ1Wn1qo4SWNAWF2ZCjEVAIa3efmAMCKifJ2y4DDSjbOFHeDLZjCdG1NtvaPRhnVfBSnKcvpgR/3NAvY9OnkebfeZKtzv8LDJTriJM59IqM+J59WMg3ukDSfU40/c0OmHjyS6+MoPxSuwLqT+/wmAHM00kT8VOG7FcVJ9HpArqLVsqJgsRKbGwhkYwq2DlOM7XlpvAs6TAkzJHQlXojqsf4/BoHR7bq2wFSVjss2L/iLIVJKGZ5pF/Qtm6laDXM0in1c8h4f/uei/MVuVKsacKmlg/fUiN9uGC1HbZpY5w8cUX45vf/KaiS91cOF10x4kmOj4uBwfnvrO+sGp+7KxrMetnGLNVJyVICv7HJ/YgZmuf+QGzBW7NFtlFxmD2LlJzap4tx3zOIzzb4R1H0VdSyXbGA51tHGd5/bkG2M0kQAnirEcw+zjP1j7rOEpze/5s7bOXw5aOY5wwx4Oc7QhmRpTm9gKz9a/wHlo0p8bbQ3LgGPoe/yVeMuSh0+nwmc98Ri60eDy/rRonFkGi5Ntcnj9f0MS6hoaGhoaGhsYMSKKAsTeegH/rU7LzS0Vjixz2snTpUm3cNE46mljX0NDQ0NDQ0FAh7R/E4HN/QGqkYNJgbVmJ//3f72LBggXamL2DiFLhNpfnvxWOHDmC+++/H7t378aKFSvw9a9/XfWxpJjn7373O3R1dcHj8eDmm2/G2Wef/abfUxPrZxjHc6lODo+YA4IgzujQQBJcZ3z/WT5Asz5/1jCawoV1aS7HMNuV/dnCI2a5/DbXq/ok9IAm/ZxhLMh7zHgU9MwuG+QQqTmM0WxjPMfojPFjmNtcnqvPyKxBLAI147fwbD8msqXmLKEws42AKM5syTnbXJztPM/6dULP/ftotvM823fSbO3zCc1n/cQhCXkEd7+I4O5NcsEjmtPBd8510HtqTnh8usZb+50T5xDK8laee95552FkZAQ33XQTHnvsMcRi6jlPzz//PK6++mpcddVVuO6667Br1y6ce+658hWZz3/+82/qfTU3mDMw0ePQvhH84Vdb0N9TmiBZ1+jA7R8/C60LvW/pdYn38QN/2ImdW/vwnltX4ILLFigK63RWwF0vd+Le17qRnWKTyNLAuXodgntHsOGCJrzn1uUwmpQ9oV9/sQv33bkd4WBpEmhDsxPxWAYVVVbZ2Wa6K8yB/jB+8MRBJDPEYhHomuY+UW3mUZUniSdpuFbo0MWXjpHPxOPiZh0ERBFK2PFsR7hEKOloFj7Wg8MjCZzdogNv7QeoKQ4ZoFBt8mA0RWwIHRhKBpCfYq8HiYJOrMDuoSSWeY24oGEQBm56EqgXwXQGJkaPFiFY5gYzSvmwLUMsIFmstVJwMqV9lNIUpP5egCQd+qpB8dOSAzk74GgB6LzsKiPtfLakWTBaEVyzETFjFi7JCnuyv8QNhnh5J601SDFhDCbcuPsosUEsdYP5gJvDCqETkrUOGY8RYEoVYSpvlB1Y0oIVj/ZkMZAsTSRdbHegwpiFjuZkq8d4vtTZqM5sxwqXXvalf7xXwKsjpQmUHr0RdWYd0kIalQYDEvnSJFALx2O1xwSeCaMn5sSmwUCJ8DYyLN5T5UElF0WONSOCQYAqnkdBorF9rBIvDkawwGaHiSU2i1PdYCTk9nmw49EompdYsPiaCDhL6XmO5zzYOpaFV8fjRnsQXqZ0LnYRN5iAEVmRRizOoitYeh69RiPMPI94NosKB42RXGmSqF40ItPjRc9QBh+8UMLGxT0lC6R0jseTHZV4vT+BtdVmWE1DoKassMgywWPwIJQJIZf24N43cohnS91gzl1kRJr3w8HYseWghGCi1Dt9odeGsUQaFSwP6rUxRPtK53LlAjfCPA0dx4ALJjHWO9XOEqipt8tuMO1LlB2s/KNx3Pvb7eg86sctH1qNdRsbSsc4msEDd+/E9s29shXkRZcvOGn2tW9XgmnbJ74PRvfWk+7OZKb6p5Pd0x/96Efo7i4k2a9btw6f/vSn4Xa75b/JY0h19ZPJ6aQ7Tsa47Or78ZwTTFfUfv5Nje/BgwexcOFC+d/EqnPjxo146qmnyh6XyWTQ0tKC9vZ2PPts8Tf0G9/4Bv7zP/8TBw4ceFNXZjSxfoZ+aERBxN+ePIKH7t1NrLhxw/tW4MLLW2fdtZ7ttZKJov92fZMTt398LRa0K4v//mASP3nqEF49PIblZj2MnSGMDcVKiiHddPtKnHtxs+IOMbGffOT+vXjmsYPyY+1OAzqPFkUXw9K47Op2XHvzMtk68OfPHcXjuwYmdwrJKy6ts6PXn0AmK2CV1YBj03zWF7TakWjKIEKncFWrDTzvR14qCg4Ta8euARb7RxNoMHlxdCiDcLroXV1tM2Blcw6ibgQVBieS+QyiuaKoMrF62HgzBpN+6OFAt59FX7QoPI0siytbjFjm6wFLmxHPGRHNFYUnKZRUyztRl+1DBjrsFCvQk5raTqHZ7MEKUwK8mIdEXDmG95EAzOJAOpoBlx4ULQDOxQAvgpqywJCyPMStL0IaPILoivMQ8LIQp/is87QV3qwEY3YMGVM1ErosREx1HzFiy6gVT/aFcJ5Vh+v4IXDZKcKRMUDwtCJrB3KiAeFsCmkhUFIoaCztw4NdYdh5PVa4DIjlprZT8Og9CGaCsqXihgoPTFypC00448CdR4IYS2ewzOlCIOOHMGUMKg1OGJgMcmICqzwuOPXk+IrnUZLseGOUxcFwFJd4vVhhToFGcQEhUUYkGQpJagTdMTee7BMxOsVO0szpsMROSkKNgRq14vAjQM/RovjWG1hsfLcTNRtHkYcBByNmHIkUzyNL0bjAbccVph5kJA6PxKqwtcRnHagzenF4OIVMTkSTw4HDgQCEKdvirS47JF0M8VwKplANtu5JI5UtLjCW1JrwsUuDqPMGsG2wBg8dziCSKZ7nSrMRq6sosLwfTp1TXugk88XzrKMN6B1x4dE9KSyp1sNTGcdYpugCo2c4WPJevLg/hQqLQS5GdtQfKykYtZIzwv9ULwwmHczNDhzoLo4R+W5aXG9H4MCovP4li/mLr1SuDZHNCnji4f14/MF98r8nWLjUh9s+uhZVtXZsevoIHrxnFxKxYh/rxu1r206Cfa3mBjM/LBkf+tBa/PWvf8Xdd98NURRht9tlH/VLL730bU8iPd10x4kelx29P4Z5DmI9Hk1hVd2bE+tTmUmsP/LII/Ju+gMPPIAbb7xx8v6BgQHU1NTgn/7pn/Cd73znuN9LE+tn+IeG7IaTS8hzsT376R0vlRRcmgr5bvvMV87HmvXq3rMPPbQPj9y1U7X9oita8YFPrpvRyvH7334e+Skez1NxVVhwoN6CmEpFRJOORX0ij5Fpu+wTcCyNv/uiHnEoW/ARsXi4pxEvHivd9ZvK+9YbEUafaruVqsLzHSHVsInz62xYWjmoWsFUT1vREyfFiJT7yNM8bhjZS66RK78BzYHa8B5QjLKVI9F8w7EEopK6TaFHVwOBUreb5CI22P4/e+cB3tR1/v+v9l4e8t4TMMbsECCQzcjeiyZpkzRpOtOm69+90vaX7jRtdrMn2QkhhARCSAjbYAwGvPe2JUu29v95j5dk3SsDxmCb83keYaSrK90z7tX3nHve79uyT3S7MyoPdTqneAZTfzSKO1zMO10IjUyNFamU7VY4uyfN6b94FGgPEpDBSCVS3JRthEIanHApFJ0vHVq/uI3hlu4YvFwtXgc5XVbseaJJ1HEpd3E0Whfb4A0eTAURo9Khx+tCn0+4nUkQO7v1aHUK244qpFLo26w4UCN865bG6hefq0Vxh7it6A0FUeiDuNWiJJCAL1op6ZQw8dIEbNrvhlekDjK0WvRs72QDaCH0WgUe/uUFMFvEf6R/++MPQhIqBUMJ4abPSsD+3Q2i+9/z/SU4a2kGTiZcrE98usp2IKtuM1pb+/vOxRdfjB/84AewWCyn5Ximqu44WfWys/qfYxbr89K+PS5incT4Aw88wNasZ2SEXkvorsz06dOxcePGY/4uvmb9DIdmpMeKfUSylZEij5aUREI5yprY0fanpTJiQp3t39MHe5/4Ce1weeEIuiMwEo/XH7KEYSS0Yrgvwvezz/D7I67NdbHvEMft84oKdbadZf0UP0a3342AhzKTiuD3hCzXERp0+WipivhXwB+0DEQIddBsvOD+ARLZ4rXgh0dUqBNuH31+hMXX8MFN7SD6/X5IgxIXCaEcpYyOUfpBb483ojUqJesSE+rs871uUaFO9Pk8cLg9EfuhI4JnOR2aPWgpixBs2VaECUYfqK+KQ8cvJtSJHrdHVKiz7U4PDKNctyJdM3y+AOzdka8pdltk/37O1MLn7kPL1rfQvmcjNLF6JCYm4ic/+Qlbn8w5M8R/MGTJSY+xUFdXx/7GxYXfpaPXaIb9eJgaETUcDofD4XA4x4m9sgTlz/4GHfs2D82mk9MHF+qTxw3GP4YHkZKSwmbqBx80Iz5W3O7+ySm5PHxOXKFQoK8v8oTBSPjMOofD4XA4nDMKb28Pmja/iu6yHey50hiN2IWr8Z3v3AaDgeJLOGcKtbW1IctgxjqrPrhEhnA6nVAqQ40yHA7HcfcxLtY5HA6Hw+GcEZATk61sBxPq3j4HCxqNKjoX1kWXwusUt+HjTN0Mpkaj8aTHBAw6vVRXV7Mg5UEoaJkGBxSwfDzwZTAcUdwuL9Y+vxf/+csWdLQ5BL2JP3z3IHNbsMb3jyJHkp0Xiy8+rcThg8JBe3tKm7H1cBvS8/rtsEZCVowrr5gheozNjXa89fJ+5M2wQk7ejyMwGFVISzVjsUULrTJ8PTO9VpRmQWyWBSZD+GhaLpMgf4YV+8sM0Eh1gseg9MeDnAXTzMLbp8UZ0dDtQ7QyWnC7WW5G9VEFsvTCdZCo1yI72g+TMo4Fs45EIdWgqdcIpcwKmSS8jAqpAgVR0XBnFgHycG/ggFSO7oRZqO1xwR8It8qkO4W+gAF6hQZKaagV5jBx2NEagM8fJbhVgihUS1Vw6dIEt/tUMWiSaiGXxgs6g8skOjQ69DArhctIAbTJ+ih0uYwicxAyBAImLLSqoVeEr3cmS80oeTzWH9XAHxCe8fD4rNhr88ID4Yu6M2CG3edGhkG4DuLUJvREy5C9ME7QQz4uQYeVF0qxPCEGMrJoGoFeroJJEoUUdTyzCB0JvZZtjEVunA5GVXg7yqVSFAQsSO52I94YXgd0SGdPNyApug8pRpHz2WzBoZYAdHLhMuoDZjR96EG6X7gvRyv08B2RYLbZJOiNHq1RIsWnREFuDFQC56tOo0BRqhnPPrqd2S6OxOXx4clPjsKfHQVzdHhfJ0cZcpC55uYiZvsoRFZeDLZvrcahA82C2zmTF4+9E7Vv/wd16//HhLoqOgEZ192P+HOuYR7qnMmF/yQ8xovzzz+f/d20aVPI6zt27EBPT89xi3XuBnOMnGlR2Ts+r8aLT+1C+4BDikotx6VXF2DFFdOhUMhwcH8TnntsB+pq+q3VyG42Z5oV1RWdzFIxLtEApVKG2iDrtbOXZeD6W+fAHKVFc5sDT7y6H9v2DDsy5Mcb4GvuQVtzDxPZV99chGUXCnu1u1xevPPqfnzwVik8A0F9MVYd9EY1qo62M8eH7PxY1FR0oLe3PyBPG62FfG4C9jTbmQCdmWJm9pGdA8GlOoUMRQY1jhxshc8fQGaGBY0KCers/aJApQBuvkgNXVQTfAEvtDITPi/TYUd1/3Y6yoJ4C2q6etDd50G8QY2EaAnqeoddZOYnGaHX2dHjdTL3EltDLNZ97kRgYIQ/LVMDbXwfGp090MjlODddhzRLA6TS/jKqZQbIJQr0eDuYbaM3YMX62i44ff0BebEqNWbH6GAf8NRO08cg09gOhaz/GKVeKYz1bsgaD7JLlduShdKoKNhk/XUkl8hQEJUFk9INCSjoVQuXzwk/uocChskDvdPdBH/ADZnEiLIuLQ50DTvhLI6LQq6ZnvdCAg26XAbUOYYdVJIDGmT0NEHq7kBApkarIQMHpX1DGt2oMMCsomBIKqMMDm8cNjd2DwWHmpVaJGnJ4rGdDV4StDGQStoQGHCBoXrNM8cxj/R+TOjztsKH/r7s9ctxoDMOxcza0I9oZRTKmiSo7Oq3IVRIgbvnGDAtlsQafaYJhzsVqOoZDkJaYIlDtsoJCVzwQYPtNiW2tA27j5gUUWhwutHuckDHRDZZfA578qf1aaHY7kZDZTc7t1ZcZcGiVc2Qy/vL2OkyYH2tDmXdnUy4J6tjsb++B73e/na2aJTIjlOhxtnKPjHDEIOWXupXA31VooAZsTjQ0snsG7PUJrg22tFxtH/mUKaUImZJGnbZellwdHaCFjNnOeDXtof5/dvd7iHP9oqu4fP5nDQjEs129PmcUEnU6N6tw6cvDNdB+hILpEsDaPXaoZbKEd0Rg+3vdmGgqyIu1QBka3CkqwcKmRQzdQbUbmuBayAANsqkQmy0DmUVHcylZka6BR0HW+EcEOk6gzLEcnbzwWb864MyNHb1O+GoZRKcrZCjcXcTPB4fcqdbsebO+UjNiBK0nI1LMLC2qKkctv08a2k6rr9tLqIEhP9EdYMpuP9JyFQ8YU/YbPrhnWjd9h78XjckUhmbTY8qPAcS2fDA12Nrx4c/vBxJSUmYCJxpuuN462Vr5b/G7AazOONb4+IGQ1xwwQXMr3/nzp2wWq3weDxYvXo181gvKyuDTic8wScEF+vHyJl00vz1tx+jeJdwpDL9oOVMi8VnH1cIbieRTbPhJXsbBTMfqjUKLL1iOt78tBJuj09wJvuiWQlYc8ts6PTCMx1NDTb86ecb0NEe7OU9TG5+LLq6etHSFJooZ5DoabFoSzGivFn4lmeKQYV4rRI7xLZb5Vg2T41X9tgEM0eS6J+TZsKRnoYQL+9BlCQi4mPx7qZe2Bzh28k2+pJz9JidVQ+FXNiCTyuPx6aGXtQ4hOtgpsWMi5Ld0CtD/cYHUTiV6LTLUK0Qdr0wKYyYbkmED8I2hL6ADOXdGmysb4aQbwf5na9MiUGTkwY24e+g3Dp5EiOq4UCv4CeQTWEcdrU70NwrHIiTaYhGvKYX3oCwFWOsOgrJOhncAeEZ0h6PHu+Vx2BzkDgLJtkgx+1FWpR0Ngu6m2hkCsw0xuH9xka4/D7B2Xq9LAnbqrvhcIc7uEgCASz3ReHis5thjBZu59KOBLx+MIAGm/D27Gg9DAYPGpzCtqFmuQHK3RqUrxduR71Vh8xrrfDGhiZEGkQGJfy9CdjV1Bri2T6IWi7FymgzPnukEY7u8DJKZRLMvDkJxbt60Nkq7FKTvyAOrbV2tDcK9+WcNDOkrQ60BA38RyZzc81LxNbDwlaNSWo57pyXigsvzBG1r33xyZ3YtqUSQmZBJODvuW8JZi9IwVjgSZFOXUKjYDo7O1lyI5rRJCihzbe//W2kpQnf5TsVyY6OlTNJd5xIvWypfAh6wxjEur0XSzO+eVz1++c//xnbt29n/3/zzTdZkiwS7MStt96KSy+9dOi9ZAF69dVX48CBA1i0aBET6bTsau3atSgqKjquY+Vr1jlhiC1ZGVx2Ein9N1meOR0e0RTlNOteVtEuKNQJry8Ar04pKtQJmu0XE+pEa2sPOtt7xfc/2IoKpXgZau0ueCKUsbbFiz3VlLNT+D0Ojw8+KfmBC99kc/sCqKiVCgp1gpJ9yj0eUaFOdLmcqHGIb6+w26BXim/3aN1ocGtE7wN2e2zwBIyiqeJlEh9qHQERmU32fD40OHwQewfdSGhQqNDrEfF9p0ysfW5RoU609tkRo47QD/o6YNWKt6Ne0YOKdvELdJ3diwoblUGYXp8HxTa3oFAn6L4EZesVEupEQCKBNt8tKtQJq7oHDTZx4XC03Y64gbsmQnR57VDsEbfM7GlxwGy1iWQQYGaXzBJUSKgTfV4/you9gkKd8PsC6NjtERXqhK3agfZm8Xasa7RDXSfeT2gmvCVOfCa5vs+LhPzYiPa1fn9AUKgTrj4v+46xivVThS45GzL1sc/YTaWERiS0g2fFKd07JZ4hMabValkG0ptuumnCiHHO2PAHObqc6P7Hy9KlS5GZmcn+f8MNN4Rsy8vLC3keGxuLTz/9FCUlJaisrGTCfsGCBZDJIlkMC8PFOofD4XA4nCkDrQmmGdD333+fPc/NzcVvf/tbZGVlne5D40xyFi1adNz7FBQUsMdY4GKdw+FwOBzOlODQoUP40Y9+xJLO0Az6bbfdhjvvvJN5W3OmFr6T5AYzGeBincPhcDgczqQPIqWZ9KeeeoolpKElMbQEprCw8HQfGmecCIxxGYzYct2JCF+4xQlj8fJM0XXp02fG46wl6VCQZYZIsNf8xanQaoVnMcxWHWQqOcxG4TXpRo0cgVYHGuuFA+ZcfR7s39OApBRhC0EKaNPMiod1dmiQUTDkEnO2Xg1adS7EHL0KM3xkdSe8f0aiFkaNggXXCZFkVkGtkECvCLfPIwwBJXTtASTohLfrNDL0KeVwuCyC2/1+KWo7LLAqhK0gqeVmRUehrTc8zfEgB1oSUNFuhV9kZsGiNMHh8cMfEC5jvV2LmnY1C0AUIkZFQboUhCkc/OPzK3GoWwW3T2zNuAwyCbm+CNvr0aJ3hdQCu1t8LXKKPgZyibCFIFHTE4OUKCXkIn09zaxFh1sBicichlauhVEhhV6uFi6BWw3nATliAsLrqZUSOSorTChvFC6D1yfF5/vjkSQVbmciLyoaCYq4fn9NASyBOGjPskIqFy7j/OVGzI6TsYBgwTL4jHD2aKCVCbezXqpGj04PQ6Kw3aXKoIB/mgHx6cLtrJBLsXReMmbmCdeBFAFMs+qZnaIYKTPjMMOghkxkkmymXoWDW6qYxawYcxamQKcXLmN0rA7TCslSlDNR8bn70LTpFTz00ENMqFP20eeff54Ldc6UgbvBHCNnWlR2VXk7nn10B46W9TssRMVocePt87BgcdpQoOkLT+zA3p39rjH0Q9dvo5YDqUwKW1cvXnl2Dz77uJyNXhVKGRKLElBa3w23xw+1SoasVDMOlbezgErSS0VJRjSXtTMbNZlciosvycfl1xcyBxmC3Bpe/t9uFlxKzhUkuhvrbOgZsFaMLYxDrUWNugHrtgKrHsrSNtjq+4PTEpKM7HPrqvtdJRIzLGiK1+Gw0z3kGpFn86DmYH+ZY+L16MuyYG9P/+eb9ApkZmhxoI0s+IAojRIJRi0ONPd/nkYhxex0LSp72uD1+6GVK5BmMuJQezt7vzQAZNtjcHRXF3r7vGzAk5kbg13dTrh8AUgkASyYZUC3sgs2t4uJ7kXJFsyMb4Z8IIiw3WHFJ5VA80BwaV60BV5lF3p8/c9zjEak6wPo8faXOVZtRp7ZBa2if/DT6jDhtYMm7G/tf55i0GFZhgcmTX+IoUqqQpxWB4+/30FFLtHBooqGWt4fAOj0yPDWYRM2VJLtYQB6hQKzEnVwS5uYxYtCIsE5CWYYFC0IwMuErkQSiwYH1UGAWVR2uOKwvdUOh9fNvLZnR0cjVd8GmaQ/CFEuiUVrXx+c3v4yWVQxqLL3oMfbH2xqUJjR1udHc2+/W0+W0YIZFieU0n73H7PSgGS9DAF0sOcyiYmJex/628nm1mJjvQn7Ovq3W5Q6SNw6HGzprzO9UoHpiRpmi0jHrFeoUGAmMdrK+p0UUsSoY9DuojrwQS6RQ6uIQll3K/xsqkcCaUU8dn7aA2evl/n/T5sTheroTmb0yOpdYsWRIx509JBFJnBxkR43LauFUdtf5pKqRDy6QY2atv4yz0gzIBBjR5ev334ySa9nt/hrbf3HTP1Mq3Ohw9vfrjro4WiLRkljfx0l6TVIrHKhZWd/u8YlKXHT93TQxDSx51KJFnUOM3a09jvjSAJy9LQnY2t5D7z+APRKOaYlalDdS+0aYE43Sb447Ctzotftg1IuxVyzBp2f1cDr8rERY+L5KSjV+dDt8rB2LjQZ0bS9Dc6e/naeXxiPO64rRIK139P9s511ePLV/Wjr7K+DzFgd1N0uNNZ1D00EUID6oMtTdJIRiNPj6MD2xHg93FFqlA3sb1XJUNjrR9X+/jLHxulx4+1zMfesVMFrnt3Wh1ef3YNPN5Yj4A+wa9aqK6bjkqsLoFSN/Sb0qXKDmfPbt86oANO+9gbUvfcY+trqkWs14L777sOaNWuY68Zk50zTHcdbLxuOPAzdGNxgHPZeXJjzjUlRv1ysHyNn4klDtxW3flKBliY7Vl9dAJXAD1bxznqUFDfgsmtnwiCQaKX8cCtef7MUpW0OtHeFO3vExWiRYFKjr6YbzQOiOhhzlAZX3TgLn2+uxKGScAs+jVaB1Jxo1MXpUCxgtUj+zfPNGpibHDhyqJX9CAdDdxDSZsUjoJShYXcj82MeSdoMK2wFZhzptQs6e2RFGxBtkKLT34kuV3gZ43U6WNwaNG3rRVNLeHKpKLMasdPN8CY6UecIrwMSj+en61DRGRgS2cGoZDIUxJswzepGjyfcvo68yDONMTjYLMf6im5BZ4+58WZcnueCTEo2heFl1MhiUdlhwbMHbOh2hbuLUBKd87IkyLeQgA23zJRL9GjpNWNToxf1ToEyKlRYEmeEXOJBuyvcSpEEsVoWhaqeAI7Yhj3bByEv8nkxUbgg2Qe5lPpJeBnlEis+b1JgY0Mn3AIOLinaaEh8SrT52uH0hpcxWWfCTIsSLp8dvQODo2B0cj26Ww348j0P6hrD29liUiF5vhENnVIcbgivI71ajjXnKFFSI8WWQ+HbmSCepofG6kbZwAAwGObzb42Cq1eB7ZUOeGgUPIKZJgNWxduRNZvaOdyhRSqJxsajUfjwgA8dA4PYkDoya5FokKPiqBcNHeF1YDWoMF0pRWOMBBW28DowqBSYJdfhiqJ0zJsZL5g/4eV3D6FqTwMqDjSH3aYezJ/Qq1fhQE0nc48aSW5uDLSBAJp2NTAnl7A6mJ2IO761iOV7EKLyaDs2f3SUCXVr/MlLO8/F+smn6+A2NH78IvxeD2QqDV57+M+46KKLMFU4E3XH8dTLB4fHLtZX5E4Osc7XrHNEoZmJJedFjp6fNS+JPcTIyo2FJsGA9qPC5nDNbU4kq+SCQp3o6ujF5g1HUX44XKARvU4PWhxuFDcL+66RYNnX1Yv4UmE7ShLvVXsaodMpBYU6UX2gBdLpGlELvvJ2O6zRGnR1C9sMNjkcMHZoBIU60dHVhwSFB1UCQp3ocXuwpymAGpvI0iCfD/VdPUixCG+nmdByWys+rNSJWvDtaurCVdM8gkKd6PW1YkudXlCoE7W2HsSqNIJCnfAGetDWZxYU6qyMHhcanT5o5cKe52Qf6PQ6cMQmbPFHNpm729uxIlXc6tEbaMEXLSZBoc7K4Gxny26cImWsc3RjmjlGUKgTDm8PuqqNgkKd6Ox2wdwgweFO4Trq6fPi/d0qVLcJb3d7/ThQ4YBeJlyH1LL7Wzrg7DQKCnVif7cd31zRIyjUCX+gHbWtsehwCpextssJkzcKDR3COQha7C4k5ptQ0S3siW53eeCIlgoKdYImBFYtTcf3n90juN3nC6DsQAu643Sia1UPH25D9oDlohC0jK66slNUrGdkR7PHZMVta4fMJW4HOhUgcd76xdvoPryLPdcmZiNm7gWYMUM82zWHM5nhYp3D4XA4nCmCx9YJv0p80DrZEx/99cpCPProo7BV7UNOrAk333wzbrzxRrYsbGRCJM7Uxh+QiMZdHev+kwUu1jkcDofDmSJM5aRI7Xs+we9+9zuWtt1sNuPhhx/G2WeffboPi8MZd7hY53A4HA6HM6GXvTR/9jrad2+EPlaPefPmYdmyZZg/f/7pPjTOGZbB9HTBrRs5Y8JO68UPCq8HHyQlwchcNAQ7oFQCjVrOHDPESEo1DznCCH++AVEitmtEWpwecQnigWLRVh2iEsRnovRGFcxy8e/XyGXQusXTB8skEmiUMshELAKpbnReGQsUFSNBL4dBKX4MBrkWaql4ynWdXIcEnfh2k0oFl0e8DiRQQBshqYhCKoVcQtvFbitKgIAScol4OytlSiik4t+hkmpgUAjbJBJRSj0CAfF2lkALq0Y8GEknV8KqEe9HCqkMrl5VhM+XQK5SQC7mIUgB0xYp6wtixFlkMOvE68Bq0CBaLV6GOJ0GyWbxY7TQeRTQRG5nmfgcjkIqQZZVL96X6XwB9WXxdtbJ5bAJBK8OUtfcI7qenLDG6xEfI95Xo81qJIpYuw4Gpbe3CscFEG6PDzv2NcI/mX7JpzCurhZUvvIgOoo3s+fXXHMN/v3vf7MAQ86ZjS8w9sdkgbvBHCM8KjsU+iFbv6USz71ZygT73II43Hn9LCTG9duwjeRwZQceebEYR6qGAwgzyUqxzYm22m7EWHXQG1SoKu+30yOS08xYc+cC5BfEoavDiZef3s1cYQaJidXhhtvnYv7ZaXD0efHEpqN47csa+AZ+ZE1aBe46LweXzU2Gz+fHB2+W4p21JUOBZ0qVDGnnWFGaYIcXfszoMKFlc8eQFSQ5T2QUxKG0oxcOlxcZRTFo0PvQ6uzfTswyGuDZ3ARHsxOJC6LhnC1DQ99wgGGG1oyunW40VTuRQFZ0ajkqa4cDQVPiDdA53Ggu74ApRQfDSjMOu7pCxNelOX6kWxrR51VhS3USPq3pt44kjEoVTG4zthfboVFJcctFSihNTfAH+oMoQ2wF/YDSF489jQ44PQM2iVIpMvQx2FPlRK/Hj1vnK7E4qwkBDAdyOlyJ+N8+Lxp7vMgwmdm+zc7hMhbFmXBRVhuMKjsUUhMkkMLtDwoUDUThzTINdjb2Ik6vRnKMDHXO4YDjWLUecRo5bJ5O6OQqZOiN6HQPu9qoZGq02sz4uLIbarkMs5IMqO1rYUGlhEamQLQsGnvqu5jX9j1zDMiPJUeYwSBKEscx6OxrZvVSZU/E5qZe2D2uIZF9ljUaSxPaoJG7UGGLxbpaL9r6husgSWHFru0SNLZ6cdZ0FZbM64HTP9yOWkkUPtyqxr4KN1L0KsR7AqgI6uvJiXrEnuVDu6ETar8W7mordh0ZDtKMM6lw1hw/YGmExKdAZ1UyPtvfM9SXydc/I1aHfTVdUCgkWDhHh1pvG9y+/namgd7ydAMyo+qZhWZ5Yyre2e+FzdXf10lcX5irw9zMGijkHsSqo6CS2eELDJexpycRf31LiaoWH3JT9fBofKgLCuhdkBKDby2ehhSzDkeb7Pjb+wext3q4jBkxOujIarGmGxarFvqZRhzoHA6GjddroPcqcLDSzs7NO8/LweVzk9mAnejs7sP/1pZg05c1UMqkKIzTo6qkGV5PfzvTgD0t08JcnYjU2Ykoa+mBc+B8pgH/5Rdk4/rV+VCr5PhicyVeenoXC1Jn7SyVICc/FvW1XXDY3SgoSsAtd85HQtKw6Nu2twFPvLKPBb5np5lx141FyM+MwsmAu8EcP91lO9Gw8Xn4PS62tMd69uXY/Ic7YLVaWQKkVatWTenMpFx3RK6Xtw7+Z8xuMJdPu2dSuMFwsX6M8JNmGPJGJ+FdXhPq+DDyx1LICnLD1mq8veEIKN1PTXG/x3MwmTnRTEyfe3Euzl+Zyzzbgzl8sAUv/W8XCooScclVM8L8jytbevD3dYeQEq3FXedlw6gNnSntaHOw/VsdTtTP8KJTFjrDp/XLkFOhhavZjXaZFHUDns2DkPdy+iIrOiVeRB1xoHVPqFWihAT+5QnotPqgrFfg0Jfh7iZ5mRY4HB7EKGSo3tsYdi8ueVEM+orkKEqSYm5iDWTSUPeSNmcU1h0xwtenxq49fXD0hm7PTlTg8nO9UKuBKrt9yJ98EDlU6HXGoNsZQH0rUN81PPggjCoJ7jtXigSTHe8e0WBbfWgdUIvkR0fD4XHh4iwPUs2NYWVUy6zw+F3Y22TCK6V2BEbMuE+zGqFQ98GqVcHm7vczDyZBa4RZIYXTrcb7ZXY4vaHuJvEGDZKipZQ6CYcae2FzhbqbpBoVuGeuHBa1FzZ3D9z+UIcWj0+B4o54tPT6cHFKH2I1oU46Xr8MO1vjsbfVg5ZyPXaXhtahVBLA9edpkZTUjSPlJry1Ndx9oyhah0BHL+JnyNAa34wAGe0H17MrBvWHtUhPkkCfXIvAiHaW9plwuMTCBlzlzXb09IVuj4tSIHe6DCa9D3MT26FUhDrluD1qbC9PRos9gAtntMOgDT1fpRIpErXR8Hr78MoWM9bvCa1DOvUK88xwSr346vxsLMkIT7L10f5GPP1pBaIDQAX5mY+YqUqdEQW7VQaTTIV9hzox0nApL8GI76zMQ0VZO15+9+CQ8B4qo1GNRJmE+fc3NdphH+G4pDOpYc6PgUqnFJwsIF/2t1/dj327G9iAvSFooMzKKJfiokvyMf/cTDzz5gHsOdASdtfr3LNScdvVBTALWNMeD1ysHzt+jxtNn76KzpKt7Lk2MQvJK7+KgM+H9+5ewsX6GY5tQKy/UfrfMYv1K6ffzcX6VIKL9WG+89uNIbPDI/nR1xdi8VxxO0eaIX//jQOi25ddmI2v3rsI48l1b7wulvSRYdqhRUubsE0gMcukRs0RYTtKInlhMvZXCtsQEnOTjajYHS5yB1lxvQVzVoYPZgZpbo/GT56JsOxFLcUFqyIvT/p8bzy6eoUt/IjZWXLU9QjbBBL/b7EKGqV4Gb6oycB75eL95OxUEzxy8TKqYcGWCnFXC4NSCbtbfDkF8ftzbaJ2lESM2gw/hG0Iif97PRdby8SXTBSlWUJml0dy/hINOrR1ottzjBa4/MN3k0YidRnwxnviy1pIHf/+6+H5B4JRSbVw+cX78ivrs1BcJWw3Sdx/yXRcMT9FdPvaDw7j6ddLRLenT4vFri7x788wqmErEz+XaNmQuk68HxL/evpaGE3iYvo3P1wnav9KJMxPRmmEdrzpsmm44ZJpGAtcrB9HkqN1T8DV3n9tiV2wErELV0EilcHd3cbFOgeD59LaA2MX61fPmBxinQeYcjgcDoczRZisPut059V2ZDdaPn8bAZ+HLXtJWH49tEnZ8Nj7B1Iem/igjsOZynCxzuFwOBzOFGEy+KyTX/oL91w05Ive29uLhx56CJ8c+gTGKBWKihbi/vvvh8VCCyZDoX18A7EanDMbf0ACH/dZ53A4HA6HM5mYDD7rtJyFRHdSUhIOHz6MH//4x6ipqYFKpcLdd9+N2267jSU5EoOLdQ7BrRs5nAhoBIJHg1GrZJG3qyPvT64NkbB197FbpmI4elzwjghIDKbP5YWKOYQIIwuQu0bkY4hkNUlIFZHrQDJKHXpckfcPuCXM+UQMlUIGuUT8MyQBKVTSCNvZbbfIx+BwRS7DoMuHGD5/5M+HVxYWsBiM0k/hpeLfoVVQG4l/h98vgb03spMEBelGQinu9NiPL3I/8Xsi14GUWiHCR5BtqNcj3g5+vxQev/h2Oo3U6sjt5JdE9jeT+CPPciojRocASo0EkbqKSimDPILdpUIhhSxCJZFzlVQZuR3IkjISksnk8TZJoGv42rVrmTAnoU4OL4888gi++tWvRhTqHM6ZCD8jOMfNL759Nq68KCfMTzo10Yjf3rcEcwviI+5/2XUz8bVvLQoLCNPqyN3Cik83HMXffvcxmhtDA//cLi/eeLEY9935On73k/WorggNzPP7/Njw3iHcf/eb+Pl338WB4vDgxy076vCNX2yA9HM5svrCLdmyevVI2SiFr7wTc5JNYSKCXGYKtErUHu1A/ow4JhSCMSQaoLkoE5t7+pA2NxFaXaiaI3vK1LmJ2NDrgu6iLOjjQ90ryE4yb4YVm9/twGv/F4fOhlAvYZ9Xin0bkvDib/uwsLUPBXpVmHvFilmJ+N/XF+Nr+ZcgxxQeGCjpjsf2zYmw1ftRYLSEyd10iw6pFh32VXuQpk4I80aPUuoQHYjDL9YB7+zLR5879Bj6vFpsaUzBju4mLEg3wDLCu1wtUyBVmYB1u12oqUqDzDfCG90vRW9tGt5aJ4WqyQqrbISfcgDIdUbD/okP+p0apHvMYWW8PEeHny3phVQigVoW7mJS3ZiAP7yUg7v+o8N7O7LgHSGq2/qMeO5IHLrja7HqQhW0ammYE8vSJWrU6+pwzmItzMZQQWxQyzErzYJPtjngP5oOrS+0nWUBGXStaXj7PQkOfZkGWe+I2/0BCby2VHxcokRytgZ5qeH+8XNjdEiyefD8wxY0loWnWe90xOKtg7F4crcc1R3JYam1XT4TdrTGIJBeh5Xnq6BWhW6Pi1KhoMCAh3YdxN+3lMI+wnHH1tWLx//1Od5+fBc7lugRuQ40ChnmJxrRubUWy50+ZIxwZpLLgCXz9fCkdCHtSj0yZoQHeE3LiqKqQCDDgpQCa9j2WfOS8Pt/XgadSJ6FQxUd+MEDn6C4qw9pC5KY+0swsXE6pGVGoX5XPeYnGKEdMSiw6JWsbOv+twuP/H0ruka4Q3FODJ+7Dw888AB7uN1uLF26FC+88AJmz57Nq5RzzPgDkjE/JgvcuvEY4W4w4dQ12vHYy8Uoq+jAjZdOwyXnZUWc4RqJ0+HG6y8W4+N1ZcjKi0VDbRd67MPuHiSEV1wxHZdeMxP7dzfgxSd3oq3VETIDv/zCbFxz82zmnfzsYztQG+RtTcw7KxU3fnUuHB4fHn2xGPtHOEJkZJjgy/XC4/cgsVSOihFWjHGJBviitWjq7kOeWYOKkmYEgqwWLdFaREVrUV3dCcuSNOZ64Q6a1Tdq5MjTa1BV2oKMAitKu3rRM+B9TagVUswxatC+tQYZ6Ra0tjjQHSQIyOt98UUxWHBpC9pqjFj/XACNdaHuJGkz47BPLWPHcd/qaShICRWvlfZGbKzfiW57ABWl0dhbMWL/WB0U5gC6vG6kmnUoaRr2cWd1oFcjKUaCZlc3EhSxKK6zwRNcBxoFrp8NFCZX47AtEVuabHD5hsuoksphVcRgT103UjSxONLoQlefJ2RW8+xsPfRRdQjYYrB9txyNncNrbulyOjvHCLuhA2aPCu4DAdTVhw7kcnItcGT2IT42gOtmuCGThrazUmpBAH502L14Y2sqNh0YUQcxatx5UR9yktuwpTEWX7S0wx9098YgVyPQGoMv9/aFeZwTWrkCcYjCl7t7MD3ZjKpWB2xBTjtKuRQLCrToi62DodeKA/slaOketsykQeHiAgOiM+sh9ZlQXKFCzQhhOC3GhIbaPuj9EkT3+lBVE+q0k59jxLIVPZAbvdhZH4W9zaFWjSkGHZZleKBX21Fpj0Zxe3uIZaZJoYWr0YLd+90oyDPiYFc3PL7hvmxSK3DHglysyE3EJ+sOs4Gz0zlcRpVKjuTpVhQ32TAtzoDuqk7YuvpC+nJKUQK2wY/MTDUk0U609oa60ORqolD7qQN6uYLNrtY0hLrA5KSY4Guwg6T5TV+bh6J5yRCi09aHp9eW4JNtNezuwSDWKC2sEqCtshPpWVE4WtYGX/D5SlaQmRaUNtpQmGBC/cGWobwMgwmVLr+uEBdekj/q3bWRcDeYfnpbalDz1sNIV7uhVqvx7W9/GzfddBMkYpnzBPB4PNxn/QzGNuAG81zxo9COwQ3Gae/FLbPumhRuMFysHyNcrIvj7PNAqz7xxBSUqOi1Z/eIbqeZ5rIR/sfBJKebUVcVKkyC0ZlUaNQohhLMCC3XSOnzhgiLkaSkW8IGAsGYLs7CriZxC8C5GRbsimDluNSoQeOWatHtGdkWVB4V3z8mTo//+88VoktPKIHQij98DKdbeMkC7ZWRr0Ftt7jF3ow4Ew40i1sxrp6nRrWrQXR7tDcFmw+KWwROM5uw76C4RV+6WY2eQ+0h4isYrUaKX/+a7raILYGS4Gf/y0JDZ6ivfDCXXgbU9Yk7TliRhP0t4tvT/Yn4fJ94Hc1KM6O4WryvZifrUe0T70cWpRyB7XbR7JqU/CjqUgVsESwti9LUaOwVPwZlZzp2R7BKvKBXgyOba0S35xXEoaxE3E4y5exYlOWI97MYiQaN63pFU4GTSH7xb5ewwYEY3/rVR6geIfSDmROlReVAciUh8mfG4RD5xouw+qoZuO4rc3A8nOlinZa9dO77FE2fvsaSHC2ekYm//e1vKCgoOO7P4mL9zMZ2Bop17gbDGTNjEerEaPMpgxkMx7LdpxJfc0rCJ9Iad4ISqkTcPsqS1lF2D5mtF8LrjbydZgcjrRGXSaQhs6Rh388SAY1yDGPcPlodjGbw4PMGRIV6//70BZG+JADPKA3lpTSvEfBH/Hwq4yifP9r3j9bPqB9E+A76fs8oZRjM/Cq6fZQ15h5v5IYKjFIG/yjfT20QqRrpXJWPEhPiGeV8piVzkbePUgcjsztxIuJz9aJh43OwHemflNGl5uNf//oL8vPzec1xxhhgKhnT/pMFLtY5HA6Hw+GMC67OZtS+/R+4ulogkUgRt/RK6DMKYTCEx2FwOBxhuFjncDgcDmeKMJGSIjlqy9D4yUts2Ytca0TC+TdDY03hyY04JwV/5BuNx7T/ZGFSiPX333+fJUwoLy9HbGwsbrjhBtxzzz2QyUaxfgOwadMmPPfcc9i3bx9b55abm4t7770X55xzzik5ds7oHEdckfD+Y9x+Mj5DMu5lHMVa7lg+gx1E4LSVcdQ6GOP2wDHUwuj1OLaDGGsdjNJEx9TQY+0rJ6Ovjenzj+FkGfP5NuZ2mrguEqc6KdLIBEfBtoxPvfM6sswKTJtWiJ/97GchSY6C38/hnAi+wOhLUEfbf7Iw4a0bn3jiCVxyySVYvHgx3nrrLRY5/otf/AJ33HHHqPv+4x//wHnnnccuHH//+9/x5JNPIjU1FcuWLcNvf/vbU3L8nNE59+JcXHRpPnOLGElmbgxzR6EgU7I1HAm9fvf3l+K2exYyW8SRxCUYYLXqMdOqR4w53DQ72qxGoVWPqBgtc34ZiU6nRO70WLZONjUjPJseHRNZOMoOtGJWnEEw4G9uogk9RzoxJzHcCpKYlWhES2cvsmYnQCGwFjc1OxptKhky5iQyN4qRJCQbmUXkw3/Zgo728MC9hpYe/OZfn2O6SokkS3gwjlmrwMwUMyR2KTLMoRaDgxZ8c7VGeErsmGYJD8KRSyVYoDWg+YUupHtjwnQQPU+SxeHQAQ8KY6LY+0dCn+s7bMOC+HD7PCIrXoeoHC8KlkfBZAi36Yu36hA7JwYbDmZCgvAySKDF1n2Z0KsVSIsJD77Tq+U4a5YBrbYAkrThlp7kWR/vS0TpngCy9NECnw/kmmJR19uLoiwzZAJijvqHcm8zzo7WMRegkeQkGBDQ+DEz3gKDQPBkqkGL3AYfCuP0rN+OJDZajcuu1+O8LDmSDAJlVCiQJolDxT4d4pUCfVkqw6Wps/CDpYU4JyNOsC9fMzMN93x1Ic69OEcwH8KiZRm489tn49JrC8JsTYmUvBhUSVTIkMdBIw8vY7LWAFWVAvmZUTAbw89na7QWiQVWfP+5XahuEw9Wvu9r85GXGd6OGrUct18zE1//1mLMmpskGLxK53Nbcw9y8mPDRDs9P+eCbFxytXhQ5O4va/Hje9/Cy0/vRl+QI9CpTIqkS8k9ZQ9tYtZQgiN60P9feuklPPvss5DL5bj22mvxzDPPsEDSwffQg3upczhTxA2GInRJXF922WXsxB/k8ccfx5133olPP/2U+bOK8d///hcpKSlYvXp1yOvXX389G/U3NDSwRAzHAneDGX/qarrw/GM7ULq/CdZ4PROm1RXDDigmixoxVj3Ky9qYZeINt83BWUszQpIhrX1+Lz5ZfwQqtRyp6RYcPtgyFJSoUMqQWJSA0vp+t47pSSY07G2EZ8AhhX6Ic6ZZUVfdhd5eD/uxrqvphLNn+Ac3KzcG7a0O5reclTfw/47hW86xhXGotahR19WLXKseshYnWoL84uOSjXBHaXC0tQepUVqYe72oDyqj1ahCkkyKKipjjBaaZBMOBLlamLQK5OhVqDjQzAR6UqqZlXFwNpYSTl167UysuGwaCzZ85f0yvLnhyFDAHQ2I0gvjUdLhgMvrZyL9SJMNDtdgHQRQmGNBg8eBrj43ZlpM6NzTCVvH8Exd+sxotJgDaHL0YZpJD+X2DnRVDjugpC40Q3meBM1eG6xKE+rKdDhwZNiBJTFGA0u8HIfbbYjXqWHtkqBqf3uIfZ4x24LiBhssOiXycpSoCTQPzYZqIEdyiwkH93RAqZQhdWY09vjsGDTY0ygk+O45MmTE1jG7xoaWFPzfGwF0O/sriT6mMNWMylYH7L0eZgtp03fAGRg+xoJ4EzxyO7rcTiQqYlC8U4665uF+MD1LC3VcL5qcPUgxmNDZJUdl23A/SNJqoXDKcaTRjmSzBikdfWgNchchf/1AYRyKm+2INqiQlKjGQduwQ4tBKUdGlAElzZ3QKuQo8CjRtL5mKBB5ZF9efI4JsYXNkMgHAh8DEvQ4E/B5XQ/6vF5kamKwp9gFu2M4MPKc+Rogtg12bx/mRKfiyozZsKiGRf7Oujb8a+tBVHc6MCcpCt9aPI0d0yBV5e149tEdOFrWygaya+5cwPIkDEJ5El54cif27qiDJVYL5MZgR89wP7IY5ZhZqER5Tyv0CiXi7Xoc2NQxdL6qlTJkpVlQVtHOLGHT8mKws6MHg/HklOfh2oVpuH15FnQCgxv6afv4ixo8/XoJuu0uLF+YituuLoAlKL8DHdvzT+xES5Md6dnRsNv60N4yPAig88vv96OxzobMnGisuWsBMnNiIERTvQ3PPbGD2cwOYo7S4IZb57JBzFR1g6FspO/dvYQJcPJM/9WvfoUPP/yQbbvvvvtw4403nvQ7EdwN5szGNtDXH939KLR67Ql/jrPHibvmTA43mAkt1l988UXmv/ruu++GCG673c5up9HsOglyMegiKzR6//Of/4wf/ehHo4r9YLhYP3W898YBvPbcHlFHhtkLknHPfUugEnGhOVTSjH/+aRMcQZ7twZisOibMu5qFZ+YowUpsvB5VR0OTLgXPppNoPyhi7SaVSZB8XgaKi8Wt3wpnJ2J/caOoC8zc7Gjsr+oM8WwPZnqSEd1H2kIGEsEkppjQrFWgTSSJi8GkgizNhPIg3/pgtCoZiswqlO0VtreTKaSYPS0K5R8J203SrGvuTRl4v8SOgEi0/uJsC45sa4BPxM0nb04sGuM74Qp4hcsYMKKmQ4JOr3AdZMfIoHXKsatcuB/o1TLMLFSg2i1sxaiUSZEpteLjHcJWipRS4JyFFnxWJdxPiCVKE+rfKxfty8nnpWGnug8uEQeXfKMe+KgRvUGDpZF9ecE9Ukj0wu0YCKjw2bZ47C8XaWeNBL+8IR9L0lNF3WlKW7pQmBAl8vkBlJW2IDc/FlKRHAsfbCrHnzaXwy3S1+dk6dBa3AGnXaSdrTo06RVoFZmlpsHOi99aIijYCYfTg8bWHmSnhd9NGHR2eerhL7D1k0rB7TS6u/TqAlx9c5Go6CzZ24C//e4TUVepxedm4obbZ05psU4TX/fffz8+++wzNqP+61//GhdffPG4fCcX62c2toG+/siuR6EZg1jv7XHi63Mnh1if0GvWd+/ezf6O9GGlKPK0tDTs2SPuzU2I3WbbsmUL+0uz9pyJh1ariGidRklKxIQ6ER2rFRXqRHeLI+KaVEePG+qghDUjcbt86A1KBjMSOna/Tfz7CV+PK6Jdo83rFxXqbLvTIyrUidaWHrSNyBAbjL3bBZ9D/BidVMY28ToggR1oFA9io7J1NJDVonhFu7rcokKdbe/rExXqRLfChU6veNzK0TYfZKG5kULo6fPBJfcDItXg9vkRwVKdWVHaeiLPdQR6XBH7sqenDy5lhHa2u6AQEepDfVkvvppRInGhtV28Dp29Aah84sJOLpOKCvX+z5ewZSORUFl1okKd6Gz1iQp1ot3mQmuE8KR2u4udK7rwVTMMnVYhKtQJWnrGLDHFCPS/J9LscGd7b0T719am0CRcUw2fz4f/9//+HxPqKpUKf/3rX7Fw4cLTfVgczpRhQov1lpb+RDhRUeE/FtHR0UPbjweapacHrYMnwS+Gy+Vij+CRHIfD4XA4E4GJ8htFd1cefPBBbN26FQqFAn/6058wZ84cNvs9Xgx+9nh+x0RgqpdvrPjOoADTCS3WaRmL2Aw5vUaj+eOhpKQEa9asYQEwjz76aMT3PvDAA+w2HofD4XA4E42J8hvVUbwJb+//AEqlErfddhva29uZg9upYMOGDZjKOJ3imX454GJ9omA2m4fWqOt0obdqaRYh2AZqNI4cOYKLLrqIjfw/+uijUW2jfvKTn7DgmODvo2BVDofD4XBONxPhN8pesR/tuzYgO8bIrBmvvPLKUzbjTEL9wgsvZL/pUxV+R58zKWbWp0+fPiS04+Pjh173er2oqqrC5ZdffkyfQ+89//zz2Qn+8ccfD31uJGjdHT1OJZVH27H+7VJceEk+snJjMRXZX9OJtdtrcdPidOQmCAd0FBQlYHphPEr3NYVtMw9Y1n35WRUWLkkX3N9k0eL8VXn45IPD8I9YK0suGquunMHWrL//+gG4B5xgBqHXc6dZ2frUXqcbTkf4bciUwnh4tQoYu/tgawuf+UjMi4ZfJkF8rA5NAgGc5AKj7XYh26rD0SDniUFiDSpYej2YkWjAgYbw4Ea9TgFzjA4xOiWq9zaGZXYgJ5zLri1Au0SCtz46GraWlhz3pmfHwKuUYq/HD8eI1OkSBDA3xgCtyweTXonunvBF3fnxBsh9fsRYdWgTKIM1wwJZnw/ZZjWOdoWvuU7SK6F2+5GVakZ5zbALyiAWrQLWLiDKaEKpuQuBEeuF1QEZkhoNiJVJsdvfE3Y7Uy0HvrtMDqU/gAfflAw5wQzVAQI4W6+GcZ8XPZly9EjD10zPiDdCk9yLLqcCdS3h/SDPooG61oU0kxrV7vAyJho16FGpYJ1rRcuu8CV7xhgtFAolZinlKHaHr2nWK+WwRumhWZGKxg9rw2IcBh1hWndLEDurCZCFtmPAL0FLXQoSLSr09HnZGv2RnKVXY+c7B5G1Zg6iosMDtQ4UN2LLx+W47JqZLGj5eGlqc2D7ZzWYFa1DcXt4PzGr5UiSSpGUHY2DR8MDBMgRJjvVjDSVLMQJZhC5FJgfpccb7x/CDZdOg3ZELAst0fhkWw377Jsunw6LMTyOo3hnPXrsLkTH6pi700gomHzuosjxTXS9outWCZ2PI8to0TBr2pON2G/UqUqK5O3tQd0HTyLg8+LSSy/Fddddh1MNCfWpLNanctlOBr4zaBnMhHaDqa6uRmZmJnNu+cMf/jD0OtlCUZT5Cy+8wGyhIlFXV8cSINEIlYR6YWHhCR3LeLrBkF3Yq8/uwacby9kPMumSxedm4fqvzIbRHO6LPRmhILCHNxzG+n0NzJqNBONlc1Pw9fOzYdSG+2YTOz6vxotP7WI/oGQ5mJNvZQMal6tfWOUXxGHNnfORLBI8VlPViWcf3Y7Dpf1Cac6CZNz0tXmIHfBDb2vpYdZyu7bVsucpaWZmcUgWbITeoERiyrA1YnSyEQGrDuV1/dtVShmmJRpRv6cBXo+fiS9thgUHq7uGfKnzs6JRWdsFZ58XevJkt2hRWdI8NIjInGFFhdODTocbSrmUeWjXlLQwhwoiLScazRKgsauP9YtpWdGobrAxhwt2zPEG6HrcaK7odyRZuCQNN9w2F1EDXuINzT14/OVi7Czpd6bJTDGxYxkcRJgMKkSlm7Grzc6SCmWa1DD1eFFT128JqFHJkZlqxqHyNhZQGWdUI1EmYdaSg77UZGFJ7UKBt1qTCpa8WByo7mTtzAY/ebEodbnR1eeFRi7FbKOG2W8ODiJy0i3o7O5jzjUkvmYlmtBwsAV9vf3tnJxtgWO+HPVKJ2uH7L4o1O12oNvmGvJYl2ZqccjdX6ZrCpW4aHorArAPeaxv2RePJzf211m+ToWYOjuaBgYJZIGZsCwaB2Js8EuAeIMGSdES1Dk7hjzWY7xx+OQzN/pcAURrFMiVy3H4cH8dSMn5ZkYMDqpdsPu80CpkbKbxQFMXfAOX10KjAb4tzehpdECukCJpdiIONtjgGhgsZmSY0W6Vos7Tx6wlZ8abUdXpgM3lGfJYtx5xonXAnSdlZhzqXV60DwyEYqM1WHSBHJrU/r7u7IrBpu0aVLX0Dll+kr/8/pou5vKZqVUitdWJugHHIxrgXXpNAVZePh1yhYydc3Ru7Pyipr8vy6W4cFUerrihEBqR8zUYKtdr68rwxoeH4R5Q2JkZFjTIgfoeNyilwvxoPeqPdsA54PKSktB/XtYOWJ3mZ0WhudWBzoF2tsZoIYvXoWQgl8CMKC0CLU72HiLKpMatVxXg3AFhXV7diUdeLMahgXNDq5YzQX/peVnMCpJZSz6xA3t31ve3s0LKhPlgXyYL0Wu/MhtLz8s6ZuvBndtq8OKTO9kAlurs4kvycdn1hdBoFKfMujHv63+BTKUZ9+RHv/vd7/D5558jPT0dL7/8MtRq8YD2k34sHg9barNq1aopLWi5C13kevnrl2N3g7lv4eRwg5nQYp34xje+wRIqfPDBB1iyZAnq6+uZUKf1cTt27AjJYpqXl8dmzd944w32nAJQyZqR1tBt3LgRs2bNmnAnze7ttXj8n58zBxIhV5SvfnMR5p8tHgg7GfiopBF/frsUjgGRHQyJiJ9dORNni9xJIGH+5kvF2L61WnAGl0T8pdfMxJU3irftF59WMjvGwjnhSVAGbdfeeW0/DpUIByyzpEMpJuyp7GTe5SOJjdIgI0aH4qrOIfEVUkaDEvnxRjTsa2IzeCNRa+TImBmPpvIOdAokNSJBnDknEbW9Hia+R0I6oig7GjesyMO0mcN3oILZsa8Rr64rw6FyYZvB1GQjDGY1Dh5oEUzBTHcJ0snffXfD0EAipIwWDeLzY7C/wQ6HgMWeVqNAdn4Mais6mTAfiVIhxczMKNiPtAs6ZzAryKUJaHQEUF0jHEiXlx+Fr6yxQ6kQtsz0eWKw9jEDynf0DxhHEpdiRPzNJlT7m+ALhDt7mBRayMusOLSjDb194X3ZoFMibVEsSp39HvUjUcmlOCugQ125Da1B3vyDsMHdvDjU632o6RK2WpxjMADlfTg6MJgayfR8EwJJGnxWKmw3mR6rQ67Di+rtdfAJTCtRYrDFyzPx7toSJljD6sCiwTe+v5QNlMUgkfzAf79Ei0hfnjbdypIONYr05Rk50XA4vagUKWN2VhQTz0cEZuIJGtBmppqwblOFYF+mQcF50+Kwbu1+eATciGgmfO5ZqbjmliI2kDte3C4vPl5/mCVdSkgavhsx2X3Wg/3Ud+3aha9//essdowyhFNm8FMJF+tnNrYzUKxP+AymlIX07rvvZiNoWgqTnZ3NLgzr1q0LEeqDM/GNjcO3ISlr6eHDh9mJTUtmaAYg+EGfcbohT3AhoU44nR4c3B++FGSysauiQ1CoE91OD4qrh5MCjUSlkiMjO0ZQqBMkOPbsqIv4/YvOyRAV6kRBUSJqB2bDhaCEKC0Oj6BQJ0h4dXv9gkKd6La7mU2ikFAnaAbZa3cJCnWCZqDtnb2CQp0g4VnT6hAV6sT8wgQcjVDGmjob2pp6BMUNQTPxfZ29gkKdoCyzTn9AUKgTNHvq63ELCnWCZl+d3S5Rizu649R61C4q1Imqig5RoU7IFG1oL++f8ReiudaGPjgFhTrR7XHC53AJCnXC7nDD5fILCnWCklB1SQOCQp2g/tXR5hQV6sRRh1NUqBOlh7qxp1x8CURVqwPOOpugUCeaG+xsWYiQUB9sZ0qCFImjNV2CQn2wL/d09AoKdYLahvq5mFBnn1/egfJycU/Ng+Xt2LGvSbQv08x98a46QaFOUMKzwazAJ4JSJceKy6aHCPWpBM3v0e8ycfXVV59yoc7hjFwGM5bHZGFCr1kn6BYX2UL98Y9/RGtrKws61WiEb/GVlZWxGfdBKEHDXXfdJfrZx5q9lMPhcDgcDrB//36Ulpay31rKJM7hcMafCS/WB6GMaKM5uIz0TSe3mONxjOFwOBwOhyPOa6+9xv6uWLFCMAcKh3Oq8I9xdjxCrrYJx6QR6xwOh8PhcE5v7hPKUkpcdtllvCk4pxV/YGyCezKJ9Qm/Zn0qY+vuQ6/DzQIMhSCHBlefF91dvaLrPz9ZfxjNjacvu2pjfTc2fXgEPrIKEaEozQK1QjhfuF4tR0FKv5++GInJRsTECgdMUeBhwezId1yOhUhr2pPTzJg1LZY52AhBAaZF06wsSFIIvVYBtUoGjVbYtYAs+BQGFYwDtpQjIbcRClKNjxEPpEmJ0eHoIfG1xGTBl50kHkCTHK+HNVrLAvyEoADTGbPimcOFEHqjCmp1fzmFYOW3ymE0CK8DlsskMOoUzD5PCDquaKueHacYGQkm+HtjRLcHvFGwRrAfjE3UQyPRQMr8WMIxKzUozKV2Fi6jTqOA3iCBQSV8PitkUhRlx7D+IgT1r1idGskG8XZON+iRnizejumpJmRaxYMLU6K10CUZ2XkjhCVaC1msljmjCEGB2h1tDjgdwuvy+9w+VHb3wmQUtr2VDfTluAh9OdGqR2pivzOMEFlpZmb5KUZ2mhmzp5P9qvD2BCv15QTRvmwyq5GedeIzxl6PDx9/cBgtTcJBvpOZo0ePsuA+vV6PmTNnnu7D4XDOGCa8G8xE4WRG8vt9fnxEtmYv7mM/emQRFpdgYIFbg62RnReL1mY7urv6mMi74vpC5r9OlmODDibPPb6DBT8qFFJcfNl05q2tGuEzPF709Xrw1sv7sP7dQ/B5/UhKNWHNnQtEgxybu3vx0PoyfHygPwCQfkhXzkrENy7MhUWvOiaHhXdfP4D33zgAz0AgJ9XRLXfOR0Z29EkpEwna5x7bgYaB4DZy47nyplm4YGUepDIp8wN/5MW9Q44qCrkUV16cg2tX5LFAWArCJJvE7QP+8KSHZqRHob20Bb12F3RkBZlsYqJ6sJ0z8mNR4/Gize6GRiFDQawOVSXNQ7aGqekW5ohDNnNSuRQpsxNQ1tSD3oGA3cRYHcxuHxqPtPdbfi7PxHW3zoFpwPKT7ClffHIXs5QjkmdY0ezzo2UgyJE821MTjMyHmg4pNdHIAsgG7fPInvLalXm48qIcKBQyNjijOhr0kyY3nvSCOBzs6GVBxGajign7YNeZaQVRaEt2wCZxQQM5klvMOLinfShgNzvFhECjHR0NdsjlEmTlx6LySL99HpGQYkKvUYnKVieT0dOyo1HTaEPPgAe+NUqDOJkUdQf63XxW3RiFORd1wy/pD3KUQo29u6x47hU7AgEJsq16yDucLJiSoMFy8jlWHIjrhk8CJJu0iDUH0NDbH/gsl0hxbmI+VqTMgEqmQGu7E0+8ug+f725g25mlZmE0mhPs6JG4oZUpESWNRnF999DMzeJ0K+49Ox+JRi1rz1c/KMMb648wq1AiLcnI2ry+uYcJ6exFcTjkdcLhGWhnvQZRbQHUlPbXa35mFPMw7xqwNbSY1DCmGrG7rT84NTfBwLzVGzoH2lklR1a8geU5oGPK1SkR1+BAY1V/GaltE2cnYKvXgz5fAMkaOXK7PKgZGADSgDEnPxa1NZ1w9niYoL1mTait4caSJvz7wzI0d/dBq5BitkHD+rp34D51BrXjgG0oXcbI1pTOqb6Bdk6K07NzqmrAOnVaVhSrD9tAAH60RcPsGY8MHHNOupmVfzBg16hXYs2VM3Dh4nR2vIfK25l146CPPw0Yr12VjysuyGblbajtxnOPb8eB4qahvnzB6nxceYz2lELs212P5x/fiaYGW39Ohyum45KrC1jQ6alygym4/0nIVCfukCGGx9aO+2dr8dBDD2HBggV4+OGHT/p3HPOxcOvGMxrbQF//3dZHoR6DG0xfjxM/Wzw53GC4WD9GTtaFlgT5Uw9vQ52AMwfN4Ko1Crj7vMwjfCSUlOTaW2Zj6ycVQ+IrGEpqcvOd8zHvrMgJPMYK2Sg+/8QOdAm4WixYnMa8z8X84XdVtGPt9hrctDhj1Bl1IWgAs/b5vczBZfG5mcfsf3ys0B2CDe8eYoL9mpuLwspBQnbTtlrsOtCEm8nxwRo+07trfxPeXH8YvbXdaBVo58QUIzQ6FewKKcoEXDESzGokyWWQ+Pw4OuBnHozeooE5NxoybwA1exrCkuXQ4I4EB7kJCSV+GvT59ipkqKrvRs+AZ/sgg4KYxNFtVxcgNir8YrhrWw3Wv1+GOpcXdQOCMJi0ZCM0Wjm86V7UysLv/Fj9OhiqVJB3ulE74AEfjMlCA1gj3GoZiuu64R8x2013K9KSTFC4fajf2+9zH4xWJ8XN37NAppfiv0/0obM7PDFUUZIRcrkf1bludMnCZ4pnJpgQZ1TgiowixGnCz/nigy14bUMZbIl9aJCFz6Ja1UaofQZcV5iJhanh1qQkWp9aux9d3S7mYDISvZkSKkVB5gqg4ovmMAcXljAo3QKPQoo9tl70jkh+RYJ4ZoqFDcIqW3tgC2tnSgylgdbuRqlGinoBl5u5BjVi2nvh6O5Dk0CCLvLYX3lzEZ7dXYddleG2oMkGFVIDErK6QVlF+DWNBnc0k06ncSkNGEdMHZE3eiZdJyRAWUU7PN5A2B0ZEv00E3/L5TOgH+HgQvkMPvysitXvmiumI8YS3pfJS37blipceWMhkk7gmkR0tDvxzCNfYs/2cGcqult07a1zsOVANX509/JJ57Me7K/+9ttv44knnsBVV12Fn/70pyftO477mLhYP6OxDfT1X382drH+yyWTQ6zzNeunmE/WHxEU6gS9njfDKijUCZoJeu/1EkEBN/iD8d7aknEX6+RJLiTUB4U8CXYxb/i5mdHscaJQQqO771uK8YLuXKy4XDzDLQ0OKOnKYOIVIebOjEfJ1mp8LNLODbU2pM1LQplA5k6CEiBFJRjQMJDMaSQ9nb3QVXejTuQ2e6/Tg1ef2wOPW3hpEglb8tn2Z1rChDpBcoiE0x9/uExQqLMynpWKT0tbUCcwaCSq62yYtSoalQHhJVotUgdiLDJUbRG2Wuzu7IM6SotDbKY4fEBGx11X1w2pSB07HX489rt2OJONcAnUA41vdtfZkHaVHl29wks69jd24+LUhYJCnZg1zYpibzPeOXpEuIx9NlyUES0o1Am6A3Hx0gz86h9bBbf3dLmg3tOFNpFzjZac7D/cho4E4WUvtDJtb3UnrEZ1mFAnKAnW1p4+NgtfL9KXdtn7cIFajqaDwtspudWrHx/Frk5hq8Y6uwsx0TrUlghbLdLMOHnwi9mSUgKvozWdQ7PvI6FZ+5LDbfjR1xeGCXWCZthXnJPBHmLMW5TKHmPhwN5GQaFOUIKpN98sxb424RwHJxtdcvZJ9Vknf3US6uSv7nL138mhZTAcDufUwcU6h8PhcDgcDmdS4QtI2GMs+08WuFjncDgcDoczKrT0gOjoODV3CTicSPi5GwyHw+FwOBzOMJRFnKiri5w1msPhnFy4deMphoJAIxEd2x9sJYZKI4c0QqtFxZy8tYonUga5XMrcbcSgAM3DAoFowVQebWeOOWLUVHaEBU2eTHqcbtH14McCBbWNRmwEm0RCalZBJmKfR1iiNaJWkITZrIEhQjtozGpoI2wnFxiDThExEDdSPySULmGLw0HUAVnEz9Cp5VCJ2OsRZp2CWQmKoY/XwGwV365RyxGlFq8DuVQCd0B4Pfsg0SLZlAcJdEsi9ge7ww2FXLwjGGOV0BrEb4CaySElUh2oZTCKWMMSSpkEhgiNIJMA8gj9jJBH+HxCbZRH7MtaixIqrfhnWHRKmCMcg96gQltP/1rq8eBYrlkOrRfSCO0YHaVh59RkZ8aMGexvSUkJnE7hOAUO51ThC4z9MVngy2BOMVffXITMnGg8/8ROtAYFVaWkW5gNYf6MOJy3IgfPProD1RUdIeLMEqXFgb1NsMbrmVCrDnJXMBhVuOaW2TjnguxxL8N3frKcBcqufXEvHPZhMTNzdiJuuWM+4kX8vI9UdTArtcOVnZiZF4u7bihkjh6DkN3ZC0/sRPGuemZZSPWRNyNuaHtXZy9efno3vthcwRwWbvzqvJMaTEs/yhs+q8Kzb5aix+HG6nOzcNNl01gA3LFy+GALnn10O2oqO5lXc4/dhbaWfju9QUcfcsuZXpiAFVQfLxTjcFBAscmigT7DjE2NNiQsSkaG3Y2WPf3WcgSJ06tuKsJ5F+fAZnPhlWd24/NNFUMuGmTjmZkbw6wPyW6SApaPHGyBf2DsQ7aA1iWp2OfyoM/jQ9HcRNSVNA/ZJBJL5iXh9mtmigaXFh9qwWMv7UNNgw25GRZ0dPWirbNvuIwDFnw7NrYgPc2IQL4fTdLhvh7jVSFuvxSH9tXBmmBgorm6crgOhiwuS1uQSPWRZsb+ATs/Qq2QYmasnllckiVe7vT+Mg7WgVQhRcaViThqsMPnd2NWYQwObiQryP430CBp+VmpuO2qAhiNKmyorMBLpaVweIaDMKdZjYDSgZervsARez2uzJgDi4Ad3ursbKSajHiyuBh19uEBXkxAC81RJd49WoHST9tx141FmB5kMUp19r+1Jdj0ZS1iLBpmvThoSUhodDLknm/GEW8HtHkKZNqjcGBTx1AZaVCcVhiHfRSA6/KxXAYltZ0YNIQh2ViYakZlqwNVrQ62/XCjDc6gQW5RjA7OOjuqdzdgUW4MDrs9aA9yhMm3aKHo6MWOI+3InZsEb0M3OhuH29GcbkFPtgWbG2zIsOrYsdF3DdWBWYmkFA12tXUhYZEZMR1AdUlHSDvHzY/G3u5umIr0yPIoUb5jOKhaKZeiME6PmpIWZgWZlReLvQ3dQz+w1JczC+NQ2tWLOx7/EjcsSset52Qyl5yTRbBd64ycGNx14yxkJA9fs5odDjy1rxi7mpqQ8LVY6It9qN82HExLExfXksXl+VmorG7GW49hUpOamoqUlBTU1tZi+/btWL58+ek+JM4ZjP8MymDKrRuPkZPtkevx+JhnOIle8uEl8UXiahCajaNkQ2+9so95sDPf6RGzyVm5MejscGL2/GQ2CNAdg1/5yaTH5sJrz+9hPsU33j4XcxamCL7PZnfh6TdK8NHW6hBrNkqQsurcTFx9YS4+eu8g1r91cMhffJBF56QzP+cdW2tYXZDTSTAFRQlM1CcEif4TgWbOaCARLJgGreVuvaoA5y1KjWgT2dXhZAOJzzdXhrxOoors7Wggsoo8oFcPe+UPDhCoXp5/5yAsGWYmvkhEh5TRqoeqtA1F063sh99gDJ0NPnKIBgg7mE90Z0cvOttDZ7wGB3cOtQKNVg2qR1gtRuuVyFAo4O/qw503zEJhvrB7SWuHE0++uh9bd9WHvE7JoHIzolDXaENivAGH6M5IINSRY/qsKLRY7UivV6Pqs5Yhr/xBqI4o2Q65/dRWdYa1c0pmFDoUUli0CtirutA9ogzxiUY2UAkky9FdIEFzX2gdRKs0MDaq4W7w4+s3FWFaVqgjkc3lwgulB1DS1oTkGBnqnKHuJUqpHBcnz8D5SfmQS8PFoM/vx7qKcrx18DCsTQYc3NMR1peXL0xhPuCf7azDS+8eYp7jI5P5dPe4YC3QoMXcA5s7dFY/SWuA9wBdtFVokATQ1N0XWgdmNUxaJdweH3P0CRbOgzPUyVFadHY6keABKkb0dRo0peREo9zpQqZMhiOH28P68owUEzrKO6CanYCdbT1DXvkEnR0zU82o73AiPV2Hsu4uuEbUwXSLEc4DNkRnGHAYfbC7Qts5y6SHrLIP0a4Aeuts4X05wYBAjBYOnQJdGjlqOkK3x5nUuPeiPJxfIJzv4Vix9bjw3Jul+HBLZVhfJmeZay/Jw4a6Crx95Ag8gyPhAfJUZtg/6ML8whRcdeMsaAdcak6Vz/qc37510t1g3rt7CXODIR588EG89NJLp9W+kVs3ntnYBvr6jzY9BtUYrBtdPU78afmdk8K6kYv1Y2S8L7RibHy/DM88ul10+8w5ifjBL87HRObfz+7G+i1VotvnJZtQPpBgRggalJQfFrarJNIyo/Cbv64e0zHe/L132ZIEMf7y0+XISRfPavifv2xhXs1i0GDqsmvFM/69t6cef3izRHT77HQLHrp9geh2su383tfWIhI1c+LhjrC86IVvLkZarLgl228f+hw7BhI+CVGQE4OSI+LtNDfZhIoI7UzJrWgJlBixcfqQu1EjoaUW9psiL0v574oViNaIX9z/sf8jHLEJW2YSN2cvxKK4LNHtr7x/iIk8MSiZ0aGgO2YjSc03oDlDfAmWVqrE0d2R7/RQVmBKiCTGLEhRN5D0Soi8zCiURTjGzLmJ2NHQnzhMiNn5ZpR0C9vPEjkxBhxpE//+OKkc6vf7k26JUVkUB1+EfH4vfXsJUqJPXLDSbPp7n1SIbp91UQwOycT7+vyEBPzwrEUhr03WpEiUDOnpNQuYfSOxc+dO/OIXv0B0dDSeeeYZNolB26SjrYs7iXCxfmZjG+jr9388drH+f+dNDrHOl8FMcEa9SzMJbuOMdoiTIYnuaIc41jKOvn3ULxjlDXSMo3zHGL9irJ8/GqN+vmT823H0Lxjb54+5H5yCMo7ezmPtyyfhGMZYyFHrcIzbxxOPrRN+Vehdl7EhwdfePAKJpJw983s9KO/ow5HWalz4wFpI5Qp8+MPLh2beORzOyYeLdQ6Hw+FwpggnOymSEIb0AtirSuBqb4AxZ864fheHI8ZYg0QnU4Apd4PhcDgcDodzzOjT+7M891SJL93jcMYb31gdYSZRE3GxPkbG00KQoKDBsWyfCMiDAiqFkMmlp70ORv+O0Y5xtFNJMqY6kkbyeaTtMmlkK0ipBLJR1pT6R5lmkI5SzaMdY6QA3WNqg1GOnwKWIx2D5FjKOEo7jbq8YdQ6irxdNlodsTJG/nx6T8RjGHX72I5RKhlbX6Z+MOa+PMoymdGu25IxtuPEvyqPDX16AfvrbCiH3zN+tpkcDqcfLtZPkLqaLvzp5xtw/91v4PPN4oFIY2XZhTm47Z6FzEt45A/WBavycNd3F2Oic/vVBbh+dT5zDQlGo5Lj1qtm4Ac/OAc33zFvyDUhWMSvvGI67vvF+bj7viWwjPB3px/UJedm4js/PXfMx/inHy7D/MJwB4mkOD1+9Z3FyEo1R9z/K19fgFVXTA8beJALC9knvvPaPrz8v13o7Q11vxjk4sIE/PSKAubYMVIUzI8zwL2pCn/73cdoFgkMJL/tnz2wggXbjoRsQWPj9ZjR4sQ0a3gAaapFg7N8wD//34f44tNQNxuivdWBf/1pM2q/qEVhhiVM7Om1ChSlmNG8s54FwioVoW4p9DyvwIot8CLx7FRoR/iC0+ddeEk+7vv5+fjmD89htpwjSZ8dh8rZeuhXpsCaHB4IlJIfDfd5cdB4LEg1hDsDJWn0yCxR4U/3rcfOL2rCtlO9Uv2W/aMNGZ5YSEbILb1chQR5HP6zowRryw7B4wsVe84+D556bT9eee8Qs/jTqkNXGMplEszKsKCzuAmzEo3MZWhkXyZrx446F1JbTLCqw4OmctRmmD5xY57ThyyBXAeZcXqkZ2lhTJBimkAdkQ1mboIBtUoJcvNiwgSpxaTC9HOj0JDTjZnzY9gxB0MWpjkz47C3ycbaWTPCIpGsFufkWnDY1omZ8RaY1KGBsNRtZsZYUFvRi1yNCVZduMf9nBQD5s6xY/43LEhMF2jnvCgYbohG/qwA8pPD+3JKjBZ5OXrcv24nPj4aHqTa1tKDf/1xM7771dew4b1DYfkcyOHqqYe3YedbB1Ek0JfJK51er3+tDjPtJugUirCBTL4iBrtfbcWfH/2SOShNRZSmGCiN0SzGordJPLCewzkVGUz9Y3hMFrgbzHFGHzc2tGLje5XYuK4MvqBZutxpVmYhKCSWTgaOHhfWPr+XWT3mTIvFLXcuYF7kk4mmNgcef3kfthc3YtmCFNx2TQGizcPuHbbuPrz67B5s2XiU+ZBTfZLf9iB9vR68/ep+rH/7IJLTzFhz5wJki9gMnig79zexY+y09bEBxmXnZzPLumOlsb4bzz++EyV7G9ixNdbZmNd6sKi+7tY5WLw8U3D/nj4PnvikHGu31yAjSgtLdTc6jw47c5A94YrLp+PSa2dCpQoPOSHLz80bjuC15/aygUOsVYejZaGuFXHzE3FEJWOe20UaBZq31CAQdNWiwQX1L7JDJHvR99aWhMxExiSb4IvVoqrBhhnpFrSXtqA3yG/fGKuDNt2Mg9VdyMmORkXAh5YgK0aTQob5PqC6uAl5063Mdz45bbgvu11evLu2BO+/WYooqw6OmUYcCAzXIbXG2W412r5sglqjgGxuDHZJQq0c56TpYQ/Y4PX7kd6qQcU7jSFX5hmz4nHLHQsQbdXhnVf344O3SuHxDAu3pDkm6C6WodlnQ4rGipJ6B5yeYYeVOJ0Ot80sxLyEBHyyrQZPry1BR5CVokGnREqCAQfL25GdbIK/0Y6OhuGBllItR/yseByo6ULCwACqNmggJpcDM86PQZWiCwaFEtGHJKjZHORSIwHiFqWiNOBjSj8tWYMD3V0hU7q5RhPamtyw93qQm2DE/tpOBGvTXLMG6i4XmlsdmDYnCtXRnXAF3RiO9WuhOqpERUUXcvJicdDlRleQywwlY0qyaLC/thvTU4zokvehtW+4nXQKGbJjjChp6kKaWYe+zgCqG4fFKyWDmplnwuHubsQaVJid6YZPFVRGP6A9FIPS97qgUMpgPMeMQ6Zhv30iWRKLynIfet0+5KTpccDWFRJ8WpQYhW8vnoZkgxbvvV6C918/ENKXB/Nb5ObH4uP1R/D6C3vh6Bnuy6ZYHdTpZhyq7sK0NDOclZ2wtQ2XQRutgvWKWBz0diJNa4J9txcNlY4QcX/tyjxceVEOensdk9K6UYz6Dc+iq/QLWGacjS+f+N0pDTDlbjBnNraBvv6NDY9BpRuDG4zDiYcvnBxuMFysH2fn+N6dL6K9RXh2lGYIf/l/K5E+wsP5ZEJe1KciS+l4QrNNYgl3BmdyhWZXg20KSfSOdjv/RPF4/XA43TCP8DM/Hp5+5Et8vO6w6PYrb5yFK64vFN3+2bZqPPanT0WdMWjARjPpYpDf+g/veSMk2VEwcpUMBvLcFpmpp7ol7/MjB1tFvyNvVgLKisUt9pIuyMTmVnGrxUumx+En1xeJbt97tAX3fbSbNJsgiSol2t0euETWpmgokc6XNthbQoX8IDSYSUkzo6pc2KaQZp4z7k1GiV3chnB2TwK2bRGvg3kZFpR/KZ6aPa0wHnsjZMtNTdbBWdIGn1u4FlR6BeqWR8PpFWlnqQSxLi1qggRmMFIEsHihGuUucavGNEcivjgibtW4YEYU9nSIW27OjDJj9wHx/YtyNEgrqKNfI8HtSpcaO2tlcEmF60AukcFnM6HF2Se67GZ5DVB+QNySs3BOIvZFsBWdNicRByPZy56ThJ2HukS3z5sZj+/eWjClxHpH8SY0bnoFuqQc7Fn7CBfrU8gyeqJjOwPFOneDOU6cTpp1ERaJNKvZF8Hf+GQw2YU6EUmoE5GEOhElsATgZELZN8ci1Bmj3F7rjeDpTmioj0X4jF6H8IBxEK1WLirUCa/LB2+EY6C+PDIx0Uh8rsh93T3KPUbPKHcs5HqFqFAnHJKAqFAnej1+uLrFy+jz+iOWkT7a5Y5cBkdv5Hb0jbI22jvKdpfNKyrUib4ej6hQZ59P16SgOwYj8UMCr8IPRFh27BYR0UPfEbGVaPAbeX+fn+4OiL/Hq/LCFWFg7g344BqxLCkYfyAA5yh92TnK+eQfpa97bZHrwCmy/G0yozD139X0RBjMcjjjie8McoPhYp3D4XA4nCmC29YOmUv4btLJxO/uQ8Dnha+vB42NoXeXTnWSJM6ZiX+MYn0yrVnnYp3D4XA4nCnCyU+KJPI99k4m1mm9WHDSJMp4ypMkcTgnFy7WORwOh8OZIpyKpEiEx94BqVINtTUVKrN13L+PwxnJWB1dJtPMOr9PdZzc/NV5iI0LtwyTK6TIn2HFGy8Wo7pCPFhrLBwqacbvf7Je0HLsVOByefHac3vw4K83MuvKE4FsLn/zo3XYtS3cPo/Yv6cBv/3xB9j04RG2bnqi4fX4mKvEAz/7EOWHQ11Wgllx+TTMnJMouG32gmRcsDov4vdMn5WAiy+bJug9Tg45ZHUZKQj5iX9vYw5FuhE2iQS9Rg4Yt33jLMRYw3/U5UoZ0hYkozdai9hUk2DgJbm4uPo8SMsQdiRKWJaEjtgAZmYLB+3MyYjCmqXCjjhEVXcX1h4pwdk5eigF6iBeqsCsci+WdypglITa6xFmiRTL2+VIzYiCyRwef6BSy3HNLUVYc9eCEMehQaQyCeKWp6OhRoV4mYBtZwBIQRxqPTKk5ccIlmFuQRxuuWU2zjk/S9C3OyovGk3JBmTNSRDMNWCN0sIYpUHqwmQoNaEWgYQ2WgvTRVmYprbApBDYLpehQBeFaL0S8abwOlBIgPM0Smg+6kWKSyAOJADkOKLhL3egQCTOpChah8AhO4oU4ddEIkNvQG+3H0VpFkFv8mSrBgGNCr0d6ZAEwttR5tOhsS4ZyYoEaGXhfVnrl2HWUQNmV3oRJwmfe1JJJVgAPdxRWsQI9WWpBPHnpKEiRY/YGcKCc+GSNFx/21zMW5QquH16YTzW3FSEFcsyBMuYmWLCrVf3+5JPJZyN/ZbF6mjh6xyHM974xpIQaYxLaE413A3mBKKy1Wod1r1xgNnLkQ1YZk40ujp70THguEA/AMsvysE1NxeF+aOfCCS+XvrfLnz5WXWI5RhZ3uXNiMOp4MvPqtgxDJaRROT5q/Jw5Q2zwvzRhaip7MCzj+7A4YPDjgwzZycy0ZmQZEJrcw9eeHIndn9ZO7Q9Izsaa+6aj6zck2vPeKLs213PbBmbGvrt40iALT0vC9d+ZQ6MAmKI2L29Fi88sZOVj6wQqbyFc47d4qy+tgvPPbYDpfuamGc7OchcsDpf0E6SBhLr3irFO6+VwDUQ6ExtQzaXR6jeJRImHK9dMxuGgQBa6r/BlnYpBXFo8PjQ1tk75ApTkGZG24EW9Pa4mXsKueU01Q9b6JFrDPlXd3f2wZJjhnOOBYe7h11gsqMMsLf6UNfSiziTGt+8OA/nzQj3tCd63G68VFqKDVWVLDCQiFZroPLrUVzrgEoiwVk9StR/2QjPQHCmzqCCaYEVXyromCVY5Faj48tmOAcCaMniMj07CkcPtTK7VRJfN9w2dyhY2+v146P3DuHNl/exgNPYwjjUWtSo6+qvA9JfRdkmOAzt6Am4ECs1wtGowdHG4TJOizfA32BHW7MD8TE6fO26mVhYNCxiaGD37GPbUXmkHWqLGqr5SdjdbB+KIU4wqxHvBWpou1KG7HQLDlV0sGMjzAYV0gwq1BQ3sgRYsUtSscfhYvabrJ1VMuSlG1Bq72KD3OkmM2rqetE5UAcKmRQzkk041NDNAk7n6FVQHOlAe4tj6JqVtcCKipw+9Ei9SPIZ4CkFauuGnWqys6JQjQCanG4kG1RI8ARQUTkcXJiWaoItUYZqdx/MSiXiJTrsrxoe1CdFaaFVynCkyQ6dWoa8HANK27vgG2jnGJ0KC3OAgKYBCEjh6krB1qNO9A3UgUElR36CFtW9zUDAjxkdJrRs7hiyRiV7x6SFCfhC54YbAcxU6tFTZke7SF+OmWFFQ6wGNQPbiaI4AwJ7muBodbDz5pY75mPazOG+eqC4kZ2PDXXdLBD+xtvnYv7ZacPtXNOFR17ci0PlHcy+85YrpuPipRnsu8fb2eNUusEE/D4ceern8PR0IfXSe2DInDm0zd3dhvfuXjKu7jDcuvHMxjbQ129b9xiUY3CDcTuc+N/KyeEGw8X6MSJ0oSWLwSce+oJdwIUgof7DX18wJu91+ux/PLBpSHyN5JKrC5j4Gk/++cdN2LVtWEQHQ7OWP/7dRYKzk4N89H4Znn98h+BMOc0onr8yl/nHD4qvYEgQX31TEfMVP5387z/b2DEKQYL4B788T3RQQSK4eGcdZs9PhnxEkpVjpXhnPdKyophlpRAkTH91//toDvLyDiYu0YC7vrMY2XnCx0hi+x9P70bxEeG7BSQ8Ci0alIrY1ylVMqSsSMdmt31IfI1MFrMqOxn3Ls1nYlSI6u5u/PqzLbC7hR1W8lTR6Hm9DZ0iNoQJ6Wb4vX4014V6cQ9C4oqSVxXNSxbcTgPuv7x9AB9XCNsQktCck2PC56UdgrdPFTIJrpmVhLtWUQKw8DJSApnX1x/Go7vr0CPiLjI/yYjOwx3otAnbs9AsbYNejvou4TXJSVEa6FRyHBax5IzWqzCv14dykXakvpywNBG79rQKZmsln/+Cgjjs39cEr8C0FInS6QsTsaPBPjSQCCtjbhQqPTZ09wk7pMxKMqLZ5kaTXbiMqWYtErd1obFK2A4yKlYLRX4MDpW1i/blpGkx2FovvL9aIcPthYm4cXW+YGZin8/PJhUK5yYJ5jqgdv6yuJEluTLqhydrppJYtx3dg9r3HoNcrUPO1/4AqXz4zg4X6yexnrl1Y8R6+cp7j49ZrD+z+o5JIdb5MpgxQD/+3UGzMiOhGR8S9GOhsd4mKtSJ8VpyE/IdIj7URHdXHzrbnaPOqostaSH7PCqDkFAnSDBUV45/GUejKkI9k1BuaRL3E1cqZWz27USFOjFrXpKoUB9MGCUm1AnaFmnQGGPVoz0oedNI7A53SLKYkZBNpE0eEBTqBL2uUElEhTrR1usUFepEt90pKtSJ5tpuUaFO0LmYEpR8aSRUv51+8eVlJD5tdr/oOkePLwCFTiko1AmJRAJTklFUqBMdPW5RoU40dfaKCnWivqOXPcRo73GxWeNIfdnR7hEU6gQljnL3eASFOkHnudPhFRXqBIl0MaHOytDpEhXqRE2XE0014u3c0epER2tvxL7cMTBbL0Sfxwd1nE5QqBP0Op3PQkJ9sJ3PKkoMEepTCZpVb/niHfZ/y8wlIUKdw+GMDzzAlMPhcDgczjHRse9TuDqa2Ox99JwLea1xTht+n4Q9xrL/ZIGLdQ6Hw+Fwpgjj6bPu6mxG0+ZXEPD5EDVrGXwuJ3sEQ9aNHM6pwO+XsMdY9p8scLHO4XA4HM4U4WT7rHt6OvHCPRfBbDbj+9//PpRRGsyZMwe/+c19oomPKCkSh8M5efA162Nk6QVZLLhOiJxpsWMKLiXIDpJcCcSCwchijiwdx5NlF+WwwDIhps+MR1Kq8PENQpZn5ijh9dZkg3n2skwkJAsHdxhMKixcko7TzdJzs6BWC49tyQ0oM0fYvu9Y6Opw4sUnd6KqXHhGytHnxSMfHcEOke2DwcwLlwrXEzl9LLswW3QN7iAXLkkTXW89PScaS5ZnQGcQdv6xJhqgdgQQrRVep2vVq7E4PS5ifMe+D2uRphHuB0qZDMunp2PuWSmC2ymw8bwVuaycVF4hqH4MxsjriFfMSoRepJ3TYnVQ66WIEnE/ijOq0Vbbjfpm4dgBW1cvyrfVIjNaOCBKo5Rh5bwUZvkohFwmQY5VjzlxBuZQMxJ6bXaiCdNjdJCL1MHZubFYcn4Ws64UIjnbDEuOFEa9SBljdQjEyBEjcj6bjSpm/ZkqUkYKfo32S5BjNghuJ9ea7IAShUbh7VSsIrMJWTPjWJuLXW8i9eWMJCPi3D4YROogJUqLqrI2tHUKx0e0tDvxxCv7UDPgCjURfdZ1Kbkn7aFNzILVasVDDz2E+vp6xMXF4cEHH0RKSgpzfBF68OylnFM5s+4fw2OywN1gTkJUNgWuvfjUTuz4vGYoUO26W+dg8XJxH+njgTzVN647jNdfLGYBYCRGcvJjUV/TNRT0R0KE2dGJ/EiOleZGO7NW3Lujjj0Xsi2LBAVAvvXKfnz4zkFmR0dBl6uvLsCqK2ew/9NrH757EG+9vJ+9l36IySXmyhuLBL3CTwedHU68/L9d+OLTKvac7BrJiWcp89E+/pOelfmdg6xeqMzM8vPCbFxz82zojSrmKvH+3gb896PDLPCQWDbNim+vyEe8WVgs0cCNLALrqruGbBXX3LmA2WAeCyREnnx1Hz4fcAuJMqtx+9UzsWxhv0jusbnw2vN7sGnDUQT8AWYnGZ8fi+L6buZZSwPX1EVW7HfY4fH5mfi6YVYGbp6dyVw2hAISyVN/7fN7hyz4MlbGoz7ZjS53//OFiYm4dWYhYrXaIS9+chei4Gsib4YVt9y5AKnp/cGjlUfbWR2Ul/U729Bgl+ogX0QEj6Sjx4X/bDiMdcUNLNDSqFEgM1WHUlsns1rUyKTIUpuxv6ILXn8AaoUUhTF6VO9rZm1KtpqXnZ+N61fnQ6OWM/eQje+XsRwMTqeHqer4s1NR4vehi57TQGlmAu69KBexA5aa24sb8fjL+9DU1h8Mmp1iQqDJjo76/oFA9LRYNMfrUNXRLyjTo7Uw9HjQMGCVGJugB+L0ODQwcEiO0uI7K/OZWB+yg316N77cUjU0KE652ITa1DZmwaT0KqA/Eot9OztZG1E5UguisMffAwqPVUokKIIOFfva4fb4WJmz82Kw29aLXq+fDRwK0yyoaLbD3udlz2clGtF5pIP1ISJzTixqtV60O/ufzzAZIP28Fbba/mO2zrWiMUONup7+JR2ZJj0UVX1orOxv99QoLUwuL+oG7CPJkYryB8yY1T+r20z5Bl7dj217+vuyxahCqk6JmuIm9lxjVkO1IAm7W+wsaFivkmOaSYPK/c2sb6tVMlyzMg9XXkiTFTJWTnLzeW3dYfZ/mVSC1edm4abLpkEr4IM/ksnqBuPqasUlgf1Yv349VCoVHnvsMUyfPh2nE27deGZjG+jr1732vzG7wbxyzW2Twg2Gi/Vj5FgutKX7GplYWnnlDGiO4eJ9vNi6+7D2+T0oK21Bo4DrBc2WXfeVObhgVeSEO2OBLAQry9ux8orpom4IkSB/7k8+PIILV+cxBxKhWeYP3j6Is5dnDomviUbZgWYmGGmgcSwe80LQLPp///aZYDvS7PUla2bjzeoOHKgLt5dTKaS467wc3HB2esTBnVqjwJLzMk9oILG3tAWlR9tw5UW5TKgJHf8ba0uwu96G7t5wZ4+oeC2yz4rD7YvzkGTSivaFh/+yRdDRSKWXI/P6ZKxamIdCa3iyGhLFG949xO7YLDonI2w7DXQ++7gCfX0enL8il3mTHy8l5HH/RQVKbB3o8YY7uMRrNEgMaNB2qB1dAg4sNNC5aUUuNr95UDCJmEqvgPXcTFxzQQ5LGjQSj8eH19aVoWR7HeqE7p5RQp/FqXCpZP0CU8CgJX1aLGYXJeDmpZlQCnjz0/Vq/dZSVOe3wi0PL6O51whJpRkHJC50eMPbOVauQK5bjaNOD+oE3IQMGjny44zobbChsTq8L9PgLnNRHFBjR/O2pvAiyiWIvzgNfZCgfGcLS9Q0ksIkIxYVJuDiS6cJ5h/YU9qM99cdRuWOOngEnLWicqMhy4lGw6G2oQFjMAlWHTsP1n5QhmYBNyK6m/DNNXOwYGCQMJXEOp1HDRueRUzzXigUCvz5z3/Geeedh9MNF+tnNrbTKNZvuukmuAVcy84991zce++9GC/4mvWTyPTCBPYYL2gmN39GPDZ9eFRwO1k8bv2kfFzFOlkI0uNEiU8yshl5McxRWnaHYCJDiajGmozq4P5mQaFOOOxubPqyFgcEBCLh8vjx3p56UbFOwvTCS/LHdHxF063sIUZ6VjRlsUH3UeGlOR1NTmR6VaJCfTBRkJj1qKvHC89nDhReKnwMJMpowCgGDVDojsdYKEgxQ34A6OkQboem3l6k+mSCQp3o6OrD1i1Votl+XT0emGptgkKdoNnc8+Yn44MndwkfoD+Axi3V6BLIQjtI1cFW/PSWOYJCnaC7DRtQAne3cBm7NDaoU6LQUSVstdjq9SDeZEBds7B1qb3XC4/dJSjUBy0/Hfu70HOwVXC73xtA28f1aBZZfkXsq7fh/vuWCgp1Yvb0OLzzv92CQp3oONyOBJNGUKgTjS0ObN5eIyjUiS6bC5/vrh9VrE82SKg3f/YGOks+Q0ysHj/5yU8mhFDncAYJ0FKWMTi60P7Hy+uvv86E+de+9rWQ19PTx3e5LhfrHA6Hw+FwwoR6++6P2HOaMbz66qt5DXEmFP7T5AaTlZWFa665BqcSHmDK4XA4HA5nSKi3bH1zSKhbz74Mq1ev5rXD4ZxG+Mw6h8PhcDicIaHetmsDq42E5ddDnz6D1wxnQuI/TTPrX3zxBW699Vb4/X7k5uaydew02z6e8Jn14+TAkX6HCTG3kN3bazGeWBP0Ed1RMsZgIUjUVnXi8MEWTGXILaQiQjueChJTTKKWnxQPmpNmhlkrHqQ8LcmE001mqplZ6QmhVEiRPsoxxiUYIgboZuREdrCh4MiGWuG10CeL3FiToE0ioZJKIdcrIRNZKy2T9FsVitkkEhQE3N0lnsBGp1OxehKDbEOzRaxdiXiTGnUicQGDpOrF61ktUyAvxsScT4RQSCXISzAKOv0QUgQQ64eoTSKRlWFBTKz4uvvMrCikJYkHf6UkGKBSCn//oFsXWZuKQdfT7PRIfVmGnDQLFCLtTPupXD7m1DVRkiK5u9uO+0GuLw0bn0fr9nUI+LyIXbCSCXWe5IgzUfH7x/4YDFgNfrhcwvErBOUbWLRoEZYtW4aioiK2hp3ckZ566qlxLSt3gznO6OMVtz6LZWfl4GvXzkTsgE2i1+PD+ncO4e1X9qGvz4tpM+Nwyx3zkSwSODZW7LY+vPbcXmz+qN8+T8i27HghC8jXX9iLjz84zKzazl6WgetvncMCPqcKVG+vPrsHn24spykkLD0/m1kvUuDu6YBExEv/24XtW6tDBCrZDGblxsDW68FjHx/BWzvr4Bto55Tofgu+RTn9Fnynm4raLjzyQjEOBnnAL5yVgK9dX4j4GN0xORy99lx/mwz15RQT1tw5XzRYO7jeSChfuCoPV9xQCI12fCw+S5u78M/PDuJQ6/DAYJrRjPqGPrTbXUgwqxHvDaDmyLAozo3TA21OtDbaWf8iwX3k0HAQZXyiAXKFjFlskv0lHf+Fq/MFvfDJFeaDN0vx9mv7WUDmoNXitbfMxjkXZDMXmA1bq/DsGwdgG7D4JDvJmbF6VJX020nOWZiCm746j+U1EOJodwterdyJekd/MCzp1vmxGbgivQhGpQYVHXZWB3sbhst4Vmosvnl2PpLNOjR19eKh9WX4pHTYtSZfp0JsvR2N1V3Q6pWw5kajuK4b/caOQFKcHnfeMAtzZsTB7fLi3dcP4P03DsDj9oVZ4FL/X7epAs+/XQrHgN2lTqPADZdOwyXnZorW27o3S/HOQL1RzgsS1K0DwbAs/8AF2bjmliIYjGqUV3fikZeKcag8qIyzE9m1Pi5Gh6ZWBx5/uRjb9w271mRbdZB39qG53jaqleupcoPJ+/pfIFMJW7uOTHQ0mLyIZtSffvppvPLKK+z5Pffcg0svvXTo/fS+ieSdzt1gzmxsA3398ueehWLA0vdE8DideOuWNWGv//KXv8SvfvUrwX1aW1sRGzv8++vz+XDRRRdhy5YtOHToEDIzT45l90i4WD8Bsa5QatlMDnnw5sUb8NKTO4c8nweRycgnPA9X3TRr3EQEzRC/8sxuFM5JYu4fYm4Io7FpwxEmmOzdrrBZv8uvm4kVl007Ieu7iQL9EDGf+hf2DvnSD0Izu1fdOAvnr8oTTbIy3hzc34Q3XipmooTE18gf+iNNNjz84WHMyYjCDYvSRWf4TiefbKvB+k8rce2qPMwtiD/u/elOx6vP7MGs+UmiopVE57o3DuCdtSXM+SgYk0WD678yB4vPzRy3PvTeoTq8XVwLdxdwpDE88dEMmmFudiCqz4tKAXcT8nunpqXrAQn3wcHJIDRI+cpdCzBtpnD9MW/0/+1iolAo/0CPw43n3ipF3dE22Cq70N0ZOmNP+QzIbvSSawqY08xI/AE/tjQdxd62GlyaNguZxvAB4cdHG/FGSQ1uLMrA2enhTj27Ktrx+PoyWJsdqC5uCrOTjE82IhCjw8KzUnHZBdlhfbm12Y4Xn9oFa7wBl19fGGaB22134Zk3StiEwleuKoBlwJdeyGL2ucd3oKUptJ1kA37wdIbdcPu8sPwD1M7Ulz/cUsV88mcLuD7t2t+El94qhd7pQUVp+F1IGmivuSs8t8FEsm6kmfT37l7CEhgRjzzyCPNPJ+6//35cf/31mMhwsX5mYxvo65c+/dyYxfo7t96C2trakHOS8gnQ41j54IMPsHLlSvz973/Hd77zHYwHXKyfoFgfJE8mRYvAD/cg3/nJcjarNVEhT+7br34+4nsefORK0Rm5yQDNpt1z88sR3/PvZ65jiYg4Exda8vKTb70tup2E2JOv3Tyux/CHN0uYbaYY52lUqNkmvhSOhOLRgWRNQpDA+9WDq8Z0jPfc9FJ/8iUR6POPNUnWiUBJrp56eJvodrrz+OPfXoTx5IffeBPNDeLX5ft+fh5mzT1xC9oP3i7Fi2KWmmQXuSAZ3/3puZNCrJNIJ7FO3HfffWz97USHi/UzG9tJFutjPSf37t2L2bNn4xe/+AV+/etfYzzgAaYcDofD4ZyBPPvss0NCnWYEJ4NQ53BOd4DpSHbv3s3+ZmdnY7zgYp3D4XA4nDOMTz75BP/4xz/Y/7/xjW9gzZrwtbsczkTGf4rF+kcffYTo6Gg2iz5ISUkJfvaznyE1NRVXXXUVcKaLda/Xyxb2WywWqNXqU74/h8PhcDhTAWdDOf763mssaJRm07/61a+e7kPicCY8RqMR3/rWt9Dc3Mxm0Ts6OtisOrnDPPnkk9DpRjdVOFEmXqSagMj+4Q9/ODSaIbFNmaNaWlpOyf6RoIDOSDaK/cFk4vZ7EwKJBNoIx6hQythjMkPBvuoI1nFkrSeTn57gUs6xQ1aXkYKodRFsIE8WkSwI2TFEuB4QWr2KXRdEt5+EMpDzSqR1/ZGsJI+FQTcVMSgQOBJkRzmekAtMpPOdXGDGel0erQxymZQFq05EaL06WTSSi8X555+P7373u6f7kDicE8Lnk4z5cTwsWLAAn332GZthp/OG1qcfPXoUn3766bgugZkUM+vf+973mH8lRdsuWbIE9fX1uPjii7Fq1Sp8+eWXkMlk47q/GHML4nDH9YWINWvw7toSvP9m6ZDl2EgLvokMOaD86eHL8cqze/DZx+Uh7g39dm9zmYXaZEalVuCPD1+Ol57ahW1bqkK2LVyShhtumztujj2ck0eMVY8//OtSPP/4ThTvGg7yHLTgIyvD8eabF+chPVaP/248gq4gX+04kxrfujgP586Ix+4FKXjhiZ0holZnUOKam2dj+UU5KD/cimcf3YHqIP9zcmq55OoCrLxy7AlofvPX1Xj9heIhG9ZByNaV7F3J5vVEaG604/kndjCnlflnp+HG2+ciOsgf3dbVi5ef2YOtn5QjNcPCbGyDg+9JQJPDy0WXTsN4sWd7LV54che6Op3Im2FFeVlbyOAhOy8Wa+6aj/SssQXYkj2jyaJmfbGpYdgJjAYBqekW7NxWg9/8cB1umWC/AX6vG7XvPgK/uw95eXPx29/+dkJZMnI4x4M/MMZlMIET2zctLY09TiUT2g2mpqYGGRkZ+NGPfoQ//OEPQ69/+OGHTHC/+OKLuOGGG8Ztf6Ho41vvW4t71pyFhUWJIdvJIuyFJ3fiaFnrkP+xkNfuRGZQRPT1enDzHfMxc3ZoGacChw4047nHdrBZL/LCF7PJ40xs9u6ow/NP7GQ2hiRAx9PdRAh7rwdPfHIU7+6px/WL0rBmSSbUQXeg3G4fs5kk3/BFyzNxzc1FIYl5SESTa8ra5/cyd5Qbb58XInxPBjVVnXjuse1ob3Hgxq/Ow7xFqSf0OS6XF++8uh8fvFUKj8cfcqfj0mtm4qJL87H5w6N486XiEBca0oA5+VZ2HEXzk1neBss45W1obrSFDeIIqlPqI2R7ed2AtefJvC7359g4iLdfLUFquhn1dd1w2IcHcfRVS8/LwrVfmQNI3KfEDabg/ichUwnXc/Nnb6C7bAekcgW2vvMyZs2ahckId4M5s7EN9PULHnkJCs0Y3GB6nfjo6zeM2zl5xoj1//znPyzwhW4xLF26NGRpC1Xs5ZdfzgT3eO0v1Dna2jsRHWWOeAtWyMN4skAigh4n6tk+GSC7Sur0Ql7enMkDCSVa1nE6B8Vurx/KCOfKaNeDU3G9oHqiBEwnyjOPfMnyFIgxfWY8SvcPJwoaCQ0SvvWjZRhPvn37a2G+8sH87I8Xs4HDePHpR0fxxENfiG7PL4jDvT8867QkRRpMgER3lclajs6X3/3ud+zu8mSdVedi/czGNtDXz//Py5CPQax7e53YeM/1k0KsT+hlMKWlpexvTk5OyOtyuRzp6elD28drfyFGS0gzmYX64LKY05Uc6FQxmRM8cYYZiwA9WUQS6sdyPTgV14ux1pPPF3k+xzeYs1uMwKkZgEdivAd0o32+f5Q6PJnokrNDfNZpjbrBYGAWjQqFggWUXnLJJafseDicqW7diDNdrHd19ae+pgvNSGgU1NDQMG77u1wu9ggeyXE4HA6HMxE4nt8o8lMnB4uUlBTceeedbGZ6MjN4/JO9HKMx1cvHmSJiffAWnV9g5oZeGy04dCz7P/DAA+OWiYrD4XA4nLFwrL9RNLP+wrsvsN9DitH6+OOPp0zFb9iwAVMZp9N5ug9hQuP3SdhjLPtPFia0WLda+9cYkpflyNnx9vb2oe3jsf9PfvITlno5eNaCZiU4Ex8Kw5jowb2T4RjHG14Hk4XISzgkiNyPA2NcB3NM/WS0zeN8qo36+ePw/cf6G9W2cz3i9XosW7Zsytg00owzCfULL7yQLe2ZqvA7+pHx82UwE4M5c+YMZYgKtsnp6elBdXU1O1HHa3+VSsUenMnFoZJmPPvYdljjDbjpq/MQG6fHRMLn82Pj+2V47/UDOHdFLlZdOYPZ9p1JkPiigLw3XtqHs5amMzs/jWbq/uBOVlx9HuZysmdHPXKnW3H4YEuIbqf8BReszsfqK6fjo/cPY92bB0LcYojs/FiUH27Dc4/vwJU3zBrVh34khys78OiLxdBqFLjrhllITghf0kh8/+fns/OerBqDMVk0uP4rc5CZM772idSPu7v68PYr+5hlZTCDFrgne/H+sfxG9bXWoafqAKRWA7MxnmrClsoz1coUzFQuG2cKzaxTtDrNiL/00ktYvXr10Ov0nBI6UKBMMCTAlUolEhISTmh/zuSFrNle+t8ufPlZNXteV92F/XsamBi+5KoZUKpOf1c/uL+JCYr6mm72/I0Xi5m3PflVzz3rxGz1JhtMuD22HRVH2tnzdW+W4ovNlcxW7+zlGWf83YaJwrYtlXj5f7vR0d5/G56cVhKSjCw4u76mC9ML45llZlJKvzPW1TcXMe9xsq/ds72OvZeceo4eamXbN7x7iH3msdradttdePr1Emz8vHoo98O3f/MRLjkvGzdcmg+tOlTEkHXnz/+4Als/qcArz+xGT48bF12Sf8oGghTEu/qqGawPv/z0btanqQ6CLXBPxyxp284P2V9yQ8vMzDzl38/hjCd+/9iCREeLjZ9InH4FEwGy5nnwwQdx9913Iz8/H1dffTX27dvHMpKuWbMG55xzTsj78/LyUFRUhG3btp3Q/pzJCSVr+fn33oVrxIwWJal66+V92LG1Gg88dBlOJzSb/syj28Nep8Q5//zjZlx27UwmeKYy27dW4+EHPw1JvEV0dfbi0X9sRVlpM75676LTdXicASjx0YfvHAqrj8Z6G1vuceWNhbji+nB/brqb9d2fnst8x198ahcCQQmZCHu3C0/+exu7+/X17y2JKNTv/vmHcAR5thNeXwBvbjiCT7fX4qk/rwwT/PR8yXlZmHtWCpvljk889VZs5CN/9/eWsEkCSj51Oixw3bZ2yFy98Ng70HXoSwR8Xlx77bWn/Dg4HM4ZItaJu+66C4mJifj3v/+NZ555BjExMfjVr36Fe++9N+y9tNRlcFb9RPbnTE56ne4woR5MZ8fpD9Lp7Ix8DBPhGMcbyioZKatDV4e4Tzbn1NHZLt4O1H5yeeRlWyqVPEyoH09fd7l9YUI9mI7uPtDHy0Qm1Cgb8enOSExZTE8XHlsnXJ5GXBnTjQ9idGwCi7J3czhTDT9fsz6xIE/YY/GFLSsrG9P+HA6Hw+FMZshn3dvrwPbPH2Vrnm+55ZZJm/yIw4mEn7vBcDgcDofDmYw46srIBg2xsbHMBYbD4UxuJvwyGA6Hw+FwOMeOvWIfdAMmC2S6wOFMRfyBMWYwDUwe+2R+b4wz6UlINuHC1XmQSsNPPLKKu3bNbJxuzj4nk9nfCUGuES1NduzcViO4nbY9/Jct+GT9YfgjrAWe6MxZkIJZc5MEt1Ew4IrLp2EqU1fdiX/9cTO+/KzqhPbvIYeU/37JXITcLvEYjbFCLipJqSbBbTNmJWD+2cM2uEIUzk3CnAXJgtsoCHX1VQUR97eY1LhmZa5gcKZGJcdtVxdAJnCuc/rxez1w1PQHCF9wwQW8WjhTfs26fwyPyYIkQKbHnFEh2y1yl+nu7obReOpdBjijU1vViece24FDB5ohkUqw7IJsXHNLEQxG9YSpPrJ0e+npXSyYkgYSSanmEP/qgqIEZomXkGRiguzdtSV4/81S5mxDpGdFYc1dC5CdF4vJyp7ttXj+iZ3MCUetluOy6wpx8aX5zP5uKuJ0uPH6i8X4eF0ZfL7+hs4viMOaO+cjOW30QEQaoG368AjWPr+XCXYixqrDjV+dh3njZPlJ+QA2vHcIb760D71OD2JiB75v0bF/H1mn0vnY1GCDUiXDJVcXMJcUxTG2c0NzDx57uRi7SprZ82ULUnDbNQWINmswGRnv35DBz8+544+oe+9RLMxLxcaNG6fkenVKivT++++zOwdT2Yuc647I9TL3D29CrqZ7SCeGt8+BXT+9YlLoOi7WjxF+0kweaOaSZvDIe3ki0tfrYcJr66YKOHrcYdvJn3rFpdNYOdpaHWHbybHuwkvycfPX5mOy4vH4mMd80fxkZnc3VaGBGM2m27r7wrZRUiGy64w00+zoceFPv/gI1RUdgttpcHf/r8Zv9rS7qxc7v6jB0vOyTihXgdfjw5aPy1E4JwnRsSf2o7q9uBE6rQIzxjmx0VQR6wkX3IyuA1/ge7ffgN///veYinCxfmZjG+jrc377FmRjEOu+Pgd2//zySSHW+Zp1zpRj4ZJ0TGTUGgWiYnSCQp3wef04WNIkKNQJuhdWWtyEyQzNrp57cS6mOrWVnYJCnaBZ9tJ9TRHFOs2kiwl1omRvI8YTk1mD81fmnfD+8pPQzgtmhdrxciLjrD3M/s6fP3kH8xzOseD3SyAZU1KkybMMhot1DofD4XCmCK4uyhobQEFB5NgADoczeeBincPhcDicKUJmtBaFhYXIzMw83YfC4Ywv/kD/Yyz7TxK4WOdwOBwOZ4pAAZdLly6dkoGlHE4wUn+APU6USJmWJxr8bOZwTgPZ+TGwRAsHViqVMhTNS0ZisrB9HllUzlmYMs5HyDkZZOREiwZWKgbaORLkZJQ3Q9jyk5h/9vi4wQxCjj3kSESONkL02Fx457X96OxwjutxcI6PGTNm8CrjcKYQfGadwzkN5ORb8ceHLsPbr+7H+rcPwuv1D4mvG2+fxwTe6qsLsOHdQ3jz5X3MQYbInxHHrB1T0ke3/OOcfjJzYvDAQ5fhvREWnLMXJOPmr81DbJwh4v5anRI//f3F+OLTSrz89G50tveL4sQUE265Yz7zPR8P3G4f3nu9BO+/foD9n/rotV+ZzVxhJBIJ/D4/Pl5/BK+/sJcFSr/7Wkm/Bedl0wT90TmnlmnTpnbOAg6HkPgC7HGijGXfUw0X6xzOaXSFue4rc3DO+dlsdvLs5Zkh4otEz8orpmPRsgy8+VIxps2Mn/BON5xwVCo5rrqpCEvOy8Jbr+zHwiVpzMrweFh0TgZmz0/G26/th9GkxoWr8yGTjY8oJn90Sr5Es+qDkKPNE//6ApvWH8GKK6bhnVdKUFPVObS9r8+LV57ZjS0bj+L2b5yFvBlx43JsnNGxWCyIjZ28eRg4HE44XKxzOKeZ+CQj7vzOYtHtZosGt91z1ik9Js7Jh7z/7/z22WMb3K2Zg/Fm84ajIUI9mPLDbfjovbIQoR5MY72N5Q/gYv30kZqayu5+cDhTHckY16xPpozgXKxzOBwOhzNFSEo6vrs2HM5kRRoYm1in/ScLfHEhh8PhcDhTBC7WOZypB59Z53A4HA5nipCYmHi6D4HDOWXLYCRjmFkfy76nGj6zzuFwOJwh4hLEHWrINjQ+0YhIS6Ij7c8Zf6Kjo3k1c84IpD56BMbwwKSBz6xzOBwOZ4hr18xm3u7PP74TTQ22odfptVvuXIDUdAuWXZiDZx/bjsoj7UPbk9PMWHPnAuQXcCeY0+0Gw+FwphZcrHM4HA4nBLKWnP6PeKx/5yC2bCzH5dcXMvvIQbJyY/DLP69k2957/QDOX5WLC1bmQTpOdpKcY8dsNvPq4pwRSMfoBjOWfU81XKxzOBwOJ/zHQSHD6qsK2EMIsgc854Js9uBMHDQazek+BA7nlCA9g8Q6nwbhcDgcDmeKwD3WOZypB59Z53A4HA6Hw+FMKiRnkBsMF+scDofD4XA4nEmFlC+D4XA4HA6Hw+FwOKcbPrPO4XA4HA6Hw5lUSAf80sey/2SBB5hyOBwOh8PhcDgTFD6zzuFwOBwOh8OZVEgDY7RuDEyemXUu1jkcDofD4XA4kwv/2NxgaP/JAl8Gw+FwOBwOh8PhTFD4zDqHw+FwOBwOZ1IhPYOsG7lY53A4HA6Hw+FMKqTcDYbD4XA4HA6Hw+GcbvjMOofD4XA4HA5nUiEZY4DpmIJTTzFcrHM4HA6Hw+FwJhVSv589xrL/ZIG7wXA4HA6Hw+FwOBMUPrPO4XA4HA6Hw5lUSLkbDIfD4XA4HA6HMzGRcjcYDofD4XA4HA6Hc7rhy2A4HA6Hw+FwOJMKSWCMbjCByeMGwwNMORwOh8PhcDicCQqfWedwOBwOh8PhTCqkPMCUw+FwOBwOh8OZmEjPILHOl8FwOBwOh8PhcDgTFL4MhsPhcDgcDocz+awbpWOYWfdNnpl1LtY5HA6Hw+FwOJMLP8bkBkP7Txb4MhgOh8PhcDgcDmeCwmfWj5HAgB+nzWYbz/bgcDgczhRk8Ldj8LfkZDP4uXa7HQqFAlMZj8cDp9PJ6nQql3W8+8xkx+d2wjuGmXWftxeTBS7WjxG6ABIpKSnj2R4cDofDmeK/JSaT6aR/bnt7O/ubkZFx0j+bMzX7zGRFqVQiPj4eaz/87pg/iz6HPm+iIwnwIdsx4ff70dDQAIPBAIlEgqk2eqdBSG1tLYxGI6YqvJxTizOlPc+ksk7lctJPLYmuxMRESKUnfwVqV1cXLBYLampqprywm8r95FT2mclMX18f3G73mD+HhLparcZEh8+sHyN0oiQnJ2MqQxe9qXzhG4SXc2pxprTnmVTWqVrO8RTRg2KOvmMq1t2Z1E+CmeoDrxNFrVZPCpF9suBDNQ6Hw+FwOBwOZ4LCxTqHw+FwOBwOhzNB4WKdA5VKhV/+8pfs71SGl3Nqcaa055lU1jOlnOPBmVR3Z1JZORyCB5hyOBwOh8PhcDgTFD6zzuFwOBwOh8PhTFC4WOdwOBwOh8PhcCYoXKxzOBwOh8PhcDgTFC7Wpzg+nw8HDx5ESUnJCSUQGOv+pzJ5xJEjR7Bv3z6Whvp4ocQT+/fvR3NzMyY6VVVV2Lt371Aq6hOhp6cH27Ztw/bt2zFRqaurw549e9DR0XHCfaK8vBylpaXwer2YqLS0tLBynkjfozI2Njay/kB/J3KOO+qvX375JbuenAjUD6ieqF9wQqGEfVQ3g1lMJxvUd+k3hq5Lo0HXd7rO0/V+tP5+PO/lcCY0lMGUMzV5/fXXA/Hx8YHU1NRATk5OwGw2Bx555JFTtv+pYtOmTYHMzEx2rNOnTw/odLrAb3/722Pa99lnnw0sWrQooFarAzNnzgxYLJZAVlZWYO3atYGJxt69ewOFhYWB6OhodqwajSbw7W9/O+D1eo/7s2655Rb65QqoVKrARKOioiKwZMmSgNFoDMyaNYuVk47X6XQe82c8+eSTgaSkJPYoKioKJCYmBv72t78FJhJtbW2BSy+9NKDValk5qd+uXLky0NLSckz7v/nmm4H8/PyAyWRi/YLOz9zcXHbeTiRefvnlwGWXXRawWq0BmUwWOP/8849rf2r3NWvWsHOU6onKu3jxYtZPznRqamoCy5cvDxgMhqFz5frrrw/Y7fbARKerqyvw85//PJCcnMyuaQUFBex6tHr16kBZWZngPr/73e/YeTJt2rRAQkJCICMjg13/x/peDmeiw8X6FOWLL74IyOXywE9/+tOA3+9nr5HQJoH2xhtvjPv+p4qjR4+yC/Jtt902JFrfeeedgFQqDfzrX/8adX8S+HfeeWegtbWVPXe73YF7772XlfOtt94KTBTa29uZ2CEx19vby1778ssvmYD50Y9+dFyf9eqrr7IfRRJ6E02su1wuJjjnzZvHfswJ+uGmH/Obb775mD7jz3/+c0Cv17N+MEh3d3fg97//fWAiQSIrPT09UFdXx543NTWxQTENVEajpKSECV8S+w6HY0jUXnXVVazvFxcXByYKNKCkgUVfXx8T2scr1qndo6KiAgcPHhxqy4ULF7J+Qp95puLxeJjAJZFO1weivLw8EBcXF7j66qsDE50tW7awQcajjz7KykLU1tayiQi6Lnd0dIS8n67nEolk6PfH5/MF7rjjDnb9p9+BE30vhzMZ4GJ9irJq1Sp20R68CA4yd+5cdjEc7/1PFXfddRcTnCMv7JdffnkgNjY27PiFZv1GQoKdRDDVwUThN7/5DRtAHD58OOR1GlgolcpAZ2fnMX1OY2MjE74kXGkGa6KJdZoRp3J+/PHHIa8/8MAD7HWxGbdB6IeYBpl///vfAxMZKh+Vh8obzIsvvsheX79+fcT9H3roIcH30cwhvT5Ry3+8Yp36O5WH+v9IoUevP/HEE4EzlRdeeIHVwfvvvx/yOt1Botcn0oBNCBp87du3L+x1ujM08tygiRiarBh5TaaBG91NoAmXE3kvhzNZ4GvWpyC0Pvejjz7C8uXLIZfLQ7ZddNFFbG12pHWfY93/VLJu3TrMmzcPFosl7DhbW1uxc+fOiPtfd911Ya/JZDKWbONE10qPVznT0tKQk5MTVk6KJaD2OhbuuOMOJCcn44c//CEmIlROtVqNpUuXhpWTeP/99yPu//TTT7M4izVr1rA10rSWm9atT8RyEhdeeOEJlXOwH4w8Dwefj+wnkxWxejr77LOh1+tHraepDNUNXavOPffcE+pDp5v8/HzMnDkz7HWdTsf+Bl9/d+3axWI7RvYDo9GIs846K6Ssx/NeDmeywMX6FKSyspIJuMzMzLBtg68dOnRo3PY/VVDwUG1t7Uk/zjfeeAPd3d1YtGgRJgplZWVjLuejjz6KDz74AE888UTYIGwilTMlJSXs+I61nBTAGBsbi1/96ldITEzEzTffjFmzZiE7Oxsff/wxJlI5lUolkpKSQl6PioqCyWQatZwkyO699178+Mc/xsMPP4xNmzbhP//5D+677z7cddddWLVqFaYCVE/EyL4vlUrZ4HUiXIdOZ91QH6fB7US9Rp8ITz31FPsbfP0V6weDr9XX1w8Fpx7PezmcycLE/MXmjAkSmgTNPI3EYDCEvGc89j9VDLqhnMzjJFeFb37zm2ym/v7778dEgcoxlnLS7PL3v/999pg7dy4mKlQOEqwnWk66m0KzajTreODAASboqJ9cfvnluOSSS9hMe25uLk43VA6aQZRIJIJlPZZ+S6KcykiCPT09HdXV1SgoKMDXv/51TBVGuxZNlDt8E+maQOKdBrsT4Rp9vLzyyit46aWXsHr1anb35Hh/k2j78byXw5ks8Jn1KYhCoWB/aTnASAYt7AbfMx77nypO9nF2dXWxHwm6/friiy8iISEBEwUqx4mW0+/349Zbb2Xl+fWvf42JzFjKSdCyAOIXv/gFE+qDt7///ve/o7e3F4888ggmcjkHyzpaOWnQQbf06S5CU1MTs6cj60e6g7B48WK2FGAqMNo5PhGuQxOxD9Hrk61utmzZgttuuw15eXn43//+d8LX+sny+8XhHA9crE9B4uPjh2YZR9LW1hbynvHY/1RhNpvZ2vKTcZzks75y5Uo2U/nyyy/j4osvxkSCynGi5aT17Fu3bmXr1Unkkb86PWhwQkHm9P8T9b6eSOUkBgdYtPQlGJpxJiE/eIv8dEPloD7ncrlCXqf2oMHiaOX817/+xQYf//znP6HVaodmVOl1+kwanEwFRrsWTYTr0EQ7V8hrnfrRZKobyvdAEyW0LGzjxo2IiYk5rn5AS8oG78gdz3s5nMkCF+tTkLi4OBZESLNtIyGxRj/qJF7Ga/9TBYmv2bNnix4nQcGnx7L2nZZIUDAq3YK94oorMNGgcggl9xks5/z580X3peUWCxcuxOuvv47vfve7Qw8amHg8HvZ/Wu88UcpJCVJG/tAeSzmJBQsWsL8jE0ZRG9NM22Dw2kQoJwkqCtYOhtqY4kVGKyfNolP/p5n1YOguAol3mm2fCgyevyPP8c7OTtTU1IxaT1MZqhuqB4rbOZFzZaJAyZxocoT68ieffBIWx0EMLt0Tu9YXFRUNxbkcz3s5nEnD6baj4YwPP/nJT5gPc7CnLNn7UeIUSjAzMrEG+aoHJ9I4nv1PJ//+97+ZzRdZuQV7dVOSJPKxDoZ8rKmcg57EBPk0X3DBBczuj/zHJyrk+U7lJGu/Qcj/nhI6kd/0oBc+QTaWVE6yaYzERLRu3LlzJyvn//3f/4W8Tr7RZDnZ09Mz9Br9n8pZXV099BolylEoFIHvf//7Ifs//fTTglaJpwvyVid7ULLeDIaOm6w4g8tE9qNUzmDbTsp/QOXZuHFjyP70Pnp9ZPkni3Uj5Q4gD/ngNo6JiWFWrEL2hDt27Aicqezfv5956o9MAEe+9FTPg3kKJjLU1tS+dL2m36FInHvuuYG0tLQQb/3B/k5Wpif6Xg5nMsDF+hSFPGUpYQZlb6NsnO+99x7L+kfZ4gaTsAxCSXXoIkYXsxPZ/3RCnujLli1jx/X8888HPvzwQyZCaVAR/KM/mCiDyhksyimzIr32gx/8gJU/+LF79+7AROK6665j5aIkIiTSKKsjie3NmzeHvI8SgVCZRsvYORHFOvG9732PCdm//vWvgU8++STwne98h4mSV155JeR9JNSEhCmVmwaaP/7xjwMbNmxgn0NJkiih1Gi++6eSf/zjH6xclMWR/NFJdNGg8S9/+UvI+yhhF5UzONENZTmlTLvkJ03ig3zbH3744aFMjaMN1E4lNMgYPKeoHebPnz/0vL6+PuS91B8p4VEwdL5SPX3zm99k/YHqjbK+Uj8506FJFRrcUSIw6kP3338/qyvKzDzRoQEpJT+iREV0bo+8/gYPWAm6ntP1b8WKFSy/APnMU3btc845h/0OnOh7OZzJgIT+Od2z+5zxgeyp/vGPf2Dz5s1sCQAtESBrt5G3zsn67ZlnnmGWfjNmzDju/U83tGyAyvDhhx+ydbyFhYXsOAcDDAd57bXX8OCDD+KBBx4Y8iZesmRJ2NKS4PXPZOM4UaBAUbI1e+utt5ibAfkUf/vb3w5pM+LTTz9lPurf+973cP3114t+3g9+8APs2LGDte9E49VXX2VLkmiNaUZGBu6++24WUBkMWdNRQBrZM37rW98K2UY2jVRX5JBC61NpmdPtt98+FIA6UaB4gieffJK5mpANHx3jyHgJamt67bzzzsMf/vCHodeprz/22GP44osv2NIhWqtLdURuMBqNBhOFn/70p6K2mWQ/SZ74gyxbtox5xD/++ONhlpz//e9/UVFRwdYzU78WypFwJkLXqBdeeIG5INE1j9qfgownOoPXKTGEzmta+vTXv/4VxcXFbDnmoIUprUMfyfG8l8OZ6HCxzuFwOBwOh8PhTFB4gCmHw+FwOBwOhzNB4WKdw+FwOBwOh8OZoHCxzuFwOBwOh8PhTFC4WOdwOBwOh8PhcCYoXKxzOBwOh8PhcDgTFC7WORwOh8PhcDicCQoX6xwOh8PhcDgczgSFi3UOh8PhcDgcDmeCwsU6h8PhcDgcDoczQeFincPhnBCUEl4ikYg+/v73v/Oa5XA4YeTn54dcKxQKBdLT0/GVr3wFhw8fPq01Zjabcffdd5/WY+BwRsLFOofDGRPr1q1DIBAIe3z3u9/lNcvhTFG+853vYMGCBUPP6f8//OEPj3n/GTNmDF0renp68Morr2DXrl1YtGgRWlpaxumoOZzJCRfrHA6Hw+FwjosPPvgAq1evZv8ncb1z506sXLnyhGpRpVIxsf+LX/wCHR0d7LM5HM4wXKxzOBwOh8M5ZqqqqthylVWrVg3dXdPr9ViyZMlJqUW1Wj30/7a2NrZU5sEHHwx7X1FREVasWBG2L93V+/TTT3HWWWex53/84x/Zttdee429RktdoqOjcd5552H9+vVsW1dXF/ue7u5uPPLII0NLdJYvXz702ZH253DGEy7WORwOh8PhRGTTpk1DAjYjI4O9RrPh9Py2226D3W6HUqlkzx966KHjqk23282WwPzud79jnz04CDhRaCDxt7/9DU8//TT7f0FBAbZs2YLrrrsOV155JcrLy1FZWYlf//rX+Mc//gGfz8cEOC3JMZlM+PrXvz60RIfKTYy2P4cznsjH9dM5HM6UR+zW9549e9jMF4fDmfzQDDOJV+LSSy+F0WjE888/z16LiorCn/70J9x1113H/HkHDhxgwj6YnJwcvPnmm2yWfixs3boVdXV1MBgM7Hlqaip+//vfs+/7/ve/D7m8X/osXbqUPY4Fmqkfy/4czljgM+scDmdcAky5UOdwph50bn/++edDS14OHjzIlpAsXrz4uD4nOMDU8//bu39X7qI4DuDnsVqRsClFYWEli8loMVkY/AEiJX+AjAxGRqOZSWGTLDIjg9GvlOfpc+p+87Pn63vTc3t6veqW78059/ad3t9Pn3PO83M6Pz9P/f39aWRkJPe/lzE2NlYL6oXBwcH08vKSpqamcvCOav53lB0PZQjrAEBdIpzHItAirB8eHuYWkr6+voa/wahU9/T0pK2trfT09JSWlpbqGldU+t/r7Oz8cG9iYiJtbm7m9x8dHc3tLuPj43UvZi07HsoQ1gGAL0Wlu+hXj4p4GBgYyJ9nZmZyZb2pqSl/LhZzNqK5uTl1dHSks7Oz2r1ot4l5oyf+vcvLy0/niX3bPzM7O5vnvrm5Sdvb2+nh4SH3x0c/ej3KjodGCesAwJeGhoZqLSuxA0ocGhR/RytI9JcXvetxLS4uNvxN3t3dpaurq9Te3l67F4tWu7q63gT4cHR0lG5vbxt6TltbW5qcnMyBO975ddiOHwxR3W90PPwEYR0A+Ks4vOjg4KC2XWKE1Pv7+9wOUkbspnJxcZFPMH18fEzz8/MfKtq7u7tpZ2cnv0M8d3V1NfX29tb9jOXl5bSyspJOT0/zO8fe8BsbG7lqH20thdg55vj4OF1fXzc0Hn6CsA6U3g3m9dHhxeXIbvi/7O3t1arrxeLy4eHh1NLS8u25it1g4orqeSwKjRC8v7+fF3G+trCwkLdTnJuby1X3tbW1tL6+nsd958TV+P/p6enU2tqaQ/nJyUneJ/314tiYO3rwu7u73+yzXu94+Am/fn+1QgMAAPinVNYBAKCihHUAAKgoYR0AACpKWAcAgIoS1gEAoKKEdQAAqChhHQAAKkpYBwCAihLWAQCgooR1AACoKGEdAAAqSlgHAICKEtYBACBV0x+2jLHreLTaXQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 700x700 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "alex_jointplot(ds);"
    ]
@@ -126,36 +190,108 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'matplotlib.figure.Figure'>\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x700 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "alex_jointplot(ds, vmax_fret=False);"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'matplotlib.figure.Figure'>\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x700 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "alex_jointplot(ds, vmax_fret=False, marginal_color=8);"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'matplotlib.figure.Figure'>\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x700 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "alex_jointplot(ds, vmax_fret=False, marginal_color=7);"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'matplotlib.figure.Figure'>\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x700 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "alex_jointplot(ds, kind='kde');"
    ]
@@ -169,9 +305,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'matplotlib.figure.Figure'>\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x700 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "alex_jointplot(ds, vmax=40);"
    ]
@@ -185,9 +339,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'matplotlib.figure.Figure'>\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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iwvKYMWMwcuTIDn+e9dHvxnz5IeQiWqTcBCtu0irUjRX9fCjd6eqrrxbJTPS7lZOTg19++UX8zGra51qqo3+eV1J3ea7G/Dzrt3Myxli7F+vUczps2DAcO3ZMZ2VCSoShiZ0qVaseWrQyUDrGjTfeKE6bN+xbrpnUmp6erlM0U8wjnW5vzW0bonYKujRE/0CM7Z9IRzy3mu9DEYy0sI4p7FuVm04raGZmZorHo4KdftY0wk6rab7++uti/kTDFTQ74mdIkyzfffdd5OC0WNyJziDYSSHoDuq/3pGRkfjhhx/EQTjNfaFJ5LTaKU0yvxyPb+y6y3M1xudpbM+HMdbJJ5jSqLqXl5feNhIq0vUVuc3x5ZdfiuSWGTNmiFG3mgmmDSeHkobRkNQnT//8a7a39LbsyquQi5Eq7xafeyvGQSGZtHoFzVt8fsNtfr/jwEeeyDxuh7OH0nDL1Y9hivcbHT5pk1rGKPWF8tRpRD1IcaPIju+uqCWGCvYBAwaI1+bJJ58Uf1N44iljjDF2mYp1SoKhEcvGRqZb6vvvv8cjjzwiJgf++uuvBkfmJ02aJEZKv/32W63r6WsahZ81a1arbsuuvDTsQxXKRB63A3pdlhU0zRWOIofdVgqCjCokyBuRkJCAjkLfm95nBw8ehLm5OQKk67p1oV7D1tYWn332mVhHgZJiFi1ahDfeeEOcgWCMMcZYnVb1qtCpbGo9oL5j6kVtq7///hv33Xcf/P39xaSzTZs2aW3v37+/SKCpyUmnYvu6667DnXfeKRaSoYWUqM+dJrnSQUSNltyWXVnUs5wlnxCfe0vjq9NfLhOFpEIIZuI0lqJQThSTTn/66SexGNaVRJGm9D6j0WMXFxfMnTsXL487dEX3obO3BbzyyitipP3DDz8U8w1ocjD9vCjOsqGG7UyMMcZYd9CqYp0mbNLEMCqsqbd80KBBohCq319OiSkODg7NejxqS5kwYYL4nEbbGqIe9ppindBkVmpt+frrr/Hdd9+JNJC//vpLTP5sqCW3ZVcOLaBFI9/Wkg9s4H/ZH58SZXrgdpyUvxYpLHQwSAXhlVBSUiLmXvzxxx/i6759+4qRY4ojBbpXsU5zCppqQ+rduzcqjg9AsrwDscd3Y/3ym+AvXSPOuNQoRy5WJjx4WQYHGGOMMaMv1qnQuvXWWxuN2Jo5c6ZoZ2kOKvrp0hK9evVqdvHVktuy9ldYWCgOmIi7NKLRxY/auoomLZzk7HNAFIy0CBGdZWkv1M5BZ4k+//zz2gKVvh+9t6lQ7+je+Y5QM6eAWpUMoWx5BykMDlJvnJN/Q7Gcjhh5FfykKXCRLkVw8jpKjDHGuqlW56x/8sknjd4mJKR7JF2wlqN2Bxp9NoMj7NG+7xMTyUqMatOFWqBoUR5qp7jpppsu60FCSkqKWKHzx8V/QgElTGAFH2kSNp+0xeY3VhrlgkctnVNgSE22vJlEMxfuRSzWiNz9OM3fKJASRdHOGGOMdVetKtaDg4PFhbGWqqqqEskfxEUadFl71Q2h2E4a7aZVM2lE/5133sHRo0fx7LPPNrtVyxBKFaL2KhpRp9FzBVQiL95TGg2VZKZ1W2Nc8Kg92peCcBNSsBPJ8lZkysdQICfASxrf0bvGGGOMdYjWhaEz1krbtm0TWeiUBlLVzqPq9VESCxXpFA+4a9cusRItRSlSLztFhTa1Ki21uNS0sdDnNA+CFl+iRcFqIgfpbJL6RAjsFEFX5DkZKzrj4SmNhI3sixjNapQhB+fl5fjmG2vMmTOnXVcQrv9zbgxPdmWMMdapi3VaUOjEieokj8biHcPCwlq7X8xI0VLzZPLkyfh3l+qKTnCk1REpAWjq1Kki0//8+fNioSIadb/22msxduxYsZ1ShLQfQxYpJTP7vYcSZCJfjkMFCmu3W8MPbtJQnD9RCStJN8WEtQ5NPg5TPIB4+V9kyEewcuVK7Nu3T6wsPH58+4y003tlhs/iRnvsebIrY4yxK6lV1dKWLVtqJ5ga0pIJpqx7iImJwaFDh0T8Hi09/++8zR00wTEXshyBCtgiXT6IElxAUdEy/P7772JUl1a+tbOzE9GCNBmW0oooqjRJzhaLGhHqSXeUwuEiDYCFVJ1UlKe50O7Pp7uhVqJA6XpYaXzg6hotzso8//zzYiCAVj9tj4WUmuqx58mujDHGOn2xTiOQDVNgysrKxEjljz/+KJZ9r4liZKzhqPqYMWOuaOa53uJLAjwxEh7ycGTIR3HVVWZiAirFPNL6AXTRurkkiQmxdlII7KRg2MBP5LmzK8NWCsCXXz6DjRs3itVP6UzH448/LvLY6eCKVyNmjDFmrFpVbVDmef3c8/oefPBBTJs2rV0j8ljnpq/vl9JfKPKTWlHoYK+zxBjSBFfKeX/qqZmi6KPFmi5cuCBG1Gk1TSsrKzEJValU4u6QNY2PuLJ2bWfKzs4WLUy0KBtNUqZJvdHR0WLeAWXZ33bbbU0udMa95owxxrqayz40SP2+tIT44sWL8fbbb1/uh2ddgL6+3yz5JBLlFJjCHm9OPYUiJHa6GEMaPafCXF9CTMORdnZl6bYz2cFEvhbAflyQT+L8ifVY+dMmceDlJg2DlaS7eBL3mjPGGOuK2uU8fnFxcacZOWUdo2HrSa58TvR7u0pDYK5wQLkmn380rE3vKfrcBj4okycgVvMX8hGDIiQjRv4DtggQ8Zk0SbWWES+sRL37NLeC5oVQAAC1c9HZIVJUVCTaheisQmhoqDhb1BCfcWCMsW5QrNM/C5qER5F4CxcuxIIFCy7XQ7MurkTORKGcCAkKOEmNtykw1lKmkh08pJHwxChk4YRIjsmTY8WF5hd4SWNhKbkb1QtLBTjFh1IUalRUFOLi4sQgSUPUdhZ/Olcs1FVNggVcYS/1AC1BRbn2fMaBMcaMsFinlJfG0mAoVo2SGhgjVDwRKpzUknWne1HqxzsawmeKOj+1ZAN/aZqYNJws70SmfFSc0aGLg9QLzhiArjr3g4puKsipvXD//v1iMjTNqWhYiFObmSlsoZIsoYQpyuRsuEgB0KBCHDSXIgslyECJnIGL2As3DIMDenfo82SMMdYOxXqvXr0wd+5cresojo+y1fv374+RI0e25mGZEdLIVciUj4vPnaR+6Iz0xztqK5QTOl2PPdPPVLJHgHSNKNqT5K3Ilk8iWz6FLJzEhx+Wipx2mkzc2VGhfr33FyhBNrLkEyhAvFYvjwJqOEnhsJdCYQkPkVbUMKGI4kRNJdva9qFyOR858hmk4aAo2FPkHUjDfuzaFYCbb775ij9Hxhhj7VSsU+JCU6kLjJE8nBcLCFEuuR2CO+2L0lS2dpmcd0X3h7WdmeSAIGkGikXRvkUU7BQ5u337dkRERIiFsIYNG9auK6K2Fo2k//PPPzgvr0QlqttbqI3MSvKGA3qKAr1MztcqxJt79oHmjbjIg5EjnUWiZiOKkY758+eLBacefvhhsdqvPtzXzhhjHYODotkVaYGhXnWFVHO6nrErhxatCpFuQZbmFPr0zxAZ7bt27RIXiuTs3bs3AgIC4OnpKVpLqKg3g4NoL5FgAgVUUEINtWgvMWtW21RrC9vKykr89ddfWLJkCRISElCBAqhhAyepr1iAy6zeCrlUrLcp+Qg9YKcIwnnNKiScPoqvTi/Hjx9sgb90rU67Gve1M8ZYFy/W8/PzsXr1arG64ODBgzF69OjL8bCsiyuXC0W/MKFig7GORJNM33zzKVRVVeHPP/8Uk+FTUlJE/zddaka0E2ilWln/gaUJLKGCJW4esgP26AFzuIjCt6WFbcO1CKhIpwWfaOEwSnMhNOLvKYXDUxoFhWSC9kBtM67SYFG4J8jrUYpsnJd/Fwc3WvGXRpykwxhjRlWs0wqlNHH08OHDCAwMxCeffCIWRxoxYgQuXrxYe7tHHnkEn332WXvsL+tCsuTjkKGBleQFC8mlo3eHMcHHxwePPfaYuNQU6zSKTZ9ToZxx/IQowDWoEhMzNXIlNChDBYpRgSKUIgfFcioycUyMwDsiDI5SGMwl52YXtjVrEahkK2TjFNLlAyhH9Ui5ChZwkQYDsh2cFQHtVqjXR7+jvaX/IVr+BcVyOs5ofkSI4lbYSL7t/r0ZY4xdxmL9jjvuEDFhAwcORGxsLGbOnIlBgwbB19dXLIBEI0Hr1q3DF198gfvvvx/9+nXOCYXsCuU+y0fF586ddGIpYzTRtP5kU1r86uRXv+ntA6+Sy1CCTKRrjohUFcp0p5HoZHm7uNDIvSPCYQnDI+o1aDXWHPmcmPxKE5wJtdl4SMPhLA2EUjIRk0OvdARmL9yLaPyGfDkOZzU/I0RxC2ylgCu6H4wxxlpZrJ85cwbHjh0T/Z40qk69nePGjcPu3bvFQhw1C21QogAtxkF9l1ysd1/FSBURcQqYiJFHxro6yiS3gieqpAoxsdMEFshBtFidN08+jyI5FUVIFWeTnn46SpxxDA4OhrOzs+hfz8nJEYMcBw8eFJM5U+UssVAY9aS7S5FwlgaIIr2jn2MobsU5LEeufB7Rml8QrJgpkmYYY4x18mKd/snQPx8q1IlarRZZ62vXrtVZEY+K+AMHDlz+vWVdRhaixEfKt6YCgDFjQ+9rJ4SL+MQKuVi0s1BMaYF8AWfPnhV/Mxs780Rxi26KoWI0vmHkYkeitptgzMR5/CFiHs9plsNfuqajd4sxxrotVUsmkdKS1fXR1zY2Njq3tbW1rV3qmnU/JSUlyJWjxefOUv+O3h3G2p2JROuCDoKrNAj5mng8OjsAiYmJohc+KytL3Mba2hre3t4i9pZaB58ZtqNFsYtXEh08BOGGSyPs5xArr0F09DWNTppljDHWPjrPcA4zGtQapUE5zOEMa/BCQqx7oSxzWsW5scKWeuM7OyrYg3ETzmIZ8uQYzJs3D0uXLhUxl4wxxjppsU7LXX///fe1X1PfZcPraq5n3RdNMq5ZsbRhrB1jrOuglpgQ3IIo+VsUFBSIpK9vvvkGXl5eHb1rjDHWbbSoWN+/f7+46Lu+IUqKYd1PUlKSmIRM6y3SQkiMsa7fm++Pa2Hjul1ETt57771499134eTkpHU7XuGUMcY6uFgfNWqUSHhpLu5t7J5q3iPU/kKJGYyxrk8jVSBzYx8kyesQi9OY0G8WgqSboJLMxXZe4ZQxxjpBsd4wj5gxfasy1hTrDlJvfoEYMyKWkgd6S/filOY7lCEXF7AWPXFXddoTr3DKGGPthieYssuG5iqkp6dfivKsjvhkrLuRZY1oF2lMU9s7K1o4qYfiTpzWfIciOQXR+AWhuL2jd4sxxowaF+vssqkZVR8zZgy27+W3FuueaEXSR4f+CTXsDN6mUE6AldQ1k5LMJSeEKu7Aac0PyJfjcQ6/wxtXdfRuMcaY0eKKil0WlMO/ZcsW8fmkSZOwfeFBfmVZtx3xpkK9sQz1MjkPXZml5I5QxW04o/lJ5LDLtG6r5paO3i3GGDNKXKyzy+K///5DRUUFQkJCLq1yy8U661yMfcT7SrOWfBCsuBnRml+Ri7P49NNPsXDhQo5rZYyxy0xxuR+QdT+0dPqKFSvE59OnT+/o3WGsyRFvQxcTcIJRS9hJQQhUXC+iWumA/aOPPhJ/DxhjjF0+XKyzNjt69ChiY2NhZmaGqVOn8ivKWDfiKPWGtzRBfP7TTz9xwc4YY52lWP/9999x11131X5NoykU3ce6n5pR9cmTJ8Pa2rqjd4cxdoU5SmFiddOagv3DDz/kEXbGGOuIYj03NxelpaXi86qqKpSXl9du++2333Dbbbddrv1iXUR2djY2bdokPr/xxhs7encYYx1k2rRpePHFF8Xny5Ytw3vvvccFO2OMXelinXoSHR0dxR/lP//8k0fSGdasWYPKykr07t0bPXr04FeEsW5sxowZmDdvnphkSgM4ixYt4v8TjDF2JdNgqEhfsmQJ1q5di3///ReZmZkICAjA8OHD27ofrAuitqeVK1eKz2+66aaO3h3GWCdw7bXXQqFQ4LXXXhPtkkVFRXjllVegUnH4GGOMtUaL/nrSypQzZ84Ul19//VVEddGEwj179ohLVlYWzp07hylTpojirW/fvq3aKdY17Ny5U+RS29jYYOLEiR29O4yxTuKaa64Rxfn8+fPxzz//iBbKd955B+bm5h29a4wxZtxtMNHR0aJAo3514uXlhRdeeEG0xHzyySeIiIgQC+LQyDvl7TLjHVFPTk7G4sWLRbY6rVhKZ1noOrp01aXUGWNtW3Cq5m8AXfr06SP+P1BLzLZt23DPPfeIop0xxlg7jqxHRUWJnkR7e3v4+fnB1tYWiYmJ8Pb2Ftvp41tvvSUuNRNRmfGhf8qTvV/HBXk7JCiw9oSMDQt+q93OC8sw1r00tuBUlTwa8fIanD++HUVFd+Lbb7+Fi4tLh+wnY4wZfbF+/fXXIyEhQUw0pXiurVu3wsfHR1zojy8V73l5eeIjZW4z45UrR0OCEk5SX9gofIxqKXXGWOsXnGqIrjOXnXFa8734/zFr1iwR7cgT0hljrJ1y1mn0/P7778fDDz8sindqi6F8XRMTExHh5+TkhJEjR4qedma8I+u5OC8+d5ciOnp3GGOdnIXkgmBpphjYycjIwH333Yft27d39G4xxpjxr2CqVqtFEszcuXPxxBNPiBQAivLr16+fWNWSGadVq1ZRl6pYatxCcu3o3WGMdQFqyQbvv/8+hg4dKtokZ8+eLfLYaUE9xhhjhrU6S8vT0xMDBgzQuo5aX66++mpxYcYpPT1dtEERdymyo3eHMdaFJqHm5+fjueeewxdffCHifykh5sSJE+JMrVKpFBPWeSVsxhi7TMU6tbrQpYavry8iI7l4M3aUs0+LIFnBCzaSf0fvDmOsC05ClWVnlCIMKfIOfHr8J/ywcAd8pauhkUrx4Nd+Hb2rjDHWqVy2VSootpEuzLh71VevXi0+d+NedcZYayehSoAPJsBK9kSMZhUKkYQYeSV8MYVfU8YYu5w966x7+frrr8WoOs1JsJK8Onp3GGNdnIPUE70U90ANG5QiE9HyLzh/vnryOmOMsWpcrLNmOXPmDP766y/x+Z133smvGmPssrCU3BGmuB9Wkjc0KBNJYj///DNPPGWMsUu4WGdNorQGSnGgj5MnT0bPnj35VWOMXTYmkhV6SnfBQQoTE0xpRexXX30V5eXl/Cozxro9LtZZkzZu3IgjR47A1NQUjz/+OL9ijLHLTiGp4CWPE8EF1G5HMcC33347jh8/juTk5NoLp8UwxrqbyzbBlBknilp77733xOe08qCrq6v4h8kYY5dbhVSI6GX+kGRHXJDXIvb4XmxccTP8pemwlNxQjlysTHhQRAczxlh3wSPrrFG0LHhWVhb8/Pxw991386vFGGv3xBhnRV+EKx6CheSGKpQiVl6JfMTCRLblV58x1u1wsc4M2r17t5hUKkkSXnnlFbFiLWOMXQlmkgN6S/+DvRQKDSoRp/kb8fK/KCkp4R8AY6xb4WKd6ZWRkSEmeJGZM2eiT58+/Eoxxq4opWSKYOlm+EgTIUGBXJwV82aio6P5J8EY6za4WGdaaPJWQkICnnzySaSnp8PLywszZszQmuBFiyMxxtiVIEkKuCsi0VNxN0xgjZSUFNGS98MPP6Cqqop/CIwxo8cTTJkW+kc4wu9e5MhnoYAa6hPhmPVX9aqlNQrlBFhJPvzKMcauGGvJG6HS7QgfGo/Dhw+LeMdNmzaJFr2goCD+STDGjBaPrDMtf/zxhyjUJSgRpJgBO0WAWB68/sUEPMmLMXblqSRzUZzPnz8f1tbWOHXqFG677TYsWrQIubm5/CNhjBklLtZZLZpMumTJEvG5j3QVHKRe/OowxjoVmvA+bdo0LF++HGPHjhWte/T5ddddJ/5+FRQUdPQuMsbYZcXFOhN27NiBN998U3zuLA2AmzSMXxnGWKfl7OyMd999F19++SVCQkJQWFiIzz//HFOnTsXHH38sWvoYY8wYcLHezdEELfrHtnp1dV/69ddfDw+MEqNXjDHW2Q0aNAg//fQTXnvtNQQEBKC4uBhLly7F9OnT8fDDD+Off/4Ri7sxxlhXxRNMuzFKe5k3bx4OHjwovr7//vvF6eVNry/v6F1jjDEdsqwxmEbVt29fvP/++9i3bx/+/PNPHD9+XKwVQReFQoHw8HAMGTJE3I4WeaNVUOl6xhjr7LpNsU59jbQSp62tbbdf3Idei1WrVokR9aKiIlhYWIh+z//973+igGeMsc6oHAV4dOifYpVTQyitSi35wVQOQDZOIVeORikycf7oFqz6cYu4DU2gv+ne8Rg6dCiCg4PFxcPDg4t3xlinpOoObR40evzFF19AqVSK4vTaa6/Fp59+CicnJ3Qn9Fps2bIFixcvRlxcnLguLCwML730kkhVYIyxzo4KdUqlMqRMzoMatjBT2MMGvgCmoFTOFkV7LmJQKCeiEsXijOKxY8dq72dubg5vb2+4ubnB3d1dXBwcHGBnZycGeWoulpaWoqinQY/mrDlBj8Mj+IyxtjD6Yn327Nn4+uuvRd/i6NGjkZiYiEmTJolJSHv27DH6P6KyLIvCfP369aIvveafC/3Duf3220VfJxXxdNaBFjzKzMzs6F1mjLHLykxyEJPm3TBMtNJka85gxk22yM7OFn8faSE46muPiooSl5q/naTh/B362szMTAz+nNmdAxWsoIQaSphCIalhARc4SX3FbcuRi5UJD4qWG8YYay2jLtapMKcR9GeffVYU6oRGTj788ENMnjwZv//+O2bOnAljQSM9OTk54h/P2bNncebMGRw4cABpaWlie0VFBS4cz4ezNBAWGIBf95Xh1yd+r73/d1jJCx4xxox+RVSVZIG/Xi2HGl4AvGAua1CGHJQjv/ZSKCdRyQ4ZGlSiFFUoEZ/VqEK5KNIrUFz34DIgS1XwksbUfs0YY21l1MX62rVrxagxjaLXN27cOHHKc82aNS0u1k+ePAkrKyutAjkjI0Nn5IUuNaP2rq6uYhSm5vr620hlZSXKy8vFpf7nVFzThT4vLS0Vo+IlJSU6F9pGI+M0Kk7fpyG1Wi0SE4YNG4bP7kiChcK50VPIjDHWrdppJMAcjlrb8zQXYCrZarXcaORKVInCvQy5mlgoJTWUkmn1dXK5uL6xFh3GGGsNoy7Wa/qwGy5FbWJiItIAak53tsQDDzygVRBTMR1/OhcK6BbJRIMq+Pa0E9/TEEOnW+tr6vvUfC+/XvaiR5IizAIDA9GjRw+RgmBqaiqKfYV0sZnPlDHGWH0KSQUFrGACK5hLBdrFPKfdMsbaiVEX69QSQmxsbHS20XWNLZpRVlYmLjXy8qpHnBOjSiDVi6enERU1LCHBpN45Tzp1Wv2RtieeLoQCqkvX1p0XrbmVBhWXeh7Vlx5bKT5SYkH1RQKN59giAGrYQAETSOL29I/DBAqoYQJzcepWOmWGnNO2OLQZOIQsALsuXYAiORVWkgcq5VKDz7tUTocGpW26zeV4DN4Xfv35Pce/Z139b0sF8kRfPPW416ysWjM4wxhjzWXUxXpNq4m+P47UvqKvZaTG22+/jQULFuhcn49YdHqN/S9ozv+Jy3GbK/V9eF/49e/O7zl+zp3+9e/TZ5HW11S0U6oMY4w1l1EX6y4uLuIj9XPX7zMnNNpBy1Ub8sILL+CZZ57RKu7pPo6Ojka3uielINDEW5qQq+8shLHg52lcusvPszs9V2N+njRoRIU65bkzxlhLGHWx3q9fP/GRetN9fSlvt1phYSHi4+Mxfvx4g/elHm+61Ed5u8aM/jka2z9Iffh5Gpfu8vPsTs/VWJ8nj6gzxlrDqEPGKQWGRtR/++03reuXL18uUlduvfXWDts3xhhjjDHGuvXIOo2EL1q0CI8++ih69eqFG264AcePH8ecOXNw2223YcyYS1m4jDHGGGOMdUJGXayThx9+WEQZfvbZZ/jqq6/g5OSEl156CU888URH71qnQe0+r776qk7bj7Hh52lcusvPszs91+7yPBljrCUkmXOkGGOMMcYY65SMumedMcYYY4yxroyLdcYYY4wxxjopLtYZY4wxxhjrpLhYZ4wxxhhjrJPiYp0xxhhjjLFOiot1xhhjjDHGOiku1hljjDHGGOukuFhnjDHGGGOsk+JinTHGGGOMsU6Ki3XGGGOMMcY6KVVH7wBjjDHGGGOdXWZmJn7//XecOHECFRUVCA0NxaxZs+Ds7Kxz29tuuw3l5eU6148dOxaPPvpoi76vJMuy3KY9Z4wxxhhjzIj9/PPPuPfeexEeHi4KdFNTUyxbtgyHDx/G0qVLcd1112nd3szMTBTm//vf/7Su9/Pzw6BBg1r0vblYZ4wxxhhjrBGLFi0So+kvvPACFIrqLnKNRoORI0ciKioKqampMDc31yrW77vvPnz66adoK26DYYwxxhhjrBEPPvggbG1tta6jov3qq6/G7t27cfz4cQwdOhTtgYt1xhhjjDHGGtGwUK9x9uxZ8dHOzk5n2549e0TLDI3Ah4SEiD72wMBAtBS3wTQTvdApKSmwtraGJEktfqEZY4x1XzQ9rKCgAB4eHrWn0C8n/h9lfNr7PdOVlZaW6p282ZrXuGFNR73odGmOo0ePYvDgwejXrx8OHDigtc3NzQ033ngjBgwYgJycHPz00084deoUvvzyS9xzzz0t3lHWDImJiTQRly/8GvB7gN8D/B7g90Cr3wP0v6Q98P8o4/29bK/3TFdVUlIiu7opL8tra2VlpXPdq6++2qz9SE9PlwMDA2Vzc3P56NGjerfXV1lZKY8bN042MTGRY2JiWvScuQ2mmWhEnSQmJsLGxqZlR0SMMca6tfz8fHh7e9f+L7ncah43Li4ODg4OMGY0yW/9+vW46qqrYGJiAmPV3u+Zrqq8vBxpF6sQFeMNa5vWn3EoyNegd2CiTl3XnFH1vLw8TJo0Sdx31apV6Nu3r85tGsY5KpVKzJkzB5s3b8Zff/2FJ598stn7ysV6M9WcJqEfKBfrjDHGWqO92ihrHpcKO2P/H0XFuoWFhXiexlys1+DWW/2srUxgY9WG9iCNplV1HbUmTZ48GSdPnsSKFSvEBNPmotYYkp2d3aJd5WKdMcYYY4x1KZKGLlKb7t9SxcXFmDZtGg4dOoTly5dj+vTpLbo/ZbKToKCgFt2PZywwxhhjjDHWiLKyMrHwESW8UKHecBGk+jZu3IgjR45oXUcj8S+//DJ8fHwwY8YMtASPrDPGGGOMsa5Flqovbbl/CyxcuBAbNmyAr6+vSHahS31z584VyTCE2moef/xxpKWliVF0anuhUfWIiAgsWbIElpaWLfreXKwzxhhjjLEuRdJIbWyDadl9aTS8d+/eBrd7enrWfj5kyBDs3LkT8fHxOHPmjIiI7Nmzpyj0W4OLdcYYY4wxxhoRHh4uLi1BxXlrC/T6uFhnjDHGGGNdcIIp2nT/roKLdcYYY4wx1rVoLl3acv8ugtNgGGOMMcYY66R4ZJ0xxhhjjHUpklx9acv9uwou1hljjDHGWNcr1jVtu39XwW0wjDHGGGOMdVI8ss4YY4wxxroWjVx9acv9uwgeWWeMMcYYY6yT4pF1xhhjjDHWpUg8wZQxxlh727BhAxYvXlz3z0eS4ODggODgYMyaNQvOzs4d9kNYvny5uPzyyy8wMTFp1+8VFRWFV199Ve+2p59+GsOHD6/9etGiRdi/f7/Bx3r55ZfRr1+/2q9LS0vx008/Yc+ePaioqEDfvn1x7733wt7eXue+LbktY6yDabpPzjqPrDPGWBsUFBSIgo6KRCruDh48iHfffRdLly6Fqalpo/eNiYnBH3/8gVdeeUUsYy3LMtLS0rBkyRLMnz8f//zzD0aNGtUhP59Tp06Jfauqqmr3Yj0jI0N8r+effx4DBw7U2tZwqe6RI0ciICBA5zEee+wxZGZm4uOPP669LisrC2PHjkV6ejpmz54Na2trfPPNN/jggw+wbds2BAUFteq2jDF2JXGxzhhjbbBx40asWbNGFNjk119/xYULF5os1BsWoBMmTKj9+s4774Snp6co2Ddv3txtfj40gj5t2rRGbxMREaFz3bFjx8RBzrXXXgsPD4/a61944QVx0HHixAn07NlTXHf33XejR48e4gBr+/btrbotY6zjSRpZXNpy/66CJ5gyxlgb/Pfff6LYppFYQqPhU6ZMadNramtrCzc3NzHaWx+NGj/44IM6t9+6dStuvPFGpKSk1F73888/i+tqDigefvhh3H///eJrGsH/+++/8dRTT4kDg3nz5uHw4cO19/3ss89ECwy57bbbxOPQhQ5CmnP/mjMOdJ/3338f7e3bb78VHx944AGt6+kgatCgQbXFNzEzM8PMmTOxY8cOUZy35raMsU7UBqNpw6WL4GKdMcZaaM6cObUF7IoVK5CUlCQ+nzFjBk6fPo3169eLrx9//PFWvbbUk02F8ZgxY3SuX7t2rc7t6bbURpKfn197HY0Q03VU4FM7x4ABA1BYWCi2Pfroo7jjjjtEL/bkyZNhYWEhinnq1yZDhw5FWFiY+Pymm27CLbfcIi41vdtN3Z+UlZWJ70/9381F+0kHB/fddx++/PJLredjCH0fOjDx9vYW+1JfTk4OHB0dde5Tc93u3btbdVvGGLuSuA2GMcZaaNKkScjNzcW5c+dEQfrGG2+IyaCrV68WH5955hlxOysrq2Y93muvvSaKU0I90wcOHBCj1vS4bUX7SK05hEbBqbilovj111/H3Llza29H/eL0vQmNMPfq1Ut8fv3114sR5hrNuT+xsbHB77//Di8vr2btJ02q7dOnDwIDAxEbGytG6+l70JkL6uc3hF7z7OxsPPHEE1AoFDr97tHR0Tr3OXv2rPhIB1mtuW1nRmc9GOsOJE6DYYwxZkhNfzm1i9DI60MPPSSSXKhwHzFiRG37SXPRxMaagpQKT0qEoR54Gllvqoe7Kf/73/9qP6eiu7KyEiqVClu2bBEj2DUjx7T/rq6uTT6eUqls1v3VanWzX4f+/fuLRJj6E1mppYWK91tvvRUnT55stAWG9qn+86xB+0cHEV999VVti8yRI0ewbNmy2gOP1ty2M6NJtk5OTh29G4y1P033SYPhNhjGGGulnTt3ikmRVKjWtEpERka2+HGo572mrYYKRRotpomUVKhSUkpb0Eh1fVRof/HFF2LfqS+e9v+ll14ShWlztPX+hnr0GybO0ARbOhNARXzN6HZDCQkJ2LRpk5gjoG8E/9lnnxUtO4888og4W0AHRXShwpzUb3tpyW07M3q9GGPGhYt1xhhrAWrPqCmsqUXjzJkztf3qVDyuWrVKfF0zmbMto/fUY16/V5oSZsrLy3VuSy05htRMfK2PMtxTU1NFvz0dXKxcuVJEJr733nvN2re23r+56GCg5myDPt999x00Go3OxNIaNOL+6aefihYWaql57rnnRFsQFeGkfkxkS27bmVELFWPdgaRp+6Wr4J51xhhrgfHjx4s8dRrBpLYXyuGmgpg+p9FgWsSH1O/zbg1KU2mIJlFSQkxJSQnMzc1rr6ds95aifaaoQ7q88847YoT8o48+EiPMNSPohIrh1tz/cqCEGTproS9XnXqzv//+e/GaX3311U0W/fUTeijphnrU9WXYt+S2nRFlwhcXF4tJv4wZNVn8IWjb/bsIHllnjLEWoD5yGjmnIpIyuO+55x7xNRXRU6dOrR11b0uvOU3UpEKU0lbqJ8JQEUnFc/1VT6kN5OjRo81+bBoR/+GHH8QKnTWoj516suv3OtOBAYmPj2/V/VsS3UgLSFFOen2U8EIj95QOo6+Xnp43peBQBjqNiutD+0699Q173OnAilp56t+vJbftzOhArn4qD2Os6+ORdcYYa4V///23NiqQRjJp0Rzqd26N+mkwFCFIrQyUjkKTG6mfuwZFKlIcJKXNUCFLq4v6+/vjySefNNgK0pClpaUYfaXJlNTPbmdnJ/rN6cDgxx9/rL0dtfUsWLAAEydOFD3cNNJObS40+bU596+JbmwOKvapb59Gg2mknFZ2pUQYmjRaf0XShoU0pb/U9JQbeq406k+96LSvlPZCPytq22mYhd+S23Z2dKBHZ4AazldgzOjSYDRtu39XIcmc89QslPdL/zTz8vJEJBljrHuj3nTKLqcWCSqwaaSX2jFa0n5ABWnDxYTo74ufnx9CQkIM3o8mXFIxSbejFBkaFaYCnyIla3rUKUGlpp9eH/pbRrehv20+Pj7o3bu3zm2oP37fvn1ikiuN6FPhXnPw0NT96b5//vmnmPg5bNiwJl8LOvCgjPq4uDgRedmvX7/aXHd9aFEmmpRKz7kp1HtOr5eLi4voPW8Y8dja23am/yE1j08HL3TwRK87RWwaYzIMndWhxcfo963hxGRjwnVH469L5uEA2Fi1/vczv1ADpwGxXaKu42K9mfiXhjHGWGcv1s+fPy/mTdBqtnQgSfMImpt131Vwsd695dcU6wcvQ7E+qGsU69yzzhhjjBkJakuiHns6M0BnXO666y7RtsSYsS6KJLXh0lVwsc4YY4wZEer7p0nAtAotjULOnj0bL7/8stYKs4yxroOLdcYYY8zIODs7i551ysSnvntaE+D666/H559/Lk77M2Y0K5hq2nDpIrhYZ4wxxoyQWq0W6UGUDkNrA1BCz5IlS0TEKEVqUl87Y12Whot11k3ExubgwgXDqx+2VUVFFXbtTDC4ncKIdmyPR1VVFzrEZYyxLoTaYWiUfdGiRSJlqLS0FL/88gumT58u4kbXr1+vd2VcxljnwCPr3VRxcQVee3Ubhg74BkP6f4M3X9+BkpK6RU4uh40bYjFs0LeYMnEZpk1ehtOnMrS2Hz6UivGjf8TUSb9gVOT32LMr8bJ+f8YYY9VoEa9x48aJxaY++eQTEadJ1+3fvx8vvviiWDPgrbfeEl9T7j1jnZ0kJolKbbigy+Doxm4Y3bhyxWm8/MIWJCXla13v42ODt94Zj+nXhbbp8Wmk/oU5m7D273Na16tUCtz/4AA89MhAfPDeXvz4w3FoNNq/LTfN7IU33hoLd4/qrGjGGDMGVyq68fjx42LhKn3c3d21cuOpDeavv/4Sefj1V5Cl/aOVc2lhJVoQy9TUFJ0JRzd2b/mX3uvZOym6sfUrC+cXVsFhRNeIbuRivZsV61lZJfD3+qjR2ySlPQ0bm9b/cb7rtlVYveqswe0jRnpj5w7Do+j3/K8fPvq0emVIxhgzBleqWB8mvQmVZKazvRy5WJnwoEiKaYgWvKJFtWhhry1btohFvur3vVPBTiPxkZGRIrudRuQ7Ehfr3Vt+NyzWVR29A+wKk5s+79PWRW2bunvT27vQuSnGGOtEzCQ7qCRz3Q2N/Fml0fahQ4eKy9y5c3H06FFRuG/dulXEPe7evVtcPvjgA7i5uSEiIkJcaAVfynVnrENo2pjo0oWmynGxzhhjjDFBqVRi4MCB4jJnzhzExsZiz5494nLkyBFcvHgRq1atEhfi7++Pfv36iUv//v1Fq01Hj7yzbkJu/CC0WffvIrhYZ4wxxpgOKroDAwPF5Y477hApMocPHxaF+759+0QhHxcXJy41xTvlu9cU7vQxKChIq0+eMdZyXKx3M0qVAkqlhKoq/YeUKqUCleWaRltUKosqYGKlNngba4vGkwRs7Rrfbm3d+HZZLoQkWRneXlUMKMwgSfr/QciUdCDLkExM9G6nSa8VxZUwtdK/nZQUVMDc2vD2phQUlMHa2vC8gMKCclhZG36Nm1JaWil+ziYmre/n6wqJRqamSiiVXAgwdiWYmZmJvnW6EOr1PXbsmGibocupU6eQkZGBDRs2iAuxtLQUGe81o++9e/fudBNWWdckaSRxacv9uwr+L9fN2NmZYefee8Qkz4Yi+3hivJcP7h+2Blv+iNXZnhOVgS23rMI/Y39CzLKTkBskucgFyaja+hw+nvA5Vi8sgbWl9v2DQ4Gdh2Kx+Oe3sHFXIjy9tLc7OQF/b0rFc6/NQXHlk9DIadqPL1egrOob5FdEoqhiFqrkWJ0DCTl9FeTDkyAfvwlywVGd51B87ASSXpovLsVHj+tsT9qfhW8iN+Lj0H9w5PtYnf751Oh8LJqyBU94rcbad0+jsqJlTW95eaV4fs5G+Ht9jEceXIuM9CKt7eVllfji7QMY4f0NHrruTyTEtDwD/8/VZzGo71eIHLIEWzZfgDH6/bdTGBD+FUZGfIfdOznyk7GOQJP8Ro0ahSeeeEIstrRt2zZ89dVXIrudCnoq1IuKikS/O62c+sADD2D06NG499578fHHH2P79u1isiBjrHGcBtPN0mDqW7H8lIhwVMkSBnu4I3pfptb2QeM98fSHEfBwN8eJD/Yh9tdTQL0C3a63M/q/OgKOfewhRy2FfOpnoKpuYY0KMzd8tHUoFi2V8fmSfEy65l9IUt0fZlnjgD9+mYzZj1nh9UUluOPedVAo0+vtgSXMlI9CrbgXVfJ+lFS+Bg3O19tuArXibpgpHweK4iHHvQ5oFegS4HwtJN9nUZkrI+vn31ByTLtAN+8bDsfbZqJMssHGF4/j6A9xWhNgvYY6YurHA+AQao3Vr0dh3Udntc48uIda486PBiL8KvdGX2sq+pf9dAKvztuG9LS6At3W1hQvvDwCDzw0EDvWxeOtZ7cjIaZuKXC1qRL3Pj0ADz0/COYWjY/kR5/NwpzZG7Blk3aBPv3aEBHJ6eNri64u6mQ65jyzQSdN6Mabe+HNtznyk3VeVyoNZoziM70TTEvlHPyaMFNvGkx7oZSZ8+fPi1F36nenS2am9v8ZEhAQoNX3TpNYG+t75zSY7i3/0ns9Z3Ngm9Ng7MfFdIm6jov1blys17RjXOe1DKXF+ltPVCYKzB1ujqI4AyO8EnDNu2dgUnjA4PcoHN8DcNxmcHtV+Sgo1dsNbldKkaiSdxvcrioNh/lJynTXP8qtkVyR9G3v6vYXfU9BpcLKTZORl1iif7tCAvrbIuZAtsF9eHr1SAyY3uBUQT0fvLsH818x/BpMmxCMQ+uSDW4fPcUPX62ZbnA7jdD3CPoMFQZG+s3NVSKSsyu3xVB+f/+wxQZbuOzszZCQ8tQV3y/Gukp042f7posJoB2FBi0ozz0qKqr2kpioe2bM0dERYWFhCA8PFx+9vb21incq1jdv3iwWeTIx0M7YGTTMtW8pY607LluxvikINpZtKNaLqmA//nyXeH25Z72bs7AwMVioE2rzqCxuZGVTmXrA9Re5NUzUZWhsbVS1aSkM1F/V30IubvTxoaHtjbSjVJQYLNTF41dWorywke0aGeVFjffRlzWxvbCo8dVhiwvL27S9vLzKYKFOSkoqdRag6mrKSisNFuqkqInXiLHuoFzORxXK9GyR8NSQrQbn8lx5ruJiIRejCKkoRDKK5GQUIx2xyMaBLTQAUz1pVQlzWMETVpInLOEFcziJ5/EdVqKzaizXnrGW4mKdMcYYMxI2Ch/9OeudlWQvCnFXDBJfVskVKEIS8uV4FCABhXISNChHPuKQL8eJ2yhhChv4wVYKhC0CYQr7zhcX2bXHRroGDeesM8YYY4xdUUrJBDbwh43kX12PyZVi5L1AFO/xKJATxZmDHPmsuBAq1m2lgNriXSm1PkmLdSEy56x3GtSbtnHjRixfvlxMUqEV1r788ssWPcbOnTvFTPSYmBiRAXvLLbeIzFjGGGOMdV4KSQVreMNaogSzEZBljSje8+VY5CFWFO9lyEG6fEhcFFDBVgqCg9QTdgjuWmcZGDOgszSvGUSzwyniieKhaFY5XVrit99+E1FR1Df20UcfYfr06Xj44YdF1JSxo+SMaZOX4ekn1yEnp1Rne3J8Pp669V8EDLWHk7uFznYbB1NMG+WFMrU9LH10k0SU5ir0vrcSKvcswCFQdwckJTQhYag0S4dS6qt3H5XSAFTJtH2w3rejQgqDjAIopaEi/UVnO4KgMVeh0nsAoGyQFUkH3WYeqOjpBZv/mUJpq5vNrrCxgs095rh6yV44BOtm/6qtVRg6wxTje6fAN1z3/iq1AkPGOyH562NI2pyk+/1lGX9/dxZRK1MxapgP9J2pHTjIHUlZBeg3yg0qle5r0D/CHS++NwqGnD2TiYcfXIuISC84OOhOLPPwsMaSH6bD1LRrd70FBTvg3Q8mws5O9+fk62uLH366rkP2izF25VCvOvWueyhGoqdiFgYq5iBEcStcpSFihF2DSuTIZxCjWYUjmg/ER2qpaRjDy4yARmr7pYvo9GkwBQUFsLa2Fp/b2dlh0KBBYqS9OQoLC+Hr64uxY8dixYoVtdd/+umnePzxx8UKbEOGDDG6Wdm5uaV4Y8F2fPv1kdoJeY5O5nhl/mjMuqcvKsqr8PW7h/D1e4dQWlJZmxYydJAXzu7LhKZSg+HD3eFytghybvV2hQoIGWeDstiLqCwsh9d4S4Rf/xssbOqyzmUMBVIygNIcyK5BKO13EVW2MbXbFaDCOw8yEqEAneJUQ4Oz9bYHiUlQGpyDBDdx0aAuilGCJyTJARr5BCTYQSEFo0o+WHseTFHhCPPkICgyj4pFkSo9eqHE5RCgqJ54KJfaoPzv6SjalCHuYznOGeppf0Myr45KrCo3x7FPHseuhU6oKK5Cj3HWCLA4AVVJdRKODAnxdv2wabMSBZnlCBlsD++KQlSlFNTuo88kbwx9bShsfK1x+kAGPnhyF04doO9XzS3cGglVBTgbnSUKTHtHcxw9fLF2e7C/A7ysrHDmaCac3Szw7FvDce3tPfT2Y+bnl2Hhmzux+ItDtZNLbWxNERbmgn17k8RiQY89MRhznqe8Y+M5LZyZUYwFr27Djz8cF4siPTV7GJ6ePQxmZl37YIQZt46ObuwOqJwpRhpy5NPIlk+jBHV/e83hDA/FCDgi7IpMsr0cUZldqe7okDSYf0PangYzJbpLvL6dvlivr6XF+u+//46bb74Zq1evxrXXXlt7Pf1gHBwcxMINn3zyiVH90sTF5WL86KWioNEnIsILlcmVSIrTvxCFl48Npts5oupMXQFan6WLCa554zDc/Kpn6TckS5aocBuAMs9NBvZQBZU0EpUyxRjqTy9RSqNRJe8R8+n1USACGkTRT0XvdpPCfqhSp0Cjrp/ZXkeT2gOQFVB4nNK7vTA5EAlv3gp1EqUR6KpQW+CCahCKdl/Uv/9mSlRcG4Afvjitldleg/5PBExxwtrN5wwmuNx2Yxje+/wqg6uYpiQXYPSI75F2UXtRpRo9ejph2fIZCApygLE6cvgiHBzN4Otr19G7wliTuFi/sqi0KUIKMuTDyJKjahNyLCQ3BEjXwFLyaNfvz8V6+8nvhsW6UQ9FHTp0SHzs06eP1vX0Q/bx8cHBgzQqa1yoSDdUqJOzZ7Jgmmn41E9SQj4URRaoMrC9KL0CTgEnDSYlSnIRqlwM55EDlaA/oY1GLYrtjcXw0Ui44VXvKqyoiNZfqBOF+5nqgHgDrDxjYKVJM7gHJuXFsKosFXupT1VpFS5E5egt1ImsAcoKKxuNWswrLTNYqJOcnBKDhTpJiM8z6kKd9B/g1tG7wBjrpOhsZE3co498FdLk/UiV96BYvohT8ncIVMwQfe2s65I01Ze23L+rMOpi/eLF6pFPJ1rHvgGaaEqLMxhSVlYmLjV4SWTGGGOdhaH/UaVyLlTQnaPU3TlIYbBGABLlDchHLM5pfkeodAfMJPt2y1mngAy6tFZb7tstaNrYd96FetaNulivqqoeH1YqdU+T0HWN/SK8/fbbWLBgQbvuH2OMMdYahv5HGV4UiREPjEY5ClCMi8iQj8AFA5t8YSqQhzve9Ya9fUsKezscOXIEx44da/ULX1zcxIKArNsw6mKd2l1qJqlaWGinndB11ANvyAsvvIBnnnlGa9SCljxmjDHGOpqh/1FdblGkDmCpcUWZnA0zyQ62Cr9m9Z/fcMOMK74aKZ/Rb4LcfXLWO310Y1v06NFDfKR89YYj7hcuXKjdro+pqamYcFD/0lXi7a69LlTvNkrOCOvjjJ4jXcQkx4YUSqDXKBec7auErNZ/esgn0h6Hfr8J5aWOereX5EagaHlfyLn6/6gpqgKhKjaBosrQgY+7iGhUIMTAdntIsIUC4Qa2m0MJfygl/aMlmkoTxHzzBGK+ehKaCt0IQFmWkLTyHmRrQiGrdbcLnoGwdFDCzFF/T3lFgA1KFBJcvHWjJImznyVMFUr0CnHSv93ZAuUVVdiy+YLe7ZQCQ0koQ4Z6GPw5P/oERWG23l9rzuKF5zYhO7ukTY/DOrf//j2Puc9ubHSeC+ucuur/qI5EGe3Jmm0iKUaC1KKedRMTkw65MGb0I+uTJ08Wk0zWr1+PyMhIrUWSioqKMHXqVBgbe3sz/PjL9aLQe272BjGhtCbL+2JqIbZvSxBfh/ZwhIPCFBdOVscS+vexR0Z5CTbtqi4Qfb1tMMPNHbaHq4s1hyBL2DhKyDl+EXnHgdh1j2DgfYkIHvwDJElGZZk3co6MQPHxZJqmiuJ9/WA5dThMxq+BpCqDpLGFSVlPoPQoZCRAVaoCzCNQbnockGiipCmUUj9UyUdRJaeKCaBKaRCqZIqGpAmriktfn0YV9lzKYO8DWc6EjBTxtVLqD42cXL1dxqWCvwoaVB+sZe6chiOvRiLvXPVziv31VfRfsAdOI/8SX+dFReLovGuQcZAKlzyYu4TCf2AV1KlRl85qOiK/yhuZ2+g1TYKFlRrOI12QtCcLciWgsVMjxtMGO3dcFJNLTUyV6D/KTUQ3lpVUwdRSBZ+Btji4LwUV8RooFBImRwZg3+kU5OSWQqWSMHSYF44dTcOGdbHiQgdeb70zDt4+tiLdYNlPJ/DqvG1IT6ueXBrexwXFxRWIOZ8jvp40JRAL352AwMDW9WGei87CnNkbsXlj9bLevyw7iVfmj8Ld9/YT+8uMQ0xMDp6fsxHr/q3+3fjl5xN4cd5I3P/gABH5yZixKZbTEC+vQ75c/bfNUxrb7okwrJ1puk/PulFFN9LqpqGhoVi6dGntdbNmzcKaNWuwfft2kQqTk5ODiRMnimKdesnUarVRRTfWV1FRhU8+2o/VK8/i6BHdmEGK7o4c6iUKy937dBf0ISMGeuIBZ2dkH7gIuVL3reLc1wIRt0ajaHca5HLd/BSVmwPsH8mF0uwwIFfnmWtROEJjFYIqZTRk6ItCtIIS/SAjGRpU/5HVpoYSgyEjGxqc1rNdAalyCPY9Pg0Ja/X3cfpMMYOZaznOLZUha/Q8x3BbuARUIXlHNjTlujk5lr62uGDrgNXb0lCUrzsPwsnDAt5htjhy/CIy03RHMG3sTREw2AGnY7NwIa764Kk+ysCf/VyEKKwO7K8+MKlPqZQwcrQvHn50EKZcTVn1rbPwrZ14d+FuvSk1/fq7ioNAjkns+v7v/b148/UdKCvTfS+HhTtj6bLrjT5JqCNwdGPHKJEzkCrvRqZ8HDI0YoVTX8VkuBg4+9peMYyt0RXrjiv5uuSu6tnm6Ea76093ide304+sz5kzBxs2bKjtM6eFjGhVU/Lkk0/innvuqb0tFd8NF49ZvHixOJVEhTwtkJSUlCQWQvrjjz+aXah3VSYmSky/LhTz51Gmua6aIr2xw7Wdh5Jxew+13kKdZBwrRtEAld5CnVRezIZUrAFM9RTqRJMFSS43UKiTQsgoNFCok3JokAoZdYszNfgGKC+ONliok4R/S6G2VUPW6H8OGSfyoFaY6S3USVF8HmK9rfQW6iQzpRgOQRZ6C3WSn1OG0qIqvYU6KSmpxOqVZ3DyRN0CH/XRwlfRZ7LaVKiTFctPG4yTPHokDaeiMrlYNwIr/zitt1An9B47fjSNi3XW5dtd8hCLNPkAcuXo2usdpF7wkSbAtJ0SYBjrtsX6o48+ittvv13vNg8P7VNY+/fvh7m59sQaMzMzfPPNN/jwww9Foe7o6AgXF5d23WfGGGOMXVllci4y5KPiUi7W46CGSgn2Ug+4S5Gwkrz4R2JMNE0s2dKc+3cRnb5Y9/NreqZ2jYaLH9VnbW2Nnj15AQTGGGPMWFTIhcjGGWTLJ1EgJ0C+FPGhgjkcpXC4SUNgJukPRGBdnCxVX9py/y6i0xfrjDHGGGue7rAoUqVcijycR658FgVI1Mrgs4I3HKUw2CIICklV23Pe1gWOGOtIXKwbOXt7c/To6YQzpzP1bu8d5gyNBjgVpb8fOjjIAQpPSyBVf7+1uZsFCuACK+lCdRN8AwoLC5SlWsHcniKo9PV0m0GRZwM4UsxhdcKJNiWkTFdIjo6Qpepkm4Yq0wKhdKwAVPRHW5dUHgL7UBvknK1e4a8h2yBbqG1MkHFY/2tk6WkJhYMFEKP/D77a3gzOtmZQqiRU6entN7dSwcRKATMLFUqLK3W2q0wUsLIxEUk+OTm6/2RpGsawCC/k55UhIUH/c/APsMPJE+kIC299i1fkcC9En9X/Gnt4WMPf3/C6BLm5pVj7VzRuuKkXzMz4z0pnRu8lmoOgj4urJQKDuJ+3K+vsiyLRAkNf778D7u4U09uyBYL27NmDbdu24fDhwyjOyYGTjQ2cJXsEBwdj1KhRGDlyZLu1ubZ0f1n7k2UJchsSXej+XUWXSoPpSF15VnZlpQZffnYQC9/aJTK6iaurJXz97bB/L0UtAkMjPBF7PgcZl/KWbWxMMTzMCym7cqGpAiYMc0aPkgoUJxWK7UpTJSzCXXFwSz7KijXoNwoYM/QskH4pVUaSoPIPRfbJfFTml8Gqjwpej5yHwvxw7X4pqvpDeaQQUk4WNHa2KB9uh0rLA7WjJIqyMGjWOKPqVB4kazVUt5tB47aTnlH1A5QHIfWrEUhfKUFhCQS+mgeLgf8B0qUDiyp3ZK2YgKTFKshU8fqGIvFQBcpyql8DUzs17ELtkLY/XXxL5wFOKE4tRtGlAxOluRLWvV1xakseKstk+A+ygItVAYouVI+ySCoFzHp7YvfmMhTlVsEyyBzptlU4fayu6A+NdMKeC8lITi+Ep5s1ens54cS+ukKp5wBnxOfmISY+F7Z2pugd5oJ9e5LEpFHSf4Ab3vvwKgwe4oGSkgp88N5efPTBPpSWVr8G3j42Ipf98KGLIhXmf/f3x8uvjoKdnVmr3itbt1zAnGfqIj/VaiUefXww5jwfCSsr3QnZ9OeDMt/nv7JNZHX7+dth4aLxuHpacKu+P7sydu1MwLNPb0DUyeqDdJVKgQcfHogXXh4hfvfZ5cdpMC1PVikpKcGOHTtEyMSuXbtQfinIgP7uWFpa4q677hIRzV5extmL3pXrjivxuuQsD4ONRRvSYIqrYH/zyS7x+nKx3o1+adIuFuKVl7ciIT5PRDkWFWmPdFtZmaBvPzeYaBQoPVWOokztdBRTtQIzx7jBSQOcOVOJ9Avao8CUtz7t7gqEeCWjKF2B4gu6I9GutyjheFU0VPEqKGJ0E16qgnxQ3rscmmNuqPxHd7RfGWoLxTUFyDscgNg3LYAK7SNjix4SAuafR8VFO5yf54iqAu37S+bmKHUIRlmZCXJO56A8T/s5KkwVcBnggooyGXFR5chNLtNZOKrvJGuYKUpx6pwK8ad0Fw2yibRBclUJEioLsD+KMuO1De7jDhtJjTJVFXYe0o3MDA6xh729Be64Kxx33d1XJ9/8woVcvPT8ZuRkl2D/vmSUl2vPknFytsD810aL+7bl4G73rkQseHMsgoP1x/gdO3oRTz2+DocO6j7HiZMCxEFGY6PxrGNVVWnwzVdHsHlTHBa8PkacgWPth4v15hXrVJDTCPq6detE5HJpaanWHLarrroKY8eOxalTp3D11Vcb9cJBxlB3tId8LtaZsf/SZGUWw9/740ZvM9TcDcUF+mMIyeRAD2SerR5h1+euB/OBC4aiFIGQu2RYaPTHSZJyVSRKjscb3F5iHo6YdYYXblE7W6M8o0GV3sC5GK9GlxrOt3RCbrzhVR1t+tkj4ZDhPsi0cRL+2W34NRg+whu7dupv2yFTpwXjl99vMLg96mQ6IgYvMbjdwsIEF7Nmoz098L+/8OuySwtG6fH8S8Px4ssj23UfGOsquFg3XKzTquIHDhwQCxhu3rwZhYV1/1/odpMmTRLrowQFBYl45oqKCvzzzz9crHf3Yv3XyzCyfkvXGFnn5lLGGGOMXVHUynLixAn8+++/YqFDWrCwBvWdU3FORTqluDVcP4UxgdNgGGOMMcYur3K5AL/99ht27tyJhIQErRXKJ0yYINpcaOFDhcLw2VPGuhseWWeMMcZYu64omoOzyJAPI0eOxg8/2Itec1q0cPz48WKSKK0srlS2vqWBdUMaqfrSlvt3EVysdzMmaiVsbU2Rl6c/2svO3gx21mYGe9bVpkoorQ3/QaUJmKWyFRrLIsnJdoZFI/MO8/MdoUICJANN5YVldpAVhZAoc1IPcydALlGiolD/kuqSpSXMHExRmmUg3sxMCcmmkV8NFVBi1cjSZ5IMCzPd5JT6XFwoqtIwZxeLRrdbWqlhbq5CSYluFCRxcKiOgaQ4SH0oFaiwoBwentaNfp9G99HZsk3bWfdwLjoLAYH2UCp5pLS75axXyMXIwglkycdRgeo+dA2q0KtXL9x6661iJN3CovG/dYwxLta7HYplO3z8AZEKs+ynE7XR6JQ4cuesPiJFxNzMBN+9cRjLPz6Jyoq6orTfYFcUp5TjwIlM9B3jgsID+agoqiuIvfvYQl2hwfolBeg5fiT8zY5DVVq95DOptPVAbKo/tj2fD/8xczDqmjWws4yu3V5U6osDW67HhU3FcA4PQZ8hx2Ejn6rdXgY77IueiqN/VcDezxy9wgpgmhZTu11pLiH4GgkWZduAcCtk5fZB/Pq6f1oypar49ED8gTJA1sB1mCvS9qdpLTlcHuaCvWeKURidjQEjnVF1IA9VpXU3qOhvhv+KLyL2wAVMHucP9yMKaHLqXgOzHmaINi/CqQ2JmDjED6dTspCUWjfZtX604X3b4zHnmY1aGfc1SS70s2iMn58dDhy5Hy88twl//Vn3GqpUEoYO88Kxo2kY0GcxXpk/GrPuqUuUoT7RZT+fxKsvbxXF+uznIvDEU0Ngatry4/Y3F45Dn76umPfiFly8WDchLDjEAYven4jxE/xb/JjMeGSkF9X+naH8//c+mIiI4d4dvVtGr71z1puTkx4XF4c//vhDpLlYVFbCAmrY2vrXThQdNGgQt7mwtpO7zwqmHN3YzdJg6qPYP8rUphGv9z6ciAEDtf/4xp/Nxf89vRsp5/LhZm+F2EPZWtvtXc0QHGCHivMl8Aq2RPIe7QV1TG1V6D9BAcfSU8hU9UH0pkJo6i0apDRVYOCtFeg/8DdEn7kWx5eborKkrvCVFBICJ5ojxGsTYnMisPMPWxRnaY/4B460gY95DHwGlcDF+SRQpL2wkWwfhPhDnsjJc0FakhXyYrRTYmwCbKAyUyGrsBKnNWpEH9FOeHH0sEAvb2sUJhfimG8pthzQTnChkeupoX6wOF6Jgj4qbN+TCI2m7jnSQkihg52w92QKHn1iMJ58eqjWokEUk/j14sN45+1duHlmL7w4b2SLM9Ipeu+52RvFSDudMbkQp73aHmW1U/FsaqoU2dr0c2+4oNLCdydgytVBaI2CgjIsens3lv5wDM/MjsAjjw+CiQmfzu6u6D29+ItDWPjmTp0zeDff0htvvDUWbu5W6G6MJQ2msejFM2fO4JtvvsHWrVtrr6NR9FtuuUWMoqvVjZ9xbC5Og+ne8mvSYJb2a3sazF1Hu0Rdx8V6Ny7WCRWWNNG+sdn29wWtRFqc4ajGSQNckXHE8HLMPcY6IHWP/hVSSeB4B2Tu083qrmEV7oZTW/Sv3El6ja3E1PHLDG6vgA1W/t9VaMwmjTkKsrQz1+vL6leBk6cMP4fJQ/xxbI/+VSHJkwuG4ZEXhjSaed2WNoGkpDz0Cv7C4Hb68dLoec1iSvocOnY/gkMcW70PbX0OzDgs/f4YHnv4X4Pbx4z1xZ//3IruxpiL9ejoaHz++edi0iih/yfjxo3DnXfeibCwsMu+D1ysd2/53bBY5571bq7hgjv6yPVGivXfoInNTa2R28b7S/X7WFr+8NW3afwhoGlqexufY1uLXAlSk69h/RF/fZra3hQu1Flz3kdtfZ+xziMjIwNffPEF/vrrL9FiRwkulOZy7733IiAgoKN3jxk7uZn/4Bu7fxfBxTpjjDHGmk2j0WDZsmWiUC8pqV7FmYr0hx56CD4+PvxKsitC1kji0pb7dxVcrDPGGGOsWUrlLMyePRuxsdUrNPfp0wdPP/00wsPD+RVkRi8zMxO///67WNCL2rFCQ0Mxa9YsODs76709/Z589913YtI13WbmzJkYNmxYi78vN5iyJtFEz8Zv0MTmth68NnF/uYm3cXO+vdTEb0JT63M0+Rylpvu927K9qXYm2r+mbkOnsRlrq6beZ81pvWOdD/19SNPsx1n5Z5w9exaWlpZ48cUXxYRSLtRZh6bByG24tMDPP/8s5mp8++23YmXdwYMH4++//0ZgYCBWr16tc/vNmzeLORunT58WawnQOgIjR47ERx991OKnysU6azQf+bprfsNxZRZ8B+oGo9u5mqFHhDMOJ+fBNUJ3YqKpjQl8Rzkh/mg+3CJdoFBp/2JQGoxdpAvW7SyA6UAPKM2VOgcJFgNd8d/xfFgNd4C5g26SQMAIO9hUlCA5dyJgqXtkW2ETin/XTQV8XEXyS0NW/jZI8nWElb0p/Pva62x39LSAzzA72GWqETnYS2c7pcFMiQhE/pkSDBvhpVOIUBrM8FHeWPPBaSx54zDKGkzwpOSMzz89gEDfT0QyT26ubj7yb79EoXfIF7jnrjVISdZOs6nh7mGNNWtvQWgP3Z8DpcFs2HIn1m++A0OH6SY4ePvYYMBAN8y88Q/8u/ac3sdnrLluuyMcC98dL9ZzaGjmrb3x1bfX8IvZxVTJZYiRV+KC/C9kVIkihUYXZ8yYwRGMrOMXRdK04dICycnJeOWVV7B//348/vjjeOCBB0RBTgerd999d21LGCkrKxMj7sOHD8eKFStwxx134L333sPzzz+PZ599FufOtex/LafBdPM0GENRfO+8tQtffHYQFfVy1icN8IdlshJ5aaWiSD9/MBOl9XLWe/dxgHuZAtnnCuAT6YTMs/kozqxLWHEKsoStgxKZx3PgOMARUYmlSI0vrt3u5mWKIb1NkX8kHdah9jicKyPqZF1xamOrRmQfJ2TsyYaDnwUCg4shJdVFKaosJITfVA7ryn2QTa1w7MxobPjKWmvBpp7j7FAWmykmXJYGOmDtpgxo6q2dFDrMCenxRSjKKYf/UAccPpCKstK6G3j0t8G5olxcuJCLMRG+KDxeipKcujhJjxBrlJlqcPZkJgYMcUdBYhlyUup+gT38rfHEe8MwcrofdhjIWX91wWjcdXcfnDyRLgr43buSardbWppgztxIPPbkEKjVurPgKyqq8OVnB7HwrV1iAavqx9LOWf9lWXXOOi2MNHCgu4hyLC+v+zlfNTlQFFtBQQ6Nv1EYayJn/dV5W/HzjycQ3scF777fvXPWr1QazDDpTaiklsW/NqZcLkCsvAqloGheBVykAdiW8Am8vHQHL64UToPp3vIvvdezvxnY5jQYh/sONft3km5H37ehN998Ey+//DL27t2LoUOHiuvWrFmD6667ThzU3njjjVoFP/3u0Fkpul9zcbHe3B9qNyrW+4UtRmyMdt54DXNTFWaG9kDcQe3M9RpUFE4b44WkLekGH999kgt2/XvR4Pb+U5yxdV0yZAOnqMaPd0Bw1jlIBlpDbHpZY9d6GxTn6r+/pZMJzkgVyEjXv3CIqYUSVkFmOHfSwHNUAaERzji9w3CU48CJHji0IcXg9v43e2DpyhMGt0++OhAb1sWiqkp/a8roMb7461/D8XdpFwuhNlU1uoLpuFE/IPqs/udIBwLbd9+NXr319+Ex1ly8gumVLdYHYC6Uku5ZjdYsepSQkCCKEOrTtbe3x0svvSRy02kbJb90FC7Wu7f8mmL9ayrWWz/1Mr+4Eg73N79YN+Suu+7Cjz/+KNYZoB52QsX422+/LXrW/f21Fwik3x/6Pdq0aVOzvwdPMGU6MjPqRrsbKimrRGmB9sJEDWPZyosMZ3mTwuLG+69zCjQGC3VSUV5usFAn+SkKg4U6KcqsQGYjK/yVFVdBzjecua6pBDRFjfd3V5Y0/hwz0oob355ebLBQF9sb+RkRVzerJleyzc01/BqUl1chv8GCNoy1Rluy+1nL2Sh82pSzTjnqVEzQaXwq1KmQCQoKwqefftroqqWMXXFyy1tZdO5/qfivz9TUVFya4+jRo/jll1/Eqrw1hTpJSqo+I+7q6qpzH7qORthbgnvWGWOMMVYrLS0Njz32mCjUaSIdTajjQp0ZK29vbzFSX3OhEfHmrjNALS4mJiZionV95eXVA34qle6YON2+tFR3flpjeGSdMcYYY0KVXC4m0aWnp4uFjWhEXV+fLmMdTZabsehiE/cniYmJWm0wzRlVpwPZSZMmifuuWrUKffv21dpuZVV9dru4uBhqtXY4RlFREayt6+bTNQePrDPGGGNMTEBPlDeKAsTFxYULddYt2NjYaF2aKtYLCgpEFOPJkyfFBNKrr75a5zbBwcHiY3x8vM6CYvT7VbO9ubhYZ/XegGV46fnNCOvjDEcnc72TDp95dhhmvTUAbgF6eqIlGfajLPGX4iI0PXRjFsVjDLZGdHEBXAboP6p0D7dBUVkFekQ46d3uEWCNojJLZPqGQNaT11xha4WDCneoRzlAaab79lbYqZA5ygxOo6xhbqd7YokSVAaNckdvdwe4uVvq3l8BhI50QpGqAm6B+vvCg4c5Ir20CD7h+kej3PtZI624GAMH6e//7BXsCPsqU4yP9INCT4C7p6c1bGzUeG/RbpSVNT4/oDFvvj0Orm66z1GlUuCxJwYjvK+L3vvVJMrMuHY5Dh4wPIm2MccPXMR916zB6h9Pc747Y51EBo4iF2fF5NGFCxeKgp2xTku+sjnrNSPl06ZNw6FDh7B8+XJMnz5d7+3Gjx8vPm7dulXr+gMHDqCwsBATJ05s0fflNJhmMuY0mPpxfmkXi8R1VAyGhbtg395kMdFx4qQAvPPehNo4v/LSKqx6Pwq/LzyJsuJK2PW2wDllHk6dyahNhbl6mD+CjyuAnCqo/cyQ6CjjxOG6BJW+Q1whx1eiMKUMVi6msA22QNTuuhQZyj2nx045VwALaxP06ueEuN1ZqKqsPnflGWqFIQFFMEtOhqxSIt4jEFu2FqD00gRXJ08L9PK2RuG+XHFYWjncEhtPpyA3u7pXzNbeFAN6uSNlZy5kDdB7iAuKk0uRk1Qdtai2UMJ9sC127adowyr4hNshp6wUcedyaovawREeSD6aj+L86uJdYw2cOlb3HAdFeCAnpkTEXdp6mELjp8Du/XVRjP36uyEnpwTxF/Jgb2eGYb08cGJ3Wu3k0oCe9ihQVuD46XSYmSkxaLAHDh5IRemlvPaAQHsRszh5StBliekcO84Pi96fgNAe+g+Wjh29iGef3iDeF4SOJe64qw/mvzYazi66hX9DWenFeP/l3Vj5w6naU5D9I9zxyv+NRq/+XBgw43Wl0mDGKD5r1QTTcrkQJzSfowKFeOP/nsWTTz6JzorTYLq3/Evv9azPh8DGvA1pMCWVcHxkf7N/J2nS9TXXXCMKcCrUKZqxMRMmTEB0dDQOHjwoDnzpfTt16lScOnWqdmGx5uJivZsX61SoXzXup9riq6HAQHu8PH8kbrixl97t6QmFeHnOZvz2V13xVR8VoLcNDsWeTSmoqJflXcPUXIlRo31wakc6Sgp0R4kVSgmDJnog9WAeCjL0p5MMnOSIqOgCXLxQfaDRUOggR0SX5ODsGf0xhYGhDhhk5Yy4ffq3O/hYoDhUxvYtCXq32zuZo99AV+zYlCAWOWrIwsoE/Ue5458d51FcrPscTUwUuGpcAGL2ZCEvR/c5UkE8YLwHDp9NQ0qK/kWRbripJ75bei1aK/pslojYm3pNiMHb0Ej+Gwt2iMSfhmgBnJ9+vR6jx/gZvP+eLYl4/Oa1KMjTTdqhg7snXh2Gh18Y3OrnwFhn1tlz1hPk9ciWo2AKOxyOXwsfHx90Vlysd2/5HVSsL1iwAPPnz4evr69If2lo7ty5YsGw+hNQb7jhBkRFRSEiIkIU6ZIk4Y8//kC/fv1atK88wbSbo8LLUKFOYmJyxGiuIS4+VkjNLzI4ySMntxRpBWV6C3VSVlKFwqIKvYW62L8qGcU5FQYLdZJwocxgoU4unM3F2WL9hTiJOZsNb2vD/9yyE4qRa2s4ijEnswQ5eaV6C3VSXFiBrKISvYU6oRHtkrwKvYU6odc2O73EYKFO9tRbOKk1QkIdxaUx9D7RV6iTvLwyRJ3MaLRYPxeVpbdQJ/S4R/amtnCvGWMNlcv5qGokmrZhljpJSUnB/fcvhZ3sgPfff79DFzxirLlkWWo05rk5928JWrG3d+/eBrd7emqvEO7s7Izt27eL3va4uDg4OTlhyJAhUCpbvpATF+uMMcZYN8pZr8lSrykuaEEXipiLjIwUp+4Z6xI0bcxZb+F9w8PDxaWlwsLCxKUteIIpY4wx1k1RH+5///0nPr/jjjs6encYY3rwyDpjjDHWTe3atUukU9BIu74+XMY6Lbl1iS5a9+8ieGS9m6OJfTffYrgH66rJgXB0tDC4/cD+FFhamsDcQv9xn5+fHVQWEmwc9OeWOriaQzKV4Oip/3tY2JhAYauAa4j+mESVqQL23uYiOcbQ8/Ppb4++Q9wMPoeBQ9zh2ccOkp4oSOLd1w6+7rYwNdPfZxYS5ohJ1wfBxk7/c3T1sISjqTncXPXP/LazM4O9hRm8/fVHPZqZq+DqbIHevZ31blcq6WeofwJwjd9/O4Wflh5vU0zi9GtDYGWlP5LTP8AOEZHejd5/QKQHfAL1P0eahHvV9YGt3jfWNRQXV4iJyvR3g3UOe/fuFR/Hjh0rIhsZ62o963IbLl0F/2Z2czQz+ZvvrsG6jbcjvI+LVvH12x83YsWqm/QWaBnpRXj4gbWYMGYp/ll7HrY2ZhgytG4iKhXw40f4oSq+Ats2xCNLU4KwES6isCQqEwX6jXITky93b0xEQk4+eox2hvpSQUwJKKHDnVFkWomtG+OxPy4NXqPsYWFnUvs9/AY7QOmmwv5NKYg7loMew5xh717Xq+kdZgtFDyXW7jqPY/svolc/Z/gE1BWL9PnoPj4o2FOCA9tTYB1iDq962ei2bmbwjrDHieMZiN+QjT4Ozug/qK7op+L8pQ9GYfWBW3Hv0wOwLupO3HhPL7HvhIr7yFHeUGZLOLsuA565lpg4wl/k1dccSIyN9EVIlR2i12eiPKECw0d6w9Kq7jn2G+oGG3tT7N2UjOwThRg3zA9OTnUHNiNGemPn3nvw2ptj9f58T55Ix5SJP+N/d/+JRx78B+NH/4jDh1o3kfPOWX1x6Nj9Wgd3FhYmmDd/FPYfvg/9Bxg+ICJhA1yw9ujteGpBhNbB3TW3huK/k3fixrsNHzSyrm/litMY1PdrvPbqdvF3g/5+0N8R1rEoVo7QxDfGWOfE0Y3dPLqxvqoqDZZ8cxS5uaV44qkhMDXVP1r+3bdH8cpLW0QCSEN9+rjA0d4CaVH5yEmrzjOvLyjYHv4e9rgYX4DUuEKd7W5eVggOtEdqehHOn9ZNcLF3MMOQcDcU5lfg9KFMne2mFkqEDHVCdkUptu6JR8NxZDpYGBDpDkuYIG1nfm1mew2qs/tEusLUVIkT+9NRcimzvT73gTZw7W+Np9+IgIOzhd4Ffxa/fRCJR/OQlVis+xz8zKEOMIGcXIX0s7rFiq2LGVx7W4mfw6mjdZntNcytTRA0zBG3zArDjTf3Mpiu8tzsDfj26yO1me016CDhzll98P7/XVV74NBSe3YlYvnyU5j9bAS8vFv++5CaWIDFiw5i2swQDBqhPYOeGZekxHw8dP/f2L4tQW/kJx1o3vO/lsWYdUWdKWedJpj+mjATjo6OGDlypDjjtn79ejg4VK+j0dlxdGP3ln/pvZ7xYWSboxudn97dJeo6HllntZRKBe5/cADmzI00WKiTb78+rLdQJ8ePp0NVCL2FOjl/LgcVmiq9hTq5mFSIvMoKvYU6yckuRWJuod5CnZQVVyH+XB626CnUCRWuB3akIHVbnk6hTuiaY7vTcCE2T2+hTlIP5eO663voLdRJn8Fu8Pew01uoi+dwoQTuFRZ6C3WSl16KqkKN3kKdlBRUoCylwmChLm5TUoGvvjysU6jXFPI/fHcMOdnViz+1RsRwb3z40aRWFerE3dsa8z8Zy4V6N3DoYIreQp3Q3xH6e8I6BsXJUaFuZ2fXZQp1xjpyBdOOwhNMGWOMMSNRKudCBf2DJTXKkYvU1FRRrNMotb29PZKTk8UkU+5bZ6zz4WKdMcYY60aLIlHD31NDtiILUUiSs5F5PB0zVi7GyoQHdRZ2YayzkjWSuLTl/l0FF+uMMcZYN1oUqYZCI0GCEmaSPdSwa/d9Y4y1Dvessxbr0VN/hCCxsVLDydZw1KPKREKVFaAwMK+RjnPdLc1hZiAmkagdVTCzM3ycae2uhou74X3o4WcP7wBrg9spBcbaTX8MIzG3NUFKUoHB7WWllSjVVNamwjSkkCTYWpnCxMTwrx+l3lha16XCNOTgYo6Mi4aTNBLO5CHIR3+cJfH0tMa5c/rnBZDszBLs25aE1qqs1OC/f8+Lj621c0cCsjL19/2zrsPTywbW1vojP5v6e8LalwZV4qOCx+1YVyR3n551LtZZi1HU49Kfr4N3g8mFk4YGoJ/aBSfXp2FoH08EBGoXi0EDHaDwVmH5+tMoCZHh3ke7YO7dwxFTQj2R9U8Whts5IXJwXRQkcfa1gMcgG2zcEocEk0J4jrSFVO8dbOmihv0IS2w9koD8vHIMHukBE3XdDWyt1LhzeCgCL5hCHVeBUSM8tApipYmEwNGOSC0qwoEDqfAebg8bl7qinb5X4EhHFKorsPDhHXhm6r9IiM7V2seNf8bg6r4/4cdvjsOupzm8emq/RqFhjugV5IQTay8i0M0O4QPq4jKJW6AVXPtbYcvGCzAzU4nkmvroIITiHPduScLksB+x5MPDqKio/odL8nPK8P7ju/Dg8DWwSVLhhshQWFnUPUc6CKK4x+zsEkydtAyPPfwPMjOKtRKBfvr8GCb3Xoq7Jq7EIzf8jcS4PLTEtq0XEDlkCW6esUJ8pK9b4sKFXNx60x+4+qpl6N/nK3z15SGxX6xrGjTYA4eOPYCZt2pHc9LfD/o7Qn9PWMeQUfN7xaUA63rkbpSzztGNzdQdohtbs8DJ++/uwaY/Y+ElWyHuRI7WdqVKQs8IZ8Sm50K2l7D/oO5CKCOGecMmTYX+Tg5I3ZmNhmv2uA2wQ3RZAeCmxIG9yago1y7agno4wENlAYWdEgePpaKwoFxru4ePNRxdzOGrtIRZdBUK07V7OS2dTWEeaoG0slJczC5CanyhzmI9vfs7oyS7AkWaSsSd1n6OdDBw8xNhGDszAO/P240d6+K1ttPo+uAIT1SkV8LL0Rpnd+um2AQNdkRaQRHUbioc2JOiMxpNiy7RC2PnaI4Th9J0UmoCe9iLvPfMuGJ89cpB5GZqTy5zcDOHaYAJsuUypKYUIDlZ+6yAnZ0pXpw3Ev17u+HNZ7bj7AntfaS8+P89MxAPzh0kFmhqLKLvxbmbsHrVWZ1t110firfeGd9oegwl2ND76eMP96O0VPs50hoA774/EZEjGl94iXVue3cn4aUXNmPMOD/MnhMhcvq7i84U3VgjUbMJKfJOuEnD4CoNEXGOXaFnnaMbu7f8S+/1tEWj2hzd6Prc9i5R13Gx3kxcrBv2xFVrcWiL4RUJHUdZYetu7SK2vocjwlG6RX+UIzGLsMTqg7EGt/uH2CGuwQh3fTSiHpFjuCWEnHUuRUGOdqFfn19PO1w4bfh7+EbY4dBew4sNTRkRiJhtWQa3e46wFXGThoSGO+LsCcP3d3G1gOZi46uTnrXIRXl53Sh8Q4N93JAUl29w+5errsHYqf4Gt1Ou+5efHzK4/cGHB+LdDyYa3E5tMzQab0hIqCMOHr3f4HbGOrPOWKwnaDYgVd4NNykCrtJgLtY7Ga47Gn9d0t4ZDRuzNhTrpZVwnbutSxTrPMGUMcYY64ZqetXlS73rjHUpchv7zrtQGww3qjHGGGPdkALVbUgaVHT0rjDGjGVkvaCgAJaWlq1etKGoqAgWFhaQDMV0MMYYY0a+KFKNKpSLUfVyuQDlkuE2P8Y6I1muvrTl/l1Fpx9Z12g0eO211+Di4iJWV7O2tsbdd9+NnBztiX6GREdH45ZbbhH9TfQYVKyPGjUKmzZtavd97w5oqWpVIxGExMrKcGwbkUwaP3iqn+iij6mZCgqF4ccws1BBZWb4MVSmCphbGp7oRo+tNjUcJSkeo4l9VDax3UTd+OOrzZQGoyAJTfxs7Odgaq6ERSPPUamUxETSxpTXS53R+xj1o3la8T6wbGT/mrOdMVa9KFKZnNfopVBOwIf7RmHut+MQ0McBE+/xEQsi0f9YxroMjdT2SxfR6Yv1uXPn4u2338aPP/6IwsJCHD9+HLt378Y111wjCvnG0KQBKsyPHDmCvXv3ipH1xMREsbTypEmTsH///iv2PIxR1JF03Dr6d2zaEYewUS6ioKzPM9AG76y6Cj+uvF5MLKTUkfocncwxLMITX+w4juIRJrByM9ParrZSwWmMPXbsS8Wgfh7wCbDV2k7FacQoLxTGlCEs0FnEItZHxS09vkelJQpcFfAcrDvJtNdUd7wQNQVL9l2PaXeH6BTEAb3t0TPYEQVnSzBolHYUJHH3t4bNQHOs3ReD8FFusLTSLiidXC3Qf5gb/tlyXkwitXHWfg0sbEzQe6QL4rdmY/QAH3j6aMdZ0kFC5EhvZJ0oRr+ebiL9puGBRN8RbjhXmIcinyoEDtDeTkKGOiLdqRRqEwWGDNNNeujXxxWjvL1RkVSFiJFeUKm0n6NvkC3c+9rgjrtX4bX520UKUH0X4wvw4s0bsOOTWEyJCIS9vfbP0cHRHP/3ySTMmz8KjRk5yhf/brgNvcO0c7dpfx5+dBD+/OeWRu/PGKteFMlW4dfoxUryEYV5aGgoTExMUFpaKlJgWnvWmjHWjdNgkpOT4efnh6eeegrvvvtu7fVr167FtGnTsGLFCtxwww0G7//XX39h+vTp+PLLL/Hggw/WXn/mzBn07NlTHAgsXLiwWfvCs7Lr5GSV4MN5e/D7kihoNHVvHw8vawS42eFCVC7uer4fbn2mj9aINOV5L3h1G375+SQGD/HAieNpyM+vS2CxsVDjnr49Uby7EC4D7bDvQjrS0ou1oiCDIh1x7Gga/ILtoMnWID2u3sJAkoyQYU44G5MlCkRXyQLpJ7VTZvoMcIZJZiWUKgVm/F9/9J6qneV+an86PnhyN1LjCxAU5IBYilqsNwnFyc8CSicV4qNz4T7QBmv3nUdFvahFN2dL9A1wwZlDmeg71A0nG0Qt0uIwI/t648KeHIQMdkTS2XzkZ9bFSSrVElwjbbDvcDJCejqh9GIFshLrXgM6mAgZ7oSo0xlw9rBAVlUpoqK1oxbHDfQFkjVQqZUosqvAruPJWtvDwpxRWlaFwvwyDPB1Q+LuXJ2sd401kHghD7597LB5bzyq6v2cvbxs8ObCcbh6ahB+fu84flp0FGUldaPu5vYmsOpjhp0HEnHXPX3x8isj4eDQvHQKQpnqXy8+jLde34E+fV2x6P2J6NWbF85hXVtnSoMplXNE8ktxcTFuvfVW2NnZYePGjegqOLqxe8u/9F5PfWNcm9Ng3F/ezGkwbUVFeWVlpSjM65s4cSLMzMywatWqRot1V1dX8ZH+INVHI+yE2mJYyz147Z84tj9N53pa1ZMuHy2bgsk3Butsd3K2wCefT4GdvRk++mCfzvb84nJ8tOcYrh8Zgr16FtKpqpRxdnsm+g12xZkDunnlVFRH78mCp781yk6XI12jGwd5/HCGGJFfnnsLTPT8kvca4oKvd1+Lh8P/ROwuikrUHmrPvFAMXABUkSqs3hWtc/+LGUW4mBGHaSMDcWCrdpFMCgrK8c/OGFw9OgCnNmfoPsdyGSlb8zA60gcH9URB0qH12Z2Z8OppjY2n46HvSHvzoXhxFqMoswIVCbpnn06ezAANoI108tQp1MVziKl+3SwHmmHDbt2fQ1JSPmbdsRq3jeyFE1t03wclORUo2VaBt18Zi/vnDUJLKZUKPPTIINx+ZzisrQ2vJMsYa5ua/5G5ubkoKSmBuXnzD6oZ63Ayp8F0ClFRUeJjSEiI1vVqtVqMuJ88ebLR+w8ZMgT33HOPGD2nwp5aYHbt2oWHHnoIAwYMwP33c2Zza5SXNRHz1UQbWFNdYuVVjT++pqLxk0Gacg3kRjqkKis0UDbSI04TkDUVjbdYlTexomZlZeP7WNnES6hp6v4VGr2Feo2ysiqtEX+dx9fQ69j4PjRcnKml7wNFG2OxuFBnrH3RCL+jY3X74Llz5/jlZqyT6tRpMDWTSPWdMhSnQFINL0JT44MPPhApMjNmzBCjBjR6EBYWht9++01MVjWkrKxMXOqfdmGMMcY6g7b+j6JWEroEBwcjMzNTDH5Re2hXQPtd/6OxMvbn11ayRhKXtty/q+jUxXpNxKK+tnqaXNrUZBgq9gcPHiyK9BMnToginf4oPf744xg6dKhIhKHRd31oUuuCBQsu0zNhjDHGLp+2/o/avHmzGFWvqqoSPbt09pmikbuSDRs2wJg1bOFl3VenLtadnJzEx+zsbFhZWekU4jXbDVm8eDFiYmKwdetWUajXPOaSJUtEr978+fPxzz//6L3vCy+8gGeeeUZr1MLb2/syPKuur6mc+vbe3mQfTTNy9Jv8Fm3eLnXqx78c+9DUDXg5A8baj6H/Uc3JWS9HLnr1ioSbmxtUKpVIRqOBrMmTJ3eJRBgacaZCneavUZqNseIz+k2Q27gKaaeNV9HVqX8r+/XrJz6eOnVKZ4LohQsXarcbEhcXJz7Sab76aKSdYqpiY2MN3tfU1FS039S/sGpvfz0BA4drp6iI19VChadfi8DYqX4Ge6A//Xg/Vq44jcjhXnqLuZtv6Y23vpog0mT0ZZuH9XeBSZ6EyBFesLTW/SPdu4cjBljZYdQwdzg4a0cIEjdfS/Qb6IL3pm5DarTuaeOsi8V4/Z6tKDatgkcv3Z+5mZUKYaNc4ZdphfH9fHW2q02UmDwiAOkJhegfoZtZTE85MtIbSRcKEDTaCQo9rfMOQy2xPTcZ7mNsoLbQvYFPmC0qVBpMHhYAJ0cLne09ejrhtz9uxLLfZsDPTzvukni4WuGqQf5QuinhEaLbCmZlq0bfkW7wLLJEZB/dqEczMxWef2k4Xv16DMbdFKA3s33Gw71w42PVB8jt4fixNEwa/xPuvmM1kpOufIuaeC9/tB99e3+JH747pvfsX1sdPJCCsSN/wIP3/Y20i7qTpdsqP78Mz8/ZiGGDvsXGDYb/FrLOydD/qObkrMuyhGcjd+LOwJVYcM0eMQ+M2kXpfy0Vv13hQjp6H67U82T60fu4rZeuolNHN9KIOo0UzJw5U4yG11i6dClmzZoljqwnTJhQe31WVpYYJaB+dkJxj8899xzWrFkjIhxr0AgCPe748ePx999/N2tfOLpR15/LzuDdF3chPaUIU24Mxtx3RsDdW/88gG1bL+C52Rtx+lRdiktoD0cxQnzmdCb69HXBu+9PRMTwurMXSefz8H/P7MGefxPh5mUFHzcbnN9PCS3VbJ1N4RhiiUN7UmHvYIYRIW5I3ZVduyqZqbUKDgNssW/PRbFoUdggZ1zYk43K8uqJk3TdpCdDcd283lCZKbHik5NY8sZhFOVX9wnSwUS/SDdknSpCYVY5ekY6I+V8AfLS60atfAfbY3dmCs4n52BYf09UZFQiJb6gdntImCMqyqsQF52L0B5OsIIJ4k/XJbB4BtjAydoMiUfyYO1vhnSXUuw/klK73c3NCv29XZC0J088X/tgCxzcU7fd2lZdG69oaaUWRTQlqdRkpZeWVuL/PtiLD9/bK9J0xg3xRfShzNo4SSqsBw3zROqJfJTkV6BPpBviTucgP7uuF5biMPcnpSLxYj6mXROMt98dD19fu9rth7am4MOndiMuKgd9hrvimY+GI7ivdub95ZKdXYLXF2zH998eRVWVXLtY0uznIvDEU0OhbmJxqcthy2Z6L2/A2TN178UBA93x3ocTMWiw7kFsS1HE6avztuKnpcdr38s2NqZ4/sXheOjRup9ta9Gf/GU/ncCr87YhPa0u+nTqtOqfrZ9f3c+WGWd0Y8MYxyFPJWPbtm249tprMW/ePHR2HN3YveVfeq8nvzoRNmatP6DJL62A54INXSK6sVMX6+Sjjz4Sp/pooijFNNKiSLSC6ejRo/H7779r3ZbiHGm0nRZAqin26Wvqb//4448xaNAgJCQk4Pnnn8fBgwexZcsWRERENGs/uFjXr6iwHOdPZaPvEDeDr90H7+7B/Fe26d1GBfGjjw/G62+NFZF9+vz15Wl8P/swykv1p4+ERThDjipBSa7+yThOodZILyhDbqr+U8P2nuYo9JBx9rCeOMiakeZeLji9SzdqkZiYKeE42grbN8br3U4LF02cFIDD/6YYXN44bJIr/thxFuXl+p/j+BF+iD+Ug6JC/c+x1wBnLF4zHS6u+ntOE+LzcO/k1UiMydO73dbeFH0CXXH6QIbBVWAf/mgIbry7t8GR5hO709B/VPutgJiaUoCIIUuQnVWid3tIqCMOHm3fhKdFC3fhjQU7DL6XP/5sCmbd07fVjx8bm4Mxw79Hbm7dwVJ9dFCwdecstMWs21dj1cozerfRWZM1f8/UOmhmxl+sv7S2F15++WWxwve6des6fYQjF+vdW35NsT7vqrYX66+v7xLFeqfuWSdPPvmk6C//7LPPsGjRItFzToskPfvsszq3pW20uEMNBwcHHD16FO+99564Ly2yRBNq+vfvL0bqG0ZCspaj0dzGCnVy4YJulncNKl5NTJQGC3Vi72husFAnxdnlgIFCneSnlCC3sG7xpYZykkuQUWZ4e2FeOQroexhQUVqFkgLD358WjiotrDRYqJPcojKDhbrYx9xSg4U6yUorNlioEx9fW1xMrBvxbygvpwy5mYb7XEuLK+HurD1vpD4a7W3PQr2mbcNQoU7iYqvTo9rThbjG38vxjbzXmyMnu9RgoU7i4tr+HBt7DDoTk5p6+VtuWOdGc7robDPFG9P6JjfeeGNH7xJjTZLl6ktrde6h6i5WrJNbbrlFXJqSlJSkcx0V7G+99VY77RljjDHWtVE7Iq1kSoNa1GZ6/fXXQ6ls/5YyxpgRTDBljDHGWPujeV329vZISUnBxo0b+SVnnZ7cjSaYcrHOGGOMdXM054tG18nXX38t8tcZ69Q0UtsvXQQX66zd3XZ7uJj8p09oiCMyjhXi5J40g/3uS1efgO8ge73bbRxNYelqBu/hlCyju11tpYLTQFsRk6g00X2703U9RjmjT7gLzK10u8LoMYdGusPfxQI2TqZ696HXYEeEm1nAy0d/Ek5wLweUypXw6aEbo0g8/K3hBQsM7qe/59vFxRKBNrYYMkw3RpFY2agRHuSCPz85jaqq6qSb+srLqvDD20fE/c3MVXonwA4a4QE7bzNY26v1P8chzli/7DwuJujvZ969MxH33/sXTkXpn6AacyIbr83agqM7m1512BBvH1vc879+Yn8bMjdXYWiEJ959ZzfKyqqTbuqj1+X3T07ikzl7xRyE1ti8KQ4pKQXw1ROHSfr1d8V1M3qgLYJDHHDr7WF638uUehMe7oJP/m8fKipaXkhRlsDPPx6HrZ0ZHB31TyCcfHUgIiK9WrPrzAjcfPPNYuIeRSP/9ddfHb07jLGukgbTWXAaTNtQcfHlZwex8K1dKCgoh52dGSJ7eSJpZy5kTXVRPPmOYDzy9hA4uFqgpKQC77+7Bx9/uF9MeiMT+vvB9qIJMuOLoFBK6DncGXFHc1B8KWoxuLc9HCuVSD9dPZHSK9IBp+JykZlWPSnR3c8KLg4WIlWF+A2wR1FOOTLiquPrLF3VUAWrcWTPRfG1f6gdeinNURZVvV1lo4Lc1xqHd6eJGEQXH0v09bRA5dFLE/bUEtIHWuK/A0koKa6EvZMZ/EPscXh3dYGqkCSRsZ5yKl9EI1pYmYgCL31nHjSV1b+G5hHm2HExGckpBTAxUWDCUD9U7K9ARWF1ceba1xpJZYWIO5dTfSAR4YWyM2Uoyqh+DfzC7fDAR0MQPrp60u+utfH4aPZeJMdUZ5Hbe5jD2scUh/dV71Nwb1rBUIPYM9XPwc7eDP16ueLU7gwxMZYOJKzs1Ig+klWbCnPHc31x2+w+MDVTISW5AC+/uAUrlp+qnWh63wP98eK8keJnXJBbhm/mH8KqL0/VRi1OvCUQjy4cCmfP1q2WeOTwRcx5ZgP270sWXw8e4oGkxPzaiZH+AXZY+O4ETLk6qPr221Px4ZO7EXMyW3zt4GqOh94YjKtnhTS9ANelJJ0X527Cn2uiLz1HCcMivMR+FBVVwMHRHK8uGC1SYPQdSLTGvr3JmPPMehw9Un0QO3SYp5hAm55evaIhHfwuen8Cxo33b9bjHT6UKl6zA/urYz9t6ACvjyv27kkSP5eAQHu8894ETJoceFn2n3VcGsww6U2oJN01JhpbIOmzfdPh7l49WLB69Wp89dVXYr7XN998I0bcCW3vLAsmcRpM95Z/6b2e8PyUNqfB+Cz8t0ukwXCx3kxcrF8etLjLwld24sTqiyjJ0U03sbQxwY1zw/DRN/uRmKi72I2ZWonbhvdCcUw5Us/rpptQQTxslDuyc8pw5nhdDnZ9vQY7w1xSImaf/u3Ofazh6GyC8q25elc4Mw+0gFmQBeQ96ZDLdW8gu6kR11ONPXuSUXjpQKI+W1tTjB7qi4KDRShK1x3lVZpJUI8zR+GZUhTE6SaDSErAZ4wDSi6WIz1K/0j3qFv8kFZQLDLq9fHvZwfJRsL+HXWZ7Q1HeAO97XFka6o4MGnII8Aaw+/xw6L3d6NQT9KOk7MFXnhiOFZ/cAq5GbopM+ZWJnj2k+HiAK01aIzhxx+O4+svD+HYsXS9t7lqciDCrByx4dcYvdt7DXHBu2smwc7JcGGz5JsjeOG5TSgp0R2td3a2wPTrQjBv/mg4OFz+qDs6WPp68SF8v+QYok7qP2Mx/doQ/PTrjEYfhxY++vLzQ+LxGqIi/eaZvUROvalpl8gb6LKuVLE+AHOhlPSfBTREDWtIUnUhrpErcUb+AeXIh7s0HK7SEFHQr0x4UCwm2Blwsd695dcW61fDxrQNxXoZFev/dIlinf86syvK1c0KwY722J+jv4ikBYk2/Rent1AnpeVViMnKBc7rbwPQyDLOns9FVnL1CKQ+pw5kwB6Gf8EzjhfAy88KeupwoSSmGJ7OJsgwcAPpYjngq9RbqJO8vDLIRRq9hTqpKpXhlKlGapz+THS5CiiPr0D6ecMRe3vWJCKtzPBrEHc0FxoXg5txLjobdiZmegt1khJbgA3rYvUW6jUL++z6L15voU5KCiuwf0NSq4t1GhGfPCUIjz38r8HbrP8vBtlqw6/Rqf3pyEwparRY37g+Vm+hTjIyiuHvb98uhTqhUfoJVwVizjOGJ/vVjPY35p+/z+kt1ElsTI7IVOdC3XjYKHxalLOuQwJ85EmI0axEhnwEHtJIqMELZTHWkTrHOS3GGGOMdQqO6A1LyR1VKEOKrH8RMMY6msxpMIwxxhjrjqglxlsaLz5Pkw+gTNZ/lo8xdmXwyDpjjDHGtNhKgbCVAiCjChfl3fzqsM5Hw9GNjLUKJWd889VhlJfr7ynPyyqtTkKx1j9dQm2uhK2VGq7OFnq3U9rIsLHe8AjWH5NILINM4dzLcNJI4AAHeA403IPp2tMasj/1fOrv87X3M4e5o1pM9NS7j46mcLewgqWF/r54SysTKKwUMHfQv11pIsHG3sxgWgoFmIT6WCMoRH+EIOkd7ogBfQw3pYf0dkSfYMPbvf1tYOmmhqHwBxs3UzEZ2NRU/4tgZ2sGtYUKFrb6n6OJqQLFcgXSkvX3lFNCzY8/HMPZM5kG93HP5kSRXGNIRLgngsIdDG53D7bG/p3JYrKqIaPG+EKt1v8cHZ3M0X9gdeqOPkVF5Vj8xSGkp1WnCbUU7dfBrckIC3U2eJt+A9ywaWNco48zfmKAwW2BQfbw8+d+ZKaftzRBfMzBGcTE6J+ozVhHkeW2X7oKHllnlwXFK7795k4M7v81nnlyPYYO/Abr/ovRKr5WfnEKt/Rajr+WnIWpuQrhEdqFVvAwR1Tay9j2XzysC00waXgATFR1b9FRo32wa989eGnhKHx6fDrueqs/zCzrin7HEAso+6mwcWccdkcnw3GkFSwc63LDHT0tEDLECTGHs3HyUAZch9jCzrtuIpaFgxreIx1wLjoHW7emIDfMEhahdQWz2kKJ0HGOkHKKkbDpImx8rODSt67QkZQSTIa64GSeBmfWZiLS0g3jh/jWbacCMsILHubW2PVfIhKkIriNstMq+v0H2sPe0wJR6y4CmVUYNModarO6G4T2ssOEXs7I35IF29gyTB7lARvbuudIE2NHDHBD/t48KA6XYvpgX3i41z0HB2czRAzzRNbJQqTsyMPEPn4IDqjLsLe0NsHgkR5ITSrE1k0XYBVsBp9wW60DCc8xtjhSkoH/1seI1JdBg9y10njGR/jBHRbY9t8FFJiUI3SEk1ZueNAQB8BFwqpfz2By2I9Y/M4BrYO7PbsSMXr493j0oX8ROWSJiE3Mz69LxTkXlYVZk1biuVnrkX6yAJOGBmgd3Hm72eCGQaEoPFKKqH3p6DHQCW6+da8BHUAEjXTEqbgMLHhqK24esRzHD+rP+X/okUHYc/B/mHBVXcGrVEq4/8EBOHz8QYwcVffzrY+iLAf1/VrEJQ7o8xU+/Xg/Kit1M/ANOXUkHbeNXYFXHt6MnDPFmBIRAMd6E1k9vawxcJA7jh6+iOuv+Q233fwH4uNz9T7Whx9PwrqNtyO83sGblZUaC14fg32H7oM/F+vMAOpbd5TCxOdLlizh14mxDsLRjc3E0Y2GrV8Xg2efWo8LF/L0LrLyyD2D8P38Izh3TDcqMaC3PdTWKuSUlSDqqG48nZe/DWx8zXDnA30x48aeOtsp9eXb5w/iTHJWbWZ0fda2agwKd4e9bIro/VkoL9Ue8adCmBb8kWQg+kQ2inLLdRI5+ke6wE+hQFliIYouVme21+c22BFFsoTo5DJcjNEdKfbsZ4s0i1JUFmsQe+JSJns9PsG2CHCwgapCQuyB6izw+uw9zWHnbw5XSYn03Vk6A/7mDmqoellBhoTUPTmoKtcuCk0slLAfYoMcqRIJh3NRUqCdbqJUSfCPdEA6ShAXnYOsDN3nOGiYhzi0P34xA/Hxuj/nfv3dYGmiAnJlxJ7WfY5BPRzgbGuBwopynDisG7XoF2SHp9+MwIo/z2D5r1E6213dLLHgtTG4cCwHP39+XKfwpbMVAQMcYAkTJB3IQ2mx9nM0USsQNswFJXIlTp7KQG62dkoNHUzccHcvPL9oJKxt9cferf0rGr8ui8LcF4cjLFz/WYlz57Lx1GP/Ycf2BJ1tPXo64f8+noTIEd4wpKiwHIue34Xl35zUSXCxsTOFTx9blCiqcPBAau36AzXMzFR4avZQzH1hOJRK3XEYOmBe8s1RHDyQInLhPTwNn51iXTe6cYzis7alwTRQKufgmOYj+PexEyubDhkyBB2Noxu7t/xL7/W42de0ObrR//2/ukR0IxfrzcTFumHXTPkF27bGG9x+47AeOLtTf040CR3lhF079Ec5kkkzgvDxr1cb3B51JB0zhv5qcLu1tRruufrbampY2atRmGN4Zcvr+jkg76z+OElS1s8Jp6iQNsBljC327qhexEef4RFeSN2pf2SUBPe1Q9UJw9/fwtUMKSn6YxJrJJhVoLJBIV9faZCMBANxkcRzsC0OHja8AumUyAAc3VG9oJQ+tErqwZ36c91J8CBHbD9i+H0Q6GeP4hjd3PkadBbGtqLxfGnJXYGMi4bbUpZumIGho1u/gietoPr6/O0Gt9MB5/c/XmtwO43w3xT5m8HttEDVuQLD7xOSkPqUWJCKdS6ddVGk5oiX/4VVj2SEhobi448/FnnrHblAEhfr3Vt+TbH+zPS2F+sf/NklinXOWWeMMcaMRLmcLyIXLycnuT9iTsci8fRBjF/zLLYkftBpFkhirDvgYp0xxhgzEm1eFMmAcs14JMjrkSEfRXGx4QXXGLtiNBJkSoRpw/27Cp5gyhhjjLFGuUqDYQZHVKIYv/5quO2QsStF5kWRGGu+xiaqUXKGq7fhGEXi7m2tlRbSkKlaicoKw73WuemlsLAy3Lfm6mEJOxfDPZzWjqawdjbc62xqqYLKxvDjK1QSVFYGchwvTV40FONYw9xSCYXS8ItgaauCytzw97BwUsPc3vD3sHYxhYOb4dfA3NoE9vaGt6tNlbCxNvwa0URcK8u6VBp9LC1NxO0M7qOV2mAUJHH3sIKVjeHv4eJlCUd3w3MTbBxM4WogDpOYmauQm2W4758maB45bLgnn3h4WDX6XlabKBpNhbGxNYWFpeGfo6OzORzqpcLobHcyR0Zq66IiSUVFFY4dvdhonOShg4bnLTDjpZBU8FFMFJ+vXr0aiYmG55cwxi4vHllnbfbl11PxxVdXw8VVuxCKiPTC9t134+2lE/HumknwCrLRSYL5eMNUvPn9BPyy7Sb0HqCdsOHibomIoV7Y/UsC7hrwB/ZvTNLanp9divce24kXrl0vcs0HRXjoFF+PvzIUKw/cii/PXIdrHu8hkk9q3/xKCX6jHbBbSsWa1Bh4jrETOe/19YhwFpnwK3emQzXUCaZ22sWibV877HSrxDebo2AZYQ47T+1CyiPUBiG97ZC3NhvjwjzgE6Sdje7mbYWrB3kCG3IQFmQFv3pRkMTayRSjJtjD9kwS/Pw18I2oi1kkaisVfEc6Izu6ALZKIHiUE6R6BTFFLYaPd0SgZSEiNcUYNd5FZJzXoMKyz3BX+KvN4H4ImDrcX6cgDh/kAidXc5zenI6xQ3zh2uDnHNbLGdMCApC+Nh+j+3rD20/75+zlZ4PB/T0Q9V86woNdRMZ7fU6uFug3zB2Ht6aip7MDBg10136NbU2x8N3x+HPdrVgXdReuv7OnVkFMBxIPPT8Ya4/egV+ibsKtz/SByqTuOdIBwrX398Cvp24W77PZb0bqHNz1HeIKWwdTPHnrP3jxgY3IStc+zb9rZwJGRnwnIiVn3rgCcXH6J3nefmcfrN90B/r2c9U50Bg82AO//hKFiMHfYsvmC3rv7xdsh39P3IGpN4doXW9mocKQUZ5IiS+AY4Uao4f7igPh+usP0HV2JWrcMPRXfLxgL0pLtNNimrJ5UxwiBi/ByIjvMev21UhK1J7QTAcqE8b8iLEjf8C0yctw+pThSePMONkhBNbwRWVlJd56661G1yhgrN3JUtsvXQSnwTQTp8E0LS+vFG+/sRN//xmNVxaMxs239NbaXl5WhV8+PC7y1m+f3RczHukliowaFFX3+5IofPrGPgQFOuDCgRyUl2hHLY66zg+PvTMUBzYm46tXDiAvS3silW8fW2SXliIkzBFzF42Ep6924Rh/MgeLn9yP3JxSnCjPxLEY7YLDz80Woz28UJ5VCVMLFeKOaccQ2tmbYGx/J1QkFiHOBVi1Vzuiz9xMhckD/VF4thQ+QTZI3pmpFbVImepWI21x/Fwm+oY6QrWnAJpy7X949sMcEBdfBP9QC9jEZUAu0k6psezhjLRUwNLFDNmxRShK0x4Ntgu1RrlCAbWZBEdNISqSG6TIuFkixsISeXmVcFaYIOdkgdZmlZMKeb0ViE3OhYOzOU4c1I5apGx7v0EOOBOTiWE+HsjeWaD1R48OiFyH2+DkuQyEhjjh/O5MVFXWfxFkhEQ643xcNnyD7XDyUBpKirQLy+DBjojPysfI0T4iD9zZRfsA4cjeVLz+1DZxQPfieyPhE6h9kBN/JhcfPr0bpUWVePr/IhE6wElre1pKoYhIPL7/Iqxs1Th1JEMn8vPxV4Zh7LX+ePXlrfjj99Par4GZCo8/NQSz50TAQs9ZE3ovf/ftUSxauAuBgfYiLrG0QWzotdeF4q13xsHbR//iVvu3J4nnaG5hgtSkAqSnaI+YewbboNxSA0mSYFIAJJ/X/jl6+lpj7jsjRZpSYyif/cXnNuOvP6O1rqfn9cycYbjjrnAsfHMXln5/XCtOkn53KW/+xXkjYGvL6TPGGt3YUJ4mDvLAf0Wh/sorr2D69Om40jgNpnvLv/Rej33ieli3IQ2moKwCAR+v6hJpMFysNxMX681Hf8SpgGjt9jVfn8aiR3Ya3B7S3xHRRwzHJLr7WWHFuVsb3Uc7y3d0cqzru8E9CJnxhidRmQ9S49gR3bzwGndGhCJju+GIvV5jnJCyw/DqnIHDbVBx1nA7gqm3Dc4dMTxyqrZUws1cu3hrKFZjh8IMw3GV8f2qcDrK8D5OGRqAmN26ufA1Akc74sCORqIaIxywZ6/hOMt+w9zw2/abW/0+ao5rBvyM6JOG30s+w+yx74DhfXzz7bF4/KmhBrd/vfgQZj+1weD28RP9serPmQa3x5/PwVW9fjS4nZ5+U4ObG8/Ogre/4dVur536q8GRfhI53Bu7dxlueXjqmaF47c2xje8EM5pinXLXb/xAiR9//BHW1tZYsWIFHB21z5a1Ny7Wu7f8S+/1mMdntLlYD/xkZZco1jkNhl12TRVQbS2wGi4K1KqHaOPpW1qAqDFtPbkmtfXxm/X82vY9mtrHdv/+bX0fXYb3SdMvs9TB92/696XJ59jEA3AnROdSKudChcbXXGiLcuRi2LCp2L59O86fP4+5c+di/vz5tb+P7u7uHZrBzroPWVN9acv9uwou1hljjDEj0R4569okzI7YgVKEIk4+hNjj67BzSRmcpf6ikF+Z8CBnsDN2mXGxzhhjjBmJ9spZb8gcjvDTTMEF+V9clPfAUeoNNbTnjjDWruQ2ThLtQhNM+VwVu+zKSluWQtFQ/SQPfRqLOCRSvcQXfaoqNVCrDEcEkvpJG63Z3lhEYfX92/ab2azXoLF5AyJyEu36HJt+DZq4fxPby8urGm3hoDkJFEXYlu+hVLbtT6RJE+9lZROvUVUj8ypqHr/+JO3WPIayiZYFkyYe/wp0I7FOykUaDHspFBpU4rzmD1TJhufAMHa5yZyzzljLpSUX4pk7/sNw72/w3f8dbjRPujFXzwrB3C9Hws7ZTKcoCB3phBNx6fAaYgsnL91Mba9htjhRnoVJ43/C8WNpOttPb03DywP+wx1uoRgf7quzfUCgCx7qEQ7zEiXCh2lHSRJHTwuEDHECzmowYbi/TrHl7GCOWRE9kH0gH0GjnaGqF5NITC0UGD7VDqqYZPSbYgsLe+2KmdJiKIox8VgxFCGeUDvpPkeXwc6Qi8rQZ7Q5XEJ0tzv3t0eFrRnS7VxgGqQ78Uvtb48sZzcoVAr4DNaOgiTWvuYwGWIFdYICYyN8dbqm3Z0tcfeQHlAeKcGIUR46B1dm1ioEjHbEsQNp6DfKDeZW2s9RZSIhdLQTjhy7iAGR7iI7vKGBw90ReyYHD1z7Jy6c052ou/avaAzq+xXGj/5Rb+733t1JGBX5HYYN+hYbN8TqbD9zOhPXTPkFUelZ6BHprLPd2c0C/Ya6IfNkIa6K9IeqQdFuZ2uGKZEBWLLgED6ctxslxRXQ5667++KjTyfDwVH7OdL7ZmKkPxL25OCxm9Yi6UKDxB4AK1ecxrSpv8C5n5VIfmlo2FgvrNp/q7gMGa279LtXiA2c+lph6tXLsOqPMzrbU+Ly8fwN61GyvxTTIoKgaFB1O/0/e+cBHkd1ruFvtvfe1HuXbDXbKu6FFmpoCUkuJJAAN40EQmgJ6YSEksolIRUSQmgJBEJ3w01usiXbsqrVe1ttr3OfM2trtdoZySAbXOblWWTp7MzOOXN29zvl/36zAr9+4mI8//I1uPveGsYBZyYKhQjLV6Tg90/ux7e++Q4mJ0/fPmmeMxOyTz2DuhwSqOHBCJPhlLdz5OE59fBuMCcJ7wYz9wznX35Rj/97aA/crqhoycrX44HHV6FmXeqH6pyOSR/+8L19+NeTR5BUoMFkwIeOlqiVolQmRMXSRHTWjUOdJINdF2AE4MyZ25tuLsV3HlwJuGk8e1c96p6PtVpMqtbhjf5OODx+XFOQg6FtUwiHojORqaU6jLt8GOpxIq/KjJbdozF2kgm5atjlfjQeGcaVVVkIH/TCMxltA12yHJpEObp3j2Pxai1k43Z4hqIuM1K9FOI0I5o2TsK6SAe/I4jxdud0uVgpRFa1Ft4jA1ClKCGUCjFxZCJmBl1fasPRHS4mcZPIJmfaYyZ5KwxQjIwBYRreBDOObh2LCQpMqdTDNerH1LAX6qUaHNw1jKA/OtBKKtRgROBBc8sYU0f/fjd8U9HVE326AgGTCA31w8iqMaKtfQITw1HhprfIkJylQePOYWRWGNA35kBfV1ScKtUSFJaZsX97PzJy9cysf3tTtA5iiQCf/3oZbr9vKfr6Hfj2Xe/i3bejApxozM/duAjf+8FqZoD4nfs34Z//OBzTBp+4NAcP/Xwdk1DoJz98nxGYMweTJcUWyDwCDHU6UVaVgMZZdpKpWVqENcC+xkFmANN/eAqT49E6JqSocPdPl+OSa2P90U8wMeHFD7+/FX/+Qz0WF1gg9QjR2TIZkxPgi3dV4IvfqkB7+wTuvvMdbN3SHdOXl1eloL/BDrVaylgyXnxNTsxr/Pf5Fjx8zza4nH7YFmmwfVcPQjP68qrVafj5YxuQka7D0w8fwLOPNsA/w04yJU+DEakX+5sHccuXynH/d1dAp4sOmDs7J3HPt97Ff19rw9JliejusmNw0BUj7h/8/ir8z02LPpLg37ONc8UNhg0H3YOm8F8Rhh8/eOyb+MY3vnFaX493gzm/OdHXW267dsFuMLlPvnBWuMHwYv0k4cU6N1+97nW8/e92zvLH/34Rp4g5Gf7zbDPuuuktzvLcUiO2N/VxzuSnJWmwbMgM3ywv7xNIFEKoTFKMd7NbNRLfcH2pGm37uC3+Vq9KQvcmbpvD5VcaMLUzNqnTTDRLknHoDW4bxMxaHUJtA6BniK+ZyNM02NcQRtDL3gZStQhimYDTqlEoESCQL0XnIXa7SaK9VtYkoud97jZQXqjDlvdiB0MzKVlvxab3uC0CyWz6/h0DnO4i5D7vbO5nBodc2TuJl7/DwV5HMjOcmqZFSzN7HYggXl+Wjoa6+BWZEyxbk4y6Tdz38VsP1eKWOys4y1965gjuu/ldzvLiKgs21Xdz9mVy/Xv238J4r7NBBsuVpb9Hby+7bSfZMvPJxbloquNOaHTvX1bi0s/kcZZ/94FN+MWjdZzlX7tjKX700FrO8vOVc1msE4ZJsGn4FWQtNuKJJ55ATU3NaXstXqyf30ydEOu3Xrdwsf67588Ksc7vWedZMFP2uZ0HHPaF7WMUSebupm5PYM4tN1N2P6dQJ/jdIfgc3OUkoY/fPfc+/DCHSD4BHZi7POSbuzzoCXEKdYLfGeQU6gRSP499jjr6w0wCIS6IgA555r5GIpQXUu7zkj3oc/czLqFOsE96OYU6wesNYsrOvVWDzELPl/UzME8dyGrQXMgkcwcK2O2+Ofuyw+HjFOoEhVI8ZxuQczsn534/yue5xvksO0kdeM4/LFQFjFQJsw3m/vvvR1dX18d9STw85wy8WOfh4eHh4eFZMElYjYKCAjgcDnzta1/D+Dj3aiEPz0KhaeKVTn34xynI2fJRwVs38vDw8PDwnCOc7qRIcxGknPjSl76Ehx9+GJ2dnbjtttvw0EMPQSaT8cmSeE6bG8yHZSHHftTwYp2Hh4eHh+cc4fQnRZoLCt9dvx8+LEUX/Rw6GnZixwu3IJFajn/13MYnS+Lh+ZDw22B4Fsw3flCNojIzq882CRp88c+Hsf1d9sDD115twfrVz+D552LdO06wc3sPfvHbXShdYWP15SYuHRqlFGuXp0Mujx97JiSosDo/BRkrjFDqJXHlCp0YeSvMsOWpoE+KD8qSyIXIXmVifibmqOPrKKRQtMKCQY8XtkVa1jrkLjeAnnLDXG5iLTeWGCDye5C1ygCKxXfbmKeGIySEpjIRgllWkARlohICswb5q0yQaeL3MytNEpSsNqKwWge1VRpXLlWJUHmxDsuzKSRkKuPKiTVj/kozukIeWIo0rMGnGbVGqMdoFFfE9wNCUYUZ2nEhqquTWX25CwqNWBaW45KVaaw++ynpGuTpDbisNhtKZXwdLRYFKioTUVWTDOMsm0SCRiNB7fIUJKdomEdcG0iFuOvbNbj/0ZUoLGXvy4uX29DndCK/lP0+brgiC9d/sQRcbN/WjV/+H+nLCax9ObfYiO89uhr3PrCctS8nJqpQmmXF97+2CfaJ+JlT57gPf759Dy7JzUBigiqunJyTWC3aNX4kZsX3ZXJNxSss+MOv92Pv9n7WOpD36cb3jqFySQJr+eIyKw7UD+IvfzrA+NzzfDxJkbSC9I/tIRcYoRNkIl/wWQghxRSOYRQNfFfgOfXQp+BxlsC7wZwkvBvM3JAv5uf/eAiPf3cnJse8yCk0MH9rPxq1Gbzgyizc8/MVSErToLVlDHff9S7ee+fYdDkRU8RarrjEgsEBJx64b1OMiM/LMiJBrkDzwTGoNBLkLzahfsfAtD2dKVEBRaoUdXv6GPG1bkk6fDt9CB4PjFTrJcgrMqJ9e8QNJKfWhL7DdrjGIwF3YrkAWUtNaK8bQ8AbQsZSA3r7HRjpi7jEUAKgsNaCzoYJuOwBpC/Sw+8Jor816rxRWWVFoMMLx6AXllwV0ixh+FqjLjGGEgMCU344upxQ2BRQJiowsj9arkjXY8InxVDjFGR6MfSFWhzdNgr6eMyhNUOGvGwhJhtGIJQJoS624sjmqengUrlJCmWOGu3bRxnhn7fCCH/zGILHrRZFSiGkRSY0bxtDKEAjf6UeZtcA6MmIBR8tEmAkKwWbt9nhdQaRWa7HoN2N3mMRq0UiMVdXJSN81MvYPVrz1Yx95OChqBWjZqkaR0btGOx2wpaqQoZZg6Hd0XJtiQLHwg60NY/DYJLjonwbVLunpgMX6TQZ9qoD2L9/CAqVGMvKEjG4bYoJ9GXayCaBKyeMrXXdTFKgZVVJjEB0OgPTwrx4kRV1O3uZPlhVnYyWljGMjXqYctI3lixNZPzZPZ4gLrokCz/9+XpkZkY858kx//zDIfyC9OVxL7KLDJigfTjUHHVQWVGZgqkeL4b7XcjMi1iU1q5ntyjt73MwffnF549M/y0/2wirRIGWxjFodFJ89bvL8JnbF00nYSK2iPfe/R7+82oL42JTU5mMjt1j8B23DdWbZPjGD2pw7ReKmN83/b4NL36nAc6xSF8WyCmEqsV4a28HfL4QlixJRG/vFAYGIragxDf+E8uyMHTAAddUAFmLDRhxu9HRFn2/XvbpPMaO0pKgxKHGYcZLffu2nuny0jIrY0nZ1WlHYqIaiUkq7N0T9bwvK7fhkccvYNqa59x3g2FjjD7EJEuiEcLPnrgPt99++yk5L+8Gc34zdbyvH73leqgl8ZNwJ4vD70f+H/55VrjB8GL9JOHF+slBxM0P79iM155rYS0nftJX3FqA3z65BwEWhxQyu/el2yrwt6cbOF0tPrE6C8cOTmBi1MtpfyfppeDsYl8KTs3TQkWJ0N8Un4iGYEyJiP7GHcOs5WqjFFllBhx4Nz4ZD0GhFOET620I1HUDLA4uRNwmr05C/7YBhGb4XJ+AHKFaloaG9yfhmmBPtlO6Tg97uxsTPex1NBZrIQ0F4DrGbuEnT1HBmh5GqJV9BjWkV+KAwYz3N/axlmvUEnyiKg2tbw6xzk4IJBQMFxjR/s4IQn62JwDZFxug3jsJONgdVnzLdWhomIJziL0fGJeosM8+xIhFNrKy9cxs8qFGdptCsuryo4fW4NrrI4J3NhNjHtz/jY34+wuNrBMwCrkYX//fpbjrwRqIxewpaTdv6sQN170Mp5O9DtddUYCf/XoDDOb45FaE115pwU+/uRUjPey2okXlFhQGdOiqj4rsmcjSJOhKc2NXHft9tJmUWF2cii3vsTt3KNViXPT5XPzfk3tjPNtPIBJRWLs+E1s3d8LL0pfJKgrxa7/7nlqc75yPYp3QH97GJEsilo6/+tWvsHLlygWfkxfr5zdTx/t6082fWrBYL/jjc2eFWOf3rPOcUnQG2Zz2dsQaj8yCsgl1AhEEpHwu+7nhcTenUCdM9nih6Obe4dXdbIce3PZ3Yz1uOITcFn6OMR8c49x7Qt2uIER+LwIcVot0kEbAGWAV6gQyvxxweDmFOmGgKwAfh1AnTLU7oKC5j/f0OCFU+8B1p4QTLoyElNznd/jhmvJzLiOG/TQkDmIJyfUEwOig4OcQ6gTlaJhTqBNcx7zommIX6oT2tgnIFdwfcWSWuaCQfdsOQW+UY9Lr5VwpJZahSqOEU6gTWlvGOYU6YdTl5RTqhJIiC6dQJxzeNwzlHJae3i4/+kTsAzbC4KgLQ2PRxEazcTkCOFg/xCrUCcEgzSRhYhPqBOK20HCA27ee59wngaqFCyR/Qh/uvfdePPXUUygsLPy4L4vnHIA+jwJM+T3rPDw8PDw8PKcFks02GWtRUVEBn8+HO+64A/397Ct6PDw87PBinYeHh4eHh+e0QVEC3HfffcjNzWW817/+9a8zWxl4eBYCvRCP9eOPswVerPPw8PDw8PCcVuRyOX7xi1/AYrHg2LFjuOuuu+D3Lyy7Nc95Dk0t/HGWwIt1nlPOJdflMm4VbGTk6nHtp4uQlsZuc0gs+Ij7RXZOxJljNga1DEu1VixdxG4dJ5EIkZtjREaVgbWcOLroV6kgXiXn7P22Ki10WQqIWWwSCcWLjcjTK6DWsge25OSqIRJIIDWzB3nJrEr4aQmUKfH2eQShRgqvWIaEYvaAF7mKwoq1DpRfzL5XWiACyi+SouBCJQkhZ31O/nolTEVqCMTsH1bGRQZcXiyAVsO+5zs3zwDIRVCYpezHpylgkgpgS2Pfjy0zi9Ei9yGUy14uUArhJoG85ez3USimYCxRYtVSdgcWQnVNMioq2G0SCRd/IpvVxvEExE0mFKah07P35dJ0M2T7AhjvcnG6wOzd088EurKh10iRIlLhwPvsgcrEkajxmR5cuCSNtVxAUVi/PAPpy42RQAcWCqpMuCo9A3IJe1+pKklCkkENtYa9L6fn6GCQSJFgYY9fIO/jT91QhNw8I2u50STH1dfy+5M/6qRIXnrijHr4MYmBgQEmMJTsWxeJRKirq2OynHZ3d6Ovr495hMPssUw8POc7vBvMScK7wXwwiA/0L7+3C8/9vpEJTiOuEv9731Lc+LVSJiDP4wng8Ud34ReP1sHrDU5b8NXvH4TLFWAE1rLqZMYybsruY7TIdcvyIDoShnM0MhuTWqXHzt5+9A1G7OgqKhPgHQpgqDsinvJKjYCdxkh75HddiQLNmMTR5ohVYn6uEWWUCe6GSLCqLlMOj4FGU32k3JKsRLJVjc49EacNi02OigwdhnZFUmjLjRII8pXYtn2QCaTTaMVYW2HE1O4x0CEaYoUQSUv1mDw4ADoQhkAqhKbYhr7dEwh6QhCIBUiuNsB5dBghd4CxzhCVJOLoXifcEwHGSSO92oiRViecI5Fg0lVXCJCpaENoPLKELEpLQ90OE/qaIgF+OTVyJGpG4e2NXLMiw4TBYQ36D0ZsC21FMiQlu+BqjzjdyJO0oGUqjB2M1EmRoIA2VYKpwxEBKTYo0Kw04x9vRs6nN8hQnm9F5/Zx0GEaco0I2YsN6N8+CjpEfOkFyF1mxMDuMYR8YQilApiXGbGvbgQ+T5gZSKhrVdjZ0Aenw8/4l6+pSUZ2awCYiAT1CiqN2Ndix+RQ5L7kLjVhrM/NPAgJFRq0T06itzPSBrklRgz73Whqi1hy5uUbmX2yR5si9zErSw+FUozGhkidiXh++JH1uODCLNa+OzToxHfu34R//uMwc191OhkKi03Yub2X+d2gkuHG4jw4tjsY+0uxXIj19xRg3d35EMuE8PtD+PUvd+ORh3dM92ViH9nYMISpKT/Tly+uysJUkwf2sch9XX99Fr7y8DKYkyKiuPGVPvzrG/UYOxbpu9ZyLeocIzjaGbkPi4ss0Puk6G+OBI+m5GuhFIqmLTQTMlUwaiTor59kftemytGZ5MO/69uZ30m/XpaSiJZdkTbSmmSw5qmwc0cPE1Cr0UqQt8iE/cetUYmFZna5ERvrOhEIhhmXnTvurMId31wGuVyMQCCEJ369Fz/76XYmOJzU+Qu3lOGBB1dCzzHYOd/4qNxgyvFtCCn2QfTHiQRqZjsMwUH3oIP+F2PpaKCKkIINCFB2vNx960klTuLdYM5vpo739UP/89kFu8EUP/23s8INhhfrJwkv1j8cRw+O4JVnj+Lzd5Qzfs2z6eqaxA8e3Irdu/rQ1RXv7GEwyFC9OAlJPXIMHY53tZAqhTBWqzHi8KJ5b9Sv/ARCEYWy2gQcC01hS10XI7Zms7YqDVkCLfbuHECQxaWmoNyMYq0azroJBFzxrhfGAg1EKRLg8CS8x8XXTDRpSphzFRhucsHRG+/sQWanTYu0aG3xYfCoM76OGhHyajWoSG5FqDvqc30CSiSEL7EIPocA7qbe+ApSgLIoDTQoeJq6mIFE3DUWJUAgk2DqUB/CvngnHEmWGbs8VvTscsA9Ge8yY81WITNdBffRKTj64516VAkyBArk2NIziM72yfjX10lxRWUKxgZCaGuItyEkSalyq03odNuxf89gXDkRhyU1VkzCj107I6J6NkuXJTICnYhMsgLDxtN/OYj7vr0RU1Px9zEn14CqRCu0+2m4huPLjRlKLL4vE9/75fuME01cuVGOikUJEPcC3U3xfV2uFOGW+8oxuWkSTW+y1FFMQb9ci/FAEG07IgOTmZDBXVG1BRaRGN3bxhA+7ks/E/MSDToNPhzbNQGvK/4+pxfqIE0Q4/CBYcaGdTbJGRrkVplwzw+WIy1NF1dO8iOQgQqZbS9ZZI0rP585X60buZigm9Eafh40wrBR1bCgEv/s+RQv1mfA646526Xxc59bsFgveeaZD/yebGlpwfPPP4+DBw+itLQU999/P+vzbrjhBtatXmvWrMGXv/zlD3StvHUjz2klf7GZeXBBvvCLisx44Z/RhDEzGR/3QjRFsQp1gs8VgrcngOa2eKFOIIl0GvcPYa+H3TOdsHFXF7zqBFahTmjaP4KSfAmrUCeMNU0h16zHEItQJ0x1uaBJUbIKdYJ7xIfJKbAKdYJvKgihx84q1Al0MASpvRMTzRwfWjTgOtQFSixkFerMNR4eYGbZ2YQ6wd8+AoHCxCrUCUNtTmQnKViFOsE54IUvR8gq1JnXn/ShZdyDiQb2LSV+Twh9nQ7s74oXsQQy+3to5xB6aG4bwt11/fjLM1dyCnXCc88eYhXqJ2wYr9CmYmyY3S6SzIL/57mjrEKdKR/zgHbS6Obw9/e4gnjv6TYoDrPfAzKLP1XvQtsUez8hA5RDO4ZRqFKxCnXCyJ4peIsErEKd0HlkEqkaHatQJ5DkWNffXMwq1Am2BBV+/NO1rGU8PDPRU3nIEFyGjvArGKR3gqbI5++n+EbiOaNZuXIlhoaGcO211+I///kPHA5ua9yXX36ZEeY333xzzN/T09M/8OvyYp2Hh4eHh4fnI8dMlSJEedFFv4VBegdef/11fOlLX+LvBM9JQdORx4flwxz7u9/9DgUFBcy/ScD0fGRlZeGaa67BQuEDTHl4eHh4eHg+FmyCKiRSK5h/P/HEE3j77bf5O8HzgZIi0Qt4fFBOCPWPmrNmZr2hoQHt7e0wm82oqqpiosk/CMFgEHv27MHIyAjy8/MZv1ceHh4eHh6ej5dkag28GAdN9+K73/0u1Go1qqur+dvCc9azc+dO3HjjjYzTEdGdZB87mW0/52bWR0dHmT1Ca9euxV//+ld84QtfQHZ2Nvbt23fS5yCBAMnJybj11lvxl7/8Bddddx0+8YlPMEEFPB8vxDljfNzDuEuwQdxC8ipMUBm4g0hUCVKYErlTtqdkaZGdxW6fR8jM1MGWwW6jSDAlKCCxcrsryHRiCNUSUAL2UbpQJoTKSDPOKGxQQgpytYgJJOV+DSVEOhVnucRsgDyRO0BGma6HKl3HfX6rGhITdxuIdXIoDRImiJENEugrUIk4rSDJ37VKKRNEyQY5b7JFAo2Ju52tiUokJHO3QWaenrHt5ILYDB46NMRZPjrghlnLblFI0GikoDRCCDisIEVSATRK2Zx9mdh9qnTcfdlsU0CbyO2eYshSwpbF3QYJ2Wros+eoQ6IMBhv3e0WtlzDXSK6VDZlcBNe4HwE/e/zGqWDrli60tUXciT4MHR0T2LSx85ReE89HkeV0DfNdTybWvvWtbzETdDw8cxKmFv44HrA680Ey7Z4KdDodM+hctWoVE4hK9rAXFhbiz3/+87nnBnPRRRfh8OHDjDgnyRTIG/mKK67A/v37mYhcMgKfixdeeIEZyTzzzDP41KeiwStvvvkm03g2m+2kroOPyj71vP1WO75917tMQF5iogpJyRrs2R1NQ02sGB95/ALmp2PMhxfvb8CmP7QzloEEY7oCdnMQO+r7GIG0tDIJLXVjCPgiQkJvkSM5S4PGnUOMZWBijQ7bD/VOBxCq1RIsWZSIlu0jTIBiVVUyxjvcGB+K2ByKpUKULrOiZ/ck/O4QyspN0E2GYT9up0fEeVK1EccOTTECJqVIDZOcxvgMp4/Uai3Mkk5Q9nHQOiOGPGnorYuWm4p1mHKEMdLsgtwkhTJHjXZig3j8XWnJUSHNGoavdRRStQBFF1Cgu1qYoFKCyKBBUGnD0J5xUCIB9IsTYW8ailhBkjpopNDl6+FuOsZs0FPkp8Pe7oB/IlJHgVQEeW4yjr3vRMgbRnKVBmLvGLyDjumBBPLS8dpbQUxNhJBcrEUoEMbAcctAQjaxlzzmgn3Qi6RMFVL0Egwftwxk6lCmQ8+EH30dTshtYnizKNTV9U2X5+brcblZBF3XBMJKKTptCdixaQzh48Gw5hQlrFYFevdMMgMeRbUc2/f1wuuJBEnqTXIU5BjRsnM08qW/XIcdR/owORkJklQqxSgrt6FuVx8CgTCuvCoPP3l43bTHOgks/uevGvGXH9fD7QggoVSNDo8dbe0T0wOJlVWp8B8NwDniR262HjlSJYYORgNFE5bocHB0Et09U9AlyxBIBnbtjdaxqNAMZUCEnqN2xv5yUYEVTTtGED7el5My1Fis08C/38FYfhoqDGjfOYagLxL0rLZJEc6SYEtdH+N8U1mdiN56OzyOyH1WaMTIWKxH045hhEPAkmobfG1uOId80wMJY7UWG/f1weMJIr/cBPu4FwOdkWBVIs4Lasw4eGQIkxNeZBUYmHq3HYmK5rKlCXD1+jDe50FKjgZff6wG1Rel4FTR3WXHfd9+D6++0sIEAX/la0vwrXtqoFSenNuD2x3AIz/biV//og4+XwifuDQHD/18HdLnGKSei24wVdSPIaLOPrtM4sX+y+2X4Pe//z3zfa9UKvHwww8jMzNz+jkJCQkQCAS8deN5ztTxvn7w0zcu2A1m8T/+Gvf3Bx98EN/73vfmPV6lUmH58uWMnmSD7OQgu0FOEAqFcMEFF+D999/H0aNHY/r2WS3WDxw4gLKyMjz66KP45je/Of33+vp6lJeX41e/+hW++tWvch5PRkepqam4/PLL8dRTTy3oWnixfuro7JxkRPobr7fFlS0utTIi4Yu3luOz/7OIEV8xx+4fx9/vqmcs+t6sa4d/loNLcqoG6VYtk1ipef8oPM5Y1wulSQJZgQRBhDHa7IJ9JNb1gvhxLy1PBO0BfIMk4U2sg4tIJMDKWhtkjhCm3CH0H4mNBKcoGsWrjJD5/EhOnoSgvyOujqHEbPT2aBCWStCxLX4G0VCogV8kgMUsQPBQHzDLwcWUK0Fm8RQoiQx9u+wIeWNnOSUGBRQpWghFQKCvH2FnRJifQKCQQpKWAp+HwkATDUdfbBsQQZyxUo2Qz4MdLQo0H/ehj1YSyKs1TwvF7oPxDi+LqkyQe0LwyARoqIu3GdSVKjEgdGGpTYK0o0MQzPoUCiTqcdCvhkQuQe/OcQT9sU9QJknhTA9DKBag/+AU3FOxLjUKvRiKEhk8dAAd7RMYHo69jwqFGN/8VhVWlKbg19+qQ9fR2Dowg7taHQadLhi9MlY3olXLkqByUBiTBbFrf7xLTWKFBgNhF0xKOSOiZ2fLI37wRpkcqSo5qB0O0LMcXNSpCggtMvjkwMb6PsazfSYGkwx5OSYQb5ve5ilMjcbOBhFv9MoyM8LeMA4MTzADiZmIxBSKq61wOQIY87jR0hzfFytqE5hylUCCzvp4l5vaS1Nxx2PVSMzQLGh17bFHduLxR3YxA4mZJCWp8aOH1uLqa+feJ/rKv44ylps9s+pIPge+9o2luOvuGubfHyfnu8/6yXqxE+/1NvoluDEAERTIoa6HlNIxYv6EDzvvs35+c6KvH/jUTQsW66XP/QU9PT0x70mpVMo8FirW2SDPvfjii5ng1K9//evnhlh/5JFHmOUwMsom4nwmer2eaSRincPFK6+8giuvvJIJWCkpKWH2rJNReUVFxUnPqJ+AF+unju/evwm/eKyOs/ymLyzGr357MWf5jve68fmL/81Znpikhr+X3ZruBGMCz5yR4LXJSRjhyExJWF5mRf8+dhtCwtU3hYHW+MHICSYti7Hv1VgRPZPsWi38zeyZLQmGHAWkDhZP9Rlbb+QibkspQs9EFqsv/Ak6rVZ0NnJvFcupMaF1B7tlJiF/pRlHt45wlq+5QAN1WxdneTDVjP9u5r6PcpMYhyfYbRJP0Kaxw+PmPsdF6Rnoa2O3UiQULjHjyB7uOpSusOHA++x2koS85SZs385uuUlYV5YM8z52O0yCOFWGN3q4bUeJLaeBnvtLRZQqwnAvd1+2lqlw+AB3HVfVpuHwNu5ruOHORfjyT5fhw3LwwCBWVP+Fs5ysgPUNRydr2MhI+SXGRrnfT+9u/hyWLps/2c7phPdZP3mCtAdN9F/hpocghR6Fgs8jjCCe676eF+s8OPFeqr/+8wsW62X//POHHkB/GLF+YhKaxGZ8//vfPzcCTNvaImKHzI7PhvztRDkXZKsMgWyBIcJ92bJlGB8fZ4zsyYjm5z//edzM7cxZ+Zn7lkjn4OHh4eHhORM4l7+jSFKnPHwGTfRfmMDTZvpvyMCVzIz6iQfhxM9zlXO9fucj+4/rUhJ7+UE4o8W60xnZT0n2rs2G/G1sLH55fSZEmBOIUK+rq2NcYGbO2BNj+q985Susxz700EMfaNTDw8PDw8PzUXGuf0dJKDXyBZ/FkfCf4aaH0YF/4803Dcy+9RO88847OJdxu9kT6fFE+LD2iydYyLHz8e6778JoNDKz6Cc4dOgQHnjgAWay+ZOf/OS5I9Ylx5c3SLpWuTw2fTL5m0wmO6njiYPMCaFOIPvfH3vsMWYfO5dYv/fee2P2yZNZi5SUUxdMxcPDw8PD82E5H76jpJQeeYLPoin8F2YP+44dO/Dkk09OC/UNGzZALBbjXOVcWi05PVALFNwf/Nif/exn2L17N/Nvr9fLbGs5kfSIWDRedtllzL/JthoSU0mynZJZdDJ5TGbViTvMn/70J9ZJ6LNWrBO7RQKpLNmfNJPBwcF5vSpPfHDN9lQn+9ZzcnKYQFUuTjbAgOeDo9XNPcgSzeMo6vMGmSBUrj3naoUYDikN/3FXmNkQ5wy/IIQpu5+1nDhRiBXc1yCgKIhl3CnrCSFKwgT+cSHjds+LvIZUSLYjc36UyHVh0F4KdIC9EYQKKQQCH8JeP+f5ZVoKXo7FKUpIPmzmvkaDbm77PgmHVeUJQqK521AgEUMgCE07psxGpRJD4RXBPSso8QQKtRharQwed2SFbjYiEQWhkvsaSdvL5rnPcuncH6EC2dxfBhKpEKACzN5z1vMrhJBKhPBxWCVqtBIow2K4ZgXYnkAooSBSzt2XlbK5xY5aMnc5NU/UU0+3nXHe4dpyqFBKIBYTh4/YYPETkHs4H+Q5XHvWSVC4SvXh97WeqZwv31EKyoJcwafRFP4zs4X1hz/84bRTBxHq57JYP5frdrayYsWKaReXmQ6DhLy8vOl/L126FNu2bUNXVxfj/ELCQ0lCpbS0tA/1ume0zzpJfkSY7alOhHpfX9+8SRNqamqYn2zbZYh/O1mi4PnoufNb1fjz01cwTg8zMRsVuGhZJv77h2bcdNG/0HYk9r45HX48fM82fPW6/yIjV4+c4tj7JxYJcHltNiw9UiwyGlFZGV0uZaBo5Naa4JAGIBQJGKeL2VRUJCDHqkd73ySyVhkhnOUbnlukx9JcI3p3jyNrlRkSVayYs2UrUVGjQf0LPoxpK0CrYuso0qsgyc5E8FAr1lwdhjUnVkQQr/W0FWa0bpmA32aDPCPWH16sAJbf7MHinHdRfnEfkmvkcQpTX57EWDv6aA3k+fEfDPpFNmhsFDJtLSi5XAJqlt60laqRWU6jxNeC666TQa6MbYOichG+efMYqsNbcfuXfDBaYj9GjElyLF5qQu/bI8xP8vtMlHoxcleY8f6bkzikTUc40RB7AVIRXNlZ2L05gOxMNTIXx7YBsS5cusoC02QIF+iMWLMsMa6OF3w6G/84fB127rkZN3+xLM43vLTMyliFbjrWhaTVWsbacCZZ+XosKrCge9skamqTodbE3qfkVDVWl6Vg6K1JrF2WCos1dpZEZZFAv0KJF7Ycha1CA1tGrDe6WiXG1bW5mKzzwpergqZIE+dLn7bSDG+vD5+wmrFiVl8mtamtSUa6QAWDWIrSWmvcwM6wTInWFAe2DfYhaRVxB5rlRlNsRFG2Cc7dXlyyPIuxuJxJQYYBN5bkIvSOA5dXpcNklMd5wlctS8IbjzTjB5dvxEB7bEDzxIQXd33jbSwqfBIb1vwN9SyOOYScHANzn9asS4+7z7fcWo7tu7+A+di87Ubc9r8VzDEzWbkqFdvrPo/Coqh9Gs/Zh5pKQTp1OYRCITOjTmY3z2BvDJ6PCppa+OMDQnQnmUlne7Al2yTi/MILL2RsyD+sUD/j3WDIVhcyA56RkYFNmzZNz8wQD8wf//jHzP6fmdtbiKc6cYlZv3799N+IiwwJwiEj8hNZT8kSBgk2veOOO/D444+f1LXwbjCnHpfLj0ce3oknf7sXtWXJOHZgAs4pf8yM2Ge/vAhf/c4yvPefY/j5fdsxMhB1tSDdoawqAV1tk8hLMkA3JsbocQ/0E6RW6tE+bkdYDriFQbTOGgBkFxqYGXq/K4hUsxbNe2PdTRLS1bDqFXB0ebAox4jeHWMxM/oqixTmHBUGDk6iuEqHsb2jCM+YIZSoRchZIYV8rAny7FSMN45Oe6ATBBIhhFlp2PtmAJbFRow2TcE9Fm0D4uWeuVwP9I0gd7kX6QmNoO2zHFgS8tC+SwtaokE4GIKrM9YhRZNrgohyQ0QFoDAI4OmI+n8TxDYThiYTMd4VRmKhEI7DseUSkwrdYhsO7PPi+k/6IOtuBsLROgpUcvTI8vCffwMFlWb07x5HwBOdCRbLhUhcasDhPSPIqDSi95AdroloHUmSoYrVetiGe0EnWHC4MQx7f6xdZGq1EV2dDlhsMqhcYUy0x86Wm8q02Dc1BVolxDd/WYvFy21xjiN3feMd9PZMwZagwr69sU47KSkaFJlNcLV5UZBvZlxuZs7oq41SWAqUOHJwFMvKE9G7c2LaA50gVYlgrdQw3uqmpSpsa+iFwxGtI5k5rl2Wgp59k6gtTka4JQDHsC+mLxfWmBBudcGQqoBn3I/Jzti+bK7UYdfIJCQKMUy0FANHYpfIk4o0cNIhjHu9GLZ4sbs+mrOAQPzG87R6eLv9yM0yomWWk4/OKoM8W4KDhwdxWUkGxrbbEZ5hJynTiKEoU+L9/f0orbShc+8EfK7oqoZYKsBVdxbh6m8X4/nnD+P7D26Jme0mA6YbP78Y3/3+KhhnCf8TvPrvZsZrnQykHnl8A0oWWfFBOHxoGN/65jvoPGbHj3+6FlddHf1++Ljh3WAWhpeewJf/nshYOZNskOQ7/Je//OU5PfvM646522Xf1bdAJf7wq2bOgB8VL/3htNmpnkrOaLFO2LJlC5NtlGQwJRvySVazX//610yyhJn79QhkDztJdLRr167pv5GESmvWrGH2DH3uc59j9g2R/erkdxIAMF9SpRPwb5rTx+9+vheP3b+Ds7yyNhF7t8cKj5mUFJghb+Q+v8wgwjbnIOd2CiKUsox6TM7yXJ/JJwqSMdTIvX9wyToDhndx29+VXaWAp7GdszyUmYu9r3IHE5VdHMay9P9yH6+woOHtRM7tFCS5kS3FzrkthuCUZsPVxW1HmbZCCW8rt9XiUekK7H6V2y4yZY0JBzdzWwDmLzViuI7bitGaq4JvlkifCUkmdO/EJyEUsi8Yko+6RQVPoquL247yf4oL0bWfuw2WrEhA61Zuu0rRehleer+Zs/zqZbkIbuW2y8ws1gKzvPtnIjNJ0DTi5dwCRgZ3W61DTFZgLm7IyUffYe6+vKo6ET3buYP3jWsN2LOZ21bUvE6NF+dog7XrM/Dv/1zPWe7zBSGdZ3vRfJyKc5xq+KRIC4P4rP+27nJm+yrRAKQ977nnnuk9wjOTJp0r8Lpj7nbZdx6J9TPr04wFkqaVCO6//OUvzOy6yWRi9gGRUfVsrrvuurhlhqKiIjQ1NTHpXcmMOgk6JaPx66+//pwekZ9NzLfnlWsv6wnouS3VEQrQnEKdOZ4GQsG5X+NENk3Oc8xXHl7Y8Zj3+DCnUD9x/hNZT+c8x5xPWFgbkcyaCzp+vnsUoDmFOoGszAUXeJ/nLZ+x4sAGyZQ7F+Q+zbUwGwrSc+YHIP1soXWcry/O9V46kRF27vK5O8KpENlnmlD/KPHTUwjh1KRLP7OgcMfSzaAoAdx0HsboXfjW7T/Bb/+3AToqOyZpEs/5AX0Gu8Gcas6KTzQiwMnWl/l4+umnWf9O9qbfddddp+HKeHh4eHh4zhw0glTGp/xcJh0XM5lOR+j96KHfhopKgAS6j/uyeD5i6PNIrJ8760U8PDw8PDw85zxklSyD+gT0VB6T2bQ1/AICNO9JznPuwot1ntPOvGER8wxuORzfTt3xxz/8F3KOk3mNuU8wd/H8EwAnVcmFlS/w9As+wbzlC3+J+fsS9fH2o4/iNp/mvjzfBZzhYVQ8ZwhkO0wWdRXkMMGPKXTRryMUmmevHc85ObNOL+BxtsCLdZ7ThtsVwGMP7MCqzD/hn384FLfXleyt/c0vd+OR3+zE4pW2iOf0rO/08uoE9HXbsWRFIlSz7PMIJeVWhP00rLVaaKzxnsO2QjUcqWEUlZuRnB4fQEL+trQ8EQarHBmF8cuoeosMlSTgzu1FUmWshSBBphGhZK0Jzm4nEqtMcUJFIBFAXmPE/211wleYBko06y0noKAvS4J8qh+LL1FDrIz39c5aoUaibAABUykoZawFIEGUkAKBRI6sdWLIE+LLlWka5F8cgKVMCFmqKa5caNCgS16B/cdMkOfY4s+vlkJemIGmOg3EWRF/2ZlQEjFGk4uwsd6JlJVGxt0lpopCCpkrzLA3O1CyygKJPL6O2dVGjPS7kLzSBLkuPpYkoUyHfioAyTItVInxvtv6PDVG80T42gWvo+NwJHPxTMY7nHj2qvdxtSQR5cXxDiPEneSSqiwMTbiRvTTe0lWmEqFspQ3DrQ7kLjczgZwzEYoFyFhlxMgRFzYsz2C8+mPaiAKWVSehrmcAutUqpt/EtUGlAQpPCCk1Rigt8X3ZXKiB2CJDYYURpvT4hBrmDCVsFVqstqSgMD/eqtBiUWLNsjR0+RzIqNCz5h9YuiIB9k43MmpNcZqa2FvaVhuwv3kY+SvNcVaQxO2lfIUVOc0UbqjNjbOCJCxdkgh7qxvfuf09jLP4ou/Y1oPlVX/GDde9hK45gp15eAhCSoocwfUQQgonepnYNB6ec5Ez3g3mTIGPyv5g/Pf5FsYTfbA36t5RVG7Bd36xirFb3LypE3ff+S6ONkWdNdKStcgx63F47zAy8/QQiQVoORR1pTCY5EjP1aF+5wBsyWqkWjVo3xMVZkqVGFWliejeMQG5RgRJoQRv7Tw2HXdJkuCUVSficH3EkaSszIb2nWMIzkgstKjGiu4WO5x2P0qrbOjbZ4fPGY1gXVxphnAwCHuPG9m1RlDddvjGosFcxkIt6CCN8ZYp6Mr02DJoR1tn1N2jJF+JqwvCCLQMQZVlZAICXceidaC0aowK09C2dRKGTAVKK8ahmjg6XS5Si2GtVILuPwKBUgVamwRPU+d0OS2UwKWuQN/OKQjEAqTUSKD17gRFR2acSPhi0FSG8UYHwr4AXLZ8vP6cGB5ntA2WXSiHJTQA/5gL6uIU9Oz1wTMetZtMXSaD1dCPwMAoQukZ+PtGFTrao22UnqNFolSKwQY7EhdrEXCHMNIa7QfqJBkkyXK07h6DLUcNiUKI7oNRYaY2SJBdqEP39jGoE2QQpEiwf3fURUYmF2HlUgsm6iYYBxi6WIk3tvcgfPyjjIjIq/+3CDd/twJSiQDv/7QJ2x9tRtAbnXVz1crwSmc3RsfcWF2VBscBDzz2aB2KyywIjYcwdMyJwmozHB0eOAaj9zmxQMMI9r7DdqSU69A55UDXsajLjCFdDrcljD37+5GbZ2T8v5uORPt6slmFy9KyMLBjEsYUBVJtckzujTrhEP/+hHIDenaOQqaVQJ2jQsv2qG0o8WFPqDWiZV+k7yRX6HBwx1A0uJSioV+hwvutvZia8mH5shR07ZuAZ0Zfrq5IAjVAY6zXjZIaC4JHPfCORN2CEkq0CAXCGD7qQMISHQ6OTqK7J+oik5KpgUWtwLH6CWQU65AUEsJ9dIZbT4IIDVk+vFfXjYwMHZI1KrTtj/Z1rV6Krz1YhU/fWoKhQRceuG8TXnz+SPQ+y0S4485l+MadVZDLz34zAN668fQxTjehJfwPZC4y4JFHHmEym54L8Lpj7napu+JWqMQfPjGYM+DDsld+d1a4wfBi/STh3zQnz4++uQXP/OYge4ejgLU3ZOJvzx3iPP7ydTnY+04fp+tF1fJkdO2eQGCGz/VM8ooNONwziskpdkcEg0UOo0CGiQF2q0alRoycND26D7Jb/IklAly+0obx7Rw2hCTXwmoTXn6T2+bwm58xQ36A28pRVZYKxdBBUBwWKtoSE0SOTtBe9jqG1VZIJZMQ+tlnJ2mJGi9t2YC2fexWjjIlhVXrZRjYNcaZ4RTLTfjXi9xWi+svSEXHm0Oc5WkbLDiwcZDTZCanxojGA6PwcmQoTc/XoHNkCuMTHPfZKsd6lR7jbRx2j0oBjhUK0L43fib+hM//2to0tG3ktuTMvNiEd9+ODpZmk3WxEa+828LZlz9Xkw/LTj+zOsSGpUSHng4nPFPsbaC0SdFL+TExxN6XJVohvBk02hrZ6yiVCnFDeS4Gto5z9mXTRQa8+Q53Ha+4IBOut0gbsS8pB9Yp8cr7xxCc4dk+k8rVidhU3w2nk70vksHO3gNfxNkOL9ZPLx3hV6Eq6WBE11//+tfpLJNnM7zumLtd6i6/beFi/dUnzwqxzm+D4TnlDPdxe2ET0dLby+0jTXC5A3Pa03mcAU6hThid8HAKdcL4sAeTg9ye6iR1u2eS2w8y4A+DdrGnd2eggZG5ypmEUHPvrVQKHJxCneC3BziFOkHoG+cU6gTK78BQD/fre100XBPcbUwm6nuG5jaT8nGI7BMEXME53SAdUwFOoU4YHfVyCnXC+JAHjn5uv3G4wghMcrcxmaUOzJiJZr1Gz9z32ecMzdmXp1w+TqFO8Ez6OYU6wTno5RTqBL89FJOAKu76fCGEHXP0RRqYcHMfH7lIcjz33k+Rk7Qldx0H+52cQp0w0D/35wUPDyEBtUyeFY/Hw7i/OZ3c30M8PGcbZ4V1Iw8PDw8PD8/8eOlJiMA9gDtXCVBTuOmmm5js5u3t7UzSxCeeeGI6cznPuQd9Hlk38r2Yh4eHh4fnHOHcTYo0HxS+s24fPCjHMfp5tDW8xmQqv/vuuz/uC+M5TdC8WOfh4eHh4eE52zgfkiLNhRxGZNKXoz38L/z9739nsp2TTOg8PGcz/J51nlPO575Siqz8eGs4Qs26FHz9zmVYVsWeEjon1wBnOICcJfH2eYSEVBWECgFya4yM48VstCYZkjO1WLk8DWJxfPeWKUXIX2WBdZUGUpWQNahw1fJUpKVooDOxBK5QNJKWa7FN5Aadwh7YYig1Is0vQXGegbW8oNCIrkEV3MkprOXKZBXj5CLNyWAtl6eJkH5rG0w3yFnXxoQKCQzL9FCVpzK2irOhhWKEbGW4+tNj0FvjlwEpAbDkAh1kWjEUNgXrNegWWZEpECAnV81aXlmpQ5bci9wl7OVZi8VYmTeA9Zewt6E5SY4svQyrlidAwOLLrTNIsGGRAZ9ekwrZLMtPgkIuwjUr05FYaYCUxSaRBMjqVpiQZ9HBbGWvY3GVBeOSAHSZ7MLHWqGByx9ARiF7X0/L10LjF2NVeSpr+eJUA1aptMhaaQDF0pdhFmFflhuetRJQ4vg2EKuESFplRuXKBMiV8XUUiikUr7SiPM0GkzG+DuSMNTUpGFAHIE9lvw+mpVoIAhTjzsTGkhITbDTNuNGwIcmUY1wYRhXH+91kk8NqVmJDTQaEs+wwCTqdFA/+gBdaPB8MM1UGE1XK/Ps73/kOsy2G59yDDi/8cbbAu8GcJHxU9gcjEAjhb789iN/8aDecU34kpanx7YdX4MJPZjPlxDH0H88ewoMPbGZs23R6GQoLTdi1s2/aj72iPAGh4RCGupyQyYVYtMSGA3WD8PsiAXE5hUYowyL0Nk1BICS2iza0HhxjAkQJtnQVxEYh6vcPMr/nV5txpGsUQ0Ouad/pygwberZHAjFLyyyQjlMY6YiUEy/spDIdDmwfRChEw1SoxJjIi+bDEYcU4gt/YVUyEg96AHcYymQFBHoFevdEHFKItZ+xRo93mwYxOu6F0STH4mwLunZELfiKanUoFPZBNGmHSCmGabEeriPdoI9b8CmzLRBTbgQGhiGQAem3+aFOexc4nq2PFmTAvq0Yjm1ORn1pKxIgCbUAnojFHqUyIiBIgaepO/J8Sx5cfUEERiN1FigV6KWK8dozQuaDK6tcBZvcB0dbxAlHKBPCXGbG8P5hhH1hKFPUCEmVGDpwvI5CCpqlRmzcN4YpewAJiTKsK1bCedwek6Ars+JQgw/j/X6oDQJccFkQONYOhCKNIMpMwnv1GrQcDkAsFaCy2oyxPeMIMoGLgCFfjQFRCIcbJxjP9vUrrRAcnYJvMhKUSNq91yjExt2R+7x2WSI03UE4+yP7dhUmKUx5anRvj1gnahdrMeAIYPC4S4xUJYK1UoPtu3oRCISRkqOBRC5Ce0PEIYVYiFZUJWByrxMBZwjqZBnCyUI07ok43ZCxRHG1FT0tdkyOeqExSBkB37hjaLov51QacXBsBO09E9ApJPh6ZR5Eex0IHQ+UNhRoQeI4hw47mQHY2HIJ/tVwDA5HpI6ZGTqsUSYgvNcbGTDWGNHV7sTU8eBSrVUGbaYCDbsi15RVboBz3I+hTue0h7q5TI1NOzsRDIWRl2+CmhKj88jkdF9etjQB/l1OhDxhqNPk8FqEaN43Nu2hnltrwuEjI7BP+JBgkePifDPsu8eYIFSCtdKIoU4v7L1eCNRCeMsU2LFrAMFApI5Ziw0YcbvR0TYBkZhiLFwP7R+GxxUJoE3L1iKgorGvcZB5vc/duAgPfn8VTGb2wdTZBu8G89HiCY8i4zMNaGpqgtVqxR//+EfYbPF5JM5keN0xd7tsv/jLC3aDqX3jt2eFGwwv1k8S/k3z4RgZdOHtf7fj6hsLGX/s+Hb14QcPbsHzzx3B5GR8UBRJLnPhykx0N0xieCAiomdChNKKNamY6vKhty3qAT2T/CozjvrG0djIbrVYUmTBWlkSOnew29dZslRwZ9LYvKUrLrETwZqgxP8sykD/xrFp8TUTmU4MUZUOLdvG4GVx9iBJgq64Vg7zWCsCE/F1hJBCwoVGpH/yRVDhiOieTdCzEqF9UtDj7HaRlDkbI+0pcLf0spYLrDY09y/G6K7BafE1E2WiEvIMI45tHEE4wFJHoxS6pVoE9g0g6I6vo0ghgnWFAZqRowhOxacFJ8miHGnFaNpCYaqXxcGFAhJXmSAac8Peyu4OoivXw+WmMHqA3XKTJBVyGcQ4tJXdipEkGhLkibFnUx8zOGOz/Cwps2Dnll74Z3i2n0ChFmPxchsadw7BeXwgMROxVIiqtVakHfHBw+JGRAZ36hUmPNHZgbYOdkvMdUvTUDipRXcjex2TF2kRVFI4uou9jrZsNWTpYtRvGmB1qTEnKFBZbMW+TYMI+uPvs0ovQVWtCYK6cVanHJFcCGm1Cf/Z14+J4fg6Er/54rVWHG0dRV8X+31csiYJ3/hxDSoqE3AuwYv1jxYvPYHfNV7EzKx3dXUhNTUVf/jDH2AwsK94nonwumPudtl+Hol1fhsMz2nFbFPiM7ctYhXqBI1GisRENatQJ/j9IUzavaxCnUAEx0Cvk1OoE44d5hbqhMbDwxhu4raHG253onfQwSrUCUMDLrgmAqxCneCdDEDgolmFOsHvCcHj8LMLdQIjHEc4hTpBKGzhFOoEeqQNno4BzvLw0CD8Aw5WoU5w9bvgGg2wCnWCd8wHlS/AKtQJ5O8quFiFOnN9wTDETje7UGeeAPi6uYU6wd5g5xTqhJEjU+id4/jRThdj68km1AmkbMLhZRXqBLcjAI8rwCrUCQFfCEo7WIU6gSTIGhpwcAp1QkPLMKdQJ/Q22NF9fLacjcE2B8b6PJx2kiMDbozbfaxCneCc8EPhoTktLclqyLjLxyrUCaRtJ4e9nEKd0Nc+dc4JdZ6PByLoiCMMmVHv7u7Gl7/8ZYyPc+QU4DlrA0zpBTzOFnixzsPDw8PDw3NOQrbAEMFOZtRbW1tx8803o7+//+O+LJ5TAH0eiXXeupGHh4eHh+cc4Xz1WZ+NH5MYGIisJgqFQjz11FP46le/ip6eHnzhC1/AI488guLi4o/7Mnl4TgperPPw8PDw8JwjnL8+67OhcMfSzaAoASPcX+6+ldmzTgQ7cYf54he/iPvvvx+XXnrpx32hPB8SmvdZP3UEAgFmySkxMRFicbyNHA/PsuokWG1KxhVmNnKFCHqNDEnpGvR1TrFaLSYmqaETSnGMY69u+iI9QgHBtCvMbMrLbDBL1Oh+f5x1L6+tUA1pYhAdrRNMCvrZEKcbpw4QK4UIuOL3MyvNUuhUAqiNEjjG4vczy9QihAQqiM0aBEbi60iJhaAoHWgqGxTdxlIDCn5vMWCUAmOtrHX0axeBSk4B3dnCWi5LtyFRDzh6KNAse7Y16WooLAKMyIQIsezZViXIYE52w94sgd8eX0eJVgIvlFAaVQiMxacBF0hEEKiUMGQJMd4eX05cZ+RpKoj0QkwcZr/PhjI9ZF4BBvdwBAqX6iBVi2Af9LLf5zw1KIsIxyQTCLDs2bYmK2FRyqBQieF2RhyHZqI1SmGSyaC3yFj3bBPbULVaClWqElPd8X1dIKJgSVCjmDbj0NER9mDqxRZkTejQsoe9DQqqtKBUIex4j33fe3qRFsYkKXrb7QgF4xshKV2NRL0U3QohfO74+0xcZ8ZlYSbQ1DfBcp/VIgSUIliSlRjuja8jcZ1ZeUkanHQAzY2j8W0goJCco8H7W7uwYmUaax145uZ891ln5XhXt1gs+POf/4zvfve72Lx5M773ve+hvr4ed955JxSKc8N1iOfc5JS4wRw6dAjbtm1DdXU1Fi9ePP13ssz0wAMPwOfzwWQyMW+Ss3UUy0dln14cDh8e/sl2/N9v9zL2eYSqpUlwdnkx1u85bvWWiMP1w9NCqajUDGoKGDwu7hbVWtF1dBL2scisUnKuBmNyL3YfjiyFllcmYHjIhd6eiCBOTlYjy6pHy+6IaCgvsiHJq8BQcyT4TWmUQFoowVs7jzGf9SkZGqh1Uhypjwgp4m1dXG5BPWNPRyPNpsYnUhMxvuu4LaKIQvpyI+yHxhB0BiHUiBDMM6Bh2yjCIZoRXwXVJtDtLvhGfBDLKdReJYC4rxVhXySAT1NohYweRnhiNOIK81kB1DlbgHAkyDBM5WH4tSJM1UXqbFxmhFLWCrgilnu0KgFHGpbi0KuRgXJGrRjJ1i4EhyN1EGpVUGeqQQ0eYn53SbLR1FiE4YZIG4hVYlhLtZhq7ANCYYjNavjkJvTtjohBoUSAoovFSJdthTDkQVCiRY+zCp2bHUzAJHE4MVVY0XfQCc94AFKVAKUXUwh3toMORMSgNCcVTXsVGD8WYES5aYkFPfsm4XNE2sBcosWAJ4julsg1LVtphLDLAc9IpM6aTBVGJWIc3h9p90LirT4egL0zIhZViTJQKQoc3Bm5z4kFGsZdpv9IpB8o9RJYitU4eNxq0ZimgMgiQtO+SBtJ5UIsq0yAb4eTCSKWWsTw5Ymxb9cgI/qJw8nS6gSg3oOAPQSRWghBhQJ1u/qnBXFllQ2mziCCA34IJRSyao0YPziGwPGAXGLZ2TccwmCbi7FmDK6U4fXWLoyPRwJui/KM+JxJAXNn5L77spOwr4nGaE+k3JQqx8oKD4xDkcHchDkF77Rq0XU8cFpjlKKyWIfw7lFQ5L90BZrkNA4fiLSJXCXCqkorlHvtQICGyCrDcIIcB3eOTNtXptcYsPVAF5zOAEw6KT67OBXe3ZORwR0FxsLzv00jGBrxQCIToGipBYd3j0wH5FZfnII7HqtGcrYWoVAY//hdI371/V2MFSQhu8iACdqHQ82R1/zk1fn48U/XIin5zHZpOFl4N5iP1xnmue7rkZQU8fsPh8PMLDvZGkMkUEpKCn784x+jsLAQZxK87pi7XbZs+PqC3WBWvfPLs8IN5pSI9YsvvhiTk5PYuHEj5PLIiJ6MWtesWYPc3FwsWbKEEfOjo6Po6OhgRrdnG/yb5qOhpXkMP/juFowdc6PjwDhrEpXMXAOEPgqtuyKCdCYqjRjZpUZMwIc3drUjNMvBRSoVYsnSREhoIdp2jSIwy8GFJOC5sDoDKqEY7zZ0YsIev5y8eKmVmSHsbJ3EyGC8u8nKRQlYptGDHvbA0RnveiHNVMNpkEM0EcDU4fiZdEumCEvXeKCWTyDcG5/MQ2wWIenzTnh7VRj8RzDOwUUgIzaJKoyPavH+U1qEvLFBNJQIKL1CCHPCBEQThwB//CzwsHA5BvqS4O0cQWAi3qFFlp0AWiZBtnUf5L54O0iPMhtdAwUY7aUxciR+ptycLUbekgAGe6Voez++jaVGKZTZeoz5QjiwI34GVqESorraAH+Ixo6towjNMich4rKyxsTM1O7bNQKfJ36mnPiGQ0ShqXEUDpZZ4vQleogUAkhbAvD0xV+jukQJp5GCoj8EV0t8Gymz5PCkCmGeBHwN8W2gSZAisVAFuyuMpu3xM+ECnRCjS2hkysIobBqDYNZ9psVCTGSkQSIJIGPiMASh2Nn+sIBCp7kA/XYVFEfsCLO4EYUqdBgT07C2e0CPsNhNFunQq6DQ0DeCjs742fyKXBPWJxpxcNSFvY3x70dzkgLpBXpc+5Ui1H4ifqZ8fNSDxx7Yzgj0Tbu64syIlEoxvn1vLe64swpnO7xYP3PE+gn279/PWDsODQ0x+9rJXvbPf/7zkEgkOBPgdcfc7bJ5/R0LFuur3/3FWSHWF+wGMzw8zIj0Z599dlqoE373u9+hpKQEjY2N+Nvf/oaGhgbGPumll15a6EvynMPk5hlx2YZcVqFOGB30wDcVZBXqBOdUAMc6JvDajrY4oU7w+ULY9n4Pjm4djhPqhDBN440dHdje1c8q1AkHdw/B7QqwCnXC1oYBBIRBVqHOXEOHA4kiilWoE4Y7ghgeEbMKdUJgJIi+p5Mx+Gy8UGfq4A2ib5MHm3+tixPqBDoI1L8UgtjZwirUCZbQNoh9dlahTvC2DaCkuIVVqBPkrjZIlV5WoU4YaQugo8PEKtQJvjEf3HYPq1AnuJ0h1NXb8f7GeKFOIIl4dm0Zxt66UVahTmjZPoreAQerUCd07plAgkvMKtQJjkYXMsNyVqFOcLV7kBeSsQp1wtSAD6Nj7EKdEJ4MoahDjOIj8UKdQAVCMLS0I2v0QJxQJwjCNDKHjsDc62YV6gThvkkUeQSsQp0QODwJJ+VlFeqEfS2j2Bn0sgp1wkifm/GfZxPqBINJjmtvK8ZGFqFOcLkC+PnDO1iP5eFZKOXl5fjHP/6BDRs2IBQKMTPtn/rUp7B7926+cXnOLbFOsoPV1tYiIyM2NfqmTZtw2223TY9QyajlpptuYsQ7Dw8PDw8PD8/HDdEmP/nJT/DTn/6U2a5L/Nj/93//l5lx5z3Zz3BoiuwF/fCPs8i6UXAqliNmzqgTyJISeRARP5Pk5GQ4neyzTDw8PDw8PDw8HzUURWH9+vV48cUXcd111zG/v/HGG7j66qvx8ssvM3vcec486PPIZ33BYp0EZpB9X35/dBn1rbfeglQqRX5+fsxzib8pcYXh4eHh4eHh4TmTUKlUuPvuu/HXv/4VeXl5cDgczKz7LbfcgrY2NicuHp6zxGe9qKiI2epCgjKI/REJNH3wwQdxwQUXMIL9BMFgkNm7TnxNeXjmYmrSy7i/EIcVNpQ6MWQqEbwcKc9VVhlMQQVGR9n3lGcn6WGCHF3d7PtwjSY5rGYl+rvZ95xrVWLkGxU4zHH9EpEAErkY7DuZI8hUQghEQJi9CjAmBUCNi0H74vciE0ISPSD3Ah72lSpVjgjm0QBGDrHbpeqLgxDlihGoZ399gVoCS9kkxo+wzzyIZIDCFoKbfcs6aFDQGsOgBADNMSmlNYcxLKUQ9LHfZ6NVALVOCMdkvIUgIS1dhDEZjcFe9jZKSpNCrhKi7TB7P5DrxYxLSn8r+31WKIVITKTRx375zP3TaAWgKJpzhkaqEjGuQGEWm0SC3ixk+oLXyV5HnVUKsU+OwCR7bxKYdQhoKIin2PeMi3VyWFQ0HBz3SaEHknJcGDnIXk6ca/KMYrzLXkxMYGBTytEsoFhjRJjnSCl4PUHI5OxfN8O9Tuh1MkxMssdPZGbpOV6dhw0+KdLcCZJOBhK8+NBDD+HVV19ldMu+fftw7bXX4qqrrsINN9wQt5vgg5KQkACBgE8gv1Do88hn/ZS4wTz33HNMBz5xKqVSia1btzLBGyf4wQ9+wHT+wcFB5o1wtsFHZZ9+iO/yj76xBbu39iE5XQOtQYrD+6N+05ZEJRKSVUyAp9GiQG6mAa07oyJFrhNDuUiKLbu6oVKJUbLIgl07+xA67huukotxZVkuBndG7O9Sq/TYsq8Lbk9EMYuEAlRXJ6P74CQ8ziAKasw4eGQIkxNREXHDqhRkDXrhG/FCvtiIf7SPoWWGqF9bloSccSnsx1ywLdZCFAjA0REtN+RpGHvCsSN2aLNUCEkk6KuPXA9zTSU0LriqHaKRoxBo9QiIkzDVGP2SoZUauKU5GNkzDLFaCmuZAsL+RvJGZsqFGgGSb/BDrtwMGiIMdl6K976biYAj8qEklNNY/mQHzKv/DVA+UAMr4P4jQE8cF7zEgm+dHrKEPaCCE3D7VmLnzxZhsjX6xZJ/ZRA5xTtBuQdB6/IxcVQFb3e0DpQlFa5JObzdoxAnWdHelYRju6KC2pwrRWq2F66WQchsGjgEFrRvjXpyqxMkSCsRwt4wAIlehkmzBe+9NTX9warRC3HphSKguZPxZ3elpOE/r3imB3dSmQAXXaiEoLWPscgUFiThrY1uuKaOjxooMJaabYfH4Zr0I7/GjL7mKUyNRgNJL7lAjZzgMPwjTsjyErFnvwz9bdHVw8xyLVRBP+ztThgKtBj1UOg5HL3Ptlw1NEoBhhvs0GcoIdOJMVAfHRzqU2VIyxFi/OAIZGY5AhYd6jdG21BtFKFsmQL2+iGIlCJYF2sx1dBLIqAjT5CSeqejcXPkmIoLFEh0HIIgELlG0scM5Qnwd/Ug7PWDSs/F3jdkcI5ER07VN/iRYdwL2mlHwFyELS+nYmTG5GHqMjXkITvcvVOQ5hnxQq8Pu5uidSjPt6IorMbIYQeMuSoMKQM40Bh9v5rTFIxzUcO+ISYXwbcfXoELP5k9XT4y6MLP792OV589Co1OiuRiLTbu7GKCvJk2UEtwz321uO3LlRCLhTjb+ajcYMrxbQipD++Qca4igZpJkPRB8dMO9NGbYUfkzSGGBinUWmio2Di9kz7f8QRNs51p2OB1x9zt8t7qO6ESLcANJujDus2PnhVuMKdErBP27t2LV155hVlGuuaaa5CVlTVdNjExgccffxxpaWm4+eabcTbCv2lOY9tO+hi/5WefbJgW1idYtMSKiRE3bCkaNO4dYmboZpJfYoLEK4DAKsTOpj5Mzpqdy8zSQaWSIFmsgrSTwnh/7AylPlEOSZoIk14fBC4KfW2xDi0qnQTJJRr4x1z4hEEFV1Osc4dQJoSnRI+3myewxmTF2O7Y2XoimjJqjfANOqFNVqB/1yjjPz4T21ITJgbcWH3lAAze3WQZKvY1EjPgGBLBGUrBUL0TQWesc4cqQwdjggMJNf3Q5u4AFYx1UAkLE9H4+qXwSikU3PEfUOLO2Guk9QjvqwbdAGgqekAFmmPKaYECQx2XoP1fCSi9rAUSZ0PcFHNQVY6xAwL4BMkRT/aZHysUBWFGNhp3qZFcKIT3aBfoWcmllPkJ6GmVwZgugad1ACF37Gy5IsuAhhE50lIAs70vbqZZmqhFs9cMCkIk+8fhHYxdcZAY5RjTm3F0gIIjEEBvc+x9VmjEyFisByYd2JDjg691KLaKEiHozHTU7xUgJUWGkT2xbUz85M1LzTjW7oYtXYW+HaNxyaWSlxrgGvEiNU+CqUNDccmltAUGdI8IYE2WIHxsNC65lDZTDaWOxhQtw+GGMOz9sX1dlyTDkmV+JOvHIAw54R+InW0XquSYUuRifCCEpSuaQY0ciymHWIJxyRLsf8sMaxpgPzQU15cDJVY8d9CBZQk2DG+bjEsulVijx/6hcSjSpdizqy8uuVTNuhTc+7MV2P5eN37zo91wTsXWMSNPD7ckiPxSE3744zWw2lQ4V+CtG89uJugWdIX/Cx8iA2QjVYxU6kJIKNUpsZFkg9cdc7fLe7xY5+HfNB8dv//5Xjx6P7c9W+kyGw7UsWcfJaTka7G7lXuJU6+WYdGYYc5rCJqAqXHuFN1fqzbB2cK+bYYgyE9G5zZ2u0lC3joTBrfHip+ZLL/RhxTBRs5yr6oATW9wzyBosoGabz6FuXBUkv+zW/QRVIezQHl6OMuDw6tA97NnQCUMjl+O0W3DnOWCrAIMbI/PzHkCZV4ihvdNcF9fmgr0GPfxlEgI5+Tc/siv+8xMki0u7rhKAF8b933yJhWicwv3NZqrrWjbxG43SchbrYPnUD9nuTZXC3tLdIZ9NkKtFPUtc88y3/TJowi72LeUEFJqaYSGuTb3AO2Tl2BgJ3cdXdnZqH+Lu1yySolXd3RwlheUmtB0PBkTG2QWfmPr53GuwYv1s58Q7Wdm2QfpOtAIQwQ5UgTrYEbZSc/a82L9FIr1VXdBuYCZdReZWd/yyFkxs85vmuL5+JlnbWe+tZ/5FodOZu1o/nPMdxHzvsDCyueBmvcCmBc5veULPv3CTnBSi4QLvk8LO3yh5z8Vt3mBzXwK3q8LK+fh+bgQUhKkCi5AkeAWKKlEBOHBsfBraKb/Dj/NnjeD5/RB824wPDw8PDw8PDw8s1FSCSiibkYadSEEEMNOd6Ax/CTGaC7bAR6ehcHPrPPw8PDw8PDwfADItheboArFgi9Nz7K3hV9ER/hVhEmaaJ7TDs3PrPPwnDqmprj3ghPC86yri8VzjyklYiGJYeQulwkgFHE/gZSJJXPvA6bmuQZizzd3+TzjYmpuF1WBcJ7jheT4OepAEQvHufb2keufe783BPO00bzXOE8bztPGlEgUuUyu4yWiuc8hoCCSzPca89RRtLB+MN/xwvn6mUQw52uQMmou5xSKAiVcYB3Z3UBjrEsX8n4m78Uwhw3kyUCOdc4Kwv6gn0k8PCeLnDKhkPoCkqiVoCDACF2PJvov/LYYnlMKP7POc9ro73Pg8//zCrJSf4UHH9gc9wU6MeHFXd94G/f/cBMKl1ugM8piyqUyIdYuT4e4LoSLKjKQmKKOKRcKKZSuSMCRgTEUFpqRkxsfRHrVJ/OxecdN+FX9ZVi8zhZXnlqqw3CWH+0iO/KqTHHlKbkaWEvU+PXhIQiWmBlHjJkoEpSQ5Cegu24C6SvNEMljhZDcIEH2SiP6tw8hqdYMqTZW6YgVQmSv06N7owfDWAFKo48XyMklmGhyI3GJEsrUWbanFJC4Xo2yW14DZKmAIi+uDlAWA2ILlEf1EAVK48u9Bej72Y3Y/1AN3N418R8LkmTQ/koIXEchSCmJV2syPSZ8azGyYwiqkgwIFLGDAoFcilBqMQ68EoAkNwMSgyK2CmIhrFU2KBxNyFwlgTJllruCgIJ0URLeOCDFIXEWZOnx90mSn4StI4k4ELZCWWiJb4JcE/pkSTC5BFhUY4kb3CVlq7FhiQlN24RQFKXHDTwkJhU8ibk49J4dtmoLxIrYwRW5r+IqE17a1A9VrYG57zMh/SJ5pRn7ttpB5yUw/SamDYQUzEts6G8JQJ5phiYzPtjJUGbBqEOKlEwlkkt1ceXZVSrc+PlWaC0OqIvinSZkKSYYS5Tw9DshyigAZvk8U1oTWiY3YN/rNNSlqRBIY/sysdG0Vplh7D+MmsuUkM/qy8RTvmyDAdmto7h5dSosttj7LBILsGRFEtqPjqOw1IyUzNi+TO7J8upkLBpT4dHKt3FsB3cQKhd7dvdjzYq/YnHRk3jmrwfj4hhaW8Zw1eX/RH7Wb/H4I7vg97P72/PwfBAElBDJgjXIE3yGCTp10n04FH4KLprbGIFn4dDn0cz6KbNuPNfhLZROHvIF+Otf7sYjD++AyxW14EtMVOOHP1mDq68twDN/bcD3H9yC0ZFowhqdRorq4iQ07hhCaZkNmj4BXD1RgS+UCqColmP7vl6kZmsxEnDjSOtYzJd9dU0yjhweRUKiCj97dD1WrU6PubbtL3bhj9/ai2AwDH868N+97THlVSVJ0DkksI96GcvGnTt6Y5K9LMk34FKjEt5jDmiKrTi8eQpBb9SeTpMkgzpJgf59E0ivNcLRPI6APdoGMr0ExgItY+GYvEwPkX0IwfGox7hYSWHxVQHI7PUQmFMw3iWDuyvq300LBBCm52GkwQ55ggyFn9oNfcL7sTdAVQb4ugFKAohNgKsxpjiYWABfwhjCcMP+9gXo/JkMmPGhZVlBIevKvRDSXYCgDHRba0z2JlqegDDMoEc64JUtRefrFIKOqOgRapWQJZngOtoNcUYWmrZL4ByMHi9RCZFeo4TzSDd0+UbI6GGEx2bYDIqECNqK0Vc3BWmihrFsPHIg2k9IEqKLLlUyFo4CuQTNPjN2bo5NfFSzWs1YONKBEBx6K3a/FZv4SF+ixlDQj/EBD5ZWGEERK8YZus2SJ0NKpgeerhEIs9Kx+79++F3RfqBJkCIhR4GhvWPQLjVic8M4JsaifVWjlaBmsQkjO8aRUKHHQLcbE33emNWeJes0cB8agiZDC4cdGGuJ9gNKSLzgDXC2jUGql8FJknjtja2DdZkRg50uRnOvvmAUaaG6mHLKlgnnsBDBKQ+0+TrQvYdjApEliWaIVTRCo0OMZePGP2kQmNGMulQpknOBqaZhmCut8Hb2I+SKzkjTSgUGFJk4tNGOnFo9VGPjCI5FTxCWCdGWr8Hr23qRU2SEY9KHnmPRIDyRiEJZTSKO1I8gMVGFIkoDb6Mn5v1c+dk0XP6zxdDY5k5CMzLswncf2Ixn/9YYE6BaUZmARx6/AHn5Rjz8k+144jd7EAhE36/ZOQbmc2L9hkycK24wVdSPIaJiJz54Pjp8tB3H6FfhxSiEkCKTuorZ434C3mf91PX1t2ruWbAbzIU7fnpWuMHwYv0k4cX6yXPrLa/hH38/xFl+wYWZePstbmu3G1YUwPUue9ZJgqZKgWfqmzjLc/MM2LX3Fog4luO97iCyU3+FKRf7UjlJjrQs3YaOOawa71iWh8Gd3OUFG0zof5/bAjD/Yj1c9bN8rmeQsVYFqpu7jupFamRd/jdQAo70oAIdQLsBmr2ONCVC/ZP/A3c7x3YAikbV3V0Q2o9wXkNf77UY2cxtpehNrsDR19izgxLyL1FC1l/P/Z4zZ+PPz8o5Zz+sqVKMD/oR8LPPN5DkSDaVGI4R9gynZAGhpFyFqSZuq0RTlQntm7ltCpVrjXj/PW4rxhVrEtH3Hnt2UULhch1c9dzl2mwVeo96ODPdkpWZ66/ZClGY3aqRDO5ERjNCI9x9sXHsYrRt4razrLhaBtdB7r4qKsxDz0bufjBeasAfNnZxlpcVW2A9SJH9cKxobDL8cOAKzEVx3hPo7mZ34yCiv7wiAfv2ctu7/uP5T+ITl+XidMInRTp/CNE+dOMtuNCPvNJEPProo8jLy/vAGUx53TF3u7x1Hon1uTfK8vB8CNwzZtPZmDnbzgZ9PBMlF8EZM2Ns+LwhTqFOkClEcLi597QGQ2F4ZyWsmY3fN/c1hmcl/ZkNFZr7/AEPNecOcoHAwy3UCbSXU6gzr08HERif48uCCOTA3NcYdM+9hBj0zV1OB+c+f8gfmnOZ0uWmOYU6wecNw0vN0UZhgJqVtGc2/nnq6JunLwbm6cuBebZOB7xhTqHOlLuDnEKdQIXDpCHnfA2Pc57soPP0ZUF47vtIzXN8yBcmmbs4y32u+YP1XLOSaM2EzLTP95kzX/nZhEaQChE190oEz+lHS2egKfw0wmEnk739T3/6E1JSUvimP4XQdGQrzEKOP1vg96zz8PDw8PDw8JxiT/YM6jImmzvJ4n733XfD5+MDm08l9Hm0Z50X6zw8PDw8PDw8p0Gwf//734fBYEBrayt++ctf8m3M86HgxToPDw8PDw8Pz2mACHUi2AkvvPACmpq4Y5F4Phg0P7POw/Ph+e73V2Lt+gzWQK+a2mQMDTmxdFm8tRyhckkC9o0PQ7xGxmo9bl6shtsfwtW1eVDK4w2fC4vMeOL3l3BeW2/PFG78zL9RuSSRcaCYjV4nw8U1mdAaZEjLnmWTSKwYZSJcvDwLrR4flIviA1IoAZC2wozRvgBM5fEWg4TUWj2cgz5oFieDZjGIt5RpIQo7Ic3LJZYZceWqLC00GhqO1k8iHIq/RsatxZIN2roYtNQYXy7VI5hVjpJf74O2In68LlYJUfhpCsFJBcJylqA7YlViKUNq+R7Y1rC4TlCAucqM/Ow6lF8XYoJVZ2OrIG1AI5i8iHhwxpXL0ixI0Ltxyy0hqDTxbZSZL8U1q7343KdEMNni28hglaG21oycRVoYUuL37yp1Qmz4pAJJyR5YC2MtBgkiGVDzaWBpwWGUrBOx3uei9Trk+hxYvyq+jQnLa8ywTXlRus7IalGfWa6Fig4iabkFQln8fVBnqgGVHKkrzJDp4vu6NUuAK7/oQMC4CFDH25b6RVrsn7gU79athEMa/34MC6UYla2EVuZGUln8e0EgAjLXGDHSSUGak8paR3taFt45KEBoUWLkDT6LUL4RnYMULlqeCQlLLoPCPBOsEhV0q/QQq+PbWZomRV9xGN+66S0MD0Tdcmbz1J8uY5xdZqNWS/DDH6/Br5+4iHGGmQ25pju+uQyXXJrDeW4enoVSXV2Niy66iLES5WfXTx30eSTWeTeYk4SPyv7gvPZqC+69+z10ddlRUGhCOESjuTnqfFFcYobXG0Rb6wTS03XQ6qU4WB91rcjJMqBSZoZ7vxdKqwTCbAkO7Ir61poSFRClifDOnmPQaqW47zsr8MVby1mDS32+IH75eB0e+/kuuI8Ho8nlIuYLnHgzB/xhrK1OR++hSdgnfNM+7mXVCWhuGIVjyo/lS5IR7AlhvC/qnLGo2gJlpxe+QR9si7Twe0IYb3VOlyeW60C5fHB2OWHMUUKtByaOjE+X63K0kCuCcLWPQpkohyWXgvtoz3S51KqB0iqFr6MLIq0U1goZ6N4j05ExApUKqioJZKa3ibclYCoGHaiPBpdSClCiEmD0APloQyi5DCFpE0CfuEYR3F3r0fIjMwLjNNIuksGoOQi4TzigUBCm5UJMNYMKjgOGPMDvAJxRB5SgYjE637PB2e6DOkcLrXUK4aFoHWBOw+G9OejaHYYmVQ5dogiTh4ani1WpaljSvUB/B4RaBWRJZriauqfrKNSp0RzMxmsvE+EuwKUXiyFo7QJ9PHBRqJJgypaK/77qZlR0ZY0Jk/WT8DuD05afCcuMaNk1hoA3hGUXaaAYGYR/4nhgpoCCdnEi2uv88IwHULhBjARlB4KjUbcfQXomdm7SYag9jJRSFbQyH6baoi4y6kID9gyFcPSoE5lZapSaZJhoiLrIaLPUcIrE6Nhvhy5Bipx8BYZ3R33EVYlyqJIUGNwzBrFGBDURudvHQIcibUB8282FGnRvH4VERmH19SHIh5tA+yN9WSAVQ1tsBTV4GOFQGF1Yhd2vS+GZOF4uorD4E1IU6bZAFHLCrS3Hsb0yeIairkukDToPBuEc9COhTAth0AvHDKtFc6kecnocgaEJ+C1mHBgzor0+Wp5coEauNoRwxzhgUaBDrULDruj73ZSqQNhGYde+PhgMMlTlJ6Fj+/h0AiStWYa8HB1Gt49DIBcisEyKLXt64PdFAliVajH+976luPFrpRCzJH4ilrFP/HoPfvZTYhnrx/WfLsIPfrQGtoTIQIQIpb8/04gHv7MZI8NurL8gEw8/sh45LCL/bHaDWS34LR9geobgpSfwXPf1SEpKwuDgIK644gqEQiE888wzKCgomPd4XnfM3S6vL70fSjKz8iFxBb34xO4fnxVuMLxYP0n4N82Hg4jxb935Dp7+M0lQEl9OBPG6DRnY9F5njP/xTK5dl4+u7RPwHBdfsylfn4Dv/nUNzBYl5zUsq/gDjnWwWy0mJauRrdGhpZHdQk9vlGF5fjKat7InaZEpRbhweSJ63ma3xyNCqfRyE8a2d0+LrxgoIO9SI9BxGGEfex0tNQnQYS9oD/vsoiQ/EdoN24BgVATHIE5GUCcFHeay4DMh+GYtqIHD7MUSOWTFyaCGY728o5WUwO65BL6Dh9lD7CkKo/IL0PIfF8IciWjSNxghGmpC2M0ehEVlZmHsaACBcXZbT2mKAQebzLB3sreROlGGotIAnEfY20iskSJzpQiBo0fZX18ixri+Cl3kPrNVUUhBXZOCvndHEOboy8nrEjC4cwRBN/t9TqixoPugA+4Znu0zSSxVoyqvEcEx9r4ssWix80gJ+g5EB4wzUZgkqFofwvAu9jYQKsRQFKWibxO7HSXJECuqTsS7/xpjBt+zIZPrZZdYsG3jIPwcjkqFayzoqp+Ea5K9jtmVBuwdHOKcSc/M0+O/DZ8FxZG2eKDfgZ6eKc7VO7vdiwP1g3E5GE43vM/6+QfxVP9t3eWMVSPh4YcfxpYtW3DZZZfh9ttvj3v+bEtHXnewM3VcrL+25IEFi/VL9/zorBDrvHUjz2lFJhMxGeK5LJJCIRouZ4BTqBMm7F5OoU5wjvo5hfqJWXUuoU7o63VApeF+K0yMeZnX4MLrCsI3xW39Fg7SCLkD7EKdQANhjw/gEOrMOdxO0BT3NoCwfZxbqBMCvaDDc/nRjkLgG2XToBH8nsiMOucF+CEIO7lvNE2D9ns5hTrzElM+CDiEOkEwZUcguigRh7dvEvbO+C0tJ3D0exFI4vYTD5DX93D7+5NZbJpYfnJVMURD6A5xCnVCyOHnFOoE94iXU6gTnH1OBE3cfdk/bMdoB7eVo3vUD9fQHNfnDiDo4H59UrfJCbAKdQK5/eP2EKdQJ3gnApxCnTDU78LwEHdf72ieYGbjyUCfjYRENfPgQquVfeRC/aPET08hBN515MyAwh1LN4Mi++bIQJEO4Rg9jicaXsaWh2JXdD5IsiSeCAvdynI2bYPhxToPDw8PD885Au+zfuYiRgk66dcRhBtCSCCmZkwynUWe3zwfPbxY5+Hh4eHh4eE5zQgpKaTQwgc7vBiHGNwrwjzzQ/Mz62cWQ0ND+Pvf/4729naYzWZcc801KC4u/sDn8Xq9uOuuu5ifX/nKV1BaWnparpeHh4eHh4eHZzZCSgbQdoQxd2ZhHp6zymd9z549TNSkWAYAAMdsSURBVNT066+/zvzs7+9HeXk5nnrqqQ98rnvuuQf/93//hz/+8Y/o7OzE+Q5xT/jFo7vw61/UIThPSnAu3ni9FXff+Q5GR7j3+l5zXSHS0+MtBgl5+Ubc+IVSLFpsYS1PTFTjuluKUbEmkbVcrZPgqtsKOV/b5w3imV8fxKraVNY9rgIBhQtrMrGkJAFSGXvK8/KlCVDZpFDqJOx1KNPBYgpCncAe6GLKVUEmC0KTzr6PVpmoACWmoMhkbwORRo4xVxKCZg73ALEEznAx3JPrItGq8WdA2LsSGK1lFmLZCDtqEBCkMoGkbFCWHPjdRkCqZy2n1ZlMWwoN7HaVAoMJap0bujz2fiAzyWDLs0NdaGU/Xi6BsZiCrYb99SmxAKlrNFhyjYgJ6I1/ApCzWg91pg5COfuCoqzIiqMeKwRq9n3vwpREjPmlUCSwz4ap09UQicPQ53DcZ5sMGl0I1sXsbSDViZGYQyFzOXu5SCaAsdiIcEY+42ATh0AA9aI0LN4gZJ7LRtYKHVSJUojV7H1ZnW+GBxLILez9QJulgUkehi2LvQ0MKQqmi6UV69jPb5RCrBchewm73aVUKUJinhpLapLYnCAhFgvw+TvKIBSy1484vjz790Z87zub4XDw+7Z5zjxCx526BByfxTwfcGY9vIDHWbRn/Yx2gwkGgygsLITJZMK2bdumo6SJ6H788ceZ5AKZmZknda6NGzfiggsuwK233oonnngC//rXv3DllVee9LWca1HZ777TgbvvfBdtrePTovlnj27AmrUnF3jV1jaOe771Ht5+s535XaeLWieyfZESR5ZfPLYLjz+yCx5PEBqNFPfcV4tb/7eCsWELhcL40x8O4Ec/2IqJcS/jf/yVry3Bt+6pgVIZERYbX+zAb+7ehaEeF/NF/omb8nDbj5dAb2YXFu++2o6HvvU+eo/bzyXnauBVhNHYGAnEXFxkRaFbC/fRyJe6IlkCZ0oYe/cMML+nZmphUSvQeSAS0KczSFGYb0bLjlEmwM2YIMeKEgk0rX2RF5SJEE5NR/MWO0K+MOONnVMlB93SCRB7OqEAmpIkDB2wI+AMQCgTImmZDsFjx0D7I0GHpkoLQkMjCEy4yBQMxNlZOPCuAJ6xSMBe6aUhFOYcAD15PJg0oRCtO1Rw9ka+ABLXh1Dw+W0QUpHEGzRVAs82A4LHInUQpmsgucQFiA8cV+lZmHo9H+5tEfcQkV4BXYkM1PAhZhMlpTYhLLYg0BlxkaFkUihLTRA69pGISkCiRQCFcB8kVosALRCBSiiCv6MLtN8HSiKFIDErYsUYDJEzwqFZiq6dgG/CB0okQOYFcmQkvQExHQlgdYsrMXRQBv/wFGMvoi+3wajbC2Eo4tYzhVK078mAoz1inWgsNSA1rQHSQMQuciKci63bqtG5KzKAtBapkWBwIdgzwvwutSigTFJj/LhNqCxJjaPQYOu24+czCfCFq4JQdrcC4TCEOhW6Q7l478XIgFYiF6ByrQaexiGEfCGIVWIYig0Y2jMEkNskoKAvS8DAgSn47AHGOjK9VodARzdob+Q+y/KT0X80CNegF5SAQvoKHeT2ToSdkWumk1PQ3qbAWFsk0DJ5qQH2Pg8cx21DsyqFWFwygGBPxLVFnmmDiHIhOBTpFz6ZFY29i9B+/L6acpTIynIBvb2R+6yRQ5JkxcjeiLONzKKER2XG4Y2ReyBVCpG/Ug37wUGE/WFItBLo8/UY2n3cCUckQKAoAft22eF1BCGRC5GyVI/GuuFpq8WCGjP62xywD3shEFLIrTWhsWGYsUEllFRYEBgOYrgrUsfcahPauyYwMhhpg+RCLVzwo7Up8hlVsy4FDzy2ClkF7FaLBw8M4q5vvIO6XZH3o82mwg9+shqf+vQHX4U9HfDWjTxhOoh94YcRRhClgq9BSulZbR4/qj5ztjJ1vF3+XfY9KIULcIMJeXFl/ffOivY9o8X622+/jQsvvBBPP/00Pve5z03/vbe3FykpKbj//vvxox/9aN7zkBtRUlKCq666CkuWLGHOdb6KdeKMctNnX8Hrr7Wyll9xZR7+9PTlrD7GJ3js5zvxkx9tY2bmZ0O8059+9ipkZ7N/oXZ32fHM0w245YtlsNriE7GMjXnwu//bi+s+VcR6Dq87iOd/fQhL1iahYImZ9TVcTj++/uk38P5bXazl+TVmWGg5vO+zO4PoK5Tw6mk0bR1GmMXUIitfj/JMFRLauyE4Lr5mIrJoEbJaQXV2I+yId+YQ6+VQ5VkRHuhDcGwq/nilGMYyK1r3CdHfGL9UKpYBa77oRnDcj/46ljpQNIq/4YTJOAzvdnaHGMkKC0JyGcafdgIsiyqKbBOUaWL4W1uAYLzTjchmhDRDA/fBAdDu+DpSSj3C2hy42ocRmoivY1iswpS6EonmOiiptvhySoopaiVkoj7I/EfiymlagGH/BRCG3dAE9rDW8Zh7Lbra0xFs6mRdb9AWmDAuU+KF18bh98d/DJZXSLC+ksIbz1Jw2ePLzakylC6Tw354DN6x+DaQaKXQllhADwzBPxT1ZD8BJRNBVpAGsWsEoX4W20+hAL60fAz1CjGwP+rZPpOaqwXITBtCoCO+DQkTymKMORMh6mwGQvE3Wp5mgk9hxr7XXfC748tNmTJklckxdnAEfhYHF4FehslMKxobxzHSF7+6JleLkFdlRkfXJDrbJllnypetSMbYqBtNDSzWqBSNghUWXPvlYlz4yWxwQVb3fv/k/mnP9plUVSfj789dNadj1EcBL9Z5puguNIX/AjFUKBN8M8Z+lBfrH/y99O/zSKyf0dtgtm/fzvwkAnsmycnJSExMnC6fj69+9avMrPxPfvITnO84HX5OoU545d/N00mDuHj5pSZWoU441DiCQw3cFoKpaVrc/50VrEKdYDTKcd8DKzjFvkwhwv98u5RTqBPGRzycQp1wdMcIfNu4Lfwm9rkw1u5mFeqE9qMTyAo7WIU6IThsh1HqYBXqhMCEBzKhi1WoM8e7AhjtZRfqzPFe4MA7enahTqApND+p4RTqBP/7w5h8iahi9nJ32yiCY05Woc5c4+AYfH0SVqHOXIJrgpl1ZhPqBEHAidT0ZlahzpTTPugNzaxCnUBRYVg1OzmFOiFDsRHo7GUV6gR70yi2HvKzCnXC/n1+7NynZxXqhJFuLzzuMKtQJ/jtPsjgZhXqBDLLrghNsAt1QigM6eQAp1An7HgpxCnUCXrXIahdPaxCneDpGkVPB8Uq1AnEBtLnCrEKdUJ4wgsq6GcV6sz5HUFMTnlZhTqBWLZ29djZhTqBphhP9rmEOuGF54+wCnXCrp29OHZ8ZYmH5+NkjG5kfmqpTM48ATwnD00v/HG2cEa7wZAZdMKJhAIzsdls0+Vz8dJLLzHZwt58800olSc/s+Lz+ZjHzJEcDw8PDw/PmQDXd5SXnoQI3F77PB8PAdqNEbqe5JGGFlnMTPpsn/VAgOQciU6QzPw3TzxhmmIeH5aFHPtRc0aLdeLaQhCL4wMxpFIpPB7u2VECSe9722234cYbb2S203wQHnroIXz/+9//gFfMw8PDw8Nz+uH6juKTIp2ZDNDbEYALhRUpuP32YpaZdR3q6+tx8ODB6b+43dzGDTznF2e0WFepIlsliChXKGIdGkgnPlHOxRe/+EUIhUI89thjH/i17733Xnzzm9+MmbUg++R5eHh4eHg+bri+o/ikSGceDroHU3QHkwjpwQcfxEUXXXRSx/Er+nNDn0c+62f0nvWsrCzmZ3d3d1wZ+duJci42bdoEq9WKu+++G7fccgvzILaNhN/+9rfM705nxC2BbeaeBBzMfJwLEKcE4vzCRXlFAmSyucdw1TXcgxarTYmsbHZ7PcLUlA/P/PUgPB725T2yF/7vzzRgfHzuVZO50OplyC3mruPiYitsi7jvpzpbBkMau8MMwZagxJhEA5pjz6FEJ4I+wQ+BhP3tJZAKQUllECqkrOWUENAnAdpE7vugSVJCbOVu58QqEYQJ3GmrxSlmqAu56yjQGeCjExj3FjaCIj1GR9MQptjtx4KUAqPjyQiLOIJ/BAJmMZgWazk/REfH8+AVcPe1CX8RPJIcznKHqAiaOfqiMlmNslx2C0OC0SKDUi/B8Uzhcah0QhgtIQjl7E+gJEIMQQFayX6fSf9xi3SAjrsvGjJlSCnhtngzlugwrszjLJel6GEt4SyG2KRCQhrFWUe5Xsw4ABFrTDYoqRAK4hCkZr9GYueZolPAwuHYRNAmS2FO596iaCpUYt/eiEMTFzVzfCZlZOoYC9hzjXP1O+pcI0T70B7+F2iEoUc+Yz1Ndguc7INnDo6L9Q/7IMefLZzRYn3NmjXMz/fffz/m742NjZiYmMDatWvnPJ4IchJcWlVVNf04IfDz8vKY38+XNwMJsPrUtS/iU9e+hNaWcVTXJkOnjwopk1mB3/zfxdj0/v9AKp1brP/8sQ14450bGOeXma4OX/36Uuxv+BJKFsV7ZRPTob//rREVi3+PL9/2BioXP4V/v3w05jlvvdmOpeV/wO1f+i/KF/0ef3yqnjNobC40Oin+vefTuP+xlcy/T2CxKHHJsix4DvrQ1DCGxBod1NZouUQjhHy1HFt6+7B1Wzds5WpYM6KrN8ROcs2KVOjGRXjvX+M4IE1BKHVGoKsAyFynRlrGEEJNB5BTYUdSVaxIMCy2QKqXoX9jD3xBBVRFqTFZpnUFBhhz5PAebEJ+2jFUflICgTj6gWLMUSJ9mRIjdQPoPSKEKDcXlCzahzXpQlTfNoo0y1sQutohzc8EpYzWgVLKoapOgELTAK16I5KuBKTJM4SSWAxhRhHcQ0GMbe+F3V+OkDpq50lTAoxgLQ5sWoSjLzpwuGEtHJLY5GKj9Ers3rQGh//pwJEjZXBpK2LKxWkpEBkNcO0/ivF2M3zKKtAzPopcVAEaD16Fw8+GsPelfPTYL0cIUbHnpVJxpPUaNP5DhoOvmtHj/ASCwqjoDwiMaBm6Crv/YcVE4wgMpVbILNGVOaFCBPPSRLiHXNAd7sJtF8lQWBhtA5FIgOUrEmFxCdHy1hBM2SokLoqen6JorLhcjtWLh+HdfxQpOV6kVMcKJarAgkahFm+/PIJN/Tq48tJjBneiVAtG5GnY87Id+/fr4UktBERRByZlihrWMi38R9uRp2nChs/RUOqjbaSwymBcZkT7ATteeNqAnVMb4JNGg7IFSimMy6yQhzug925D+bVO6LKjfZ2SiKBelAHHEAXP/g4sXU0jrSzaRkS8Z682QiMLo+PNQYQ1OmiLY4O6JUU2dIV0OPzqMLKkciyttsYM7QoWGVGZYUDfG6PIccpx0fI0pm1PkJCjhnmxEps3d6FtYBy5q0yMz/oJtEkyqKrleH1fO9au/Cu+cvt/OfM5/P2fn8RzL1yN9Iyov7tCIcZ3vrcSu/ffguQUXsjyfPTQdBht9MvwYQJS6JBMza1ZeHjOSutGwsqVK5lA0n379kGv1yMcDuPaa6/F5s2b0dbWxvztBLfffjtSU1OZ5UEu/va3v51X1o1kpvpnP92OXz2+m/E6n4lOJ0NhsRklJRbc/90VzO8fBOKN/off12PTxmP4/g9XIy+fPSHOocZh3PHVt7C77rgf+QxWr0nDXd+uwW9+tRtv/jfi2T6TxaVWPPbLC7FkKXtSpPkYH3Hjkfu3w9Hmw9D+KcadYiYylQgF5SbYwz7sbh3E+GjsjL5ILMCSqkRovGJQfWGMd8eKBaK/SlcZUGoeg1k9BH9vvAOLNCsVI0N6BAMCTB6Od73Q5uoh14WgVAfhPBzxCp+JxKbHiC8ZNCXF6L4h0KHYt6zcIkNCkRh5S9qhCdcBwVnJYCQKovIhUnghpg8D/lnB0gIJfKIlGGuywNPnQnB8lnsJRUFdkgxQPhzbZ4GzM97dxLzECHNiPzoa0jDWGF9uKNYhPa8fcmUAvo6IZ/tMxDYz5IlAb3sm+t63R3zpZyCzKpG1cgouhw7dG90Iz3IjEmulSF9BkmBJ0fYeEHDErtyQREj6RRbQgTCc3Xb4Zt1nSkhBUJyIgwMSCAZDGD8W8f6eSXq1EUqRFznGMXiOxd9HYsc5NKlCs12G5t3xDi4phWosTg7CH1CgbetExK98Bro0OfIXB2C1uhlfenpWojKRToEJWQa6x3To2DsBvzO2L0vVItReAlSWdEDo6ADtmrVqKBQiYFqMrqM2THUH4O6fVU4B6pIkDAxLAVcQE63xQfUJlXpQghD6p8To2hfvsGIt1sApo6EXi9G7M+KPPhNDphKTNhrj0gD27OxDaFZfNtsUyE43wCUNYuu+biYnQ0wb6aS494EVuP3LlWCDfMb98vE6NB8dww9+tPqMEum8deP5BZFW3fRbGKTrIIAIBYIbIYIyzkv9XNQdp5up4+3yQvGPoFiAdaM75MW1hx44K9r3jBfrJGPppZdeiqGhIWam/dChQ0zg6AsvvIAVK1bEPFcmk6G0tBS7du3iPN/5JtZJ8qLykt9zlpNl6knXt0/rNXzty2/gL3+KBs3MpnZ5CrZvixepJ7jyqjzGu/3D4rL78SnDc3M+Z8jkgX2CO+Ph5bmZGDwSSRjDxle/5IKwM36wcYJAYil6tkaS+rCRvFyPQBu3BZ8gKQXNW7jfqoZMGhsuJ16M3MhTHUCY213gWN1F8A9w2wROKcox3hBJLMSGsigDw3WDnOWFV4igGNvLWe43FKJ9Y4j7+pO08PSx2yAyCAVw2dm3nJxAZlbAO0e23W5NBrrruZ2fLvu0AP4j8dvyTjCQUoDNr3Nf4+JqExx18SL2BJmVYmSIDnOWh2RK/Pu9ub/o77nrPcDLvY2sse1C2FmE+Ano9Ez07uK+RuVSK45sGuWuw3ITOrZxl8vKFPj3ofgB2wmMiQocGuI+njDhvJszi+mZCi/Wzx+IrOqlN6GfjuwKyBFcCwNVyOqlfi7qjtPN1PF2eb7oxwsW69cdvv+saN8zOsCUQPzU9+7di507d6KjowOf+cxnsGrVqriAU8KTTz4Jg4Hdn/sE1dXVeOqpp1BWVnYar5qHh4eHh4fnfBfqadTFjFDn4TmnxTqBJDSqra1lHnNx0003zXsusmd9vsBUHh4eHh4eHp4Puke9m34Hg3RkdT+NuhA2wVK+EU8TdJjiNHo42ePPFs4Ksc7Dw8PDw8MzP3xSpI+HMB1EF/0G7IhsZ0ykVkFH5cUkPyKJj3h4Pgy8WD/HUSrEUCrFcLnY9yobjXKMjbphNMVvKzpVmMxzZ45Vyud25DFZlPM63SQkqDgtJ4ViAVR6CZwT7CnTFVoxNDqac8+6VCZkAlG5oAQUQmIZol4eLEjmriMtljIBfrODDk8gUoghkAQR9rOnhRerZIBEDvg59irL1KAlAlBejr3AQhmE8jn2/gkFEKvmnoWQzJMgOEhx2/cxLyEXgxLSoEPsdSQOJwKZCOFZgdInEOtkEEOCgJ39PoqUYojUUoBjz7pALJj7PlMAJea2eiQolHO3kVQhhFNAgeZwORIpJEwwWtjPXkepVgq5TgzPJPv7mZSRe825Z10khlDJXQcSaCtWzr0XXCKfs6dDqBAybcUVDaUg90EsQDDAfp/VWgmUTu7PLINBxrjCWG1z59k4X+GTIp1+ArDjqd2fnc6uTtzpfvSjH8HZNA6ryII777yT2a7LBltGdp4PB30e+ayf8QGmZwpnc6BHX+8U7r93E15+sWn6b8RCbVlVEhobhiAQCnD/d1bgli+VnbagrX+9dBT337MRvb3RwLbUVC0KDUb07bUjoVSNdrcd7R3RWQiLVYnv/2g1bvgMW7Y3wOHw4ac/3o4nn9iLpCQ1fvLwOlx6eS7r69tHvXj6vv1498/t03aQ5JS5tWY0NY8wrhNpS/TYs6sPgRmCuKIyAZp+ITx9Pua5nQcm4J2KCqnkEi1C/jAGW6Zw2dUi5FCtCNmjLhsiqwmtA+lo3BhE7nIltPQkPH3RNpCaFfBpzDj0nhNZSyTISx+GryfqKCNUySBKSkbPtglIrRrQCg1GDkQDVYVSIXLWS2DDNki0IuiKZaCGD0VVPyVAyFCKwZ0+UHQYSRcDssAugI4GcgZVZRjdKYZ/NABhWh5czX2gfdGBjTzbBr21B0JfN0b869C+UYzAZDSduSpDB1v6GMQTbXBrS9G5Xwn3QNRNRWZTYUxiRP0mJyrX01hR2gR6JOqbLVAq4VdnY2DHOCRWI4JCFaaOjsSI+EBKGjb/xwe9VYTq6gC8Tb3T5ZRQAHl+Kg5vDkIgpJC1TIaphr6oaw4FKEsSsWdXGM7JEGovkiPU3IewL9oGmiIrBjopjB/zQVNpQVu9PUYQ2wo1IHbqE01TWHyhHBpfP/yj0TrKEzWQ6GSwHxmGOzkZdc1yDLZHyzUWKYryVfDuHYUmWwM/JcLQDNcc4uSSsUwDd2MfdMkSpBb54WmJBl1TYiFMlWZIJxvgoxXYM7Ac+99wTwtiMmBMrTGirWkSVNiPm28dR4JnLxCK9lWfuRD/+mcGelsFqLlYCUFnP4IzBLE23wARPPD0jkOYmY1j9UF4RqIDH12mCmKVGCMNE9AsNqKvz4/xrujAR5MogyBNhv11Q8gp1MMYEmK4yREzkNAsUqNuxyC0aTJ4jGE01A9F+4lchLylJmze0w2NVoKMTD2TF2K6HwgpVFUno7FxmBnX3nP/ctx6e0WMHeSZDB9geu4wM1C0oaGByeUyOjrKJGp89NFHUVERa1d7PuqO08nU8Xb5R95PFxxg+unme86K9uXF+nn0pnl/axfuvvNd5kvP6fCjoyN2SY74phMP9drlqafl9d3uAB752U788ff7sawoCUM7phCcIYwFIiChRofdR/vx6c8U494HlkOjiXf3IOPLfzx7CA8+sBlDg7EWe+s2ZOBnj6xHTi57UqTWvaP43dd2wzUVgFsQRFtTrOuFOU0BsVmEiTEPCnVGTOyNtbdTm6RIyFNjqN0JS5YKrdtjZ6rVWgqfui4Azdgx2JW52PI8hdCMCUKhhELZhUqEugchTrfi4Lte+FyhGA/vpVdKoPMcgzTRgr56L3wTsTOM6kIbJvuC0KWKkGE9AIk31oFFkWOC0jROFB7G2vVwtcc6f6iyFbBVDUFAOzDZmQHHwVgHGEqjA9Q2+EcmYSiiICOibwYBSoueoXUY2u9FYrkE8ol9zEBg+v4IJJiQVqFntw9Is2H7G274vdFyoQi4/PM+pIsPgDKnon+3F4Gp2DrK81Ix1e0BZTJg5w4hhrtjZ5pLlsuRqR2FUC5FT4ccw83RwQPBUqCAyexDOBBGJ7FT3Bs7m27LkKJ0URihMSf8cj06d8Q6/Yh1Egiy9OhvciApX42hHbH3WaYSoPJiEdDXD3WOCRMH+2OsFmmhACNJOajb4UPuYi0Eh8YQmnGficY2LzFjqMUNS64C1OAYAhOxs+GpVUpoREOQGaVQS7tBTcXago5J8rC5Lg/+kBBOOoyeptj7nF8KfObKdkgDk9hxuAxb/xW7amBIEGNJlRChgQloM+RwHokOgAiUXAq/JRN99W4YC7To3zUyc4zHJP1SlpnRXm+HcbEGu/cMwzvDalFAUVhSY4X7qBuGfBUONI3BPh676pG4TIOjg+MwJSpwtH8MPQOx96Go2AyfL8Ssmnk9AbS1xfbVgkIT85m1clUaznR4sX5uifV/dF3HOM898sgjCAaDyMzMZP5N7KNPFeeC7jgdTPFinedcf9OMjLiQlfrrOZ/TN/wNqMl2gdPEb++tw7OPNHCWX3ZzHu55ciVn+ZbNnbjsYm4rRpIYpeHIbZzlROyvkv0RoTkSLi1JsWCsk9viL6NSj2N7uW0Ol6+2oH8rt/1c+koTOucoL1ithLCT2yLQVChFuqGOsxxSCTxjc+9yUyYKEZ7itqM01UhBjXdwljupWniaOjnL630XYstz7Ns5CKuuksDErAKw4zGn4L//niPzpUUCnWvuTLeDlAQ+J7cd5OLFcky2sWcxJmgWm9Ezh9XiiuvE8B/htiFEVg6OvsV9/sRKLVxH4735T2DKFWJR5jbOcpJh9ntPr+baPcWQnarAeFfsYGYmV1wThLeV23LTl1yIY5u5+3q42oQtG7mPz1tmQnMdd1/X5svwagd3P1OrxXDM8s2fzbHerzNb+s5keLF+7uAMD6DyywOMSx1h3bp1ePDBB1ld6hbCuaI7TjVTx9vl2dyHFzyzfkPLt8+K9uX3rJ9nkNmu+fgwWUM/CPNdATXPM8LsW12n4doPPH1+ikJ4vt1f8xXPVz7fNc7XxPPtpZvvBPO8fuQccxeTWf6FlM97DfP1s3na4GS66bx9Zd52nv81FnT8gsvpeZ8SnsfxYKG7Nud9LyywjiezUXO+9zwPz6nCQXejhX4e7q0iSKVSfOUrX2Fyt7Bt1eQ5vYRp8vjw7X42fWzwYp2Hh4eHh4eHZx5bxj56C/rpbQgjAJutkNn2UlJSwrcbz2mHF+s8PDw8PDw8PBy46WEco1+Fk44EPOtRgN/85jfIycnh2+w8dINpaWnB888/j4MHD6K0tBT3338/53NJMs8///nPOHbsGMxmM66//npUVVV94Nfkxfp5BnGNmA/BSTxnLoLB8JwODcR9Zk7meXkSIDvn4fOcnuxZJ8+ZGSwXxzznEMz3GvPUYb4lU7JzYU6DvHnuEQ0Bs5+ZmmOPwfztJJjzVpDXmPMS57vNczsAghLMvUY5bzel6HmvYb77PF8bzbvyPU95eN5+NM8JBAKmL81l6jVfHU5mx9Q8lzAn873+fJ83wpP4PJrvc22+z6T5ys8meJ/1U0eYDmEYezBE14FGGAJIkUKtg5KynvL96Txnh1hfuXIlhoaGcO211+I///kPHA7uuK+NGzfi0ksvxSWXXIIrr7wSBw4cwIoVK5gVma9//esf6HXPjU8nnpPGYJDjv2/fwLgszGbRYgvefu+zHzq4lHgff+X2/yI38zf441P1nHvfb7qvDDfeWwqJNFatCUUUklfp8MQr+3D3ne/AbmcPiiPOD3/482WMt/psysptzD7lKy/7J1pbohaHJziyexhfrHkFKTk6ZBTq48rN6UokVKjRQI3DsDT+/EarDOvXGGEbdqFqlSlu3zax4EtdacCWhhHIVuhBiWI/DIgbTNpKMwYbJpG+0gyxcpZipQB9rRHvH/SiJzULtCY+aE5dlIChHhWOjl8Iv8wWV26XFeH9/etwoH8tvKr0uHJ5mhYJyyVQJ/sgz2Xx/FUbEDAuwrHNVrhE8TMAfuhxtPt61D1vgV1dA3qWGqNFEkxpa6DoGcCF10shVcxqAxFw3VecqM5+B9mXSiEh3uCzEGdlwjcAXHiFBOaUeF/w7EodUi1CmBfpoM2Mv09JpRIsv9yFT148gkXL4483pMhRsEyPyQkaxnJTXLnKIkbFFWKkilpReZk8TnDKtRQuv92NJOEeZKzXMM4oMW0gFMCeUoCtG4NQLLFCOMu/new0H66S47vt7XinWIqAMb4NJAUWHOzW4b9tV8AhSYkrH5UU4MV9lyI/X4vUQm1cuTlDifRyPewCwFYZ39dlVgnCyxX45U4aPelpccO6sEyGY7ZCvLXTDfEyC+PBPhOBhELycgtkh+24fF0CFApRXHxMVW0CJC0+rKxJhM4oZQ0+FUxRuLG0CNlJurjyihIbCoxGrCpMQWFOvMMT+Rwjn2dcwaXdXXZ89lMvoyT///DSC1Hr2hOMjXnw9a+8iZyMX+P3T+5DiMPj/2zzWffRdv7xIdrASXfj8bqVjC3jD94tg+mybVCUNCNjkQ6f/uLF2HrgJbzV81283H0r75d+nvK73/0Ozc3NjK++SMQ93+3z+XDjjTeitrYWL774Ij772c8yIv2ee+7BXXfdhdbW1g/0urx143kalU2+lJ763X785IfvM7NS33lwJb5wS9mHmlWfea7Jyag1W2mZFT9/dAOWVSezHtfbPoVf3bUT21/rRmK5Bq2OCXQci9pJmi0KfO8Hq/DZ/1nEOhPtdPrxs4e244nf7GWea7OpsG9v1L9bLBbg9i9X4tv31SLoCePJ+3fj9b+2RL2pKaCk2oruFnvEZ71SF+ezXl5hg25QBE+/H7UrTPAeGkfAGXU40Rdo0e0B2prsSK814ljLJOwzvKkTMlXI1ingqrcjsVIP14gP9hne1CqrDPpMJXp2jkFTqMFIKIy+GRZ8cq0Yy6oVULd3Q25VISxXY/Rg1J1EKBUge70MCfQ2BIVqNA9VoW1j1H2E3Nvc9UqkyvZCIvbAVKkBNXQICEeXFQSJeXAc8yI46QFtK4L90BDCvqj7hjLXAkt6B8T+TvQ5LkTzW3L4Z9xnTaYGaflDkE41w2dchP5DIniHopaaEosKQ4IE7H7Lg2UXh7B6yUFQ4/3Ra1Qo4ZEWoGfzFMQWM1x+HSaOzKijTARBThK2veGCxixFRpYMI3ujAzGBiELCMhNGGicgltBYvC4Ef2tHTPSQMCcDm96Twz4aRm6VEQN1Ywj5ovc5sUIPyuGFu9+JogsVoPt6EHTO8JpPN6FrUIdje72ovTaMVEUTwvbofRKZDHCGEjG4x45Acioaj8ow0j7jPpulyCxSYXLPMHy5MrxBTeLgkagLjEolwaeXpKKgwQmJWYkJqRYddVEHFqFEgKrLRCjVbkaIkqOurxYH3prhs04BqbUmtDdPwucNIaPcgNadYwjNSDyUvcSA4IAPjkEvlMs1qKsfgGdGXy5YpMcGswuqwVFMpmVj+64QHDP6sjVThUyzCN7GCVgrDHAPeeHojdZRYZPBmaTAlm3DyCvWQ+cXYuRodNZJphVDV6rGru2DsKQrodRK0F4fvc9imRDWKjX+tb8FWo0Mi9LMaNg1FLOiVlxtRV1TPzPYue87K/DFW8tZc0N4vUH84rFdePyRXcx7+wQrV6XiZ49uQH6BCX/6Qz1++P2tmBiPTgqULLIwn1k1y+MHRwuFd4M5OywZ/9p6JV577TX87W9/Qzgchk6nY3zUN2zY8JEHkZ5ruuNUt8tfM38OheDDu0C5wx7c2PGtD92+xFd/+fLlePPNN+PKXnnlFWY2/YUXXsA111wz/fe+vj4kJyfjvvvuw49//OOTfi1erJ/nbxoyG04E3UJsz2763CsxCZdmQj7bnnn2Klx+ZR7n8Y8/tBMP/mALZ/nNXyrD47+8cE4rx2uufIHxY2YjI12HhFE5HJPsGUyVGjHCJqDnWKxP9QnICsA3y7MwsY/dwo+0n6vKhLr3ogOF2VxyUQoG3+K26LOts2Dnu4OczhcVK/XQtg8xvuFsaLM0GGgLIeBit0qU6cX49O1vg3ZyWPCJJBgaXwp3W1QcxSAQwGtdjP73uS340jZYMLqzi7M881IdtPbNnOVu/RLU/1McTWY0C0W2Ge37wgh62O+zwipFSW4HQg72FRmBXIIDAyUxAjKmXExh9SdpuA7H+o1PQwGZF+kQbm3krEOnejXe+gt7BlWCeoMav9neyLnqdEFFKtL2ihGcMZCYiTFLAdeIPyYx10xkGjH8BiFGZwwIZyKSCiBeLMXhfdGkUzMhg/VLVqXhyLvc9/mCS20Y2cjd1/UrbTj45ghnX06oNaBu7xBnBlNbgRp7+gbg4ejLWr0UrzV8lkmaxsWGNc/EJFSaCdnusnptOt59m9su8k9/vRzXXPf/7J0FeBvH2oWPbJEl2bIkM3NiSswMYWpTZsaUuWnalJu2KaR0y0x/GVNI04bRjgMOOGTHzMwk29L/zCgGWbtyEseJYd776KbWaKWd2dndM7PznS8IpxMm1kc/NboDcJp7GNXVhvNj7ty5dBZUpTJ9MnUmGK+643S1yxfeK4Yt1m/If3hExDoR48uXL6dr1r29vU2y2AYFBWHdunUn/FtszfoEx85++Ovuamv4/cjJDZs8ajaHfoheWMuTHr4XpVLKK9QJdTXtUDTwL5AmCZK0Qv7H39rOHvTwpD7vtY3rbOP3EyfwCcxeOtt7zFrUadu6eYU6oaOxC12tZsrru6Bv519bh24telq5BzMUnQ7aZvNryMnTC3OQ7Jjm6Gnvhr6HvzOQbJvdZr6is17LK9QJunYtbSfe8i499B1m/Lz1gIWuw+wa745W823U2KY1a43a0tSF7k7+vtpW28Ur1OnvN3WhXcC/h2QQoG0x0wY6PdpazB/H7g7zfbmnXWe2L3e2dvMKdUJro5ZXqBMa6zuhsTN/g64xc00i69PNlROGumYxxhc9+k4U69ejQp8GXZkaLi4ueOyxx+j6ZMbEEP8DIZac5DUcSkoMkz6Ojo4mZeQ9MsN+MrA16wwGg8FgMCYkDfoc7Ne9j0p9Rt9sOnH6YEJ99KPTC4b9Iri7u9OZ+t4XmREfLlqtYfKLa127SCRCRwf/xBIXbGadwWAwGAzGhKJL34Yi/WrU6A3L2iRQwVWQSl06rK2tz/buMc4gxcXFRstghjur3rtEhtDW1gax2NjkoLW19aT7GBPrDAaDwWAwJgTE5rQWWSjSrUYX2iCABRwFsXATTEMX+gPjGRPHutHGxua0xwT0evAXFhbSIOVeSNAyGRyQgOWTgS2DYfDS3t5F3RJuuv4PlJY0ca79fP/dXWjv6Ia3j6ntGiEm1hU//nAQ6du5g/a2rS3CjlXFiI5y4SwnVoz3PhDLu495efV4+cWtSExyh1hsutbXzk6GwCl28ElRQ6Ywtccj74WlOCFksj009qbrYIUiC8Qnu6FABig8OALaiNVinB06unRwDzS1zyMERNmhpqkLjhHcQUqaEBsUdrbCK0HNWe7kK4eTDeAU72hiBUmwcrBCl6MKmngHCGWmbSBRijB5rjXqLRIAK1ObQwhFaLBKQn2XO4QajjpYCCAL8oZM3g5rb+46Woc6I7/ACjI/0/V5BIW/A/IP26HHMZizXKBxQXmZA9SRrpwG6lInBTqsbOGU4AhLqWkdpSox3ONVsPCdBEu56ayIhVQEeag3JkfpYe0kNS0njjLJdjjcqISEw0KQIA92R/YhB1i6uHLXwc0bTZWW8IzmPs6OQTZQdkkwK96L05/dx1OJhb5CRM5XwlJk+gGFgwS2IdbwSLWjFqGDIe/5ptrDL9QW1vambSAUW8BxmhLtqm44cPRlsk9xiS5Q9QCugdw3Lp9oNUoauqCeyl1H62AbbNTWwC6Ru5+ofeUok3bAL0UDC458CSpnK8i9JYhJcYV0kBUkwcZWgpBkRzz8wBrOdeWdHd34bNluBNpp4OJiOnNFHGVuuS0CTz2TwmlfS4iJdcFvvx7Gtq1FnOWMsQuxZ8zWf4dc3a9UqMsEDgiyuAmeFnNgKTC1eGWMDbGuH8ZrpJg5cyb9d+NGY1OFnTt3oqWl5aTFOnODOUEmWlT2778eweOPrkdxsUGky+UiPPRIPO65LwYSiRBbNhdi8YNrcehgdd9NMC7eDfv3VaK5WQs/PxWkVkJkHeh3nbj8ymAse2E6nJwVKClowkuLt2DNyty+cr9IDYrqm1BY2EgDyJ56JhXX3ziV006yra0LK17ejrffyugLLvXwVFJXm8w9FdTxITbOFQf2V6KpybB2zNlejkQfN2Rvr6EBcMS2kdhH1lcZbvpW1kK4hSuxM62MDkRCIxzQU6dDVb5htkUstsCFiW7A3gZ0t/VAOdkGx7p1yDrQ0Cd2AhPtUXK4CU21nXDwlMPBTo6SXf0OLKGxGliWd6CppB0yBwk6/CRYk1bS53EdHGIHmw4RKo82Q2otRGScEqKD5UC3ISDP2ssaYoUItVl11NtbMcUJuzY1o6PF0AYqFyn8AqxQtbOGDiS8U1SwaiyArsUQUCdVWSLo3HaI6zNp0GiXJhg7VnqhOtvw+0KZACFzLSEozYZe2w2plxM6Wi3Rmm+og95CALGvPyr3NaOrSQuZuxJ13UocS+ufkZo61wo2nWXQ1rRCYieHTmmPom39g72AmQL4+x2Evq4CAisZqnsisfN74gJjKFcHKCBT6NCUXQsLiSUkk1yxb007tMcDWG3drWDvIUHV7lrq/e2eoEFrbjW6jx9nmZ0IvvFAx5F8GhSqCHJHQ0E7OqqOBxVKxGi198fh9c3o0ergEGaLkiYtSo4ZLC+FIgHOPc8KitJC9LR1wcpDjdomFYp2Hd9eoEf0RQI4WB5GT1MzLNUqHKsJRMYf/VGV9hEq1FZ3oq6gDXI7MWwnWyNrW3XfcZaFSLHfsg4Hj1TTc+uGZBckFlXA4rjPt06jRm6rM/J2NFLh7p5gh8OZtWhvNgRe2jpI4eVrjaLttbTfeSVoUJ3dgpbjVosSGyHU4bY4sr0aPd16uEQpsamxFIeKDJaXUrElFkT7onhnIzrauuEXpMIknRSdh1v7B6EJGhw91IiWWi3svOSw1oiRv7u/L0cmqGFV3Y7W0jZYOUhwzFuAj7cf70gAZgW7Y2qzEnXZrXR/xBFW+D09D13H6xjgq4anzAaFexsgkljAI06FzXuK0NpmCIB1cJHDxd0ae3dU0GvAlARHZBwtR22d4XxVqaVGlrObfs/H24vTUV5gOI4iqQXs4qyxeVcRtXJMSHTDq6/PRugUR07LWV9fFT0W+/f3uzYRR5jnX5wOF1frMeMGEyd4AUKB6YB0os+m1yELpfrN0EELASzhKIiBA6JhMSA7mxYN1EPd1ZV7QH6mmWi642Tb5ROPN4btBnNL0QMj4gZDmDVrFs12umvXLjg4OKCrqwvnnHMODh06RL3a5XJ+R6vBMLF+gkykk+aSC3/Cf6v7RfRAfHxViE9wwzdfc9vXkZnssAhHrF+bT5MTDcbaWoy7F0Xj+7cPoGOA/3EvRCjNucYfT700DSoV9w3n2LE6LJz3HUpLud1NyP5VVrQgL6/fs30gUUHOcNMrkHuA24rR0UcOtasMh7dw29u5OsmRPNUBa1aXcbpeyGxEmJLoiJw1VejpMv0AESbhsxywalshmpq7ODM2njPHEw7HaqCr5w5CsY9zwsEDWlTkcZf7xyjhr6lFT1kFZ7ldiBh6uQJZKzmLYespwqTEHtRlcM8uWihkaLELwo6fm6DjMO6QKiwQe5EMxRtr6cDGZHuRHlHXCHF4lQ7NldzWIW6pjji6W4vaAm4rRO8EFaTaFrQWNHKWO4bKoHHsQH0Wjw0hEcQCD2Ss57bUtHcRYu50OQ6tbICeoy9bqQSYeoEIG77UQdum57SCdJrpiD1bq9DG5eAi0MPlXDkiG8ugaOB2J2n39kf6AQEq8vq98wfiHWoLmQ4oz+K2HbX1laPQXYtf0/tF9EDcHK1xdZAftGvqOdPuipUiyOLU2Le2grMvS6wsEDpHhTe2HkRdi+lxEloIsGhaKNIyy1FRw73EYGaqN47l16GwiPs4Bkc6oKK9FVlHuc9HkswtwdmV5mvgwtZdisseC8X1t4bx2tc+tmQdfv7xEHo4bEOJgCdWjvPP8R8TYj0CS2ApGP6a27FGFxrxccY1JsmK6uvr8eabb9IZTUJgYCDuvfdeeHp6cn4P2d5iyNTHZ4aJpDtOpV0+dn9z2GL91uL7T6p9X3nlFWRkGIKRf//9d9jZ2VHBTiBJkBYuXNj3WWIBevHFF+PgwYOIj4+nIp149f/yyy8IC+O+HvHB1qwzTOBbskLIy603m/6bWKIRazUuoU4gs+570ys4hTqhu0sPhYWIV6gTiouaeIU6obCgEWVl/OW7DpVD22OaDbGXyrxWCLr561ha0Ypjzh289nRtxH6vuYdT3BC6OnUoaungFOqEHp0e2uY2XqFOaChpQ0Uev71d8cEm+PhxC3VCTZYW9V2kjbl/o6GwC23+Ziz+WtpQ2wNOoU7oaNGhvkrIKdTp9l0CFGRp0FzJ7z1fV9LFK9QJ1TktUOu5BR6h8kAbhM3cIpZSW4eSDjv+7y/rRkWRJadQJ7TX65FzSAVtWw2vFWRbSxe3UCfoBfBsAq9QJ4gaa1CRx38zyt/fALWZ1YwNua3YKTDN5NtLSWUzZPY6aHkeB2sbuyBs6ebty53tOmQ2tXAKdUK3To/9TXW8Qp2QV1bPK9Rp+dE65LXzH8f9+6pgnc9/K2so7kBkKEem3gH2tUSkcwl1QmtrF51tH65YP1PYWHhAKDh1ATOWExoRoT1wVpykeyeJZ4gYk8lkuOuuu3DVVVeNGjHOGB76AY4up7r9yZKcnAwfHx/631dccYVR2aRJxvlk7O3tsXnzZmRlZSE/P58K+5iYGFha8tvz8sHEOoPBYDAYjHEDWRNMZkBXrVpF/w4ICMCyZcvg6+t7tneNMcaJj48/6W1CQkLoazgwsc5gMBgMBmNccOTIESxZsoQmnSEz6DfccANuvfVW6m3NGF/o9YbXcLYfKzCxzmAwGAwGY8wHkZKZ9M8//5wmpCFLYsgSmClTppztXWOMEHqdAHoSDT+M7ccKbOEWw4QrrgrmdGAhpE7zxEWXBkIiseQN9rrg4slQKrkDnDxdbSCTCKFx5F5Taa0Wo6y5FTnZ3OtsW1u1WLcmD5MDudcaCy0tEOfngpRwD94jGxvvCrdEWxrgx4VrlBISfxEsOWwSCX6T1bBRSSCVc7eBs5cCEitLyDXcVmBSBxHaZN3QuMs4yxXWIois5LD04rYQhMgCbY5KOEZwlwssgMmxalgcX1fHRbOHF9o8NJw2iQS7ECW6pXYQiLjH8+0qR9SLxRDZcs9Wqd3FUKq6IXfmjj2wtJGgTm8FkRt3UI+F1JI6ybhMteGto02gLcRB3FaRBI8kJZSknOd63OXmDlcPObXn5MJtkg20MgksFdxtYO0mg0wugLUj93G2sBOiWNYJhRf3uSCWW6JcJkajE7eFoM7SArlKDRyj+AOf/JPs4JKo5u3LmmglUrzd6HnBxYKpjvDz1UPC05etvKygtQJkau46yh3FsJVI4e3KbdWospZikkKNyf7cMSJiiSUWXB6A2GluvMfZJ1JD7RT5SIpyh+sUG/AtQ3aeao2Vq49SRxg+zjnXH7Y8cTLu7jZITeUORmSMDnr0nTTB0TvvvEOFOsk++s033zChzhg3MDeYE2SiRWXvzaygXsYZO0rp325uNnjhpRm48OLJff7mjy5ei9WrDK4x5EZnsFELg6WlBaqrWvH0kxupawx51GQlFSI10gNHMqrR2dFD/c2Dw+2RmVaO7m49LCyByYkO2HWwHI0NnRCJLHDn3dFYsjQRCoVBKBC3hieXbqDBpcSujlgz5uTUobbGYOWWOMUVikYRynINwaX+0Rrsra5CXonBFcY/QA2RyLLPbjIkyB6OWhmqjhicNmw9pOh0BjJ2G+rs7W2LAGsVyvYYgttsNRIEBtj1WT/aOkrh5K3AkXRDgKGVXIjgSHsUpNWhW6uDTCmC+1RbZG+tpkGKxCPdJkmO7XtL0NrSBYnUEpHRLijIqIO2Q0c1ZXy8K1qPdqC5uhMCCwHCUjWwLa6GvskQwCcIdEBWQTeqCw1Bif6xGnQWtaOlzBAo6hlqDZVIh/qjhn12ClNCo6hBT4VhH7vs1DjQ4oijGYaAPpcABSbb66HLMQyOZA5S2AdIUb+33FAnJzk0Phboyiugf3dLpDgqDcKONc3UElCuFmNSsBIN22upTaJIIkDYHDm0OaXo6eiGpVQIm2AnlKTXo6eTNIIAwlBnHNndhrY6LfU3D0pRQ1hQCV2rwXrRdooDagu0aCkzHFenWDuUHWlFc6WhXBOqRHNbD6qyDcfNK8oWmp4WdJUZ6qwOIC4wWrTkGIJX5T5qGoHfkmuoo87WBkU9Hji61dAvVJ4yWDpIkL3LUK6wFcE/RIWibbXQ6/RQ2EvgHSRH465qeowspRZwjtKgfFcdejp6IFIIoQ5VI3drnSEQ0wLoSJJh3f5SNDdpIRJbICnWHdqMDnS1GqJVnRKUOFJQh7rKdtqXU6fZI6atDKIWQ51r3V3wd7YAxXmGvhwS5QBhmQ5NxYbj7DLZhrZdSZbhOLqHKiHt1KP2qKFNFB5W0DoLcYRYeBJnHF8FKlXt2LS/2NBmDjIsjXKBJbEp1AMSexk6beyRtc6wvaXcEoJIG+zZXoXuLh3ktmL4h6hRSNvE4EvvkGiDzL2VaG/phlhqCc9YFVbvzkVbRzdtp/Pj/NFzqBtNNZ3UU90nQY3tB0vQ0Gjoy9MXeGHpaynw8DUMOv/5OQcvL9mC8uLjxzXEFnW6Thw9Wts3EdDSrO1zefLzVsHd2hpHMg119PZXQSWRoOiAoU1snCQQ+Fpi647jdfZS4sWXZ+Lc8wI4r3m1NW145qlN+PrL/dDp9JBKhbj3gRg8tDgeVlbDX0Jxptxgplm8O6ECTNv0VTim+wltqITvVA0efPBBXHvttfScH+tMNN1xsu3yvtPbsBqGG0y7rh13VNwzJtqXifUTZCKeNOSx4rffZCE/rx4PPhwPmcz0hvXv6lxsWJePxUsSoLEznSnemVGG/72cjsI99agsNXWEcPOygYO3AkfL6qgl42CcnRV44qlkfP/dQWzZbGrNZmMjRmS4C9QtYmTvMJ2NF0ks4R2vQrm+FelppfQmPBDyBCE1zgMSS0ts3GnwYx5MfLQbgiVqlO5uRFujqUOK9xQV9XyuPdyCxgpTdxVHPwUkHmJkFFegKN/U9cLRRY4wf0dYVQpQxmHBp1CLERFjg8r6Hhzd0e9z3YtYZomgWDs4oAfVGdVUfA2EeJH7pNqivAdIX9uEni5Te5OQJA3CXbRoPVSO7uM+1wNRh2rQKJVg3foeNFUbRPNASBKdUG8hlI1VaC83deKxclGg284eR47qUX7ItFxhJ0ZIrBy6xh5UZprWkQriMA1qm/XIJQODQRAv8uBUFQKcG9FysAj6wc4exDd8qgsK6pXYs7YNWg6XGrdoFZ1Fr97XSAcSg3EJtYGHmxjNOU1oKTdNyGPjpUCriwSrSyqRd8zUNtTeSYa4ABfU1ncge79pHRRKMRamKJFXp8eObaYOM0QQR8U4w6ZbhJxtNXQgYVRFCwECE+3QZanH3vRK6jo0GK84FTydtPDNrUd3i2kdrSfZ41iPLTL216O+0rQvu/rbwM5NhqNFdSjLNz2Odq4yKCdZQVZugdKD3H3ZOdYG598VhNT5Xibl7W1dePeFDKxPK8C29GKTNaW9+ROUejEOpFXSgcRgouJdoJcCG3YXUieXwcyc7Y33PzyH5nvgYs/ucirYSTI2MmA/XTCxfvqp0e9Dvu5v6NAFIazw/b9vYc6cORgvTETdcTLt8q7jO8MW63dV3j0m2petWWfwQmYmrr4m1GwLzZ3nS198RMe4wMtBiYxSbjtIkhzJ2kPKKdQJ5eUt+OLzfVT0c0ESHtWWtaHmMLdVY1dnDwp312NXO7dFIBHvG7aTdMBS3sfkaTtL4OWn4BTqhPz99VDFOXEKdULlsRYo3GScQp2Wl7UCnnqUZXHXoaVOi/yyLhTv596eCM+SIw0QNHL/PhGuuevrcaDbklOoE7K21iI4pYdTqBPqDtQi294bTdXc3vWlh5sQP1nOKdQJ7WUt0CrsOYU6rWONFnU1MnRmmQp1QldLN5qLWpF72FQkE8iM9qFN9XAOMsyimqAH6veW4XC1jFOoE0p21sNlqi2nUCeUHWiCq72aU6gTmgpaUOUMTqFOqK5oQ5VnO3L3c/f1lkYttudoUXiE5zh39ODg/mpoarlneYl4P7SlGlqlwR6Ui4L0esydIUI7h1AnNB+tRrWLjFOoE0pzmiBxEXEKdUJNaRu8PW2Re7CWty9bdQg5hTrBSibCZYtC8NzrWzjLSbKybVuL4aqXmwy8e9mVVgaRt4hTqBPWrcnH/v2VvGI9ItKZvsYqHfoGCHksWccLOn03SrABdfos+rc1POAsSEBwMHeGZAZjrMPEOoPBYDAY4wStvgk94M9PMNYTHy37YxY++ugjlO4/DB+lGldffTWuvPJK6vwyOCESY3yjZ24wDAaDwWAwxhrjOSlShW4Hnn/+eZq23dbWFu+99x4SEhLO9m4xGCMOm1lnMBgMBoMxqpe9FOnXoEKfBp92NaKiopCamoro6OizvWuMs4heLzilLKQDtx8rMOtGxrBoqOtA2nqedcLH8Q0kbhzcZZaWAuoMIxbzp98NDLKDtTW3dRzBL1ANNY8VJMEnUAUfXxVvuYe7Dbzcua3nCBo7K8jt+X9fqhBCYMt/KlmKLGClENHgOC5I2whsLGigKB92DmIanMeHjasV5C78bWDjKYeDD/caXYLSUQqBWs5bLlSIqSMIb7nEAiK5iNcKkrxvSdpAwt9OUlsxxDb8rhtSRymsnbjt9QhqXzkkLvwBgRIHBTTu3DaKBGJPqHLkn78QySzRrbDktYIkDikyuZjXCpLuo40UVjxWkAQHexls7fjraO8hh5rH8pOWe8rg7Ml/HJX2EohV/P1EKBdBquQ/BkKxBXxCVPS85evLYhtLiK34+7JUKURTHf+a6sKcBri48PdVEvTp7sMfDOboKsfkQG6rSIKNUoKSYtPg1146O7qxcVU+75p4xpmlQ1+HQ/rPUKnPoH9fcsklePfdd2mAIWNio9MLhv0aKzCxzjglyI3su48OYG7wV7hh3m+49byVKMjhDqy74d5w/Lj1MoRGGXtiT5qqgdxXir/+zaHBXmHhTkblwSH2WPXfVXjn/QXYvW8RLr/SOHjIw8MGX397Ab744QJ8f+gyXH5fiJE3ulIjweJ3k/DRlguwY/fNeOrZFMiJoDyOTCbEhYkBiCxWw3OPFJfFTYJa3S9kiLieluQJ23Yx/t56DHaxCmjcjIWSV4IaB5X1eH39LjRE9MAhwFhkuEXaosZTi79XH4OrpzUCp9qZDCTsghX4fO0B7LavhWucrYn4mjfbCu552Zjh1oComWrq+tGLtb0ETolKrMsswU/l9ZAkamAptTRyUXGMd0BZXiesjrUgIsWeWkr21VFsAb9Ue1S1tuODlc2o9nWjNn59CADpVFdsqlRi979V8Ay3NRH9gXEqzJ/ahdbt+VB4KWEzSW1ULvPXIEfmgr9/robeUUxdVwZi5yOHX6QtitZWoU0nhFOcvZEgljlKIYy0w7pNtahs6YRXqh0sxf2XLiIu3ZPtkJ3TjP9bJ0e7vx8sBzgXWUgsoQz3QGMZ4FyTh7j5CmrH2FdFCwGmzrZF0qRyuFXuxbRzhdB4GR9nl3gVCjVafLwqFwV+UthMsjYqt51ii4POlvj372JMctUgJMLBqNzXX4ULg7yhX9WCSIktIuOM+7qjmxzzo12h3NyK2E4rJCe7GvVlG7UEUxOdkHugDvtrauE5TWM0uCM+6fGzNAjpaURoZR3mzrKnOQt6Id+VOMMB0+VaFG+uhyLIBVJHY1EvmuKM7+ul+Gt1IRzDrOHsb1zHiLkueGffebjvfwn4LONChCUb18Ez0BaaQDlW/puNBjstfGON+4GDp5zmMFj9Ry6uCPoRv314yEgQV1e04pEb/8Ot56yEuEaAGUleRvkcyIA9MckdxcWN2FVUidAURyhs+utILDIXLY7E6qzr8PPvl+GTzxdSN6m+fmAhQHyiGz2v77/nX1yw8AeTfA5r/8jFgqn/h9su+BOXJPyAvTsMFqaMs0OtPgtZuo/Qqi+HEDJ4C87HTTfdBEtL/sEggzEeYdaNJwizUOonM70cy+7biIOZBr/ygTdLIszvfDwGsgGieKAV5M9fHMIXb2XCQmmBTRmmVoyRUc7UxYH4td96WwT1bB9I+vYSPP7YesyY5Y0HH44z8T/OP1SPNx9Mg7ufDRY9FwUbtfEsZWlJEx5/bANqClvglCdBZ5mxY4TI1hIt4UBlWyssG4HSY8auF8QvflqUJzprulEgbsK2gwZP9l4sLQS4IiYQ8koLNCm7sGm36VOHsFgnNDZ0wMpBjA1phdAN8qebGeoJ/3Zr+LkLYZtXCEG3sXuJzkWDQ63W0FsLsWN/GVqajevg7a7ADA8VpJaWKDvS0udP3otEI4blZGu0tHejsrYN5QUGX+te5HJLXDzbGrL2dhyotMLBzFaTGeSABDu0VHcgxK0b4lxTpx5NhBM66jtQIbTBxv+aTB43+keqYdnaA7WDBFXptdB1G7eBU7A1ZFKgRylG2vY6dAxycHH0ksNRI6UDk2NHGtBca1xHR1cR5ibrYCXoRlNxF1qJ485ArMRodvZAQ20PAjQ1sKgydgvSCy3R6BSAggKgQtqF3fuMy4nwW5jkCnVdN8psLPDvdtM28I1Ro6apDYEaFXq2tkI/yITGZqoCRWiHu1oOUUYLdB3GDi5iPysUWBt83IlIJ24xRm3kokCQqxpyC8CxoR664/kG+n9AjEoPDRqaujFJ1wGUGPdlSysLuMWo0NHYhq0Nltiyu8GkLxMbxK7Gblz9TBjizjdNNLb2h1x8+VImhEpLpG0vHuwaiqgwZ9i0iSB3kGBXehm6BrkRTQq3w32vxWH3rnJq2djSZFxHe3cZpC5iCEQC5B6rQ021cR3tNTJE+jvB1lqCpa+lwsvfeLDb0qLFqy9tx9o1edBqe3D0iLE4J/kc7rgrCldcFow3nkzD1v+KTJ4UXHBNIBYvT4TGgf+JxonArBtPnB59F01yVKXfQ/+2FnjCT3ARdOjB90WXw8HBgWYrXbBgAUSi4Xvgj1aY7jDfLm9q3hu2deP9tXeOCetGJtZPEHbS9HNB9Lc4vM/UB7qXt76bj3kX+/OWP/X4Brz5+g7e8htumor/vTsfI8l1lt+ZeDgPpNSnA6VF3PZ0BOcoG+zJrOAtnxfvg31b+cuDUhywbpsh0RAXtyS5I57H6pHQ7qzEh+mGxEhcyKyESGgzLy5y7YDGan7XiOBgO5QeT7rDxeUXWKF9P7elJqHW3wfb/uW2YiSEJ9qjMY3b4o9gE2SDvVncT2t6vdmJ5aM5LgxqpsmZ+LAPkqKtiH8f1yh9kLGdv6+T2eW9W/iP86wEdzRs5m9D70gV6vfy/77IQ4p/irltR3u5SWM+9EjuIjcdrAxgp6cSB/bwHwfydOqCRYG85R+v2I0VS7fxlkdHuyA/g7+OLgHW2H88gRUXZNlQXjv/shVCXtG9sBv4RGgQM1K+wq6d/H11TrQ3DqRX8pbf+3Qc7no8BsOBifUTT3KUq/+F/iuAAC6CZLgKUiEQWKBDX8/EOgO959Ib6veHLdYfqLtjTIh1FmDKYDAYDMY4Yaz6rJMnr3U4hFL9eujQTZe9eArmw1rggU400lwJWvAP3hmM8QwT6wwGg8FgjBPGgs868Uv/OOOaPl/09vZ2vPPOO9iwYRe8YIOwsDAsXrwYKpWpMQDZpqeHO7EZY2KhG2aQ6FgKMGVincFgMBiMccJY8Fkny1mI6HZ1dUV2djYeffRRFBUVQSKR4Pbbb8cNN9xAkxzxwcQ6oy8pEney5hPC3FLY0QZzg2GcNDKFeIhy8wE/8iG2F/D5PB6nprqNPjLlo76+A11d/DMvba1dEJKoPL7fFwIiM9ZzBHNWkwTJEOVSkflxchePzWMv3WILWJj5CalCZLYOJGDPbLkAsDRjs0jQDuHIQCwrzaEXDTGrIRGYvUKRAEkLM98htbKApRV/O+stBOiwEA7rOElE5ttAMMC1hose86cCLKUWvDaJ9PflQugGuP+YILSAboCjymDIWSQVm6+jvsf8Ha17iLulBTmOZpCKLWFh5pyXykSQSvj3kTjGmLPLJI4z5DfMYc5SlH4HhqEIGJyQa/gvv/xChTkR6iRo9MMPP6RuL+aEOoMxEWFnBOOk+eiP83DTgxHUSWEg/kFqfPHvhUiZ62V2+0ceTcB7Hy6A/SB3BeJ/nJDohq++2IfLLv4JeXnGQWnt7V148fktCAp4D3Nm/B/27TUO7Ovp0eHD93djavAHSIj5DBvWmwZwrvoxG/NDv0a2bTPUiaZezrIIKxzwb8Kh6loaBDpYELsF2EAzVY69+yqRlOwO6SCh5O1mi4vCA1C4sd5g+2hr7Eaj1lghNdETJesbcHHkJHg4Gwe1EDtJYk/33fYSfGRvhYZBftM9QgH2BGnw5IFytPnr4TzF2F6PaJ551/jjiz0X4a4D8xF4gatJHcVR1jjoqkNFWxt8kzUQDLoKuAcp4RaopM4ZXqkaCMXGQkrtJYN9lA2++qMBBb5e0MuMFafAVoYaNz8c/Kse4Qn2sHUwbgOpjRCeqWps2VaGtggFpINsEi3EAuhSbLAquxwIEkETPOg4WQCqFAV2dlajxKMDqmjT45g03RrnhXVQW0ZNpLHFIKHFyw5/iNR4fbMO2V4+0A1yHeqxt8cRYRA6t3Tg3CQvelwGO7HMiPZAzbomzIz3hEpjXEdrWzGmJjlh3eYidMVbwWqQv7uF1AJW023w14ESFIcKIZ5k3AbkmFglq7GvogWTPNQImmpvUgf/eA20Nj34Wt+F2lDT5QIWk+xxxEqFTUf16J7iYuKBL/ZSoc7BCcLdWpyT5GnSl53drREd6Ywv7tuN9+/egZZ646UV1VWtuPO2v/HYM+vhGa+CZpDPv1QuxORUB6zanQdhpAgaP+M6GqxRPSDNA87x8UJEsOlxikhwhkAPBKrVSIx0MymfO98XO/bcYnKe9UKsF4kFY9nuRsxN9IF40ODKy8sWU8MdsWZHPial2sPK2niiQeUopfasT7ywEYtu/hOVFcbOSYxTo0ffieXLl9OXVqtFcnIyvv32W4SHh7MmZZx0UiT9MF5jBeYGc4IwNxhTco/U4cWHNmPvjgrc82QsrrlrKm/iHy4aGzvwwnNb8OnHmYiOccGRwzWoG5AshcyY3XN/LB5+JB5r/8vD0iXrUFTUZGSfR5xjnnwmFUcP1+DhB/9D1gFjO8nzzg/Aiy/PREdzN5Y9sBEZm4ytFoOn2MO5WYrOth5UeHdi805jq0U/PxUcpTJUF7bCaYoNtqYVG3lDu7hYw9XVGgcPVGFepC8Kd9RD29Fj5I/tHGSNjB2liItzQ/H+RiMLPqlMCLdoW6zelYvQcEcU5jegsrLfuYO05yXJ7phW0oxqFwW+Km5CTp5xkFVSrDt0x3rovjz4VgJC4o397I/9V4F/HtiDxjYtypwE2LfL2HnDa7ItFBYitFR0wm2SEoe3Vxk9HiSJeJwcZKg81AznKFscTK9Ct1ZnlGgnNVIG5bFSaH29sH9rKzqb+x1YJNZCOIbb4kBaNdzjVMjPbjByoSFe75HxDtDtboJloAyZ1Y0oL2oxGoBExDuj43AHJE4i5KMZOUfqjOoQEekEZbkQjlYWiPHrRtth4zoS73edVof6Bi0yFSps32pc7uFrjQV+Oqhr6lGl9MP+9Y1GdpIkGVOPrwjpeyuQGOOG8owGaAfYSVopRdCEKbA9vQRBsQ4oONyAprr+OoqlloiNdkZnWgsU4QpkldWjqrTV2AM8wRmKAx2QesiQ19aB0hxjNyL/WA3ySxpgKbdEt1yPw/uN+3pYmD1S9Raw6hCgwkaJQ2nGbeQSoMBkez0ElS3odLXH4c310A/oy0o3K7S7W2JPVhUiopyQm16Lrs7+42xjJ8G1y8Ix4wZffPLRHix/fisaG/vrSOxa48NdkZNWA99IDQ4V1pj05dRYTzTuaUWgnwbiWgFq8o1dajwSVdiaX0oTVJEnCjkHjesQFGmP/PpGQCjAS6/OxLz5fuCiprKNOtT8/n+Hjfqyi6c1RPZCZOVUYWqYEz0vB9pJ2tvLEOprj5yMGvjH22PH3lJqJdvXBjYSLHksAbffFQXREE9VBsPcYAwQz/Sjum/hNsUSUqkU9957L6666qohn6gOpKuri1k3TmCajrvBvGzzEayGseSrXd+OJU2LxoQbDBPrJwgT6/y0NGuhMJNhdCheezUNzz61ibc8Mdkd27bwZ0klyZMOZhkLl4HYaWSwqrNAdzf3o2wiCrQOQFUVv71dSKgDsg7w28tdFjcZh7fy70PkdBfs3sBvHeeVqsKf247xloeHOyHTjFWkp5cS+w7eTkUfFz1dOsxz/AptgzzZeyH3yRA/e5Qe5bfImxxnhyPp/DaG06e5onA9f7njDA3SNvK3gU+cGrsy+JPQuAbYYP+xSt51hnKFEEsdLaAf5OXdh4UAP+hsUV7IPzt6XpgHynbxO044TbPDoU38/cBlpgpbN/L31alJjti3ld8icFKIBtX7+ffP2kGCvQ1V6OFZmkIFscIFzTVmLDlDVCg/wG8nKU9W4sA2/jqq5inw+/ps3nLyxGmrmfN1Vrgn5Om8xbDxlGJ9eQlvBlGSz2Fn9W0mORYGsjD8G2Qf5LejdI+zRYYZK8eUVA9s3mSaB6KXBx6Kw7PPT8PJMNHFOln2UoVdKNL9RwNg42ZNxhtvvIGQkJCT/i4m1ic2TRNQrLMAU8awGY5QJww1n6LtNB/5T5KdDFUu4rfapsJHO2CmmAtza+DpdwxK6mP6G+a/v3uIdcFdgxIjmZRrdbxCvXf9eJeZdiQCeOBs+SnVcYTLyTEwFxDU3amDnnssYkCnR/cQfaWny/w+DE7eZFI+1PruIbbv5hto9JZre3iFuuH7dUMfxyF+Y6g6dg5xPprbP/r9tI0FZtuAT6j39vWhYkaGumYMeRyGbAMzFxSGaXvqO5Cv/xN1+kP0bxv44O2338bkyZNZazFOHb0h7mY4248VmFhnMBgMBoMxIrTra5Ct+wEdqIEAFvAQzIYSAbC2No63YTAY/DCxzmAwGAzGOGE0JUVq0uejQP8PdOiECAp4Cc6FXODMkhsxTgs6PXFqGo7P+tg5EGNCrK9atYomTMjNzYW9vT2uuOIK3HHHHbAcwjqOsHHjRvzf//0f9u/fT9e5BQQE4K677kJKSsoZ2XfG0AwVWDRU3NFwy0/sO4a7j+Y/YGYFywl+v/nyE9mHIb9DMLq//4Su2SPc14bfRqfjQA+vfOTPx2G2wWnoCyNdh4mUFGlwgqOBtoyff74FXno5AgOj8MQTTxglORr4eQbjlH3Wceown/XTyKeffopzzz0XiYmJWLlyJY0cf+qpp3DLLbcMue1bb72FGTNm0AvHm2++ic8++wweHh5ITU3FsmXLTuduMobBTbeG4c67ozidZKKinVFR0UKD1qwG2ecRiM3hZ1+ej7femUdtEQfj66eCh6cNfGLUcHQztfhzcJHDN1YNVzdr6vwyGGIJR+wku7t7EDrFwaSc7BPZt72VVQiItTMptxQKEJLsgEN5VfRfrnXlQfH2qCxqwXRqn2dax/AwJ0iaLDAz0QtKpbEFIGHSJA2USiluvG4lykqNHUQIhccacNsFf8A+SA4XH9NHz7b2UoQmOKJD0gOPEFvTOloL4ZWqxuG6WvhEq03Kice15zQVfig9Bk2y0kTokL9dktQ4UFSLyan2nJ7Y5HuP1dVRu0wun36/YDXcLeWYG+8Ntb3pcfbwUSJushuK3FwhdTato9hehmI/F1irJPCcbFpHhVKMxCQ31Ld2wTXCtFwotYDjNDX2ldZQK7/BEItIv2Q71OS2IDLZmdMbnfSPXVUV8E+1oy5AJuVTNTS2IijJAQqVaRyIc4A1mr264Renof3WpNxNgetivRDmL4Ozv2kbyNViuCTb4pi+DY5TlaZtJLPEOS+E4p4v4hF/oQdnXz7vvkCs+HgObroljLMvX3ZFMN7/6Bw8vCSBujkNJircGfpmPdxS1bCyMT3OTkHWqFdrERbnDDtHY6tHgqunDWIiXPHQuf+g8Ch/IPCKr+YiLNbUClJuLcKSl5PwwafnYM48X9M2EFvS87mwsAFx8a6cffm6G6bgocXxvL/995/ZiJz6EZ56fANaWvqdn85kUiSlhdcZeykEHn0JjsiL/Pf333+Pr7/+GkKhEJdeeim++uorGkja+xnyYl7qDMY4cYMhEbpEXJ933nn0xO/lk08+wa233orNmzdTf1Y+PvjgA7i7u+Occ84xev/yyy+no/6ysjKaiOFEYG4wI8/hQ9V45KG12LSxEN7ettR3fd/efucMJycFFd4ZO8qoTeHzy6fjksuCjJIhLXt2Mz7/JJMmXgoNdUDadmK1aCi3kgqRGuWBIzsMri2TY+yxaXcR2jsMwWIkD0dcvDsOHqxCc5OW3qwPHqxGY0P/LFVMjAuKihpRUdFK7SZLiptQXt7v3pE4xRWKRhHKcpvhO1WN6tY25OX2+8X7B6ihEkuRd6Aebv426LbSGdlN2rvLIHERY+euMmoJ6e+kwtGMfocVG40EDkEKaiFJBhKBQXZI217SN0OgUIip1eXd98Wgp1uHD5bvxGdvZvYF3ImEFoiPd0PBngZ0dnQjNN4ROftq0dpkiMwk2iQy3hm1OW1orOqAb4IGu/MrjJxyYiNcIawCtbP0jLRFRlMljhT2O28kT3JFbJcatYdaYB9kjWLLTmQd7q+Dm5c1nFQK5O2ug72nHN0OwI49/Zaadg4yBPnY4VBaNVQOUgT5alC+rQE47okrtrGEZYQY29JLIJFaIjrcBZVbm/oCVCUSAS6aawtpTjn0PTpog53x5dpaNLf29AmuKYlOyD9Uj+b6ToMt5KEOtNf2R6cGxmjQUdqBxpJ2OMfYYm9VPYpL+gdCIaH2sG6zRGV2C9xClWjq6kLR0X6HFUd/ObrlAuTsr4WLrzWalV3Yvr+/jsRfP9rFGdk7aqBxlsHLXYn89H6bQoWtCB4hKhzZXk1FrXyqBL/uyEbP8ee2tC9HeuBIhqHvzIt1h1NmG9BxvLNbCCCOtseBPQ3oaOmGZ4IaOw9WoGmA1WJqrCsscrvQXNGB8Mvccf6KMKjc+wXy3rVl+Oj+nSg+3IgpM5yw6M0YeAb3D2T2Zlbg4QfWUPvDKVMd8OprsxGf6N5XTvIkPPbIOvzz9zG4udkgxMEOpRn9bUQG1+EBDijYVksHEuIgMf5Ny++bKbOSCxES4YjM9HLqABMR6Yzc7bXoPh4ETAZ9l94TghufCIecI8id3Np++/owXnt8O2qr2nDeVZOxeHki7J36Bzr/rDqGRxevRX5eA8IjnFBT04biAfaw5PwiQcNHj9YiMsoZK96YQ//lIienDkseXkttZntxdlZg2YvT6SBmvLrBkGyk3xddTgU48Ux/5pln8N9//9GyBx98EFdeeeVpfxLB3GAmNk3H+/oLso8hFZgO6k+UDn0bHm+7dUy4wYxqsf7dd99R/9W//vrLSHA3NzfTx2lkdp0Icj50OuKQYTqL98orr2DJkiVDiv2BMLF+5njz9XQ89/QmXkeGc871wydfnAe5nNuFZuuWIlxz5W+oq23nLPdwsYHAUoDCYm77OpVaSpOlZO7htkokCXKiYlyweSO3tZvQ0gKXTp+Ejf8W8tQQmDnPGxv/K+gTX4OJmuWC/VsrjTzbBzIpxg6ZuZVGA4mBBAbawarRAuXF3DaAdvZW8HFQIS/LOPHUwBlIj6m22LKNu44kIVbsdDf8tO4IZznJSHnHrKnY8k8x72PKmDmuWLMxz8jneiApCR7QZXZBO8CzfSDqEBkqq9vRWsU9e+nmLoHOWo+sI6ZPG3pn0yMCHFCSzj1DK5ZawDVZhXXruY+jpYUAs2Z7Yec/Ziw5z1Hjm/WH0M3jBnRhcgBa0tqNPNsH4hFhi39LC1BV38ZZ7ummxA0W9hCUcreBUCXGYSch9u/lthWVy0V46t0UJF3tw+vMcjS9GsHJxt79vZDbx/ZtxYiLd4PloMRSvXz78X58+MBO6tbDRVisI3YdKUdDE3df9va3hbBZgPpy7nXYZLDz3cFLOQU7obmxE4W5jQiJcOB1drn3rtX47pssznKiMx9cHI+nnknhFZ3r1+Xjsot+5nWmuvLqELz6esq4Futk4mvx4sXYunUrnVF/9tlnMXfu3BH5TSbWJzZNx/v6Mqvhi/Un28eGWB/Va9b37NlD/x3sw0qiyD09PZGZmWl2e77HbFu2bKH/kll7xuiDLOkwZ51GkpTwCXWCm7sNr1AnFJU1mV2TWl/XAYWCWxwR2tq60TQgsdFgiDCrbzW/ZrSptZNXqBO0DT28Qp3QXNvJK9QJhQWNULbw+1DXVLfDVs+d9ZHQ2tyFqgZ+33kisMta+f3AdXo9ytvazK4nrG/u4BXqhJaGTgib+cs7K7rQWst/HEqKO9Gm4G9DkpxKW8tvwaft0KGqmb8fkePXPCBhDhcNLZ28Qp2WN3VCxyPUCXU17bxCnVBY0giBpemSll6667WoFGrNnksSJ9OlVb2Q2Ws+oU4g4jUxyfx11FljzSvUCaR+fEKdlpe3wqqZ/1ZVW95GzxU5j7mItVLCK9QJEonQbD8k01lkeZq52eHysmazFrIF+fxLdsYDPT09ePzxx6lQl0gkeP311xEbG3u2d4vBGDeMarFeVWVIzKFWm64R1Wg0feUnA5mlJy+yDp4Ifj46Ozvpa+BIjsFgMBiM0cBouUeRpysrVqzAtm3bIBKJ8PLLLyMiIoLOfo8Uvd89kr8xGhjv9Rsu+gkUYDqqxTpZxsI3Q07eI6P5kyErKwvXXnstDYD56KOPzH52+fLl9DEeg8FgMBijjdFyj6pEBv74YwfEYjFuuOEG1NbWUge3M8GaNWswnmlr43+qxgB0esEwrRtHr6vTmBLrtra2fWvU5XJj9wMyizDQBmoocnJyMGfOHDryX7t27ZC2UY899hgNjhn4eyRYlcFgMBiMs81ouEfV67NRod8ObxsVtWa88MILz9iMMxHqs2fPpvf08Qp7os/ohXtR9yghKCioT2gPpLu7GwUFBX3lQ0E+O3PmTHqCr1u37oS2I+vuSMDBwNdIs2d3OW6+4Q/szOAPWBvrHNheiWeuXY/szH53kMFMn+mFadO5lyg5HXdy+OWnw7zbE9eYW2+L4LTPI2tPlyxNxKOPJ8HKynSsSh7iJCS5w9NTSd1ouEiJ9oCrlRyuTtyLZKOmOEFIMvX5cPcZtwAb1Ak64R3KPdi0c5OhXdaDgBhTK0iCUi2Bm6sNZsZ70UBOrqDBRx5NwKLFkdRFYzCkXaKTXeAQJKe2jCYI9AhI0ECpkULDYZNI8IvUAEIBPDy510uHTLKHtkcHt0DuNrD3lKFHqEcwz1piWwcpWu11kCZb0f0ZjMjaEvpQEVxSlbDgSLcgFgtw5Xkq3DxbDWu56QcEFoB7si1KPDogUnPPWUyO0cBPJIebq6nlJ8EjVIlKtEETwB3g5ORrDSedDCmhbpzlpP8oFCK4xHO3oVwpgqe3LS6Jn0yDWQdDHGHmJfmgIl4BvYTjUm4BdMTZwtXXhgbTcuGWoMTH/7eH0/KTsHFDAW658U8cPcJ/vpqjOL8RK38+Al8Oy0+CtUYMubMYkYnckyfEEYb0kUkpdhCKBJx2kqTswxW70NKs5Vyi8fvXh/HUnetRU8k9S/nv6lzU1bbB3YO7r0ZFu+C88wPM1jMl1RMzZnnzXo/uuCsKpxu+exRJikSCPkf61awrRa7uF+jQg4ULF+Kyyy6jwvlMvQhn8vfO1ovBD10GM8zXWGFUu8EUFhbCx8eHOre8+OKLfe8TWygSZf7tt99SWyhzlJSU0ARIZIS6fv16TJky5ZT2ZSTdYGpr2vDs05vw1Rf7odPpafDjVdeE4rnnp8HewdRPeSxSW9GG9x7dgX+/PUZPEOLRfN4tk3HbsijYqLkDHX//9Qgef3Q9ioubDO4jcW7Ys6ccbceD+pJTPPDq67MRFGzPuX3WgSo8/MB/2L6thP59zrn+WP7qTOr0QigqbKTWcn/+kU3/Dg6xp0FiOdkGCz21WorJgf3WiAE+arjKFTiy1yBciFf2pCg7bNhZiE5tDxVfwW52OLDDYDdJfOPD451weF81Wpq6qB2f8xQltqUXo6fHcNolxLqh/lgbGqo6IJZawjdWg+27StBx3E4yItwJumodKgtb6EAiIsEF2Vm1aDoeXOoTqEKzRRf2HzHEb1x0SSBeWD4drm6GPlqQ04AXH96MTf8U0L+DwuzR0qRFUZ7BCYeI8QA/DXLSaqg1okuANTolOhzNMtRRrhAhKMIemdvLadDvQGvJXl9qYmGZSY5LWzfsNDJE+Tth//YK2makL0cnuKL6cAuaa7WQyCzhGaXCzh2l6NIalrlNiXZEdUUrda4RCgUISLTHjr1lfR7VU4MdEdSmRNuRTircVYnW2JVbgdpqQ/Cnp48SntY2qNhjEJzTkpUI0Deh47gAldjLkKNU4td1hjo5BlujVNCCw8cFqK2tBLMCvdC9pQP6HsDRSw4HtRSlu+v7PNY1cUqso8elB0oHKVR+VtiVVtY3+CFuKM372tHR0A2ZjRDeU9U4klbdZyfpFa/CxtISFFY0QiK2xPRoTxzdVYOONsNxDglzgKJRiLpjbbTNAhMdqGUiCSQmOE+yRqFVC9IOGewfkyLd0VLagcpSQxCwi5sCs9zsYLXneNBvkDV2t7SjIMdwnJUag788GSyT42LnJ0O9jRZ791f2DfCIN/o998XQY1pc1IilS9Zj5e9HaTk5/267I5IOcm1s+ANSe+lo78aHL+/Cp6/vRufxQOkpkY7oqu5GdWEb7cv+iXbYf6ASzceDtf2C1HTfcg8bzr/wOCeUFDShusIgst28bOBCLD/3GI6Ld7gKFY2tdEBAsHeW4+EXEnDBNYH074OZVVh230ZkphtcnRQ2Ytz9RAyuvTuMnpvEWpLYNq5elWvoJxJL6vKUudvQl+0dZHh22TRcfW3oCVsP/rnyKG23wsJG2mZ33h2NRx5LgLW15IxZN0ZgCSwFQx+j4SY/ev7557F9+3Z4eXnhhx9+gFTKH7B+2velq4sutVmwYMG4FrTMhc58uzwl+mTYbjDPdd0yJtxgRrVYJ9x55500ocLq1auRlJSE0tJSKtTJ+ridO3caZTGdNGkSnTX/7bff6N8kAJVYM5I1dGRGferUqaPupFn1Vw5uX/Q3GupNbclIApx33p+P8y+cjLHM2h9z8codW/q8vAdCRMQTn09DwnxuR4m2ti689MJW/PrLESquB0Nuug8vicfSJ/gtOH/64RBsVVLMnuPDa7u24uU0avnIBUk6FOCsxt5NRLCaukY4e1jDI1CJ/Vsr0N5q6i5Ckvj4R2qweXcx6upM3UWIN3pKvAf2HqxEWZnpDCcRTzOTvNBQQMSXqasE0RFR011x29IoJKdwP5HY8Hc+PnxlFzLTyrnrGKKBvb0c2zcRX3rTS4K7jxJ2fnKs31bQN5AYiKOjHNFTnJGdVtMnvgZirRQjJs4V+/dV9omvgRC/9PAUZ+zNq0I+h3MGGdydm+yH5spOHDnIPcsbE+uCy1UCtB/q9+YfiHSSHb7s1mPNlnzOGZUAPzWucPFD2cZa9BwfSAxE6WaFlilCbEwrRGuLaV+2VUsxPcoTJbsa0Fht6m5CBioOKTY4dLQG5UXNnH15WoondMU6lBzhthX1SlajuL0Jh3meTEVGOsJFKkfaVu7j7BVoix5X4L+t+Zx9mSQRu/KqELy+Ip2ee4NxdJLj86/OR1IyvwMMEcn3XPY3SgtN60ie9CSkuFO3ooJj3H05OsUVTfWdOLKfu45h0U60P+zZwV3HiARnOij99oMDnH3ZL1CN5Eu88Npraeg8nn9g8NO7hedPwlPPEqvFkxeg7e1d+PTjTMyd5wv/AE3f+2PdZ32gn/ru3btx22230dgxkiGcZAY/kzCxPrFpmoBifVQvg+nNQnr77bfTEbSTkxP8/PzoheGff/4xEuq9M/Hl5f0XcJK1NDs7m57Y559/Pp0BGPgi33G22bK5iFOoExobO7FpE7eAHEvsXl/GKdQJjbWd2LeF28+cIJOJEB7hzCnUCURwkKQr5rj08iBeoU6YMdObJkLigyREqStr4xQ3BCK8Ohu7OYU6oa66HY3tnZxCnUBmkOta2jmFOoHM9ldWtXIKdQIRnkVHG3iFOmH6Od7I2s0tYglHs2pRWNDAKW4IxXmNqKpv5RTqhMrKVvS06jiFOoG839jaySnUCWT2tbaunVOoE8h+HSys4RXqhH17KniFOqHjaA2ysqt5H31mH6tDe1UHp1AnkARJxHKTS6gTGuo60N7WxSnUCZ1tPehp1HEKdQLpX2Q5Cp9QJ1Qe4BfqhN27K7GfR+QSCg43IK+sgbcv5x6rp8tCuIQ6/f2KVmSk9yd34uLgnipOoU4gT1Mqq1s5hTqBHBvSz/mEOmHvzgr64mPP9nI6OOXry8cO12H1P7mcQp1AEp4FHM8KfCpYWYlw970xRkJ9PEHm98h9mXDxxRefcaHOYBgFmA7zNVYY1QGmBPKIi9hCvfTSS6iurqZBp1ZW3LMGR48epTPuvZAEDYsWLeL97hPNXspgMBgMBgM4cOAADh06RO+1JJM4g8EYeUa9WO+FZEQbysFlsG86cYs5GccYBoPBYDAY/Pz888/033nz5nHmQGEwzhT646/hbD9WGDNincFgMBgMxtnNfUKylBLOO+88digYZxWdHtANc/uxwqhfsz6eqaluQ1MTSW3PbatGHBraWrWoquRO+97V1YPPPslEbq7BHeFskJNdiy8+28u7BpYQluxEnVO4IJZyIfH86cwJkyZr4MFjq0YCzWbyWKadDLPn+PKWEZeYhJnu9Le4cPGwRsIMDxokyYWNSgIruYjXCpJYSKqtpTRIkwviNqJ2tIKbN38AjLu/LXaYWUu8YX0BJoVxu+YQfCap4OJpTQP8uPDwVWLaDC/qcMGFxs4KMoUYMgW3MwN530FuBbWdFW9aexuNFO7u3HUk++XloaT7yYd/mAaiQP51wlI/FSb58c8E+njbQuYggQWH5SdB6WqFuBlukHJYfvYG0SpkYihU3OezSGKBmBlutL9wQfqXxlUGZ3/ucoJLsA0mhXJbehICp9jDO5i/jdz9lfB2VfH2ZVcXazgr5dQZhQuVSorS0mY0NnLH2RB3m9KjTdA4cAd9kSBajYMVdXbhw9PPFv5B/MeJWDkGR/D35ZBIByTO8uDty55+SsyYyd+XHRzlCAszf00yB4kxIQGmfPEXY5ljx47R4D6FQoHQ0NCzvTsMxoRh1LvBjBZOZyR/T48OH32wB8uf34KGhk7Y28uoCwMRW71HIzbOFQX5DTRwj1ilPbo0EbffFUVvdr0OJo88tBbZR2vpjZUENBFXFLmcWyicbkhQ5MsvbsN77+xEV5cOgUF21EaR+A1zUVncgnceScf6n/Pp3+RGOv/aANy5PAYqB6sTclggDhVvvb6jL8gxJtaV/mZ4hNNpqRPxlF784BocPVLb58az9MlkLLqdeLZb4FBmFZ67f1Ofo4pYYombH4zAbUuiYCUT0SDMFx7aTIPbCEQQTUlwxI4j5airb4daY0WdZdLTDFaQhNhoV7QVdaKmrA1SuRBeUSpsyyihN3xC6BQHGuxHAv9EQgtMj/VC3p7aviBHIjy0cj32ZFXQNr3y6hA89/x0KjgIJDB36ZJ1+GOlwZ4yPsIV2spulB0PclSqJPAP1mD3tjK6T0Qk6fR65B42DACtZELctiQaNz8YDrFESAdnix9ai/VrDXUk/TEx1g0lexvR1tQFjaMVPLyVfXZ5hLg4V0hy9Giv7ILYxhKWEWJsSy/pG+AFRtijsKERuYUNEIstEBPrht27y9B+3NZw8mQ72EKEwoONtI7EvvLYoVo01hsCOYn4FTsKkbbHMFi5LcUDSS1d0B4f5IrVUhy1V+KztYbzy2eKCpWdbTh2zFBHMlieM8ULuq1a6Lv0cPVTwFYmRvk+g9iyFFtg2gMBmPtEECQKEW27lxZvxr+/GSz/yD4lxLvB4mAPOmu7YaUSwS7EGge3V/UFOcYudMMtr0fDyceaBqEabA33QHs8yDEgRIMubQ/ysxuop/qMeC9U722hbUpw9FGg3bYHu/aV99kaFuc39XmH2zvJ4OepRk66oe8GhGvQ0qBFWX5z36DYd4oaB7ZVgCSGdpisQIW4DQcPVfflH0iN8kB1ejO6OnRQeVihw0mHjD399pSx8W44mFWFxoZO2r+eeS7VyNZw3U+5eHfJDlQWt0KqEMI9UomdaWXo7jp+nKfaobWZ2IY2UYvO8HhnHMysRtvxvuztb0vtS48eqO1zdcnPrkd9jWFg4OSmgIOTHPt3GYKIQyMdqH1nb19W2Unx4LIEXHJjMD33MtPLqXUj+Q2CTC7C7Y9G4cb7DX2ZeMeTaygZyPb2ZWJP+dgTJ2ZPycWa//Kw5OG1OJZTR9v03gdi8NDieBp0eqbcYOIEL0AoOP02ilo04I5PffDOO+8gJiYG7733Hs4WzA1mYtN0vK8/avnZsN1gXuq5aUy4wTCxfoKcrgttxo5S3Hf3ahzMMtxABhIcbA+FtZjOph84YFpOPL+ffjYF332T1Se+BuLqao1XXptFbcdGkt9+OUL9icvLj3s6D+CiiydTAc3nD797Qyl+ef8QrnpwCkLiTn72qqCgAcue2UwTkFx1dcgJ+x+fKERAfvDuLmRn1+LJp1NM6kHGtiu/OYIt/xbivmfi4OFr8GwfyKbVBfjkjT3IqajDoWxTV4vJkw1OE6J2AXIzDZ7SA3H0VMDKXYwenQ4ZO0wTZDnZyxHu54hugQ7r0wvRM+hZHh3cPZ6I5mYt3nwtHe3txg4u1Oc7xhNinQUVRr2e7b0YBLEznFwVWLw8Cc7upjO9f/2RjQ/f3Y3W4g6UHjN1/pgUqqGzzJoWMRr2mTrAWPtL0ejUg/qeDmzdXWJaRycFfHxsYQUhDm+rgn7QgxsbWwn9Da1A1+dzb1RuJcSTM7xgLbDE2xsqUDfIjYgkUpqc6EDzLakOCaGtNHU/CYqzg52jFc5/eSocAkzbIG19MT55eTesygRoPGhaRyKIrVwluODBYETNdzUpJ4O7Vx7bipqKNuzhsNS001ghdrIrukgd0wvQNejpFUkYFBLpCJHeAoW76qnbzOCEQaEJjnSAkn+oHk3HPdv7EOjhnqBCi0UX9AU9aCg2nS13jbZBg0SL2ro25GSbPsEjHvtPLEnCb28exu4Npn3VwVsOK2cR7aN7d5g6uJDBnZefiva5PdvL6EBiIAobEQKnOtDyvTvKoe3UmVhBhsU500Hm/c/GQ6kyFqlksPTTZwfpdz/wXDyc3EyP4x+/H6VJ1ohIJ9fYU4G4+Dx4/79Y9ZepMxV5WvT8C9OwbVUOXvvigjHnsz7QX/2PP/7Ap59+iosuughLly49bb9x0vvEfNYnNE3H+/oSi88gGYZY79S34WUdE+vjitMl1u9Y9De++foAb3lisju2bSnmLSezyUTw8xEZ5YwNW67HSJIU9xn27+O3OvzqmwtwwUVj2xt+uDxw37/49KNM3vI5id44sJnfZjAg2g6b9vDbdvr4qpBnZvkTWVozWKQPJlitobaSfHy78RJEJrjwlj9y43904MLHBYn+KNvEvxSAZCf9IYN/+6mBjqjK4rYAJCjtJDhSb34JmLfEpi/xEBfnenujKsd00NnLfb8kIepC/hTu3z6ciX9e56/DzNv9cMN70bzlW/4rxC3nruQtd/ZQoLyoxWyElB3MP5lycJOjqoR7KR0hIEyD7L2G2Wwu7FIU2LC9kLf8goQA5G7m394vRo30nfzXLDKjns9jS0qQKYRoazHfl9NKb4Ha/tRv2sPlm6/3445Fq3jLw4MdUbqvGrndS8acz/pAf3Vih0x81a+77jrce++9OFswsT6xaZqAYp0FmDIYDAaDwWAwxhR65gbDYDAYDAaD0Q+ZzSTU1Zku32MwzjQ65gbDYDAYDAaD0Q/JIk4oKTGNM2EwGCMHs248w7i48tuyEdzdlbyWYwSFQkSdGfhwc7M5q3UQiy3hwBNc2hugud9MqnDCnt3l1DGHjwP7K3nT3p8OSMBl3tFTt8PkS3M+EBd3fptEArFyFIss+bd3VZh1rLB3kMHOnn/dqr3aChoNfzlxgbEdFKw3OBDXXD8k6Pm7AcVSbmH2OxRKESRW/G2gtJNCpebfRzd7BZztFbzlcmsRVK78bUDsJDsbTANPB6J2M782uNWi22x/aKzvgFjCfxl2VMlha+Y4O7nIoXbi3wfiAmOj5neIInaSYmv+NrawAKxl5h2m5DJuu85elAopr00iwc5WRq9rfNg6WEFpz3+cictSbQV/7MVwOZFrlkUjILLkr6OzmzWsZPztPFYIDg6m/2ZlZaGtzTSomsE4G8tg9MN4jRWYWD/DEIeRH36+GF7exi4iIaH2+GfNVfjo03Px37prMHWQzy9xeomKdsb6dQXw8FCalBOf6/+9Ow9ffnPBiNfhux8vxmtvzjERSjNneyNt181ISOIOyCOWa5cn/4hLE3/EdXN+RXaWcVDasWN1uOTCnzAt6UukJHyB7VuNA20rK1qw6OY/kRT3OaLDPsafK4+e9pvyT59lYW7wV1gY/g1efHgzWpoGOWgMQfr2EqQkfE6DS4mlpIen4bFxL8Rt4s9/rsD/Pl+AH7ddjinRxsfR0UmO5BgPHFtXixQHN6SEG7clEdAr3piNP1ddid37bqVWjQNFv1RqiaRkd1RXtaGzoweJSe5GgpjYAi5M8Ief1hYtuZ2YluwF2SCxNf8Sf/xz4Fr4BnJ7XadtKMYFUd/i168O0/0nlnoD8Q6wpVaEK//LQUs4oJxkLCal7iLUJPbg2w2H4OmlxJSpDibiKz7BDdt3lKBTo8ekOGNPbeLZH5jqgMxCgzViQiLxwO8vFwstcH18MBKbHOFaJsWFSf6QDvANJ+11wTWT8e/B67B0zQxc93Yk5IO80f0j1fB0keHHm3bii8u3o6GEW5jMu38yHl07Ha5BxoNkhZcEnTEW+N9HO3Fx3PfYvd3YKaWqvBWLb/gXD137Lw2MDI0ybgMbazEuTgiA1T49IgR2mBfvDcGgQXFqsieEjRZoa+5CWIoThCKBUR2nJjpRz/iCw40014HM2jhEyTdGDb2DAJu2FcI7UQWlvfGgwCPUFnI/KXb+W4qZ4Z7w9jDuy0E+djg/xA/7V1fAO0gFr0Dja5qDswyxMa7IX1uLWCdnRIcbByurbKVYkOCL5ox2BEo1SIn1MConNo6TU+1xsLIWZd2tCExxMOnLyUkesOkSY1H8Snz45E6zwcSnArFrvXLaz/Sadc3Mn3Fkv7G7U1VeC14/fzNW3XsQ17tMxvQQD5NB87sfzMd3v12Mn7dfgbGOh4cH3N3d0d3djYyMjLO9O4wJjk4//NdYgVk3niCn2yO3s7Mbb72xA599vBcPLo7DzbeGUy/vXogI+fzTvXhl+Tb4+KmwZ1e5ibsHsU0j9onzFvjRQQBJWHImqa1tx7JnNlGf4hdfmoFzFgZwfq6uph2vPb4Nv3xxqM9fvNfX+Ko7puCWhyLw/vu78M7/dvb5i/dy6eVB1M/591+P4OXl29E0SDwTC8dXX5sF/wD+ZDgnApk5e+6+TThw3MO5FztHGR56IQEXXhto1iayorwFTz6+AT98d9DofSKqyHEivsv3PRiL2+/s98rvHSD88uUhvP1cOiZ52yF3dx06Wo2Ps3+0BvtqqpE82wNPP5sKjZ1x9PuOtBI8/OAa+r1kP0jSmoF4e9vShEySLgto2qUoyW4yKtc4W8HW0wr1LR14/I0UxE3jHmyVFzfjpUe2YPUvxvZ0JBnUlGgn5OfUURu+PdvLjWaTicBKiHMHjnahM8gC/+7ON3kyQtqorLQJnl62yMqqRlOj8XGeOtURoiYBrO0lOFxUi4oKY4cUP381zTfgbCGDZ60cNXnG7icqNyt0uOpR3dOBJ9+ahoh4Z6Py5ppO/Lh0Hw6tKYeTvRVKdho/WRHLhZi9NBAzHp4Eodh0hrSnW4f/3s7GnysOQTfZElt3FKNLa/x06LyrJlEf8FU/5eDdF3agtdl41j4kwgENtR0Ictag52gXmquN28ApyBoFFi0QyS3RUdWNykEuMU6eCvq0QdveQ/tVwWFjhxWVgxRuvjaorGiFhdoCWXuMHZ2IbSyZBKjObYG1hxV2p5eZ2CQGxzlgX04VEvxccWx7LXq6+48zOT2IVWRpbhMC/DUo2FlnYifpE6PG4apa+HmoUJ/VhpY6rVG5xxRblGiboVcJkF1ej7Iy477s66OCs1wOqx5LCNstUJJj3Jcd3eW46+VYzLyUP8nZiVBf2443nkrDT58eNOnLVywKxR2PRGHTB3lYteIwugbZSbrG2+K/ikLMusAXjz+VTG1aCWfKZ30k3WAIK1aswPfff39W7RuZG8zEpul4X38Aw3eDeQNjww2GifUTZKQvtHx8/OEePHT/f7zls+b44NeVl2E089Sd6/HDJ1m85UGpDli31ZCYhIuYWBdOv/FewsIdsXn7jcPax1jnj6hQ4uOn7ZdjShS/L/xN1/+Bn388xFv+5DMpWLwkgbf87y+O4sVbN/OWh6c4451155r1eZ7s9y7MEWHpgK7jSXi4+PbApfCcbOob38vtF/yBDav4j1N0sit2bik95eMcEemEPbv5lxuQp1EkURgfEpEl5rW4wRxvFZ0PtRv/xf3t6RtwbCO/LemVn0Uj7kYf3vIPXtpJRR4f4XHONFkPH1MmO0CaxT/dI1UJcajJfGZMsvSlpdFYBA/ENtAKx47wBwiGxTlh74CkVoOZluKFrE38tqMxia44tsU0v0AvPuEq5GXyLzNTuInxdxV/P6H7qLdDTw9/O31/6DKarfVUWXb/Rvzfe/t5yxcm+aFyYyNvecT5rnjgtxSj98ZqUiSSDOndHedRn3XCrl278NRTT0Gj0eCrr76ikxikzGLg460Rhon1iU3T8b5+/2kQ62+OEbHOrBtHOUMlmB0LCWiHrgNGtPyEGOo7hnkcht8Gp6EfDHcfh/r6oT4x7OM8/DYY8jNDlg9z8xH+/TPRF4fbj4aqw4k8mh52Ow/7+4f6Apw1tPom9ODklu+ZR4D7YzZCIDCIcZ2+G/n6JuShDhd4vAdLgRC/Ft3WN/POYDBOP0ysMxgMBoMxTrCx8Dity2BMEAC2Oj806HPQjiqoETRyv8VgmEF3/HWqDGfbMw0LMGUwGAwGg3HC2MKP/tuIHNZqjLOGnrnBME6UkbQQJIjM2PcZykf/eItY4A2nDkNtPzBg81QZ7m8M9zgM9fvE1cPs9kILs1aQJDDOcojf6DZjl0kQWJjfB8sh1qwS945h9QMz1niG7S3NthNpn6HqiKHsKE9g1nE4x9FCOES5yAIWZtqRlFkO0VeHKhcOtY/DLB+qHwmFgmH3Zf0Qa2mGum6b+32CxRAujMdXjIxbbAX+9N9mfTF69PzxEQwG4/Qwzi8pI8fhQ9VYOP87TA36wMQB5HRy/Y1T8dY786id3eAb1qLbI/DhJwsx2nnkpSTcuTSGuoYMRKYQUaeVL3+5EC+vmAVbW4mJeLv3/hj8/Nul+PSL8+DiYm1yQ73qmhB8/9PFw97H7zZegukLvEze9w5Q4ZO/zkdQuLG13mCIleV9D8SaCE4bGzG1T3ztlTQ8uXQDmpu515LOvdoPSz9OgcrBykR8TU62x98Hc3HZxT8hL487MM/RSYH/1l9Lg20HExLqQB1hmpy74Rthasfo6m8DebgU557/PX76wTRItrioEdde+RtWbTuGsGRnE290G5UEwcmOWLM7H1NTnSC1Ml5dR/5OTHFHXXoLteqztTUOfiPfd/udkfj598vw9bcXwN3dNNAnNdYDAfVKXBDsB38/0zpEhTljusYdcl8p3KeaBsk6TFagemo3Zs79Gn/8bmr5SdqVtO//Dh2AJkVpIigV9hK4JGnw2X27sPKFgyaBui3NWrzy6FZ8sDwD0ckuUNiITQZjYSnO2HKoBJMS7KnL0OC+HJnojKLKZgijxbDzNjWpd41RYo9NLQSTLeAZZFpH32A1Av01cJTLEBRpbHdJcPG2RkC4Bj2VPYhOcDERpPZOMsyL9YY0U49pxBZyUF+2Vhr68pG0ahrwbKUQmlgtRqe4onBnHYKSHGCjkXD25ezCOrjH2MLO3TQwLDTOHsHdUtw9aTKCAuxMyiOmOOFSV3+EOqsxOcy03MNfiSmhDnhm7jps+SHfpLyosBHXXPErAnzewYfv7zbJ50Acru67ezXe+3o3d1+WCen7X+8+DMU0OWQqY9tTS6EA7qkqfLvpEO6/6h/qoDQekQhUkEAFPXRoBX9QOYNxJpbB6IbxGiswN5iTjD4uKqrEO2/tpy4tJDFML8QX+tXXZ2PKVH7HkOFQX9+BZc9uxuefZCIu3vBbRISNJYrzG/HiQ5ux/q98nHvFJDzyUiIcXfo9umuq2/DMUxvx9Zf7MW2GF159bTYCJvVbMra0aPHqS9vx7ts7ERxij1dem43YuNMb1LTpnwK6j9WVrXSAcf29YUM+3RhITnYtHnl4LdavzafHKTu7FrU1/QlbnJwUeO7FabjiyhDO7YmLx6fP7cYv7x2E+2QlinTN2Jfd705C7AnvuS8GDy9JMPFHJxCbuS8/34dnn94EkdACnt62yEg3vpnOiPQESnVoa+6Gw1QF/k4/hp4BM5FEkJH+RewQ33w9HW+sSDeyDZ3kq4GzlRw5B2qpVd+Ow2Wob+h30nFztkaQqx0O7KhERKwzmos6UV/W3wZWKhHkoRJsSi/q+62g4H5x2d7ehddeTcP/3siAh5sNgsQa1O/tt2IUWALKZDk2HiyCtUKMaEdnOhAYSGicA5ry29HdpYM+RIgfdhyBbkBU4PQZXnjltVlw91Bixcvb8fZbGegcIMDj/Z2QpHdA3ZEWuCdocHRfHdqa+tvAwVeBq1+PQMRCV6z85gheXboN1eX9+6iyk8JnksHGcnK4PQobGpFb2O/iopCLkRzmhqy0Knj62VLhfOxQv0MLOXbzYn3QuKcVMo0Y5U4dWJ3ZLz6Jzp4X54uWox10Wz9fFXK31wL6fgXuGa1CeXULmuo6ERChwYHtlUZWi+7BSjTptCjKa0ByrAc6M9rR1dp/TbP2kaJao8WBvZWIjndF9ZEWNNf2z6KSZEyu3jY4kFaJ4Ch7dJR1ob6k/zjLbETwDlPhyPZquAUq0djVibzs/jYQSywRGeOM/Iw62LnI4Gtjhda9AxxWLICGJAl+P1wAqVSIVDc3tG8zTn7kFKdETmk92tu6ERRih9yttdAPuAOHTnPEojdj4ORvjTdeS8ebrxn3ZZLfgvQ/cq5++nEmnn9uCxrq+/uyu7M1JrtokJVRhdA4RxwsrkFpRb8Ad1DJcHGAP8q3NsJlqg0OtdbhaF6dUXKx25ZE4+YHw9HR2TYmrRv5yNP9gWp9JuwFEdhc/PYZDTBlbjATm6bjff1O4gaDYbjBoA3vjRE3GCbWT7JzhIe+hWM53AlSyAzhhi3XIyzckJJ5JCgtaYLrGchSOpKQ2SZnd/4sqCXFTXDjmF0daFPo5KwwuxxgOBCv9+aGTmgcTv0i8NB9/+LjjzJ5y5c+mYRHlybxlv/x0xFce93vvMsuiLj4b/01vNsTH/Kw4A/QxpMkRiYVwslRgbwBAnJwXybe5+lp/LNm01I9sXFTIW/5ZdMDcXgNvw3izGt98dxnM3jLd6wrwQvz10PP4zap8rRCU2UHejq4W8lKLsR+2zqUVRsL+V7IUxAy4M3cw21TSHrX/bHhyNvMb3PoeIEt/vyLf91u+ExnrNqYy1tOnhhkb+e3OfQLUCGjqAKdXdyNYGstQazWER3N3MdZKBZA6iNBUTa3zSAR+snRbihJ47eDtJulRMYG/n6QNNMdh8wc50kpdkjbyr/91Cn2cMjq5D3Olk4i5DV0oaeNex5MKLWA3tEStUVtvMtyOhMF2J5ewrsPs+f6YM2/ebzls2Z6Y+0605n6Xs5L8cPODfz2stMWeOHVr6aNK7FeqctAgf4fWMMLGSXfMLE+jiyjRztNE1CsMzeYk2RwUp6BEN/f1taRXb831oU6wZxQJ5gT6gQXV/PbDxeSyGg4Qh0n8HhtcNKfwVhILMyujzbXDwnW1mJeoU5o6+hGk5m+SvpyU5P5vtyuNb/ut3tQgqvBCHvMD7ZsrMS8Ao7uY6uOV6jT/WvtRoOI3zu/q0tnth3JNw+cbeeiucF8G3UM0QbtnebbsKlDyyvUCQ1Nnejo4v+Obq3ebFZP8rBBwN9EFJ3W/Prv7kFJgQYzVBt2kSROZj4iaNHxCnX6+x069HCPxyg60pfN+M4TGhs6h9XXm7Vd5suH+P2xiESgpieJFvx+8wzGSKKbQG4wTKwzGAwGgzFO6NA3QIghRmCngR59J/ToQTfaUF5unOjrTCdJYkxsN5hTZfRnqemHiXUGg8FgMMYJpz8pEjed+kboQB6JWBglTSIZT1mSJAbj9MLEOoPBYDAY44QRT4p0HK2+AZY6MRQCZ1hZaMbmdCVjTKMf5lKWsdRV2XOqk+TlV2fBy9vUNo24dCQlu+PFZVuxby93wNpw2bqlCHNn/h+n5diZoK2tC889vQkXnvcDta48FYjN5czUr/DXH9mc5evW5mP29K/xxWd7qbPJaIMEnxJ3lHPmfotdO/kDyu6+Nwaz5vhwli041w+L7og0+zup0z1x1z3RnP7ukyZr8PKKmWaDkO++4x/qUKRSG9skEmxVUuqA8da78+DpqTQpl0qEmJfoA0dLKwT6DbgJH4c83U5Icqd2hVOmcjsSnRPtC1m1JcJjuYOtI6Y549olYbx1KNxXj9+fykJUijN1DRmM3E2CukldEE+TQGJrWi5RCyFMFSN0iiMcHU1tEOVyEZ5+LhUr3phN25PL0/38BH9UaNthF9rvWNSHQA/nJCXa6roQGeXMWYeUeZ545qVUXHv9FE7f7rBJjnDuliM12ZPTY97VwxqODnLMT/ChrjeDcbaX45LYSfBIVEFhb1outRbCJ1UDjbMMTh6mdbAUCeA6zRZpoirIgqScdwdVsgL5TY3wjlBx1tEvRo2chnq4Jpj2I1qHKUo0d2oRluzE6U3u4auEXCGBOEUNCyvTNpA4S9EeLId7spq6CA1GZGuJzumWqA/shtxVzBl86jxNCQeRDJM5+jLx/l+Y4AenZivEhbhw1uGiSwLx/AvTcf4FkzjLp033xFMvp+KKRaGcQe9BYfZ4+MVEjDda9IaAXSuY2oQyGGcCHbNuZJiLyhaLZXjrjR14/dU0agNGbtaVla3UxYRALtg33hyGJ59JgVo9/BkOIr4ef2wDfv35sJHl2IrX51DRdCYgv/3EYxtQUmKoIxGRxOf9sSeSoFRy3OgHcWB/JR5+YA3Stvc7Msyc7Y1XVsyCf4AGhYUNeGzxOvz1Z7+zRniEE1a8MYe6kowG1vyXhyUPr8WxHIM7CBFg11w3Bc8umwY7e+6A1FV/5eDRR9ahIL+BWiESP/nZPCKeiyOHa/DIQ2uwcUMhbGwkeHRpIm67M5LTTpIMJIgFIbEibG01BLwpbSUIDrZHehppdwEVjs88lwqNnawvOcxAS7ukSDe0lmlRUdLS5woTmuCEtIOlaGjsoJaZ5HdysvsdUsjxKS5qog40Id72CLeyR9HefncR73AVqlvaUZTbCEd3Oe5+JQ4zLuFug9Z6LX56Yj82fHSMBgYS1O4yWDmLcXBnNSylAsgSrLB2V0FfYhtyjqUEuKFha6shFTqxdTxUhIbjQYMyuQgREU7YkV5Kg0qJ+Hph+fS+YO2urh58+N5uvPTiNhpwmjjFFYpGEcpym/uOc3icE7TZWrRVaaGeLEejpAs5Wf1tEBCpQUltE4oKm+DuY4PHXk3BzIX9dSQDO9L/9+wuh4NahhR/d+oC0+sm6eSlgEhjSZ1piItNSKQj9qaXo0trGJRrHKxg7y/HxvQieu6dE+uH8t1NaGsxHGeZtQjBU+2Rv62WDnJ9EjTIza1HfZVh7bJIYongWAcc2VWFjrYeuEYpcaiuFkVFjX3XrFlxXrDZZ4Guum4oQ2QoQDNyjvTXMSzaCe1lWtSWtMPBWw5LtQUO7Ol3gQkMtYNtmxi12W2wcZBA6S/DvrT+iQtXXxvIFELk7KujPvTEDz0nrabPTlLjYoVgDyVadtTDQiyAOE6F3Tur0NlmiD5VqMTwC1ajcFstnRGTJEuxNrsIdXUGO0crKyFmRHihfXsHejp1cIlV4kh5LSpKuftybIgL1K0SlOQYrmmESfF2SC8sQ0lV83F72FlITvHsK9+4oQCLH1yDo0dqaT6AF16agQsumtxXfiizCs/dvwmZaeWwVUtx/3PxuPyWENq+I+3scSbdYPR6Hfbq3oIWTQiwuBIqQUBfWYe+Ht8XXT6i7jDMunFi03S8r9+CzyAehhuMFm34ZIy4wTDrxhOE60JLksXcfec/2LCugHMbksjoj7+vGJb3+ob1Bbjqsl/6xNdgHlocT2cIR5KrL/8Vf/LMhDs4yrHq36uM/NAH89EHu6nIJQ4jgyEzirfeHoHPPt7LmVWQCCUy6Hn4kQScTe6/ZzU++2QvZxlJ5vTLyst5BxWkXqtXHcOCc/2p08yp8O/qXISFOdLkR1w0NnZgWtKXyD3GnTTJz0+FDz45FzGx3DdQkizm8UVreS36iPBwi1JiDY99HfGTvixpMopX1xt5eQ9MFjPjZl8sWhFDE8twUXygActnrkdzDfd6W48kFX4tzUVpGXeimeBAOyrGs49xWy26e9jg9bfmYO48Q6r0wVRWtODlu7dgx0puiz+SxIskLtq2ppjzqY9IbIFzb5uEe16Mg0RqWke9Xo+PXtuFX146hFYed5DQVAdkZVejuoLbhpDM0lo2CFCez90Grj42UNiIcHRvLWc5mWW3mGyJ9VsKePvygkhfbFxT0DeQGAhJbJY03QOb1hZSD/vBEEE8Z5YPDm6t7htIDCZ2phsqM5vQxHOcg+LsUVPWhqqift/6gbhNskGmpAaHjnBbXrq52SDcxQG70sp4+3JkuDP2reV+AiqVC7Hw/sm4+4lYzidbJL8GeTI4Z54vZ64DcpzX/ZmHyEQXqAYksxtPYr1Ofxg5uh8hggxhFg/AQtDf35lYP30w60bz7XLzaRDrn44Rsc7WrA8DklClsoL7hkKoq22ngn44Yp0k2eET6oS9I7TkZiDmlvVUVbairKzZrFjPOlDFKdQJRFzty6zkTf9NBMP+vZU42+zN5N8HMoNLZs75xDpJ6DJw9u1UmDvP12w5WZLCJ9QJx47VY2oYfz/08FRSgcRHQ10H5PXGGSkHQpLS6Jv0nEKdQN6XiIS8Qp1QW9TKK9QJ1WWtvEKdcDSnFt08v08gs//BwfyJxMhASFvP7yFIxGdjcyfv8iwyC24tl3AKdYJAIICvu5pXqBPqKtt5hTqhrKAZwnp+y8vSvCbIbUwFZC+15W3o4l7R0teXaxrbOYU6obOjB00tWk6h3mf52azlFeqEpuoOXqFOqChqQd2AJFqDKTnahCPSWv7ykiY4wspsX26v49+/jtZuuNnbcAp1Annf3PlMjvOs88yfr2MZMqteottA/9teEGkk1BkMxsjAzjIGg8FgMBgnRKV+J9pRDSFkcBbEs1ZjnDV0zGedwWAwGAzGWGMkfdY79LUo1K+m/uqOgmh0owPdeuPfItaNDMaZQH/8f8PZfqzAZtYZDAaDwRgnnG6f9S404uOMa2Bra4uHHnoI2gIlIiIi8Nxzz/EmPiJJkRgMxumDWTcOk2uuC+UMMiLExbsNa706ISnZg7oScEGcPkRCC2rpOJJcf1MYtabkInWaJwID7cxuv/D8SXB25g6M9PJS4oorg3nXvBOXlYsvDcTZ5uprQ6HgsM8jEDegyOhTvzlVlLdg6ZJ12JvJHRvQ2qTFh0/uxM513MGfvcHMfO1EnChuuGkqp4PMQC65MRhSK+7xe1SSC668OoTTCpLg66tCl0QHlQv3WmE7dxnizuN3LiKOHj+uPgznKdxBPmIrS8y50R8Lz+t3nRgc2HjzreG0nlz2eQTSPho784F3867xh0LJfZw9J9lCIRVD7cD9HQ7ucuTk1iI/mzt2oLqqFf9sPAbPIFPrV4KVQoTzrg+klo9cCEUW8JmqwqREO04rSPJecKIDfMLV9LNcJCxwx9XXTaHWlVxMCXKAt0wBlYb7OHt4K6ESSuDsxn0+2znKaIyGRwC3lSNZTy/WCOEZzt0GIokFrAOt4BXHvbCeHFu/ZA1SEjzoMeeCWCxecmMQb18OmKKBXiWAwpb7OLv52WD3rjJUlHDHR5QWNmH54s04doh/3fzZ9llXWnidtpdC4AEHBwe88847KC0thaOjI1asWAF3d3fq+ML1YtlLGWcCHbNuZJxMVDYJIn380fX4/bej9G8nJwWee3Earrgy5LQ0JPFU//jDPXhx2RYaAEZuWHHxrjh8uAb1dR19QuSF5TPg4mo9IgcvL68ejz2yDv/8fYz+zWVbZo6WFi1eWb4N772zi1r/EZu1Bx6Ow/0PxtGbO7HPe/+dXXh5+TY0N2vpjfiWRRF4/Klk2NoObQ15Jigva8YTSzfgpx8O0b/tHWTUBpHYN5KgspOF1Pm9t3fhlZcMde4V1U8+kwqNxoq6Sqz6KgcfPJ5BAw8JqRd64d5X4+DkyX2cycCNWMsdzDL44JOgV2J/SWwwTwQiRF5+ZAv+/S2X/u3gIscjy5Ow8EqDx3RtbTuWPbMJX3y2jwZaEjvJ6FBnHN1WTYMLZVZCpEZ6oii9Hl2dOiq+Lno4GJc8GsoZXEq+4/NP92LZs5tpQDbhougAyHOApkrD7GD0xe64akU47DzlfV78xM6y1z4yMcmd+saHhBqCR4k9ImmDnRkGNxAy2CXlZOB7ItRVtuH9pTvxz9fZNNDSRi3BpEAN8ohloA6QKIRwjbTB3vRKGmhJ6uUbpUbajhLat4krzPX3hOPOx6MhV4ipewhxRFr+/FY0NnYSd0nMj/dF0+EONNYYzt/ZV/jirpdjYe9iqOP6v/Lw4sObUZxnsBUMjLBHYWMTcgsMA4GoIGc4dEpRfNRQ7jFZiXbLbhw+ZHBI8fJWwllpjZzM2j4Bet9r8UhY4NFnB0v68i8/Gexg7e1kmO/jAcstHYBeAEulJdojhNiUVkyPq1whQnS4Cyq3NtFgYaHEAvbx1tixqxQd7d20ztFxrijc1YDO1m46cJiS6IS8g3VortfSv4MSiNNNFWprDMc5KcYdXYXdqD8eTOoZrcL2mjIcKzXUMSXUDc6NMlQcM1gvuocqUd7dikPZhjq6BdigQ6bDgQMG+0jil//Ka7MxfYYX/bukoAkvLd6CNSsNfdnOSQa1twybMgyTG/ZqK0wL8KQWmqQfkkGa+xQltm8vRo9OTy0/b1sShZseCIdYIkRnRzc+XrEbH7+6m9aZBJpefecU3PtULBQ2/MHXY90Npl1Xh9TH6vDvv/9CIpHg448/RlBQEM4mzLpxYtN0vK9fi0+H7QbzNW4eE24wzLrxBDmRC+2mjQXYsrkI9z0QC2vroS/eJ0tNdRuee2YT9SonPr+DIbNlzz4/DYtuN59wZzgQC8HMPeW49/5Y3icK5sjJqcMXn+7FbXdEUgcSrlnmd/6XgSuuCukTX6ON7VuLsXZNHu57MPaEPOa5ILPot9z4J7KPmh5HMnv99NIUpH9XjIM7+n2se5FYWWLRs1G44oEpZgd31tZiXHVN6CkNJLavK8KurWW4+aEIKji59v/15Wk4srUKjRzOHp7uSkyP88QNz0XC2deaty/cfP1KTqcdpVyC6+ODccV9UxAyy4lzoPPBu7vg5GyNSy83FQ5koPPt/x1AS0sXblkUDkvLk3+ImJVeiW9f3o/czTVoazB1D7HzkcPaV4r9RypRXmYQlAMhA51Fj0fh3Q934dBB0yRittYSnJ8YQNs4LMn0yYy2sxsfvLIL//x7DNt2m9pJWggEOCfeFxKdJb0m6DgsXKKjXTBjvjeuWRzGmVyKDO6+fj0TsnVa6JpNHV6kAVI0ewtQv68NLVWmLjY2rhLIg6Wozm1FVb6pM5a1SoyACDsUVTTgMMdMNBncTY/1Qn5zI9btLeRMTnVx3CQItMDm9ELOFaaTE+wx/TxvmkSM6+nRtrVF+Pjt3fh3ax5a20yPY1iAA0KcHZC5r7xvwDgQTz8lbn4wEh+9ugsl+f2e7AOfJix7fwZmnOsz7sQ6OY/y9H/AOjQfIpEIr7zyCmbMmIGzDRPrE5umsyjWr7rqKmi1ptfC6dOn46677sJIwcT6GPM7/fH7g1Tk8UGWZGzYcv0Z3SfGyfP2mztoois+pkd4oibDVAD24hOswtd7LzmrTf/Ijf9h5TdHeMvvfiIG9zwVx1v+3bdZuO3mv3jLiSf82o3X4mzywkUbkL6ymLfccZoNtphZhhaY7ID127k9zQkzZnnj9z8v5y0niY3CQz7k30E94Kwzf7Nae+R6uPtwL0shvDtrI7LX8VuT2qTYYu9W/nLfVA12buHP5kuynKbv5F/CRWbId+by28OS7K2KRvPhVWmlt0DNk5iMMCPlK7MZh+dEe+NAOn8do5NdsXMLfx0uvDYQL306e1yJdSLUi/VrUabfAp8pajz11FO4+OKLMRpgYn1i03S8r199GsT6Nycp1qVSKRXmN998s9H7Xl5eiIqKwkjBAkwZDAaDwWCYCPVy/Xb6N5kxHC1CncHohXrBCIbhBsOXUGIIfH19ccklZ3ayjAWYMhgMBoPB4BTqboIZOOecc1jrMBhnETazzmAwGAwGw0Soe1ksgBJ+rGUYoxLdWUqKlJaWhuuvvx46nQ4BAQF0HTuZbR9J2Mz6SbJra6lZt5BVf+VgJPH2UcFWxR/UGBE5PH/bg1lVSN9uGsw2nsjcU4Hdu8rP6j5MCrTjDdAl8aAhUQ6wteM/zoFR3HaeZ5KgcHtem0SJ1BIBIeYtPX1IX7blD8SOiDTvYEOCI48eMTiDjBR+kRpOm0SCWGYJG7kEIh6bROJoZGUthELBH4hNgoCrKk0DM3shTkg+vtw2hoSIKGcER/AHYjt6KrDvAP9abIJ7FP/3S21E8IvSwFLI3QhCsQUmR9pzOv0QBBaAhdoCChW3TSJhSoQjPDz414uGRTghIITb2pXgF6iG1Eywe0lxk1nLTnI9DYlw4O3LxAIyNNKBM0CXYGEJdFj0oLFxZBIRnUpSpA59/Um/iOtLvv5PukadJD1yEaRQoc6SHDHGu3VjU1OT0auzkz9PAck3EB8fj9TUVISFheHXX3+l7kiff/75iNaVBZieZECDr/BlLLg4BI++kgwXD4PLBbFre+/tnXjlpe3UojAl1YNaiAUFj4ygqq0hrjCb8eXnBvs8Ltuyk6WhoQPPP7sZn36cSa3aLr8yGMtemA4nHn/0sQhpt2ef3oSvvthPZ5CuvX4KnnluGvVyPxsQEfHEY+vx6y9HjAZbK96YjahoFzTVd+Ljp3dh5UeH6TEhuPvb4L7XExA/j9+z/ExyeG81nrt/I/Zs7x/8zFzog8dWJMPdmz+ocaDDETkmX3+5v68vTw60w6uvz0LqNK8h240IZeIs9OjjSdRGciQ4uqMaH96XgZyd/W4mPvFq5Bc1ora8DU5eCgg1lti7pz9I0jdMjbLWFuTlNcDBQUYH2TvS+wf6/gEqiMVCarFJ9vvRxxNx+51R1A5wMJ2d3Xj7rQyseDkNbcfdTEifJbahpA+TZZc/f34Qrz+5HfXHrSCJePaKVmFbhsFO8tyF/lj+6kx4enL7m+duqcYv9+xB6T5D9kkyQIm61gvnvTwFNk5WKMyqp21wYGO/8I+a74pb34iGi78NKopa8M4j6djwS35fuWOQAmUWrTh0pIYOOiKDnHB4WxW1vyR4+dviiTdSkTzHE+3tXXh9RTreen0HOjq6TSxwicPRtx8cwP+eTUdTg+FGaq0U4+4nY3HNnVN52+1/b2bgtVcM7TY1zJEK6oL8RlpOxPn1N07FU8+kQGMnw8HMKiy7fxMy0/r78uzzffHoq8lw87JBcV4jXnhoMzb83V9H7ykqVGvbqbPRUFauZyrANAJLYCmQnFCio97kReR6+OWXX+LHH3+kf99xxx1YuHBh3+fJ50aTdzoLMJ3YNB3v65fhE4gFwwgw1bfhR9xi8v7TTz+NZ555hnOb6upq2Nv3a7uenh7MmTMHW7ZswZEjR+DjY94V6lRhYv0UxLqlQAormRCLHomCX4QGjy1Z1+f53Au5edx6m8EnfKREBJkhfvqJDZg9xwe33xU1ZNIbPojoJ4KJCKfBs36PPJqIu++LPiXru9ECuRERK8Pnn9uChnrj2S8ys7v0yWRqd8k3szbSbN5USD24SdIhIr4G3+hz9tXivcd2IGKaC664PxQi8akd55GEuML88EkWbn80CilzT37ASJ50PPPkRsyd78srWold41tv7KDiq7XV2ILP0UmO516YjiuvOj25Dbj60H+fHsNfHx9FS48W2ftMbQgnR9uhurMN7XIddnC4nxC/d3JoyfUgPa20b3DSt32gHR2opaRyJ0Ui3ujEQcjeXsaZf6CxvgNvPZOOPfvKcbCgBpWDZuxJbgNiN/rQ4nhIJBye9z06bPsgF3t/LsG5L4TCO8H0yciWH/Lx13tHcfHDwYhZaDpg3L2hFO8+mYFqcTs2pxfRgcRAAvzVcLFWYOGFk3D9vWEQD+rLBQUNeHzJenj72GLJ0kQTC9y66ja89sR26Hr0eOiFRGqbyGcxS7z48/MMg49eyOAuNtYVxOz+hZdmmuQfIMd55f8dwY+fHcSdS6ORNNv0WGxaXYC3lqWjTdSN7TtMn0KSgfZrb5rmNhhNbjBkJv37ostpAiPChx9+SP3TCYsXL8bll/M7FI0GmFif2DT1ifWPIRqGWO+iYv1WFBcXG52TJJ8AeZ0oq1evxvz58/Hmm2/ivvvuw0jAxPopivVeZAESaq/Gx3c/XoRzFnJnXRwNkBkrleIVs585cOR23hm5sQCZTXN3etPsZ/JL7qOJiBijF7LkJTr8E95yIsRqmx4Z0X148ZZN+PvLbN5y12lKrN6ax1seHeuCnTv4LQSJwNu07YZh7aO70xs0+RIf5PtPNEnWqfDFZ3tx712recvJk8e/Vl+FkSQ89EPkHuO/Lv/8+6WYM/fU15iSXBBLl6znLV9wrh++/+mSMSHWiUgnYp3w4IMP0vW3ox0m1ic2TadZrA/3nNy7dy/Cw8Opvemzzz6LkYAFmDIYDAaDMQH5+uuv+4Q6mREcC0KdwTjbAaaD2bNnD/3Xz2/kgrHH7toGBoPBYDAYp8SGDRvw1ltv0f++8847ce21ZzcBGYNxSj7rGN7/Toa1a9ciMzPT6L2srCw88cQT8PDwwEUXXTRiB3HMzKx3d3fThf0qlYpmkDrT2zMYDAaDMR5o1hfj9df/jwaNktn0m2666WzvEoMx6rGxscE999yDyspKOoteV1dHZ9WJO8xnn30GuVw+cWfWich+5JFHoNFo6JogIrZJ5qiqqqozsr05RGILkyCvgdBgMuXIBJeeLkgwo9LMPkqlQkg5gtHGEiRYUaHgt46Ty0W89nuM0YOVlcgkIHEg5ixNTxfWKvPns63K/FphtcqK1wrydNXB3DWJ9HPS34dDYaFx0OZgtF09p7x/pwPiAiOX85/vJJDcZlDg6smiGuI4kWD/U82OONKQ9eoF+j+pi8XMmTNx//33n+1dYjDOqnXjiRITE4OtW7fSGXZy3pD16ceOHcPmzZtHdAkMYdQrlAceeADvvfce/v77b1RUVNCGIfY4CxYsoBebkd6ej5R5nvhzz9VYteYqPPJYAhW1AyEWfOs2XYfkFG5nh9ECuXHt2b8I11wXaiIiiN1bRuYtcHQa2/aN5Ma9e9+tuOSyIJOyiy4JxK69t46YYw/j9OHhqcSOPbdgzjxfkz58481hyNhz64g3992vxOLRD5Nha28s1hw9FHj++5n46MeF+P6ni+HlbRyQrVJL8cb/5uKHXy7Bf+uuoTaCg51aiMPLDz8PP4X15rQbsej2COrzPpDpM72QtvNmBEzi9yw3R15ePS696CeETv4A11/9O7XQHEh1VSvuWPQ3Fj+wBlOmOMDH17gNyICZ2MF+/vX5GCn++TsHMRGf4FhOLZKS3U0GdzGxrti49XrEJbgN63euvnYKfll5Gfz81Ubvk8mZhER3/LnyKGakfIVdO/mDic8GOn0XcvQ/ogedmDRpEpYtWzaqLBkZjJNBLxj+61Tw9PTE3LlzMW/ePPrfZ4JR7QZTVFQEb29vLFmyBC+++GLf+//99x9tqO+++w5XXHHFiG3PFX2c6vcennx9LvWSHkh+fgMee2QtMnaU9fkfc3ntjmZ2ZpTh4Qf+o17xL6+YhVmzR8Yv9GyybWsRFj+4hlrKvfLarFE/mGJw88+qY3h08VrY28vx6uuzR9TdhIvmhk58+uxu/PVFNi6/LwTXPhJmlBiIeIUTm0niG05yFjz5TArU6v5Zd2Lb+Pmne7Hs2c1ISfHACy/NgLvH0L70J0PWgSra14uLGvHiyzNx3gWTTul7iEf5ipe3U6/3zs7+CQ6S1OuhR+Jx591R+OKzfXjpha1GLjRksBAb74aDB6owd74flr0wDc4uhtwUpxviyLXk4bX4b3Wu0fvuHjbUk760pBnPPj8NV10dclqvy8TD/t3/7cSKV7YjJNQBR4/Woq62va+c/BTxXX922TSIJd1nxA0mTvAChAMcywZCspPW6g/AAiJs3vsbpk6dirEIc4OZ2DQd7+sXCD4athvM7/pFI3ZOThix/v7779PAF/KIITk52WhpC2nY888/nwrukdqeq3NUV9fBzk5l9hEsl4fxWIGICGLneKqe7WMBUj/S67m8vBljByKUyLKOszko1nb28Ga2PJHrwZm4XpB2Mrd8aCgevP8/fPKhwe2Ai9Rpnti0sZC3/PwLAvD1dyMXeEXw934blRX8mWD/W38N4uKHN5tujq+/2oe7bvuHtzw5xQPf/XTuWUmK1JsAqbS0lFrLkfPl+eefp0+Xx+qsOhPrE5um4339PCrWT91yuUvfjj/GiFgf1ary0KFD9F9/f3+j94VCIby8vPrKR2p7Loa66Y1lod67pMCC5M8ex4zlBE+MfoYjQE8X5oT6iVwPzsT1Yrjt1NNtfmVn9xDlZ2I6aKh9GOmEZxZDDBiH2r/TiY2Fh5HPOlmjbm1tTS0aRSIRDSg999xzz9j+MBjj3brxTDCqlWVDgyGQiVxoBkNGQWVlZSO2fWdnJ30NHMkxGAwGgzEaOJl7FPFTJw4W7u7uuPXWW+nM9Fimd//Hej2GYrzXjzFOxHrvIzqdznT8Q96ztLQcse2XL18+YpmoGAwGg8EYDid6jyIz699++y29H5IYrfXr+TOvjjXWrFmD8UxbW9vZ3oVRjf4UvNIHbz9WGNVi3cHBgf5LvCwHz47X1tb2lY/E9o899hhNvTxw1oLMSjBGPyQMY7QH946FfRxpWBuMFczf0Ea6G59IPxlu+bAZ8vdP/0+e6D2qXL8VaoUCqamp48amkcw4E6E+e/ZsurRnvMKe6JtHN4GWwYzqxbsRERF9GaIG0tLSgsLCQuqbPlLbSyQSulRm4Isx+tm6pQgJMZ/hqst+GdIP+mxA1q6+985OTPJ5Fy+9uJW6hkw0iPj66ot9CPR7D088th7Nzf2P8hmjh9ZWLZ59ahNW/XWM2hEOFpwkQPuue6Lx+VfnY/GjCZBwrN+PjXelLlOPPLQGDQ0dJ70P+3dW4LKkH3HTgt+Qe6SO93O//H4ZomNcTN53dJLjw0/PRVS0M0aSSy8LwnPPT+PM50AscD/45PSvET+Re1SbvhKNOEZn1YmNMRG24+VFONv7cKbqyWCMarFOotXJjPj3339v9D75m3ikk0CZgRABXl5efsrbM8YupSVNuOHalVgw51sczKrGX3/mIDrsE7ywbAva20fHur/NmwqRGPsZHl28DhUVLXhx2VbEhH+Mv/7IxkSB+E4T/+m77/gHZWXN+N+bGYic8jG++zZr1CaRmYj8/OMhRE39GK+9mobKylZs31ZMPcWDgu1o+bTpntiecROWvzKT5mF48ukUZGTeigXnGhKD+AeQz9pjR1opystb8MF7uxEx5SM6SDuR41xX3Yali9ZSob5/ZyW2rS3G+ZHf4uUlW9DSrDX5PLHuXLvxWrz/8TlwcJRTl6B774+hOSSuvOr02jXyBfHe/1Aczedw2RXBfW3w6x+X4dsfL4aXl7Hn/JmiTL+V/kvc0Hx8xp8VL2Nio4N+2K+xwqi2biR89NFHuP3222nyhosvvhj79++nf5No9q+++sros1KpFGFhYUhPTz+l7U/EKmgsWPxMNEiylsSYz9Dayi3KJ03WYGfmyCfMMcfHH+7BQ/f/x1tOZiaJ4BnP/PbLEdxw7e+87iA33DQV/3t3/pneLcYgiH/9e+/s4mwXonkfeyIJjy5N4m23997eiaWPrqc2sFwQ3/mPP1toVqjPDf4aTQ3cT1zsneXYUnATrwBvaupEVVUr/PyMExadSYjHPbnuDLTAHel7yGCf9U59Iw7rP4cO3fhn0/dISRk/1xdm3TixaTre1+cKPhi2deO/+tvHhK4b1WvWCYsWLYKLiwveffddKq7t7OzwzDPP4K677jL5LMkk5ezsfMrbM8YmTY2dvEKdUF7Wckb3h3Mfys3vQ3lZM8Y75GmCuamBslFwnBjmzxdy/MRD5GCQyUW8Qv1E+np7WzevUCdUl7fS7x+cobUXko34bGckJgmSzhZafRM6UIU5iyXoXK2iE1hJSfyDKwZjrKIfRhZSun3f/41+Rr1YJ5BZ8BPxhT169OiwtmcwGAwGYyxDfNZJZsaMjL/pmudrrrlmzCY/YjDMoRvmUpaxtAxmTIh1BoPBYDAYJ0YTCiCoq4O9vT11gWEwGGMbJtYZDAaDwRhHNOiPQnXcZEEsNnWoYTDGB/pheqWPnZl19myMMeYJmKTBbXdEcq5htVVJ8cyysz+zdPkVQUhIdOMsI64R+XkN+HMl9zKu/PwG3HjdSnz+6V6za4FHO+ec648583w5y4jTyD33RWM8c+hgNa654lf88tPhU9q+rq4dD9z3L158fmQdjm6/MxKBQQbXl8FMn+mFCy6ebHb7OXN96bHmwtvHlrqmmIMEkN72SBREYtPbk0whwuLlibC0ZLcuPnT6bjQij/73rFmzzLY1gzEefNZ1w3iNFdgVjzHmkclEePX12diafiOSkg1JQSwsBLjx5jBk7l+EWxYZ/PbPJpMm22H12mvwyecL4eysoO/Z2kqpgD+WU4dtW4tx9RW/4YKFPyAnu5aWE0H2/HObERvxCRV49929GtOTv0TGjlKMRTw8lfj5t0vxw88Xw8vbYGVHfKmJP3X6rpuROs0L45HGxg7qM54U9zn+WJlNB17nzP2WivcTgQzQPv04k1offvpRJl56YRtiwj/hHdwNl/hEd2zbcRNefHlGX6Cmh4cN/u+7C7Hyryvg40PmbPlxcbXGdz9dTG0LySCs9xx94ulkZOy5BbNm+wxpg/jg8wn4K/MapMzz7Hv/3CsmYXXWtbjlocjTUs/xSjMKoYMWarUaISEhZ3t3GAzGRLBuHC0w68axw68/H4a3j4p6L49GWlq0WPbMZuot3lBvmiiGeETfdW80fv3pMIqKmkzKiWPdHXdF4aVXx+6sWWdnN775+gDmL/CDs4txduHxRPr2Elx95a+orjJNG06SChG7zgce5p9prq/vwPnnfIe9mZWc5TNmeeP3Py/HSFFV2Yo/Vh7F1deGwsrq5BO0aLU99DjPmu0Ndw/lKe3D+r/yYGMrQVSSK8YyZ8q60Rvnohp7ce/S6/DCCy9gPMKsGyc2Tcf7+jSLdyEchnVjt74dG3V3MetGBuNscNElgaO64clsspubNadQJ3R16bBlUxGnUCeQ4fXGDYUYy0gkQtx0i/kMxOOBAweqOIV6bzbbTZsKzYp1svSFT6gT1q/Nx0hCEgwN58kUmSUnT7iGw4xzWTKfk6ERhmtDdPT4XlbGYOiHuep8LM1UswBTBoPBYDDGCZ0wLKNjS2AYjPEDE+sMBoPBYIwTPINsMWXKFPj4sCcSjPGNTqCnr1PefgzNrTOxzmAwGAzGOIEkQkpOTmaJkBjjHt0ESorE3GAYjLNATKwrXHgCK62shJi3wA+TJms4y4lF5TkLua3xGKOLiEhnuLtzBxNKpULMm89tZdmLnZ0VEpMMDkdcXHDhJIwkhYUNeP3VNOpow0VtbTteezUN5WXNI7ofjJMjODiYNRmDMY5gYp3BOAvExrth175b8cBDcTQIb6D42rX3Vix5LBHbM27CC8unw9q6P6kJsaYkFpXERYQx+omMcsbOvbfikccSqDjvZcG5fsjIvAW33xlldnulUop/1lyNT784z2hwNznQDn+sugJffXvhiOx3R0c3lr+wlVpEPvPUJmob+X9f7UeveVhPjw4ffbAb4aEf4tmnNiFy6sd487V0dHX1jMj+ME6OwMDRHWTPYJzOAFP9MF5jBWbdeIIw60bGSJGTU4fXXtmOK64KwbTppl7jlRUtVDglp3ji4kvZTXisQpJbvbJ8G3Urmj3H55QsP1e8vB129jIq8on140iwbm0+HrhnNQoKGk3KoqJdcO/9MXj15e04sL+KM8HX2+/OR4KZpwETlTNl3Tht2jSsX78eAuLxOk5h1o0Tm6bjfT3B8u1hWzdu77mHWTcyGIyh8fdX44OPz+Utd3RS4M2357GmHON4e9vi/Y/OGZbl5zPLpmGk+erzfZxCnbBrZxk+fH83p1An5GTX0fwBTKyfPTw8PMa1UGcwJuKadRZgymAwGAzGOMHVdWwnj2IwThTdBBLrbM06g8FgMBjjBCbWGYzxB5tZZzAYDAZjnODi4nK2d4HBOCPojr+Gs/1Ygc2sMxgMBqMPH18Vb2sQ21A/fzXMLYn28eHfnjHyaDTclq8MxnhDfxr+N1ZgYp3BYDAYfTz9XCp+WXkZFeUDIX7vW9JuxNvvzce6TddRD/mBBIfYY9V/V+GBh+NYa55FVCo2WGIwxhtsGQyDwWAwjCDWkqm7bsa7/9uJ//t6Px5dmoRLLw8ysnDcsOU66r3+xms7cOttEVh0ewQsLdn8z9nG1tb2bO8Cg3FG0A8zwHQszawzsc5gMBgME0iyLjJLzjdTTuwBr71+Kn0xRg9WVqfuO81gjCV0Aj0EAuYGw2AwGAwGYwzBPNYZjPEHm1lnMBgMBoPBYIwpdGRwOsztxwpMrDMYDAaDwWAwxhQ66CFgSZEYDAaDwWAwGAzG2YTNrDMYDAaDwWAwxhT6YXqljyU3GOazxWAwGAwGg8FgjFLYzDqDwWAwGAwGY0yhm0Br1plYZzAYDAaDwWCMKXQTSKyzZTAMBoPBYDAYDMYohc2sMxgMBoPBYDDGFLoJNLPOxDqDwWAwGAwGYwwmRdIPa/uxAlsGw2AwGAwGg8FgjFLYzDqDwWAwGAwGY0yhFwA6wTC2x9iBiXUGg8FgMBgMxphCR+X2xFizzpbBMBgMBoPBYDAYoxQ2s85gMBgMBoPBGFPoJtDMOhPrDAaDwWAwGIwxRQ/09H8TQayzZTAMBoPBYDAYDMYohc2sMxgMBoPBYDDGFLoJtAyGzawzGAwGg8FgMBijFDazzmAwGAwGg8EYU+gm0Mw6E+sMBoPBYDAYjDFFj0AHvUB3ytvrcOrbnmnYMhgGg8FgMBgMBmOUwmbWGQwGg8FgMBhjip4JZN3IxDqDwWAwGAwGY0yhG6ZYH862Zxq2DIbBYDAYDAaDwRilsJn1E0SvN4zAmpqaRvJ4MBgMBmMc0nvv6L2XnG56v7e5uRkikQjjma6uLrS1tdE2Hc91Hek+M9bpFnRAMJyZdUEnxgpMrJ8g5AJIcHd3H8njwWAwGIxxfi9RKpWn/Xtra2vpv97e3qf9uxnjs8+MVcRiMZycnFBR8dKwv4t8D/m+0Y5Az4ZsJ4ROp0NZWRmsra0hEAgw3kbvZBBSXFwMGxsbjFdYPccXE+V4TqS6jud6klstEV0uLi6wsDj9K1AbGhqgUqlQVFQ07oXdeO4nZ7LPjGU6Ojqg1WqH/T1EqEulUox22Mz6CUJOFDc3N4xnyEVvPF/4emH1HF9MlOM5keo6Xus5kiK6V8yR3xiPbTeR+slAxvvA61SRSqVjQmSfLthQjcFgMBgMBoPBGKUwsc5gMBgMBoPBYIxSmFhnQCKR4Omnn6b/jmdYPccXE+V4TqS6TpR6jgQTqe0mUl0ZDAILMGUwGAwGg8FgMEYpbGadwWAwGAwGg8EYpTCxzmAwGAwGg8FgjFKYWGcwGAwGg8FgMEYpTKyPc3p6enD48GFkZWWdUgKB4W5/JpNH5OTkYP/+/TQN9clCEk8cOHAAlZWVGO0UFBRg7969famoT4WWlhakp6cjIyMDo5WSkhJkZmairq7ulPtEbm4uDh06hO7uboxWqqqqaD1Ppe+ROpaXl9P+QP4dzTnuSH/dsWMHvZ6cCqQfkHYi/YJhDEnYR9qmN4vpWIP0XXKPIdeloSDXd3KdJ9f7ofr7yXyWwRjVkAymjPHJr7/+qndyctJ7eHjo/f399ba2tvoPP/zwjG1/pti4caPex8eH7mtQUJBeLpfrly1bdkLbfv311/r4+Hi9VCrVh4aG6lUqld7X11f/yy+/6Ecbe/fu1U+ZMkWv0WjovlpZWenvvfdefXd390l/1zXXXEPuXHqJRKIfbeTl5emTkpL0NjY2+qlTp9J6kv1ta2s74e/47LPP9K6urvQVFhamd3Fx0b/xxhv60URNTY1+4cKFeplMRutJ+u38+fP1VVVVJ7T977//rp88ebJeqVTSfkHOz4CAAHrejiZ++OEH/Xnnnad3cHDQW1pa6mfOnHlS25Pjfu2119JzlLQTqW9iYiLtJxOdoqIi/bRp0/TW1tZ958rll1+ub25u1o92Ghoa9E8++aTezc2NXtNCQkLo9eicc87RHz16lHOb559/np4ngYGBemdnZ723tze9/g/3swzGaIeJ9XFKWlqaXigU6pcuXarX6XT0PSK0iUD77bffRnz7M8WxY8foBfmGG27oE61//vmn3sLCQv/2228PuT0R+Lfeequ+urqa/q3VavV33XUXrefKlSv1o4Xa2loqdoiYa29vp+/t2LGDCpglS5ac1Hf99NNP9KZIhN5oE+udnZ1UcEZFRdGbOYHcuMnN/Oqrrz6h73jllVf0CoWC9oNeGhsb9S+88IJ+NEFElpeXl76kpIT+XVFRQQfFZKAyFFlZWVT4ErHf2traJ2ovuugi2vf37dunHy2QASUZWHR0dFChfbJinRx3tVqtP3z4cN+xjI2Npf2EfOdEpauriwpcItLJ9YGQm5urd3R01F988cX60c6WLVvoIOOjjz6idSEUFxfTiQhyXa6rqzP6PLmeCwSCvvtPT0+P/pZbbqHXf3IfONXPMhhjASbWxykLFiygF+3ei2AvkZGR9GI40tufKRYtWkQF5+AL+/nnn6+3t7c32X+uWb/BEMFORDBpg9HCc889RwcQ2dnZRu+TgYVYLNbX19ef0PeUl5dT4UuEK5nBGm1incyIk3quX7/e6P3ly5fT9/lm3HohN2IyyHzzzTf1oxlSP1IfUt+BfPfdd/T9f//91+z277zzDufnyMwheX+01v9kxTrp76Q+pP8PFnrk/U8//VQ/Ufn2229pG6xatcroffIEibw/mgZsXJDB1/79+03eJ0+GBp8bZCKGTFYMviaTgRt5mkAmXE7lswzGWIGtWR+HkPW5a9euxbRp0yAUCo3K5syZQ9dmm1v3OdztzyT//PMPoqKioFKpTPazuroau3btMrv9ZZddZvKepaUlTbZxqmulR6qenp6e8Pf3N6kniSUgx+tEuOWWW+Dm5oZHHnkEoxFST6lUiuTkZJN6ElatWmV2+y+//JLGWVx77bV0jTRZy03WrY/GehJmz559SvXs7QeDz8Pevwf3k7EKXzslJCRAoVAM2U7jGdI25Fo1ffr0U+pDZ5vJkycjNDTU5H25XE7/HXj93b17N43tGNwPbGxsEBcXZ1TXk/ksgzFWYGJ9HJKfn08FnI+Pj0lZ73tHjhwZse3PFCR4qLi4+LTv52+//YbGxkbEx8djtHD06NFh1/Ojjz7C6tWr8emnn5oMwkZTPd3d3U3270TrSQIY7e3t8cwzz8DFxQVXX301pk6dCj8/P6xfvx6jqZ5isRiurq5G76vVaiiVyiHrSQTZXXfdhUcffRTvvfceNm7ciPfffx8PPvggFi1ahAULFmA8QNqJMLjvW1hY0MHraLgOnc22IX2cDG5H6zX6VPj888/pvwOvv3z9oPe90tLSvuDUk/ksgzFWGJ13bMawIEKTQGaeBmNtbW30mZHY/kzR64ZyOveTuCrcfffddKZ+8eLFGC2QegynnmR2+aGHHqKvyMhIjFZIPYhgPdV6kqcpZFaNzDoePHiQCjrST84//3yce+65dKY9ICAAZxtSDzKDKBAIOOt6Iv2WiHJSRyLYvby8UFhYiJCQENx2220YLwx1LRotT/hG0zWBiHcy2B0N1+iT5ccff8T333+Pc845hz49Odl7Eik/mc8yGGMFNrM+DhGJRPRfshxgML0Wdr2fGYntzxSnez8bGhroTYI8fv3uu+/g7OyM0QKpx6nWU6fT4frrr6f1efbZZzGaGU49CWRZAOGpp56iQr338febb76J9vZ2fPjhhxjN9eyt61D1JIMO8kifPEWoqKig9nTE+pE8QUhMTKRLAcYDQ53jo+E6NBr7EHl/rLXNli1bcMMNN2DSpEn44osvTvlaP1buXwzGycDE+jjEycmpb5ZxMDU1NUafGYntzxS2trZ0bfnp2E/isz5//nw6U/nDDz9g7ty5GE2QepxqPcl69m3bttH16kTkEX918iKDExJkTv77VL2vR1M9Cb0DLLL0ZSBkxpkI+d5H5GcbUg/S5zo7O43eJ8eDDBaHqufbb79NBx//+9//IJPJ+mZUyfvkO8ngZDww1LVoNFyHRtu5QrzWST8aS21D8j2QiRKyLGzdunWws7M7qX5AlpT1PpE7mc8yGGMFJtbHIY6OjjSIkMy2DYaINXJTJ+JlpLY/UxDxFR4ezrufBBJ8eiJr38kSCRKMSh7BXnDBBRhtkHpwJffprWd0dDTvtmS5RWxsLH799Vfcf//9fS8yMOnq6qL/TdY7j5Z6kgQpg2+0J1JPQkxMDP13cMIocozJTFtv8NpoqCcRVCRYeyDkGJN4kaHqSWbRSf8nM+sDIU8RiHgns+3jgd7zd/A5Xl9fj6KioiHbaTxD2oa0A4nbOZVzZbRAkjmRyRHSlzds2GASx0HoXbrHd60PCwvri3M5mc8yGGOGs21HwxgZHnvsMerDPNBTltj7kcQpJMHM4MQaxFd9YCKNk9n+bPLuu+9Smy9i5TbQq5skSSI+1gMhPtaknr2exATi0zxr1ixq90f8x0crxPOd1JNY+/VC/O9JQifiN93rhU8gNpaknsSm0Ryj0bpx165dtJ6vvvqq0fvEN5pYTra0tPS9R/6b1LOwsLDvPZIoRyQS6R966CGj7b/88ktOq8SzBfFWJ/agxHpzIGS/iRXnwDoR+1FSz4G2nST/AanPunXrjLYnnyPvD67/WLFuJLkDiIf8wGNsZ2dHrVi57Al37typn6gcOHCAeuoPTgBHfOlJO/fmKRjNkGNNji+5XpP7kDmmT5+u9/T0NPLW7+3vxMr0VD/LYIwFmFgfpxBPWZIwg2RvI9k4//77b5r1j2SL603C0gtJqkMuYuRidirbn02IJ3pqairdr2+++Ub/33//URFKBhUDb/q9iTJIPQeKcpJZkbz38MMP0/oPfO3Zs0c/mrjssstovUgSESLSSFZHIrY3bdpk9DmSCITUaaiMnaNRrBMeeOABKmRff/11/YYNG/T33XcfFSU//vij0eeIUOMSpqTeZKD56KOP6tesWUO/hyRJIgmlhvLdP5O89dZbtF4kiyPxRyeiiwwaX3vtNaPPkYRdpJ4DE92QLKck0y7xkybig/i2v/fee32ZGocaqJ1JyCCj95wixyE6Orrv79LSUqPPkv5IEh4NhJyvpJ3uvvtu2h9Iu5Gsr6SfTHTIpAoZ3JFEYKQPLV68mLYVycw82iEDUpL8iCQqIuf24OvvwAErgVzPyfVv3rx5NL8A8Zkn2bVTUlLofeBUP8tgjAUE5P/O9uw+Y2Qg9lRvvfUWNm3aRJcAkCUCxNpt8KNzYv321VdfUUu/4ODgk97+bEOWDZA6/Pfff3Qd75QpU+h+9gYY9vLzzz9jxYoVWL58eZ83cVJSksnSkoHrn4mN42iBBIoSW7OVK1dSNwPiU3zvvfcaHTPC5s2bqY/6Aw88gMsvv5z3+x5++GHs3LmTHt/Rxk8//USXJJE1pt7e3rj99ttpQOVAiDUdCUgj9oz33HOPURmxaSRtRRxSyPpUsszpxhtv7AtAHS2QeILPPvuMupoQGz6yj4PjJcixJu/NmDEDL774Yt/7pK9//PHHSEtLo0uHyFpd0kbEDcbKygqjhaVLl/LaZhL7SeKJ30tqair1iP/kk09MLDk/+OAD5OXl0fXMpF9z5UiYiJBr1LfffktdkMg1jxx/EmQ82um9TvHBdV6TpU+vv/469u3bR5dj9lqYknXogzmZzzIYox0m1hkMBoPBYDAYjFEKCzBlMBgMBoPBYDBGKUysMxgMBoPBYDAYoxQm1hkMBoPBYDAYjFEKE+sMBoPBYDAYDMYohYl1BoPBYDAYDAZjlMLEOoPBYDAYDAaDMUphYp3BYDAYDAaDwRilMLHOYDAYDAaDwWCMUphYZzAYDAaDwWAwRilMrDMYjFOCpIQXCAS8rzfffJO1LIPBMGHy5MlG1wqRSAQvLy9cd911yM7OPqstZmtri9tvv/2s7gODMRgm1hkMxrD4559/oNfrTV73338/a1kGY5xy3333ISYmpu9v8t+PPPLICW8fHBzcd61oaWnBjz/+iN27dyM+Ph5VVVUjtNcMxtiEiXUGg8FgMBgnxerVq3HOOefQ/ybieteuXZg/f/4ptaJEIqFi/6mnnkJdXR39bgaD0Q8T6wwGg8FgME6YgoICulxlwYIFfU/XFAoFkpKSTksrSqXSvv+uqamhS2VWrFhh8rmwsDDMmzfPZFvyVG/z5s2Ii4ujf7/00ku07Oeff6bvkaUuGo0GM2bMwL///kvLGhoa6O80Njbiww8/7FuiM23atL7vNrc9gzGSMLHOYDAYDAbDLBs3buwTsN7e3vQ9MhtO/r7hhhvQ3NwMsVhM/37nnXdOqjW1Wi1dAvP888/T7+4dBJwqZCDxxhtv4Msvv6T/HRISgi1btuCyyy7DhRdeiNzcXOTn5+PZZ5/FW2+9hZ6eHirAyZIcpVKJ2267rW+JDqk3YajtGYyRRDii385gMMY9fI++MzMz6cwXg8EY+5AZZiJeCQsXLoSNjQ2++eYb+p5arcbLL7+MRYsWnfD3HTx4kAr7gfj7++P333+ns/T/3979gzQSRHEcfxfsQjojomIjBCKojVaiksYqpU2qNEmROkiCIFZWYmkKS1NaWmul0XQiggTbqFhY+o+Ad7x37BL/cZtdwg3y/cCCG5zZJSD8fLyZieLk5ETa7bYkEgm7Hx8fl83NTXteuVyWgYG/0WdhYcGuILRSH2U8EAWVdQB9WWBKUAd+Hv3bbjQafsvL1dWVtZDMz8/3NE/3AtNOpyOtVkumpqZkcXHR+t+jyGQyflD3zMzMyNvbm+RyOQveWs3vRdTxQBSEdQAAEIiGc10E6oX109NTayGZnJwM/Q1qpTqVSsne3p68vr7K2tpaoHFepf+j0dHRT59ls1nZ3d21919aWrJ2l+Xl5cCLWaOOB6IgrAMAgG9ppdvrV9eKuJqenrb7QqFglfVYLGb33mLOMOLxuIyMjMjl5aX/mbbb6LzaE//Rzc3Nl/Povu1fKRaLNvf9/b3U63V5fn62/njtRw8i6nggLMI6AAD41uzsrN+yojug6KFB+rO2gmh/ude7rle1Wg39TT4+Psrt7a0MDw/7n+mi1bGxsXcBXp2dncnDw0Oo5wwNDcnKyooFbn3n7rCt/zBodT/seKAfCOsAAOCf9PCi4+Njf7tEDalPT0/WDhKF7qZyfX1tJ5i+vLzI6urqp4r2wcGB7O/v2zvoc7e2tiSdTgd+xvr6umxsbMjFxYW9s+4NX6vVrGqvbS0e3Tmm2WzK3d1dqPFAPxDWAUTeDab76HDv4shu4Gc5PDz0q+ve4vK5uTkZHBzseS5vNxi9tHqui0I1BB8dHdkizm6VSsW2UyyVSlZ1397elp2dHRvXy4mr+vv5fF6SyaSF8vPzc9snvXtxrM6tPfgTExPv9lkPOh7oh1+/v1uhAQAAAOC/orIOAAAAOIqwDgAAADiKsA4AAAA4irAOAAAAOIqwDgAAADiKsA4AAAA4irAOAAAAOIqwDgAAADiKsA4AAAA4irAOAAAAOIqwDgAAADiKsA4AAAA4irAOAAAAiJv+AEon+YVXPkstAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 700x700 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "alex_jointplot(ds, cmap='plasma');"
    ]
@@ -201,9 +373,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'matplotlib.figure.Figure'>\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x700 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "alex_jointplot(ds, cmap='plasma', marginal_color=83);"
    ]
@@ -219,27 +409,94 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'matplotlib.figure.Figure'>\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x700 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "alex_jointplot(ds, kind='scatter');"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'matplotlib.figure.Figure'>\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x700 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "alex_jointplot(ds, kind='kde');"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/paul/Python/FRETBursts/fretbursts/burst_plot.py:2568: UserWarning: \n",
+      "\n",
+      "The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`.\n",
+      "Setting `bw_method=0.04`, but please see the docs for the new parameters\n",
+      "and update your code. This will become an error in seaborn v0.14.0.\n",
+      "\n",
+      "  jplot = sns.kdeplot(data=data, x='E', y='S', ax=ax_joint, **joint_kws_)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'matplotlib.figure.Figure'>\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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3Hr377ruRXhIAAMROZK64uFh0pHmCbQi6d+8e6LoAiDl4RJ6sM23ZbwTFJyVTtJNz1DCqObCXaPN8UbrBx4Any1jNHBkAACwn5n766SePo7AYNEAA4B+TJ0+mffv2iZRk1uEDYubwZfc+gS46vjN99NFHYnQZ2x6x2TdsSwAAIIRi7oQTTnDrYq2qqqJ///1XnJAuvPBCOumkkwLZNAAxCVv58JQSJvvIYRQXn0CxxLnnnksdO3akRx55hL766ivatWsXPfzww5SZmRnppQEAQHSKuby8PHHRY9y4ccIXa/Roey0MAMA7b775ppiKwF3ga9pbc2xXY+HPjBYtWghD4fnz59MFF1wgJkccdthhkV4aAADEVgNEkyZN6LTTTqPXXnst2JsGICphk+3PPvtM/J+NuGOtXkx60bEhMkfneHxfTk6OGP3FUX4WdBs2bBCGygAAANwJySwdHqhtVZd6AMLNK6+8QnV1dTRgwADq1asX0fx5MfUi6HnR1fU6j4rLP6P9G1fS/c+/Tg+/OYXefvQuGjt2bMyJXQAACFtkjsfycJ0L1/3wN+vevXsHa9MARC2rV6+m77//Xvw/lucHSy86eWnaspDanXEDtRlxFSWlNxd2LTw14tJLL6Wff/4ZUToAAGhsZI7tEzx1s5544ol01VVXBbJpAKIWThNqI9b8xaempoYGDx5M6enpiGgrcASu2aFHUXq7w2jHvC8obseftHTpUrrhhhtEze4pp5xCycnJIj2blJSEkWAAgJglIDHHBcm33367y208V5G95fr06UPHHXdcsNYHQNTAQm7YE9Oc6USesbr1+7lCtNTEdaW5r86z1NzVcJHQJJWyDu1LB9t1o32b/qbyfxbR2l3raN6KlyguIYke/mI+ZbQ/nGbffzFGggEAYpKAxFzPnj3FBQAQWDqRyxJKv3uL4hISqUXPwZTe5lDLzV0NN01y21Jm5yOo9eCzae/qRVSydDZVlWwX/9+7agE9nbmLbrvtNvGlEgAAYgmM8wIgAuxbt5QqizeJKQ8t+52C18AP+Jg1734sdbzgP9TujOsprc2hQhz/+OOPokHi9ddfF76XAAAQKwSlm7W8vJy+/PJL2rlzJ/Xt21fU/wAA9LHV19HOX6eJ/2f3OZESU2GMGwicnk5v201c9q79kw7Z+RutX7+eXn75ZZo2bZqI0nXq1MnlMa1btxYlIQAAELNijic8cGPDn3/+KfygeJYiFyIPHDiQduzY4bzfNddcIz5QAQDulK2cT1VlOymxSRplH4lJKcEgsUk6rW5zElWlFtGuBdNp7ZJ/aM75V1D2kUOpec9BFBcXTzXlJTTztlGoqwMAxLaYYwPPlStX0pFHHim+AfP81aOOOoratWtHjz76KKWmpgqbBfbNuvLKK2FPAoCG+tpq2rngG/H/nKNPoYTkpjhGQYJrEdPbdqVmXY+m7T9+SOXrllLJktlCxOUPuwTHGQAQtfgs5v755x9atmwZrVixQkTlqquraciQIfTbb7/RunXrhK0Cc/bZZ9P+/fvp66+/hpgDQEPZit+otqKckjOzqXkPdH2HgsSm6VQ44koqW/ErbZ8zhcrXLaOqKU9Qq8Fn4/0IAIhKfC4e4Ugcp1NZyDHs78Recxylk0JOwiJv06ZNwV8tABaGa0tLl/8s/t9ywGkUn5gU6SVFdT1d8x4Dqf2ZN4uaxKrSHbR52su0cOHCSC8NAAAiF5njE1F2dsO4HYavZ2a6F283a9ZMROcAAA188sknVF9TRU3z2lGzLkfh0ISB1NaH0CHn30lbp79OB7aupf/+97+ivnfcuHGWb4TQM6HWA00fAEQ/IZnNCgBwhU+6X331lfh/7rGjKc7iQsJKJKU1o3Zjb6aime8R7V5Bb731lqj9feihhygrK4usINZ4SkhJSYlz2gXDv5MzbesOHqDqvbupvvog2epqKT4phRKaponGj1l3jkXTBwBRjl9ibsOGDfS///3Pef333393u03eDgBo4LXXXqPa2loRKeLxVCC8xCckUu4xp9MtXU6mV199lRYsWEDnn38+3X333XTsscdaYmIIURq98NbvoolGmCWvWUy2miqqLttJ1eUl+hupr6Mbtv8sRp8NHTpUNKsBAGJczHG9iV7Nid5t3OkKALBb+kyfPl0cipy+J4t6LhAZuJ53wIABwoNu8+bNdOONN9Lw4cPp2muvpfz8fNO9LIkZzUVqvnLHRmEyXbljA1WVFAmTZI7C8QQRvjDcVBOfkiqEa13NQardX0a1FfvE+48FLF941OKll15KvXr1ivSuAQAiIeYGDRokOlR9paCgINA1ARA1NUx80uV0Hnd/9+7dm5a1LIzY+oAdNhL+8MMPhYXSRx99JOyUeHrEaaedRqNHj/Yp9RqKOjSO3HKmY/Xq1cI9gP081837U7yHtCSmZlBiyzaUWtCJMjr0oKa5bcUMWxV+HM/6valnMi1ZsoTmz59Pv/zyi7gMGzaMbrrpJsrNzQ3qPgAATC7m+FurGb+5AmDetBjR/k2rqOiHXyguPoHKq1pRs4iuEEiaNGlCN998M5188snC4JzTrl988YVoUtlQkUAZ7btT01btKblFK0rKaC5qzySNNR9mYc9RQRZrW7Zsoa1bt4qfGzduFLVxzuepqaH62hpKaJImmmbEpVV7cUlMy6KKrWspKbOF8NfTgyPASelZNGzYQLrsssvEc7777rviS/nMmTNp3rx5dMcdd4gULKLFAFgbNEAAEERYyMmTK5+IS/78QaTBco4cSql57XGsTUa3bt3opZdeEpGrqVOniihd7YEyKvvnd3Fh+PXjJorEtExhc1JfX0ePP/6PmH7TtGlTceEoHV9YFPElLS2NDh48SBUVFaKzn0cd8mXXrl2ikYFnx67ZUeZMkUq4cSEluzU1yS4giiNqN+QoMa4s0IYZm60hWpyQkECXX345HX/88ULArlq1iu68806aMWMGPfzww5SRkRGEIwoAiAQQcwCEiNJlc6h67y6REuNpDweLN+NYm5Q+ffqIy8UXX0wjHpws6tIqd24W/nTcHcoNBrLJgGvV3lj9h5sQk/D9u7TKcnad6iFEYF4zEW1Lbp5HKXxpWUjJmTlO4XZgyxoReWtM53PtvjJnx6vLGjuNotL9zahkyY+0ZsqXQtjxFB9OHxsBixMAzAvEHAAhgKc87Pr9W/H/3GNGUUJyExxnC8DRqYxDelJ2nyHiuq2+jmr27aHaA+VUW7FX/ORGBNl4wM0JfKH6er63qFOrq6qgY49oR61atRKijaN0LVu2FPVp/JNv54jdyNd+NUyRhiparNL6+LOF3+HGz56jecvX0AlnXUatT7qAUlt1cLsv5toCYG4g5gAIAcXzvrQbBOe2paxu/XGMTYCacjRC+3uudWQhpIohGTEzEmLs93b31QM91tRVVlaSGUjNP4QKhl1CuxZ8TVVlO6lo5rtUMPwyatblyEgvDQDgBxBzAASZA1tWU9mqBeL/PA8UBsHmwCjlqMLdn6n59pGF4RSNkYTLAApHXkW75n9D5euW0rYZb4tUcvPu5vTfAwC4AzEHQBCpr6ulotkfif+36HGciHwA82CUcpTUlJdaRjQGk/jEZCo89f9o+08f054V86joxw/EVImco4ZFemkAAB8IuLKWO7+4WFgiTCxF3QgAsUvp0tnCkZ+7HnlsF4ht0Wh0SUpvTmaDI8ith5wnOq+Z4l+/pF2/282uAQBRJObKyspEuz1TV1cn/JIkU6ZMEeNxAIhV1qxZQ3uW/Sz+3/qEcyghpWmklwSAX7CtSt7AMyjvmFHi+s4F02nXwu9wFAGIpjQr+xFdccUVdMIJJ1BmJvstIRIHgCxof+KJJ0SEulmnPpTRsTcODLAsOX2Hs7IT0bmd87+m2sr9RDQw0ssCAARDzI0cOZLefvttMWfyu+++o927d9Mhhxxi2kHVAISLZ599loqKikR6Nf/E8+GoDyyPqJez2aj4t2lUsngWTZnShSZMmBDpZQEAGivm0tPT6ZxzzhGXjz/+WDinjxgxQsz84ws7m69du1aMhznrrLMwzBnEBLNnz6bPP/9c/L/VoDPF+CUAoiVCZyMbFf/yuRgF1qJFC7r00ksjvSwAQGPEHNcE8UiaAQMGiOuFhYViHAzD4u7FF1+kwYMHi8jdunXrxBBrAKKZ9evX03333Sf+P2bMGPqitlOklwRAUGnZ92SqrdhHtHm++ALPpQQ86xUAYNEGiJUrV9Jxxx0nXMy5Pqi4uFgMiJa0adOGHnnkETHn8J133gnFegEwDeXl5XTLLbeIerm+ffviBAeiluzeJ9BFF10k/s9zXbncBgBg0cjcGWecQZs3bxaNEO+//z7NmTOH2rZtKy48qqZZs2a0d+9e8bNJE4wvAtELD0rn+iH+MsMzK3mu5YEDByK9LABCxnnnnSfSrJMmTRIX5vLLL8cRB8CKPnMcfbvyyitp/PjxQtzNmzePrrnmGjFU+scff6ScnBwRveO0KwDRCNvy3HXXXbR06VJRR8rND1lZWZFeFgAhQ061GD58uBB1NTU19Pzzz9NTTz1F27ZtExe4GwBg0QkQycnJopOVL+3atRPRuauuukp0uvKJ7txzzw3eSgEwAXwSu/vuu+nnn38W7/9nnnmGOnVCnRyIblynWhTSnrbH0O7FM+neZ16h5+espcyOvWjmbaM8zqMFAJhQzPEf7RFHHOFyG6dWTz31VHEBINpgk+zbbrtNRKM5Ev3444+7/Q0AEAuj0FoNPpMS0zKFD13psjlkq6slm+30SC8RgJgl4HFenErlE5uEI3PHHHNMsNYFgKnYs2ePKCdgIZeSkkLPPfec+BsAIJZ96PIcI+tY0PHfBJcgAAAslmZVYbsSaVkCQDTBNjs333yzMAXmGjlOrSIiB4Bd0LGv4rZZ79GsWbOEmONmIDTAAWBRMQeAVeHCbS7u1oMjcdzgwPYj7du3F0Xf/BMAYKd592PJVl9PSX9/Q7/88otokOPGiLy8PBwiAMIExByIeVjIDXtimqO42059XS3tWjCd9v7zu7iekt2aPn7sMQg5AHRIb9eNHr3oWHrsscdo1apVdOGFF6KmFAAr1MwBEI3F3Xyx1dfRtu/eovK1iykuIZFa9juV2p5+DWVmZkZ6mQCYlsMOO4wmT55MXbp0ETWmbF/FI8BgWQJA6IGYA8ABjykqW7WA1n/0KB3cvY0Sm6ZTu1HXiSLvuPgEHCcAvJCfny+mQ7AfHdfP8YjHq6++mnbs2IFjB0AIQZoVADYCrqqg4u++pPK1f4rjkVbYhQpOvoyS0pq5mKZ6wtvvAYhWtH8fHJVj/8VXX32Vfv/9dxo9ejRdeuml9H//93+UmIjTDgDBBn9VIOZZvHgxbfrseaqrrqS4uDhq2W8E5fQ9meLi4w1MU/WpKFpHqfkdY/54gthD/+8jlar7X0I75nxCBzdvoZvve5RmzpxJ999/P/Xo0SOCqwUg+oCYAzHLwYMH6YUXXqAPPviAaiv3U5PsfBGNa5rXzqtpqh415aUhXC0A5kbv74Ovd7zgbtqzYh7t+HmqsPm57LLLaNCgQSJ617lz54itF4BoAmIOxCR//fUX3XfffbR582ZxPeuwAZR/4gUUn5Qc6aUBEFVwhLtFz0HUJLctDaVVNHfuXHFhGxMWdTzr9cgjjxRRcQBAYKABAsRcNI49sC6//HIh5Fq2bEkPPvgg5Q44DUIOgBDCDUVsvv3JJ5/Q0KFDRcMRzzjmBgme480R8l27duE1ACAAEJkDMQGfODga8MQTT4jrHAUYOXIkTZgwgfbt20e0aF6klwhATDRJtG7dmq677joaNWoUTZs2jX788Uf6559/xIXnHWdnZ9OWLVvERKFWrVq5bYcfH6/UswIAIOZAlMMeV7/++iu98cYbtHTpUiovLxd1OjfeeCP16dNHCDl0oQIQoSaJxN5Ud3wX2rduOZWvW0oV29ZQXPFe+n31s0T0LCVn5VJam0NFd3mTvHZUd2AvzbxtFBUUFOAlA0ABkTkQtfC3ex7+Pen9T4X5L3vFNe8xhIp7n0D/+f0A0e/2aBy6UAEID0ZNRE1z21LugJFUtmohHdy1hSqLN1Ll9vVUs6+Uyv6eLy5cz9qkZRv66qs4GjFiBLVp0wZ1dgA4gJgDUcfatWvpnXfeoR9++IGqqqqEkMvq1o9yjxmleyJBFyoA5iApPYtS8w+h5GZjqe7gAdq/aRXt37hC/Kyt3EcHNv9DL764UfjX5ebmisaJI444gnr37k1paWnO7SAVC2INiDlg+TQqp0mrq6tp3rx5NGPGDFqxYoXLiKGDmUdSs859IrpOAIB/JDRJo2aHHiUutvp6qiopopIls2nHjvVUVVJMa3etp19Xrid6byoXwVLTlm0otaAzJTfPpTmPjhOROwBiBYg5YFlqa2vpu+++oysefYMqi9YL019BXBxltO9OLXoNpsVVlZSaiw91AKxub9KkZSFldesvZiUnpmbQga1rRMTuwKa/qapsJx0sKRIXW10tnXvuT8L2pH///tSvXz/dRgoAoonEWIrglJSUULNmzSg5GV5iVmXnzp20aNEiWrhwofCp4td0/679IpXKKdTm3Y+lrMOPoaT05uL+B7asifSSAQBBJj4phTI69BAXpnrvbtq/mYXdKtq34S/av38/zZo1S1yYtm3b0tFHHy2EXa9evahFixZ4TUBUEfVijoc933PPPfTKK69QQkICHThwQLTEv/TSS5STY+zmD8KTHvXkB1dUVCRer02bNtHGjRtp9erVTpNfCYvzZi26UfMeAymt8FCXEVwAgNiAv8i16HGcuBws3UF39IgXnxVLliwRlic8eYIvH330kbg/+0tyN3u3bt2osLBQXPLz88XnCcyLgRWJejF3yy23CFuKb7/9lgYPHiw6HIcPHy66oebPnw+/ogjBQm7o419SfHITqi7bRdV7d1GN4ydfr9m/R6RLurTKoqSkJOfj2F+qa9eu4ls2+1BxEfRpr//mccwWACB2qDtQTg8tsFFSZnuizu2prt1B0Rlbse1fqti+Tny+rNlRJgyKf/rpJ5fH8hd+FnSZmZmUkZEhPm9SU1NFcwX/lBe+3qRJE/H/9PR05+/5/+rnFYNmDBAOolrMsXDjCNytt94qhBzDRbHPPvssnXzyyTR16lQ655xzIr3MmIjCbdu2TUTX+LJhwwb6+++/acuCFVRfV6P7GE6bstBr3749denSRbxunCrhb9L8gSmBRxwAwJsFStOWhWKkGMO1teXrltHYdnW0Z88e2rFjh7iUlpZSTU2NyAoUFxeL/28oOSA+i3Q/16oP2i2PNL9nX7yCYZeI/9eUl8AXD4SFqBZz06dPF2lWjsKpDBkyhJo2bSrcxyHmGg9/+PGHIl92794thJt6YVHNH4wqfJ0/VEWtG3/wNs+jlOatKKVFK0puYf9/Vcl2+pdstKlJNhFP+dlVRbR4qct24BEHAPCHhOSmlJzegqaUcPTuUKJOJC6cCWA7lPqqCqqrqhSRvFY9WlB8YjLVV1dSXfVBIeD4d/yzek8x2cjG31btt1cfFNtPTGuGTAEIO1Et5jj6w3TqxH+tDXAYnCM+K1eu9Hub7F/GQlCOiJI/5UWi/Z16m4xW8YVvkz9ZeMrr2tv4J08rkNf1HscXXpu6DXkfeb/KykqX22RXKH8Tfe2118T1lJQU53r5vnK96n7K61zTxl5uWrTijY8516VwhI1/clri5b9rKK1NV8OZqCzmjExGnc9TXurjKwcAAMpnkpfPlqS0LErKbGF4H26uUn/P9il1VRX8HxxmEHaiWsxxpIjh+gctfBsX2BvBAkUVKXv37hU/ZSNFuGFxtGn3Porz8Ny2ujpql5PhVrPh8zY2bve6DSMSExPFMeULp0F/XFdGyc1bUVJ6M/FNNSEtg1bFxdMq1ngbiKp2/ktNcttQ1Z4dhts8WLpdfDiKD8gA7xOMbVhtLdhnionjj9eZTHv8a/aX2X+Wl4r0LdfXiRnQmi/1AASLqBZzchiz3h8PR5Y8ibJHH32U7r//frfbuaPSzKz1II7CuQ0AAABEPe0JDycs6rjJAoBgEtVijjsdGfYiU4vmGf62xO3pRtx55500YcIEF/HHj8nOzo661nUePs/pT65t04tiRgvYz+giVl7PWNrXaN5PDiqwkGMLFACCTVSLOZ7Xx3BtXLt27Zy3s6Eke5edeOKJho/lujFZOybJysqiaEamSaMd7Gd0ESuvZyzta7TuJyJyIFREtcMqd7FyRG7KlCkut3/yySei6P+8886L2NoAAAAAAIJBVEfmOJL2xBNP0LXXXisGro8dO5aWL19OEydOpPPPP5+OP/74SC8RAAAAAKBRRLWYY8aPHy8cuF9++WV6/fXXxQivu+++m2644YZIL800cDr5vvvuc0srRxvYz+giVl7PWNrXWNlPAIJNnA190gAAAAAAliWqa+YAAAAAAKIdiDkAAAAAAAsDMQcAAAAAYGEg5gAAAAAALAzEHAAAAACAhYGYAwAAAACwMBBzAAAAAAAWBmIOAAAAAMDCQMwBAAAAAFgYiDkAAAAAAAsDMQcAAAAAYGEg5gAAAAAALAzEHAAAAACAhYGYAwAAAACwMBBzAAAAAAAWJjHSC7AK9fX1VFRURBkZGRQXFxfp5QAAALAQNpuN9u3bR/n5+RQfH/w4Cs5Rsf2egZjzERZybdq0CcbrAwAAIEbZsmULFRYWBn27OEfF9nsGYs5HOCInD2pmZmbjXx0AAAAxQ3l5uQgIyHNJsJHb3bBhA7Vo0YKimZqaGpo5cyYNGzaMkpKSKFrx5z0DMecjMrXKQg5iDgAAQCCEqkxHbpdP/NF+jmIxl5qaKvYzmsWcP+8ZNEAAAAAAAFgYiDkAAAAAAAsDMQcAAAAAYGEg5gAAAAAALAzEHAAAAACAhYGYAwAAAACwMBBzAAAAAAAWBmIOAAAAAMDCQMwBAAAAAFgYiDkAAAAAAAsDMQcAAAAAYGEg5gAAAAAALAzEHAAAAACAhYGYAwAAAACwMImRXgAAAMQqs2bNotdee815PS4ujlq0aEGdO3emSy65hFq2bBmxtX3yySfi8tFHH1FSUlLIn6++vp7mzZtHn376KRUVFdFll11GI0aM0L1vdXW1WNfixYtpz549VFBQQKeffjodc8wxLvdbuXIl3XfffbrbuPnmm+nYY491Xn/iiSdo4cKFhuv7z3/+Q7179w54/wAIJRBzAADQCPbt20eXX365ONn36tWL/vjjD3ryySfpvffeo5SUFI+PXbduHX322Wd07733Uo8ePchms1FxcTG9/fbb9N///pe+/fZbGjRoUERen7///lusra6uLuRi7t9//6Xjjz+eDjnkEHEc+HkHDhyoe18+PizCKioq6MYbb6SjjjqKfvvtNzruuOPoqquuoldeecV53127dolt3XHHHXTkkUe6bKddu3Yu1/nx/PxarrvuOtq9eze98MILQdtfAIINxBwAADSCH374gaZNmyYEGPPxxx/Txo0bvQo5rZA46aSTnNcvuugiEW1iQTd79uyof304Arlo0SJq3bq1iM5NmjTJ8L4sqlgEz507Vxw35uKLLxZRzVdffVUIvK5du7o8hsXfyJEjPa5hwIABbrctW7ZMiMdRo0ZRfn5+wPsHQKhBzRwAADSCGTNmCFGRkZEhrnM07ZRTTmnUMW3WrBm1atWKSkpK3ITMuHHj3O4/Z84cOvPMM0V6UvLBBx+I26TgHD9+PF155ZXiOkcAv/nmG7rpppuEcLznnnvozz//dD725ZdfFilW5vzzzxfb4QuLVF8eLyOW/Jinn37ap/1lIecLnFZlunXr5nL7YYcdJn6WlpZSsHjrrbfET474AWBmIOYAAMBPJk6c6BQ4XOO1detW8f8xY8bQqlWraObMmeL69ddfH9Cx5dotFk6cetTePn36dLf78305nVheXu687a+//hK3sQB888036YgjjqD9+/eL31177bV04YUXUvPmzenkk0+m1NRUIfbef/998ft+/fpR9+7dxf/POussOvfcc8WF7+/L45mqqirx/PPnz6dgwseVo3CcxpYcPHiQpkyZQh07dhT7qYX3n0Xp//3f/4nonXqcjOD1syBu06aN2EcAzAzSrAAA4CfDhw+nsrIyWrt2rRAsDz30kEgVfvnll+LnhAkTxP3S09N92t4DDzwgRAazc+dOkXLkqBdvt7HwGjn1y3AUjUUKi5sHH3yQbr/9duf9uK6Mn5vhOjQZ6TrjjDOoSZMmzvv58ngmMzOTpk6dSoWFhRRMhgwZIqKhN9xwA02ePFmkPzkqyKlUjiaqa2W4maRnz55C6K1fv15EEXntvA2uzzOCX0uO8vHzxMcj7gHMDcQcAAD4iaxv43RkdnY2XX311SJaJAv3ZXrTV0444QSnsGABwR2tXIPHkTlvtV7euOKKK5z/Z6FTW1tLiYmJ9NNPP4lIFa+f4fXn5eV53V5CQoJPj09OTvb7OPgCH59nnnlGiGmulePaQo6ecVSQo3UsKiV9+vQRHa1qAwenTFncnXfeebRixQqPKVbeV/X4AWBWIOYAACBAuFifI0IsZBjuqgwktaptgGDBwbYcLDg4mtQYixKOSKmwEOOOT06Vcl3e0UcfLUQjCy8WP95o7OODkeLmphBOIx966KHiNrZx6dChgxByvB6O3slaPC0s/jhC+fzzz9Pq1aud21DZvHkz/fjjj3TqqacGPbIIQChA7BgAAPyA03SyXo5Tdf/884+zXo5FwBdffCGuy2aDQGFxxzVuLBAl3CHLHmtaOEplhGzMUGHxs337dlHvx95sn3/+ubDueOqpp3xaW2Mf3xhYyHG3qlaEjR492tns4Q0WoZ6aJd555x3he4fGB2AVIOYAAMAPTjzxRNEMwGlRFlEcDeLrLLQ46sNmtHyd7SwaA3eDauF0Ine4VlZWutzO3nb+wiKP18ieeJyK5KYHjlapETiGRU0gjw8VfJz37t3rdru8TVszpwfX2HE0Vc9Xjjt1//e//4nXkiNzAFgBiDkAAPADmVJkMcARIp5UwNdZZHFqVEbtGlPrxo0ELCi4W1TtaGXLExZX6tQITgcuXbrU521zRO3dd9+lmpoa521cR8eNDTk5OS7Ckdm0aVNAj/fHmsQfeJscAX3jjTect7G45aYGblTghg0J19CxT5wKd6hyRJG7W/VqBPl4cncwG0FzzRwAVgA1cwAAEADfffed07KCpxGwie0111wT0LFUu1nZR427WbkL88MPP3Sp++LoF9fkcbcsCxKezsC1YmyU62tKMC0tjX7++WcRUeR6uqysLFqyZIkQjtwdKuG08f33309Dhw4V3a0cqeM0Kjdn+PJ4aU3iKyyuOIUsvfVYzHJNIvPss886xSVPy2ChyJMZuAGFu1lZzHKTA3ezqh2qLDK5HpGtUzjSxmbDXIPITQ1GEx248YFFITd3AGAV4mwcUwZeYV8i/lDlUD633AMAYhuujWNPMx4LxQJMFsyzcPAVFhZas13+fGnfvj116dLF8HFcuL9mzRpxPxYvHD1jAciWKbJGjjs1ZT2fHvxZxvfhz7a2bdvS4Ycf7nYfFle///67GIvFEUEWdlJcens8P/arr74SDQT9+/f36XiyONVj2LBhbp+7XO/Gx4F/stDjKCl30GrhbbL334YNG4RVDM9XlX55erAZMgtDPpZWOofI7fPoMdlhHK1wVJjNufnvLRxzgyOFP+8ZiLkQHFQAAAAgnOcQiLnYfs+gZg4AAAAAwMJAzAEAAAAAWBiIOQAAAAAACwMxBwAAAABgYSDmAAAAAAAsDMQcAAAAAICFgZgDAAAAALAwEHMAAAAAABYGYg4AAAAAwMJAzAEAAAAAWBiIOQAAAAAACwMxBwAAAABgYSDmAAAAAAAsDMQcAAAAAICFgZgDAAAAALAwEHMAAAAAABYGYg4AAAAAwMJAzAEAAAAAWBjTi7mamhr67rvv6LLLLqM+ffrQ1Vdf7fc25s2bR+effz7169ePRo4cSe+//35I1goAAAAAEG5ML+Z69+5NL7zwAg0aNIj+/fdfcfGHKVOm0ODBg6mgoICef/55Ov3002n8+PF0ww03hGzNAAAAAADhIpFMzoIFCygjI0P8/+abb/brsfv376drrrmGzjjjDHryySfFbf3796fq6mq6/vrr6cILL6Sjjz46JOsGAAAAAAgHpo/MSSEXCJyeLS0tpYsuusjldr4eHx9PkydPDsIKAQAAAAAih+nFXGNYvHix+NmzZ0+X25s1a0Zt27alP/74I0IrAwAAAACIkTRrY9ixY4f4mZOT4/a7li1bUnFxseFjq6qqxEVSXl4eolUCAAAA/mF0juKmQb5EM3L/YmU/KdbFXF1dnfiZkJDg9ju+zdOBevTRR+n+++8P6foAAACAQDA6R/3000+UmpoaEwd11qxZFM1UVFT4fN+oFnOcTmX27dvn9ubm27Kysgwfe+edd9KECRNcvvW0adMmhKsFAAAAfMPoHHXCCSdQdnZ2VB9GDsSwkBs6dCglJSVRtOJPRjCqxVzXrl3Fz3Xr1lFeXp5LxG7jxo10yimnGD42JSVFXAAAAACzYXSOYnETzQInlvY1yY99i+oGiJNPPpni4uJo5syZbibCBw4coBEjRkRsbQAAAAAAwSCqxBxPeLj44oud1zt16iRsSJ577jlavny5uG3Pnj10yy23iKgdT4UAAAAAALAypk+zTpw40VnkyHVuv//+u5gKwdx4441izJdk2bJlIhKn8tprr4lQJQu9du3a0datW4VR8GeffUbJyclh3hsAAAAAgBgTc9deey1dcMEFur/Lz893ub5w4UJq2rSpy21NmjShN998k5599lkh5LgwNDc3N6RrBgAAAAAIF6YXc+3bt/f5vlpzYO0kiW7dugVpVQAAAAAA5iCqauYAAAAAAGINiDkAAAAAAAsDMQcAAAAAYGEg5gAAAAAALAzEHAAAAACAhYGYAwAAAACwMBBzAAAAAAAWBmIOAAAAAMDCQMwBAAAAAFgYiDkAAAAAAAsDMQcAAAAAYGEg5gAAAAAALAzEHAAAAACAhYGYAwAAAACwMBBzAAAAAAAWBmIOAAAAAMDCQMwBAAAAAFgYiDkAAAAAAAsDMQcAAAAAYGEg5gAAAAAALAzEHAAAAACAhYGYAwAAAKKEurq6SC8BRACIOQAAACBKqKioiPQSQASAmAMAAACiBIi52ARiDgAAAIgS9u3bF+klgAgAMQcAAABECQcOHIj0EkAEgJgDAAAAooS9e/dGegkgAkDMAQAAAFHC/v37I70EEAEg5gAAAIAoATVzsQnEHAAAABAlIDIXm0DMAQAAAFECInOxCcQcAAAAECVAzMUmEHMAAABAlIA0a2wCMQcAAABECYjMxSYQcwAAAECUADEXm0DMAQAAAFFCSUlJpJcAIgDEHAAAABBF47zq6+sjvQwQZiDmAAAAgCjBZrMh1RqDJEZ6AQCEm7MumiJ+FrfJdPtd3pZymjr5HLwoAADLUlpaSs2aNYv0MkAYQWQOxK6QK4x3vThul/cBAACrijkQWyAyB2IKjrqxWOMIHG2xizfxf+X3AABgZSDmYg+IORBzQLABAKIZiLnYA2lWAAAAIIqAmIs94q1mhtiYlmtu2eZOHwAAACAaqampoZ07d0Z6GSDMmF7MsXh74IEHKDc3l1q3bk0ZGRl06aWX0p49e3x6/Jo1a+jcc88VnT28jdTUVBo0aBD9+OOPIV87AAAAEE4276mgdevW4aDHGKYXc7fffjs9+uijNHnyZDFAePny5fTbb7/Raaed5jVKt3fvXiHclixZQgsWLBCRuS1btlDz5s1p+PDhtHDhwrDtBwAAABBq4hKSqLi4GAc6xjC1mNu2bRs999xzdN111wnxxXTs2JGeffZZ+vXXX+mLL77w+Pi5c+eKN/WECROoW7du4racnBx6/PHHqa6ujj7//POw7AcAAAAQLjjNiikQsYWpxdz06dOptraWRo4c6XL70KFDqUmTJl7FXF5envhZUVHhcjtH6BhOuwIAAADRQlx8vBByiM7FFqa2Jlm5cqX42aVLF5fbk5OTqX379rRixQqPjz/66KPpsssuo8cee0zc/6ijjqLNmzfTTTfdREcccQRdeeWVIV0/AAAAEE6S0ptzGwRt3LhR1JmD2MDUYk42OWRmuo9d4oaG7du3e93GM888I7pgx4wZQ02bNqXKykrq3r07TZkyRTRTGFFVVSUukvLyBmNZAEINGxs7x405plPQ1noYHAMAPJ6jkprnkq1yK/3zzz8igBGtHbvqz2jFn/0ztZiLi4sTP/XsRDiMHB8f71UM9u3bV4i4v/76S4i43bt30/XXX0/9+vUTHa0cvdODmy7uv//+IO0JiBpBxWytD+kcV+fzOp6zT+8Saptu/xvYvD+OioqbUPGipuJ+MEAGIDYxOkelZOVRefHfNGPGDGrRogVFM7NmzaJopkJTImZZMcfNCtIAMT093U2oyd8b8dprr4kW7Tlz5gghJ7f59ttvi3q6//73v/Ttt9/qPvbOO+8UjRPqt542bdoEYa+AFVAFlRRTBalE2yoUQUWZIRdU/NwDcm3UsmmtuF6Qmkjb0itpAc+RDcPzAwDMidE5KiGliQhgMKeeeipFa8SKhRzXzyclJVG04k9G0NRirnfv3uLn33//TW3btnVpYOB6APab88SGDRvEz86dO7vczm/0goICWr9+veFjU1JSxAXE9gxXFkxLtjanor6V4vbi7WkuKc9QCCmX56ZsKmp9gPLzDorfCRHJawjh8wMAzI/ROSq5eWvaWHqAUlavFqKHvVWjFRZy0SzmkvzYN1OLuREjRog34ieffEInn3yy8/bPPvtMdLmec47riaykpIQSExNFPR3TqVMn8fOPP/6g008/3Xk/TrWyGDzxxBPDti/AekRSKEGkAQACoVnn3rRrfkuy2WpFk6BRKRGILkxtTcL5/kceeYTeffddev7552nr1q0iLXrrrbfSmWeeSSeddJLL/TnaJv3omCuuuEKEna+55hrhKcedrPPmzaPRo0eLOry77747AnsFAAAAhI4mee3Ez6VLl+IwxwimjswxN954o6hve/nll+mJJ54QNW9sLcKCTgv/Lisry0UM8pv5qaeeEo9lE+Ls7Gzq06ePqJvTWp4AAAAAVqdpXnui8nW0ePHiSC8FhAnTizmGZ6vyxRscuTOK7gEAAACxQGp+R6K1P4pgBo/B1DYQgujDEmIOgFB1q+b1rRTNBbJjdGlJoov9BzcZMKhhAwBYheRmOZRfUCDGevEs82HDhkV6SSCWa+YACLWQ69+xkk5vV0OjO2TSsa1a0Klt7Nf5dv6902cOAAAsBHupMr/88kuklwLCACJzIOaQUTYWddMG5NA0NgB22H8UFbdwsf7IoxrnfdXHAgCA2cXc119/Tb/++quwKIlmCw8AMQdiGD1hxqItj8qoeEAWUd8kp7+bZNCk713GakHcAQDMRHV5CdVXVVKLFh1ErRxbdnF0bsiQIZFeGgghSLMC4AccwQMAALNSU84zzePoyq/W0drkdrRmRxlNnTo10ssCIQZpVgAU1EgbR+mKKEFzfGpo7uQxOGYAAFOSVtiJEprYJ9Vk9xlCe1bMo4ULF4rRUJmZqAGOViDmADAAKVQAgJVp0rKQUpq3otra/TRz5kxhtg+iE6RZAQAAgCgls8sRzjGYPPkIRCeIzAHgAdnFyg0Rsl7O3vUa+oH3zudme5TCeGfHLcPPL37Ha1DZWi9+hGpNAABrkdn5CEreuoDWrl1Ly5cvp169ekV6SSAEQMyBmEYKJkbrKceCSAqpPr1LqG26jQpSSZgL78q10fy8g7SEsqmYMsV2gimeVCEn/fDkczP8/NsqiDZrum2LWttFZijWBACwHgkpqTR48GCaM2eOaISAmItOIOZATKIVSxzx6p9eKm7jCRDO6Nciorz5ZUK0LdF0s/KECKLQROZULzwWZtO2p7l10hpF5UK1JgCANTnttNOEmPvhhx9owoQJYswliC4g5kBMwkJHeMpx9K0wy3FrQ5RLplHztpSJ63OvGe62jbM+n6Ib4QumiPK2LTnNgqOHMhXL8D4Nuus78X8IOwBim06dOlH37t1pxYoV9OWXX9Lll18e6SWBIAMxB2IWn0TXNfo3q+ItGBhtT7tGt4ji+Dq3iKKkmOw1ffJxiNQBEBumwQlVlc7rNeUltH37dmEavGTJEvrggw/oxBNPpDZt2lB8PHogowWIOWBpnJEphqNTYZrOEKxtuzQ5aOD9UEWYVsiN7WH/wOY6uvzUGlpW0pTm74wLS3MGAMC8psH1KWotbRxd8eVastUn0IZ9Nlq7ayMNuvFJ+u3lO6mgoCCCKwXBBGIOWFbAycjU2NxSp6ApqkiipSWJtGBdUzF6i+vdgi1mPEXl/H0urVDztD2X1DBl0meO2+0drkl2ESfq+FgIBn+/AQDWMg3WknPkUNq54BsqX7cMNiVRBsQcsCSy1o2FDHd1yrfyrsrEhnSjsyHA/PgqvLze74zgrAcAEH007zmIdi/6nqpKioRNSWFhYaSXBIIExByIgpFb7m/nqTxyK0TCpjERLzcrFE4NM/CHAwCEmMSm6ZR1+AAqWTJbmAifeuqpOOZRAsQcsDRWSiVqa9765O0R3nUiktjb0UHr8Iez2r4BAKxBiz5DhJj7448/aP369XTIIYdEekkgCKCVBYAwIcWZSBEvakpLlmaL2j7+yRe+LRzNGwCA2CUlK5fS2x8u/v/+++9HejkgSCAyB0AYiQWRpnrfuZgso8MWAFPQvMdxRAs20fTp0+n//u//KD8/P9JLAo0EYg6AMHnEBcOuhDFb9E6bPqYxSSKFrAd736mpZDPtBwDR6DOnR2KTNOrcubOY1/rCCy/Q9ddfT61bt4bvnIWBmAMgQFxEl2xkUOAJDP74vGm3J6NadusRO6KujidWbK0PSm1dY21W1CicnF8r4VpAMRLNJSpXZrgGiDoAQuEzp0ccLc86krbuWkL/vv8ZfVGSRbP/exF85ywMxBwAAaL1fTOKoAW6PY5isaBjQcSCTgojIfJak4uoC1QIyefUu92fx/N6eX5tkXZ+rKMOMI/cRVwgzwcACNxnTkvZyt/owNY1tG/9chxWiwMxB0Aj8FeEeEsv6o3v4shXMTV1q0ELFnoTJthw2YkX2xSPxwC+dwCYlpb9ThVibu/qP2j37t2IzFkYiDkAwoBe9EvrOadG8qRA0nrqqcydPDzo61NTvDIaKGa8ino3e60bomgARAepBZ0praAT7d/8D02ePJl69eoV6SWBAIGYAyAM6I3tUmfKiohb3yRnbZleHVk4Zs2K591CDdFAx2iwUD8/ACD8xMXFUe6xo2n/5sfohx9+oH///Zc6deqEl8KCQMwBEEaMZrEKEWeCEWQQbADEFqmtDxG+c7YDm0RnK1+A9YCYAzGLm6DSpDpdbmfLDfW27WmUN79xESvR6MDROY2Ai4T1iNYbjlOscjoFGxqLdc0vg9gDIArJOWo4xf/yJv3222+0cOFCOvrooyO9JOAnEHMg5lH93Ny83Rzipn9HVzG3Oe+g6N4MtJtUr9FB21nq0oTACGuP4As9PXuR3tm1lJ9aQ0UVSdQ2fbeYVFE8ICsma+ZicZ9BbJHcLIdGjBhBM2bMoOeff17Uz8XHY0CUlYiz2WwNxlDAkPLycmrWrBnt3buXMjP1zVyB9fDksybR9ZELkrDSiw5KUTUg1/VPc1sF2UWVw+4j2AJDiLoBWeL/sTy5wVvENhaOAbDeOURuv/vEtykhJdWvx9aUl9BLo7vRnXfeSZWVlXTrrbfSkCFDTGskXFNTQ99++y2deuqplJSURNGKP+8ZROZATBPpE7Ouz9vWeipq3YTmkz3VqRrwqvNbQ7EW4P6ahOp4AxA502AtcXTTjztoT24f2v3HTLr2gWep8Pc9NOvOsbArsQgQcwCECJ4AIXBE2hjnRARFlPklouDbFhYgbEEsmAZryT3mdCpf+yfV7C+j/VtWB31tIHRAzAEQZLQ1aJwu7ZVtr7lblmsT6VJRc7c0WzQVBFqT5cmnjoEgAQD4Q3xiMuUOOJ22zXqPSpfNoX37rscBtAgQcwCE0H5kydbmtEQTmVPnlAY6QUKIxQFZzto2dbC9fA45G1Zdk7oN7e1Gz6W3b4GCZoLg0NiZugAY0azr0VSy5EeqLN5EH330Ed1///04WBYADRA+ggaI6D4JWuUEqJ3UwEgrEcZ9sH25i70Kd+UWpNqbKRin9YjBfbmr9dhWLcTtv+4opaUliUFtwtCbPBFsC5hoJRRiG1i/AeKIB6cFnGaV7N/0N2387Dnq1jqLvv76a8rPzyczgQYIdxCZA8CqxfnsT1cYLwSPajqsDrZX070MizOmZVOiXZX852+jotYHqHhr04ZUbWG8m5Bj7P8vpc37D1JxYZqYFBFMVFEqEfvmWJdRVM9I4GoFbbQJnWjbH2Ae0tsdRqn5Ham2ppheeeUVevDBByO9JOAFROZ8BJE5EEpcBImCVojojgNjdKxT5O2qSJLdsUZRORWtTUkoLUo87b8vqWBeq/TIYzj6yMzf6bqPEEAgUlgpMie2t345pfz6P2H98f7771PXrl3JLCAy5w7EnI9AzIFQIL3dpODiZomWTWtF1Ew0SiiCy4li7suihe/PBr8Mm/wyajpUPsaFKIpYqYLOLU3r2H8IORBprCbmqvfupuP3zad58+ZR3759adKkSWKWqxmAmHMHaVYAIs3WeiqmNCHm7LVsDUJO1sCp9WyM/XaOttlc/oztqVP7Y+2pV4doc6RErS7c9PC4T7ByATFGdXkJJVS5TqwJBDYSPvmUk+mXX34RY76++uorOu2000xpIgwg5gCIKNou0yK3e9TQ3MnDia5puI8UZkWUoNzf/XuZrJvzp5sURfUAxKJpsB5xdPu8PbQ7uzvtWTGPLp74IK044ghq06ZNELYNgg0icwCYpPvVl20a3SfQdbo1Dzjq5NS0rrfGA3/WDwAwr2mwHq0GjaV965dRzf49NHv2bLrkkkuCtm0QPCDmADAw/WV7jvzxdc7aNFlQzylO6eEWaQHjNChW6u5cvOYmfa9b/K9tHOifXmrYOKB9jHic4zlVVF87JljHBqIRgMjBwjCn78m04+ep9O6779K5555LKSkpeElMBsQcAD7ANWxS6DB9RtpNem/87UPnzFTGW1OBGgljsagl4DFfRiiNDkbRO1l/p3a6Gq3bG7IDlgUiHxu5/VB2wgIAQkuLXseLma0lJSXCSPjSSy/FITcZ6Gb1EXSzxiZ6prYc/VItMGS0zpug0wo5dTuMbHgIRNC5WXuo3auKTYlYn6Pjk59fNRlWO0G1vnW+iFP5HHKEGaM2cqgGwM7nUUSt9jn92X8AzI7Vulm17F48izL+/o6ysrJo2rRp4mekQDerhSNzy5cvp3Xr1lHLli2pf//+lJjo39Jra2tp0aJFtGvXLuGX06VLl5CtFUQPeqnJJW2a0xJVMDmNems8ig8Xr7gtju1oRJcv2/FmzWEkEuXtLEB5VmyHzDzaUF5My0qa0nzHrFi5DmcXrBdBpSdY5Qgzdb8YHl+mFXGjOpY6jYyJ9jktWaStCsZ/AWAOMjr2og6V/9DWrVvp7bffpgkTJkR6ScBKYm737t00ZswY+vvvv2ngwIHiZ3V1NX322Wd05JFH+rSNTz75hG644QbKzc2lTp060fr166mgoIA+/PBD8U0GAF8IVpQoZNEmh8UJU1Rsj7y5pDmV27dVNKWC1HLx02mBEoQ0qC+PdYrP7Wm0Oe8gFaTaP4bUEWPSVkXePxBxK3FOtvBjjVbH6mPrgPmIi4unK664Qsxq5XPqOeecI86jwByYXsxdeOGFtGHDBiHiWIxxhG3UqFE0cuRIWrNmDWVkZHh8/NSpU+mCCy6gyZMni8JNyYwZM6iyshJiDlgKT7Vr0k9OpjyLyWEYLKgXI7s4GsbCjUWdFHj21CdboIyhcKBGKNUonowqytRvrOJsatHMqkXdIQinz5ye71zr1h2pe/futGTJEnrsscfojjvuEL9r3bo1/OcijKlr5pYuXUp9+vShp59+2iWky2+kI444gl544QW6/vrrDR9fVVVFbdu2pdNPP53eeOONRq0FNXMg1BjWvTnTlA3RJXXsld4oLud9lHSn3uxTbS2bv5EbVXi44GeULxj+dtEQjVKPpzqejKOVjFpTaaX9AuGrmTt03NOUkKJ+kQseiRlZVF26gzZ9+ZK43vb08ZSQkkozbxsV1igdauYsFpn74YcfxM/jjz/e5XYWeFx8OXPmTI9ijqNvO3fupLPPPpt27NghaubYvZrTs61atQr5+oE5MdNJ383uQzOqi3E2WBRmNdTVeRFwLNz65Nk7blXR5mwy0NSy+epH53wOhtc6vk7YmsixYpKvNiXpWrgYibbGHHtvzR/y91YQP7xGZ03l1uDUVILYItg+c1pSsnKp+eHHUtk/v9PuP2ZRwcnobDUDphZz//77r/jJ0TUtfJv8vRF//vmn+MkpVu6+6devH5WWltKyZcvoxhtvpCeffNJw1hxH9fiifusBUTIHdUyScw6qhAWTkSdbMJ6X0Y1gKfVc/LOYMmkJZVORjmecL2sT+0iZzto5ifp4f2rb9P32Sp31bVJwqkJOjhRT695Y1AmROcY+O1bdN3nc5THwdX1aASSOHx9jxRvPSPSamXAINb0ubfV9wiD6Z24ieY7KPXYUla9bSpXFG6l8zWKqqRkgomXhQj5XOJ8zEvizf6YWc/v37xc/09Lcv2Xwbex54wkWbgwLud9//110sTJPPfUUTZw4kdq3b0/XXXed7mMfffRRUegJogM1IqR2e8qh9nydBZQQQY5xWaGCT5xqhEwW6Ac1OsXNEFsbrFJ4tJdP9iKq2NSYArtG3+xdp4GgHv+26ZVU0KPSaWEijYr9bXpApMp/1FQ9sB6RPEclpWdR7oCRtGPuZ7R70Qz65pv2ERnzNWvWLIpmKioqoqNm7vLLL6d33nmHysrK3BoVjjrqKHG7p+jcLbfcQs888wzddNNN9Oyzzzpvr6+vp8LCQmFzwlE6X7/18Js1VPUOIHy4CZ8AI0KBPKcejX3OxhgR63nfqahRQV/R6xzVPk//jpVOcZifav/2KSxSlMkTiAzFVslBtBPsmjmjc1SofOa02OrqaN1Hj9DBnVvoqrNPE5mucEasWMgNHTqUkpLs0f5ohF/TnJwcn94zpo7MseBiiouL3cQc18B17NjR4+PlNwWtpxzXzXXu3Fk0UhjB40owsiQ6CeeJypemhsZ4qaknY5mm1eJJqLqkKDk9W6g5CQhRFVhzhPZ5nOvdQjRtQI6beXFDKth7DR8IDjjO1iXS56i4hATKH3I+rf/4cfrpp5/ot99+o8GDB4d1DSzkolnMJfmxb6YWc2wOzCxevNhFkLGQ27Ztm7At8cQxxxwjfuqlY9m/LjvbYZIKLImZowoe6+Q83N9rYbwDOVM1fzy5djwWV/ptYdEYDzd/j3WkXxvD7luTTp4IZUQXgMaSmt+Rmvc4jmjHEnrooYeoR48e1KJFCxzYCGBqMXfSSSeJRofXXntNeMTJZoVXXnmFEhIS3ObDsadc8+bNxeOYo48+WnS+8iw59sORUyMWLlwofOs4/QqiA/UEfdYXn+tEe4LT1KCXojUqtPdWk+TtMYa/14zMkhSk2mg+HXSpx1PFQEBTJbSpW0cDg0zdWn1Cg0vhf5h84wJpbNEzPw7k2DvXwiiND0ZrYqz8+oLQk33kUGq3opSKioronnvuoRdffBGecxHA1DVzzM8//0wjRoygIUOGiEkQPNaL3yyPP/642ziRJk2aUO/evWnBggXO21auXEknnHCCmPxw0UUXiaYIrqPj62x94s10WAKfOXOiPVFyLZYcD8WNDerc1GA0Gaii0agDUKJrlcFo6s98nU6gdpdq95NRx2Bpt6viqw2JjP5pRSPz2V/257HqCT9cUV3tayZHqTFFFUm0tCTR+ZqFskZQGylW7W/UukW5JtmMEqj/IIicz1z3iW8L77dwwWbCDx3fmh555BExnYknQ1xyySUhNROGz5wFxRyzadMm+t///ifGcHExIPvGsc2IlosvvpjatWtHDz74oMvtnGblRgoWdsnJyXTccceJN5w/+WiIOXOjF/2IVGTOLSWq3ldZn3YYvS+F/1pTWUZOTNAbZK+uKVRNFY19rmgnWJG5oK6F0axHb00MXs/gEA2mwZ7MhPetX0475nwirrc+/hxq0rIwZGbCEHMWFXNmAGIOBM3nznECVY2BPUVrgiHGVAEhbvdz6oOeL5kUpHLtcv0cKVQjd2YWA0aiG0IGWFXMhaubVY8dcz+lkiWzKS4+nvJPvJB+evBSiLkwvWdMXTMHQLTgFA2cYm1NyogmG53appI6ZOY57lns+FlJ07Y3zvNOCi/V143ooFsNlp7wY5EmPeVYnPWfSDRtXo64rhVyDWsn6pBJtKG8mKgH0fy8gyLaqJ0CYUa0kVb12Jhh3S4RNQcQnMBs5A0cQzX7y6h87Z9U9MP7tOr8fmEd8xXLQMwBEKF5m5JXV3F4bp/4f1FxC2fUzB+jX5lydW57TFOi7fZalSXfNCetCY8cCyXhiOGogbudwtL24GvO38XdM0787J29Q0QNWdTxGnmb9jrFclG/J+uuGBaCA3ITiXqXiIkWLEq1dWpmiIR5GzNmmveOw8BZ1mkycryblcaVgeiGI3IFwy6huoMHaP/GlfSf//xHNCXyCE0QWpBm9RGkWUFjUJsK1BOyuM3PdKpaVH/10fvo2FYtXMSXCgsxjpSxIa86sUGkcTW1UVLQySJ9NeLG/Lqj1NlsoaZRpSjTmg7L+iuZztV24srpG9rmDUScAqvTBOYmFtKskrrqg7Tps+eooHanmNb0xBNP0MCBA4O2fdTMuYPIHABhQD3Zusw8zTtI/c+1dzkyN/72oaEIkt26Y+/jW0td5qF6EnJcx6ZiT+/q+6pJ4TCpTYGOBUqD8FLx1rCh3QdRt5db6SLkPB0vb7WHvD7ZRKIeu2gROKqxs9HvATATCclNKH/4pdRv989iRjo7T7AV2HnnnWc4Dx00DkTmfASROevhqc4o3Ga5nmw/ZBRMRtBUOxWBxnZFTWeqTQeqKJLCxsg6xdf166ZGFX8yl9/pRNWMumJ1x4YZdMjysRJr1zynWrcnj0EwrWjMAiJz0UEsReaY6r27adr/9RdOFF9//bW47bTTTqM777xTuEo0BkTm3IGY8xGIOXPgS32Q1gpC4rQr8SOVpz2RasVGY0Sd1rPOF786WRunIvzANOav8nZBI6wv9JojVH8yRkbYtHNVtTYtqqAb26PSLbL41aYk5+MlRv5sfF8pVp3HSZM2Vo9fpGbvBvP5gvWFAkSGWBRz068eSPn5+cK4/7nnnhNz0bt16yamRbCNWKBAzLkDMecjEHMmHIvlwQvLcGxTAP5ZkR4bZuhZp+LFyLixZsnO59bxJpPoCTmj7Wjr6/Qer72vOrZM68mn3bYUgJJw1ORBbIFYNA32ZCb87kVHC+NgOZaTzf6rqqqEwf8tt9xCo0ePDijtCjHnDmrmQFQSzBN1pCMgRqJIpZgcJ4f59hSsp07Yxj6/Icc0cjteHq9y1iveI7Qs4FRBF2oi/T4BgKkp30P1Ke5ftsJPHF3x5VqKi1vnvKXysNOpX9VKMU7z4Ycfpnnz5tHtt99Oubm5EV1pNAAxByKG1o3eSQTc581kTeEtwqONRKnpV9Uf7sdt7zpr6XydMOFPOjgSXafaY2OUdtePptbQ3MljyAp1cOE+pt5S/hCq1iGtsJMp0qxGPHzV+TRnzhx6+eWXxbjORYsW0XXXXUdnnnkmZro2AqRZfQRp1uCi15EoCWcRu9FJlYmUoPTUtCFR7UA4pajnD6fX2frtlqZ2818Pok5PKEqRyM0XLlMqfOwc1du3YBzfSKfA/UXbyaymjhltJ3Oo3/tGrzEjm3F8HTNnpdchEsRazZynOjo2El67dq2Izq1YsUL8rnv37qI54tBDD/W6HaRZ3UFkDkSOrfVUTPYPnfnKZIJAIkjRYPvgaS1aaxPp78Yn/s15B8WJl6cvsGgzEnQswoRoUKJquvV4OrWGfFJvm57ojPSpPnU+iYXxdXR6uxo6tpU97ymmRBAJcXnWF5/7Jdy121W/CMi1Dpr0vamGxDtF3JgkGtWx1KWBhNm8X7GPcRzTcL33OUVf1LoJbUvnphT776SdjTi2DuNnvp86EcOlTnFMkrMGkgWq7Kbm1yGcs2eBdejcuTO9/fbb9Omnn9JLL70kRN0FF1xAJ598Mo0fPx6TI/wEYg5EBI8f7H7UTgULozRTY9z1A7FG8SvC5WhGkNGdl1eW2yN0jokNLOrk9IYvN5TT/J32btG8LQ0ixyiioiK7YsUUCacQ5JTlcPIVXqNqqSKtWLQ1bf6Mz1L3XQ+1i9UKhPNLjJ6g4/Fxm0WEzvW0wMe4iP8mthoMb1eEv3w9xGPkZDoADIiPj6ezzz6bjj/+eNHtOnPmTJoxYwb98MMPNHbsWLriiiuoRYsWOH4+gDSrjyDNGp2oaS9Gb4JBICdYT/WA3lKM3gSWdjSY9J6T6U81badaffgaffQ0dsvb2gPpSFa36UuKWe9+giB0LhuuW91+I7Zrplq5YK7N03sWETk7SLO6pln1WLVqlYjS/f777+J6amoqXXjhheLC/5cgzeoOxJyPQMxFL24RND5pB2iyq92uUZRIL1Xla52cKlpUfzmt+NTbrrfn0LMgUaNf6pSFYNq8GNmVqM8l913PU09b08dIE2ERjZzvXzTSyFcvmo2Jtd6M4rhLkCptNBBz3sWcZOHChfTiiy8KccfwfNcrr7ySzjjjDEpKSoKY0wFpVhAV6EZRVPNZD6IjVCdjn4yNHZMgxuaWitvlFAfZfSpFiN4kCJ7JSmQXLhvKS4m68YNLXYrXqbiyYZKEgp6Xm9wu0UGXUVu8FrUOSuCDn5xe+jp/fJ2hUHMRD/Lx2ro8pc5ShWsu1Zo+9Rh6quvzNerJx1Mek4a1+r7dQAlnxEvUYVKm+/HVMWIG5qW6vIQSqhqmrZgJ9p7bvn271/ux2HvkkUeEdcm7774rHsPNEm+99RZdfPHFNGDAACopKaFt27YJcafCvnacvo01IOZA9CEjV0paLNSRDBkl8iU1q0YCtXVfsoZs8379iB4/D99HO9KLa9BUiipKhbhhYVZc6Nodqe1i7J9uL8gX21e2q4oXtQOWkRYfvC3DtKwmgqhG+cTz5VbSto6Vrs0UDp88iT91eSrONQ2wd/zmj6xzzr4tHqBfmyYfIyNT6nHmqJy/HbxBeQ8FsK1rhu12Rim1Y+K0UUpJNEQXgdl85nzznvNMPNmOvJj2rl5EJX/+SGt3/Uu/TLyXUlq0ppy+J9Pz6xe4mA7XlJfQzNtGxWTzRMjTrJzbLioqEiM9tAraSiDNah0a68Q/6K7vPKZcGWmroh015c3OwagOSx07ZpTCVOv7XKNzrvy6o9RFeGhTlNrUoRQs2po7TxMddI2LtalqzX4ZjQJzpi0NxnFpn9sIX7z3GOex8ZAmdat5DKKvnlbEqTN35Wvg7fgbbevqo/eJ94S2o1m1phHb/qa5Ker0YgmkWRtHXfVBKl36E+3+YybV11SJ29IKu1DewDOoaV47v9K40fieCYqY45ZiDody6LNXr17O25966in6z3/+I8Z35OTk0DvvvEMjR44kKwIxF91oo1Uyxca42HEo46D0oirqPNS5j5xi+Fzq4xntzFVPfmNGok7afYjHOwbPq1MQWHBqI3jqY6QQ9acezFMNnBs6Ak/ct5Eizuj1Y2SKWP7fW9eoP920geA269fReMNoI3Oeon96Xc1yG2wBI6Ny8jWW7weReteJIDMQdaEFYi441Fbup90LZ1Dp8p/JVm+PuGd27kO5A06nuPgEiLnGcMopp1BZWRnNnj2bmja11+eww/MJJ5xAXbp0ob59+wqxt3v3blq/fr0lR3dAzEU/eoauLIS0vmrBKnp3npAdUT4VX060egJGLdRX690YbSSOkSd5Ri9C5lVMaIyf1SaBcFpteEpZinXqCGO9CJlRk0ew1u9pzq6vokrvy4BbZJdRIrLa+4j7hXBOLXAHYi64cBRu14LpVPaPvfOV060ZnfrQzOfvoh49ekTFWzCskbmdO3dSmzZt6J9//qEOHTo4bz/vvPNo5cqV9Mcff1BycrJY1BFHHCGG67IhoNWAmIs91BNvQDYcPnrLGT6vw4iVRdJodgR2oPrHyXooKdzcuj89oWkS8XTiF/ukGfEUyIQINyHiY5NKuCYgMNpjGSrzYW0qV0ZnnU0mBug1ihiNWvMEBFx4gZgLDQd3baWdv31F+zauIFtdLXUvzKZLL71UXFQ7EysSVjHHs9Xuv/9+EZVTadWqFd177710zTXXOG976KGHRP3cpEmTyGpAzIFgNUUwniJeMjo4tkelEHFGEx0kXoWdFq0gY3SsSLSGvnoRSsbTfF2tiNNG8/SES7jngnoa6aYSiNh0S6tquohVISnrLqW1CiOjpno1hlr07GkCSZND5IUGiLnQcmDrWtr+08fUhvaI+nw2Gx43bhyNHj2aEhISyIqEVcx9/fXX9Oqrr9L06dOdtxUXFwsxt3TpUpcauv/9739C9L333ntkNSDmYodAGyjUebMNNh8NNXdauxFfBB2foK/uVuE2d1UKONnwoD6Hi4jzEqEzEmTayByvQ2tA3BhvOW2tFyOPmWqLoq1TDMesUjXNrr6G/s4pVUWr2rHsqTFFC7+WRkbWLvhhaKyb6tV0fiP9Gnwg5kJPVdkuurtnPH388ce0efNmcRtnDG+44QYaOHCgS+drtL1nGm1NwinWP//8k6qrq0U6lfn+++8pJSWFunbt6nLfLVu2iK5WAKyC3+LB4YPG81ILUl292kQ0xYe5m2o0q4gSaNyANvaauDHnNfiebSh3bld6zUnkyd/NX85DN2uHzIYwnOx6VO1Inp98fqPHrBn50akTOLT+dp4eHwpEtLIwy3HN9TX0lv50g1/r1vb/ql59blHaR84x7qoujLenenXEnksqdYvn46xGAcfe15BOZl9Cp7gMc9c4AMEmLi6Ojj32WBozZgx9/vnn9Nprr9GGDRvo5ptvpqOOOoomTJgg6vijkUZH5th6pFOnTkL1cj0cN0LwPDUuQPzqq6+c96utraXDDz+c7r77bmH6ZzUQmQP+YJS+0ouKeDLf9WRFokbktOgZ5mobO/S6W9Vtf7XJMcs1CJ5qvhBIyk8rCGWNoWoVo43w+VW36IFAI5O+Pr9Rd643GxeOCLrV0Tn+z+8jnt/LqB2uvtigeNon53PrNGXILxXRMi3D7JG57hPfpoQUa9eKBUpNeQm9e9HRwjiYOXDgAE2ZMoWmTZsmtAqLvVGjRtFFF13kbNb0h3AbEofdmoRDmueffz7JTaWlpdHcuXNFw4PkgQceoEcffZR27NghFmc1IOZAYzGc18poTsx6ViTatKSnVKrhODCDcVh6YkE71sllGkUQIzHejovR/jj3yzFFQy8draakX15Z7hJtNJuwMBr/5q3rVVuPqHY2S5yCTYfGNHh4em5Z5+eLr18sEC4xd+i4pykhxX+hEi0kZmRRXJzr50jNvj20a9F3tH/DCvt9UjOoZb+RlN6hu8+p10gYEoddzDHctcrqNz09nc4880zq2LGj83d79uyhZ599ltq1ayeidlYEYg6EZA6sBxpjVeGCUQRHFQwDsmjUwN0iamMkiNRondyuuk5f1moUedIKMbWpg1O+emJAK376jNzjjMqpEUdpomxWIaet1ZNdtYy/tZZaqxuXpghlEgfjLcLn6bl8fW7G36hftIKaucizb+NK2jFnirA1YdLbHUb5J15ASRnNvT42EobEYa2Zk3A+mi968JBcjswBEAsY+YCJEyenwPjEpkkJ6qU7N5TvE6mwj//9wM3MV6Kd76puyynIuJS1o2vTxJcbymn+zj3i/7zdPDrgVqMmUR+nyxh7zR6n8Hit6rQF1bxWpoV5X+RILQnvJ7uv6D2XrDPL63tAjOXikVxi2+NdhcLzx5xv8FrYa93yyDWyaDZ4H9VoqUutpdhfu0Hq08s/EJEv1RfQyFx6SZvmbqlaSZH0i+a6PGedoMLWeuNRbQpexXEjay0BCBYZ7Q+ntAvvod1/fC8mSezf9Dete/9BajX4bGrWrZ/lGiTCOs4rWkBkDviCXlG/thtR3qb6x+nZj6jRKRfvNqUGSaa2VFHoq5WJjFgxqmhQYaGg3b66JlmzZzRaTA+XCJ+eTYoeGkEiU4i+jE7ztUZR+zjD1LaftWW+mgMbGgorpsZ6o+O0Il9v/q4a7dP10tPzFdT74uBYj5MwWshYHUTmzEXVnmIqmvkeVezYIK5ndOhB+SeeT4lpzSwZmYOYC8FBBcAn7zKlu1SebLUTGlTfMRZOn/3lKua0Re/amiU1MqaO75KNAYyMyLlMS3Csy6gOTW/up6d6NSkcfU0Z+ppGVuv4PM7AVR/jx1QN7VxXvdFuvsxMNfKB8wft9jwJS23qlkd8afdBr0lGe4zU7Ro1PUDE+QbEnPmw1ddRyeIfaOeCr8lWX0+JTdKoYPhllN7+MLf7QsxFCRBzoLGoPnSMUe2Sxy5GbbREJzKnNylCayqsbtO5LcWeQhWZRtFDuW1O2TJ66WL+nRCgXmaNNnY6h15XKwsYvWihc00GYsyok5TRCiBvfnO6dYya11mi15jCBDpaTNsNLUfTab38dA2mUeMWdCDmzD1FYtvMd+ng7m3iesu+w6ll/5Fi1mvM1cwBEG3ondTVKFYwzGztQuGgcy6oPX1Y6oz+cF2Ti8hy1I1JEca1bnp4S7XqTXKQNWW8z+yVx2xLrxTiR8xcvfA8VwHwVY1jbTnuT+DcbiJNnTzGflvDMBiveBJUTP74htfhrC8+t0cmlQiT9v7+4PW19LEGzBfT3lDiFLlbiKYNyHGZP6v3HpQiUnvsxPHFHFcQxTRpWUgdzrmNdsydSnv+mke7Fn1PFUXrqOCUKyjJIO1qNpBm9RFE5mILrZjgKI+cO+prqs3rthXvN5nOk5EkGUXjmihGTiOQaVNt2lLAYkYT6TE6QasRJr0RU2qNljpKitGbA6tNGfo7NUNdrxotVCdpqOln9XXwOpPWxNMNhDEw4zjmqt2MV6saDb5GCfWmUkjU11uitRbRWxPwDiJz1mDv6kVU9OOHVF9TRYlNM6jwlMsprc2hpo/MQcyF4KCC6MCoEF7ru9aYk5onOwn5f6OOVz3bDrke3eJ/ndmpLvdRRpFJixJtylYVmb6MtpL76M0QWft8Uqzyvhmh9dkzk0gLZGyYFKt6RscSI0Emj5O3Bg23WkIdKxttM4RejaavmP21CCcwDbYO1WW7aPvsD0WTBMXFUct+IyitsAvNun20acUc0qwABHoiuiZ0zyFPusJaYiRbiDSloopSTWTOXcg5oz2auaAFqaWuUSxOo27RFLcro8hYUDhK5Vzq49Qh8J7W7SIeB2TRoEnfuwlIOTqLn49Fae9s+/7Jzl2nJUmQau0iOX5KFe19xtdR//RSN2NdxiXqy/u+xfULgxCC1JAG5/SohF9rEYntbb/OrzO/F1RhF9A+n+G+HxJtTR7D+ySFuPb5QeipKd9D9Snuc36B79RWlNM3rz9JU6dOpZ9++olo3RwaekgSZWfrG2+bAUTmfASRufCjTQ3xCUMt2o8GR3mPY5FUFL86o5FVLp5kSupWoqZJJdrj6anTVqKKDTUyZ9R16kv3ZSjxVKMW7jV4nZLgQ6ODkY+hmlJXG2ac8H19PP56aVndZh2lAUfui5gd7Gf0NhZAmtU6VDtSqjxL/sMPP6Tnn3+e6uvrxZjSJ598knJydOqEQwDSrBE+qKDx6FkreEpDRepEYWSB4Uvq0VfxxujNVNVOS1Dr6GTqTkWKOa0FisvxlHVnGksNF4FgIOL08NcaJBR4azgwshbxlp725GnnDV+95/TWpfWg06LnGefy3Eptoe7IMOW956u3ndH++LJPsQLEnHWo1tTHLViwgO68807at28ftWzZkp566ikxaz7UQMxF+KACCsnMTo4AuHihBaFmrdFr00QuGG3k0NMafRZCmkiZtAvRsx2R9VN6a1JRa/O0UTMjg1iPv1fx4qPWmDmgjRGIevV7/vrMqREnJzpGui54GbPmbwONRH0dVPElkRFbxuW1luvUqb+T95eNNer7W222mfvIKT6v2df9iWbxBzFnHap1mh22bNlCEyZMoA0bNlBycjLdd999NHz48JCuA2IuwgcVxAZ6xrCSQHzBfH0+rRGsUVenW4ODj92MKnrpW7VgX61vc/Eq89CN6e8+C8Yk0dgelULEGk3L4MjkqwszGl1nZ+TPJnFGhDW1YowaMZa4eMpphJ6/EVxfjI61vniMVrS7oJNKFfcNkR2JXvmEPMYuUymC6EtoBiDmrEO1QefqgQMH6J577qG5c+eK69dffz1dfPHFIRsDhgYIAMJAY6JL2giTLydNT+Of1AJ5p1+cZirE5v0Ns1hlylaL1pbC3iNlE353WiFn7z6VAz4TnbM+1akMwYCPU5+8PY5Un/4XKRZ4x94zjr7K2yeEgMfJG95EhtJZ7OwSdUkpNhVChI+HrHuTKWz1mDZWCOlFbVUrF/EcvM4xSZRHlc7I2RLSex1q/F+H0vgQCqSAbKgdtL/XuKlDvobeIqkAhJO0tDSRYuUaug8++IBefPFF2rZtG91+++2UkNBgMBwJ0ADhI4jMRQ96J8lwnDCMCteNolm+4hb10dQ8qVE0r95sOtMKdGen6qUVHZEhSbCMlY3sPNyiOZquXiN0pzI40Gv6UEdfaYVqqGsCPc1YVaOFUkRqx3QZTbgwWr/PlibK/RsrVoO5TTODyJx1qPbBU+7jjz+mp59+mni8/QknnECPPPIIJSV57vb3F6RZQwDEnLXR83NTCYfDvaHHl07aywUvKVNtmkqv3kkvpeoyzULj2eYNKe6kuDKa4aqX/gxF2s6nOj5HlFLtBJZo08Vawj0BQRVxowbupmsPdxeN0mfQ7XU0GDWmHXmmTREzRvWAqsiVkUgXC5UoS4mGAvjMWYea8hJ696KjqXXr1h7v99tvv9Hjjz9ONTU11K9fP9EkwfV0gcLPFx/f8PmFNCswNZGKjAkcXmqqoLMLGd9ETDBwCrMt7nVsErdZmQbwfdinzZ6eanisc1+3NqViakpLNLVyUgAw4qTeo9Kty1Wa+OrWqP1vHH25odQ5oULrS6d22fJUi2NP5/9V04bz9on73vjbhwHX2LmJkom1zjFoLoJEJ3LIz8kjyti3T9vYoK3XcllHiFOOnv4+eF0s3LSm0SxAXcWUY2yaAS72MTrvG+d7R4pBTRS0IRrYEN315wsACA/wmQsGcXTFl2spLm6d13se6DGKin6YTGu/+ZG+XFlMrU84l+LijL9QehKQM28bFbApMUyDQUxghqiBkZWF/tgte92Qpw5SV1NguTUbtU2vpAV8/62u0xOkcJK1bartBAsFFgyMdvKDHnz76HvG0baKciEoJn3TlCZxKnDY+eL3IvqlI0Aa6uya0nyH4a1YV2G8MMP1lCZVa9o4wsbGu3ojqNqmJ1IRp6+VyQX6x17v46+G5k4eHhRDaH+/0DhfX0f0mOsEpfC+d04LKt7u+MbvjLw18XvuraeUq50ayqMaZ12e1g9vW0WDkIOHnDlJK+xECU28fxEEwSG5WQ4lZbSgzdNeogOb/6G9f8+n3GNGUbhBzZyPIM0KfEFvbqa3iJOnxxj5f6nNCHKeq8qXG8pdI1RK9ESdB6sdE8aCTltbp2fP8dlfiiedZpvaei7GUyrQ27HRSyvz+C+xPWWtjN5oMzN1OrtEWzWzdBntTFy1Y1gdbRYKA2a9mk5ZA6mKOu2aIu31aBVQMxfdlK1aQNtmvif+337szZRW2LnRdXpRVzNXXFwsOkfWrVsnDPvOPPNM6t69u9/bOXjwIN16663i53XXXUe9ezvm3vgAxBzw191f1mXxie+rTUkuRfr+uPs70WlwcI/M2ZHGrhK3mjjFxkI2SEifOkadFSrFnjovVTdq6MmiRUnZMXK9qgGt2Ce92kAVRdC5iUTNNiIhLsQoNUedm5w3q0Y35XxbdaauGwYpcb0ZrnoGviq+HAO1uYTtX/QEvlqbJ9eEyJx/QMxFP0U/vE97Vv5GyVm51OmieyguPiFsYs70adZFixYJY74+ffrQGWecQcuXL6cjjjiCXn75Zbryyiv92tYdd9xBr7zyihjLMXLkSL/EHAC+YLf9qBQRDHlC5FqyY95wTGt4kE/m++jp5R/QtHk5nrsj9SI3fMJWZqp6imAxDdMzSt1SnkQZzhM1i00+OUuBx9EuKRrUei0uxL9WGJ9XO7YhVWStrtiTtYgNESm7QFEjd7Jm0adOXkcd4DQ9EaQew8J4OuuLz90iYaEWes55s1x/V5EkXntZO6iKOjl3lvdba74rxVVxYRqRYlzsHoG1p6tFDaBD3LJIVg2lV+150/llwpv44vVs68hizvU158fLqSIyOtc7m+jqblucopLfz1qvwXB4LwJgJvKOG0v71i+n6rKdVL72T2p2aN+wPbepxVxtbS1dcMEF1LVrV5o1a5azy6NFixYisnbiiSfSIYcc4tO2Zs+eTS+99BJdffXVNGnSpBCvHMQSzsJyZVA9n+wk6kmc/9/hnnF0apti2swprN46zvxqBM2gM1FvDdpB7HwyZSHQ4AnnuhYWGXw7r4UFHdeZ8VpkTd38vIYmidPblTrFhJFpr/33XMfWULOnNnswRZRARZrHOn3xHNEhX1KJajTp6qP3ua0tbhwLKLYJqNZNHUuhF+zuWvV1eJUS6P4ByQ2doBeexypJCDCRjuaJJq2b0KBJ3zfMOmUhqghyFlBqU4oW/h3XsTF6KXd+fe3ajl+XElpC2c7XQrtmue5pbQo0EcIkt1Fyrs+hTCBx1Gtq58RK+H3Jzw//OBCNJKQ0peY9jqNdC78Lu5gzdZp15syZIir33nvv0UUXXeS8fevWrdSmTRu6++676aGHHvK6HQ5R8oBcjuz17dtXbOuLL76g0aNH+7wWpFmBL6j1b8bdgHZBYWQXojf/1NtzSnTNcjXdsuoJ1mU8mmYclGqGK69rxYIUSm5TIHRq4hhvVhksSNTnlHV/agRTRt6uGbbbcCKEFhkdk2sOZL6vL/NX1ddeHksZKdOzbXGrbzSwqdG109HpdtZ2RTOe0tgSf9L+HtfiobM10nWMkQZp1tigYtu/tOHTZygpozl1ufxhnx8X1WnWX3/9VfxkAaZSWFhI+fn5zt97g0ducFSPTf1YxAEQKnwelM42GZoTL4sskRLtVkFFFfucAokjN9oIkstJ1tH9aBdDO90MgqVg4bSd/USsCMzcSqe9CJ+Q1aiKPbLVIPj49qUl9hNyg59dG42tCHdDlvkV0RQRKcoUz715v6sQ0RM4MpU5aWYOtRy1Q0x+MBJ0skaNXKJz9vovv2sYFY9CrWCSjM0tVURvkqGgVEWloZBjHLepUU5va+Xop1vNZaFDhPVN0hV7eu8xT8+lPh8jX/NYFWoASBKa2r/Y1Fe5elmGGlOLOY7AMXrGfa1atXL+3hOfffYZTZ48mWbMmCFGcfhKVVWVuKgKGQBP6I3X0qJX6yaNjLnWTiKjU1oDW7conGYgvEzH8U++jO6QZ69xO112oya5iYzRHew/N5RvcY1YqdMhHOOrZFG/k3722jmuA5SP1Ub9VPHgVlvVJtOlZkwb/TIShlJ4vDq/Cd3fho/VtQ2/UztkE06jbs3t/7/3Bk2USQolx9gorReb874+onb96vnCMVxX2FC/VtAgJI9x3X9PzyuEV+sDzvSyFLLvdVRFo+tYNnGfna7b4fu9vLLcmVYPBnJt6muORonQYHSOqi4voYQwCwnQQEXRerLV1VJ8UoqItvnjM8fmw3xx3qb839JijrtOGb0RGSkpKVRZ6fkNu2PHDlEjd8kll4h0rT88+uijdP/99/u5YhCraEWIKg48oUbaFlCiuBS3yVVEiX6ky5nO06mr0ooIT2lINVok66uk39wCWePksKgwmvIgHivq7+xipSDVHhFUxamauhW/l154lGbvAJXp4DHG43B8Gnum8auTtVm6DSKtG7YpH+NRyLH4m1/jGv1S0ula+FhIGxe5/rz5doGrvqa+fAmQ9xMp6dYHRNepKuRUtHWRnrwCT71xtNKM4T3q5wm1m5txdlj3ttfq8TFGrVzwMDpHwTQ4/NTs30PjDkuh5s2b0zcrl1JNch316VZAp3VwL0kwpgktWbKEli1b5rylokLj8WTVmrlx48bR66+/Trt376bsbKWinD8fevcWYu+ff/4xfPxpp50mumH//vtv0TTBvP/++z7VzOl96+E6PV9y1wBEsj5Pz1ZEom0GYNQCdWcNnbZb1JGyY8sNrd8Yb8tpkeFlaoVzFq3GZkUvuuhcU0WSS7TSm52JHnrpaU/1fOp9jX7vraZMz+/Ol8ibp32SwtSblYh8XYxqHfVGrBk9l8t6lU5pbYOK9jiEs4s4FmvmjM5RRzw4DabBYYYjcF9e0U+Uc5111lmieZOdM9iFozHwa5qTk2N9n7knnniCbr/9dvrzzz/dDgqLswEDBtD06dMNH5+enk4dO3Z0qbljr7o5c+bQSSedRO3ataPnnntO3M8baIAAocRb5MMfvzDVQ45xmdfqQ1OFViyoVhfadKFadyZP+H1G7nEzJHZ2mWoaEZx+a9vTXNash56nnDfPPa0PW2PFhLZpQ+sFZzgOTGcbcjvefObkvqvb9rWWTYpWvekhjPoFgHFpZFFfV8ea+P0gU8mquFabVGJZsHkCDRDRLea+vuoYES1duHAhHXXUUfTqq682ertR0wBxwgkniJ+//PKLi5j766+/aM+ePTRkyBCPj2cvOr2cM4u5Qw89VET39FK4AAR16LuP9iJa1G2wlYZR56AaGfFWf6aNEDmfRzHj7T+RxcMej2vj6JkYm1Xs2Me+SWL8FIs4jvipPntaaxZV0LHwYkHB+7Tkm+ZUpBwjvciY/bYaojYNkUO97krZictCxbnGEFhiSLElBZ1Mt+aPrxM/eQatVki77ourgbMaJVWF6eb95FcdnxSbciSYnFvrrIVUahlV8cv/dz6X45hJnPvmiMhqu50BiGU++ugjIeS4BIw9bcONqSNzzKBBg0Sjw+LFi0U+mg1/OYzJguzff/8Vt0nGjx9Pbdu2pTvvvNNwe76mWbUgMgc84TUdxfgYFXNuxyBCw+ilNN3GRmnEnjY6oz5OHe8ltq/YjfjyfNJWRJ1OINO4alpWom2WUMWLbMAQfF7j84xbPbHjKQUbzCidQBHEao0gozXtNWpg0UbHxP0MZsx6Wo9q9aLibursitF4NU/RRJgA+wYic9HLroUzKHP1TBEcuueee2jUqODMZo2qcV5FRUViWgOP9OJI3YoVK0Rjw9SpU+m4445zuW+TJk1EtG3BAi6t1gdiDgQbX+qgfLWV0BODKnpjuVQBIcWYJ7HEqPVyzvSd0oWo9YtTa9u021GFh9vj5Vp1PMrkdAq9Qn6O3qmdn0Z1XcEQaL7UxvmzHa35saf6OU/i35e0pXbt2to9tTHDyL9QTxB7rQ+UaCLP3tYby0DMRSdl/yykrd+9RZ1z0kSd/zXXXBO0bUeVmGM4Gjd//nxav369KAYcPHgwpaZqhlES0f/+9z9RS3f66acbbotr5n766ScaOnSoqJnzFUTmQECCTo2CeTnpaWve9MY4uYzMkjVNmpmwUmR5M6yVZrXaGjQ1UqeO3ZLRMq35sOqRZjQf1GjcGItQ1e5EpmF1PdgU4SojTi7NFzqCyeNr5bCEMTLZNaM40UuPC3TMhtUZtjI66hKZc6CddatGBI0aOJzP7yGaB1yBmIs+SpfPpe0/fSysSK69YIyo84+La/gC3ViipmZOwh0ixx57rLh44tJLL/W6LW6I4AsAwcSnbkV54u2b5KynkoJICIgBWS4nZb3OTr4u5qZynRnP7txij+AUzbePyVLFYEGq/XuakTUFb9+l7k15bjd7CYfwUsXbEudv3NN1urM/dY4JCwu7SHTsZ6/Lib7i/9sFrDM66Dh2at0WI+vi2AhZHg9fkGtRU9gStcZOK/4YKQBd5pB6aUoINoZRYJ1Ur/peku8dsQ1NNNZ+f/uxFIJOUzPHQKwBQMQxsN2LZtDO+V+Lw5HVrT/dcMMNQRVy/mKJyJwZQGQO+IunJgNZX8YRKelHpkah9DoQBQbNFHqRLzX1qrUokc8pkVMg+CSuChwZ0ZFox3apXZEuM2Z1UnlGNXt6aO0vnLerx0W9fwCdqkbCWxtdNDqmsq5QRrr0BJ2vfoPqfV2eV4NuA4XBGDH1dVPn0rqlwA0IZb1hLBKuyFz3iW9TQop75goEh/q6Wtr5y+dUvm6puN6i1/GU2fkImnX7aJdRXMEg6tKsZgBiDviKcRemu6UFn3RdPNoM6pm8ndi1z61NZ2oFGiPr6/SQPmWqIJCoY8JkOlZFnSCh1n0ZiRWtcDWq3fM37Wlk9+LP47XHkVHtSPTsQrzVUOqJJD3rEPXYynS33nF1e06dektPHdCeUqjqmpwE2J0d64RLzB067mkx8B0EzxD4w/HDxCQqdtHgefCrVq0SUThuuuSafoZ/z1nEYBJ1aVYAwoWeCay3E5i21q3/RFdxw7Yi0u9LnES32CcILNF0XXqab6mNSAUyMH2JIiRZJEgzXi0yYscCYltFoku6ThUV6m2qQGzZNIkKUitpft5B4fzPx86bJYjzGG6td06d0IvM+eW3Z2AP47c9iWNN0vBY2Kg4xWUNzZ08nMio5lkn7ekizBx1ac7X15FSluKLJ2WoTSxyPcFs/JAjuPpP5OkcUuTbx7PJ9brU0vHr41gDJjqYj7TCTjANDrKHHAu1srIyYTnCDZhcm//444/T0UcfTWYBYg4ADXrRDXkCk3M8dQWXY7SWKn4YEWVi3+q+SS5RJtUTzgg1aqIauPKgefWEqnd/PaRImE8cXbL/+bvNW3UY+rIoE+t3pIPV+rtrHV5xG8pLhfGvKuhkbZY8Jjwk3tf9bCxu+y0FuaYm0BdcoomO2jF1DJen+bvqOvj1V1Oz8mNX1j3ya8nHaVTHUjeT5wUaa5apk8cQnUFBQ60ddE/J2ydJsKAHIFax2Wz09ddfiwZLnuzA9mfPPvusXw2U4QBpVh9BmjV28GUOpVFtlF56k9Gm5TzOF1Wew8h3jtHWqDnRjpVSivS1EyKkkNNrkvA019OoM5bRdrd68itz88DTEGhHqSe7DU+vr2HK2kAQ+mU5o1MrKKN2qqCW47i0XcuhSml6XKOmXlEFKVb/QDer9airPkhbp79BrfZvEB5yPKjg3nvv9WlqVDBAzVyEDyqIDnytfTLy5PKngcGXdagdlS5obDvUCIubMHDUW2ktUIzmexpF5uQUB5eRXB5Eh7b+TGsH4qnhQjW5DVTU6AozveNo0GTgrYYsENHpy/g1jMiKHiDmrEVl8SbaNuMdOlhSRIfmZdLEiRPpvPPOC2vHKmrmAAgljpN5MWV6rhvyNGOzMMvnTkefjYadQoCfi1ObDQPXpUAQ0box9tFbDK/LXsPVlL51CBK7/UgbRTxd0vCk/z3NZQ3dmtsv1JO8pv9EmpgynTVxrhHGhmOk23jhEFq8r1zjpfXVY/QaSTwdP6M1qo/R7cbVQY0yekJsx+FvN/a+WhrdQe5nhps4lq+Vno2NeE6dKQ7+eL5pxaSKm2m0wXP48jwAWAlbfT3tXjyTds3/hmy2ekpMzRT+cexNa2aQZvURROZiF8OojkEkR69jVR1Ormdya2SP0Ri7DXUNjNE0COlBJoWfEerUCJ6jKtfZ2IkJcr1a8StHe6mD37X2G2pHrjQZ9pbKdjvWjmkNVx+9z3kcZFRw2rwc3RFcLngwTNZ97sJ4GjVwN117eKaumTMLuldX2V8c3hfVakTXYsTP1K/R6C8VT9vX21evXzgUjz5xvZGRViuCyJw1mh22zXyXKorWieuZHXtTTt+TacZNw4JuO+ILSLNG+KCC6EMvMmM4nklJm0lBItFaS3i0kvAgRjxZS8iJEHKChOpnJ9OW6vNouy21KVdtapWRgkP1xfM3xWgk4tQZr4y2fkygk67V87lTj4+RkBnbo1JMyjASVvfOaeHymnnCU32dG0YRMbmPOvf3VMMmCfh10OBLTae3bRt9sZF+hYG+f6wIfObM3eRQvuYP2rVgOtXXVlN8YjLlHnMaZXQ6gmr3ldLM20ZBzEULEHOxh3oy0g5P1zP5VSNzbt2wmnmbWjydTNWxU9ph9Kpw0TOOletlcSTntbqMc9I0ThjV0KmwwJFoR275Ogxed98N0tKMNpLp6dj5mmrUio3H+9lTnRIWv68uzPDagOCpHlAb3fI3Daq3f3rzUP0ZueXLcwcL9RgzLv6BMtIaA0KOgc+cOf3jbDYbPf/88/TXX3+J27t160a33nqrsCORhMJDzhdQMwdAEOGT8WZxErILKU+pSK8n6PnuJ34+seVRpbEZLJ+4Gz5XnGk2T/Dv5TgvXq+anlPHObnAI7q2NqclzrXtc/5Kjarka6xXRERMJ4qoFUunt6uhB16wR9xkKvOrTUlU/A35NhJLU5OnjYTy9u1zbBsW+PG/H7hMt2CkWMwfH0dUbD/uLCwuesVVVJxYQHQiP6cPViB69YAukxYaK1q00dvW9tePn9OX9K5KOIWTP68nCA7wmfONqj07ad68efTJJ59QdXW16FC95ppr6Nxzz6WEhASyGqiZ8xFE5mKbxk4TkNvQjoaSGNmW+GKnYRTl0qbwVLRCQxuh06Z9dZ/Ly9guNRrTILRcUbtt9UZN6Y7WUtbJuAo511m0nCqV6WWJtv7MaKpCIHiyRAl0W2pkVkWNhjbmeRqDnsch0Ac1c+ahcucW2vrtm9Q2YZ+wHGHz37vvvjsiqVRPoGYuwgcVAE94S8UKAkjLqds3TM95wk9TXaNt6wkPRq8ejpEWKoxswtBrfJDPYzSqyqNAlvvnY/NCoPYnwRKCet2x3mbIhlNM+erVBxqAmIs89bXVoi6u5M8fqL62hvp0aCWmOvBIrnBajvgKxFyEDyoAQfGyM/BCC0YHqTfvPG0jhxRhRnV0DAsytS5Pok3nauv/9Lpl9aKUaiep0TY44qZGAI0MeJ1rMxiR5tfYMInS5exrtEyvaYaPtVHns5mEUjCjkLEAxFxkObBlNRX9+CFV790lrqe37UY/vvYQde/encwKauYACDKebDQk2iJ9X05sHidJhABfRUowcM4dpQaTYhlV4xThkgCED4/V0jVO9gF+rfJHuteyOaN65zZ42z29/IOY67b0FxwPYAXqDlZQ8bzPac/K38T1pPQsan3CuZSSnU/Nm9stlqIB1Mz5CCJzsYuekNMzrXXWX/ko6IwiG8Goz/P2PL5sU/s4XwSmW9RLM5lC1yvNTwFs1D3qtCbhtKzBSDWJar7L8PrUFLBM98pOXb16Ol9sPQKxCZHbjGQtXLBobOo5mkBkLgLH/N+ltP2nj6m2wv631KLHcZR77GhKSGkqPOWmXz3QdHVyKkizRviggujF66zNIBTRBxs9qwp/UouG+6xjmizvrx0XpvrdydSnnr2LN1SrGBZf2qYKdYICOYbUG5k9a5ENG0YNHv5ELBv7+nszqhbrjbDYCySCa6a/i3ADMRc+ag/spe0/TaHydUvF9eSsXMo/6QJKK+jsvE+0iTljjwUAgP8no2vMd9BUIaL60THq/wfd9Z33onodISSjSGoURlp1LKFsIhHFtNujFFWU0q5Ke12dS3SsMF6MOJPPobcO3r7c7gJxK4uvUiHoGgSiMieWaoLSkOAcITa+zsWixVOEMVD4NRAieHwdXd2tgjpkptq7c3c67pDcYN78646DohbwrC8+9xrdNIoi+hsRdRHUE2tdItPMZ3+51yWC8FJdXkIJVa5fTGLL/Hcx7Vr4LdVXHxRNDc17DqYWfYZQfEKiEHCSmnLjbn8rAjEHYppgp8pMu2+NqMHzZEar95yq8OIaN1fvOk1KVOt1p/PcvC11pil78jEs2qbNk9GzBsPfqZPHBN3DTO2glVHGho9QGxUVO6JnW1wf583A1y1t67gPi0ajZhMWdyzoeA1Oz0Od5w5ZOrQwXryO6pg6htfCtxcXNvjfWfVvx8rUlO+h+hT3bvJoMvrNycmh2bNn05AhQ4S1CLN9+3Z64YUXaNnqZZTZLJE6depNN910Ex1yyCFkhGoMbHVQM+cjSLNGH3ojpcR1HZ+zcJ+U1FSlXlrTp7mbDvHDaEVUsPfTKOWmeqTJdKsa0dJLsWonafAcU3UOqzpDVjuL1ZNdisu6NPvu6zQGf6JberWW8jn1Hqf7mvuwdu1z631BCfb72FOkDwJOH6RZG49Mjebm5tK3335Lp556KiUmJtIXX3xBzz77LFVWVlJKSgpdffXVdP7551vS/FcFNXMRPqjAWpjlxKQ3/Lxh6kKNrsWGnoAwmofJ6E2v8GU4vRG+DnMXaAWzjuGw8/4OEXdqG/tYMb25qdIMWE3XqqPAVAGojXK5WZaEcPC7t4YWNeqoNmJImxcper3VY+o1hvCx0LN6geAKPxBzwRdzbPb72GOP0a+//ip+f8QRR9C9995LhYWFFA2gZg4APzDDic2tc1QZISarIViI8YnZHpkxNvgVdWWy/ozs29hWYVxRoUbrxHPread5MdnVwy1atEXdfr1LPZpAWo44nmPavBwqGLabOmQ21IlJtEJOTHBofUD8n1Oe+c5j11QIWa7VUztViezRPTneTNy/t/1YiPUEQfT41UW8tV6MApO1gNp6PF8MnZ0RTWWsGL/+elYsAFidZcuWiZmqLHiSk5PpuuuuE6O4IjFD1QygZg5ELdpIl4pRFMooHRZ2wcdzUsnuxaatNePrfLJmwcbCQ/19/niHOFtUTzS/hooogZ7XScG57asH3NLPjehq9HkqheO1+Wx+In1GDjdhF5p4jFAtaeNep8cCThV+jHZShaj5UvbT11oyGV1jnM85xvNoMok2MrnA7WO5huZyHaDec2qink4PPn7vzq+h5yef73nNStexWy2j1rA5wIkkAAST/fv306RJk2jKlCkiW9a1a1d68MEHPdbGxQKomfMRpFmtWQMnT9ZqvZbWE86J8hi9k5q/47T08HuygOJpx3CkRsJrVGvRJHqWH77ajhid1HUxiNrpEegweH8aCOT9tfYlMkWtncWqZ/NhtF1fOlD1/PTUFHYwbWv0Jkfw87p013p4Tu2a9VK7LrWIJrRGsRpIszaevWsWU9sNs6i0tJT27dtH119/vaiPS3I0QUQbqJmL8EEF4UUt+Fd9zSTypCrxVB/mjFZpTlq+FsjrzVwVt2uez18rCKPRXp6Mc/UElJtf2pgkcTLXq02Tqc0vN5TbLSccUSZ5jLkezZ6ydBeQkmBEcvwVx9raMd4/VQSrawxW1FWvXi3UdWq6kTkfGjs8TTPRzrL1JMIDnV/bmMdbGYi5wLHV19PuRd9R8W9fUeecNGrbti2ddNJJdM0110StkGMg5kIAxJy50UZkPE1mCNVJRK8b0XmCDVPKVitsGRZejDYqJWrrFAE8movTNHBt2lebkuwCSNkn9RjLmapqpDMU3ZNqk4D63Oo+zX3kFO/muwbdpNGKGoWTkVyPncA6EddAj5W2IUd9L2r9+aL9dYCYC4ya8lLa+v07VFG0jmx1tXTV2afRf//7X/r5559FNyvEnB3UzIGowJMthPhdTwq695i3NUQKcfJ0RAbV6Qstm/I32EpRZF+81SG65pdR0fwE+owNXz3Vph3jyyiyxvm7GUVtnILAIUbULlVucJBRSd4ntcbNcOxXa0f0kptEtta7mCV7eh9JrORJKL3rVCEnI6oDchNpvmiQIXvDhBLpVRH1mQ7fuIDGcznEIaeCXd+LfPpp8KXzZ9vebIXMOIklXESTafC+DX9R8bwvhAFwfGIyZfcdThMnTqS0NDT0aEHNnI8gMhdctCfJaPjQDfZM1cauxWgUVCQtVyR66Tu9mjjGTZwZ7JOuyFIiQlqfOq3Xnfrcajpar7u3sZ6EvphVN2Zag97zeTp23mhMStWw6cXx/Npoqi/b1Zv7y+j5F5rlsyVckblDxz0tZo9amfraatq14BvKrdwmIm9dunSh2267jfLz84XRb11dndNnDpE5OxBzPgIx13g81oBF0KA3FLVLjDa9Kgl0+LoWb00N/j4umBhFxVx8z3SaT7zVGBo1uajecm5NDga+a2I7XtLCbpYxBpMvfHnv6jUtcK0iw40HqhjR8wd0pkX9TEt6m63r6/oDSYnrmUXL14ajZ42po9T7m2OC3WwSLJBm9Y2Du7bS1u/eooO7t1GX3Ay68sorady4ccIcWFJTUwMxpwFpVgAageHkgzD4eolaKL2I0Zgkn4vYA+kY9RXV70523spGBD2vPGkZoq3hcs5G7V1C/SfaRRvPZFVFmRRAUgy5rMNxYlftSjiaI3A0R0iR5HJMGc3xaEykx+kDV5glnq9tekMDCaOu3f6zwWNQ1iV68xj0VD/ZP73UsNYw6PA2HZOS7Gu374fTJ7GRz2nFGcnAe5ND6bKfqHjel2Srr6PE1Ax6+OGHacSIETh0PgAxB8KGmb4l+4p+bZj7fjjTfGz+KurRghNp1BvTxLilzDR+dOI+fNLkAfYe5mQapcJEjVojhqXrRbjY804yd/Jw3ceJma5kj9hoI1lqlzJHeQpSbfRZcUN3qvDkc665xuU55H6qVhySgtRE2pbuqCXUinCH6Gjs8TB6Pd095Roo0rlN77gZdlKPsQvFPDrgFulr2ZSFlaunXrD+PrX7qN0Po9cexC482aHoh/fpwNY14nrGIT2p5dGnUu/evSO9NMuANKuPIM0aPfhS2+ZLTZPXposw15w1JrLmq2gNN3rGtipq2tGv8WIezJI9pXu9PYf2ufTQrcczSDOL9XjxCdR2i6q1gdpxZy5i1dM4NXksLFr6YEaQZnXHZrNR2crfaMfcT6m+poriE5Mo77ix1LzHcVRTXiJGdxUUFLg9DmlWdyDmfARiLrqmQejWcGk8wRpr/huU9TpSZEY1VGYs9A4FugX8Qay1dNm+fA4/RZxRnSCjtQBRTY2PbdXCbfas9PnjGbKvrkr1OJtVryGAkSlbmeLWrsOl0UKJfspuV22TiBnr0KwExJwrNfv3UNEPH9D+TX+L66n5HSl/6EWUkpXrMocVYq6ZT00zSLOCmEArztQTnERERloT9T+3lm787UMxjolxqfFxnDDP+uLzRhkC6+FyUnaY+Rak7nYRcGbAl4HuwT7xN3Y7RpMuJMUDgi9W9N5jgsJ4p8WKnBfLwk1P0Kk1dUZrM4oQyyYEf9bL6Wa7bYjr7b5i1ggvMFc0bu8/C2nHz59QXVUlxSUkUt4xo6hF7xMoLkbnqgYDc5wdAAgD2nQS13AtUVJuMgLG0ZLe2URLHQKFkYPLXYrnHWzOOyjmqMratEDQi66odV3ypO/SESjSi2UeT+ZyzSpBMTHWWadL7RkLifllbt5hAfmUBQltBEoiCvRZ3PW2R2qFkFdSuL4eJ700fUOdYI2oBVQ9ACUcfWOWXXieq7XGPfwFwS6k5GOlAbDAqIlARhgd81n11qEi3rNb7P/nGr5pbVo03C79A31oKjCKZEfyNY9FzOwzV1u5n3bO+4L2b14lrjfJKaC8QWdRSvNcqtlX6nJfTrMC30Ga1UeQZo1uPKXxdL2/QpDq01uHv3VURrNJeVSXFA5y/qZ2GoSvI8uc6/FyvPTmq2rxxaRX737BTLUzp7erEelOLc5RZh7sQPxds1a4e3pdjaKgjEt5gE4DTGPmCzcGiLfY9pnj9OmH44cJPziVX375hV566SUxUzUhIYEuuOACOuuss8T/jeBtxOtE61Az5w4icyCm0B3vxKhF343wvwoGqgGuXsE642l90hKEI4YNf+LFjjqopo1K/brMgN3uGI2lY3/iy/HTNgIIgTW+zlkfyEhvNTmlIRidweI5t9ijVfJ5vxJRz3K3mb5yJq02AurSeOBYMwtnNXLKdip67yV/9sHZ1UuZzuhwUTGJ18E5Yk123PKkBoe9i7juFIne7WmCCaJwkSWtsBMlNInchASudWMRJmvdWLw+/vjjNHPmTHH98MMPF+O42AgYBA+IOQA0MzvNjDeLDFWw8JiuJQOyaLNIH7OVh/0+LFi2VSR69aHzZj4c1JO7MvJJha+zePG0Jn/Fgz/3P7cTuaUY9dage2w9mAwHZb2aEWsAmI25c+fSQw89RKWlpSLCdvnll9MVV1wR1VMbIgXEHIgpwmUnEmxfOb3fB4IaeXKKqC2+r0Vbe6WbEmRD40nfG04acLEGcdT1yfou6bvmarHCz2Wv/ZKov3ca/XqwG1GRjQ7affQXWWsmzIi1t2t87oBvEylghRIdVFRU0AMPPEBffcUxb6IOHTrQ/fffT4cddliklxa1oGbOR1AzB6yImiZlnCk4Pwr7fXkOtXtSzsrUjnCSaJs7ZJeuWpvG+FKfpj6n3ja14jVYli7ROFs41GhT6rKRyG0qhY++gVYjVqxJylb9Tm03zaaysjKKi4ujCy+8kMaPH0/JyclBew7UzLmDyBwAUYzhyfCMxm/bLXLI0y8oTXSz2gv1Gwxr1do//j2RpiPYMeaJa9MaE7nUIkWdHIflrPfzYcyZr+sAvh8zMeHDUdMp3yeb9zfcRx1XhmNsLeprqqn41y+p5M8fqGnLdGrfvr2ojevTp0+klxYTQMwBAPxGT0jJtKM/J2F1O1rLDG2ziq8GvlqzW7f0XU/fxKy2k3RsD7sAldsOZOh9rOP1OB3j5+SOGDDMtgKVxZto64x3qLpsp7h+6qmn0j333EOpqY6wKwg5SLP6CNKsUWI30lh/NeDXcZ77yCkBvW4SlxO4xiYmVGOotCPEZKeqal0iLV682ZZE23s43DWmvk7VsMLfczSmWdkAuHTZHCr+5TOy1ddTUlozatl/JM15+ArdyQ3BAmlWdyDmfARizjr4Mq9Seqx5siGJJjd7X73QfN1nvXFj0tOOYaGj1qY1VtT5s26jGbaBjOOSNikcjVNruxoz4sqXejsjv0B1zJZaXxbK92Ugc4pD9vxePA3NTLjEXPeJb1NCSugjYnVVFVQ89zOnAXB6u8Mo77gxVF9VSTNvGwUxF+b3jCXSrN9++60wG1y3bh21bNmSzj33XFFQ6clsUDJnzhx6//33afny5ULNs7fNtddeS4MGDQrL2kFkkHU5zHzib/P2AnytWa7unEtZVD+x1uUELn7/xeeWSq15GmPFkQ2xP4rprLrPcr/19tnpEbe1nopaszmt3dNO1qhJf7jG1KYF4sem9enzJ7qk5+nG+8U1XbpRoGO8T0WQz6ce/7H32ZzNIXyc5GugCkO+cJcu3y5rDNX6P19rDIOJ9riGq0nBCn9nZqKmfA/VpxwMmfkvw+di7laNr9xFia2z6Morr6SRI0eKhgdG7zEgtJhezL311lvijfLggw/SM888I0TZ1VdfTYsXL6Z33nnH42Off/55uvnmm+myyy6j5557jpo2bUoffvghDR48WLwROacPog9PkSRnzZSXlJacnSmHjvOfilpEL+9vthONtmOQHEa/+bRHCBJ1egDfzrfJDle5z64doIni9uLCNCFy1H32epyD1GjhK55eC16XHIUl597yMeDRXWrtGxNIFNErjmgfH38+vjKCyU0iLBhZmGk9BLXrcDu2PorJxmK29zgIj2mw1vxXMnv2bLr33nvp4MGDdMghh9Cjjz5KXbt2xcsSYUwt5ji0OGHCBDH24+677xa38ZuGQ48s8NiA8LjjjjN8fEpKCn399dc0YsQI523cWbN582bheTNu3DjKzc0Ny76AyOLrRALVcHeSQb3WXDmr0mRoI0Dq/FEZoXRGJeU+KWkrvr1IEXaMTJP6O0XA6f0WxghOwGl3nsHrmKigN082KDg6fadxpE1nRJvejF0tEFUgknB9HAdXXn31VXF9wIAB9Mgjj1BGRgZeGBOQaPb0Kgs3TquqnHPOOSI698EHH3gUc1dddZXuXLcjjzySPvnkE1q9ejXEHIiak6Yz1chGtlubUxFH5hRkKs+la9RhGOwcH7bVno4uJnukStth6m3eqBz51T+91FlrJjs/gzGSKxBc0qeFdsE6Py/OdX6piMz5X//mz/PrmTPDWBhYgfr6enrqqafEeZM5//zz6cYbb/Sp1AmEB1OLuT///FP87N69u8vt/E2gXbt2tGSJ6rvujp6QkwN/mbZt2wZtrQCYATW6yOJF2wXI9YCCEHUANvjJ8fPZND5zkRuX5nU/Q5wODpeA9adpBABfhRz7xXFwhWviJk6cSGeffTYOnskwtZjbudPuWdOiRYMlgCQ7O9v5e3/45ptvxIWLNVkQGlFVVSUuEo4QAmAFjLoy7QKLXCJwwUKvkaTI+TtzpqUDxZMVitlEk1qHZ8YaTxA44ThHVVdX08MPP0zTp08XUTiulRs+fLhoJowk8vkjvY5Q48/+JZr9G4FRhI1vq6ur82t7K1asoIsuukgUdb7++use78tFnVxXB4DV0PqvqbNDnUX+IRZY0SoaDMejcee0I9oZKdGk2sWoiAitRnAC6xOOcxSLt/nz54uIHJ87+ZzLETqzMGvWLIr2Gbe+Ymqfueuvv15Ykmzfvp1atWrl8jse2JuUlETLli3zaVtr164VXay1tbXCrsTbwF+9bz1t2rQJmUcQAMAauBj5KkSq0UNt8OApFaM7uK/t1x2l9NWmJGeTR7SK7VjymTM6RwXLNLhk6RxKWz5NnGfvuOMOGj16NJkpYsVCbujQoWJ90Qq/pjk5Odb3mZOCi4WYKuZYkG3cuJFGjRrl03b4vieeeKJ4A3BbtTchJzth+QIAACpmFUJcl7itY6WYTtEhM8/ld+xRJ5s9zIY3w2ezHu9IY3SOqi4voYQq1+Ynf+Ft7PjpY+qUkyoicmeddRaZERZy0SzmkvzYN1OLOZ7vxunU7777zqVrlQVZZWUlnX766V63sXXrVhoyZIgIV/LjevToEeJVAwBA+FDFDgujBZRBxW0SNaKIr9fYu2fD4E0XcP2hapVD9no/1PqF1jRYaw7MyTq2AkvKSaWjjjqKrrvuOj9XACKBqcUcNyiwF9wLL7wghN3AgQNp27ZtwnuO/eK0HTWHHnqoiLp98cUX4jo3SHBEjkOVP/74I/XsaeAWC4CJRmoB6x9LQ5GiTMQIxVo9jTkzy7HxVN+ptW8x45qjzTRYaw7MQY+VK1dSWlqaqMlLTDS1TAAOTP8q8RSH1NRUIeb4J+eOTznlFHrllVfcPG42bdpEzZs3d17nqQ9r1qwRuWa9lCxvg7cFQCROXGY9UZnV3sLFFHl8nXOahfSzY0NkaVYcqXFrqt+evX6tYZqG7cHXKO7BcbShfB+9uirVOSYtmBMn3OaYKs0QkT426vq0mPVvIdbgqNybb74p/s/p1cLCwkgvCUSLmOOcMZsVPvbYY7Rr1y7KysoSY7n0YBPg5ORk53X2w2HjYCMw/QFEApy4QotR16aekPD1tdCze3E3HW54XuGr18O1binunnEu1yPtvefp2OA9Gpv89ddfIgDC51g2BgbWwfRiTsKhXm/De7W+cRylUyN1AJgJtyiKZmxYpE6oZj2Ra2vDpI9dAzW6liueIo2+1GNphRzPdeVoILMt3T7jlfo21HgJUbeF6LP5iTRJ1q65vL4FjrFwwylcaVZvc2v1bgtHGhiYix9++EH8PP7440XnLbAOlhFzAEQLTnEwIEuIgz55e5xzVOX4K7aQiNT4KyvgzzFxq8vycxsuUzXaZNK0RU1dCvVlHVwe1ZjutfJnJjEA0urL05hMYE4g5gAIMy6zQsleqDzfMZ3BOfoqROO2YpnGHku8FiDaWb9+vfjZpUuXSC8F+AnEHAARAMIAWJ3G1CCC0OGvz1xNeYkw5m/ZsqWw/DIaoQnMDcQcALFep+eAJwPE8gnZZbIDHxsT1C9GAtn1alTHqTX5VRs/Gltr5+n9GWpbl1j1mSOKo4vfW0Df3dLSeYu/ozJB5IGYAyBGUMc+jepYSr2za10mBMzvHfpavUj4nnmrlVPtRLh+kS1P+NjwMXFankz6XoiJYNqImFLEcZNH30pnk0fLpvbjMH9nnCgB4OPAsyWkqNN2DjdWyHl6f8rXgjuHYSQcPJ856TXHTYZy3Bg7R6B50FpAzAEQY/DJcIH4n2sqRthsKNGPYOBm6TG+zvlcwmdte1rQI4IufnS9S2jsfa7jp2/87UPnjFL5vCxk7FYhzEHnRyOLB0GQj4sZ/dl4/2QdJ78/WNBtq0gUDTlqLad8zlCJcX6ezXkHqSC14fQkG4PMZOcSjXTu3Jn++OMPYRqMujlrATEHQIzg9eTLA1LOCJ0AYW82afTLgsnp0aZJ1QULFo8Nz+eO+rwy4ma3PEmgJY50q/w9d6pGCk/WKsEkVOJMTV9z1M2TRx+/Dnz/JVub0xJHmpVfR3E/pTEIhIYjjjhCiLlffvmFzjgjiB8GIOTE2djyGXiFR4LJEDRPlAAgpOOfHERDvZbeIPVw1D55Oqahfm6gHyVlC55e2ZVUVJEkUqcMp3C1kVL19VOx8msW6nOI3P4RD04LKM06/eqBVF1dTWeddZaYrsQz0c3aCFFTU0PffvutmAzlzzD6aH7PIDIHgEk85/hk1z+91DmeSqa3RJ2ShZsTIm1+jOkGkcPZMLGFRLStqG8lzc+zv68l9sic+/vbiu91q9OhQwc6/PDDRZr1448/pmuuuSbSSwI+AjEHgEmwn+A4BWVzCjm1mxAnt8CiQf0nuiYf5ExUROg8HDtGY4wcaKTY6/2RzTMVl156qRiFOWXKFLrgggswCcIiQMwBEEH0TnRunXo42TWqZk52Rcq0HtdsFRemOcdvgYb3nRNHw4pEmFs7BB06SaObwYMHi0aItWvX0ksvvUR33313pJcEfABiDoQcb7VLDKJODYSjjkwl2o69mtpbQIk0bUCOW3Qpj8qibr+Dmpbe4vo7Pl7qfUD0mAarxsGSyy67TETnPvnkE+rXrx9169bN5f48Jz0+XuMDCCIKGiB8BA0QjU93nd7OtSNwaUmi0zcKKa/YEFrAfB2xVnuPWbUGMlwNEIeOe5oSUhyWOn6QmJFFcXENAm3H3E+pfO2flNKiFbUddR3FOcQbC7+Zt42igoICihRogHAHkTkQUoR3VWGWS5pLRfWNssqHssc6IwVfBarz8VrX+631ITXwBcBqXnhye+Gya4kV02A98k+8gCq2/Ss6Xfdv/Iuy+5wYlPWB0AAxB0KGntO+lrmTh0dnwbioM7LXZHmqMdLWKTFcq9RgYNtwPwg64Ol9KN5zjgkKLu9DNmYOg5ecp9+pKVy3v5cAxnThbyH0JKZmUO6xo2j77I9o5/xvKLPzkZSUbv9iDswH0qw+gjRrcIimdE8wU6TRelxA7JUz6H3xcJsE4jAOZtTpEp6i0OFOr3qcEytR6jC9rcnMPnNG2OrracMnT1Fl8UZq1uVIKjzlCqcnHdKsocef9wzEXAgOKvAMasOA2QlXyjBUgk5FiCUT1KWqa3SZ7LDIXt+lJ+T0InnOyHUAEb3GGB0zHTJljNPOhvJipwGykfmxlcUcU7lzC234+DHi+QLtzriBkpvlQMyFCZgGA1Nj1hMiAMF43xoJK73xVZ62qd2OHE3GHoSMkXBwRpQcRtQcBet/rl2McISOTai9RcFCgWHE7fOGCJgwyFbLDHRqUbUlCKGu9WVYsOWn1gjxpoWFHJt8y9nG0UbT3DbUvMcgKl3+M+387SsqOPmySC8J6ICaOQBAwHM2fT7JatJRenisq3JsI1zRpUC3rxfVYfhk3za90j4s3gePO09jsJwD6HuX0BKyCzqZ2tQKOX5cy6a1QogwPF1kft5B8TheQ6RrMbWpTCl+WYCKiCL72+kQyokoWmH8mfhfIhW3yXVfh4u90pio9YRs2e8UKlv5m0i3VmxdQ0THRXpJQAPSrD6CNCuI5fS2nrhgkaAXoZCigX8vb5MTLdwEn47IYyEi66o4GqWON+NoVCSiSkGrtfJTkGprzdTj50nQqKLO15SmqbwolZQqvw9UUcyIGkBu7HAcA8ZM+6OHVdOskh0/f0olS2dTWkFnWvLZa6iZCwOomYvwQQXALGjFQP+OlS6pOrWeymcLFUdUTm5Luz1GW+DOyDonLXoRKino5LZ8LZIH5iLQyJ+2O1evYUIVcuJ+JjchD5eY6z7xbUpISQ369qv2FNOmz58nW30dzZw6mQ455BDd+4XDUBg+c+4gzQpAjKCeEDniJQWYr5jxBNnYCFCoBIB8HjMeM73IYaibCvxdU96YJPu6qFJ82Sgm1y8c8JkzpqZ8D9WnNPytB4v4hCQR8as9sJcue/snSi8s0nnuyBsKxyoQcwBEMerJr2h+Aul8/NJcrvWJ0bFyqqgLRv2YVmSEW9TpdYxqRZo2lS0REdDWTXzyR/QVX1PJelE42fCxwBHVVWv8/IkiC/h4hEmoRnpCRbBMg/VIb9uNytctJVtNtehqBeYBYg6AGKAxqa7GbMtMFh+eojrBWk+kbT84ojWqY6mzZpEbH4oq9rl5zckU5ZI2ze0bkOJvfhnlUU1Y98Wl8YUyRRROrM3FVNi3hgeniNPUXTLOus1FTUMistX3lSok5e1vvXwKWZ2EpnaRWF/lKF4EpgFiDgDgLgo4QjK+zqXRQXppebO2cGmWcGxDhYvYz/ric59r9QKJuHlKn5ox9Rk0Ow3KFJEsu4hJdDagyIYBHp3ncf+voYgRjNdFdvTaRaGMTtkjfVLIBfP5jPDUqWxl6irtdazxIajJA40DYg4AoIuMaEhbCzlbV6Tutjb1uXZJ7XrVm88bCrQF8WYlWFFCr4/pSTGDx2MRQusQb68BNyhYGTYNPrhri/g/UqzmA2LOYkS6HgNEN2rKa4Hw1nLturMLpBpnKs6omUD8fwuJOr37ZSpPs41g1ep5tOZwGNGGy6OsMX/LUoB6SwGaKXUNYoeq3duouryE4uITKLWgc6SXAzRAzFkAlzSS5uQ66K7vYNUAgo4/hetGKc1wGdLqiSJypIq5fqx3tqsf3tKBiTRtXo7Xv51QiybV5JfxVpzvzb/OLB20wYo24ourudi9eKb4mdGxJyUk2+2GgHmAmLMA8kNf1MToRkoAiMz7UnviZYEkPe1oTJK9Nk4a3QbRI041xeWif1dj2VJnjdhm0Rnp+jHHv9N2dxrtnyooQjL/U6dQ33nMNMfLZT3Sr8/RpWm2Wktp9swpdm6+kPvEUVFPYlU1ph57X0OtJdf93fjbhx5nnwI7HD1LqHKdztJYKos3UdnfXI1J1KxrP6reu1v3fmxNAiIDxJxFwAcXMCtuHmEay4tQzdMU4kERQg21eYniNn5OPvkXaacE9PS9dkoVUEHvglWOlRQ/jDx2si4xaM8XBPSiZc7bCuNd9kUOp+c6Sfl68GvmLWLLx4S3IR/PI8zspyobLQnlzkUJwfKZq9m/hz4cP4yysrLohhvepSYt02no0KF0881ne3wcmwaD8INxXj6CCRAg3IQqKhRMPDVABOLK7+YP5iX9qHd/b48Jht2Et/3S67DVW5enTlwzvebeGl08ztUNYISZSzpZM/LNTMclmsd5cfTtm3HH0gsvvECzZ8+mli1b0pQpU0wxAQkTINyBmPMRiDkQanSFQxSdzDyhpmd5TJiaolP9wSIxysuTkNGuxWiGrTqj1grzZY1AHVvsiLmqsl10VpN/6csvv6TExER64403qEePHmQGIObcQZoVAJOgrdESxICQY0Q9aKF9KLx9zBjXSylCzhePtBDgUpunzIpleF2ePPf499vSOVXYIOTknFqr1roG2sigfjlxTqVgfBC16mvAAlnWFjJWF8dmpvTPH2nqtoWUlJREd955p2mEHNAHYg4AExGrJyO9CFfD7yI7bkwKEe2YKWlGq3ruedoPl32JoDlvOHHZf9kYo44ZK4x3GdNl9HhZH6nWFjIFqTai3iW0hLI9bgf4x67fp1PJ0tnUomU6TZgwgUaNGoVDaHKQZvURpFkBiN3UnadaPrOu2Uz4k6r29PjG1uNFEqukWXct/I52zv+abHW19NgdN9INN9xAZgNpVncQmQMAhB2Xk/MAe3rVpX7OIZbMcoI2yzq8NWSYZZ1aGruucO5XKGf3mp3df8wUQo7JOWo4jR07NtJLAj4CMQcAMB1mrinzVMPFqAPtQyUCVBsYQd8kFxNxqwkQtXHEuU8O9Dz3wl67GgPsXjyLin/9Uvw/d8Bp1OzQvpFeEvADiDkAQNgJpmVIsLfty/Nqa7jU+bOb9x+k4sK0kNdwqX5+srFC1qCx8JERTuf9TSryVJ86dZ+kSGar2mJKE+PhwlUTZ7ZjFGrT4D0r5tGu378V/8/uc6IQcjAAthaomfMR1MwBYA4Mx1qFsfPX22gtX6NIgdYLGvnYOTFBhMtffPXmsyrhqpk7dNzTlJDibjitGgGrxr7ffPMNTZo0Sfz//PPPpwsvvND5O75ffLzm/W0CUDPnDiJzAADrjrcj3418g72GoEzLGGNPj0o8WZ34+vziObZohKYQuuYVcoxZ12U10go7GTZAsBEwC7SCggKnkGP/OLYfueyyy+iaa66huDi2BgJWA2IOAGA5InHi13rOqSPLpKkxE6hgCtbIM4gi4As//PADPfDAA86IHISctYGYAwAAP4WcnFLBiEkV6TZ7bdd2z7Vdnnzo8sh4CD0AwWTJkiX0n//8h+rr62n06NF08803IyJncSDmAADAC9pZqtPatDCo1/NPkEG8gXBTVFREd9xxB9XW1tKJJ55Id911F4RcFGAZMcdvvF27dlHz5s2pSZMmYX88AABAfAErU19TTffff79owjjssMPE/83Y4AD8x/SvIouw2267jbKzs6lPnz5CjJ155pm0c+fOsDweAAAAiAbYEHjLli3UsmVLeuaZZxDYiCJML+Y4l89t09OnT6cdO3bQv//+S//88w+deuqpVFdXF/LHAwAAAFZn75rFVL52sUipPvTQQ5STkxPpJYFYEXObN28WQoxnww0cOFDcxi3V/I1i8eLFNHXq1JA+HgAAALCaaTBbkKiXyp2bqeiH98W81bPPPpuOPPLISC8TxFLNHEfTuNvmlFNOcbl9yJAh1LRpU5o2bRqde+65IXs8AAAAEAzT45rqirAcyJryPVSfctDFJPjzzz+n6Rlx1OawHnTLLbeEZR0gvJhazP3999/iZ+fOnV1uT0xMpPbt2zt/H6rHAwAAAAGNl+Nu56319tFvYZw1rJoGi8hcdTXNmjVLGANzw0NKSkrY1gLCh6nFXFlZmfiZkZHh9jseh8It1qF6fFVVlbioo1IAAAAAn/wDt7jeLsZtfXJ50A6er+eoDz74QGSojj/+eOrZs6cYhWV15D5Ew754wp/9M7WYky3T/EbUwrclJCSE7PGPPvqo+BYDAAAAmM3CxpdzVNWenfTtt9+Kc13Hjh3F/6MJjjhGMxUVFdEh5nJzc8XP0tJSt+haSUmJ8/ehePydd95JEyZMcPnW06ZNm4D2AwAAAAgmvpyjylb+Si0zMoQ58JVXXhlVESsWckOHDhXp42jFn4ygqcXcEUccIX6uWLGC2rVr57x9//79tGnTJvFChurxXFeA2gIAAABmxNs5qq76IO1bt4xymyfThRdeGJWih/cpGvdL4s++mVrMsRccR9Q+/vhjGjFihPN2vs4ecTwcWIUFWnJyMrVu3TqgxwMAAPBQ1I8RZJZh7+pFVF9bTfn57Z2BDRC9mFrMNWvWjJ566im6+uqrqWvXrjR27Fhavny5mOhw0UUX0aBBg1zuf+ihh1Lv3r1pwYIFAT0eAABiGdVeI2+MPSqQn2e3uWCWLM2mQXd95+zOxHgzc/rMJVRVUunSOcJXjq252CgYRDemFnPMVVddRfn5+fTyyy/Te++9J1yr//vf/9K1117rdl9OpcqoXCCPBwCAWIbFmVPQbU+jvNYHqKhYf5Y1hJw5qdi+gV6+4Bi6/as9FNe6ObxUYwTTizlm5MiR4uKN1atXN+rxAIQCPjkKI9HCeHFy5EgHnyD5ZMk+VBzlwIkRmAW99yK/h/EetQaprTuIsZVcb8XzyL01CoLowBJiDgBLC7kBWdSndwm1TbdR7+xayk+toaJ2NbS0pJI2749zSV3hhAnMCN6X1mLevHni50knnRTppYAwATEHQBjSVksom4paH6DN+w9S2/REIeLU6NzcR1xHzgEAQCDUVuyjVatWicjcCSecgIMYI0DMARBiENUAAISLiqJ1lExEhx12GFKsMQTEHAAAWMQeBF8MgC9iLouIBgwYgIMVQ0DMAQCARTze5O0QdcCIyu3ribKSIOZiDPvwUgAAAKbBk1iDkAOeqKuqpKZNm1L37t1xoGIIROYAAMCEQLSBQGCj4B49elBiIk7vsQQicwAAAECU0DE3k/r37x/pZYAwAzEHAAAARAlsSdKrV69ILwOEGYg5AAAAIEqIj48XtiQgtoCYAwAAAKKEwsJCSk1NjfQyQJiBmAMAAACihLZt20Z6CSACQMwBAAAAUQLEXGwCMQcAAABECRBzsQnEHAAAABAltGrVKtJLABEAYg4AAACIEnJyciK9BBABIOYAAACAKCE7OzvSSwARAGIOAAAAiBKSk5MjvQQQASDmAAAAAAAsDMQcAAAAAICFgZgDAAAAALAwEHMAAAAAABYGYg4AAAAAwMJAzAEAAAAAWBiIOQAAAAAACwMxBwAAAABgYSDmAAAAAAAsDMQcAAAAAICFgZgDAAAAALAwEHMAAAAAABYGYg4AAAAAwMJAzAEAAAAAWBiIOQAAAAAACwMxBwAAAABgYSDmAAAAAAAsDMQcAAAAAICFgZgDAAAAALAwEHMAAAAAABYGYg4AAAAAwMJAzAEAAAAAWBiIOQAAAAAACwMxBwAAAABgYSDmAAAAAAAsDMQcAAAAAICFgZgDAAAAALAwEHMAAAAAABYmMdILsAo2m038LC8vj/RSAAAAWAx57pDnkmAjt7tv3z5KSkqiaKampoYqKirEMY3mfS334z0DMecj/AfCtGnTpjGvDQAAgBg/lzRr1izo2y0pKRE/O3ToEPRtA/O/Z+JsofqaEGXU19dTUVERZWRkUFxcHEWb+meRumXLFsrMzKRoBfsZXcTK6xlL+xrN+8mnWj4p5+fnU3x88CucysrKqHnz5rR58+aQiEUzEc3vk0DfM4jM+QgfyMLCQopm+I8imv8wJNjP6CJWXs9Y2tdo3c9Qiix5sufniMZjF0vvk0DeM2iAAAAAAACwMBBzAAAAAAAWBmIOUEpKCt13333iZzSD/YwuYuX1jKV9jZX9DAWxdOxiaV99BQ0QAAAAAAAWBpE5AAAAAAALAzEHAAAAAGBhIOYAAAAAACwMxFyUU1dXR6tWraIVK1ZQdXV12B8fTnPFtWvX0vLly8WYF39hY8a//vqLiouLyexs3LiRli5d2qjRcvv376cFCxbQwoULyaxs3bqVlixZQqWlpQG/J9atW0d///031dbWklnZuXOn2M9A3nu8j9u3bxfvB/5pZg94fr/+/vvv4vMkEPh9wMeJ3xfAFTa052Mjp0BYjd27d4vPI/579cauXbvEvu7YsSOo97U8PAECRCeff/65rVWrVra2bdvaOnfubMvKyrK99tprYXt8uJgzZ47tkEMOEWs97LDDbGlpabYHH3zQp8dOnjzZNmDAAFuTJk1sPXr0sDVv3tzWsWNH22effWYzG0uXLrX17NnTlp2dLdbatGlT2w033GCrra31e1sXXnghn/VtKSkpNrOxfv1628CBA22ZmZm2Xr16if3k9VZUVPi8jbfffttWUFAgLr1797bl5+fbnn32WZuZ2L17t+20006zpaamiv3k9+0pp5xi27lzp0+P//LLL21du3a1NWvWTLwv+O+zS5cu4u/WTEyZMsV2+umn23Jzc20JCQm2E0880a/H8+t+0UUXib9RPk68v8cee6x4n8Q6mzdvth1//PG2jIwM59/KOeecY9u3b5/NCrz55pu2YcOGifcGfx5dcMEFhvctKSmxjRo1yvn3kp6ebjv55JNtxcXFjbpvtAAxF6XMnz/flpiYaLvrrrts9fX14jYWYvwH88UXX4T88eHi33//FSfBSy+91Clqvv76a1t8fLztxRdf9Pp4FoBXXnmlbdeuXeJ6dXW17dprrxX7OW3aNJtZ4A8n/sDjk31lZaW47ffffxcnuNtvv92vbU2dOlWIOBYCZhNzVVVVQpAcddRRtrKyMnHb6tWrhYD19EGv8sQTT4gPb34fSPbu3Wt7+OGHbWaCT8Lt27e3bd26VVzfsWOH+NLEQtYbK1asEMKIxeCBAwecomfMmDHivb9s2TKbWeAvHCw8Dx48KISYv2KOX/cWLVrYVq1a5Xwt+/XrJ94nvM1Ypaamxta9e3chVvjzgVm3bp0tLy/PNnbsWJsVuPzyy20zZswQ4tObmDvppJNEYIEFLMPCjD/D+Mt4veMcFch9owWIuSjl1FNPFX/U/AevcuSRR4qoTqgfHy6uuuoqIUhKS0tdbudvZS1btnRbv17UQAsLOhZJfAzMwgMPPCA+7NasWeNyOwvP5ORk2549e3zazvbt24UwYmEzYsQI04k5jqjxfs6ePdvl9kcffVTczsLOEyzu+UvIc889ZzMzvH+8P7y/Kh999JG4/fvvv/f4+Jdeekn3fhyl5tvNuv/+ijl+v/P+8Ptf5ZdffhG3v/XWW7ZY5cMPPxTH4Ntvv3W5nSPQfLuZBL03+AuqJzE3d+5c8fvXX3/d7Ysp3z59+vSA7htNoGYuCuH6oB9++IGOP/54Skx0Hb87bNgwURvmqe6ksY8PJ9999x0dddRRYsC0dp1cL/HHH394fPzZZ5/tdltCQoIwowy0VitU+9muXTvq3Lmz235yLSO/Xr7wf//3f2LG8G233UZmhPezSZMmdNxxx7ntJ/Ptt996fPy7774r6jwvuugiUaPFtWS+1OFEYj+ZoUOHBrSf8n2g/TuU17XvE6tidJyOOeYYSk9P93qcohk+NvxZdcIJJwT0HoqG94G8/q2yr/7cN5qAmItCNmzYIE7whxxyiNvv5G3//PNPyB4fLrjRYcuWLUFf5xdffEF79+6lAQMGkFlYvXp1o/fz9ddfpxkzZtBbb73lJtLNtJ9t2rRxW5+v+8kF9i1btqT//ve/lJ+fTxdccAH16tWLOnXqRLNnzyYz7WdycjIVFBS43N6iRQsxWNvbfvIJ+9prr6U77riDJk2aRHPmzKFXXnmFJkyYQFdddRWdeuqpFA3wcWK0730eKs9fbszwORTJY8Pvcf7yY9bP6GDuKwvXtm3butzOfyvZ2dku++rPfaMJc36ig0bBQoThb65aMjIyXO4TiseHC9nNGcx1clfYddddJyJ9EydOJLPA+9GY/eTo1C233CIuRx55JJkV3g8WNIHuJ0djuTuUv52vXLlSnPD5fTJq1CgaOXKkiNR16dKFIg3vR1paGsXFxenuqy/vWxZtvI8s6Nq3b0+bNm2i7t2707hx4yha8PZZZJYMgZk+E1jc8ZchM3xGBwvel9TUVCHivf297PXjvtEEInNRSFJSkvjJ6SYt0qJB3icUjw8XwV5nWVkZjRgxQqRXP/roI2rdujWZBd6PQPezvr6eLrnkErE/999/P5mZxuwnw9/ImXvvvVcIOSYzM5Oee+45qqyspNdee43MvJ9yX73tJ4vS/v37iygk2y6wJQ9bm3AE8thjj6XFixdTNODtb9wMn0NmfA/x7dF0bPz5e0lq5N+WVYGYi0JatWrljFLo+fmo9wnF48NFVlaWqG0LxjrZZ+6UU04RkY4pU6bQ8OHDyUzwfgS6n1xP9+uvv4p6ORYB7OfEFxav3ATF/w/U+8tM+8lIAc6pVRWOWLHQk2m7SMP7we+5qqoql9v59eAvE97288UXXxTi9IUXXhBRCBmR4dt5myxeowFvn0Vm+Bwy298Ke83x+yiajg3vC5fVHDhwwOv7oJUf940mIOaikLy8PFHkzt/WtfDJnD/0+eQWqseHCz459+nTx3CdDDdHeIP/8DkFx80SH3/8MY0ePZrMBu+Hnvmt3M++ffsaPpbTef369aPPP/+cbrrpJueFhWtNTY34P9dbmWU/2fxWe5LyZT+Zo48+WvzUGirza8zf1vlYmGU/+YTLzUQq/Bpzvaq3/eQoHL//OTKnwlFIFnfRYpIq/361f+N79uyhzZs3ez1O0QwfGz4OXDccyN+KFd8H2r8X/nJ28OBBl331575RRaTbaUFouPPOO4UPFVs1SNi+go1F2YBVhb142FdONZr05/GR5OWXXxbt5mxVoHqVsYkw+3ipsI8X76f0ZGLYp4o9idjOglvXzQp73vF+snWFhP2S2DeJ/bZU7yS2aeH9ZBsST5jRmuSPP/4Q+/nkk0+63M6+WWypsn//fudt/H/ez02bNjlvYyPZpKQk2y233OLy+HfffVfXCiRSsLcc29+wtYwKr5utZtR9Ynsd3k/Vlob9H3l/fvzxR5fH8/34du3+W8WahL0T2UNPfY1zcnKE1ZCe/caiRYtsscpff/0lPAW1Buls78HHWfo0RoM1CX+WsSHyuHHjXG5nj03+e1+vGEj7c99oAmIuSmFjTTaU7Natm5hmwN467JpeWFjoNClV3+T8h8QngkAeH0nYE27w4MFiXR988IFt5syZQqSw6FRPCgybCPN+qqKNnen5tltvvVXsv3r5888/bWbi7LPPFvvF/kl8EmdXfBZjP//8s8v92NSZ98nbxAMzijnm5ptvFkLnmWeesf3000+2G2+8UZy0PvnkE5f78YlcT7jwfvMXkTvuuMM2a9YssR02EWbDZW++g+Hk+eefF/t1zz33CH84Pinzl4qnn37a5X5saM37qRrB8pQInlTCRtLsOce+dZMmTbK1bt3a1qFDB69CPpywCJV/U/w69O3b13l927ZtLvfl9yMbAqvw3ysfp+uuu068H/i4sbM/v09iHf7SzeKfjbL5PTRx4kRxrHiyjRVYuXKleB/wZxi/x4cPH+58b2gnofAXd963u+++W7wP2CuT/14ef/xxt+36c99oIY7/iXR0EIQGnr/5/PPP088//yxSTJyCYusCbWqGrQ3ee+89YVlx+OGH+/34SMNpKd6HmTNnijqinj17inXKAnjJp59+Sk899RQ9+uijTm+mgQMHGs7t5PortikxC9zI8M4779C0adNER1bXrl3phhtucHnNmLlz5wofuZtvvpnOOeccw+3deuuttGjRIvH6mo2pU6eKlDfXuHTo0IGuvvpqUfCvwhYDl156qbAfuf76611+xzYkfKy4w5O7YzmNftlllzkbJMwC1zO+/fbboiuTbSZ4jdp6TX6t+bYhQ4bQI4884ryd3+tvvPEGzZ8/X6SmuRaIjxF3szZt2pTMwl133WVoC8P2KuwJKBk8eLDwyHvzzTfdLGdeffVVWr9+PeXk5Ij3tZ5HZCzCn1Effvih6OLmzzx+/bkJxgrwWpctW6b7u3vuuUc0pKnw+4jPU/z3wp/P3NjFtc56+HPfaABiDgAAAADAwqABAgAAAADAwkDMAQAAAABYGIg5AAAAAAALAzEHAAAAAGBhIOYAAAAAACwMxBwAAAAAgIWBmAMAAAAAsDAQcwAAAAAAFgZiDgAAAADAwkDMAQACgkcuxcXFGV6ee+45HFkAgBs8ilD9rEhKSqL27dvTxRdfTGvWrInoEcvKyhLjA60GxBwAoFF89913xCOetZebbroJRxaAKOXGG28U87ol/H+eCe0rPFNaflbwHPBPPvmEFi9eTAMGDBBzZoF/QMwBAAAAwC9mzJhBI0aMEP9n8fXHH38EPMg+JSVFiMF7772XSktLxbaBf0DMAQAAAMBnNm7cKNKhp556qjM6n56eTgMHDgzKUWzSpInz/7t37xap2Keeesrtfr1796aTTz7Z7bGcFZg7dy71799fXH/sscfE7z799FNxG6dSs7OzaciQIfT999+L35WVlYnn2bt3L7322mvOFPDxxx/v3Lanx0caiDkAAAAAeGTOnDlOgdOhQwdxG0fT+Pqll15K+/bto+TkZHH9pZde8utoVldXixTrQw89JLYtRWKgrFmzhp599ll69913xf+7d+9Ov/zyC5199tl0xhln0Lp162jDhg10//330/PPP091dXVCoHHKt1mzZjRu3DhnCpj3m/H2+EiTGOkFAACsjVFqZcmSJeKbMwDA+nCEisUNc9ppp1FmZiZ98MEH4rYWLVrQ448/TldddZXP21u5cqUQfiqdO3emL7/8UkT5GsOvv/5KW7dupYyMDHG9bdu29PDDD4vnu+WWWygx0S59jjvuOHHxBY70NebxoQaROQBASBogIOQAiD74b/u3335zplRXrVolUpTHHnusX9tRGyBqampo9erV1KNHDxo0aJCov2sMJ5xwglPISXr16kX19fV03nnnCWHG0UB/aOzjQw3EHAAAAAB8gsUbNylIMTd//nyRojzssMMCPoIc6erSpQu99957VFVVRXfddZdPj5ORQi0FBQVut40cOZJef/11sf7BgweLdOqwYcN8brZo7ONDDcQcAAAAAAzhSJmsl+OIGtOzZ09x/f/+7/9EZC4+Pl5cl80GgZCWlkb5+fm0YsUK522czuXtck2elm3btuluJykpSff2K6+8Umy7uLiYJk+eTJWVlaI+j+vhfKGxjw8lEHMAAAAAMOSoo45ypkS5g5NNdfn/nGrk+jZZO8eXO+64I+AjeeDAASoqKqJWrVo5b+OmisLCQheBxyxYsIBKSkoCep7c3Fw688wzhSDjNatijAUlRwcDfXykgJgDAAAAgFfY3HfevHlOOxAWMRUVFSLd2Bi4G3Tt2rViAsTBgwdp4sSJbhGxr7/+mqZOnSrWwM/75JNPUrdu3Xx+jnvuuYfuu+8+Wr58uVgze+NNmjRJRP04bSrhztfff/+dtm/fHtDjIwXEHACg0d2seuO8rDgSBwBgzI8//uiMzsnmp759+1JOTo7fh012s/KFo2/ctMAiafbs2aLJQOX2228XdiHjx48XUbunn36aXn75ZfE4fyZWJCcn0yWXXEItW7YUom3p0qXCJ05t3uBtcw1gx44dXXzmfH18pIizGVUQAgAAAAAA04PIHAAAAACAhYGYAwAAAACwMBBzAAAAAAAWBmIOAAAAAMDCQMwBAAAAAFgYiDkAAAAAAAsDMQcAAAAAYGEg5gAAAAAALAzEHAAAAACAhYGYAwAAAACwMBBzAAAAAAAWBmIOAAAAAMDCQMwBAAAAAJB1+X8uw/CRRoFCxgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 700x700 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "dsf = ds.select_bursts(select_bursts.naa, th1=40)\n",
     "alex_jointplot(dsf, kind='kde',\n",
@@ -257,9 +514,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.figure(figsize=(5,5))\n",
     "hexbin_alex(ds)"
@@ -281,9 +549,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'matplotlib.figure.Figure'>\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(-0.1, 1.1)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x700 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "g = alex_jointplot(ds)\n",
     "g.axes[1].grid(False)\n",
@@ -301,9 +597,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'matplotlib.figure.Figure'>\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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6aA5FbyXX+aNo23cihpL6vGMX3wd1QJh4n24K3ZmrSX2NVqsVbXQ0t4Wuyt177718lYsxxhjrzmJ9wIAB4vI9/aOl3u+vv/4aM2bMaNhOCybRYzqDJnDOnz9f9JrT69Il88bqTxLoczdH/e2NW2o68tjmKJmD9qX+RvvDzp6iLd+Kt75J48TIemf98Y9/xLhx48T3nN7vjVdEqKe+cMsKmE4cgVSmQPTFd0HeRm9+X6PRaMQIO10RoZPmBx98sFdfDWGMMca8qlinUXTq+05OThbFNMUiUi50vY0bN4pkjo6iPlaaVErRbsuXL2/oiW+MenrJ8ePHWxRAdJJQv72jj21OpVKJ1JfGN3Z21Jw6BlNuqhhND5l8SaeXuqe2p8LCQtx///0ICAhAdna2aMk6ceKE2Ea94j2Nfvaop96YsUdMoo688JYunZx4M/ode/nll0XqE/Xs//nPfxZrODDGGGOsG9JgVq9eLUbTKVedkizqJ3FSgaRWq0VWekfQSCilyOzZs0ekzFxyifuibe7cuQ0nBI3R82gEfN68eZ16LOs5JXvWibd+Qye1Gh/Y1lL3Df3enx1E7sAFyCy3YPm6LZj8u8dFj3hPT9qkQp3mdXzzzTfiY1rRkxZ66s+oHY1azi666CJxMvXXv/4V3333XU/vFmOMMebdxXppaSlOnjwpYvJoJKzxqHpcXBx++OGHDr0eXQa/9NJLsX37dlGo0/ue0MQ0Ss2gqMj6CaI0KveXv/xFRN7ddNNNnXos6xnWiiIR1UgCx5zf5aXu62/6uOGIuugOEQFJvfDVpzLQ04X666+/jnfeeUd8HDT+AvgPm9Kj+9Rb0NwUiqykeSp0nGgdhM8++6ynd4sxxhjrFVrOvGwHWhWUJvDt27evW3aCFrKhTHSKU/zoo4/ErTGK5Zs8eXLDx8uWLRMTCSklZtKkSSLikUbo6CSh8cTXjj6WnXvlBzeLt/r4kVD5t0zt6QpDwkiETlmEwm3foWj7Kuzbd36PLHVP8zroZ/yrr+piKW+77Ta8XxRyzvejN6PYTUrzobQYWv/gxRdfFFfpbrzxRrcLrIWHhzdZl4ExxhjrqzpVrNPoOcUhUrtLd6AUD+p/94TSZRqjfmRakTQtLU0kfgQFBYlkCXf/vDvyWHZulZeXw3hsH62AhaCxdS1L3S1w3DxYyvJRfngb/u///g+DBw8Wi3edKzQPgyYrU6Y/FZ30/sSJE/H+W1vQ39TPLWjN1KlT8dL3u8XiWM/95328vnafWMVW0uj31WYsxdqHF/XIiRdjjDHmFcU6pW3Q5DBKhJkwYUKXd4JGvTuz9HhSUpK4dfdj2blBJ1EupwOaiARoIuLPyuegApl6w2uLT8JkKsPdd98tFu7qbFpRR9DJIU2apAmu9PtCCwLRxOv6/P/+pn5uAbUseVKTl4mQyRdDGzUI+Rs+hTFzP1xOJ6IW3AqpXHlO95cxxhjz2mKdio0lS5aIBZCoPYWK9+Dg4CaXq6mlhe5nzB2aTPjjjz+K9/2HTzurB0kqVyBy3o2IS/9OzLWghXhooufZGpmlthd6fTopoDkStP7AU089JUb0++rqpB2dW+CJzVgm3vonT4XMR4eTP74v5jScWPE6Blx8F2Rq7TncW8YYY8xLi3VasbR+cibFLFJh0tzVV1+Nzz//vOt7yPqk3bt3o6CgQCxB7zvo7J/UyVQaPPfcc3j66adFL/Qtt9yCf//73526otMayuOn+Rxf/PiLmNxKI8SOMVfg/nX5AN1Ojx7T1QTW9pyDmMX3IXflm+KYZX/xD0RfcnenFsxijDHG+lWxTpGIlNzSGuoNZ8wTWvCKGBJHQ6o4N+0N/v7+ePPNN0UOe3p6Om6//XaRQtIdEZ4UPUqTmWlydHV1NaRKFcKmX4mAkTOb9Fs3Hj1mbdNGDkTslX9Cznf/haWiCNmf/x0h510G4Dw+fIwxxvqFThXrdFmfbox1dmJpffa97+Dx5/Qg0kkktadQ8si2bdvw+OOPY8uWLXjggQfEZOTOFOmU8vK///1PZPeT0aNHw2YYD92AwWfhK+h/1EGRiL/2UeT+8K4YYc9btxT/S1Lg4YcfhlzeqT9hHWrXaqttiZNpGGOMnU1n9z8dY26sX79e9HUnJiYiMyC8R5JIHnroIXz66aeiVYsW4Vm7di0WL14s5mG0dlWIijfqO6dVeylu9JdffkFtba3YRtn9N998M+Lj47FvZdY5+br6C7nGgJjL/oDCX5ejdN8v+OKLL3D06FExaZfSqc4W+pmhBbU8TYrlZBrGGGO9slinf5JtrTJIvcCeViFl/RsVxoRWwM0s7skkkjjUTrgOxdtXwZyTh/3/ekMU75THP378eFF0+/r6ionTRqNRTE6lBKT3vl0Hp9Xc8Co0YdJ/xAwgcTReOATUrF3HPelngVQmR/jsa6H0D4Pu+C/i7xAtyEYnWDfccAOUSmWPTIpljDHGel2xvn//fpEX3RqaYMrFOmuuqKgIKSkp4v3p06fjg6/TztlBcld00cfUN2/M2IvinT/C6SzHrl27xM0dSnehQp1SSfRxyfBPPg+aqEFNkpC4J/3s0scPx5uPXCVWg6X8epqHsGrVKnG15LzzuJedMcZY39KpYp2WBae+48YsFguOHTsm+oHpH+a1117bXfvI+lq2usuFkSNHirhP4NwV657QBFDfpHHwCYvFOxfHiZ9jWun2+PHjMJlMovWFRthDQkLEYmBvHzaLx1MkJOsZNGfm9ddfF61Ir7zyirjqQfMOpkyZggcffPCstsYwxhhjvb5YVygU8PPza3E/FTK0AuGll16KESNGiFYC1n+5m5xH7VM0Ok0Z/L0xb5yy11tb6Iv61Ze9tYUL9R7UeP7BsGHDRNFO8w++/fZb/Prrr9i0aRMWLlyI6667TpxktYYnhzLGGOuXE0wvvPBCfPzxx1ys93PNJ+fZqsqQvXk3LSuK/x5TwLp7Lfd2s26af5AE63m3omTnjzDlHMU/3/oQr/xvOYLGXwC/IRPdZrPz5FDGGGP9tlinDGvKmmascZ849YXXLxSkCY+Dy27jA8S6df4BxWWaclJxas0HsJkqULJrDaqOpSB0+hXQxw7rd0fbarUiLy9PLEBWVlYGu90u7qdWNGpDi46OhrTZOgCErzgwxpiXF+vUr56ZmdnkPvrjX1JSIpI+XnvtNTGyzlhjlem7xVvDoLF8YNhZQwV75PzfoaYgC+X7foGlvFAsqqSLGYqwGVdC5R/aZ48+9e6vWbMGaWlpIl6U5l3Q3+bGqA0tu7RanDhL5UpoIhOgjx8JXewwcQWCrzgwxlgfKNZ/+uknjxNI1Wq1SGXgCaasMSqYzMUnRWoKpa/0Rs2z2N3pjX32zP2kYb/BExA4ciaKd61B2b4NMJ04gsxlzyFwzPkInnBBn5gDQoU4FeabN28WCUbZ2dkNhXg9KsjlOj/INXpxv81WDnWwP+w1RjhtVlSfzBA3Rcp60TakjU7qga+OMcZYtxbrlPayfPnyJvfRpdSwsDAMHjy4UytBsr7NmL5HvNUOGAK5jw7e0wvdFK2gqYlIOKf7xTpPptYgbNpl8E+eioJfl4uCvWT3T6hM/Q2BY+bC5ZrqlXNAHOZqVKbugvHYXlgrSxoe47RZoI1Kgj5uGNShMfAJjYFc69ckWrQ6Nx0KQwAU+gCYS07CmJGCisPbYKsqR/6Gz8SVh+MXDhCTrRljjHlpsR4VFYUrrrii+/eG9fkWGN9e3gLT1gI4nKHunagAHbDoHpiyD4qi3WosRf4vn+Hxx3Pwpz/9SUSJegWJFKUp60Vx7Tw950Om9IE+cRQMCSNpGB3qoIh2LeJEVx98QgaIW/DEC1F+cDOKd/wgroD9/ve/F8dlzpw5Hp/Pfe2MMebFE0wZa8xckgdLWYHoh6XeWMZ6Ao0u6+NHQDtgMEp2r0Xxbz+IBd5oBVQq1i+++GLMnj0bBoOh132DKO+f1rA4/tUyUbATdXAUAkbOhGHgGMiU6oZR886gVpnA0XNgGDQOJ755DekF2bjr0afhN2wdgicugOT056zHfe2MMdaLi3VKF8jJyRGjKlqtVtx34MAB8Y+EVqekbHUalfHx8Tkb+8u8kPH0qDpN8KO2BMZ6EhWmIZMWilSiC9UnRDY7Fe10e/755xEfHy/a+WjdCOoTrziaCYXO//SETAWkag3kah0UvkHtmufQlVFo+vyUH0+rtNLfV5fTCV3M4Ibe8sbtLd1BofUVk3CrMveh7MAmVKbupJ1AxLwbIW3UB88YY+zc6dBf3/Xr1+Pqq69GaWmpKMbfeOMNsVLg3LlzRcIA+eKLL/DDDz9gw4YNZ2ufmRehCXCVp/vVe3sLDOtfqGf7vrsuEe0e9DeLJs5TggolXdWnXdHftaJiU5MJm80L/wvXRZ8esR/SMMLd0VFod5NH6eThnXfeEZNG61dtrRl9uei/P5toFJ0m4fqExyNv7VLRwkYtN9ELboNE1jKvnjHGWC8p1umfyR133CGWXL/55ptx9OhR/OEPfxArCN5www1iiW+NRiP+4dH79Hb+/Plnd+9Zr2cpzYO1shhSmQK6+BE9vTuMtUB54/Q3jW4UP3vo0CFkZWWJ93Nzc5G/54QYUXc5HHDarWJyp6PWBDvdaoyozk0TE4+lsu9F77jfsMnQRg4SPeGdmTxKE0brF3ciUqVatKjU+AWLqwHnCqXpyNRa5H7/Nqqy9uPU2v8hcv7NHfq6GGOMncNifd++fTAajWKUR6erS/O45pprsHPnThEbJjs94nL33XfjyJEj4j4u1llV1gFxEHRxyW5HHRnrTYKCgjBz5kxxI6dOncLmt7a4nbBJxXrFoW2wVBSiNj9LxJNWpu0SN6VvsBgB9xs6qd2fWyJXiMmj5Qc2i/YaOkHwHz4NwZMWigSlzvajdwUtJBV90e3IXfW2GGGXKpQIn3P9Od8Pxhjrz+QdWWxj4sSJDYU6oaQAs9ncUKjXGzNmDDZu3Ni9e8q8sgWG0jeIb9K4nt4dxroVFdA+YbEwDBojRsTNhSdQfmSbiFSkq0mFW79F0faVIkpx9zi1iLRtPpfH4XAgJSUFn332GbK/WNkweVQfm4yQ8y6FOjCix79r+rjhiLzgFrEqbPnhbeJr9U0a39O7xRhj/Ua7i/WampomhTrR6/ViEaTm6B+SxWLpnj1kXis1NVUs+U6X0ml1RMb6KproSYU73cKmXY7KtN0oP7QFtYUnUJV9EE8++SReeOEFJCYmilZCUlFRIXrka2trRW88TR7VRiWKkXRahbU3ofkmDkuNyGEv2r5KXAUAzuvp3WKMsX6Bp/ezs2bTpk3iLU2+o4l4jPUHUoVKtMDQjTLLaRGmQFeBaCOkE1i6Nebr64vRo0fDXBUG34G9c3VfEjB8GqwVxSjd+zMKN32NQ4dm8sJJjDHW24r1vXv34t577234mEaFqIe98X319/v7+3ffXjKvQxOS64t1ToFh/RVloYdMWYSld04Vo++UMkMTV+l9ulJJI+0xMTFigunPb21Bbxc69VLYKktE//ozzzyDoUOHIjo6uqd3izHG+rQOFetUhNPN3f3NUcQj679oQnJ5eblIsqBIO8b6MyrOKbqxtfhGb0BJMJQIYynLh8lUKZK/PvzwQ9ESyRhjrIeL9UsvvbRdi3/U40WR+re1a9eKt7qYYSLVgjHWN1AiTMTcGxCY8hmOHz+Oxx57DK+++mqLoAHGGGPnuFiniaRhYWHd9GlZX0YJF7SAVn2/OmOsb5FrDHjqqafw+OOPY8eOHXj55Zfx8MMP9/RuMcZYn8SrW7But3v3btECYzAYoIlI4CPMWB9DOfC0CN79998vkmw++eQTvP322yKXvvGN5q4wxhjrGk6DYd2OVq8lU6dOxRpe7ZCxPsdeVYGbPt4pMtfLB0xByZ61ePCZf+Dl30qgiUwUj7EZS7H24UVe36fPGGM9jYt11q1olO2XX34R78+YMQNrthn5CDP091Hotub7dGQ+UG9BhTqt7Bo6/XI4zCZUpO5EwabliLv6Yaj8Q3t69xhjrM/gYp11K+pfraqqQmBgIJKTk4Ft2/gIs36t8Si0JzV5mV7bMkZJN+FzrherttbkZyNn5ZuIv/rPPb1bjDHWZ3DPOutWa9asEW/nzp0LKbfAMNZkFNrTTaHz7nUpKPEpeuGdUOj9Ya0oQu4P78LldPT0bjHGWP8eWa+srBQjqFFRUd27R8xr0bLpv/76q3j/wgsv7OndYaxftqf0ZELMgIvvRvbyl1F9Mh1F21bCdff0nt4txhjrv8X6jz/+iG+//Raff/65+Li0tFQkgNCKfKx/ohVLzWazOIGjlQ3z8vJ6epcY67ftKT21YmvUBbcgZ9VbqEzbhVWrVuHuu+/u6d1ijLH+0waTlZWFlJQUuFyuFtvWrVuHv/71r925b8xLW2Dmz58v+lgZ6+36entKT6C1FULPWyzepzjHbTxvhTHGzl2xTkvIjxkzBhEREfj3v/8tRtPpxhi1RdX/U77gggv4gDDWjwWOOR+GgWPFwA6tcEoDPYwxxs5BsX7ZZZfh2LFj4o8v+fnnnxEcHIwhQ4bg9ddfF20wNTU1ndwV5s1oxVJauXTQoEGIi4vr6d1hjPUgurIWMnURhg0bhurqajz44IOoqKjg7wljjJ2LNJiEhASxat0DDzyAiy++WCyAc+2118Jut2Pt2rUiso9GVlesWNGZ/WFe3gLDo+qMMSKVyUVrJF2JpdVMH374YVitVj44jDF2Not1ytCm9peMjAzxMS03TRF9Tz75pBg5WbhwoVh2Ojo6Glu2bOnovjAvVVBQIOYy1PerM8YY8fX1Ff8ztFot9u7di8cff1xcgWOMMXaWinWLxYK///3votXhrrvuwtGjR8UIenFxsdhOf5CpVebdd9/Fyy+/3JGXZl7s+++/F72pY8eORWgor1zIGDsjPj5e/D9QKpXYuHEjnnvuOTidTj5EjDF2Nop1Wj6e4vhohGTx4sXIzMzEVVddhZCQENxzzz1IT08XOdu05DzrH+if7sqVK8X7ixYt6undYYz1QuPGjcMLL7wgFkqjOEcabXeXKsYYY6wbetZp4tDo0aNFuwO1vdCkIZpoSovgpKamYtasWQgKCsLzzz/f0ZdmXohO3OgEjq6qzJ49u6d3hzHWS9Fgz9/+9jfx/qeffor33nuvp3eJMcb69qJI9ahImzNnjmiFoUmmr776qphsSC0zrG+PqNPqjsuWLRNXUiZPntwixpNXf2Ss/3K3QuyoUaPwu9/9TuSv/+c//xGLqNFVWRpxZ4wx1s3FenJyMuTylk+nnuWbbrqpsy/LvAT9Ez7/uS+Q8/2PcDnsMJcHYc1bTScV8+qPjPVfnleIDURZ1ASUpmzAw8/9U9xz33339cg+MsZYny/W6VaPWmN0Ol137RfzArUlpxqWGDckjmqxaqnNWNZDe8YY600rxDYXNvNqyNRaFG1fJdph/P39sWTJkh7ZR8YY6/NtMPWSkpLEjfUPNDms8sgO8b7/sKktCnXGGPOE/l4ET1oIe60JOLWrYcLpDTfcwAeNMcaa4UZB1ilHjhyBpbwAUpkCfkMn8VFkjHW4YA8ccz6uu+468THNd/r444/5KDLGWDNcrLNOofg14jt4vLiczRhjnSnYqf3ljjvuaCjY33//fY51ZIyxs9EGw/qPkpKShhVqA0bO7OndYYx5eWLMRRddJGKAaQXs1157DadOnRKpMfXtdeHh4ZwYwxjrt7hYZx22fPlyEd3oExojJpcyxljXE2NiUBE3HcU7V+PFt/6HNzceRciUS2CvKsfahxchMjKSDzJjrF/iYp11SE1NjSjWid+wqXz0GGPdlhgTet6l4v28DZ/CmLEXEqkMwRMX8BFmjPVr3LPOOmTFihUwGo2IiIiALnYoHz3GWLfyH34eoi6gFhgpKtN2IX/9p7BarXyUGWP9FhfrrN3oHyatWEquvPJK8c+UMca6m2/SOEQvvBMSmRymnKN46qmnxFU9xhjrj7jaYu32ww8/oLi4GMHBwZg1axYfOcbYWaOPH46YRfdAKldg3759uPfee8VVPcYY62+4WGftYrFY8O6774r3aeESpVLJR44xdlZpo5MQeeFtYnXsAwcO4Pbbb0dRUREfdcZYv8LFOmsXmlRK/yRDQ0NxxRVX8FFjjJ0T6uBIPPTQQ6JgT01NFYso7dixQ8Q7Nr5RQhVjjPVFnAbD2mQymfDBBx+I9++8804eVWeMndN4x79udAGjrsapNR8i40g25l57GyLm3QyfkGjxGJuxlOMdGWN9Fo+sszZRoU69orGxsWLxEsYYO9fxjtqoQYi/7nFoIhLgtNuQt/YjWMoKRNRjXU47Y4z1TVyss1ZlZmaKVQXJH/7wB8hkMj5ijLEeIdfoEXPZH6CLGSoK9txVb6LiyA7+bjDG+jQu1plHLpcLL774IhwOB2bMmIFp06bx0WKM9SiZUo0BF98Nv8ETxN+oU+uWonjXGu5ZZ4z1WVysM49WrlyJlJQUqNVqMcGLMcZ6A4lMhoi5NyJ4/HzxcfmBTXj66adRXV3d07vGGGPdjot11gKlKuzatQv/93//B5vNJhZAovsaJy/k5+fzkWOM9RiJVIqQKYsQdcEtoninv1k333yzaN1jjLG+hNNgWAu5ubmYe+N9sFaWwCcsFh8Uh+DDt7Y0eUxNXqaY6MUYYz3JN2k8JDIFAtK+R3Z2tlgH4sEHHxQRsxKJhL85jDGvxyPrrIX3338f1spiMZmLekNVfiEicaHxTaHz5yPHGOsV1MFReP311zFlyhRYrVb8/e9/xx//+EcUFhb29K4xxliXcbHOmvjmm2/w3Xffifcjzr8BCj0X5Yyx3s/f3x///ve/8ac//QkKhQKbN28Wo+uUZkXtfIwx5q24WGcNtm7dKtJfSODYuTAkjuKjwxjzGlKpFNdee60o0EeNGoXa2lq88soruPzyy/H9999zYgxjzCtxsc6Ebdu2icQXmkg6a9YsBIycyUeGMeaV4uPj8c477+CJJ55AQEAA8vLy8NRTT4mR9i+//JJTYxhjXoWLdYaff/5ZXDqmS8WzZ88Wk7N4YhZjzNtH2RctWiQiaO+9916oVCqRFPP8889jzpw5ePTRR7F69Woxob4+5YoGKxhjrLfhNJh+jP4xvfvuu+JGqFCnuEaelMUY8yYul7PVONnk5GSUjbwStYXHUXFkO6zFxTiwdDleW7ocMrUWuthhUPmFYs3/3YXBgwef031njLG2cLHeT9EoEi0isnfvXvHxkiVLcP/994vRKMYY8yb2qgrc9PFOKAyBbrfXR83q44cjeNJCVJ9MgzF1N4yZ++Cw1MCYsRcuhx3XXLMRI0eOFDcq2ocMGYKYmBj+u8gY61H9olivqqrCihUrxCXQ4OBgcWk0Ojoa/RGt8EeTr5YuXQqz2QwfHx888sgjWLhwYU/vGmOMdRoV6hQr647NWNbwPrX46aIHi1vY7GtQnZsGU/YhVGXth8tlxuHDh8WtHrXPREVFNdwiIyNFHzzdKIGG3ur1+oaCnq5YtmfRuPDwcD4JYIy1S58v1o8cOYJ58+YhNDQUF198MTZs2ICHH35YFKs02ai/KCgoELGMdCsuLhb3DR8+XPSnh4WFiZH2erw6KWOsP5DK5NDHDhO3gNGz8I/pgeJvZUZGhrhlZWXBZDIhNTVV3Fwul3he8zk99LFGo4FOpxMF+M5T1ZDr/SFVqCFT+UCqVMMnPA6asDjxeJuxFGsfXiQKf8YY69fFOo1wXHPNNWI0ffv27VAqleL+e+65BzfddBMmT57cZ/9YWiwW8c9m9+7d2LRpEw4ePCj+0dAk0uxqibgUfDA2Gbd+ewwA3c7g1UkZY/2xlebBH8pPt9IkAwOT4Up0ilF5Kq5tVWWoPpkOl90Kl9MJR60JdnM1nFbz6VeoAlAoPqbiXFJR1OT1g9Ua+CWN75GvjTHm3fp0sV5fpNKKnPWFOqHkkzfeeEPc/+STT8JbTjxOnDiBmpoakR1Mt/r36W1ZWZkYMaf3aZScWn4cDkeT1xg3bpyYRPr8fgdU/iEeP1fjS8aMMdafW2loBed61bnpUBgCmjzG6bDDaa6Gw1Ir+t+prUYiV4pRe/qYineHuRo+obHn9GthjPUdfbpYpxXsyMSJE1tk8FJbTP32jrjlllvE6nj13F0Wpffr+xfpLX2s1Wob3m++jQpxGvGm0XBaKpveNv6YbtR3n5pXBonM87fM5bAhLlDXsH++vr5ISkrChAkTxC0oKEi0uEgOZnb462aMMdYSFeVSrS/kWt+6v8N2e4uCnjHGuqJPF+s0Ek3ctbpEREQ0bHeHCmW61SsvLxdvv9+4HZJGiSlOuxUSiQwSmczja7kcDsQGG5oU+R1FxTtdeoXECalcDolCBZlcBalSVfe+SiPSDEp9dFD5h0LhF4xaHx0KJRJs2lMJ7FlX93UVnYQ6JFqM+HhiLssX28/2Y87V5+ltj+lN+8JfE38ve/PPVV/9+aSrlzQAYzQaUVFRIe6jq6OMNUaDgfRzQvUKJ7X1b326WKcWkfrZ/M2p1WoUFTXtKWzshRdeENGGzVnL2p7l7056eUGnnscYY6zvGfL2Q00+jourm3zKWHO0cBclEbGeV1JSguXLl4sWaxpEpe4FmgNJcyObu+6660RnRHO0SjzNnewIiau+j6MPuv322/Hee++JEQuK2Gps9OjRIsYwPT293SPrsbGxItLLYDCgr6Gz96FDh4r0HIoh68v4a+2b+PvaN/X17yvNMZo0aZIoyPri/xbWeXTlhWKm6eoLtbWynkWx17/73e9Ekh4V6DQQ/Omnn4r1aihh8NJLL20xKEyF+a233trkfqolaQ5hR/TpkXU6IOTkyZMtinX6A0kFuyf0TXA3Ik9nt33xDyr9UahvGeqLX19j/LX2Tfx97Zv6y/eVvra+/PWxzmseFcp6BtWNFEry2GOPNbQl3XbbbZg2bRpuvvlmMSeQ1q5pLCEhoVtiwvv0cpUzZ84Ub7dt29bk/rS0NJGcUr+dMcYYY4wxT+6880785S9/aTJ/gN5fsGABKisrceDAAZwtfbpYnzJlCsaOHYtXX31VRBo27keny6l0OYMxxhhjjLHWeGpFogFg4ufn12IbrfFDLTM33HADnn32WRGr3Rnyvn7p6PPPP8cFF1yAMWPGiLd05rNnzx589tlnIr6R1aGWn7/97W9uW3/6Gv5a+yb+vvZN/en7yhhrH7PZ7HbyZkfRtM3mbUae2qDd2bdvn6gnqQedJps2RsU7Lb5J9SfNe1y2bBmee+45vPXWWyIGvCP69ATTxt/Un376SSwdTVnjF154oXjb0b5JOquiSx3cV8gYY6w70JwqmkTI/1sY1x3tr+nCfHxRia4X6zqdDiaTqcl9NDjw1FNPtflcaqemYjwvL0+MoI8cObLF9sYpMbRQ5bx588QaP6mpqWLNn/bq0yPrjWfkLlq0qKd3gzHGGGOMdYHVahWF+j8xBT5dKGNrYcdDpm0tkpjaM6pOJ9fz588Xz12xYkWLQp00j3OUyWT485//jA0bNmDVqlX4wx/+0O597RfFOmOMMcYY6zu0UgV8JJ0vY6UuCeDseBITxclSW/WhQ4fw1VdfiQmm7RUWFtapRdC4WGeMMcYYY15FKgOkXUi1lFITuLNjz6GwkoULF4q5j19++SUuueSSDj2fMtlJYmJih57Xp9NgGGOMMcYY6ypaKJMWPqL+dCrUmy+C1NjPP/+MlJSUJvfRSPxf//pXDBgwAJdddlmHPjePrDPGGGOMMa8ikUog7cKCURJqg+mAF198EevWrUNMTIxIdqFbY4888gjGjx8v3qe2mvvuuw+FhYViFJ3aXmhUnSakfvDBB9BqtR363FysM8YYY4wxryKTAbIutMHIOpiFSKPhw4YN87idVlmuN2HCBGzZsgUnTpwQyS8UvDhkyBBR6HcGF+uMMcYYY4y1Yvjw4eLWEVScd7ZAb4yLdcYYY4wx5lWkXWyDEWkwXoKLdcYYY4wx5lWk0m5Ig/ESnAbDGGOMMcZYL8Uj64wx1susXbtWrHJXTyKRiNXwhg4dKparltKQUg/55ptvcPjwYTzxxBNn/XPt27cPn3/+udttd999d5Ne0P/85z84efKkx9d68MEHERoa2vCx0+nETz/9JPKS6fiOGzdOHFt6v7mOPJYxdm5IZf2nDYZH1hlj7CygyK5HH30U+fn54uNdu3bh6aefbtdzN23ahL///e+w2Wzw8/MTMWCZmZm47rrrMGLECJw6darHvmerV6/Gq6++ek4+F+US03Gora0Vx6HxjZbubkyv17d4DN1oX+nWeAlxilGjCLXrr78eRUVF4kbvz507Vyx60lhHHssYO3ekktOtMJ29eU+tziPrjDF2Nnz//fd488038eyzz4qP33rrrQ4X2bfffjsGDx7c5OPRo0fjueeeE6/dX9xyyy0YNWpUq4+56aab3J70mM1mLFmyRBTu9Sj/mBYsOXDgQMPxvffee0XSA2Ulv/766516bFdQtBtjjLnDI+uMMXYWrFmzRrRKKBQKUYj9+OOPWLBgQZdekwpWHx8fHD9+vMn977zzDt54440Wj//ll1/E6L7Vam2474svvsALL7wg3t+/f78YuaYWknq7d+8WI9F0kvHpp5+ioqKiYdv7778vrhDQiDK9bv2t8Qhza88ndKWBnkMnM2cbLT5C7rjjjob76Fh8/fXXuOCCC5qcCA0cOFAsI05fo8lk6vBju6q7XoexftUGI+vazVtwsc4YY92ECtT6ApZWuistLRXv33PPPaJIpUKWPqbiujN+++030RJCPdONUVG8dOnSFo/funWrKMYbF+urVq3Cf//7X3H705/+JNo86vvjaRR55syZOHjwIBwOhzjhoBX5du7cKbbTqnvUTkK92o1bTep7t9t6fn17EO0TLcfdXp999pnokf/HP/4hFhppj6qqKixfvlwsRDJt2rSG+8vLy8Wy4REREW4XNaHjSyckHX1sV9HnYowxd3iCKWOMdRNfX18xkn7kyBFUVlZi9uzZkMvlosCMjo4WE0SJTqdr1+v961//QkBAgBiZpxYamuRIrTBU8He1MExPT28omGn0m4poGmF/++23m4xE02PpayHXXHONeE5WVlaLfWjP8wkVvjSyX78sd1toYi29Nh2/7du3i89LJwQ00ZWOtyc0MZVG/Ol4Nebv7y9OOHJyclo8p/6+7OxszJo1q0OP7aqSkpIuvwZj/YlMWnfr9PPhPbhYZ4yxbnL//feLt88884xolfjrX/8qPl6/fj3mz5/f4SK7ftIkocmmVJxSEguN0icmJnZ6P6mIpVH1evQ5aFlscuzYMXFyUD9aTgUr3dpCveHteX5ISEi7j8N5550nimEa0W+clHPhhReKNBi6ouAJtahQoX3jjTc2uV+pVGLx4sX46quvRNpMfS88TWb94YcfxPs0Yt7Rx3ZV89Ymxlg7FkWSdiENBtwGwxhj/RaNpFOhWR/7R20gU6dO7fDr1I+i041G2am9hEaZL7nkEtFm0lkajQYDBgxoch/FIN55552i1SQ+Pl6MjrvrOfekq893JzY2tkmhTmgeAPWQUwHtqVCmKxvUMnT55ZcjMDCwxXaaFDpmzBjRHkMTU2+++WZMnz5dnFCRxiP2HXlsV9CVDsYYc4d71hljrBtQH3Z9YU3tGjRSTe9TcojRaGyY7Nl4MmdH0eTSiy66CEePHhUFaT1qvbHb7e2etOhppJwSayj5hE4SKLP8tttuE4U3XRloj64+v73oNelKQ3FxscdRddK8BaZeUFAQduzYge+++04U4hMnTsS2bdtw8cUXi+0Uj9mZx3YFnVzQiR1jrH0kMppk2vkbPd9bcBsMY4x1A8pCp/YPivijEd/zzz9ftIJQfGBcXJyY6Fjf2tIV9UV5dXV1kz5wKuCbo3aNjqJWj/p2DyqGqRiltp45c+aI+2hBptZiBtt6fndIS0sTLS7UUtMcFfEff/wxBg0aJHrbPaHvDc0poFu9P//5zyL1hWIZO/vYzqKJvnRSMGXKlG55Pcb6OqnIS+9KG4z38KZ9ZYyxXot6qGnknApIGn197LHHGmITr7rqqoZRd3d54O1FEzUpzYUmXTbOHae2DJqA2jgpZfPmzWKUuyM90xTl2HwEnk5CGi9AFB4eLvajvke9o8/vSHQjTahtPtq8YsUKkbRz7bXXQq1Wt3gOHR86SfA0qk5ogan6xaoaT0ilBZ9eeeWVJiuTduSxXfXee+/x6DpjrAUeWWeMsW5Eeeq0CA+h1S737t0r+s07oz4NhlABSgUiFahffvllk0KVJlFSCgu1ZlxxxRWi7YZGmKlgbe+qqdQD/7vf/U6k19BoMfW1//rrr6KVhlp86l199dV48cUXRd84JbpQIf7kk0+2+/n10Y1U7FNWeWtWrlwpJu3SQlB0EkStP9RSc9lll3lsJ6IWGJoYSr3lntCVD+r7p6sdlDJDJxk0gXTZsmXi6+rsY7uCrhTQVRnqxaeTO8ZY66SnVyLtD6PVXKwzxlg3oVF0Kljriy3K+qaYwo62NtDkxcbxjjR6S600FJ1IMYFUEDdGPeuUqU6jytQrT4UlTcSktgoq6qkQrEev4W5/EhISsGfPHpEFT8Uo5YtT6gq189Dr16P4ydTUVBHhSLGMNPJN+9fe53ckupGy4GlUm64Y5ObmiqKd7ktKSnL7eGrPoeNz3XXXiV5zT5KTk8V+UrIMpc3QZOC5c+eKqwBdeWxX0IkVrUr78ssvi4m1EyZM6NbXZ6xPpsHI+kcajMTFaxy3C41U0ax/uvzb3X+kGWOM9U80EZdG7Ck1h6440EkBXRmgE5oZM2b09O6xHsR1R+vHZVX8XGhlZwYCOqraYcPFWeu8oq7zpqsAjDHGWJ9EVyeeeuopMf+ArtBQDv5rr70m2pkYY54XRerKzVt40a4yxhhjfReNqFNOfX0b1dKlS0VLD+X0M8aaohaYrt68BRfrjDHGWC9BE3YffvhhvPTSS2JlWeqT//3vfy8m2tIEVMZY/8PFOmOMMdbLUKb7N998I9J3KNueFmKiycu0SixN7nW3CBZj/TENRtqFm7fwol1ljDHG+g+a9EaLL1HRvmjRIjHqTok7lFNPK9m+/vrrSE9Pb3WRKsaY9+NivZ+rqbbii4/2YM+OnLP2OQrzjVj69m/IyS5zu722xoovl+7Frm0nzto+MMaYt4qKisITTzwhcudvvfVWkb1fWlqK//3vf6Kn/corr8Q777wjFqZirF9FN0q7dvMWHN3YTyOUaCRm8/pMLP84BcbKupUIh4+OwJLbxiMssnu+PovZhpXLD+GnlUdgsznFL8as+QNx+fWjoNWpxD5s/SVLFOqVFXX7MGxkGJbcNgER0b7dsg+MMeYN0Y1Hjx6FXq9v9bG0eiy1xFBCDC04RSu8Ur4+pcfUGzBggMiCpyz9MWPGiEmrzDv1tbqju4/L+hHzoetCdKPJYcOcAz95xfHlYr0f/tIUFVThzZc3IyujtMU2uVyKBYuHiYK6Kw7vz8d7r21DWWlNi216gwqXXjMS23/NxrG04hbbZXIpLlg0BFfdMKZL+8AYY95SrCf/+QPIVBqPj7MZS7H24UWIjIxscj+tEFtfuP/2229iJdl6tCAWLT41efJkUbjHx8eLYp95h75Ud3QnYz8s1nkF034oK6PEbaFO7HYnNq7N6HKxvue3XLeFOqkyWrB1Yyay0t3vg8PuxC9rMrhYZ4z1G0pDIGRqbYefRyvdUv863ahwp5hHGm2nW0lJCTZv3ixuhAocWgWWCvexY8di4MCBXLwzryXtYiuL1OU9bTBcrDPGGGN9ABXulCJDN2ozzMjIEEX7rl27ROwjjSBu3LhR3OofP2rUKFHADx06VNy02o6fMDDWE6Syulunnw/vwcU6Y4wx1gdXRB00aJC43XLLLaLPnfri9+7dK2779u0TI/FbtmwRt/rnxMTEiKJ92LBh4kbP5753xnoWF+uMMcZYH6dQKDBixAhxu/nmm0Vve1pamijcDx48iMOHD6OgoEAkytBt9erV4nkUF0ntMlTAJycni7fc+856A5lEAlkX2mBkTm6DYb1YQKAWSqUMVuuZiUiNaSL1KDaaEWxQu91ud9bC7qyGWh7kdrvLYccgVT42SFxweegJC6spQ57MBbPD/fYopQWm3CLookPcbjeXVqIqKx/B4wd72AcHag8egs/IEWK0yO1jLHmAIhgSqfsJKqXmSsilMvgqdW63m6xW5JtMGBgQgM5KP1qE6Fh/+Pi434dThVWQSaUIC+7cpWmnw4mjhwoxZHiYV8VUdVRtrQ25x8sxaIj7nxfGWFNUhNe3vtQrKyvDkSNHxO3QoUPibUVFBVJTU8WN8t7rJ64OGTKkYQSe3tLEV09/axk7G6RdXNjIm+ZacxpMP52VXVJkwqcf7MaeHbkN96kMKmgmRmFvsQlKhRQ3TY/HNZNjoZDX/US7XE5UWI+ixLwHTpcVBuVABKvHQy49k2DgSNsN64o34Co+CUvoIHxaMBY7s85cwInWODA6Zz+M2w7AJzII5TNmYofpTDGsl7sw3XYCNT9uhkypwPBHr8XwP18FmaoufszldCL1rVVIefJDWMpNSFhyPsb9/Q5ows4UzDUp+1Hy1tuw5eRCNTgJwb+/C6qBiQ3bXfYKoHIzYDkBSHWA7xRIfAY1bLc4bNhaeAB7S9IhhQQTQ4ZiYsgwUbgTp8uF9ceP47Mjh1FltWJqVBRuTB6OAB+fdh//4sIqfPr+buzdeRJ+/j646qYxmDozvmF7rdmOz78/ilXrj4kie/G8QbjiwiSolO1v0Es7XIhl7+5CzvFyxMQH4MY7JiBxcDD6mq0bs/Dl//aiorwWYyZE4bpbxyM41P0JFmO9NQ1mzLPftTrB1FpZgh/uOq9FGszZRH3v+fn5ominkXe6UdFeU9MyPID+L9YX7vVvg4LcD+iw/ll3dPdx2TbhQujkXUiDsdswZeePXnF8uVjv5780h/blYdn7u+GM8cUhuwOVtbYm26MDNfjDhYMxMs6OoprtsDibLmwkhRKB6tHwrQ6EfeU7cBza1vQTSKQojJyCD/bHINl4CubVm+C0Nv0chglDcTBhDKJhgs/PG2EprmiyXZ8QgQn/uhuqAAN23Pc6yvYda7Jdoddg1JM3YNC101D2wUeo3tpsH6RS6OfNReBN10IqTQdM++i0ouljlBFwGabhiKkKv+anoNpel/tej0bXZ0eMgdOuwQcH9iOrouk+quVyXJ6UhIsSB0LRyum61WLH918fwupvj8DW7MoGjQrfcMd4ZBWZ8NHXh1B2Onu+XnCAD3535QhMHdv6P+vyshqx0NX2TU0XSKFBrykz43HVjWPECYK3O5FVho/f3YmMo03jPxVKGS5aPAwXXTYMShV3+rHerTcX6+44nU7RJkOFe30RTxNZqSe+uZCQkBYFPE1qZf277uiu47Jj8oIuF+uTtq/2iuPLxXo79eVfmiMnK3D7u7+1+pgPH8yHC3aP26OWp0KSesDj9oyK4dj7IRXJ7vkOjkZl6plRfneUvlpYK6s9bp9x5yi4jqd73B76wAJoh7tv/SElLn98WNLqLiD1pK8YTffkwfETMCUqyuP2X35Kx0dvej7WARF6ZNqdre7Dhy9diEA/z8X2269swbZfsz1unz4nAbfeNwXe7pHff4eCPKPH7TffPRGz5p+5YsKYt+es/++GCWJhpN6GFmWiAj49Pb3hlpubK0bmm6OTjaSkJAwePFi8jYuLg1zed06q6xeu6g59ue7ojuOyc2rXi/UJW72jWO87vyGs01zd+CjPT2/9+e7+qHfwJdqxj23sA7quzT1o4wHtOAxtfpKuHQXv4eozXwljVIyXw6lqejWtKQlu/TYDEklmLz5cfoBmAjBqApzDLDCX5MFcnAtLySmYi0/CZipHRnEaNu5LA7BSPEMik0EVGAmf4Giog6OgDomGXOfvlf3vnhauYqyruFhnjDHGepg2KrFTiyL1ZuqgSGDw+IaP7TVVqC08jtqCE6gtzBZvHZYaWErzxA2pdVcd5Ro9NJEDoY1MFG9VAeGQeNNsQHZOSKQucevK870FF+uMMcYYO/sFh0YPfdxwcau/omqtKEJtwfHTt2yYS06Kot6YsVfcCLUHaSISROGuixkCVWCEV468M9Zni3VatGHVqlX48ssvxSIOU6dOxbJlyzr0GpQX+5///AeZmZkIDg7GNddcg7vvvltEV/UHFN8nlUm71nqBLv5hbOPpEknboyaSNqIHXW18kvZ9nW3sQ5vbu9Zq057/P209ps197CP/5KRtfB195etkrK+i31GVf6i4+Q2ZKO5z2m2icK/JO4aakxmoKcgSo+9V2QfFrXALoND5QRczFLrYYdAOGAKZ0n3MMOvbJNK6W1ee7y16/a5OnDgRK1euxI033ojS0lKxaENHvP/++1i4cKEo8r/77jvcf//9ePLJJ3Hbbbehr7PZHFi5/CDuvXk5vv5kn0giaW7rnlN45b/bMdZfgyCdqsX2+FANblkI5Jr8IZe0zBOXQQXNZgtKNhXAGZHcspKUyVHuMxxHf8hE2IwRkGtb/lENmjAYtqoaBE0cAk1ky6gvw8BIBIxMgDrED/4jzsQb1lMFGhB63nDsXZUNZ+zglvsgl8M2YCh+vHUdTm6RwYWWE1JqK3yx55oNSPjsJPQSN/uo0uPq+Bg8OdUfQwP9WmzXKRS4ZJABTtk25FWnt+jBtzuc+HzbcfwrJRdRk6OhdpOrHpcYCIndieHBWpH80lx0uB7PPHAeAjxMLq02WbD0nZ04sPcUBg0NaXFyQxGQcy9KwjU3j0Vf8MBfZmHEmIgW9/toFLj2lrGYNiehR/aLMdZ5UrkC2qiBCJ5wIWIuux+D73wZ8Vc/jNDzFosCXSKTw2aqQPnhbcj94V2kv/sITv74PoyZ+0Whz/oPicTV5Zu36PVpMBQHRSuvET8/P4wbNw4///xzu55LM3wHDBiASy65BB9//HHD/e+99x5uv/12bNq0CdOmTeuTs7L37TqJT97fjaKCqob7AoO1oogZPyUGOXlGvPv5fuxPLW4SeRedHIJ9RSb4KKWYN0GJkKhc+ik5/QgXknwD4Ksqg9NlgeakGra/r4ar5Ewih3ZINAwhVqA4B9bAgdj+dQUKUs6cYKnDAqCPDUPxjiPQxYVB6a9H2d6Mhu1StRIhE4eg+LejkKoUCBgRj8KthyjcvOExwZOHwZSVB3NJJUKmJqNsfxZslaaG7VGT45A4RAnnqVxIY+JxZFsZig7lNWwPGBGOWR/Mgz7MCIdTi/2vn8CBf2w+sw8GNbRvXIWcZBVkEimmhoZjTJAdjWvfrSfV+PhwOcpqzZgc5Y/hYYWQyywN23WKQAz0nQhfZTD2ZJXiX6uP4njxmSSbIKUMo6wunDhQgIAgLQy+KmQfOxOLKVdIETk6AkfzjKLIvmbhEFw8OwHy05n3jTmdLmz6+Ri+WpaCKuOZfQiL0EOukOHkiQoMHhaKJbePFwsw9TUpO3PFzzqtHdCXoilZ/9De6EZWx2m3oubkMZhOHEZV1gFYjaUNh4baZfyHn4eAkTOg0J37v3XdHa/pbXXHuWI8fVz2zLygy2kwYzeu8Yrj2+uL9cY6Wqx/9tlnuO666/D999/joosuari/qqoK/v7+YnT9rbfe6nO/NN99cQDffLbf4/bRM+Pxa2oRHI0K4MYGxOkx8ZJKSJVnCr/GVDI5xn9zFPb1e9x/AqkENTHJ2P2W54jC8NmjUbDpAFx291GK+sQIWMtNsJS6j+aTadXwHxaLkp2p7j+BRIKkKycg7UvP+zD0nhlI/2g77NXuoxi10wfhorXXQqtwv49muxTb8s2Qyj1f7ck8lowP1nvOg5zu64OibTmw291/L/xCtHjqHwvg7+v5Mu8b/9yE37ac8Lj9kiuH4/LrR6GvX0XKP2XEgD54MsL6Ni7WO4/KF3NRDirTd8OYvkeMuBMafQ8cNQvBEy+CVFG3oN65wMX6uWE8XY/tnd31Yn3MBu8o1nt9z3pX7N1bNzklOTm5yf16vR4xMTFISUlBX1RW2nJ1ucZKy2o8FuqkttrssVAnFocdrvRW2pGcLpTltBZBBthMtR4LdWIuqoTN6DlT3VFthrXizGh6Cy4XKgpqW92Hop0FHgt18TmO5Hss1Ila7oSvjwVVrVx5LTa2fhyMDqfHQp1UldW2Wqi35/vtbjS+r1EoZFyoM9YPe959QmPELXTqYtHTXrr3Z9TkZaJkzzpUHT+EmEX3QqHnk/i+SCJ1QcppMN6vqKhIvA0IaNlrHRgY2LDdHYvFIm6Nz+QYY4yxrmj+v4Wu9BJq55BZWh9gYG1HRUbMvRHVOako3PINzEW5OPHNq4he9Pt2hRh0R846Y2dDnx5Zp2WRibvVxOg+h8PzqOkLL7yAp59++qzuH2OMsf7F0/+WthdFYu1F6TLhs67FyR/fQ21RDkzHj0Ad2HIyuie0eNOnd8/r1GqxvXGF2b5KIuliGowXBYb16WKdetzrRy602qYTd2iknPrWPXnsscfwxz/+scnjaRIQY4wx1lnN/7ecOnUKQ4cO7ZOLIvUke61JHE+nzQKfkAHQRLRMEmut95yKbl6JtHeT9KNFkfp0Myv9ASQZGWfSRojdbsfx48cbtrujUqnEhIPGN28xeXocQsP1brf5BfjAx+VCUqjO7XaVXIo4pQ/MKUFA3YWJZlwYoAuE6e5ZQKCv29fQzx6Bsc+NQcRs99F52shA+IdpEDY5ye12SoQJHJOI0GkjxGRVd4KnDIMmIhAKX/dfh++QAaKfURfnfpRDNyICoa/ORNSfZ7ndLjOoEfveYuSapI2DaJowWqUIUMmhkLifwFReHoiiCjkiPCSTBKrlMMhliBoW4na7XC7BwMHBWP3tYdjtbr8Z2LXtBFQqObQ69/swIM4foydEobPsNgd++OaQuNH7rG+rqbbisw/3YMsvmS3iR1n3aP6/heZQse5lKcvH8a/+JQp1GmWnnnbGvFmfHllfsGCBaHf58ccfm0Q0btiwAbW1tSLSsS8anByK51+7GGu+PYKVXx2E1eIQUYCJg4KQdawUaQcLxePGDA5Gjs2Okqq6SZbDIw0wnahAxt580MJxkdsMGLpYBklEpdgeoNIhQCVBraMQJaGA4qWFCPqtHLJ3f6RYISiigxD2h9lQ+NPjKzB36SgUH5qADTeugrnIBKlSgdjzk1GbegyVOw+K10yYn4yio4WoyqmLkAyaNAQ1ucUo2FiXZmMYFAWZWonyA1lNPi7edrghY53iG4voY5dLfOw7eID4uPJoDqQKOUKnj0Dp3gzYTbWQalVIfP9yOM5Towa1UPwhGsNuuhsn7/wJlVvrPseAJ+bC5454WCS1yKw6jvxaAwYaguCvqsuptzikKKytQK2jbp9DNCo4XUHIr6lLfbFa1NiyNxq/HKyC01UBmVSCUTH+SM83osbqAM33HB+gQ25GKQ6b647toLGRsOdVojy/btJsXGIAjJUWpB4qFDeKZlxy23gkj6q7lHsqtwLL3t2FIwfqJvpqtEqRrZ5xtEgs/qTVK3H5daMwa/4gEf3YGfv3nMKn7+9GQV7dfI1NP2fi+tvGYcSY7oklY70HFeab12di+ccpMFbWtWL88lMGbrxjAmLiW875Yaw3sldXomT3OpQd+BUupwMKrS+iLrodkn6yAGJ/I+lHiyL1qejGpKQkMVq+YsWKhvt+//vfY+nSpVizZg3OO+88cclx/vz5UCqV2LVrV7tXMfWm6MbGSour8dWnKUg9WIiykpapIUqVDLHDQ1FbZkZu1pmc78ZGTAnGtJuAGgdNyG3546Kz65CYVQnDIBskaBmN4nT54NCbxSj56QjMp1pO6pWqlNCMGILKk5UoTWl6FaRe8OShotgv2nIQLofT7Ui6JjIYJbtSYatsmSKjDvVH2LXjoP5jAizSlsdBAilU+wFpvA9q9O5TaILVYQhSS1FhzXF7HBRSX+xIDcXS9TZUmVsuQOWnUWBwsA7GzHIUNspcb5zaMjLaD67SGmQfcz9RadykAQgO02HtqqNwOFruQ2iEHkNHhOGK60ZDZ2i5yFV7WMw2vPHyFpHV7w6N1N/9x/OgUnc+Mov1HpRP/99/bEJWRsufOVpYa84Fg3DDHRN6ZN/6A45u7DpzSR7KD25G+eGtcDnq/vbqY5MRPufaTuWtd3cEY2d5a91xro7LoYvmQX96HZ7OqLLZkPzDWq84vr1+ZJ2K7dWrVzd8g7Zu3YrY2Fjx8SOPPIK777674bEnTpxo0Yf+6quvQqPRiFF2ekvflAsvvBBvvvlmuwt1b0YLIUVE+mLbL9lut9Oou6nQhLxcz2k3B7YVY/wSGp11f15nkpvgM0wPia1uxL45qaQWuiA5Trop1InTYoW1sASlKcc97kPx9iNQGLRuC3VCo+gKvcZtoU7MheVQjPR3W6gTF/X8jNGjxlHleR/MBVDKpB6Pg81ZiYNZEagyu090qKixwVltc1uoE2p1ySuvgdlDoU5278iBr5/abaFOCvOqsOiqEZ0u1AmNrHoq1EnKzpNi1D+Yi/U+gRbNcleoExctuLX+GBfrrNdxmGtgzNyHisPbUJNfd1WUaMLiRL66NmaIaIVkrC/o9cU6zZp/+OGH3W5rXpinpaWJEfPGaPXTf/7zn3jxxRdRXFwsRud9fHh1Q8YYY8ybOKxmmLIPojJtt1jB1HU68Y2Kcn38SPiPmA5tdBIX6f2EROISt64831v0+mI9ODhY3NqDFjryRC6Xc6QSY4wx5mV96FXZh1CVdUDkpzsdZ1otKY7RkDQOfkMni/50xvqqXl+sM8YYY30dL4pUh6bRWcsLYMpJRfWJozCXNG3JUxgCoY8fLkbSKelFPMduE33m3YUXN/IOkn40wZSL9X4gLMIAmVwKh4f4P71BDZ3eDNPpVJjmAkN1kEEKJyrcbpdJFKh0+sDz9Q8JtHHBkCrlcFrt7vchyg/qbF+Yiyvdb48PhSpAh5LdmW63U7+675BQlOxMdb8HUik0Wg2q6vvT3VBK1XC4bLA53S9MopRqIBG/MhYPX6UcvgbPv/3UPalTSKFUSGG1ud8HP4MatX5qVFaYPc5BCA7VedxOMY7Giq6tgujjo0RAoAZlpe77+wOCNPDx8Typx2qxI/VwIYaPjuDL0V7AP1ADH40CtTUtJ4eTyOi69SrY2eWtiyJ1ZQGhhtew2XDw4EHs2LEDO3fuFKuLUwZRAP3RDNZh4MCBmDhxIiZNmoS4uLhz8neFFzfq/ST9KGfdq9JgepK3z8rOO1kpov4O789v8k/aP9AHWeml0GgViIrxx7HUYjhPB4tT4RcyOhxbzBa4pMBt85WIHZAvCtp6UkkI1p+qRqnFirlBOizwrYDc2WiyqiIY8A2FROGCKbMaR57ajOINdbGMRB0WgIGzgxHkOgorNDiaE4+07w7BZa/L9JZrVBh+1zgMm2+CVOZE2mY/7Ht9HyxlpyeCSiSIv34ykv9vJHzC5Cj6xYi9D25A5ZHchs8RNGkgxv57BvzG+MBiVyG32oUyS13sIlFKfaCWaWG0lUAKOfTKQBitxQ1FvRQyQBKMPcVlsLucmBIagBB1CZyNinabMwzfHregoNYCX3MwMo+okFtypmiOD9Qg+FQVStNK4ReihXKAH9Jyzpz8+OlViNGrkLMvHypK6EkMxLG0koYTLIVShgWXDsXCy5PF+xTluHxZCqoqz+xDQlIQSgtNqKgwixjHG24fjwFxAZ1OhFm5/BDWfHekIeNdoZDigkVDcfEVyR6TYCj3nXK6KYUocXAwR/95icqKWny5NAVbRb563X2U3b/42pEiDUYqJlazs8Hb02A6m5xSUVEhAiM2bdqE7du3o6bmzOAAzT2j4nz69Okixa29rbB9jbfXHWf7uBy99Pwup8EM+fZnrzi+XKz3s18aShOhLGVKFMlKL4Gt2QgvRf8plXLI9Eoc8JGiyNJ0IZzwABnuuMgFna4G+8ukOFzeND1FIZHgd1E6DFWVQ6qLhETT8p984doCpD7/M4KH+CPakAqZs+mIfiXCsW+7GupgLcbdooLWr+lIsblaiZQvJChNLcfol2cgcHLTf3BOuwuZ/z2JY+/sxNBHzsOAG0JajMRUWrXINVVBIdXAaC2BC02/TrVMB7lUCbtTikNlFpRZm+6DTq7A9HAtlNIabC/ywa7ipmk6EpcEhsoopKU7kWAHCrbltAiRiRwSjDKpBME6JQoOFMJa23RkMzhEC61eJU6qrr91HIJDmy6eUm2yYsXn+3EoJU8U8DnZ5U22U776rPkDcfn1o6DVdS4dpjDfiE/e2y0uC9A+hIa7/9nPy63Ex+/ubMh9bzgOUglmzk3EFdd3PkqSnTvH0oqx7L1dGBDrjytvGC2uurGzq78U6zQumJ2djc2bN4sCnUbSnacniJKAgABRnNNtwoQJUKv5Z6+v1B1n67ikXtb1Yn3wN1ys9yl96Zfmty3H8cY/N3vcrgnwwYHo1lfVO3+hFaVWzzGHj41MRKTWfUQhsR/Mh/V/H3n+BHoD1DNbX3nTMmUBXB6iGIkUgXDCcwyi0apDasUpj9tdkGPXmQF4t+xOP+TVuG/dIQP2BeHwOveRliQ2IQDHM93n29dnr7//1fWt7sPzj/2E9KPuYzHJZdeNFHGOZ9OLT6zF0dOLbbmz6OoRuOzakWd1HxjzRn25WKf2lpSUlIYCndY5aWzQoEFiwUK60RoptIgh65t1x9k4LulXULHe+W7uKpsdg77yjmKde9b7obY6n7qnMcp1lre39zGtPbvrX2hbr9HWsWxzezfsQzd8mW3vQ5vfTu62Y6w/oHaWLVu24JdffsG2bdtQXV3dJEp5/PjxDQV6WFhYj+4rY96Ci3XGGGOMdRoV5D/++CPWr18vCnSr1dqkvYX6zqk4pz50WpyQse4g4TQYxhhjjDH3nDYrqrL2o+zAJly7+qUm84KorWf27NmYNWsWt7ews0aCLi6KdC4uO3cTHlnvh9rqC6SJiehiV4PT1fprONsIOHVJZPRr2Oovk8NFfemt7kTrD2jra3DWfZ2tpYRJms5LdfMAdOlYt+d70Z7HtIbSXqg3viva2geaaMpYd/7M9TXekLPucjlRW3AcxowUsZKo026Fy2GHPcxPxCuef/75mDNnDhITEzm2lbFuxMV6PzR+ygDcet9kLF+aAmNl01zfEWMicP2t41Fod+CV1UeRUdB0EmlEkA/8QuU4kmbB0BgdymRnoiCJRibHUH9/fH38OCYGh2F8sBPyRlmmVD9nVMpxQCNH+O/uxNBVK6EubvoaxinTcGLyYCikciQUnoC+/HiT7eWacGzwHYqSzGqcF+aHBEPTJBbYXFAezYckew2cg8bCNiiIwuCb9OyXmvXIMRXDoAhGrb0KNlfT41BUEYofDviIgmJobDUciqYTVVVVGlT9rELRiWpEzQ/Ayeimk0SVTjkMx0KwN9WIpIlRyEvJh83qaFK8Tp+TIBI3KEHl8w/3tMg1HzwsFEtuH4+2/P6h6fhy6V5s25jV5CSqPgJy9beHYbM5cPGVw0UcZ+PjsHl9pkgHShoWgmtvGSdy3Dvj7j+ehy8/TsGWDZlN90Etx8WXJ+OCS4d26nVZ32K3ObBm5VH88PUhTJ4R16Wkor7mXOesdyQfvaSkBGvWrMG6detQXFwMH0oO81ciNDRajJ5feeWVokBn7FyS9KM2GI5u7MezsmuqrVjx2X6s/zENAUFaXHfrOIyZEN2w3eF04bvduXh3wzHYnA4MHWTAkbIK2E/nsJPBoTr4hVSiGhUYHhAMs8MIs+NM9refUovZEUFINNhQVKvAruJyVFjPFNcKiRxDCqoRt/Ir2CMjkXvJfJSqGqfISBAOPWKP7xWr1P0WMhG7rWY4XGciv6K1/pgWJkOAqgbyHBNkh3YC5kYFvDYQjpHjYQ/3QbVVgxOmGpjsZxJcZBI5dPJAVNqKUGvR4tfUMOw4ceYkhcr8ibF6BIfm07VfyHcH4sDPxbA3ir0cMMgf0tlOlOpMCC0ORfpWMyqNZ45DoJ8akSo5cg8Wijz0G26fgLjEQLe55npfNa65eQwmTYvr0PczI7UIH7+zCyeyypA4OAjFBaYmiyfRYkbX3DwWE8+LRVZGCT5+ZyeyMs6chChVMpHjvmDxMCgUMnRGZnqx2IfsY6WYMDUG194yVvxsMbZ/zyl88v4uFOad+d3SG1SiYJ8xd2CXrxB5q55Kg2krcpFO5vfu3Ysvv/xSTBatj1nUarWYO3cuLrroIowcOZITXM6ivlh3dOdxybpuNvTKLqTBWO2I/3SDVxxfLtbbqS//0hTkGUVBpVS6L9Aqa6y4/ettKKp2P+ojk0qw5Dw5im1NM7YbGx0QgSJznsft4XID5KpKOJvlndeTSxQ4Wu6Dcpv7qEYpJLi9uhS63N0eP0fR6AuQpnO/SiuxW4Pxz5/lMHtY6VWnlGPoXhMKT7qPrKRiI2Z6NPYe8RwXuWBaLO5cMtrjJeLiwioYfNUeFx1qCy1o9f7r27DllyyPjxk3eQD27Mjx2MqUMCgIT750Yac+f/0+0CJcUQN45UtWZ83KI/jsgz0eD8eUmfG484Gp/fJw9bZineIWabLosmXLkJV15u/I6NGjccUVV2DmzJlQqfhqyLnQl+uOrjD2w2Kd22AYwiJa/yH11ShRafFc5NIIvN1lb/U1ap3ulzKvZ5E5IfVQqBO7y4Yah+cfVyc1oFs857qL12hjH2psdpjtnq+Lmax2mCqtrRaptdWtN7E7JZJWezmbL3zUUXTCQN+PtlarbG3OAW3v6j5woc4aMza6wuOOsYs/c6zrLBYLvvvuOyxduhQFBXUDL7Qw0YIFC0SbC/WkM9abSKT0/6Zrz/cWXKwzxhhj/RS1t3z//fd48803RT86CQwMxPXXX4/FixdDr+/aAAJjrOu4WGeMMcb6oT179jRpd6FFim666SYsWrQISqWyp3ePsVZJpC5x66yuPPdc42KdMcYY60ds1ZXI3/AZnliZLVYVpX7dW2+9VbS7cJHOvIWkH6XBeNGusp5AkX+rlh/EGKkGKg+5yENDfFFQooFWToFeLfmr9DDbAIPC/YRDpVSF/Eo9XA73S09LIIWpOgoSS4iIc3QnQR8Ae1w0oDmTsNKYXR+ObJcWapm/2+0yiQpFRl+MCPOHu1AKuVSCEUEBCBgeArWP+30YMCQYNoMKQQHuj0NYiBbZFiv2ZLmfgGqqsmDZuzuxYU266H9vzuFw4qeVR/HZB7tFko8nM85PRGiE+0vX4ZEGzLtoMIaNdB/XptEqERyqw7ofUuF0uJ9oy1hHUQJRTHyA222UUnT+giQ+qOdIRepOZC59GlXZB8X8mWuvvRbffvutaHvhQp2x3onTYNqpP87K3rf7JD59fzcK8+vST3ThWsimheKAse7jCIMPtAo5MkrrPtYopbhotBJmWQEcLgdUMgWC1X44WV3XB0kS9UFwwgSLwyyWPFIiBGsyalBpqZugOi5cg/NiLLC5KsTHUlcQvtilxuGCusmh4b5yzB7uQrmjSHzsr9JiUogcYZrTn8MB6DPNUKTvBRxWuBQaZESPww8KNZyni/Bk/wD4Kqtgc9KkNgnM5nC8txUoOT05NMZPC6lEguxyk/h4YIABlUV2nCqumwQXpJJjlMWJEwcKxETNwBAtbAMDsNdUF9WolEkwzk+L4+klsFgd8FHLETUwELtKTWIhJzJ7WCjunZ+EUF8fUZhvXJuBrz/ZJwp2sQ/xAbjhjvEYODhEfHz0YAE+fncnTuXURU76+qlxxQ2jMW12gtsJq/V51iuXH4TFbIfaR4FFVw/HvIVDGhaj2b09RxT+JcXVIvd9YFIwTuVWoNpUdyIQHeuPG24fj6Rhod3zA8X6tfqf868+SUF1lRUKhRQXLBraIv+/v6bBJP/5A8hUmrP2eRxWM4q2fidWHSVKQyC+e/sfmDFjxln7nKxr+mPd0ZHjknPLTBi6kAZjtNox4MONXnF8uVhvp/70S2O1OvCfl37F/t2n3G4PGRsC50h/7Msvayg+G4sNVGDuSClKLSUwO1uOAKukMsTrQ7Ajx4L00pYpELRq6eLBvigsl+PbA+5TJMbHqnHxCDuS/E9BKmk5AiyrkcKRbcF3ci2aLld0Zh9G+AXjhwMS7M51P0o9PNQP1ioXDmU2W3TptGE6FcIhwaYaC6xuRsJDNAoM1fvgQGUNyswt03LUChl+NykGaavTRTZ6i+MgAabMiIPV6sSubSfc7gNltt//yAz4Bbj/J19WUo1ffz6GWfMGun2M1WLHd18exN7fckXcojuTpsXi7j9Nc7uNsY6iE9KfV6dh8vQ4hIbz5MX6Yj3pzpchU7m/KtfVBY/y8vLw1FNPic9FJ/c0in711VcjKiqKc9J7sf5Ud3TmuJy8tevFetT73lGs99/hDOZRbY3VY6FOivYUwR6ndluok+OlNpjMSphd7otgi9OB7AqH20KduCDB6oxa5OZ5zhrfddyMP02rcVuoE4fGib2hMSgrLfG4D3vy4LFQJ+nFRphOeZ6ActhkQXWwFlaT+8cU1dgQEaxDWaH7WEuzzYFtKadgclOoExq13775OJyeDjQtQpRWgqICk8dinfLzF18z0uPzlSo5ho0Mw/dfH/L4mB2bj3OxzrqNTq/CpVeP4CPajDYqsdty1ilDnQp1ylDfvXs3HnnkEVHg0McvvvgiRozg489YZ9BqvsuXL8fBgwfFmgRJSUliUnZwcLDbx9Pk7Q8//BDZ2dniMXSSPGnSpA5/Xu5ZZ4wxxvqgTZs24b777hOFenJysshQ50Kd9bU0GEkXbh3xySefiBPe999/H0OGDMH48eNF7GlCQoKY99Hchg0bxO/d0aNHccEFF0Amk2HatGl49dVXO/y18sg6Y4wx1sds27YNL7/8Mux2O2bPno3nnnuOJ5CyPkVyjtNgTp06hSeffBKPPfZYQ/vYbbfdJgrwm2++Gfn5+fDx8WlYZIxG3KdOnYqvvvpK3LdkyRKx/aGHHhKLjXVkoTEeWWeMMcb6kJqCbLzwwguiUJ83b554n5NeGOuaO++8E3/5y1+azPOg96nwpr73AwcONNy/Zs0aMUeEntPYXXfdJX4vP/roow59bi7WWQsnT1SIhBNPKGotIcDzxDC1RArHMRngIfmPerEt5T5QSzz3pAcodQjTe55sFaRTIL3M8z5Y7DLkl2oBl5scxtOkpUoYpDKP22OCVIgJV3ncHuijQIjc8/MpFcbX6YLM8y4gUCaFVud58ZGgAb4IivL1uF1vUKEg3/0E2Paifne9r+evc0Cs+7jLxkvF79/jeY5De+w9XIhyY+tL0jPG2tevnv/zMjgcDsyaNQvPPvusuPzOWJ8jlVDucudv7nKaW0GTWt1JS0sTb/38zsRT//bbb+Lt2LFjmzyW2mho8bEdO3Z06HNzGwxrUFFeiy8+2oNtv2aLeL/Bw0KRdawEVktdpKGIWrt0KC6+oi5qbXN2If67LRUFVWcmio53+sC6pxi/ldYifIAe0QvlqA49kzIirfXHkQMGpJ2qhq9GiyGDVMhxFVIEjKCVq+CHAOw/XgmZVIKR4f5IL65Erb2u8ldIgXHxOuTWlOL13Q6MCYvB4sFlCPSpi48ke3Ki8OFOOQpNJkT7RWJUggUO5Zn4SGWVATlbfHAsqxg6rQIDhwUgxW6E8/RO+PnIMTRaiWOVJZCGSTA9Lhh7Usyorq07DpR8OD5Ah9yMUmRmVWBCfABOSoG86rrYRTIyUAtzXhUO7TyFpGAtJCFaHC6rbtger1FiQHEtTuw/IbLNBw0NQcbRInEiQ3z0KgQODcbh4+UivWHYxCgUHyyEuaYuwpJO7AcOCcGJrHK8//p27NxyAktuG4+wyI7PaI+I8sXf/3spVny2D+t/PJPxTicRl183CrPmu79UR7nv61enYcVn+1FTYxMTVZfcNgER0Z5PLpo7WVCFd7/Yj5TDRdCo5bj24iFYODsBMhmPIzDWUS6HAydXvweHpRYJCSPxzDPPcKHO+iyJVCJuXXk+oTkdjalUKnFrj3379uGzzz7DuHHjxGTTejSqTkJDW0Yf033UUtOhfXW56ssD1l8jlOx2J9Z+fxTffXEQ5tq6YrCen78PgkK00BnUuO5341pErVnsDnyakoWtB04hNK0GJ9NbLvgzZGIwfKbbkJcXiG2Hq9A85TAuVAv/KCvUSjWO5NWg2to0PcXfR4lIgwYSuR0OhQnFtTVNtiukUlyYYMCwQBOW7Q7A3lN1+eiNjY3WIyqoHKbDOuzZWd5i0aHICD3ssUoERMpQYC5Hjb3pcTAoVfC1+qE61wlHQQ2KSqqb7oNcivikIBQ47Ai0uJCVXd5iHxITA1AsBeJrncjdlwdHs5SXsAiDOCGSBWmQUVoDU02zfdApEe+rhquiFuZau0iBaYxOsOZdPASLrhouctU7I/d4OT55fxdCww24Ysko6A1qt4+j3Pdl7+7CyZy6PPx6MrkUcy8ajEuvGQGfVvah1mzH598fxar1x2Bvdhyiw/W445qRGDmkLmOesf4Q3Tjm2e+6nAZTtON7FP+2GhKZHFu+/QSjRo3qtv1k515frju647jk3zMLhi6s0WC02BH+319a3P+3v/1NRJ22pbi4GJMnTxbRqNu3b8fIkWeS16655hp88cUXone9eQsaTUyl5x4/frzd+8oj6wwnskrxxUd7PY620+2dL651u3CJSi7DLeMHovynXOx3U6iTo78VI9QnFlvLz4x+N5ZdWA2nVIM8uG/nKK+1ituASCtqa1vGINqcTqzMqMCOY2E4XOD+NfbkVkGS54f0XXWLKTV3Kq8KEWotsnybFuH1jFYLjCiE8rgSVacXDWqyD3Yn0g4XISk+AGluCnVy7FgZRkf64nhKntvtBXlG+IfrkZXrPu/caLJin8mKGKsDxgqz25Ou1SsOIyrGD1NnxqMzaCGkR5+d1+bj3nttm1hMqTmH3Yk13x1BZLQvpp+f6PH5O1LysGJthtttuflVeOXD3fjopQUd3HvGvJfVWAqZpbZLzy/e8T1cTieCxpzvMUqOsT5DJq27deX59D8nN7fJyVB7RtXpBGr+/PniuStWrGhSqBOdTife1tTUtCjWq6urodd3bI0JLtYZY4yxHmYzlsOpMndq0SPy97//Hb8GajB69Gg899xjTbYxxjyjQr0jVy6qqqpEFOOhQ4dE0gtNMG2uPunlxIkTTXrZnU6nKPDnzp2LjuBinTHGGPOiRZEaL3pEcnJyRFSjQqHA448/LlYmZazPk3Z8kmiL53cQjZQvXLgQe/bswZdffolLLrnE7ePmzJkj3m7cuLHJqPuuXbtgMpk6XKzzLC7GGGPMi9FleEKZzoMGDerp3WHsnJDI6Cbpwq1jn4/6zy+99FLRn06FOr3vCU04pYKd1jooKqprv6UVT5944glxkn3jjTd26HPzyDprM3lDSjOuJa2fgba1XdrWdrT1fEpaamM/z/I+0FZKqGmNvLWcxtN/XFojo+c7Wnm+pO3vF000bY3VYoeyC5Ny6ieStrq9jX0UX2cr5JwG0690x89kf0WX1VevXi3ev+yyy3p6dxjrs1588UWsW7cOMTExWLZsmbg19sgjj4jJo/UoJebyyy8Xq53SRNQjR46IWolWPdVqOzaZnP86MsQmBOKhv83BJ+/tQv6pphM0KVLwhtvHQ6l0X2WWFJnw2Qd7kHa4EElDQ3AsrbhJyglNSo0aFowD6SWYMDQER4y1MJnPTBKlkm34AD/klFQjPtgAs8qGvEZRkCTOXwdrOVB9wo64QUC2qazJdn+1GlqXAWn5JhH1eKigAo5GIUcquRTJah0ydxRhaGIgcvKNMFU3TVpJGhqETJ0NodIgyH3MKKhumrQSpTPAaFSgeogDiWVaHMtoug+BfmrcfMVwjB8Rhi++T22RcqJUyBCXFIQtFdUYOzUapoNFMBnPRD2S2OGhOOAjRbBGBXlpLfILTS1SUkhVtRWxwVocTz0TR1mf3HPVTWMw8bxYt98rm80hJqD+8M1hjJ8Sg6tuHA1fP89Z9q155Jm5+OzD3di1LafJ/b5+alx5wxhMmRnX6vOnjY8SPycffX0Q5c2Ow+TREbj1qhGd2i/mXZwU/7kmXcR/Jg0LFYlTwaF1E7NY+6Snp6O0tFSsjEgFAWP9hvTctsHQyfCwYcM8bq9vS6tHk7w3bdoketuzs7MRFBSECRMmdCpOlaMb26k/RChRmshPK49i5ZcHoNYocM1NYzF5hvuiy2o9Xfh9fUi8Xy80TA+Vjxw52eWIGxKMExY7Shulp2j1SgQNCsK+/EpEB2rF70p2o1QRWmNoRJIfMk1GKGUyRCi0OJBRDlejxY1GDdbC6WdCpdWMWF0Q9mbXwHw6h51EGDTQKuXIKDEi2d8A44FKVBSfOQHQaRQYEGlA6rFShIXp4IhRIcN6ZjsN6o6P0yPPXAq5VIoAmR/2HK+Cq9HI+1ClFtZj1SirMGPR+Ym4asFg+KjlTfPDP9+PlCNFIrLxhMuFgpozx8Egl2ICpMhJyUdQuB5Vsb44aDpTtNLA87hAHfIzy8QVASrUjx4rReOQw6RQPVzFJpSV1GDewsFYdLXnuMS9O3Px6fu7UdzoBECjUeDSa0bi/IuSOp1rfuRAPj5+dxcK84w4/6LBWEyRjRrPizw1V2O24fNVqfh+wzGEh+hw+9UjMWooRzb2B3SCTz87FBdaT6GU4aLFw3DRZcP6zUh7Z6IbqWf9h7vOE8UBje79+9//xvTp0/Gvf/3rrO8vO3f6Q93RleNS8Og8GFSKzr+OxYawF9d6xfHlYr2d+tMvTUVZjcjpbi2r+/nHf0L6EfcxiCRhcjR2ZzYdfW5s4MhQ/FZkalJ8Nhbmr4axxoaa0wsyNadUSDBoqAZpRS0z1etNUfni0PZ8j9sHjwrGbqmpYTGk5oJ1CtRYnahudDLSGB2dl+aNxeh4zxFpb60+io9/azr63NjYMD32FVahWdR4g0AfObT51ah2E1lJqBvlr3dPxpiRnpMf6ATs0w92e9xOo+z3PjwdnUWLI5WVVCM4tGNRVI0VllQj0M+nzRYe1jfs2nYC/3lpk8ftiUnBeOLvF6A/6Gqx/uSTT4o2GFrG/Lbbbjvr+8vOnf5Ud3SEsR8W6/1j6IJ1ePn5tpiqmrYuNGdpvvJRM1ar02OhLl6/lUJdPN/mQrX5zGi6O/Ya9wVuPbPL6bFQJ8ZaBywOz5+DGmnU2tb/UDjbKD6rqEW9lQNRabEDHgp1QhcU6CpIV75Xpqr2xcV5QqPyXSnUSWhQ1xaDYd6lrZ/Jqi7+TPYnmZmZ4m1ioud1DRjriySSLq5g2sY8tt6Ei3XGGGPMixZFshlLkZ9fd9WQMpspZYL6YGkJc4p0lEr5ChXrB2TdsyiSN+BinTHGGPOiRZFoav6t39IKwBnIOHZK3HP3t6lw2Q9i7cOLWkx0Y4x5Ny7WGWOMMS9aFKmevdYEiazu37hPSIxY2ZSx/kIi7WIbTFeSZM4x77kGwHqVS64YLmL63AkZFYbyADVCo9xP2Aj3UyO4tBYTde6fb9BKMXOmDBfMUkOlaPnLREkpE4N0iCgG/OXu+7UHK7VAjRNRYe5j4MJj1YgaZ8OUJPf9+Rq5DIN8/DBcHwClm19oumeYwR9fbslBsdH9aNjBtGLkHSnGQF/38YihASrogqUYOcjX/T4opJjop0XyoCAoFe5/VYcmBmD1xiwUl9W43b7vRDm2Wq0IHOJ+Eqx/gA8o5XLLhky4GsVd1jNb7Fi64pC40fudkX60CK///ddWJyS3xmKx46tPUvDF//bCXNs0cpN5pxFjIjFiTITbbQZfNRZdydGd7eFy2M707nYiDo4xryaTdP3mJXhknXUKRTqOGh+JFZ8fwM8/pIrMbH2EHvZhwdhGCS15RhHLOGpsOIrTSlFjssJHIUMy5YMfKkTm6ajFmYmBOBHsg+waKyQSF2ZO0sDmX4xTdouIW5kyT4PaU/7Ysa+uIE7y94GyzIxjBwvFxxSXOD45AClOE6iUDJYrMKBShoy9xcg7HaPaOFtdrZFi7CUG5KuLkEP7oATOH++P4zlqHCu0gGa9DvX1Q87JWuytrhulCjGoEB+uRqqxQnwcq9PBWgHsS6OPK7A5tQg3TY/HNZNjoZBLReH84VcHsWV33eVpmsMyJSkYR81WlNPiLwoJhif5IbWiAgeLT39dQ3WoLXMhp6Cu6B4XpEPZiQqk5lSJjwP81Ajy1yA9u6xJ5vqRY/RxGX7bn48rL0zC4nkDoVDIxAnEf9emYd3Bgrp9UEowel4C7HvyUVNaA7lCisRBwcjKKEHqoUJx++WnDCy5fTziEgPFczbtzMVHXx9CSXldH+3GHbm4+fJkTJ8Q3a6fkYryWnzx0R5s+zVbfLx7ew4mT4/F1TePhX87JjGTnVtPiDx3iqck237NwtU3jcGUGfHtej7rnQKDtfjTk3OQsjMXn5yOFKWFsubQz/C1I6HRtj/+s1+rn8jfxoJxjDHvxtGN7cQRSp6dyq3Ahz+lYX2+EdZGeef19Go5JmlVKN2bD2NFy1Fo+icdPz0Szik1KLJWuv0cEcpAVGz2QWpqidvtocFahMbpkZpSDKutZYqMxkeBkRN9UZVQCqO91u3qpWHSSBxNkSGr2WJE9ZIi9fBRybA/q8Jtkk10oAaLBobgmx9SYXET96jxkSNubCgyUIOSmpZpGHRiMTLAH7ajJuScdH8c4qN9xddyOKNEjIg3FxasxbQ5Cfjf9uOodbcPShmmaVQoP1zUUAA3vyw468JBSKs041C6+2M9fFAQ/nLvZGjUnlNotm/Kxkdv/uZ2JFytluPGOydi6izPBbfFbMO/n9+II6dPNpobNCQED/xlFrQ6Luq8HS3WteHHdAwbGYaoGH/0N52Jbmwc4Zjx0ZOQyhUYcs+rTSIdmffjuqP141L03EIYWvk/1ObxNdsQ8tfvObqR9Q+R0X6o9FG4LdRJldkOm1TmtlAnNCpfU1kFo9VzZnqetRQlJzyvbFhYXA1fndJtoU5qam1wGexuC3VCQZJGaxWyCj1fbEo7VQWDj8Jj5GRuaQ12WwrcFup1+2CHxWFHicXicZCs0FgNk4dCnWTlVsLPoHJbqJOC4mpszyh2W6iLfbA6UK2kXHT3bTMupwt7dp3EcY97ABxML0GVydpqsX54f77HlhWz2Y5D+/JaLdarq20eC/X61pqqSjMX630AXQmaf8mQnt4Nr+auhY0x1ndwGwxjjDHmhaSKuitLLocdLmfr604w1tdI+tEEUy7WGWOMMS8kVaga3nfarT26L4ydc7IuThLlCabd78SJE8jKykJwcDCSk5M79Fyn0ylWecvLy0NAQAAGDhwItdp9EgljjDHWmxdFqudyOcWoOrGU5sPFBTtjfZLcGyYSLFmyBBs2bMC4ceOQnp6OwMBAfPnllxgypO0+xx9++AEPPvggysvLMXToULHaW2VlJR577DE89NBD5+Rr6OvKjWZoKTqM/nm42a6QSZCQEIjilHxYzO7j/3xVakjlTlTY3fdSBykNkIdpkJVdl8jSnF6nhI9BJSZp1gckNCaX0f4poZLKYXG634cEf39k+tuRdzr9pLmoQA30PnIYT7rvxTao5dArZB73QSaVQAcpNAoZajz01gf7+sAnyIpiDz3lNJHWYFChwui+712nUcAfEsgkEjjc9LHSPtAkUx+NArU17r8O/zA9auwOFHnah0ANcjPLEBrkfjJcVa0NFh85pFIJnG4OBN0fm1CXOOOJj48cEVG+yPPQvx8aoYdWz5NLWf9cFIny1D+9e55YrfTqjeGoqqrCfxcPRlxcnLiPsX5BIqV/KF17vpfo9cX6zTffjJ07d+LIkSMYMGAAzGYzFixYgAsvvFDcp9F4joCjpZevuOIKTJ48Gfv27ROPpYk4jzzyCP785z+LEfZFixad06+nL3E4nPh+QyY+/z4V1bU2DIvxRbVBheyS6obHTB4YhD9cOBjRgVpcOitBRPnt2Hxm+mJwqE7EtB3ZXgjlPhkG3xCMXL8y2F11xayPTAFNVQg2/GyGw2nD2OEhqMwxorzS3FD4DRwchIMmC44XVSFxcCB0lTbk5hkbPkd8vD+KgoGfTpoQXBmAMUnAKcuZzO8QtR5XxI/FUP8I3DjEgU+3HsfHW7JgsdX1gGpVcgyK1eFIVQUKHS6MSPJDXp4ZJVV1BTMVxmODdajeeRLZlRYkxvjB6qfG8fyqM/swwA81NVbs254Pf38VEscE4GB5ZcPJTahODT8fJfYXV0IRI8HomBBkHyxtmKxKEZXRAwOxq8wEe40Fo5JDUHOqCqWnTyzoBGFYrD9KjxTj5I8ZGBPvj4o4P2Q0+l4kBWuhP1aBrJRCUegOHBKMY6nFDZNVA4K1cA4KwM8mCxQqCcYND0VOegnMlrp9UKtkGBKmR05KPl5/YSPGTIjCdbeOQ3BoXYwk/W6t2nsKb6/PQEW1FQkTIxFVVINTmXVxk2TQ0BDccMcEDIhtPfXDR6PEs/9eiHXfp+LbLw40TFZVqeW45MrhuOCSIZArOFea9c9FkSj1hYpySn2hJJmMjAxIpVJOgWH9ioQG4brQytKV555rvTq68fDhw6Ll5cUXXxQFdr3ffvsNkyZNwptvvom77rrL4/O//fZbLF68GB988AFuueWWhvupnSYhIUGMrP/jH/9o175whFJTB1KL8c7n+5HTqCiuzxSPGx6KSpkEt85OxHlJIS2OJWV6f/G/PVAoZchILYbT0fRHMDDOB2FX6OCQKrFtmxNlxqaj0D5yKcYYfGCttqJYJcXxZqPMErgwLkgPW1ktpNEqHLSeKVjrJUepER5eiwmhMZgVkQS5tGnhV1BRi//8lIbSWgvynCZUWpuOQqukUgzU+KGm0gZDVjnKM5utHCgBYkaFo8zpEoV2WnbLlQUjB/rBGq2E3keBQ4UVsDcbhQ6SKxFrlAEuCdKtNpQ2uyqhkkkw1k8DW4UFirIaFB1vedUhdFIU8jVyhJtsKNxZl/veWES0ATK5DJJQLbZYbLA224dAHwUGymVQWJ2oOlYKU1nTqw70PVyweBgSJ0fjtbVpOHqq6c8DmaRTwye/ChctGiby+TtK5LX/b684ObzmpjEI8DCiz1h/iW5sHNH4hz/8AVu3bsXjjz+Oyy677KzvLzt3uO5o/biUvLxYJLR1+vjW2hD0pxVeEd3Yq68BrF27VrydPXt2k/snTJggDuxPP/3U6vOpTYZGG6hfvbFjx46Jt8OGDev2fe4v3v5sX4tCndCpX9aBQlyeFOq2UCeDk0Nx0WXJSDtc1KJQJ6XZtch4y4jv19haFOqk1u7E1rJqFOgULQp1sQ+QYFeJCbJhereFOjl00gxTUQjmRg1tUaiTMD8f/OWyZBw1l7co1InF6cQhUxlCMspaFup1O4ETKfnw91Cok1MZFdAU2bEvv7xFoU5K7FakaM3YbqxtUaiLfXC4sK20Gj7ltW4LdVK44ySGFNa4LdRJXq4RZp0CG2qtLQp1Ulprw44qM/J3nmxRqBOb1YHvvjiAf39/xG2hTnaYzIhZmNSpQp34+fvgzgem4vd/msaFOmPNUOtL4/9rjPUbUknXb16iV7fBUH86iY2NbXI/La0cExPTsN2TpKQkvPrqq3jyySfFJXrqec/JyRGj6dRec8MNN3h8rsViEbfGZ3KMMcZYVzT/30L95l0xaNAg8bat/4eM9TkyToPpFer/iGm1LS8N6nQ6MVm0LTNnzhQ962+//TY2bdokLjnS5RO6XCiTee55feGFF/D000938StgjDHGzt7/lvorxNQ2SnO6OOmMsb6nV7fBKBR1vUh2e8sWAJvNBqWy9TQI6k2n3nYaiafox82bN4v7rrvuOjGx9KuvvvL4XEqLoT6m+lt7TgwYY4yx1jT/30JBCV1BwQthYWHif+LevXv54LN+tyiSpAs3b9Gri/WIiAjxtqjoTHJHvcLCwobtntDE0urqajz33HMNo/NUuNNEHIp/pBYZT1QqleiLb3xjZyjkrf/oKBTSNrPvW6OUS0TMYGsojrHV7W00ebWV+GR3OCGlGbOtkChbfxFKq2l1exuvT6erbRxqSNv8XrSemkLzOlpDLy9r4/upkLW+XdnG9u5Qezoxpied7X2gSbZWi/vo0e5CyTu9OHfA6zX/36LX6xty1mniaHtuNmMp8vPzReIZrR9CV4/J+vXre/irY+wckkm7fvMSvXpPaSIpSUlJaXJ/cXGxaGep3+5JSUmJeEuFeWNUsPv7+zdsZx339APnYe55MSL9pTF/gwoP3DIW885rOs+gns3mwMrlB/Hua9sQPzAQAUEtozfHTxmAZ15agI/umozRbiL+BoRpkDRUC6PGgtGD/SCVNEsw8ZNh4YVyOEKyMX+MCjp10x9zqjsnJ+pwwnISL2zbhnyTqcXn+PVoIW55cweCzD5I0Lc8UYvy1SAhUI9Dg1WIuGBAizN0jUGFyIlROHiiAsMGBkGjbnrmIKPs+dHhOGK0IUHmi1Cflot0jYEaQ/ZUY1x+DUbrVC2Pg0GNsUoFsqwOxI6LbLEPOr0KN989EQ/+ZZaIS9Tqml6JksokiBkfiRPltRinUiDazecYHGFAkF4N4+QohIwOa7GdstAffvp8vHzLeFw1KabFCRbl0v9xwRAsmRaPs6XglBH/fHo9Hvjd11j97WHY7ed+2fXCfCP+9dwGPHDLV/jhm0Owe8jR74qjBwvwxIPf49F7V2LXthPd/vqUib9xXQYeumsFnnv0JxzPLO32z8Faz1m3GcvadaO4qVu/zcBFb23BvJe+w9ixY8VrrFu3TrTCMMb6ll4d3Uh/dOLj4zFixAisWbOm4X6KcvzLX/4iinjaVo/+UNEoBbW+kPfffx+33Xab6Fe/4447Gh6XmpoqIiGpHWbp0qXt2heOUHIvPbsMb3+2H9m5FVg4OxHXLBwMjYcopX27TuKT93ejqKCqyahv/KAgZKUXIzhMjxtuH4+hI5ou6vHzoXz896d01NrtGJigw+HS8iaLDg3w1cJVJcGJgmrMnqqGSVsEs+PMCKdOroaiNhBbUmuQHKmDQ1GN4tozC/4opFIsTByIy5OSUFBhwb9XH8XOZoXKsAG+KJXWwuy0IyHIgEMFFXA2+tUZoNcgJKMGJQdKEDMmAunF1SJ7vp5Bp0RkqB5HM0sRnRiIIoUEeRW1TRaOGh7vh4zaCgS5ZIg7bkXOkaYnkwOGhiBVr4DR6cQIrQrHUkvgaHQgIkO08LU4UJBZhlnzBuLy60eJgr1eldGM5R+nYNP6TIQPDESZTIr8RjnsdKUiISkY+0y10GsUCNCqcLRZ4k9yiA6Kw8WwlZtx6dUjMHfhYMgbjexnFZnE8Us5XoaLx0ThjjkD4adVnrUR4O++PIi1q442KdDDowxYctt4JI9q/cpbd7CYbVi5/BB+WnkEttO5/CQswoAlt4/H8NFd34fS4mp8/tEe7NzatEAfNjIMS26bgIho3y5/jsz0Enz87k5kZ5z5uaeTvxnnJ+LKJaOhM7Q8kWM9F93YGI20r7pjCu655x4xyk6BCpdccgl/e/oArjtaPy6l71wFg0/n/78Ya60IvONLr4hu7NXFOvnxxx9x6aWX4qqrrsLll1+OAwcO4Pnnnxd9f0899VSTx9LEmlGjRmHHjh3iY6vVivnz54tc9gceeKAhDeaf//ynuPRPE06bJ814wr80ntGPUGlFLYL8PS9Q9eu6DHzw37rvizsDBwfjsefnQebhslSt1Y6bvtiComr3o0Y0nnvhOCVyLAUeP8cARSR2nfI8WjjAxx+7d9pgdxMnSdQKKYJiFSg0eR65mmLR4tDhYo/bh4wOx5YCz8lCA/3UkG096XF0mHLNpQMDUVDsPpKS/GHJaMyZ7jkm8adfMvHfz/Z73B4eoUeaFLA5POyDTIqPbp2A2EjPRSLl1FP85dn01J9XNykum/v9Q9Mw0cMVnu7y3GNrkHHU8/f7jj9MxdRZ8V06Ibn/lq88rvwrk0vxf69ejLDIzv+jST1ciBf/urZhcazm/AJ88OoHV3T69dnZL9Ypc51aYF577TWRlLZ8+fI229tY78d1R+vHpbQfFeu9/reZViqlEfSgoCB8+OGHYqLoqlWrWhTqZN68eQ29e4QmoG7YsAHLli0T39yPP/4Yhw4dEoV+Wlpauwt11jpqK2qtUCc11a338lqtDo+FOvFRylFt89yrS3WGQ9J664HN1Xp7RLXF7rFQJ2abE7VttDdY2mjBoGz0VrebHa22cVCuubmNnmWpso0e9TZ62GstDo+FutgHhxMqTet/IM92oU5qqq1d2u4N++BwuDwW6mK73QlLF3vYzTXUo96zx5F1HQ1m0ZVl+h+5ceNGPqSsX9Qekq5MMG1jzlhv0qtz1usNHToUr7zySpuPW7lyZYv76JtBMY28shtjjLG+ikIU6Ao0tX++9dZbmDFjRqvxxIx5PVn/yVnv9SPrjDHGGGvb9ddfL9oDKKJ4xYoVfMgY6yO4WGeMMcb6AOq7vfPOO8X7NLre1dVRGevVpJKu37wEF+vsnBg3eQBGjo10u80vUIPaaD0+33ZcZJs3Rz25Xy1LwQirEnply84t+nVLDvNDabEKgaq6zOLmQlV+KC2TId4Q4Ha7v1qNQIMCE4b4Qu7mF1gll2LueB2mDpTDoHafdjMpVo9hE6yIinA/SSw6XA9ptRXJ4e73MUCnRNRgJQbODG+SsNJ4cmnstCgED/ET6TLuDBociB8LCpFR4n4Sa2peJX7MKMLAxKZxpvV89SqE+6sxPtzgNjud7hsV44/3fslAadWZJdMbo9SSV57/BdnH3E/+TM834pFP94qUn664/LpRYvKjO5QwRPuRleE+nvVYQRUe/SwFPx3I69TnpnkFq1cchlangp+/+30YPSEKYyZGoyt8NAqR6qNUtWxnoPjPpGEh+P7rgyIxpjPyciux4ad0DBwS3CKGlVDc5zU318UCMu9ALZ80H6uiogLvvfdeT+8OY2eNRCbp8s1b9Po0mN6CZ2V3j5Sdufj0gz0ivpFiGyPGhGOrzQbz6YmXcSE6PHjhYIyNrysmf9tyXMTWlZXURS2q/VXQzYlESnWViG8c4KeFTCJBdnldVjrVlzOG+cAoq4tv1MlVkNcGYWtqDVyirAeGi/hGk4hvpNjGgQEBOFZWBuvphZoCpDrYi3VIza0blRqbqMeAwcVwKus+h9SlRHlxBLZnmUR8Y2yABtOSaqHW1BWGLqcElalh2LKhLr5RxDaG6XG0UfE6YGAACuVSEd9IsY2jk/QoUhXD6qqbLBhiVyNwH3DicN1zYoaHICNajnxn3URdrVSGZJsa6Qfr4hvDw3RwxaqRbq1pWGzp4iFRuHXCQBjUSlRUW/H2+gx8v/dkQ+zlsAANXEU1KCyuFrGNSfGByMwph9lSN4k2IFgLRZQeR05HbQ6JMKCs2oLCyroiXauS43czE3DFxAGQy6Q4lVuBZe/swpGDBW6j/4w1tA/HsHJPbsM+UI4+5bDHh7o/gWlXfOMXB/DT96lismVwqA4arRInsspOz1kBps1JxJU3jIbBVw1jrQ3vbTiGb3fnNsRejozxx4MLBmNgWPvSAA6m5OGT93ch/2TdCREV0nGJgchMKxFFPMU2Xn/bOIwY4/7ktDOoGP/swz0N+eox8QGoqbGiuMDUsA8LL0/GgsXD2lwEi9TWWPHt5wewbnWaOG4kMtpXTDTNO1nZ8L27Ysko6A0t1wBg3Z8Gk/znDyBTtT5R3x1aIOl/N0xAePiZyNvdu3eLCEeas/X666+LCOR69DhOivEOXHe0flzKP74OhjbCDlo9vjVW+N/wqVekwXCx3k78S9N9aGGkb745hK+zSnDKQ9LFvGGhUB0swtGDhW63Bw4JgGRSEPbml4skmBbbdTJMHOiDTUdrYDK3HK2nhZGmDDKgyFyJktozeeeNRcuCEe5vhsvQcgVdIrP7QWX3hZ9/DiTNFmYiLosKp3aGIWVfGWrcfJ00MjpoYjjKAypR7nA/MppUZYCxRo4DcB8XGa1UI0qqwm/mSrjLb6GrAFcMisWnm46jqtbNPkiA80L0KD5pROHpE6LmYpKCUKNTILVZ5nq92GAtFvppsW7FYZFe0pxWr8T060di6f48VNa0TAWihZSo6L95RgI6K/9UJZZ/vA8pu3LhdLMPVMDPuH4kPj6YhwoP+3DjtHjcNjux1c/zn5c2eVyQKDBEi2mzEnDxFcmQt6Ng7owjB/Kx4rP9SPcQF0knK3/86+xWs9czUovw+t83obLczc+9BBg+KkKM5tMJCDt3xXrSnS9DpupcipJc7weJpOmVsLz1n8B0/DDUIQMQvfAOsZ0K+7UPL0JkZPedSLKzh+uO1o9L+SdLul6sX7/MK4p1r0iDYX0LjfyFDg/DqSOeM9F3pBUjxEOhTkqPlsGVrHdbqIvtJgdOFSjcFuqE1q8pNzlQYnNfqJNiSSnCDJ7bCxzyCgwIdKLK5n4vJCoLZCq720JdPN/hgt1c7bFQJxn6KhTWeP4Hnms1QxEgh9ND9LvRbMOu7FK3hbrYBxdQZHOgyEOhTk6klaA03HP+8/Hiahw8Xum2UCfVVVbsSS9xW6iLfXC68Nuxki4V6+GRvmKBIneFen384J6MEreFeuN9aKtY37/npMdtpUXVosA9W4U6oQXDXn/xV4/biwtNYmS8tWL9+LEy94U6cQGlJXVfBzu3tFGJncpZ9yRy3o04tvQZWErzUJObDv/h53XbazPWK0i72HfOPeuMMcYY6ykKnT9CJi8U7xdu/Rb2Gs+LsTHGejeeYMoYY4z1QQEjZ0IdFAmHpQaFmznKkfUxUmnXb17Ce/aUMcYYY+0mkcoQPuc68X5F6m+oycvko8f6DikX64x1WrXJgs3rM8VEUk/9wSdKquGvcR+BSOJ81QiJ9fO4PTRCj+RQz9spLcVgdEHpYTlhutdHqoJe7jnpIljpB7nNc++uv1IDH5nnfnKXSw5pqBxKlftzYto1pVoDg9TzawzwNWBQqOeEiJgABYZE0dfjvldbo5QiItoOH4/74IJviAp+wZ73ITbSgCQP8YQkXC2HQe/5OCrUcmilUvjIPewDXAiWSFFY0krvfmoRUg97nsNQXFYDKe2jhx5EuUoGhU4JH6Xn8YlQqRQFbUQgJg0N9bjNP1CD4DCdx+1WqwOb1h8T/fOdlXG8HEHx/h63633VyDtVCaebCNR6UTF+YsKtJ0lDQzq9f6z30YTFIWD4NPF+0bbvYLV2/uePMdYzOA2mnXhWdtucTpcoRigTvarSgpAwPa6/dRxGjY9qeMy+42V4ZfVRHCs0QaOUYVC4AQdzy1FfW4RplIiBBMcyy0TdNSzWH2VHi1FjrIsLVKnluOTK4bjgkiFiIl/KqVK8vvUossrqIuwkLhdGK/UoOlwBo8mKoAAfaAbpcMh6pgiLC9DB7nQht6IaPgoZkiN1yDUXwuGq2wlfhQ/UDl8cyjeKon5irAHBoXlwSOv2geIeBxmCYLSVwOlywk/pB4fTgSp7XcQhxd+ZLWHYkWtGpcWCAJUafoU+OLq9vGEfImL9UKVT4HhpDdQKGUYmaZEnL4TjdKaLn1qNEI0G6WVlYh8SfYNw9KQV5TV1E0V9FBIsHK2CWV4Ah8sBP4UfDmZqcPDUmezzqUk+cGpLUWUzw6DwgaMwEFt2n5lYGBehgdwXOF5RLXLkk9V6HN9RCJv19D4YVAgL1iI1sy4GcdCgIKTb7SitrZukqZRKcJ5SgfyUfNisDkQOOB39l1vZ8DkGjAxDbo0NZZVm+Puq4TvAgD2NivIEXx8YTFbknDRCqZDisvmDcPkFSVAp6yZpVpTX4ov/7cX2X7PEa0+cFityvwMC605g6IRwxdoMLP8xDRarA9FhemhNVhSejm4koRMika6UorjKAn+tElEBGhzMrThzHAxq+NfYcSK3Egq5FIvnD8SVtA8q9/PvKb/9sw93N8SJUib+/EuGiJ9LtY/7E9DdO3Lw2Qe7UVJUDV8/Na68YQzOmx0vovXaw1hlwdIVh/Hz1uMi9nJwjB/MxytQefrkQiqTYODgYBFZaa61IzrWHzfcPh5Jw9yfXBgrzVj+cQo2rz8mjiuh79+S28aLSazs3KfBjHn2u26dYNqYw1yDY0ufhs1Ujmf+eDceeuihs/J5WPfiuqP141L+1e9gaGXgoc3jW22F/xUfeEUaDBfr7cS/NK2jxWeWvrMT2RktF8KhxZAuvHYkvth3EutOZ3A3FuHvA1+1HP5WJ7LTSmFtNiKv9VFgYLAWYWo5rrllXEOh1nik/tvDJ7B+30lIss3IPZ1/3Vh8gj9MEXKodQocKjxTqNUL16sRHiSBzKnAgVMmWE5nT9fTKeWYOlCFgRHVkEhqYHY0TdOQQIJgdTBKauzYl6dAVkXLfYjT+KH2sAtOrQb784wtxsLD/NQYEA8YAiTILC+HxdH0OPjI5YhQB8BH4UBQcBmq7TUt9sFXGorDuTIEh9aiwHzm5KDhcygDkJuqhcJHicMlLWMvQ7RqRBgl8KkBMnMqGjLXG/ZBLUdUYiAcVjuQVoLy4mb7IAESBwejxuZAjV6FrFMtj0NMtC9qDUr4O4CMtOKGzPWGfQjU4HdXDIfxZCW+/eIAapsluNSfsIUkBuC9Lw+2GA2nfRga4w97lQVlkXqkFtedyDWWEKqD1OVCsNWFjLQScaLZWHCAD269cgSmeFjIixbqWvXVQeRml+PaW8YhLNL9H/qCPCM+fmcnDu1ruQBUQlIQbrpzoshM94SWwfjhlyx8uvIITM2Og1Ihw9BIA6RltTBVWUQSTHOTp8eK3xlPCzfR7+3ypSnihPr8i5Igc7MQFvP+Yp1Upu1C7g/vYmiEP1asWCE+J+vduO5o/biUc7HO+JemY/713Abs333K4/bQmbHYWu45HjDeV43KVPcrXtb79N8LoWslU/X/3tiOHW6KonoDp4ZhT63nRIQIgw/yjJ6jHMk9c6tgdXq+jJxdHI79bk4G6g1whWHHfs9LgEeFKuEMb1lkNzZjoAs1Dg9ZjVTMyoKRUel+5U7ib4/E5qOev84QuRLmnZ6/BhLvcKG81PP3M3JCFA4d9/x1DI4PQGqj0e8W+6hVANmt74NicDCKyjzvQ8zYcOz1kA1PRgfqkHPIc2sNreb68csXoSuoUP95dZrH7WMnReP+R2d63G6qseK6B75v9XMMUchQ4OakqN61t4zFBYuGtnOPmbctitRedOKXu/INhNlLMGHCBDz33HOIiIjgBZJ6MS7WWz8u5d/c1vWR9cve84qRdc5ZZ4wxxnqYzVgOp8rzSXh3CBo3HyfW/g/H127GhvIXsP2Nx3mBJOa9pF1MdPGiNBgu1hljjLE+tiiSJ9S3XrR9Fcr3/4ry8ru5WGfMC3jPaQVjjDHGuiRo7Ly67HVrLd544w0+msz7VzCVduHmJbhYZ91CqWz9Ig0lnrRGpZCJiYGeUOKGtdmkz+YoEaPVfZC3vg9KqbTV3125VAIpWn8NWRu/Uoo2rmUp5TKR/uLx9SUSyKVt7IOk9X1QtXGcKJVF2sqBoG2UxNPqPiha3weZhxjHM/sgh0zW+j7UJ8Z4om7jYKtbiXEkCoUU9lZ+5mhSarWp9Rg8pYdEmXqtHWcik0rFz77H50vQ5vei9d+atlnMNo8xrPW90BTXyryDRCZD5LybRBLR1q1bsX79+p7eJcY6RyrpYtY6F+usn7njD1Ow6OoRUDQroLR6JW68cwKeunsy/nb5cATpVU22y6QSXDUpBv++fSL+8ehMDIxtmSEdH+cPeZwvbnlnB346kNdiO2W2P7h0N34qNCIpOUSkZDSm0yhwxzUj8cy1E/Dk+SMRrG2aCS6VSDBOp4fqxzyMOuFAgqHlpehBQQbxvK92GqBytYzD85FqkbcrBLs/KcdgBEEla7oPGrkcoyL8UR14ArOmKhFsaHoc6G/GtOF6jBhXiKnxSsT5tZzsMjjQgBtHAUl+ZiQYglpsNyg0CPMJgNlehOH+wVBJmxaKKpkc08KCMDXuBG6fKkeoruU+jIs1IDSxFMlXKBAf33If4qJ8ERqkQYFKioThoZA0+2MXEKxB4PxobNfXImlciEiPaUytkmHQ8FBsr65FZHKIiNZskeSSGCjSZFSJgYhOaJmUMmhICJ5+eQFeeHg6LpgR1+LvLaXJPHrnRLxw01jcNz8J2mYFs49ShjvmDMT/3TYRT943BREhuhb7MCQhUOSi3//MeqQcaTkJ9VhqMZ56aDX+fNcKrPsh1WOu+ZVLRok4xOa55kqlDEnDQpCy6yT++fR65J86E3fZZF/Vcrz+tzkY4yaCMTpcj8gwPbIcTiSMDGtxcuPr74Po0eF458c0vPXpPpg6ke++fVM2HrlnJf5y/yrs232yxfbsY6V49tE1+NMdK7DmuyNwtJLvznoPdXAU/EfWTWx+4YUXUFxc3NO7xBhrBUc3thPPym6f4sIqfPr+bqTsPoWZcxNxxfWjoWtUmNZY7Pjo10x8ueMEhg/wx4MLhiC+UbFEo3Trtp7AxysOi5FNVYQeB0qbRvONjKHnDUaEvwYfbszE8t9OwO44E70XqlUgFlJkZZbh/KmxuGHxMJHsUa/WZseyvVn4cn82YnVa6PZXoCytUXKJBIiYHY2jegcUMikCNWqkFjctpibFqjFxUBUszhpY84Pw3RdG2Kxn9iEgVIWIqVocqy3D0BB/VEvLUes8M3lM5pJBXRKFnQdrEBfqg6EjjHD6NE1PUTlDcSjfIk5oZsfJEaJvmnTjdPnhhEmKcks1wjWBOFVdDGejMEaVTA249DhmLEGyfxASDeWQSc/sg8Mhw4GcWKw5WoMoPx+EhlXDKGma0BJaEopjW81ieDYkSIu0ZgkusUEaaE02FBdUIXJSOLZrrLA22ocAuQIJJjnSj5YicWAgMuyOhpx2opRJMM5Pi+PpJaLIJrn5TdNyhkcYYMqpS4a5+qYxmDIjvsl2iph8+7N9yMqpwOJ5g3DFhWdy2kmZyYI316Xjx/15mD0sDPfOS0KI75kTNpvdiW/XZWD56lQE+WtA6YUn8pruw+TREbj1qhEiW/7LpXuxbWNd7nu9dueab8hEfGIgSkuqUVFW2+RKw/yFg8UJr6es9h378vD+lwdQa7YjMlSPo5lN05Oi/H0QYHMiP6cCscmhOFRcjdpGI+IGnRJLLh2GeefFtjmin3u8HMve3dViIaqR4yLF2gl08kFfz6b1mXA1ir2MiK7Lah82krPae1t0Y3PmsgIkHP0Kubm5GDdunGiJkXrRhLv+gOuO1o9L+erfw6BVdf74Vlvgv+ANr0iD4WK9nfiXpmMqK2rh6+d51UsqoAKajew2VlxRi6te2wxroyK8MSpgE0L1SM/3HFl335yBuGZ608Kusd/25OKNZzd63O4TqEbWJIPHfZBKXBhfrcDRVM/7MO3aAJxQtrwaUC/BJwg2medRLblEgZEBNsikdYshNUcF46maEOTVeI5BHBMYDLnUc6RlZW0o1uWX1S3r6m4fHHKc+lIJy+nFktytQBo9PRSHTCbP+6ALwG8ZnqMckwO1yD9U5HE7Ldr00T8WQOshpotO8iqrLPAzqDv9M3cwrRh/fXmzh7Vg63LNw0xWVFV6Tux49Nm5GDI8zOP21SsOi0WePKGrBn95Yb7H7bQGwe2Pr0F5pee2k+T4ABxqJRbz8gsG4abLkj1uL8yvwqP3fNcie74enUQb/HxQ2spqrw/+dRZGjTuzGBrrfcW6tbIE71wchz/96U+ora3F3XffjVtvvZW/Vb0I1x2tH5fyNfd0vVi/4L9eUazzaTQ7K1or1ElrRROh1SM9Fcn1CyHV0sI8rWmrL9pzG65gq7G1ug9OlwRVVZ63E5er9U8iVbS+3QW7x0K9vmWDVjBtjVLW+j4q5HaPhTqxy+weC/W6fZTA3EYvvq2N1kCbs/X2CVqkytOKooT6b1sr1NvzM0fFeGtHigpl6t9udT/Nrf9MtjWibW7j9Wkfra18L4i18ZC/u89haX0fqT/dU6Fet93Z5tfZ1nbWO0RGRuLRRx8V77/99tvYuXNnT+8SY8wNjm5kjDHGepjVWAqZpfVF2bqTzViK/Px8jBo1CtOnTxcTTR988EH8+9//FoslkfDwcG6NYb2XlHPWGWOMMdaHFkVqSoJbv82ARJIJp3osTrr2wJx9EnNvuh/RF98Fp7kaax9exDnsjPUCPLLOGGOM9ZNFkTyJuex+ZH32ImxVZSjevgqh0y/vsX1hrF2k/Wdk3Xv2lHmNzbtO4olXtuBQWufjwHQquUjt0KhaZkhTgsiUQB0izU6E69xPOJygV+PYD2k4csD9xMrsigp8bzmO+MURbrNWtcFqRF0fjimDtNC6yfOWS4CR4f6QDNEiKML9P9iR0w1w+tkQrW0ZR0kSDTqMD3ZibGCQ25ZxtVSJcE0QKizhgMtdQogEvspQDPaVINTH/T74KgOxq9gJk61l1COxOvTIMMqRaAhxuw9KKKGpikX87HARw9kcJVROWuSHhMQaxAW77wkfqDeg2uT4f/bOA05u8sz/P7WRpveyvXjdu40LvfcSCBBCIIUQQnJcwl0KuVwu7e5S7i7/tMslOZIjIRACJHRCN4RmwL2X9Ravt+/O7PQ+kv6fV7NtdqRZgzH22vry2Y/Z1Yz06JU089Or5/k9mFetXsBTZ+HhzEpYONujOLFMxWxksWC2W3F8ian4eRPbxBef3ocf//vL6OlSL2LdubUP//GtF7H57UOqywd6Y/jrA9uxstauWupgElglPt+yKpjsKvtJU2hYWY1H3+hEZ3fRuUaN1ac34JS19arLHH4L8j4zHn5mn+JQM5V0Koc/3bMJ8608HKby84Gcxhef2YibP7QAzXV21W3M9lkwuH0A77xxUHV5XziFX67vhO+8JjAqPvSCwOIjn1iBj926Eg6nel3K7Pk+vPL8AcXWUWfmwFkcqL/yc6AYFvHOnRha/6RSuK2jc7xCUdQR/8wUdDeYw0Svyp6ert4o7n5wh+KqMcaZq2rx6esWw63xxT4doXgWv3yxVfFXJ98bS9xm5PoTGA6llOUcS2PWXA82R9PIiBIaTRwagxn0HJgQCqtOa8CNt6yE22tGPJfDg3t246WDByGNfhHVGC0wbs6jb+MIaJZC49XV6LAnkCoUi+RsBh5Oxo7NB+NKMSXxXI9l8xiIF/NLOZrCYosN3W8PIZsRUdMsoP5cGr2FCUeORosbkVwKkVwaVpbFxYqYIrZ4xRh4xobeJIeOeNEistbkxXA2gqxYLDg0Mjzm2q3g6EFQlAwL50JByiEjFh1YlHZNlA9bgmFkJBEmxoysJKAzPhHDHLsL8x0JcHQSkszhUMKDLaHQ+Dh4BQt4hkNPMgzIFJyFGmzvzCv7SrDyHOZAQNvbg8qxmL/aCuOSDIL5+LhffRUbwJt78khkJHgFHo6CgD3dE24586ttCCdzGIhmYOJoLLMY0b5/eNx6M+A1w2TkFBtG8jE6v8WtWDnGRz3CiWf+TR9agEvOblYcgYi1ILEYJFaDBOI1ft6lc/HhG5cq9oLDgwk8cM8mbHmnezyGxcurFXvBQI0NmXQeT/55J55/cu94AySP3wLGb8a+geLYEs/13sE4YqMNkMxGDrO9Zhza0qfYFgZaXIgLHHqGEuNFpJec1aSIZouGe83u7f1K3H09UXA8g+plVdjdHR0X6VU+M267YSlOWRxQBNObf+vAw3/Yimi4eM4Re8fq+V5s642CDN28Zhc+e+NStDQUbwxJgejzr3fi/sf3KGPnthjQwHPo3DvhuDN/sR8337YatfUOZPMi/vB6Bx548+B48zG/jceslIihzUU3o1PPblKsM52uosVmOp3HEw/twAtP74NYkFBdR27GKPR1F89h4sF/1vmzcP3Hl8M6TfHvycyxcoPRItq6GT3P/h9ksYDv/MPtuOuuu451SCctuu6oPC6Rdf9wxG4wjvN/OiPcYHSxfpjoF01lHn2+VfFGJy4tUyGNcL74yZU44wis3HYeCuMPD+3A/kkifDIuh4AWnxkdb/cowmEqBp7BRZ9djGczXYpgV2MR70aIyqA/o25JV2uxIZ+0YNeA+sypy2jAqfUsevg+iHJ5DBzNYK3PixrTEAqyuvUeSwewN5xFOFfq8z2GV7BhqYtFLK9uc8jRAg7GfdgwPKgZw2qPDzvCI4jn1WNoMvuwY78BXeHiDdFUGmwmLGrMo0subxZEsLA8mME6vLktirxKkxziXb+m1o6+PcOIxtRjICKdNPGZ6rk+HmOtHYucRryxrl11uc0u4JyLZuPZJ/Ygnyt3yyFdQS/+0ALFLz08euM3leaFPgxzNA5N8Vwfo9prVvzat2nMIBNf869/fq0yI68GuTl4/PHdePqdboxo2EGuXRaAeCimNGFSw1tlxfnXLcIl581SnSUiQv2BP27D5nXtyKq4wJCbm3OuW4QnBqIYiKjHsCRgxT+cNUvTQ57ccDz0+83Yvrm3xHt+DLPFgL/7yplYtKxYtKhzfIt1wsj2V9G37o+Y7bXg29/+Nj784Q8f65BOSnTdUXlcIi9/CbZpXL4qjm8iC8d5P54RYl1Pg9F5X9jVGlQV6oRMVsT+Tm3f58OBNFDq6dH2Mx+JZJAPplSFOiGXFbGnb0hTqBOGGW2hTuhJxHAorL18JJ1DwZpTFcmEvCRCYPKaQl15jZjQFOpKjJkY0qL2OOSlDOL5yjH0p/OaQp0wmExqCnVCVyyFCKsdQ6KQRTJZUBXqSgyihEwypynUCf1DCU2hTujsiWLvzgHN5aQJEZl1VxPqY0J5384BTaFOGOiJagp1Qt9wEoMVPNfJTHzn6CyzGuSGobrZpSnUCTv3DmsKdcJwfxwLmlyaj3OtZgOsLK0q1AmiKGPngaCmUCe0hlKaQp1QXWtXxlsrYyKZyOFg+5Fd/zofLK6lZ8O17Fzl/3/4wx/i5Zdf1g+BzvGbs04fwc8MYeZEqqOjo6Ojo/OB4F5xAS655BJIkoR//ud/xmuvvaaPvI7OMUJ3g9HR0dHR0TnJfNanoxAfwbU3XYtUKqUIdeLB/s1vfhOrVq1Sluse7DrHHFJVP02juWnfP0PQxbqOjo6Ojs5J57M+HRRue7IdsJ2GfsshJDp34aYv3IXqCz4Bg82le7DrHHvok8e6URfrOu8Li+d6sHX3oGreOm9gkE4XlGI3kkM7FeJ28dZrnWhodqGmzqG6/ta9Q2gIWLCnM6xZYCrYBTAsrVlgynA8LByHRL68pTu5v/aajIqjSX+y6OqhWmBKmbULTE0G0HkDGIrWLO4UGBYsxWvmrVsMFrh5DqGseq60i7dDlsllm9EsMLVynHYMFAO3wGEowyOhkbc+22HCiJtHm0Y+d73dDJtsQBTqs4BWjofJDRhYetxZpCQGhobFw8Jh4xHRyFtvmWVGKGRA5yGN4s4qC5wGGqEh9RoCq41XbAYNBgY5jQJT4hZjdxrHHVamsnBRAOaCiFaNc26swHQgmFTN1yYFpk0aFopjefN9HSNw2QXNvPXF870Qzbxm3rqvyoo9nSOob3KqF5hms0g6JfACi2xGvcDUaWDgY1kMxTWOhceM3QeCmoWypMCUFPSSzauNAxnn8EhKcd4hLjY6x6fPeiUarroDPc/+FrH27eh/5U+oPu9jxzokHZ2TCt0N5jDRq7Kn51BfDP/7p+0l1o3zZrkwMJxURBkR6jdfvQAXn9mk2NsRiBfzfXdvQHtrUBEOF1w+D9d8dAmMpqKoj4TTitPE+lc7ld9rF/gwKEkYGhm1TWRpzG12obVzBLm8hBqnER5RxqFJDh2NS/3YV0NjUCrAKrBYXM+jPRYctyystljAUhQOxeOKaJ/ndqMrGkOqkD9s68YFfgf2DUWRFSU0uDnMbS6gLzMRw3yHG3PsMXB0Cgw4WAwuRHPE0aUYg5GxgaZoJAsRyDKFrOTH/ihZXzEGgeEhMHbsHBlSYljhdmCeI4OcFJ9k3ejF5mAEWUmEkTEjN8W6scXmQkBIQ0QKFFgkCy7sDE+MQ8BowTnVMrzCMCSZwuauZjy8VURstDiRWDc2CBZs3x9Wli9fIMDdHENotCCW3Og0Wz3oToSRlQowiRbEOzzY0TkhuBfWWyF74ohISQhgUD/sxN6toXHrxuqACWsuoGCoCirCL3EggDfWpcdtE4l1Y/VCJ7aICZDbgIUUD/POGAZHi4/JOdQyz4tDHSPKDaLTbYTTbUZHa3DifJjlQiKeRXAoqdzENbd40EbsI8dsE2tsuOkzqxSLR3IjuW59F+59dDeio2KWWDfO8ZrRNdm60cihZ/DwrRt3bevD/b/diP6emKp1Y7XPgs/csGTcunH93zrx0B+2jN9YGE0cquZ6sa2vaN1IroHbJ1s3yjJe7OzEg3v2IJHPwSUaUL2HRceWiWuzocmp2C8ODSTA8gycZ9RjcyQ9foMVsAmoBoWu/cWxO3t1HT513SK4HUUbVvLexx/cgRf/OmbdWLwxmWzdOHueF72HIkqRqcNlVKwfTzu7WXVMTlaORzcYNSSxgJ6//kbxYCf85e6f4tJLLz3WYZ3Q6Lqj8rhE1v/TkbvBnPbDGeEGo4v1w0S/aA6fNzb14ImX2pQZTeLaMZVZ9Q588uqF2PJqB15b164InsnYHQKu/8RyxGNZPPHQTmVGbjIsR6NmeTXSNIVQOI2gyszoomobmFQO4Vkm7JLLZy0b3QL8bhkGhsa+kXLrPQtnQK3VCrHAYHtXGskps7OkKdLCgBM5UcJIKovBRPk2VrcY4fWksdBZgIkr34aRsYKledCgEVW1YjRgJOtGPE9h90gYKbF0ZpTcYFxU64KLF7E/msNgOqnaFCmeA6pNMmSUO3IwlAWDaQHNVhkt9l7QVOmxSOcEPLenEb1BDgfaE4inCmVNkc47XYDZnUGykEUwW/5Uwp7xY6jTCLO3gF5pQjSP4ZVMsHTxqG8CnPMHAGbKbHzOgJ6NXoTiDPYJGcTE0mNBHmSemhPA9aSQiuUwOFA+G0+e2pCZXyIgO1XsP90+s+IfvnJNHS66cr4y8z6ZZCqPPz21F50dIYzsG0Yqmi1virS8CpSFx6euX6xYS6oxEkwqIn3z2xO+75ObIhkbHVi5rBpXXzhbuRGdzJg4PtAeQlsii0iq9Log978XnNGIM8+rwR/370JntPzaa05bwG/LgwWt3CBPxVpthbTIB56m0LVjcPwGZgwjz+Ijl89FQODw8L1blJvpqcyZ70U+JykONGTWvXy5D5+4fTXqGtWbhZ1szBSxTpAKeXT/9W7E27djcZ0Hv/71r7F8+fJjHdYJi647Ko9L5O2vw2Z57z0cYokMHGt/oIv1Ewn9onl3/PqBbXjmbx2ayxdW29C3Tb276NjMYXqKGJmKaZEPvRppEIRZ51Zhq4pgGWNJrRn9eW1bPJIy0jdQ+a69wWlGVwU7xy+fz8Fq7tFcTpobJfLatnayTOORg+UdVCez3G1HKKvdNXOhw4eIMouvTr3FBp9R3TN9jE//pK7i8kuvkDCQU08XIVQx1djWr72fywIWNHoqd7y95xWPpj0o4YyDErr3aXfNnLvQh/27tcehebYb3/6vyyrGcNsNDyg2oFr84zfOxbJV2v0EnntiD/70u82ay0k6y7/95IqKMdx451NITrmBnczCDzvRltY+FouHbGh/Vvvac9XY0J7X3kdCXbqgPJ3QgjQqax+dkVeDeODf8ndrK27jZGEmifUxwX7wkZ+iJtevCKZf/OIXWLJkybEO64RE1x2VxyVyEon1mZNdr6Ojo6Ojo3NMoVkO1RfcjGXLlilOMX//93+PXbt26UdF5xicjLTus66jo6Ojo6OjU6aRWA7f+ta3sHLlynHBvmfPHn2gdHSOEvrMuo6Ojo6Ojs67QhAE/PSnP1Vy1hOJBP7u7/5OF+w6x8ZnnT6CnxmCbt2oc1QwCpVPLWKrVwli95jPiyjk1VvWUwwFzlT5XpOZ5l6UIwYjFa5VjmZhoCnkNPKkSTGmma98sdOVNjCak05cTzS6xUOSGXAUjbyKDePY+i0ci5B2+jAEpnIM3DResxQYOEwUIin1caAomXjRVFwHTypyK5Elvn+kAlR9G5JEbC9pJCX1XGpKlsFwlWNgmMr7KQiVbQVz2QJ4nq2Ysy4a1I/TGFKFnHsCLXBKXj6j8SWSl0QIZhrJCr1zpjsWnKXyOBBnGEqUygq/x5czFFhy/VbIWZ9uLKe7/k9GjremSJXIx0Lo7y/WPXzta19TmiWRmfVPf/rT+N73vod58+aVvF5voKRzVKBPHp913Q3mMNELPd4dRJS88Hon7nt8j+KvPtlN4oYr5uGq81twYO8Q7v/NRvQcKi2OnD3fq1jaEVs94oM91b2jerUbyeUMwvksGvMO7F4XRGGSSYnTbYLQYMeegRgWNznQU0ggNslbXQCFtXEO3e/0o6rZjsRKFr2GSZ7iMlBP+bDvQE7xBPdX8dgfKy1UXVEvYG5jHKlCCkzOj6e3ZjDZMKbexeLq5TLSGELA5AFHhSFhsrhhQcGLvlQIdoMVNq6AgjyxDSLgozk/NgynFP9sp8GMg4nSgr21Xg+ub7bBQGfRlTDg8a6DyEkTYtEj8LioxgieHUJe9GHTcArx/MSxIDcBp/icsBqGQMOKVEFEqlB6LGwGPzi66CP+/BY/fvVCrvRYNQhw1OYxnEligc+OYWkQBXliIOycgNl2M4YyQbgNXmw6lEMwNbEOVqbQHHWjdXNYsW1ccX4OrKc0hmi0Gq/sBQqSjGqbEbsHS49Fo8EIS28eAwMJLKixK4XL+UkHg3iuB2psild58xwPgkMJRMMT7j3E+eWSDy3AVdcvAq8hMjeu78KDv9usOJwEqm04MMX3vHqpDdZLOQxJMZxXMw8X1y4Cz0wI0lg0gz//YQtef7kdTbM9iitMZNR+lMBwNDxnNGBLIoMalwlfumw+lo7aMI6xPdSNRzu3ICMWYOl1YtPzYcXmcwx/gxWYZcTBWBJLa21K8XROmrgwrDyL+VUmHEoNow5ODDyaRKh94rynWRreM+qxNZmF28LDlS6gp6O0UHV2o0OxYc1kRcxyGtFFisQniXpi3/jx21ZhzgI/Xnx6Hx5/aEeJm5PZasC1H1uGcy+eM27ferIzVmA69/b/B4Yv2mLOBFirAxRVFDtSPoveF+5FeuAgaNaAmktugdHfMC7sX7jrQ6ipqTnGEc88dN1ReVwiW7515AWmK/51RhSY6mL9MNEvmvcGEep/fGIPnnutE2euqsWnrp3waSaIooR1z+zHY3/aDoeb/J0a92me6oudZSQ4r3Bj3xTXEZ9ggtDF4eDOOOoW+7FjOIHsJMs5i8BidoMFe+IRrBR5ZDcFEZ0klIhomHWqHweaU7BwFsR7ebQPlDq8zK2xIsXnIXN5XLAECIul7ilW1oy+ASc2dGZw42oegmkA4iTRytEcqkx2iNIgONqLoXQaKXEiBgoUqkwe0FQIGdGIHSMcuhKl41BrdiIj5sFSIj47rwZeY7Jk3l6UebwzlMU7Q/24sNYFv4kIyslWixwiGQ82D49grsOBOivxjZ/c+IhE4UU0OwKWFpQGUhJKxzqbc+Pnf7Vgd5+MJYsNaE8OjzrFF3EbBdS6OAwXgljq8iCaH0F+kmAkTysE2YvXO6OozTgQ2pbBcGjSOFDAqlOcaFw7ggLFY2ObA7sHSu0gm11W5EUR0UQW8zICWncRX/hJMTgE1PAsencPYvZ8H7o6wiWCkdwENrW40b5/GAuXVuOmz5wCf5X6BzU5F+//7Qbs3j5QeizqHRAlCbFUGrNudqOLD0KaNBIOgwnXNC7HclcdXnqWnN87kJp000qaNRH3mbbWIFwLfThoM6BvSmOkCxdX4Y6L5kDicvhLx2bsjZQ6uPg5O0beYdHXmUP1Kg+2x2LjnvkEl9GA5oABPekhLK1xYEQMIT3q209gKQZ1URf2/WEY9jkeHHII6I2UzuwuqbYh3h5WrBztVh5tXaU3UnV+C8ypPGL9cVz90SW48PJ5JU8wlD4J927B26914uwLW3DdTcthsb13X+QTkZnmBqMFEeyHnvglkr0HQHM8Gq7+AkzVzchFg/jr587Qxfp7QNcdlcclsvU7sFmPQKzHM3As/44u1k8k9IvmyNDqXjoGaUrz7//0nGoHxLHZT+oWB6I57Ufv/lg1tnZoWzWeazei+7UuzeXeFhfesbAlwq8kBprCR65NIDlJZE9lkcOHcAWbxIDRj4G0tk2igbbhtYEEiCu6GqTx0I/XNICltcchkisgL2vbRTJUAKJcKj5LtgErJBCBrJWyQeHfXqtCf1LbsvKyeXYM57S3IQxU4aVHtO0mPX4TuuooTatGMg5z+ij09at3myWsqbahtYI96NozG/H5L5+puZzcIH7xlr+odsQlEM/2lq85MFAoNmRSo3m7H9tf6NNcXrO6Bq9mtW0YPXYOTWuHUNBIgyI3a+m+OrSFtI/FpSuN6M71am8jE8CLr2nHYOdZGHvi4w2bymKggB986UwsmOutOJYWqy7ST2SxTpDyORx68pdI9rQWBfuH7gBrcehi/T2i647K4xLZ/t0jF+tLvz0jxPrMSdjRmdFUEupjYlxLqBNIc5acRr7yGPlJKSBqSBpiY/z9BUlTqCsxSHLJbLkaMiovFzVE18Q2RE2hTiAzpzRVeR3TLaeU3p/T7UOl3GpZyZ2uhDTdfmrUIoxBumhW8lQn4zDdOshTmyPJH5dESVOoE0hOd2GasSR57pUoVDrplXEQNYW6EoNy3stHdCzy08RAnlJpCXUlBpmk8VTOk9eF+skBzRlQf9XfwVw3V5lp73riF0j1F7tP6+jovHd0sa6jo6Ojo6PzPgr2z8NSN0+Zae99/vfYsWOHPro67z/0yeOzrpfk6+jo6Ojo6LxvkCLTuqs+h+6n/xfxjp2KWwxJMzj99NP1UdZ5/6Do4s+RvP890Nraiocffhjbt29XmoN94xvfUH3dxz72MeRypaYMhHPPPRd33HHHiSfWycD89re/RXt7O7xeL2644QZlZ98Nzz77LJ5++mkMDw8rtlK33367XvCio6Ojo6NztAT7FZ9D12M/Rz7Tiy996Uv4t3/7N1x00UX6eOvMWM466ywMDg7i+uuvx1NPPYV4PK752kcffVTRqrfeemvJ3xsbG9/1do/7ZwAvvfQSli5diu7ubtx4443w+Xy4+OKL8YMf/OCw3p/JZHD11Vfjs5/9LBoaGvDRj35UucM///zzlXXqHHuI48Zf7t+GeQv9ikvGVMwWAz7+2dW4fdlyuARBtdhwnsENLphFs0e9QGtutQ0DAoPGJQHV5R6/BYJDwOoqGwSu/LIgjjIr5joRHfHBzJbbqxEfFb/Rh46YBCvnUN2G3+jCAgeFRqtPdbmJNYOmeCx2+mCgy++jBYbFWQEPuhN5yHJ5DQDJPC5IJohSFgxVavs3Bku5wVEcDHSV6nIKZoxkzcgUAhr38hz2DdfCJRjhNqqNAzDP7UZnEHAZ1GMgfx8xyVi4zK3ak8LrEnDDVTQ+cYoAs8r5ILAMVjXa4F7Gw+cxlcdAAXPnedHt4FE7260aQ22DA0MDccWJiOSmT2UwmsZ/Pr8fNafVwWgqt3MkjjL1a+sQ67bDyZWfc2S3alg/Dhp41M3xqMZQ3eBAPi9hZY0djIrRvsdqwDmrWCx2eWFkymPgaAaBQg34DIsqq7rl30K/A119DKoF9XHwGewID3BY1uBU9XZ3mQ1YZBewYLYbFpVx4Dgacxf58evXO9AxpF3seyREI2n85mdv4p7/eQvxWKljzuFCrDe/9/XnsentQ+97fDqVO51WX3AzzjnnHIiiqMxAEgGjo/O+zqxTR/DzLvnf//1f7N+/H//+7/8Olp1+vnvWrFm47rrrSn5OOeWUd7+rsjxNddExJJvNKju6YMECvPDCC+N/J00Xvv3tbyuPIBYuXFhxHaSr2mOPPYatW7ciEJgQaul0GmTXTabyL3s19Krs9590KofHH9yBF5/ZP17I53Qb4XSb0dEaVNw2zr6gBdfdvAxWW1GkZwoFPLJ/H55ua0NBktBgsiOxtYC+jlE3DApoXuTDgVQOkVQePpsAj5XHnt4Jl5iFFh6OQzEMdkfBCyxqFviwvT+Ggli8FBwuI0yNDuzsjymia0lzqVe7yUDjiuUGpJl+pWDUzTswlBHRmyyKFQoyVns9MLJxZMUszKwJS9w2BEwTd+DhjAk7RnIYyUbBUCwsnAv7o8MQRy9HgTFCli04EAsqMSx2edBsHRl3gSG2e4tcs2A35JSCUUk2IlEIIitN+H+zlA+iTOwZ06BhAs8QcTwhqGTZiKyUhigTe0YG2YIXnbEgxNGiSSPDo8lmBUUV3W2imQB+tbmAQ7HiOLDEkcXlRlskjJwoos5qVQo/exMT21hTZ4PRGEOykIaJFZDNOPD2oYljUSVZIO+l0XUoBgNH45LzrTj1zENguWIBayJjwtO76vG3NuKQQ6wE7UhQYcQLmVKf9i1hZLMiaqutSNl5HJhkQbjaKkDaG0QklILNLsBfZS3xSa9vcuLjt63GnAU+pbD1gTc7cd/rncjkizG4OAYrCkDX9n6lmLJhkR+7TQwGRotHDSxw7hkCIsKg4mvuNdjQ32rFztYJYbmKeAG3BhEeTsFq5+FsdmFHbxTyqPmmr9oK0WtC61ACHEPhrKVmWOt7INPFbXCUARztwP5oUClAruG82LqRRu9QcTnLAEvnOXEgHkU6L6LOboKBpdEemjgWq2YZwTsiiOSSMLM8uLAXf3s7Ne7VXuMywmxg0ToQV5yPlgVs6Ns1pHjLE4hYr6+xYV9bSCnEbpnlwiFKRv+oJSUR+9eurset586CZZqmSIcDKSp/6a9Fr/Z0Kj9+437NjUtx/iVzQE/T5IrQ2x1Rejns2THhTLRoWRVuvm0VqmrsOJ7cYBZ99R4w/OF9H80kiM/67246BY8//jj++te/Kn/71Kc+hY985CMlr9MbJ5Wj645p3GD2/seRu8HM/9p7doOxWCw444wz8Nxzz2l2+f3MZz6DX/ziFzhSjmuxTh4xXHXVVXjooYdKLmySykJm2L/yla/gv/7rvzTfT2bOm5qalFn4r371q0cUi37RvL8cOhjGj767DtGwug3irDkeZTad+GGr0Z9I4DdPb8eml9VtEI1GDg0rqrChawT5URE+GRoyzneY0dkdRThRnlNGqJvjQiLAomuS+JxMo9uA0xbK2BFWj8HEMLi2KYAFzigYunz2llx6bTEX3hoMIpZXnzG0GxxoMIsQ2LD6cs6GOXYPUlKb6nIiwgW6VhHpFKUeQ1a0oT0WUgS1Gl7BgVcPCnilS90e0MnzqLPZsGO4tFHQGAJL46xGF147OIKMmquIDKym3ThnWT9sDvWx7hnx46lWC3rT6uPgkAW4R1x4szsCSaVrLOnieqHAo2NLH7IZdYeW0y+bgxdTWfRpnJPzzDyMDIWtGrO7PheLFQsEvPBmHNKkZkVjkG64F1gF7OwIIzW5g9YkFq72w70kBIlXf7RqYUlDJjs27FCP0WnlsGCuFRt7Q6p+PgaWwrlzLXj9rRSiSXWHlxX1DiQORhAaVD/eNX4LGL8ZW4fVj5XLYsD/u3kl5mh41x8OiVgW3/vn59HXo27FWtfoxDe+fxGMJm2XqZee2Y8H/m8jRJXrn2FppXkTac50rJmpTZHebQMlMo0R2vwiRrb/Tfmbc8lZ8JxysdL0TW+cpI6uO2a+WF+4cCEWLVoESZIwZ84cJY+dTEK/W47rnPV33nlH+XfFihUlfyd56+TD7e233674fjKA5NHb6tWr8dOf/hRvvfUWaJrGmjVrlBwiq9V6VOPX0WaoP64p1An9vTFNoU6oslgQmjRzOZV0Oo9MpqAq1AlE0A3SlKZQJwx2RRG0aHtDHwzl0JzStjBMiSKcPJlx1PKnpiAweU2hTkjkoxBYbQ/saD6GtFSpE6SoNH3SsmtUvihlUVOoE4YzEbzSpX2thLNZ2FWKaMYgAr0nXPxXPQjAUJvWFOqEWtcgBjRENiFCZSCTJwwqQl2JQZSRkGRNoU7Y2x5Cn6BtQbgvmVVNkRpjaKSAgx20qlAn5CQZIxyjKdQJ+WRcU6gTEoUYdu/Xnn0Nx/OIZQqaxpu5gozeblZTqBOGw2mkNIQ6oXcwgYJZ+6tjJJHDoVDyyMR6PKsp1AndB8PKbHslsU6aXqkJdQJ5ktfeGjwuxPoY5tqWGe+zPh3V538MBocXg288hsju9UoH1KpzP3qsw9KZqVDUERaYUuPifzI8zys/R4rD4cCpp56qaNhwOIz7779fSZ/59a9/jVtuueXEEet9fcVmImQWfSp+v398eaXCVMJNN92k3NGQR28jIyPKTPsvf/lLvPHGG6rrHkvBIT9jTD2YOjo6Ojo675ap3y2VCtRORDwrL1TSffrW/RHhXW9AyqXhXXP5sQ5L5ySmrq6u5HeSZv2d73zniNe7c+dOZXJ5jH/4h39QCqyJwcnZZ5+N5ubmE6PAdMzyRi2Jn+M4VUucyZC8dILL5cKLL76IT3ziE8pgkRn3trY2TbsdAhH05DHL2M/Ug6mjo6Ojo/NumfrdQmqyTjaci05H7aW3gqJpRFs3o++l+xUzCB2dY1Fg2t3draTCjP18/etff18OxGShTmAYRknJzufzSpr3u+G4FutjaSpJlbbmiURi2jSWsRwkYvVIBmmM5cuXKx+Qzz//vOZ7ycGafPB05xgdHR0dnSNl6nfLnj17TspBtc9ZiborPw+a4ZDsacW3vvUt1e96HZ2jLdaJVpz8836kwGgxZnRCsjxOGLE+e/Zs5d+DBw+WFcV1dXUpqS2VGFtOZtanQv5GPii1IAdr6gHUef+orrXD67doLvfPcWPHJMeOqbQfisDlEDQylAG72wB3nQyzUT0HmaQeuzgaXpd2QVfNLBdarNrHvcXPw2EwasdgMCCUkZET1WMgpd0ugUK9WTsH2Wd0QZS1c/fd4GGKiUXfRlUMECUJsqyVi03Bzjng4tVtFglVJjc+NFv7WK0MWHHdXBs4NR9GUoRj4OC0ybDyrOaHUL5gxkhKO4aBlA/z/No35/V2AUsXiOA0EvusAoslS/yKE4tW6qLfbECLij3hGPMarZjdoB1Drd+Aqnpox8DScGcKcGhsgwyfrcEEHtouJT7BiTNWaMdY7TPA54LiKKOGmaIQCObgUbHELMYgI1BrQlWz9nk/7xQrli3WjsHv4tERiyFbUM/Nz6TzeP7JvRgJpTTXQY5Ts4btJmHOfB9MZu18dcL8JQFVK1gCcYGav0jdxvVoM/W75WSunbI2LkTDNV8AzfHYtWsXPv/5zyMSiRzrsHR0jhpbtmxR/m1paTlx3GBIvs+SJUvwH//xH7jrrrvG/75hwwalSPR//ud/FGtGLUhOO0lfIZ2ifv7zn4//nRSd1tTUoLa2Fps2bTqsWPSq7PeffF7EM4/txtOP7EIuW/xid9faIPvMaO8p1gicvrIGn75+MbyuoqCNJbK4//E9eOH1TsU+rjZgAc1QONRbzPskD1AWne9BFx9BulCAzcDDnnNgw/b4uEXdYrcZ+f4EhkMpcCyNuc0uHDg4gmyuWHTnrbIAfgv2DRbXubDejhCdRmg0z9RuZLB2AYP+3CAkyPAJVrA0jb5UdFx8nub3gqbCyEt58AyPRU4X6ixRpaCTIMlmZKUoJJlsg0Yk68Nz3VGkxeI42DgzjCyPwXTx7rve7IbfGAdFFQWOQaaxuEDDHGtXikdl3o2kz4eMMCGAWMqv2DLKIPaOPATGBwrx8RgUz3WaBQVSnEkhkRewc6QTOamYXmblTKhXKu2Dyu/pnBe/2UZhX6g4Dk6BxT+trUKTPQmKkpEXBTy4N4cn20LjFpbLa5wIyyNIFXIwMga4GBe29pCYijE02uzIiAUMJJOK1/g5DXacXtcDA1uMIVmw4K1BO/ZGii4wtSYXekMyBuLFFDczx+D0Jgui4hAkWYKVteDgfgfe2J4ZF8CXL6/B7RfMgdNsQDKRw2N/2oZ1z7ZCIifQqOc6ORcH++JKMW798ipsoCTE8sXzwefkEajlsTdYPL6zXTZEBgvoCxZjMPI0Vq0w4mA2qNiJeo0mIGzB9n3J8XE4zSwgtXsY8WhG8W2vmuvFtr4oxuofmxbagLkS+tIJZWSWBVzgTcMQURwHMyvAZjCjP1UcWyfjwlsbeOzvKhYfCwYaq1cY0ZULIi9J8BhN4Apm7OgZjUGWsVoyIrlxGPFIBoKRhXdZFV7PZDFW99tcYwZtldEVTSrjtsRhx+DGEJKxYgyegAFzL+HRLRdvogMGJ9p2CjjQVVxu4CgsnmvHvkgUOVFSvN//7rR5OLPJP35OvvVqJx68dzMiI2kIAourPrIYF185HyxXLqrJV9Pr69rx5/u2IhYtHk+H04gbPrUCp519eLmeoeEkHrhnEza9NeGvvvr0Btx4y0q4NHoyHCs3mBX/9sQJX2CqRax9O7y7HlNSV0keL/lun5pCcDKi647K4xJp+ylsGj0mDmt842k4Wv7hqLjBkB5BbrdbyeQYg9yQkpx1ksZNnqiZzeYTQ6wTrrzySkVQb9y4URHXJE/9sssuw759+xRj+sk7++EPf1i50H/0ox+N/40k8j/wwAN48803FeFPINW4pP3xfffdh5tvvvmw4tAvmqMH+UIlX+D9eRG7uyIQR0XUGLyBwfWXzoXVYlCEenzU13kMoj3nNbsgO4BUQxaD6fJHqbVmG/I9ArihPNo7yu3/nHYBAa8JGbuAbQMxFKbGwNJY2GyHqzaNtDCsiM+pNFk9sLAy/MYskoXyojEXb8dyjxECk0FeLnqXl2JEa8SOQwkWfamg4uE+GeKtPsfuwnImiqpYJyixfGZStDUi4XVCYgoQ5fKiaIZyQKAdEBhLief6GLLMoZ843FApcAyJcaprCI1D0SqEk1ac1yiCpsqdakJpC+7ZlUCaSWIoUx6DV7BBTBshFhi0hssfBdp4A65o4ZBngbcGI8hLpTO0DEWjVvCClQGOjyAllue6Oikv2vfZ8cnT52G+ip82sQ59+N7NSKcKaNtf/gTHbOVhW+pDqsGE/ZEIslMaJxEf8gUuBxgUkDBFEcmWx9BkcSFzgIapNYb+g+XnnK/KCqbBAmYpi9ZM+ThYOA5Lq03w2yRFpBfk0nGgSQurfAAHungkjFGE1WKwOZHrY2DcGkOfynnvCVhQmO+GWMNjd5DcRE2JwcBiDm2EN5DFsD2o+MhPjaGKCqDnoBGDhTSCqYnCyTFW1XlwU3Mtnrl/O/bvLj/vA9U2fOL21Vi4VL1RVypJbrC2K3aLH7phiWLL+m7Zvb0fLzy1F5d8aAHmLz42M+pa6GIdyEWD+NVl9UqH06GhIVRXV+NXv/rVSd9lXNcd04j19p8fuVif9cV3Jdb/8z//U5kwJpDeAR6PRxHshE9+8pOKZiWQ15CuvaTbKZlFJ2kvZFaduMPcc889J9bMOoHY3ZDOpevXr1csGMcE+sMPPzwuvid7Wi5btqzE0pFU3ZPupeT1xD6HDBjJP//Xf/1XZSAPF/2iObq0d4Xxj997peJrTAKLVAXrvQXXOtGeUvfhJsyJu7HzjeLMpBreKiv2aVgcjnH6pXGkRO3C5otq3IjnizPRaiz3uOA1TjRomUo8Z8FfOisXTn/Z0A+6oG1zGG9ahqyGLzvBws6CkdG2DxRlGiO5jooxeAxzQFHacd7fJuPtIe39rOL82DapUdVUnEYDDNbKDkyLnCbE8to5rhfVrsYydzGVTo133jiIX/7odc3lRreAfasqpygsaqIwmNKOYWmbFQfe1B6Hhgv82FujfSyJWJ5fW9ktpH/Yi5649jqWdlhx4HXtGPynVmGDXfu6MnI0Zs3TPqcJqb46dIS0x+HsuAEH3+zVXE4aFX31OxfgZOREb4p0OBCf9Xs/vlp56keMH/r7+5VZSfJUnQj3k7Vxkq47jj+x/tZbb6G3V/2zjGjSqenZJGWbTC4TqT1//nw0NDS8p1iPa+tGgtPpVB4xtLe3o6OjQ7mLIYJ87FH+ZEin0qkDTvID7733Xvzwhz9UHjsYDAblsQR5fKGjo6Ojo3M8kI+FIfEnqyMKhVsfP6D4rhcWXoueQ/fgwL4unP+x21F3+W3grC69cZJOOZOKRN8T7+G9ZGb83UDE+XsV6DNKrI9BOj5N1/Xp0ksv1VxG7sjJj46Ojo6OzvHGydAU6XAw2D1o/uhdOPiXnyAbHkTvC39A43X/CM6mXXCsc5JCffBi/VgxcyLV0dHR0dHROeFhTTY0XPsPMDh8yMdH0PXIT1FInVzNo3R0JqOLdZ0PBOK8UolcJg9Gw/pvrMjUKGg/CCJZURyl3SqewKk4TkxGMDBK4aDmco4Gz1QubiPFj5XIk6rJSjHSbMV1kCJT0JU9YClU3k/FRqfiYvL+SvvBQJ7mo4PWtIosYmAqv19g2OK+asBSNBiq8limp8kokA1i5RgEFnyFOIlVJa/SsG0yDD/NOCglmtoYWVY5J7SgKRo8W3kb7DQx8HTlGEwGBnzFGKiK46RsQ8NCdQyGY8bdedQgLj7pVOVaDp0TC85sR+O1d8JgcyMXC6HvhXvHGx3q6BQ5Uo/1mSOBZ06kOjOSUCSNH/12A279+nP41/9ej76h0kK4XE7EYw9ux4+/+RIaGRqzasudO4i1osnIIZsTsXC2W7HXm0xNlRXOeR68+lYaTYxfETiTIfaNLUk3tv5tCLPqHfC5S4u4FPvGhT7sE0V4bQLmVpUXmixqsMNRw2L7biNqDVVlEitgtOJ0vx2R3BB8gg8cXSrqDTSPYE8t7viliAdebYQkTi1cpMFRAQhsHB9uYtFgKe8NMNvmw61zq0HPWgi4F5ZfvrwbcDXDHDsEc94NCqU+1MS+0RI3gd//CuT+QchSqegn1pYH4yZ8Z3M/7mu1IVPwlMUAeNAVM+LtoYOI5Y3jFoxj5EQB/7NewG9fScEt1sDElsZAfp9l9aIr049FNSZUWUof+xP7xvluN8LpLHIJC+pM5Y++qwUPQj0+PPE2B6tcXXZzY2MtCLbV4qu/6cD3H9+FcKLUoSRTyOLFng14nX8Hq+9worqh9HiT86txZTX2BSwQEhzmu8vPyXk+K5prgIFEHAs8HnBTit6IfaMn7cVfQzn4Tq+H1U4sMCcg9o1zF/jQtq4fzbt41BgtZQJ4RZUTswJJGGgW1abyY+ERXMgUTMgYglhW5SqLgdg3BlgfnmNz8F9UB5ujNAbByKF5eTV2tUUwN8qh0V56LMhltqrRhobmCAwMq7gdTSVgcCGyN4C9u5NY4naViXa/VcDlyzmwZ/Zg9Se9cEzpa0AsJJtXVOON7jC+8oNXsK+j1BWHCPi/vXgAX7vjcXztjifxxivtSqGWzskBZ3Gi/povgBXMyIT68P3vfx+FgnYxtM5JBk0f+c8M4bh3gzle0Kuy3x35goQnX2rDw3/dh3S2UCKMr75wNj5y2Vzs2tqHP/1uM4YHSwV83WI/erMF8AYWvIFG56jn+hjVfouynpFIBr5mJzYF45AmiUanjcXixRwOpkfQQruw/+UwMsmJWVSGoTB/lhttXWHU1NjQzQD9idJZuwU1dgTjGWU7Vg+D9ni8rCFSY30GsUISi5120BgmV9P4cgNtgN1gRygTgpz244HnRYQTE04zPAd8/RoOy2b1gaVskOQMJJS6afQlfXh7KKeI0fOr3WiyltokyhkJ6O8E0kHAXgdQYaLIx5EYASlrABk6BCFnh6lvP+j85JkpGqg/FbDySBYE/OHAMPZESh1azq924PRA0Xc7lBHQnyoVU6SZ0nyHHyyVxesHTfi3dSHkx8zDJ3nSD+SH0Gh1oy8ZQVqc2A9KplDFBbB/OAGfyYR0Po/BVKpMGFN8SnkgkI04sLG9dHZtlpfD2nl5JKQo5EgAT/4tg/yk73OLwOLWc1pwzapa7I524PX+7UiLkwS8BJj2erD7mQicfgt6fCa0T5nFndtgRYbPoyDLqPXQ6CZjPgm30QgHz6M3Hkc17cE7WxOlMbA01lIMerb1o2m2G309USTjk7ZBU2i+qgoHPSl4zUbUujLIoPRYeAUn8lIBBakAGWbsjZQ6G1kZE+icDV2ROOpMHmzqTJQcC9IQaVWUQ/fGfjTM9aItkUUkNemcooCWNT60U1l4rAb4qxKIodRVqMbkUKw0iTe+OOjGG5tKj4XXaUCgRsDBaByntZhgdPRApiauPbrAgN3ows51w6if40FHKo/wJCtW8oTsvFPr8YkPL8LIQAL3/WYDOg+U7mfLPC8+8dnVaGguv6GdiejWjdOTGuhE50P/hRaXgOuuuw7/8i//omoycaKh647K4xLpuhs22xG4wcTScDR89j37rH+Q6GL9MNEvmnfH//15B554sU1z+bJ6B7o2aVu5mR0CenlG6fKphX2RFx0V0mvO9JmxZ7uan3mR5nlubBxttqIag0Cj4JIgVQjiixdKSEvaNok9bU148m3tXMubz+Jx3emdmssliYVHqIVGQ0oFeXgIKGh3e5UkN+jwfs3lnYbZ+HFQewMGmsKHGvKQtduk4rltLXj7kPZ+XrzMiF5R+3j7OBd29mofS7L/kZAFmdFGRWrM423YeVA7hhsvNiFlnmiOMxU27sIjL2rPtJBJ48YVOaQL5d7yY3hiAezo0I7hPDOPQ+u7NZc3rbbDdLn2+8lRCmV5JPLaKSFcuAFbe7TXsYaxYu/GQc3l9XNMwNqRkhu/qTF0v1OF4ah2DJ+4mkGU1t4G11GFV57V7lRZ5zYhuW9Y8/qnaAo//b9rlSZJMx1drB8eIzteh2XH42BZVumfctttt+FER9cd04zLod/CZnvvdqexWAr2+s/MCLE+c54B6Mwo8hVElbJ8rG2iBmJeqijUCYVplk+awFVfPtrRVHP9IsnfrrwR0j20Etl85ffnp9kJmpYqCvXRICqvY0pToalMbXRTvpzI9MpxJka7v2pBOr1WXD7NOJMJ4unGKjfN8S5MaTJVFsM0A036Ik1tVFX2msIRlQtAnjQTrrqc7Mc0K5lmFRCnORYyWX+lm0NlrKe5Lphpjvc05yz5fKh0SpAYpSmNqnRObCwN88c7lv/v//4vnnzyyWMdko7OB8aMsW7U0dHR0dE5USFFlExWL6Cs1DhpxYrVuOaaa5Qmh9/61rfgcrnGu0fqnIRQJ491oy7WdXR0dHR0jjEnd1Okw2+cBMzCoK0F0dZNShfy3//+91iwYMGxDk7nWEDpYl1HR0dHR0fnA0JvinT41F7xWRT+nEA2O4A777wTv/vd71BbW3sUj46OzrFl5jwD0JlRXH3RbKxeElBdtmiOB7d8fDkuvXoBGLb8FHR5TIpLyyl+C2xGVtUPnazDl8ij0abuOb6k3oF+Fmic71Vd3uIzw9odw7lGHpxK/qzfxWH1GgNOm2OC06QSA0VjtWzB+keMEMPl1n4k39bMelHVEsaFa0ot88aY22TEIUbCSx0NyIvlPtQMzDCz9chKHGS5PAYpI6L313uw/583InlQ/VKWwxykvXsh5x2qy2Gtx5xaP74wOwAjUx6DT+BxQ7MTNs4LjirfD0pmQKMKFyyJ45R69WNxwwoe5zZGcXZA3b3jFI8Ddy7j8S+ne2HmyvfDbeRwyTwrPno6jWpHuc89z1L41GkCrjwvglMXqsdw0RoBZmccNSoWiMo6YEcww+KstWYIhvIYHCZOOadsIz7Y6PKiRjIKdWIVMlkZs/ylNoxjLKixocvEwn96vWpOeMtKG1Z+hMNCpw8GuvxYcFkDqJc8sD9vgS1dHgMNGtVsFVhjGvMC6kVX83x2BK0yWtb6FeeVqfiqrMhY7TAONIJTOecM4CHEG1HfbITfVT7WxPL9U5cICNiyqDM7VWNANIDtfQY0L1WPIeAxweIQULemFryp/HibLQbFDcbheu+FZTozG5phUX3BTWhubkY4HMbnP/959PX1HeuwdD5oqCP0WZ9BaTC6G8xholdlvzc27xzAbx7aofirux0CbrluMc5aXTe+nFjY3f+bjdi9vR8cR2PWXC/a9weRzxeLIk1mA/xz3djWG1OK8+Y1uzAQTCISy457Ys+e68HOZBbxvIhGr1nRQZ3DEzaI8/xWSP1xhAaTcFoMaBY4dOyZcIlx+y3ItzixJZGFgaOwZoUJ3fkQcqOFmcS3vUpwY1NnXCkyXGqwILIvhnCk+Mia2BquWetC3aphUHwORsaGaJ7CcGbCes/JuPHWBgP2d+XhtrNYuJhDeyI4XurnNxvxkfnAfO+AIv1MTB0oilhajhWHsuAoD1gqrQickXUD6Pnpi8gNjFoI0jQ8l61EzcfcYC0S5LQBcmcvEDo4cTBcjaD81uJ6DVYgsBCg4+O6UYQRLwUNeKovCI6icFGtG2ZuGJJcrJokTYgsnBvRHBk7GRzlxVAmi1RhwsXFIAXw540ShhIiVtRyuGZFGiImrPcMtBMbg8CBaAJensct8zzwCBNuOpIs4IUOAX/aM6IU1p7d7ECGGlYsCwkcxYLJ+fH01gxyInDRfAFz6yLIiBPH25D346GXgP6QiHmNBqxdlUFYnLCc9AgOiLKIcDYOBgZkU15sGxgZPxZOXoAxacemXQnl2C6ud6JtII7kqAUpaY61dK4F/ewgCpBQQ7vR0wn0hydSGBbXOdAzklJsCasdRliMLFr7JxxaZrlNcPfEMdIags3D4dzbXcg4yPlQjEJgBFCwoC0eLFpL7vFi9zMjSCWLVbQGA4PZ53nQt2AEBUaCj3NjKCJjMDlxLFrsbhzoKyCYyCNgEWAzGtA6PGGD2mAzw9iTQ19bFEYjB/9CL7b1xyCOFrC6LAYsWUwjau1VLDYdhVps68wiPjoOHENjvsOBXfujyOYlnLdcwPLFUaSlif108h4cSiQQy2dAZ63o2OvB9kluOQ1uE2yJPPoORmDkGTQ3OLG/PYTCaJWs3WJAk11A17Z+xarv7AtacN3Ny2C1qd8Az0R0N5j3Ri4axP03LMQ3v/lNdHd3o6qqSik8ra6uxomCrjumGZf++4/cDabq5hnhBqOL9cNEv2jeO8TZYf2WXqxZWgWBVy+TeOPldvzlj9sQ1rBi9NfYkK+yov2Qut2b1WJA1RI/3u6cEF2TIZ1Jz3NZ0PtmF7IZdcuO5rVV6F+aRyijXuQVMFvAbePQ1qZu1Wg1c7jy0xZ0S/2q7imkkRKfqcfGoRGkNRp7XNDowscXkxsR9RgomNH7tTcQeXWr6nLGZsa8fz4VhpGtxDJDZQU0qCXng6ohNzXqMYREF/ZnIshJpf73YwiMBYm8BYNTvMbH4CgOTsYHjic2iWpHg4KTr8NsOznW6hYuI2kHnu6OIpZXj8HCmlEtmJGU1K05WYpDPhXAgNyj6kRDgYKXq8arHXEk8uoxNJldOLiXQe+IxjnpEFDrEbC5LaLZ+XNpgxMbifhUcXAhNwJXLbagcWkfcrK6DaKNcuHNn2fRr2GL6fKa4LzZg50jpV7kYwgMgwahCu8cDCOvEgO5UVvldGH3wUSp5/ok5tWaUbBJ6Aqrj4PHxOMjpwB5Tn1mk6EYjAzU488vJ5FTcXAhMZxWY0f//hCi8dIGVmM0VdvwDzcvR1NLeZOsmY4u1t+7WP/r584Ax3H43Oc+h0OHDiEQCOC///u/0dTUhBMBXXdMMy79J49YnznPAHRmLKSx0Nmr6zSFOqG2waEp1AmDvTH0T+l+Opl4Iod4hniBq0PEUjad0xTqhFg0qSnUCQPJBAYHktoxJPNI5rX9yIloFDni060dw2CSzM5qxyAjieSeSbPlUxBjSSAaVhfqygokgBI1hTrBRCc1hTohIyYQz2uPQ17Ow2Em+6B1NGQ4DeSJgbbXIs8mNYU6IVFIQqa1YyjIeRjMpNGUegzkGEWzeU2hThhMJTWFurI8ksHAiLq4JKRyIlI50sRI43yQgYK5oCnUCbFMTFOoE0aGUwiltGPIiCLSeVFVqBPIX8MFUVOoEzoH05pCnRBMZcHx2svJU4zQCOluK2nGEE3nNYU64dBA/IQU6jpHjs/nU2bUGxoaMDAwgE9/+tPYvHmzPrQnA9TJkwYzcyLV0dHR0dHR0ZmC1+vFb3/7WyxZsgTxeBx33HEHHnnkEegN2k9wqJNHrOvWjTo6Ojo6OscY3Wf93fuu9/f3l/ztl7/8Jb773e/ixRdfxA9+8ANs2bIF3/jGN2Ay6cXIOjMbXazr6Ojo6OgcY3Sf9ffmu05R7aPjF8ILd30I3//+9xXfdZK7/vzzz2Pv3r34zne+o8y665xgULrP+vtKMpmE2Wx+f1eqc0Lh8pjRPNuNjgPqhXINs93Iec3Y06a+3O81wyJw4Fka2UJ5bqxVYGFkaDicRkTC5TnhxFXG5bBANsnoTk04ZkymyeQAU81iz4Ggait0n9uEXNAANsCgII+5uExgoDn4BRpuo4BQOqNaaOcyGZGXOHC0ep4yDTscZy3E8KNvFv0hp8DX+kFX1QL9vUChPAdYZgVEWRMclBG0XD4OZI2saICdsSMqTrjZTMbKOcFSAg4lMqr5+WbWBBdFI0OxEEedZEr2gWIRz9FwC0ZQlHp+fl4yI2CkMJBWL+Z1GZyQRB4UUqoxCIwJiaxBGfOcVJ6PzdEsWNkAr8mE4VRK9VjUWG1gahjs71U/FrOrzfB7GAyEMxBVjoXXxsPrl2EZZJFQqZUgrjJWMwsza0ayoJ5/bxGcaFpcQOdO9fO+abYdNV4Ogz2UagwugceqGgoHhljERl1cJmNgaMypBhIjRhwKqh+LU1rMyJllbNMYB+IqkwkVQDmjqseCpwXQFAObkUMsXX4sDCyNNQv82BrOoqtP/dqbW+/A5rcPYeXaepyo6D7r7w/ENejjH/84li5din/6p39SCk9vvfVWfOxjH1MsHgXhxHESOumhacUJ7T1zJO+diW4wHR0dyuOmhQsXYv78+eN/J40KyMUyNDSEWbNm4Z577sFZZ52FmYhelX30Iafi6+va8ef7tiIWLYpZl9cMvtqKPaO2d43EFaYgoXewWHxoFFjUzXZj40gCRKN7rDz8dgG7e6LjjhsrfFZkN/QiHcnAQOzhWjxoax1GIV8U9bVNTkR4Ft0jKcUWccGZLgw5k4jlimLXzRth6xew752icGyotir2dj0DxRgEnkFLgxN720MQRRmBGgPWXGVAiB4e37cGixeQYyjIWUBmEIoH8HZPBHmpGEOTw4YaVwZZFOM+tyqABc4cKKoYAwUBXMEANl8UbYn9ORz6+Qak9nQW91PgEfjEmQhcGwBtoCBnZcit3ZC7d4xGQCFTuxh76rxIsjIYMFhkqYKDioIatYeUYQFCvUC8R/l9yDEP7RyFnFQ8FgbaCDdvAqiBUUHtRHeCx0g2Ou76scBkxwL5IBhKRJYyoZP1YzA/4RpjZn3oSSSRErNgKRqn+qvgMZJjW4xBlC3YEczhYKI41mbWg75UEolCUUiaGAG5rANvHSpuc4HHhOW1IpKFoiMLEYUQvXimNY5MQYLLyGFNg4CgYjdZxGPwYnN3HkPJLBiKwly3G+3hMLJiMYZqixWphAEHBovbnGdzYLA/g6FRu1Cn2YBlS2hELL2KqrfmHRhotaG1NzluabhmsRkZbzdESoRB4iH3VmHTvrhSVEo4ZbYV9XOHIBqSoEkMNi9ShREURm9uLKwNh5I09keL+1kz4MTw82mEhorbsNl5XHSDGbNPJZaGwEjKiWfb7NgbKopdlqZxabMLV8yOQmBFxDIG3LfZiWf3RSdiqLNicVMQLJeALFEYOFSPFzZmkMwWx6HRa8Q5azIwOYrncSLux9/2seiPFc8HG89htiyg7Z1B5b5xzlwjzrgCSNIj477vUjKA+1/II5aUYREYzPLZsKs7Mn5jcfpcL+68ZB5qXCaIooRn/taBB57ci+SoqK/ymuEqiOjbXzzvFy+vxs2fWYVAzfHt3PBu0N1g3l93mJqampLv7Z/85Cd46qmnlN9J46QvfvGLOPfccxVRf7yj645pxiX4lyN3g/FcNyPcYN4XsX7NNdegvb0dr7/+ujKAhA0bNmDt2rWKlRJ5/ESqs0VRVF7ndGo0yziO0S+aD45UModHHtiOzr4otg/EkR/1XB5DEdQtbogMjX25PEIqs3VzqqwQKApCe1jxsp6Km4gAjwlpnsVOldk8o5nBnPOdxFYEe9eFkcuWztaTj/n5xJ2CAvoGE+O+75NZttaKOadKMAnEAUZlxlA04+CIAxZjAVl6sGyxmWVxRX0AVTwFLhscF9VjkEs3+EIUie2DqLmlBQZveSMdOVJA/mA3OqoCGDCVX+pW1oRFRhcMyQgwvLM8RNqALtdCZHgDGGoQFFU6O0s+PVKFaqSyNJYxgzDL5bOvUdaHTlgQzEoYmuQ9P4aLN2G1z4VgRsSm4dIcVAIFGjzrQThF4/XOBFKjHvyTOb/ZjoBVwmudOXSrHIt5HguavUD3CLBz9EZvMg5BQMBkRjbDYNNBIqpLv8gNNIW5ZicEoYC8rxcFuvycsyeqEQ/yEOqGkWLKt2HLuZDosaO+KQHJWm45aWEFNFqtiOYorB8cLpujZiUa1XvdcMsiVl85BIOp3EWmLVSD/SEjrp6TRcBSPlPeHrTiT9tMaPInYbKUn3Nizoh9e6rgdhbgr+sGRZdGIcs0+ofq0N/LoO+dIFKJ8nE4/2Ib6hbKeHY9iz1d5THWuU3w2QTceHojTp1d3rQsEsvgD4/uRrAniu6tfZCmXP8sS+OiK+fj2puWKf8/09HF+tET62O8+eab+N73vqdMHBLIrPs//uM/YtGiRTie0XXHNOMSegQ223vP2ojFkrC7rz05xPrIyIjSjGDr1q1KntgYn/zkJ7F+/Xplxt1qtSoXySmnnIJvfetb+MxnPoOZhn7RfLAkkjl87B+frvga80IvDlWw1ruA43BwU6/m8toFPuwMatv/GTgaudHZdy3qqqzontTsZiof+4wdorM4E62GiXXhQGyiYc9UyOzzl5qmKS2h7YCknrJC6CzY0ZUuF8Fj1MpGzBrZU3ETrXWVW3nPylvATJpBn8pOzMLOpPZ+OgwORHLqfuVjPLXbpJrqMUazw4GOiPY65rs92BvSjtEv2LGrS9vCkLB8aRT50WZZatRbXDiU0N7P5W4vQll1b3iChbViT0TbspLwxYXEHlQ7zlrzLFBUhfMhZsO2kPb5YGEtSBQqx/Dsr+yIJbQtJ2tWVGFHv3pKC+FDK2tx11ULNZcP9MbwtTueqBjDT377YSV9bqaji/WjL9YJqVQKf/jDH3Dfffchmy3e0F900UX4+7//++O2kZKuO6YZl9DJI9aPeFpiz549WLNmTYlQJ6xbt065CIhQH/NCveWWW7B9+/Yj3aSOjo6Ojo6OzmFDHGFI86THHnsMV111lZIG88ILL+Daa6/FT3/6U0UA6swwqJPHuvGIIw2FQuOpL5P/1tvbizPOOKPk76SrWKTCzJeOjo6Ojo6OztGCTBySJ/x//OMflYnGfD6P+++/H1dffTUeeOAB5XedGQKli/XDhhRskNlyko8+xksvvaS0AJ462066i5Ecdh0dHR0dHR2dY8WcOXPwi1/8Aj//+c/R3NyszKz/+Mc/xvXXX69kBugNlXROKJ91UqCRy+Vw55134q677lJmzklTAlJtbTQax18nSZJy10pep6MzHfFkDkaeQXrUmaLsxGUomInVSwWIJVwlGDsHaoSCrNGK3WQxgMmISKsUsCrrN1Dw+mh0a6f/gmbpKWWhU9ZBM0qxqlYmtoOmIYsUKEa7XbxEUSgvLZ2AGUgBpQ+/Ssj05iBxFGhWfRtihgWdACSLxgokGXIyDxi0t2Ex8IB2eQAsHI14jpTQqsdgZDh4TSwGyHZUYCigykKho8KDu2qLjL3qDojjx4Kl8qSmWBWvmYaH59CfVj+i5GyzspWOBLGMrLzcyrEwUBRyGrn5FpYd3ZL6OMgyhZEMBffER2/5OrgKB4oUylJAFhRIZrzWOPn9DGIaae00JcNqk4EK1wU7pWh0KolMHpyBQT6nPta8wCrLTyT0pkjvf5Okw6GhoQE/+tGPlEZKJJ+9s7MTX/rSlxRnO2L5OHXS8UghNX70DLIMPK6hTh6f9ffFDebuu+/G7bffPv47mVV/+eWXS9Jgfvazn+GrX/0q+vr64PF4MNPQCz0+GAoFCU+93I4Hn94LA8egymfGvvbSgr3ZjQ7FfYX8NM/zYkskhYw4UQjaaOTQGMqg50AIs+Z4EAomERmZcMYQnDwsF9RgayKOWqsJlv4CevZHSjzXm5YEsDuSAsdQaDHx6Nw1WKKoL7zAghVnxFFACqkhP+7/QxrxxISwaJptxOKLgRF5BLVmNzgqhZw0EQNHcXDyTgxnhmHmLEjmGfSkJsUA4AqLAXMy7UWLMXcLYBRL7MZyjBUJOgERSRgRgCmfxORbg1x/AcF7dyC9ZRfYUxZh8CNzkXJPusGJyxB/3o3u37wO+5wqrPn6fFQtmfB/lyUgvtOIkSe3Kb+zHz0NqXOMJclz9gEZzre2A5FB0AtPATPXAoqbNFAGF+BoBsWkkRHNeHNgEAPpidxQgWGxymuHkSPOJGZ0xAS0jdoVjrvuOFywGIi3vYwDIwHctyOByVrvgiYzrp6TAE3HMZiowi82ZRCaJKhbnAbctEgGxw5ClLx4aDeLPcEJxxgDzSLAebG9JwaPWVAsCVuDEzFSkPHFMwWc0dwPUBLaol48SGw6J8Uwz25HrVlEqpCAifXgneEo4pMepxP7zyarEUOZEPxGJzKFHKL5ibsXnmawwuOEJA+Bo41oj5nwznCkJIZzqt3wC8TLXIRHcIACsW2cCCKUcePVfhZ9qQTW+ny4qDYNMzfh3kPDCAPtAk0lkBNNeGsoiB7iAjQ2DqBwFkvDm2yDzJqxV6jD9nyps85cuxMOPgxJzqPzQBV+8/skcpPuG1qWmOFYXcBwPopqJoD16yWMRCeOhcfIYTbLoLU1hAWz3bj9o0vRVOcYX57NiXjkuf149PkDsJo51BBL1Z2lrjVrz2zEDZ9aCZf7xOhKOVZgOvf2/weGr3CXpTMtrNUB6ggEmJTPIrzzdYzseB2yWDyxrU2L4TnlYnA21xEfgbHGTVpFsFPRdcd04/LUkReY2q+cEQWm74tYJ5BCDVK4YbFYlOYDy5cvH18WDoeVHDFyB/uVr3wFMxH9ojn6bN0ziN88uAM9A6XuKg3VNoiSpHyR26082rpKp09dDgHWOjsORFJYAxrd2/oVv/Mxit7qbrQdCMJ7Xh328HnEsqUzk4uddoS3hmFzmhDiafRMaZzU7DHDFM3CxGbw4RsoiPyEhzqBpYxo3erCS+uSOOdaC2KWIUhE7Y4vZ9BgcSMrBuER3IjmoshJpW4aToMHXckEWhgZ52AAbG6Kc4ngA1xVkHgGCZZBDqWuIjQEmGU3uEQYkcd7EXnydXL3M76c4jjgytPRe4Eb8l/j6P33vyEzVDqWjVcvw6ovuMDkOQQfP4hcZ3fJcq65HtIty4AqGv6N3aDbx3zcRzHZwaxYAbrOAMq9CDDkFaE5hgwKwbQRr/Z3Yq7DjipzBDJKG0QRQb01mFVm0+ssORTk0hgp2PF8mxXd8QJuX0HDxk+1IOSxqc+Dh/Yk8ellRgQs/YrAnTxSoWQ17tmegYP2oGMwh5F06bGY57NjJJXB4oCMW9fGwLGlzZlk2YaX+wTsDWew0mNBfIoTjoE2IC85sCkYxGKXB+FsqKRRFk3RqDF5MJAawVyHDXZDHPlJN3PKWNMevDGQg4llsdIjISeVjoORscDBM8gUktgY9GB7KFQyF25iDbi01o1VvjgEOgCGToOCVHIswlkjXu3rxlwamJs+BHqKxWjGWIf1tBWygUejVYQol944U5IVL6+zY/3WPBZdbkQ3hkpiEBgO5oQPb7yVxXKHGYdag8hMelpGbowvOasJN39oAXa2DuP/Ht6JoVCpw1NLrR0YTMDI0vj4basxb5EfJxK6G8zxRz4RwfBbTyO8Z73yO0XTcC09B97Vl4ERTEfNsWYquu6Yblye0sW6jn7RfJAc7Inii/+6TnM5yXgxmwxKeowWKz1mdOzRtsWrubQB62Vtq8dasxmdbdrLySzvDz8XQUEu7z46xlA6gIMJbXvAuXYfEnntGAMMj/OSu1CJkdnzIUE7zsIvepBat0FzedQzD9v/uEVzuXteFRY2aFv/EaP72qurgHjpDctkuFv+HrRTO00pXWCRENu0tyEbMZKtbB/oFhyQoW2bmSn4kCpoPxbf09eIn71W3tVzjHoHi/93denNylTeGSQ3Xtox8IwPHXHt473A4YZFeaqgDgMDRCXlRXtO5e0hN3qS2k4Wdy1pQb1FOwdJihdAdb2puVykebTX+CrG8OTBKnQltc8ZX3sNNr2hnYM0p8mJ1k71brUE8pTt4Z9fCZqZOY+tDxddrB+/ZIZ7MPjmY0h07VV+ZwUzvKdeCeei00FNk9Kmhi7W31+xHok8ecRi3eG4akbMrJ94n3w6MxLSvbASJK182tdI0yyfLgaN3PUxyFK1durlr6q0dLr3Txfl9K+RJxV7qy7PH9n7lU5IFbzGDweKko5wnIqpIZWgp1kuTfNQ8XAeOk73msNYwzRLpWlfU8l7/nC2QU16AqS6/DBikKZbPs21Nd21TZafiEJd5/hG8Nai4eovoOFDfw/eFUAhk0T/Kw+i44EfING971iHd9IjK588R/YzU9A//XR0dHR0dHR0NLA0LsCsj30DVed8BAxvQibUh65Hf47e53+PQrryE0AdnePCDUZHR0dHR0dH50SGYhglb90+dxWG3noaIzteRWTfBiQO7ob/7OuVv082ANA5+sij/x3J+2cK+sy6jo6Ojo6Ojs5hwAhmVJ17A5pu+CoEd7WSGkNm2HufuwditrRIXOfoIsvSEf/MFHSxrnPUeevVTnzjzqew7tn9qrmrHQeC+OMv38byegdMQvnDHoedx8JzXag7wwKft7wS38DRWNbkRCaeQ+MsdXutNWc04I4rl+KaRfWgVWY/Gqwm1HSkcIbdCKtKDE6zAas8Fmx6xQkO1rLlFGgwVADxXA4Bo3oMdWYPJCkDn+ADpZSrlmI3WOGzcYh75kFmhPIVMCagZhUsbA0YSq0YhgZ1wIDCoWHwc2arxsDPnQMnH0fLlctAcyr7ubgBy351GRx/fxVolYIb2mqF47S5kPI84KhSCYEFs/JcUGay7vJxImRFEw5EosiLAdWPILJvdq4GAWMLGKp8HCgY0Bqqx5/3EpcgNQN5Cv3xavxuO41cQd05RJK9OFTI4NRZVnAqfv21diPOmg8MJqtAQeVYwICeRBVieQZWrnycyPFlClV4eQcDJ0OKM8vxCi7sDheQlwLk7ClbzjMWiLITDOUHR5fHwFAcJDkAN0+hyqRyrEDhTI8L7lQn5IK6JaAsGYFCFLC3AFT5+SBxDuw2z0MsVw2GKl8HDQ42QxUuqyugwWJXHYflbg/WnBbG2lOcpDa5jKY6u+L0tGiOB7yKd7rHbcTyKyz4v32vI5ytYNZfoa7g1RcP4BtffAqvr2tXrTPYv3sQ3/nqM3j4vi3IZvQOljrTYwo0ofnGr8N36pWKXWS0dTM6Hvg+0gMH9eHTOX6tG090dAuld8+hzhHc95uNaJ3k0FLf5MTHP7sac+b7EItm8Of7tuL1dW1KzSLBbBfgmOfB7oNhMDSF+Svc6PKEkR213iMthGZF3WjdHEY2K2JuvQO5QxFEhia+xJtnuxEeSSMcSqG23oGbb1uF+YsnOue2h+L4+Rt7sL0/rHhqL0yz6Hupu1jFSizxHAL41TXYMhRXHmsuD9jQv3sImUzROcTAATfeZIO3cRASChAYN/ZGchjOTMyqEMGeLGQQz6fgNFjgETikChNuFxbWAo7mEM6FFZu/JqsFFq5v3Debk1hUJxjw0Y6iD417PuDzgBrVMmS80iKFlNgDGXkwcSuSP3kDua3t49vgZ7dAjMZQGBoCG/CDtliQa5tYnrP40NnDYfCdAzA4LFjy7QvR9Lka0KTDEBFqCQnJP7Yh8UzRKcSyfB7MhQOg86M5mjQNZuEyINEDZFOgGhaAPfc00K7Joo50UiLjloEk8+hKpNCV6B1fauPMqLOSJj0hUOBgYmphZORxUSfKFIKZPCK5Q8rvsUw17tmWRzBdPBYk1M8ss2JpgJxjOWQLbvxhB4etgxPH4oJGM85tIv7kMdCwYFvIilcHJpxLzLAgEXRjd38cJo7BBfOM8Hm6QdHShBe8zwYjW3RtSeb9eL47juiojzoJdbbdi7xYtOM00S68vJvHzr4J56I1TQKa6mKI5eOwcCZIshH7IhM2iLVmI84MELvHIGiKhYH2oDMeGi+CJc2UGq1OpAvEGlGCgfZj+0gc8XxxG+RlDt6LjlgcyUIWzRY7rnfFUMtMcmAx1QM2LygqA1k2AMkEED8w6VgJQIYCkt2QKQ4Dlha8ni8oR0857ykGi9w20NSAUlJqZv3gmThAZcdj2B0O4IWeFOL5LOrNdix158EzE2Md6Xfj5WcMONidgNMuwOc2Yn/HxHXhsvPwukzY3xlWHGCWnGpHqGEQIi2ON2S6qHYhzq+ZP22DKUJ7axD3/WYDOg9MjMOsuR7FArKphXxOpPDg7zbj7dcnBBbxb7/hUyuw9swmfJBuMIu+eo+SD60z80gPHcLAKw8hnwgrLjH+s66FbdaystfpPuvvrx4Lhv9yxG4wHud1M8INRhfrh4ku1t8dLz/Xivvu3qDpAnHORS3Y8OYhpDSsGKvnuRFZTSNIqVsU2mUedbt5dGxWt+bjOBqXfGgBrrlxKRgNF4kn3mnHM7/ejEy4tPHLGO55HkR5BsP96gVENdUcLvs4j70JdQtDBjTm2AOI5fqKik6FOosPXmEA9KjgmYq1YEaV2QdKUN8HSaYRenIPYv/7jPoGWBbG5cuR3rKFWGqoviS3bC38X5wLwatewlLozAJ/fAFsXMPG0GoDf8MNYOZpNzuLZC3YMbIPkkb1fbO1GrVmA2gNl5isSONnm1LY2K/+mNlvZnFegxV/2qNu/8fRwM1LHHhpYBA5jXOynqlBwB4Cw6pvI2A0I1lgsC+qbpNIfOGZdDUe3qx+vpCGutevMWNfqhd5DeeiswIecFQMKVH9fHBwZqQkFp1xdZtEluZwhs2GVZyWLSYF2BYB8X3EFkj1FVl48Lc0hZCkbmvp4o1Y6WFA0SPq7xcN2BsOgKV6VGfSZYlC64ZGvPTyCLI59XFYMM8FLI8gYVC//j2CBV9begmMrHY31mce242H/7BlfCJgMhRN4byL5+DNV9rHb8KnsmJ1Le7853NxtNGbIp0YiLkMht/5K1K9ByCLBdz1d7eWNIt8Lx1Mdd1ReVyCJ5FY1wtMdY4KPYciFe3aDh0Mawp1wshAHCMUp7k8SmXhGtL2yM7nJVisvKZQJ9RTBk2hToh1hjFsV0uBKNLbl0d/SrugSIQEmipoCnUCQ2U1hTohzqZRrSHUCUTc5l6v4FdeKEAmM/4V7BhdTYymUCewTTygJdSVIGOgG8iTC+3jEcklNIU6IS2KmkKdwDMStgxo+9sPJgt4p0/7fCNulTtD0BTqBIM5rinUCQPpJAbT2rO5aTGPnn7tg12QSG8fCfkK49CdzCJg1D4fIvkkhjPa10VBymO+kCAnnwYykAlqCnVCmhIRqpDKOZJNK82MtEaSZ3LwGzMY0dgNipbBm3KaQp0QyaTBagh1QjCTQEYsVBTr3QfDqkKdIEsyDh0c0RTqhEMVfN+PBubaFiUfWmfmYm1ahKH1T2B4w7N44oknUFtbi89+9rPHOqwTFlmWjyjvfCYlluhiXUdHR0dHR0fnCCGdTv1nXFP8pfN13H333TCbzbjpppv0sT0KyLobjI6Ojo6Ojo6OzrvFufhM3HLLLcr//+xnP8Obb2p3CNbRORx0NxgdHR0dHR0dnfeR6667Dtdcc43SWfvb3/42RkbUazx03juy3sFUR+fIWLysClYbr7qMF1iYTAb4q9St/UiOd0OtA3PjGstJsR/tVAoaGVI5qILHZ8bs+eqWeYRMOq/YtQWqtWOoWejDnCan5jpq5zgQDDkBWT0GCysgKzJgKfVxYCgaAkNybh2a23ALtSgQez0N5JQI5+UtoE3q24DdiUzeAMqmvg2Z4xDcnUViu3auNtudBLtiheZyyleP/GvbIOfVE6XlvAR/fAQWWT3XmgYNA00hXajk7mHDrUsCmun/p9YYce08GRaN88Ft5BCwAC6Deg0C2f5aH4cak/Y5t8JdiwtqajWX15qsuHhBDlZePcqAjcECnwy/oH48OYpCs8UEu0HNjrKImXXDZ9Q+J+fYnOBtNQCtnsudgoA3U1ZEZPViKhk0CkY76i3axVY1ZidiOSdkWX0/BxN2bO6zoCCqH+9cXkA/ZYG7St35hOTD2xvtcMja16834sEbb/ZCrFCDsGxVLUwm9RjMFgNWrq1HoNqmGcOaMxs1162jMx3ESeyuu+7CnDlzEIlE8KMf/UgftPcZ+STyWdfdYA4TvSr73ZNMZPHIA9vxynOt48Wms+d5MdgfV2wbyRci+Z0Um6ZTxYK3mjq7UhTW11N0u2hY4MbIMmCQKxYXWmkBQtSJ7e1FR45qu4DGWA5D24idHGAwMLjswwtx+TULYeDVSzLeeq0TD927RbF2HIuhu4sUvBZj8BHPdhuPrv648ntznR2pdB4DwWLBm80lwLXciR3hYoxVVgErWwqQhMFxF5hqswf9qRAKsggDzaHB4kQqPzRebFprdoGj48hJRCRTcPMeGJigYj84JspcAhESxRhoOJT10NSoVV9BBgYGgEhr8XfaisibSYQf21j8nWFANS5AdPsBSOksaJMR9iXNkA/uBTVabJoLzEbfjiFk+oPkmwU1N5yGxq/Nh8FdHDc6UgC3fTcQ7Bi9+6hFvm0Y0qFRmzuzFbSvHtLBPYpvH+X0gbvqUrCLq4sxkGMeSgDDuwApB5lYVdpmYTedgzRqUekRXBCYBES5aL1p42rhFYxgR20TASJsiYgvOqyk8ibcvS2BN3uLY+83sbjjFAM8ZnL8ZUA24dVDLjy0Jz5u7Xh2swNZKoiclAdLMXDybmwPBVEYLS46O2DHMncc0ug2KASwN5xGolAc6xqTA6f6PeCZ4vHPiUa82BvB7kjRDtDMcljutiEnEftIGSxlxM5uLx7aUqywZBngIyuNsNsGUZDzYCgGHO3Gm4Mh5EZdYZa4nPAKWaQKxXHwCB4kcglkpOJ5z9NmpEUe/eni7JybJ4JeRihbvA6sHI9rG+vhNSaVU0wmN0apNBDbryyXZArbpEa8GU0iIxbHYZXdjjVcF7jRwuAk78cwKyEnj55zlB/7wlnE8sX9sHMm+EzEgnRsv20geptnI8rv5KbwufZqrDsYgSjLsPM8zmsyoMpGLEmLMbT11eOpXQXEswVwDI3FZiu63x5CNlM8J+sXuBD2UuiNF28el9RYwbuGkUZx7K1ZMwrbrNi/r7jNplo7br9xKRbMVnciikXSRXvYl4m/etEF5uwLWnDdzctgtQko5EU8/9RePPnwzvFi03kL/Yrda12j9k3R0XCDWfFvT+gFpicIuWgQf/3cGaipqcH+/ftx8803K8WM999/P+bNm3fY69F1R+VxGQg9AJvtvdudxmIpBNwfmxFuMLpYP0z0i+bI/NYfeWAbgkNJ9HQVv2QnQ1xbaurtiqBv2zdc5uDAsDSaT/cjUmfG1n1JZFRmcBf7rZifk/CxG5fB6yfe3uUM9sfwf794C/t3T/i+T55pq2l0ImnmsOdgeMxyfRyWoTB3lht5H4vd+STSKjEsqbZixew00jLxvy53siB+63VmAS4hh1Sh/JEoSxngE+yoMhvB0GrWfBRYygtuJAQMEQFcbrchSl70P9SP8M5+5PrLLSX5Gj+MNS4MHUwivK3cRYa1mzH7ny5E/cI8qI4tRHFPCYGGbGpEoTsOqacdyJQ3qaHnLIbhynNAxTuBXPl+SgYHuu0NkEyU4ite9n5w8Aj1cPLkaQAZh9KDQX7riVnQGoliiZ/cIJW7ChG/9afbzQCXQGzMF34SVs4EE2PGal8aNFU+TsTzPSP6UWNywiWkMdX7hPw2kjFj+0gEBprclJXHwMCNLV021PkjSItFATwZI2NCUjTDzkmI5yf5oY/CUiycvBOxPIWDiSAklVmgGpMXi1wmLHESb/qCatOjgVAIz40UMJwtHwcbZ8QFDg5Gi4iEyrGgwCJb8CElMsiIRY/3qXgEL/qiHB7em0U0Wz4OsxxWLPPReGEPh45Q+XXhNhrQWDAga6GxK1xui8kzNFY2mcD3FbDj7QjyxFZnCmevrsPtH1sKi8mg6bf+4l/34eIr5yv+6lMhfuuP/Wk7FiwJfGD+6mPoYv3EFuuEb37zm3j22Wdx4YUX4gc/+MFhr0fXHZXHpT/0xyMW61Xum2aEWNfdYHSOOvVNLsxbGMCDGzerLk/Es4hGMhjoVfevFgsSDrzaj/7V1apCnbBzMI4bP6ot1AnbN/eqCnVCMpFDKJJG25C6R3ZBlLG7NQi47apCnbCjL47FzQziorrlXDiXQKOVVxXqyjbknNLQhqG1LC1lFOQhsME2UCpCne5AXHcAAMozSURBVMDQwyDhqQl1QrZ3EHmTXVWoKzFEkxi4/y00fEYrBAlUsgNyMK8q1AlS607Ig82gWPX9pHMROGU/BiSN9yOPaG4QTo3MHjJzXGdLwCyEIaqIZALPhlDn5LE7rH48yc3UXDvxdVcfJ9JoysqNwC3wmjG4hSQsXBKp0Rn4qYgIYUm9AX2pcqFOSIspeHkjQtlyoU4oyAWEsmF0J7XtBXtTw/hIc7PS6Eg1TjqNvXkzhrMTjagmE8un0S4a0Kgi1AkyyAz4ENI5bUvKYGYYLx2sVhXqhPZIHPl0FTpC6t7woXQObg+P1qD69Z8VJbzTloB5i7aV46sbunHp2U2aM+yz5ngwa84Zmu93ukz49B2n4liSi4XA6O3qTwhI86P+/okeIBdccAGefPJJvPjii0rhKXGIORL/dZ0iR5rKMpPSYHSxrqOjo6Ojc4zJx8KQeO1eAjozCQq3Pn4AFFXsGE1SYA5meeSHQ7jse3+CpX7eEXU21Tn50MW6jo6Ojo7OMUZvinRiY6mbi8i+DRBTMRjs2t2edQ4f+STyWZ8xYp3YH4VCISVPyWDQ7lo3HYODg8jn8/B4PBAE7e6UOjo6Ojo6OjrvB5y1WLBcSKun5um8d+vG98qRvPeD5rhPkBJFEf/8z/8Mt9uN+fPnK2L9ox/9KIJB9RzLSqxfvx7V1dVK5f1zzz13VOKdiQz0xZDLabejnw5RlMbdW7SwOSrfGNnsguLkooXZaoDTrH2TxhC7N6N2G3ZCTiRFeNrLTRYOfIUYTEYOVoP2/S1NUTAS648KcHTl+2OGIssrrYOFzBgrPn5lXNp5+wTeZSLedNpbsFkAtsLxIssqWBySIlQo+zHdfmofDFpmIcva4yDLNCip8jbMFdZ/ODFIkmHaGEgxbCU4uvL5YKAZUBViMDI8BEZ7G8TVpSBV3oaZqbycVc5J7RgYyqAUP2tBg4G1wnVD4NnKXzU8w4CltWOwCQZYLdoxcCwNs4ZN4xjdB8MVl+voHF1Gz2/pvX/X6py8HPcz61/+8pfxm9/8Bs888wzOPvtsdHd34+KLL8bll1+Ot95667ALMpLJJD7xiU+gqakJ7e3FPLKTnVQyh0f/tB0vP7sfLo8ZH/v0KVixpu5drWP39n7c/5uN6O+N4szzZuH6T6xQhPdUTj+nGR6vWXktsWocw2jicPUNS3DhFfMUK8U/3bMZm94+NL6cWK2dc2ELrrtpORiBwb2vdeDB9QeRFyceX61ocuEfL52HZr+6gBwYTuK3D23Hhh0DaF7oA0+KWXsmitkEI4vas3zY7Y/CAR7OQ3bs2zNRHEkE/uy5XuzN5pDuzGHJXBf2RiLIixN35QsDZnx0eRA+ewjBTDW2hdJIFibyT22cCcvcHJw8sT0kPtUMkoWJGxyaYlFlcsFmGEK6IMDI+gGUOuek8k68NRhEhnLiTKsHvsQBUPJE8aFsDCDhtcN0ZwaBhZch9Mu3kR+a2A/O50Lt9S1wNgVRfeYp2HPvMCLbO8eXM0Yes24/HU0fMYGiGMjdCaB316QIKMA/B2CSMLgyEOtORWHLFmDU2k95RU0zuEU+UJG9kD2zQTFpoDBpJom1ALYqmKVB1OddGOJ5ZKSJ84ECA2fUAMfm5yGZHKBPvxywyCVSUk7SkN58BtbECLKrz0HKS47DxDiwkg2mgz04a2g7Au5l+BttQ6ww4SNvZgUscwvwCN3gKBsoikVOmnS8waB7pBY/+ZsIl7GAf7/EjjpHrCSGcNaM3+7rR28qhYtrAzBzxK1lIgYTQ6wVKaTEHjRYvRhOE1vGiQJJgeHRZDXBxHbBZ7SjN2VCMDNxvGmKRovNjVrLIE6RaGwf8WBPOFTy2LbK5EY8l8Sv9rbjyoYGtFgLoKhJQkA2Ark01pgH4K7y4uVQCpHcRFGvieWx2mtBo5UUwtmQLrDIiJOLXWlkxABe7Y8pPQGWu/3j9pRjWDkPsmIKZzT2oM5eg1c6ScHtxDgEzBbkUwLWd4Qxx2NDLJvHwKgtI8HGc6gXLNi2PYyA2wSHn8WBkYlrkwj4BS4n9rRGkXYJWFxvx4F9wXErWMKqJQF85iNLUOVTv0nt7Y7g/rs3Ys/OASxcWqXYMlbXanvZ6+gcDfKJ4ucca9Huq6Hz7pBlWfl5rxzJez9ojmvrRiLMibj+yle+gh/+8Ifjf3/++edxySWX4MEHH8QNN9xwWOu6/fbb8fLLLytNCj772c/isccew9VXX31SWiiRQ/76unbFf5j4nU9m8Ypq3HzrKgRqKu9jaDiJB+7ZhE1vTQhrgslswDU3LsEFl84FzZTfSEmihJefP4BH/7QNy06pxUc+sQIOp1H1BoCs6+OfXYXGWaVWa92hJH727D50DCVwx0Vzcf6igGqM2ZyIvzy7H4+90IpcfkJY05CxtMaB4QNBBOY50D0vjwhT6mRRX7AjsaMAlmGQsHNoi5SOk9/Fw1vNI5hO46MrJCypHfUeH0WUOHQlAtgfjWCB04E6Ux9oekJIFRvKBBDKxmHjbPAaY2Do0sZEBsoHA2OAKFPYEcqjM17qruKhWZxJ2tzkgsh46pG0JIt3FmPbSAHJ+4IIP7IJvksXIrAqBpqdJO5loHebC/t/txuu1c2Y/4VaGL1TLApTBshtncUX26yg8qXuKbJoQr49C6m3G+zKhWCs4dKnF7QBlH82UBgGrHUAS4T7pHEgzizGGgzTSRjzPNxbNoELFz3zx2laDnr5KuVeQdq+BWjbVLJYcviRWrMGeSELU1AC37a5xGoxTxuwwbcWm/N5zLU70GAZAEMXPfXHEBgf8lIcqawNv3rDiL1DpQ4sl8y14s7TWbCMhKe6Uni5v+ipP4ZP4HFutREUYjCxdsSIp/4kKJkBRfkwkA4rvu1ufgDUlBhShSp0xrKw80bMsScgKGM1QTjrwDuDHNJi0bt/KFM6U0ycZa5urIXDkAVVYIn1T4mwLsgUNsYdeGckhHkOB5a4guCmnPeS5EYiT3zvTdgwLKGPeLVPafzUrHw05MHSBiTypedkXhSwvd+HXUMJ+HknNnYkSmxQWYrc2DrRFoqj2WZFa3sciVTpTOPCWTaEqSwcvAGRwQL6gqUxNNp4uFIictkCbrthKU5ZrH79p1PFyYh1z+yHOOnmnljBXnTFPHzohiUwTvM07oNAt248OTjw+28plo71V34O1uYlFa0eT0Td8X4SGx2X7uHfHbF1Y533lhkxvse1WP/1r3+Nz3/+83jttddw5plnjv+d5JyTA0XE9gMPPDDtesis/BVXXKGIdfLB+PGPf/ykFusb3uzC//zXa5rLiXj+2e+uq7iOb9z5lKpn+hifuH01zr90rubyQkECW+HROEmtIQ2LSBc4zXWIEliVG4IxfveXnXjshQOay+ed4kCnV3sfDGDQtY1H6bxuKXf/fRIsp91GmqcDyEpTxOckzKwHPKv9eF6SGazrYSsWwlxUYwZo7XVYu2QY+ndob8M1C4xHex/JJ4Tc2lYxhUg2VYNK9Wkup5pWAVyF/UwLwKbXtTdANk7SVqRSgVtC8xJQyW7NxaHZazFi1s4X7Y/48J3ntFMtyO6fsjSGtKRtpXhjsx15WTtFr8roBSjtcTLQdlgMiYrH4k9tZmQrjMPnmwKwUtrnZIK1I02pWzkSYlkbHjlY2ZXkNB+DgqxuH0p4YUcL1h9Ut6wkLLY5sWW/9rVX5RTQH64cwyP/eBYCDu2UsN/87E288cpoQy8Vzr6w5ZjbNRJ0sX7ikx7sQseD/wGKpjH39h+BmdJJWRfr747YqB47NPx/RyzW6723zghdd1znrO/Zs0f5t6WlpeTvHMehsbERu3fvnnYdpCj11ltvxW233YZzzjnnqMU6kyDFupWXT3//Nt1rplteSagTGIauKNSVdVQQ6ocTgzzN2U/eXUmoExi68jYqpOEqTLOL4/XuFV8x3TamSXajSZtPTKOTp90P6shinM7vVrljkI5oHdMN9XSnPVlcmM494AiPN3PE5wtBPqJtTDtQRxwB8dKf5v2HMYVEakSO5meUjs77RWjLOuVf2+yVZUJd58jdYOQj+G+mcFznrIfDxZk4tTse8re+Pu0ZqjE+97nPgWEY/Od//ue72nY2m1V+Jt/J6ejo6OjoHAlTv1vi8eITCL0p0ok7qx7Z+7by/7bZK5RZ9KkQn3UdnRkr1seKR9UydcjsMBHhlbj//vvxl7/8BU888YTyyOTdQFoCf/e7332XEevo6Ojo6Lz77xa9KdKJh5TPof/lByHlMvjktVfgW/+iXWNHOpjqvDtkvYPp8YHP5xtPZbFYSiv9R0ZG4PV6K76fFKZeeOGFWLFihZIXOHm2nqyT/I1YOao5ynz961/Hl770pZKZdWL5qKOjo6Oj816Z+t3S29uLBQsW6E2RTjDIJGPvc/dAzCZhcHhx55136h1K3+8xxpE1Npo5STDHec76smXLlH+n5qYnEgl0dXVh+fLlFd9PZtNJ3vvatWvHf7797W8ry772ta8pv5PCAjV4nldSbSb/nAgQW8THH9yBuQt94Ljyw1/X6MQdd5017Xo+e+fpmDWnvAsbcVq49OoFOOO8WZrv3bKhG1//wpP4yx+3IpstL9brORTBf3zzRfziP19TXGfeKx++ZA7OP61eNc+3cbEbfUYGDXwVOKr8CY1XNsHfasUaEw+/udwxwmpmcNZpJvx2uxX9seJNZSkUqoxzUWd2IWAsrbkYg6eJ7V0OedEHqPh5s5QNNq4Ol9dXw8Oby5YbaBaz7W50xWVkCq7yDciA5WAB7N49kLka1TxgMe1A9q9bkV8fhpxTGSjWAdjmgFp+sWK5OBWJMyG55ExEFjUiXz1PJQQKkeZTcNDOIGKqU68BYD2A1QRqzYWAUG6/J3qqkbj0KsQvvRyir/yGWeZNSJ1zGSKLG5Grma8aQ7R5JWIW4luu7hxioF1odGfx/Sso+Czl10W1k8PFqxgEjDa4VY6FmWXwufkuzHWkFQvOshhkoC9Sjf/eQGNrfyNEqXwcEnk3Xu4TsHGoGjmxvNCVgRlmthGfnONBk6X884jYK1o5P37WlsLOhF3d5izFgO/aC2vEqFpIIMt2MJBxeZ0DNk7lvOcELHJ4EM9blfNTrSC6N1kNgyeE1S3qxZ+nzzXCPXsQF58jQK1tQYtDQCCWx6k2I5x8+Qs8PIsLZBq/+eGr6OrQLqS97ublOOXUetVla85owIdvXIpjwdTvFqu1Qs8CnRnL8Dt/RbR1MyiKRtV5H3vXT/d1dGaMG0wkElFmsz/84Q/j3nvvHf/7PffcoxSNvvLKKyVFo2SGgnQ3rTTjTlJjTkY3GOKDTuwQd20jnspFnG4TnG4jOlpDMFuI5eJSnH/JHFXLRU0LyJfb8ec/FC0gFy2rwk2f0fYwJs2X/vjbjdixZaLWwO0146OfWonVpzcovu+PEau1Zyes1gw8gyuvW6zcAHBc5bQnLfZ1jODuP21DW1cEroAJxoU27A1P1CC4TQY0+jgcSg9DAIP6YSf2bg2hMBYDx6BprgebIkkUJAlrllkRYsNI5Cds79ZUO3B+0wBMhhQcXDW8RidoasLNQpKNGEwPI5YfBEuZwdImZMQJG0SGMsLK2UDTQVBgYWKJKI1NKsWjMZKxY/1gP/JSAQ0WLyiEkZ/kyOHiPfAZ44oFJD9Cw7RjH6jopLoOWzVgsyj2i3LBWLRcbJ3ko251gjt3LZgWFhRtACxNJKMW1GgMMrm3T3OQ96+HLGaRnbUcKS9xqpmIwZB3wLR/N5hEEGnvLAzX+JFV9qMIT9vhy0kw5oYB2gQwNiA/yS2HEiCHCpB3vw2Z45FZew7SLuJ8MmbvR0OICDC+/TdQ2RRyy05Hqt4KadJYKzEc2AMmNoyMpxFDtdUlMXC0TRlj4q3OQADH2JARJ6wWKfDYeqgad7+VU4qYz1rEIygPIj/azISI4iarB92JELKSiCvrnVjhJTf9ky0GPeiKZZAopJDIOPF6lxEdkQl3lFqrGVfOyaHGNoi8aMSBmBsHJuWtGhkDVnmJTeKA8mVvYupAUZNtLymEMg482TWIZCEPh8GDrkQK0dxEDAsdLlwbKMBnSEPOs8DIIJCeGGvZ4ETKF0DamIIs88iJViQLE5aUNAwIZr14Y2BEKeacY/cimR+BOOonT5o5VZs8oKkgJOQRy3mxcTiL8KQYqgU39ndy6Arm0OLn0VifwcAky0knZ0bikBMbdqbhEFgs4A1o3T88fmNpMrKoaXFjYyih3HSfKfAY3taPTLoYA3GMOuei2bj2pmWwWHmoMWYFSxq31dY7FJ/1+Rp2j8cC3Q3mxCO0dR0GXntE+f+qcz8KS8MCVWvGE113HC1io+PSOfhrWG2VmgRWJh5Lo8n/uRkxvse1WCf86le/wh133KHk+V177bXYsWOH4uxCfNb/+Mc/lrxWEARlNv7tt4vFHGqcjGJ94/ou/OrHb0AsqHswLFlRjdvuPF21mdHhQER2Z1tIaTiixZZ3uhW7SGLZqMbKNXU4sG+4zPd9DF/Aiu/97AoYVGbaDgfi/HD/6624r7WrpJnRZJZ6bBh5O4xoTN2SzuMywnWeAV1J9acxAsvgv86tRkDDHpBcaNEsi6FMG+RJXuOTMbN+uASyj+oxyLKAveECEgX1giTSXGnFUArGfW+oLicSS7LORf619SXNjErWsXgl+OtOAyWrHwsZPKKMhLymXSQDOe1EmCr1Ip9MAFWwpbuBSY2EJiNRbkQcFCQqrR6jLIARjcizlWJwIUxp22aamBqkxUHIkxoqTSZX8ODBDiCan2hmNHWW+fMLeBjZUk/1SVHi1a5GPLyHNDNS55JZLsSpQeQ07CBrzHZc00hmuNXHQZYN+EMrh92RUv/7MViKxpdrjKiNbdN86JvyLECfMa0IbjUoOLA7zCJVUH/KxdM8RnJ27BhRj4Hc3MwyVaMt2as0B1ejLlONrU/GkUqrx1DlN8MynEJwQP3aIkL9m/9xCQLVNk0r2F1b+7FoeZXiNHU8oYv1Ewcip4KbnsfQ+ieV332nXgnv6ks1rRlPZN1xNImNjkvH4K+OWKw3+z8/I8b3uC4wJRCfdVJ48T//8z+4++674fF48I1vfANf/OIXy15bW1s7nueuhdlsVi4Yo/G9H+CZBpnR1hLqhEQ8+56FOoE0L6ok1AmD/XFNoU7o7YlqCnXC0EAc+bwEg/rk2bSQGTizi9cU6oRQNIeYhlAnBEfSkHParaIzBVFp+qJFcZGoKdSVpYpA1m7UQlEZ5KTSRjaTIV00qbj6zUQRGXIsrinUFeIjmkJdiQFZiHQl8z0RObIL2nbkkOScplBXoqQzUMkUmXg/mUlnK318ichPFwNymkJdiYFKIJrXFnbxfAYCU2kcJIRS5HhrE8oUAF47hliW3ChU8H6ncohMavg1lYIsQcoSka0dhSRmNIU6QZQTSBW0z8mslEUoo30+iSQGJqsp1AnpbFZTqBOGhpLIDCYrfobFIhlNsU4E+tJT3p1Q0tF5N8iShMHXH0Fo2yvK795VF8Oz6hJ9EHVODrFOIDPghzML3tbWNu1rrrnmGuVHR0dHR0dHR+dIEXMZ9L14H2JtW5XfA2ddC/fy8/WBPcrIyhSAdETvnynMCLGuo6Ojo6NzIqP7rM9MSIpL30v3IxcZUjqU+s+8FtbmpSV+6rqP+tFBJkXzR5DJfZxngZegi3UdHR0dHZ1jjO6zfvyST4TxwOcvKvNCJ/VxP/rR/eC4FJxz6pQUXWLDqYbuo65zJOhi/SRgwZIAvH4LhgfLi7OI24rFxmPPjn4sWHL0mjLMW+SHv9qKwb4JR4wxOAMDY4MddWYO3a3qhZNrz2wErxReqtNxIIitG3pwyYcWKM42aiypcqHOYUZ3pDz3lWNoeC0meJpkdHSqFy3OW+CCyUZj30gIosodebXFgu3DEs6rZ0FR5XnIksyhJyVCYKwQ5bhqQWI0a4PVYIDAqOedE2s9hyGL4UxG1V/WzNhAE0fNYTOQU8nx5a1g6mdB7B6EPNBdvpxhQTe3QKbsoGSNGPIm8Mkk0g6Sz13+GJGBFRaRQobiIU5yq5nYSx77UkasZBzgpIjqOEDwgaOAvKxevJkRPYhkDQiYUqoxULAinOPAMwIklfx74nTSlTAiYLRDhtp+UmAoF+baGeyPqp+TjRYXhtM0fKakagw0LFjklbGpn0dkUsfKMUwcB7+FgcjYEMyUd0gmbiu1ZgfyIgdO43ygJStW2QoYSjPjbjWTcRnM2JnmUMtZQIvl179M82jP2mCykNx6tfOegpXzoM7CoTtR3nmR4OSdStHxUCapGoPDYEYqLsDGGRHLlxfK8jQLljehvo7Boe7ycaApYEGjE0anCW2TnGImU9/oxNYN3WhodoIXtPPrj2fMtS1ghHJbUJ1jD5klJ2J7rEA0k8ngJz/5CR55pOj4snLlSqVLOqmp0/ngkE+iNJjj3g3meGGmV2Xn8yKeeWw3nn5kF3LZ4hcq8UkPBZOIjBS/QFed1oAbb1mp2CkeDQp5Ec89uRdP/nknsqSwDkDDQh/2Wjj0ZYrFZastPOR9IYRDqXHfd2K1Nm+hX3WdpCj1z/dtxevr2pQvcaudV/yVz76gBZSKwXpBlPCXnV24d3Mb0vniOMxz2zHUl8XgSFFQLXWbkemLKwWl404US1gcYouCyW82w8iyODjq0W/hONTZbNgbKoq6KrMBd631o8aaUIpKic/3YNqIRzsPIVHIgaEonBlwwmEYgjxa2EfBi7eHRAyki9tc4/VipVcGPe6GYkIsl0M0V3Q3oWBDIk/ET9FnmqUMaGQtqM4fAkVkfIGG3JcFDu0glU8ARYNqXgmq2QeKoyCLMgpbhpBftw7IFEU9PXshuLNng7aPfoAJtcS3EpRcPBYyjJAOHAQObFR+F/31SK1chjxXFFgUOJgKNgixbiUGkeIRMvkRkYlLCPmYoZAsVOHRzgQiuQKIEecnaqxYzg+AGhX1ssGPnMUMiS0W0UqyCTkpNnFzI1vwxoAZ6/qKIr/FZsJ1zQx4ZkxIcghnPNg8HFI+hnmGxSKnBZTiClOMIV0I4NHOFEZyeeX4XNXgRKNlWCk4JTCUGx0xYDhT3G8aThyIFTCUKYpdJ29CrVlANFc83jUmI86tJoI6OD4OZB0FmbjhyMgWOLzQUY0XOqLKTR7Z5ilVLlS7gqCorBKWmfPiUDKKjFiMocroQK05B4oqjm3A6FUEM0UVjxVFLCdTBTCJg8rvIcmKRyM+7IiGxz34/ZwH23qjKEgy7ByFu5pzaJFaQcnF835ImIXfhHkM5AvK8bq8zoVZNjJuxZsbnnHCxMqgqOLYx7Ju7IsAkVzxdyNjhJkzI5gp7jcNAcNZC1pHb244moEbfry2O4tcQYaRo3HmQgOGpAGl8JWMQw3vx6b9MoLxAvkywnKDFcN7o+OOTA1VVgjRDIYOFo93Tb1D6V7d31McF7vTCK/PjLb9xRhcbhM+estKrDmjETMF3Q3m+Geym8vevXvxL//yL0qvF8LNN9+sONZxKn0JTnbdcbSIjY7LgYGfH7EbzOzAF2fE+Opi/SS7aEiToYfv24L+7ii6VGaQyUz71TcsxeUfXnjUYhgJJvHQH7dhdy6PrYnyGUeBoXCGgcMZi6sq+r6/+uIBPPj7LYp15FSaZrtx2xdPQ02dQ/W9wWQGd7/dio7uBPZ2ls9yGxgKqxwmcA4RbZaQqjPJbKcTBobBwUhU8bqeypm1DnxskRWv9g+hPV4+g+w0GHC634yelIxtofLlHE3jivoqeAQRQ2nyxVA+CyDDD7rAojHfC05lBllOGyAPp0A3VIFSafYjJ0XkXm8HU2sF06A2zjRgmQV5JAJ504uAWL6fuYWrUWhqgCkxCFosnznNck70cA680FvAHpWnGj4Dh9vreDitHAp8+flAbsIk2YzWqIQH2kaQlcrnFy6scWCFh8bWYBzxSf73Y3gFE+osPF7tz2NXuDwGl4HDh5ssyEkSDkRVZvtlCiK8SBcoxPNE2Jcfi1M8Dix105ARgTwqeCcznLTj+Q4XPJYkaLZ8GwzFgaOdsBkK4Gi1JwrE67wWHkoGGz0ASsVNZ2+hCusiDuzqS2MkXT4OS+wMbqtN4KU0j7cT5cttHIvrm62oMolg6HIrRlmmMJAKYCjNYjitPg7k5qY/IWDDPhkD0fLzpdZlwNJZFLr6KOzqLR8nM81gSd4IbjiDru395UY2FDBnvg8MQ6G9NTg++TD1Sd5nvnCa8kTxeEcX6zNDrD9+6xq89NJL+O1vf4tCoaD0c/nud7+L1atXH7Xtnii642iNS2v/z45YrM+punNGjO/xZTirc9Qhs+bLV9WqCnUC+eJ77ok9RzUGl8eM+Re2qAp1QkaU8Y4s4cLL51Vs0PTSX/erCnVC54EQdk5qvjQVj1nAWbUBVaFOyIkyNsfTaLWqC3XCgXAY0WxWVagTXu+J4LGOmKpQJ4RzOWwcplSFOiEvSXiyqw9D6U5VoU6gMIhmOaIq1JXlxhzohQtUhbqy3MzAcNFSDaFOkIDEAcjvPKMq1AmG3RtgiUZUhTqBz4fROkyrCnXCUC6PdXGDqlBXYqQAhk7i3gPqQp3wYm8EW4OSqlAnkFnyd4Y4VaFOILPsf+sX1YW6EoQMhhpCskAaAKkfi03BCLJSQVWoE7zmKM5tyqoKdYIo52FiUxpCnSCjNdoNNtquKtQJ89l+kAcAakKdsCMq4vfRKlWhTojlC1g/mFMV6gSKklFl7kckF9EcBwlhDPTzqkKd0DOSw6Fuo6pQJyQlEd1cFl2kgZva4ZaB1j1DSpMjNaFO2LdrUHmNjs77QXroEL7whS/g17/+tSLUzzvvPDz44INHVajrHH4ajHwEPzMFPWddR0dHR0dHR0fFknFo/RMIbVkHwWtR+rh8+ctfxsUXX6yaZqmjc7TQxbqOjo6Ojo6OziiklC/evh0Drz6MfKL4JOz888/Ht7/9bSX9Quf4QFJSJI/s/TMFXazr6Ojo6Ojo6JA0zFAfBl79M5Ld+5XxMNjc8Ky5DF/+8i26UD/OEEktkfzen3AcyXs/aHSxfhJidxiV4ixRVL+tdLpNR7R+SZLR1TGCpha35mucZgNYhkJBIwYvxyhOLza7oL0OjwmHDqrn3hNyuULl4pJ0HiwNFDTS1lw8C6tsQJxSz+81sTT8ZgY96mnvSm5tJm0gBiWaGIhVIijNVuwsjCBp+WZDSn0TMoskBGiVxpDizJE8A0+FGGSS9ytRoGiNaQbWBjirgHC/6mKJ45FnBfAVhpuMUyU8xOqSFAdoxCBJHKqMEnpT6nnOBpoGR1d2Y2iwGLA5qJ4GTRAYDjQYSFDPg7ZyBmW8+9Lqx8JA0TDIvOb6CfE8oxwTrSfo2WEDeBcDmlePQZR4hCgWHo1zsiDTEAyVS5EY8rFPvqQo9UgzSQHprAFGXn0byRwPUSTXpUaNAShUkbruQ9oxuKwAPShD0viyNJsNkK0csnH1vHej1QCTw4hoWP18IJ9vdqf2Zwehsy2EhianZl3MQF8MVhsPs4XHB4HeFOnYImbTCG1dh8iet5QPToph4Fx0JlzLzoGY0vqQ19H5YNDdYE7Squy+7iju/+0G7N5etAIkEB/zK69dhEuuXgCOqyyutGjbN4w/3L1BEeuLV1Tj5ltXIVCjPl5dwSR+9uxevNMWKnGCOZ3j0LelX/Ffv+bGJbjg0rmqX6iSKOHl5w/g0Qe2ITmpYK66jmyPUvZx4dIAbv7MalTXTTy6HI5l8D8v7MeLOwcQcAhwmAzY1xcrcYI5xWFG5/6g8qXfcooDbZZgSaHpeU12+GxRZMUMeMqH51tTCGcn1KpfsCGV4NERSmKB34KWhggKk7yyjQwPF29FbyoIK2eCmRUwkC7aMBIo0KBzAWztj4GmKFwyy4RlgUNgaKnECSacySArpRHg3Ggu9JcUmo5QHmzOWzCcjcInOHGKFXAyEwWWcoGC3D8CDO0FeDsQaADFTy7ApAHXIsBQFHXyQAzy+idJIuf4KxLzVyNY70ABadhoHzypIbDSRAwFgxMJixkFhFGQnHiqi8aOkQm/bz/H4DZnDv5MO2TOhoJ/FgrWCRFJhG2yYEI026/4yg9l/Hi0M4LcpOeXy9wuuAxppMUUPIIH8VwcWWlCSFabbFjpNYOlY8iJVjzamcWWSUW9FtaA+U4bhtJB5ThUm8xKIemkUcBKrxtOgfxNQjDtw/M9YcU5ZozT3B6caid2lCmIjBNRKgSRmhD1Ixkrnu8hjjZh1JhsaLSQNU3yTs9wCL1ix9a/DcPm4HHGdWZ4lw2XuLAMZwLYMEzsHyWc77HhQlMXDJP8/Pfmq/GXIKP4ndebvOgYyiGUmhgHn8kEi8GAjkgEtVYLbLYCQvmJceAkDsxADTbsTsBuZnHLucAZC7vGbyyIsH6rpxaP708jJ4pYU+uA2zoAihJLPNfzYh6JQgICfHhqK4WDIxMx1rkMmD+rgL5MEF7ehuEBC3b1TJwvJo7BbI8NuwYisBhYLEgy6FvXPXGHRVNoWF6FtlAKmZyIRXV29G8fQG7UCpYwf7EfN9+2GrX16k5QQwNx/PH/NmHbxh5Va9h0Oo8nHtqBF57eB5OJU6xgz7qgBTQxfD+KbjBzb/9/YPj37myh896aHBEr0Oeffx733nuv8j1POPXUU/GZz3ympJER+X+a/mA9OU403fF+j8uOnp8esRvMktp/eNfj29raiocffhjbt2/HsmXLlGZYWnR0dOB3v/sdOjs7FQehG264AWvXrn3Xsepi/SS/aDau78KDv9uM5jkexWOdOLW8F6KRNB66dwvW/62jpGkJy9K46Mr5+NBHFkMwqs98vrZ3EP/9/H7U5mVI+4KIjHqsj1Hb4MDHb1ut2LGpEY8Vvda3bOhBVbUVB/aVNk5hWFpxlrn8+kV4cmsvfv9aB9K50pnL+dU2jCSzqGJZpCd5rI8R8JthXcJC8Oewqk5GslA6o8/RBiTSLrzemYaDcmN7b7QkH46hKZzWbIXD04tqiwOD6RHkpNKp6IDRhVQhi1zOhH2DIoZTpTFUW0y4ck4BdfaU4rEeH/VYHx/rUa91d34IO+QatKdGShonkRnP2VYvlhoT4EZSkHt2AuKUmUl7A+CxgrJXAxZH0Qd8ErJkgLR7N3KhbgSXLEFqSrMeGhzccMKWHULaGkCGJgJ38gwuhXDGjz+1J3ClBVhWaAc1Sdwr2zDXIuf3Ic0KCGfDyEvxsmZDW0NWtMfyWOjky8aBozg4eAeS+ShODfhgMxBBWjqLPJx24t7WIKrNNiQLEWQn3YAUjwXxGM+jzsxilj0NGVNn1szYPWJBfyKHqwJmmKlShxcZLHKsFcPiEN4c9GD94IgisidGAVjg8MDORpHZYcPmpyJIxEpjmDXPgVXXFyB7OWwOShhMlzY1chmMuMZLoZaN4dGIFztHPdbHIF7rAYMHe/sTqLc5sH9KMy8lBq8TSToMQ9SDrTsKiKZKZ7IX1Zlx24UjkAw0Ht7DoStW6qbjEgSsruPgNMVh4kwIZUobSDEUg0wqgCe25bF6LodBseixPpla3o/N+yT4LVb0RFKIZErHodlmhmN3HGxGRsLEoWdKczenjUe92YBkXxwf/dRKrD69AWpkswU8/ZddePbx3cjnpbKmazd8aiX27hzAw/duQSRceu2Rp4Qf/+wqzJrjxfuNbt147HzTBwcH8eMf/xj79u1T/t7U1ISvfOUrWLNmDY4HTlTd8X6Ny7buI7duXFb37qwbzzrrLOW8uf766/GjH/0I55xzDp577jnV17788su44oorcNlll+Hqq6/Gtm3b8LOf/Ux535133vmuYtXF+mFyIl80ZIa6kkXi4fCT772izFRpcenVC5QvUi0620P4zpefqbiNXz9wA4wm9e6khJ//4G/Y/I5KV85Rqs5uxOsR9RQGQoNNQGK/erfKMT7+NQm5SbO2U9nR1oR3/j97ZwEex3W14W+ZUbBilkWWGWI7sZ0YAg4zpw0WU/oLKTOk3JSCTRtwwHE4cRgNMaNki5lxJS3D/5wrWmlnVrJlWZJ936fbWDs7u2fu0Hfv3POd6vAqjINcPFeDgFa8nVQSAz4tjzx95+tn+eANCtskEv5AHJqcwtUmiaUBCTIad4r/gDEB0vmLI8ZQYW+CNyhsg0hEqeNpHFx0ua5XB02juEWoT2lChc0QMYajXdHo9YrHsC4pCXqF+DSpcrsRb9YKT+0hzEolrkgPr/oZSpQvDtKg+Geeb7LgoxbxdkhqtKL4SWGbREJnUUB6Z2R7MZvGiGan+DEXJ03AgaYO8Rhk0di5R/x40qiksORFngZw0zwl+nzCVVYJl9+Gfe3i7WAN2vDxYeEpLwRVs9XudQhWLx3kt/93DvIjiOkXNx7AS88eFF2emm5FdaV4O9EThsdfvAUnGy7WTz29tcewynMIu3b1F3jT6XS45557cO2110Iunz6zg09n3TFTxXpxcTHy8vLYv/V6Pc4++2xBse52u5GVlYXc3Fy88847Q+//6Ec/wm9/+1sUFRUhOzt73LFyn3XOhIU6MVYh3OAYadfjecAcMutA+DfGWn+sGMeTGT7md4yxfIwNHU9B4YknsI/xDZKJxzDm/hyrnU7CVk500gJ5io/9G2NtR2TG2t3jOh4meFyPGug+7vXHR+QvCYy1PDiOOMaw0hurnU7K9YEz7eelN32yGdUv/AXbtm1j01quvPJKvPjii7jxxhunlVDnjE0gSEmiJ/46ETeYQaE+FiTgqSNOncBQvvCFLzCv/scff/y4fpcfmRwOh8PhcE5bggE/Og9vRev2V+Fz9bG/58+fjx/+8Ids9JMzMwkEJaJJ6uNdf7L47LPP2H8XLhw5oyAxMRFxcXHYsWPHcX0fF+scDofD4XBOS3qritD0ySa4O/rNFFTmWFgXrMUvf3kHkpKSpjo8zjTAPpBYPIhKpWKviUCj6oTNFp5rR+/V19cf1/dxsc45KYzZPx3jEfV4qsGN9ZGxl48Vw5gh9H8oOIHfGGNCACWBjhnCmJ+Y4DcEpz6GaeF+exJGXcbcn2P9BDuegpN6XE90+cnZ3yfp3JzAcl6Q8vTC3dGIpo9fQG91f26MTKVF7LKLmR0jucHwCqQzn8BJKopEbkyhUPGrn/70pxOKzePpT5QXmlqlUCjQ1TXSlGAs+Jx1TkSa6u34w8/fw69/8Jagp7mjz4OnHtmFuuou5igzGolUgln5sfjs0yq89sJh+Lzh/tEHiltw/3/3MEs2vTG8NxsbZ8A3fnAutDrh5FKnw4ONj+1GVXkHMiPE4DjSgmUxeigE5uhnxRnglwHphTaYDOExRFs1SJ4dix27o6GVhlewk0vkcPclorrDjQJbuGUcyYR5SSaUdjhglsYxl4zRmGUWFO03IDFgg0mgVx+tUeOafB1zntHKw2OQSuRw+eOwrbkPBkV4OxCp+mhoE6XwpswDBHzJ/YYkFEcnorZXgmAwvL2DUMIbUCFKbYFaZhXYThl6vYl4vcYFp0/YvScQjEVRIIgOcy6CEoEY1LEoMyTCF6B2Cvf8l0o0aHQkoNMjhVER3tbUtsm6GFTYW+DxC9v3AWbYtG04KzYaCmn4xdSiNKGmzYj3KhMRDIY7JEmgRmNfHD7p8sOD8H0RhBS13ii0e7qRa4oRFKNkr1in9yPr/ASoNeExpKQbcdc31Lg524RoVXg76OUqxMltaO+SIUFjEWgHKbKMseiTdKAw1iooh2epzAh+2I4VZi10qvAY0mK1uGBVAOek65n142gMSiVyrVbsqJZCJw8/HqSQQtUai6J/dyHdHcNqCowmSRaFlo88WKgxQqcMjyHJoEGuQ4XMFBNs0QLtoFWwc+t/f/4UB/eGj1b5/AE8s60KT9Z2IG1hIrNjHc3cRYn40v+txK13L4HOEH7cx8bpkZJuwe9+/A6zhOVMX/wuBxo/fBblT/6SCXWJRIqoeech+3M/g3XuauafzuGEUltby5JMB1/33XcfJgolnxIOR7ipRV9fHwyGyAYKo+FuMOPkTMvKdjm9eOX5Q3jrlWL4BqoGkc/weRfMwpU3zmXC+ZP3y/H8//ax4kWDpGZYmYBvbe5lloskzpsahp0kbAkG3HTHYsxdmIjWdgceff4gtu1tGFpOgiHfqkXl4SZmuXjJ1YXMSUbI950SxrZ+WIHn/rcP3SFWa1TohLySW5p6kZhiYkWaGuuGH3MZE43w5EfjcEsvovQqxFvUOBxyA9YqpJiv16DsWCtLQErPicLuLgc8AwWcpJIgrjtPC1tiMzwBD1RBG17aA9R1D7u4ZFr1cPsDqOt2IM2qg97gQbNr+DcSDCrMTZSjzdMGrUyNzvpobNnmGuHAsWSBBpXuNiZvVqWakB1TD2mIp7VRGQunrxvegBtySQw+aHCh2TX8HbkmE5J0fvT5emBRGZFr9sOgHO5wyd1yGGq6IW0vQ1BhQFNcDkrVvqEhRrlUgUJLBozK/u8MBHVwB+hRsn+o/V1+HTrdLfAzf3cbtjW70eoadsvJMxuxNNYLiaQbEpjQ5JChzTU8omCAAvluD9S9VQjKtGgypqGE/MkHYpBKpEjQRiEQbGZJpy5fHD5qssPlH26HLEM0AuhlnvexaivU8l74g8MXyBi1FWkGDSSSXkigg8fvgSc47Ezi9Gmwt82Mku52qGVKwGvFp1VdCA4IS41cgq8s0iHdQtsehMNrw65WsnscjmGhORaz1C5I4UJv0IJXm3pR6xx2qtHLjej2SFHv6Ea0ygCPU43StuHzwuxXIvmYEhW7m9m5dfENRiw8txnSgSJRvoAMu1ri8GFjJ7M+TNbE4lBdL5y+4RgK401wSe2we51I0VnR43Wj0zMcg1VugqNPhepuO2xqLUyHg6jbOuwapLVqoFiUgL3NPdCr5ThngRIKW+1QwjF5/8u8cdjbYIc3EECO1Yrq7m44fMPH/cpUIxLMPXD5HdD7rNjzlAvVh4fdchLmGmG4UIEGfxesCh18R3Q4vH343NSbVYhZYMXBrm5oFXLkyrUo397MzmGC+tm5mdGoqOmC2+vH7FQz2opa4OwZtnucvyQJN96+iHXyd5W34y9vFqOqdbgdMrRKpLQ6UFfWAVu8ATfesQjzFg1Ph+i1u7HpqX348J0yqFRypGZYUFrcMpTgzqxgL8rB5dfPiehOdbxuMLO//Rgb/eWcGMFgAPbSvWjb9Rb8rv79rU/JQ/SSC6E0jRy88Nrb8fZ3LmPzh6czZ5ruON522V75APQTcIPptTuxLP2rJ9y+kdxgfve73+F73/ses2ucO3fu0Pvk60+xr1u3Dps3bx73b3GxPk7OpJOGRqrv++qr6Bzldz4IVfUjUX54v7DtnVQmYcWIDu0Vt8VbsjYTHx5thXuU3/kg6bF6/PhrKxAVI+77/vf7P8KubcJlEql2xez5CTi4Z7gjMJqkc9Oxo8cJ1yjP5UHSTGomhpr7hC3looxSLJ6nwXulwtZ9JPXOybaguLtedBbA4phovPepC71O4Wd5qfFK3HZhB1QK4d+QQo76vhhsbRG2aqRnCDdn2ZBmrBV1N5HZDTjqccITUnApFLPShFyzDgEI2yQGglLsalFjV5uwBSFt+oXJNrS5xOfoJUtMaA70wCMRjkEr06DUrkC9wCgFoZLKsDjGCk9A/JjLt6RAIqkWnVZS2RWPf+/xoEek8m2WRYm1WX40jfI7H4pBIkeCJgaftjWKuokYZUn4qJz8zoVjXKow4tqzmqHRC1cPtXu0eHCPBZVdIjHIpFiSbkKJXaQdgkB6dwIOP16PgNhxvzwOhjV2BOTC1UEV0KCuzYiaUfM8B1HLpVjpMODTjeLnXuHVydi2oxM+j3BDZOZZ0dzYh94uYZtUs0GFJKkU9aXCxz0VVMu+Kh+vFDWLxnDNrFh8+do5ogXgyHP9H3/4BD0hgxGhmCwa/OqvF8NgjFwpdSx4UaTjL2Q0mvLycvzzn/9ktnoEzUX/4he/yJJIxZiKIkfHy5mkO06kXbaeBLG+YpLE+u7du7F48WL85S9/GeGpTomnVBSJjlc6RscLn7POCcPj8YsKdaLH7mZVAMUI+INwu4RF+CCNzb2iQp1o6XVHFOpEc0OEGAKAO6SqoRCebhdcfnHfusY+D9wDTxWEaLcHUNYiPmGOlvjgjThdt749ICrUiaY2j6hQJwLwoXZUIZuRywGVnEa2xX+jT6uAxyfuV97l6WbfI4ZUEkCTU7yt6Zfb3ZGPh3aZBJ4IHoIOvxONDvHfcAf8kEqEBe7QZ/wuqOXi7SCTuNET4Zgs6/Rg7qiCPSO+P+hDRUjF0NHQwwKXOyAq1IlgjFdUqBNGpQPNveI3J3qa4/SJHw90LPraPKJCnfD19IoKdcILJzpd4iPKLl8ADccin3v2Cr+oUCd6ml2iQp3o6nHDHGEbvB4/6iJcwwiHThGxUnN0rF5UqBP0NI+eQE5UrA+iS8qCTH1iRenOlEJGJLBDR8NJtP373//Gpk2b2IglCa677roLN9xwA5sXzOFMFYsWLcKaNWvwxz/+kR2PsbGx8Hq9zGedjuFbb731uL6Pi3UOh8PhcDgzio8//hi/+tWv0N7eX8hu/fr1+PrXv85EEefMIIAJWjeegJXB/fffj507+4sKulwuNs3l6quvZn/fdtttuOSSS4Y+u3HjRlx11VXMm33ZsmWsEBIlNr/22musENfxwMU6h8PhcDicGQGNplO59jfe6K94nZqayhICaSSTc2YROEluMMfDOeecg4yMDPbv66+/fsSynJycEX/HxMSwTuXhw4dRWVmJ6OhoLFmyBLITSHLmYp3D4XA4HM60hwrJPPTQQ2w0neab33zzzawipFI58URfDmc80Aj58TJ79mz2mgjTO7uCMyWQG8WyVemiyxcsTcbKNVlQqoR7h+QCs2J1OqwCNmvs+40qyExqpCYIWxfJpUB+lBafvl8uWiJ8385amCxq0RhMqWZ0pZigj9MLx6BXwBoECnTChQ/I3W2ZSoGzdWrRkvJ56QZE6VQsoU6IJIsKKgUQpRae06oJymFskSPPItxO5HyxaK4OdV1JCIo86uvoi0XQpYdSItzvTtRq0eGWw+UTbgeXV4l3K0zocsRAjHhtNBxenei+KG4zorlLB1lQeI5onEaDeE0AOrnwY79AUIHSbh36fMJ2k7TtckkcZpmiRR9aGhUWlHRrEAgIx6CXa5mrixTC7eDxy1HWo0NerHCSER0D8xNN8ASiRf3XTUqyWFRCIxOOQeHUovegDLEB4f2tgAyd9RbsKxMu1ELN//HhNMR6hS0QiRSDAX6XGmqp8HGthRbdFgMss4QtLbV6KTZcpcb8KOHl7BBwJCJGHsWsIYWwSU3oTTBBbRE+7nVxOnhm6RCXJtzWSoUMa89OxfIFCRBjTqIRCUlGQRtGIjbdAqM3AKta+LyIVsmhKu9EVXn/FAohjGY1lqxIFV1Oy4zmE09u44wfcndp+vA5/PznP2dCPS0tDY8++ijuvfdeLtTPYPxByYRfMwXuBjNOzsSs7JKiFjzx8E7UVPbb/cUnGnHTnYtROL//Jtre2oeN/9k95MhCIv/KG+ZizYWzIJVJ4Xb78Orzh7Dl5SJ4vQHmd56yIAGlrX3oc1LSI5CbYUVDcy+6e/uT6nJsBgRbe9Ha1J9UmZUTg5vvWoz0rCj2d1ODHU8/uhsH9vQ7i5gtGkTH6lB2rN8VQqVXQL8sBbvbeuEPBKGSS7HQpEH71hr4KclRAszKi0V9TRf6Bn4zdY4NB1UytA64gMzVq6Ct7ELrQAJrfKoZLYl6HOvr/7zNqkJMghLH2vvdMKwaJeKNWhxp7hqyfpyXpkVFTyv8wSAUUimyrVaUdnQwyzvS/tnOKFTv6UHPQAyzsqNQ6vejzdmfHFiQqYXK5kSTo78dUk16rEr1wKDpFxdurx676iw40NL/m+TLnh6tRqOnmW2jRirD8jgrXL42BBBgFohZxigk65shk/qY6NrdkITNx9zodvfHMN9mxsKkDqjkfUNe43qlF75Av+WkSmqGWWWAUtZvk9nuUOGZIjV2NvYfH0alEnPiNXBJm9i+JWeSVXEmaOVku+hn1n9SiQ0Njk4Egn4WQ5fHhp2tfcxmkMg1WZFr7oVc2h+DXGJBl0eCbk9/W+sVBvR6Zah39G+3WqZhlpJl9vYh7/ElsXpYlM0sqZYEZYYxGkoZOdVQ8qgUCkkMvEH6fP/+Lu6Mw1t1TnR7+pMJk7RRqGvzo7m3/+90qw4GvW/IejNWrUe2SY5AsH+71VI19Eo92lz9x6BSqoRCZkJpdyvr5kn8MgSOxWHXp93weP2QSSXIX2BFTXQXXANWnMmSWJSXedE2YEG4NEuP29c2wWbp3+6qpmg89I4ZRXX9SZOJVg3i04JoCLQPtX283oCSjnb2mzqFAtkxOjR5mxGUBCENSmFwJ2F3pYMlTUslEizQ6WF/uw5ue/9vbrjJgvlr7QiQdSbroERjZ2sADY7+/S3zWXCsyoCSARvEeIMacVFAnbOD/a2TqqDvsWJ/qZ3FoFfJMVejRMsn1QgGgpAppIi9IAX7gy64fJQQLMEckxFNO9vg6O0/7s+an4A7r52D2Kj+Ds3+ohY8/OwB1Db2n4vJVi3Mbh/qBq5JlIRuNKlRWdbfDlqTCpbcGByp6mTHl0YtR1J2FHa1k51n/0DA2SoV2g40wuX0sWvS6nVZuPqm+YI1HtjxcaiJXQfra/r3f1KKmV2T8grjcDIYdINZ8IuXeYKpAD0VB9Hw3lPw9nRiVqwBt99++xkzmn4m6o7jaZf3yv4BneHEO8x9PU6syfryjGhfLtbHyZl60gT8AXzwVikT3usvyYNcYBT5yIFG7N9dxzzR6cY5mubGHjz7zAEUt/ehviXcdYRuqNnJJsjanag6Gm7/RzfUVWuzmGXkmy8VDfm+h0IFS5BowD6/H50C7ig2owpz3UE46+1orA+3nFOp5YijkTy7G9WHw+3eSHymzo1Hd4EBR7q64RVwkcmMMiDGKEWbvwPd7nAnC6tajTiJAW07XaitC3eyUSllSC+IhjTLh/Le8BE/GgNYnGCBWQ18UEX+6uExpJqMmJ0ggV5JPuDhThZauQYJGgNeOkruJgIxyGRYn2HA2Sl98AaEbe/0injsqDVgc0knPCFe48MxGLAuM4gMcxsCIX7ng8gkGnS6o/BBgx81feEFZhRSGZbGWBGrCaDRIWzNZ1FFo75PhuKuDngC4c4jSTojLkySI1HfTc7ogkWNer0WbK70o6InPAa5RIYkdQyCkgBqHC2Cz1ZyTFHIMcnQ6W6HLxgeg0FhREuNDtvfdKO1fbgOwCAmgxIpS0xo6JaipD7c8Ucpk+KqZRp090mwZX+v4PzKwjQj4jJ8KLd3whXidz5IgkGPGI0Kh6qCaO4JPx5MKgXOkUtx8bouSNRCo8xSOHzxeH6vETsqewRjKIgzQheUYn9xH/rc4TGkWDTIlANlpgCa+sJjMKoUmCPX46qFaZifH15Ii873V98vw+FPq1FxeNhzPRTqzMssahS19A8EjCYuVoeoWB08Ra1oGxgICIUKIVH9hxWr++eijsbvD+Dd14+xf6/dkAOZQGG1E4WL9Qij6R89j66j/cl8Cr0Zmx74FdauXYszhTNVd4yF/QwU63zOOiciNEK+5qKRSROjKZgbz15iUOGR5AIb3t10SHC50+VDV4cDPUeFhRmNyn34dik0WoWgUCdo9F+TbUFns7DVWrPdDbdOJSjUCbJ5dFZ0sEJKgjEEgar9jehMlQgKdaK8vQcxURp0i9j3dbhciO7TCQp1FgOzzOxBV5yw5RxJlJ0NnWwUXUioE1TwZkWqHg4BoU44fE4cbDGhrFO4CiMV+ilqc2Bpkrg/da+3Ee9WSQSFen8MPYhRqwSFOuEPOtHpDggKdcIb8KO4yz5QaEmYTncb6vr0gkKdqOuzQ6uwCAp1IggX6noDgkKd8AX9aPF2wOETt1E81t2OZJ1OUKgTPV472isMgkKd6O7xwN4AlIh4pnv8Abyyy4W+CLaXh6rsUCZIBIU60dDTC7nbgGaR7ex2e6HOVYoIdSIAOVqwrUL8cfGRJjuS/HpBoU7UdDoRU2hGU1t4BWTC7vbCHSMVFOoEDRBccl4WXvrnZ6Ix0Mi6aV68oFAnmlr6kKSQoUHk/O7r8eDjd8tExTqJ8/MvzcNk4rG3Q+YWPlbONHqri9G89UX4qaaBRALL7LNhyl7AXDU4nEHICWZCbjAzaBoMF+scDofD4UwxXnsnAirxDuqZUPTooVuW46WXXsIHBz5Ahh5IzMnBN7/5zSGRLlQQiXPm4g/2vyay/kyBi3UOh8PhcKaYM70oUsfBT/DTn/4Uvb29UKlUzOnlnnvuYf/mcM50uFjncDgcDoczpXPTO49sgy5Gj6ysLCbaCwsL+R7hRCRwBk2D4daNnAlDc61LKvsdIcSIsWoglYqfGHqdks1JF8Ns1TDnBzEUShnizcKWeIPExuojxqCzqJmjjRiUPGtViS9XyKTQ+CMXO1Ap5MwNRPQ3VArm5CG+XIlojXgMcqkUPm9klwStUgYZZcyKxSglu0rx0SwpVGzevBj03VJJ5FLfUjIqFLH+I3RyNVQi9oODjis6eYQYJRL0eSK3g04hhzxCDFalGkaF+G+QRaMkIF5qXgIJFCLWgYMY9VIoRaw/iWijEla9+HaQ44pWpozYDlG6yDckpUwOCcSPWxm0zPFIDJ1SjrgIFoZ0uGsgi1grUCWVweURn5vf0uGA2Sp+fqs1cugjtDXFEGvTs0Rx0e9Qy+F2ecVjaHewF+fkYS8/gLInft6fRCqRsGqPTz/9NBfqHM4ouBvMOOFZ2cJs21OPxzYdYjcx8kW+45o5iBmwXRtNRW0XHtp4AEUDNmuEUa9EhkmN6v2N0OtViE8youxoa7+f80ByGbnQXHZtIfv3W68W45XnDsHl8o3wfb/x9oWIsRnwcXEz/rblGBq7hhO1Mm16fP3CPCxIt6KmqhNPPrwTx460DC3XG1SIXWXFEWs3dEEFMkrVKN/ZwhJbCZlcivQCG46098HtDyBjmQ1F3j44vcPiosBkgHR7G+w1dqScG4u27ADaQpLFbBodFBVylO/vRlyMDlq1HBW1w0l/eq0CWVFaVO9rhNaiROxlsSj2dwy5kJAAXp1qworkOsikXhxsTsWbZQ44QhIL0/QW1JQCdc0enL9Ijdn5nXAGht13yOfc5Vehpq8DShjQ0qVDacdwDNRJsKms2FXRg2idFF9fHUSUvm4gtZWQoNuZgEf3e9Dp8iM3KhrV3d1w+IYFzuwYIy7I7IRZ0w2VNBoBuOENDCfUSmDAx9VmvFXRhziDBolREtQ5hjt6apkC2UYz7J5W5kyTZbSiy0MWiMEhAaySxGJLiQN2jw/zEk3o8HfC6R9OBE3UWNHcGUS93YkLMnS4eFYPpJLhGKTQwe1Xocfbil6vAdubjTjaPZz8qJbJsSbBhPkxTfAFZNjaFIutze0IBIdjSJDasG1bAF09Plx3ngaxiS3wBoZj0EqseGebGgfKPcizaCBrc6GxZTi5MTpKg7QVcrRYWqH169FTEY2DlcMxapQyrJyvhiahFghI0V2TjE8O9MI3cEyS5ixMMaOqrQ89Tg+WzjOgU96JHu9wDNkWI1ak9kGn6kJbtw2vHdKgqmP4mIzVq3DJHD+So+uhkWkQo1HDGxg+LySQo6wuCX9+mcw/JSiYZURxZxe8ITGsn5WAe87KgUWjxBv7G/Dvd0vQMWBHSmTG6KDudKOpthuJs8zoS5Cjxj4seKO0KsRINDhU1g2bSY2vnJ+D8wriRgwEPP/mUbz0dikkQWCOTY+qw83weYcTrLNyY9DS2AN7twsp8+JQ1eNBd89wkvesNAvuuXEustOsKDvWiicf3jVk9UiYLBrEDNi/WqK0uO62BVi2crjOBNltvrClhL2Iqy6YxV7kB38yOBPdYHyOHjR99By6S/awv1XmWMQsvxTv//hGJCYmTnV40wauOyK3y2tH/zVhN5iLc784I9xguFgfJ/ykGUltox0PPXMQB4qHb+4E3cCuvnAWrjxf/Gb24Wc1eOLFI4g3qdF6qBmuUVaLickmJs1oJP3mOxYjLnHkSdTZ4cCzj+9BVUUHs1sb9H0fxO3146mtlXhpdx1uOTsdVy5JCRvN3vFJJZ773z5YZxlQmuGAQzpyVC/ZrYV6lxcyiQytUgkaQsQ/YbSqYZ1vQavHjZQGH5q2N45YLldJkXplAmr0DsQ5DDjyfhtGm6fkZFjR1ulEglmD9qIWOEMEBmErNCOwQgOtFrgwqxtW7Ug3DYdXg4+q4nGszQNZtx77ikfaYlJBppvWq2GKaoNKbsSx7tYhwTmIOhiL4mYfLAo9jtS60O0cGeRZqQrctqQHEmkQm4vV2Nc8sh30CiWSjAZ0OB24eFYQGZaGkRsJCTQyG9yBblR3RePxAw64R2X1FNiM8Ct6EavRwR/shicwsh1i1HrEqBVMqO6tk6G4beTopkElR368Fl2+Xih8ehxuGun4o5AC98w3ID+2DQga0ekmK8aR29nosOHDRj9S9Rqcm9ABnWJkol+Hy4C36nTodgZRVaTF0YqRMUYZpbhxvQwKbTcqKqLw0qcj24lq9yyO0qO1uguZ83ToSG2Gf9QxZ3baUHJEjqRYBRKymxFQjNxOmduI0iNW9Pb1t1/lgN/5UDtoZVgwT42OoB2r0+SIMzaGPfItbUjF28V+nJ2pxJzUKshGxRClskArd8Lep8dfXlahrGnk8vgoNaLiFQhKga+tyENBnGXE8j6XD49+WIb3DzchTalAxZHm4b7egAVq1lk2VErdSNLqcPiYHe4Q4U0sTLfi6xflobG2G489fxCtIR0MwmZUI0EmgavXwzrwddX9nvuDKDUKxM2xoa7ThZsuy8Oa5amQhAypk/Xjx++V4aWNB2CLN6KyrA3uUY47uQU23HTXYtR3OvDoc/2DEaGQD/wd1xZi2fyJC8szSaxTYTV7yW40fvgcm/5C+yVq4TrELN0AX183Xv/C2Vysh8B1R+R2ebl44mL9sjwu1k8r+Ekzkq/89F3UNAjbIBL33DAXG87NFF2+a3s1/v67j0WXa3UK/Oup68e88IfehI93+e6GBvzusx2iy5VBCXq2RE4Xz1cr0FgzUiyEkrI0CQcGCrgIkROnQ8vBFvEYVBL834ORHSL+uCkHR2rFH8/fcJESDvVoET2M1B2N1/ZG3s4YW+TH/z9bKYFMJl4N8rPaNLxaJn685MfqoNMLW3cOsrdSy4pMiZFuNqGyS9iikPjCAi2STPSkQBiNzAyNQthWc5Ab/pAE5yhxGUpeogHF9eLfse4cFdo04vsiUWuATCreTsS7r9si2jn+8vMBSJTix5RRYYPdK27Nabeb8buN4tN7iFe/vRpWvfgUofsf/AyfDhQuEyJjrg27Qp40jCZRr4SrVPy8IdI8fnR3iZ8bd31tOc6OcA366N1SPPZ38fPfFKtDVcQIgId/fT5s0RMT2GeKWPf2dqHx/Y3oqey38FVHJyJh7c3Q2PqrxHq627hYHwXXHcLYz0CxzhNMOSeEWOn54eWR16epBGN9YiwiCfHxLI84gXUohrG8nSbm/TRWO5wUZ6ngeNppgtsxRlsGx9qfJyHRZ+y2nODxMp6jYVxtHWn5mCGMeW6M1Q5U2XUi5+7YEYzjAyfjwB6rLSfYTv3lZ8f+yMnidPVZ7x9N34PWz15HwOuGRCqFdd55sM5ZCYlMzkQ64R2oQszhjJfAGZRgysU6h8PhcDhTzEz3WSef9Ke/uH6EF3pTUxP+9re/Yf+x/TCaFcjOzsc3vvENpKWlCX4H91HnHA+BYP/rRJnIuqcaLtY5HA6Hw5liZrrPOo2Qk9imBNFAIIBNmzbhgQcegNPphE6nwxe/+EXceOONkMlOTmIuh3MmwcU654SY8OP8MZ8+jd3lneic9bGf94+j2z3BrwiOMSVhXHMSxvrIWI/yx/MbY4UQnPAHJvwbY22HBKdi+sdYvzGOGMY8d8b4jjEm/JyE3T323prgbwy6/0yIMaewjN2O4zh5TiCw05vq6mr84he/wP79+9nfCxYswA9/+EOkpKRMdWic04xAUAL/GTINhvusc06I731hKebnx4a9r1LKcPNl+Vh/tvBjzkEWLE7CzXcthk7AQzoxxczs1O7/ybtoqAtPGOxo68M/fv8xvvflV3BAIInN4/Zh89P78bXPb8JbrxTD7w8IOtL8868HkN0UBW0w3BM82a1DyidyzDGokGgJT2Cx6JVYGK2Dx+VDenZU2HKZSoaYNRnYJwsic348ZGQHMor0/FiUGlQwr8+Exhye0Ec2lrFxJjx/vw2d9aaw5c5uNd55LBG6na1YrA9fn/y7b1uZgVvnnoWlMfnCvuadCdj2qQr5GgtMSoF2MGmRYdXD6ItFjDo8AYe8zhPl8fjZmyYcaQi/GQcCUhR1JqPY1Y3FKWYoBDzmUzTRKK5Soq0hHbJAeFvLXGaU7EyFqikGMbLwdiC7zazGKLS85Uama6Q7yaAbzN3zdUgxtUEjI1vA8HYorkrGtx6NwgOvzUJXb7j1aLvLiKdKbVh5vgf5meGJlUadDCtXaCFL6sLiOfqw5bTZ81It2LU3AF1rKmTB8NFFcoPZ+5ERjYfTIfWGxyD3GVFdmYr4NBUyEsNHYA1KGZabtfjfP4OoL04I05F0Y6ruSMI/d/lxtDkF/kD4WE2vNxr7HcDFG6SIjwlfTm4wBbMN+O6WPTjUGJ4A6ujzMGvUuh01mJdoDOsgUTssSDTBv6sBa1UKaAXOi8JsHWILvSi4yoLoeLVgzYbsVDO8iQbECZx7VK/hhtsX4qxzhK9B5Aaz5aMKPPZWKUsAJ/eY0aSkW6CRyzDfrGHuM6Oh92jZA7/4AMWHmgR/50wjGAjg+eefxw033MCEularxXe/+138+9//5kKdM6nTYAITeM0UuHXjOOFZ2cJs31c/ZG129qJEfP7qQsREKF4ymh67C88/sQ8fv1cOvV7JbBpDfdbJ43zdhlxcfv0cKORSvPlyEV7ddBjuEJ/1eYuTcNMdixAbZ8CubdV45j970BZia5eYYsItdy1BXmEcKmu78OAzB1BUOpzMpNcpkL7QiBJtOwwBBdJLVSjf2Twcg0yC9Nnks+6Ax+fH3AQT6otaRsSQnhXFfJ7bW/tgW5iAMq0MzfZhez8S/DGeIGrL2hEbb4A/VouSEDcMg1qOOSoFWj6tgVarYPaVpcfahrzeqZjTivXRWHppC+QqLw6/n4D3XuyGM8T2MjknGuUWFWqcXiyfFYOvXZiLpJB90eG247363ajsaYTUZULJYQuO1AzHoFPJMStNjyP2LuiVMqRHGXC4qXPogkYFduYlGdHqb4PH70OKxoaiBgf6PCH7IkGPa+Y3I9rQiSZHLD5sDKLVNbwvLEodJB4dilvssNK/3XocbRl2T9EqZFiRrYbCWAtJQIGOikRsPdw75AJDYm9+thHdunY4gx5k90ahYo8dfSHtkJFhgjfbhxZpH9al67A6rQtBDP+GXKKDXKKBK9CG9m4znvsoHrvKe0cUjbrxHCUuWFgJP+T4tCkG20b4rAMJsrh+n3W7D0vnG9Am60CfdziGFL0JLZUyVNS7kR1ngMPtQ31niMe5SYWC2UF06RqZz7q9IhqHQnzW1Yp+n3VtYi3IJ7GnIxnbyntG+KwXRFtQUdmHnl4PFkUb0FLRiZ6+YY/zWRlGrLrQAUNMJ7oc0fioSo66nuF9EaVR47x0OWINjfAFdCju0uNY97DnvUIqg9Udhw8+dbMnVAU5JhR3dA75rBPrshPwhWU5rGjSJ++Vs3OZzoNBElLN6NMpUNXmQFasDvJOF5rrh91uTFYNFHkx2NHrgs2qQHa+FBW9wzEopTJkBC0ofq8dCEiQnW5BSUUnvL7hDnh+qgU95e3o63Ri+eoM5pNuEinSdLS8HQ9uPIDyEAcns0GFVIMKNfsbYTarEWXTo/zYsDORQiFDyuxYHGzuP0bmxOpRc6QF3pA6C0tWpOKGzy+E9QScYU4HNxhXax3q3ngEyVI7FAoFli1bhu9///t8DvoE4bojcrs8e+hBaCfgBuPoceK6wntmhBsMF+vjhJ804lDhErJxzE4LH9UcLwd21+Gff/wULqdwBUGzRcOKF9WJ2CRSBVMarf9sa7Xobyy5YBbe2d/ARtaEyE03wX64Fc4+4RgMJhUMRjUaQooZhUKez9HrMvBpBEvLZRlW7KzqhF8khtlRWkg/a2AjlIIxGFWIitWgqky4Hahzc9UXlmDD2mzRGF4/VIbfba4QjWFWoh5tChd63F7RKqs2gxqlbcIWhXKpBFefpcbhHnHrvkRpCt471DckPkeTZdWjsdKDrlEe/IMYNHLkQYLyCpF2kErwzW8YEGsTN9+rbcrAL5/zwTvK932Q3CQNYue2osc70lM9tICT3BGN8pCCSqFQ5ybdm4RPDotX9z0rx4I9Zd3wCjz9IVJjNXDqfeh0ekSrsOZ1KFAS0vkcGQOw8tpYbLeLWzkuS7ai1tsMb0DYDtIqN+DQUZ1oDNTBOrdJhsO7hPe3RCrB7IUJOCSynMg6NxFH03rgEYkhWqWB84MgOkI6AqGoVTJ865YFWLokWfQ33v6kEv94cp/ozJV5aRY0HWiC2z3c+QyFCiYRnSJVTFVqOX742wuQcpzXwZks1gN+H9p2bkHbri0I+DyYl2bDfffdh4svvnhc7kqcyHDdEbldNh58aMJi/YY5d88Isc7nrHMmDE19mYhQJ2jai5hQJ7o6naI3UcLr8aM1gm8z0dzWKyrUiZ4uj6hQZ8u73UMj3UL4fAH0igi/Qfp8AVGRTNj7vFCKCHUWg93NRtnF8PsCMMsjJ3AFveqIMZBA7lGLt4Pd7WVTbMQgAd4VMtouhMPjExXqRHuvuFAnepw+2D3i69P2qeXi7Uh09vpFhTrR0uuBRkSoEy6/FxKf+G/QSHxolVsh7CwGcd/2th4PPDLxtuzz+tDbK74+NXHnqEJXo+ly++AdVSQqlG6vA53O8Kkigzi8frS3iruY0DnjibAvCZfPIyrUiQ6XE05xC3243H4YBKarhULFlSJNMaenM5GuMWIifRB60tZrn7luLic0mv7Wf+Bu7y++pU/Nx4MP/hqFhYVTHRrnDMEf7H9NZP2ZAhfrHA6Hw+Fwxj03vX3vu2jZ/gr7t1yjR9zq66COTYHVauWtyOFMAlysczgcDoczxcyEokjeng40fbQJzub+6WX61DzErriCCXZe1IhzqgmwJNGJuMFgxjAjxHpPTw9efPFFlJeXIyYmBpdddhmb4zce/H4/PvjgAxw8eBBerxezZs3Chg0boFSGu5BwOBwOh3MmFkUSKmoUytatW/GnP/0X8oATmpQY3HPPPVi3bt2Iuem8qBGHc4aK9aKiIqxfvx42mw2XXHIJ3n//fXznO9/B//73P1x99dUR192yZQvuuOMOJtivvfZaaDQati5VUHv66aexfPnyU7YdnMiQk8uiZSnYvb1GMEEtOzeGzdUuO9YKnzd8jm5+YRyWnpOK9jYHukNcNwaJitFB5QsiyaZH3YCrQygalRxxJhWkOdEoC3GCEIqBnCJCnSAGSU6zQNLnhVWnRIfAvPNYowo6tx/p0TpUtvWFx6CUIcmkhj4/FhVF4QmBdE/MKLBBqpTBcbiZzdMPiyHVjMP7G5jzjXUgIS6U1nYHig42ISNGh4oQx5xB1AopksxaJOjVKGojF5VRMZC1nsUEpQ/okXvhDnHlGCTTqIPkqAembA26feH7wijXwN+pQIZFjorO8H2hkkmRqdQgIUGDAw12QTfseZlGWCVe2He64XYLtEOSEfvqFVgdpUMQ4dspgQZt3Wpk2WQoaw5PlKU5+TnJOti0sahxtAp6cifKY2H3aqCWu+Hyhc91TtAbIAsCNpMazQKJkRatEjF9XuTG6HG0NbwdFDIpcpMNCOqAI83DrjyhFBoNiOr2oFMthzPEnWiQ1CQ90mID6OjUoMURvi8MSiV8nRrE26LQ6AlPUpVLpFidmIP0oBKvFNUOOeKEck66DWenavDSkwcEE6PpvFi1NgvdnS40CSRf640qQK1Fml6Fqt7wZF2ZRIJsuRW+bKC4vEMw7yQr04otxc1ISjLBKGDFSCyZG4dte+tR2xi+vw06JdatyUCRSi5oB0uOUFm5Mezf5FblF5jsOndhIpJSzBCDEtPffv0o1l6UI/i5qS6KFFrUKBS6f/7jH/9g91xi4cKFzEd99Oc4nFNN4AyqYDqt3WCoCtq8efNYxbPPPvtsaDT8y1/+Mh5//HGUlJREvGD84Q9/wKFDh9iFRq/v9z52uVxYunQpWlpaWPGG8Y6w86zsU8ORA4148uFdQ/7qSalmljTZOGD3RtZoJrMalWX9wsIarcUNn1/ErNMIp8ODl545iHdeP8puqEqVjNkqlpPI9wWZPUbqvHiUdTjQ6/AyAUzWb/bSdvQOiHwSF+TV3jxwUyfrx2AAQzHRb5I7TcWAAwclx8bE6oZEvkIjh2lFCna397udqORSzInRo/ZQM7zU0SDRXWhDSa8b3QNJtXPijeip6IS9q1/UZcXqIe90DtncJaSY4DAoUdnan+QWY1QhWS5D5dFW9rfRpGaWkCQkBp0pLrl6Ni68LB9yhQwerx+b3yrBC1tKmHsPi6EgFqXO4UTO2UkmNHW70NbTn1SZmaRHUB9AbXe/2E01aqGt96G+tN+BxRytgXGuCYc7+2M0qxXI65Gh4X2yGwSUOhlyb45BrakdvmCACb8Yfxw+/NQLh6tf5M+ZZUKj14EuV7/IK7AY0XvIjs6W/u2MTzWhz6ga6tykxGgRneJDk79f1JmCKkTV6lB8pN9xxaBXwlZgxj5PD4ISCcjG/+urZUix1iHIkiilaGxNxu9fDKKrr//SNzvZhMZOJ0tqJQrTjPBZu9Ed6I8hzaqDUe9Ds6t//0crDWgrN2BfcX87RZkVyJ8tR3lv//7XK5SIUZixu6qHPaIlb/lcnQVHKrpZ54bcchZF69G9tQbegba3LUnEMYUUbb3935mXbES33I1WV//xkGLWMYebyo5+UZ+o1yCx0o3mPc39v2lWwzQrGkeqO1kSJdmRnnOeDsacJlZEKRiUors3Dtvr7HD7/UwAp2tisHe/C72O/s7O8gVqKOM7WUIpkW+Jx9XpCxGr6XdJKGuz46+fFuNQU3/bJ5t1uHdFHhYnR7O/ybLx+f/txSfvl7MYqIbCFTfMxZoLZkEqk8Ln9eOtV4/ilecOwuXysY5vyrx47JQEYB/o9C3I18Fr7EG7q/9cTNea0bXbg6bqgeMhVge1Uo7KgXMxNkoLebweh9r7jw+zVoG712TjkgVJgonYlAT+6vvleOa1Yta5oY+cvzIdN19ewAQ7sX9XHZ56dDdamvrP/9QMKxwOD1qb+ts+Jk4PrUaB6srOoYEGso4lC1khhq5Jbxxj1zIS/msuzGFto9Upp40bDIn1179w9oh7amdnJ/NK37t3L/v7pptuwle/+lXI5dN+nO+0gOuOyO3y6F5ygxm/VfRoHD0O3LFgZrjBTGux/uGHH+Lcc8/Fo48+ittvv33o/YqKCmRmZuJnP/sZfvzjH4uuX1NTI1iM4Ve/+hWrqLZjxw4m3McDP2lOHVTEiIoZ7fmsdkh8joYEeMHceFx6bSFUKrngKNbmjftRerQVXR3hI4oagxJxhXFwtvSiqSzcWk8qBbLzYhHwB9l3CJGWaWU3Wxrt9wiM7ppSTFAW2tBa0SnoJEHFW5Jmx8LR5kB9RadwIZ1EExvNPFDfLViXMifOgGgJjfa3jvB9H8SWYMD6awrx3DtlaBIazdcokFgYiw6vH6UD4mRkDEHMybVA5fShfMew93woKflWGKKVaHurBp6ecNeP6CwdEi+3YNfhIGoaw5drVFIm2j31DtQMiO5QqEOVXmiDPEGCGkmz4Ch3st8ETYcaB+BAr4CrSG6sHLctCuLJ96TYX+UTfKJQmGJG0NSHhoBADAhiXoIF3h453tvqhJCBS16GBgkpMuyrdqJHYF9EqVRIgw6+/c3oqg63nJSrZYhanYYuowQldmHrk9lxZhhavWh6qxoB6nyOIi7TiphCA+IWtUCiEnCyCWhQ2xqDg0cCqG4IH+1XqyQ4f5UGF+SmY06UsPh8p6QBHU43rpqdCrks3BWovKSNPSG76Ip8ZnU6ms4OB57deADbe50oE3CJUSokWL5AB2+jB0c/E7bFzM2wgnpiO9t7IPCgDTkJRvzs6jlIjhIWv53dLrz0TilWLU1GRnL4KDc9OXt98xEc3FPPtkeIzFnRmLMwARuunM282IXYv7sOj/1jh+DTPupgf/5LZyE2UTItxTrdP++9917WmaACRz/96U9x3nnnTVl8ZyJcd0Rul0fPILE+rSuYfvLJJ+y/owV1RkYGmxYzuFwMsfLGzc39o1FUvIEz/ZDJpFi1LktUqBM0sk6j6UJCnUhINrFReCGhTjh7POTNJyjUiUAAqChtExXqRFV5B7OUFBLqRHdNNzRtDlHLNypoFGjqFRTqLIYgcKChG/vraSqIcBLNsaYedHe7BIU60dzQg4+31wgKdRaD0wtXu1NQqPfHIMH+4i6UbRcW6kRNUQdkezsFhTrRVtaHxr0KQaHOYnAH0FXnEhTqBP1uxcFm1EmFhTpRK+tGWzQEhTpxtMWHpz4xCgp1wuUNsCk7QkKdxQAJ9jV04eMdbkGhThRXONHcJhEU6kS72w2NvU9QqBM+lx/eqg5RoU4cbupC5wf1gkKdaCrvQPYih7BQJ6ROKFwQFOqEyx3EgX0SUaFOrJuVgOvmpgsK9UERS4WJhIQ6YbFqEbckSVCoEx5vEKVHxIU6cbSiA0edbkGhThxrsOOIQPXjoRhIKF9dKCjUCRLfK1ZniAp1gpbRZ8SEOkGdFiGhPvgkYuc28boQU8mBAwfwuc99jgn1hIQE/Pe//+VCnTPt8IfYN57QCzOHaf0si6apEEJTXegCMrj8eKCLD02hIcFPU2zEcLtpLqx7RE+Ow+FwOJyJ4Pd74Q/0d+icrj5moDCd2LVrF77+9a+z+19BQQH+/Oc/c0tGzrQkOME569N3XskME+tOZ/+IhEqlClumVqvZvPPjgearX3PNNejt7cXzzz8PKc11EOE3v/kNm2bD4XA4HM7J4lDpqzh47CX272feAL75/enTtvv378evf/1rJtRXrFiB3/3ud+xey+FwppZpLdZpntygyCYnl9FCfnD5eCDbRnKEoXnqf//733H++edH/DyVTP7mN785YmR9vHaRHA6Hw+EIUZh9CfIzL2T/XnJ2Ki64PAX5+flT3lju9gb8/OfPMPeXs88+G/fffz+3OOZMa/y8gun0IC0tbWjqisUyspx9fX095s+fP67voYsPZbG/+uqr7JEeucmMBY3mC43oTzY1lR0wmNRsXufpzL6qDuQlmKBWCs/3JAcTsltsF7AXJMhpor2tjzk1iGGLM7DkRLFHXXEJRjTW2ZkzhRBR0TpWfryzXXjOKSWpqqnEea3w3FipTAKVWsFmm4s9bTMpZMyy0Slgw8hiNGng8wfRLFLGXKuRQ21RA/01SsJjkEqQEKfHwWph6z8Wg0oOndeHPgE7SiJRr4TRGkRLh/Dce51KDq0+Qv6HVAKtTAqZRLy8s1khh0Mjh8MpvC9sJhU0Xika5cL7Qg0ZTB7xJ2UySRALM3w4XA6IbCaiPBIEAjI4pMIfiNWpoE9QoKRaeD64Ri6NGAOLI1oDqVwiOudcr1PDKAvC7heOIVqrgj7NAGeR8Nx6hVoOe7MK0pQIj419CtB0c7G593ESKZtPTQmQQtA5SRaN5Jp0oji7nVBIITrnPFqthF2vhH3AoWc0ZqMKSpViyMFnNEqZBH0ic8UHOXC0BfmZUaJzztuae1kbUFsIQS5QahGbyEFs8Yaw92QyBXsR6RnxMBgMU14Uyeewo+6NR5BhkrMRdS7UOTMB/xkk1qe1GwwVYaAe/r///W9WgGGQY8eOITc3lz2uoxHwsewfb7vtNjz55JP44x//OGK0fDplZffa3dj01D58+E4ZVEoZLr12Ds6/JJeJ1tOJmrY+/OXNYnxW1s78p7+8PgdrZscJfpaE8usvHMYbLxUNeYqT+CaXlrrqLiYYlq9KZ8lsZpHOTXlJK554aNeQ1ePgDfSmOxZj7qJEdLQ78Ozje7Djk2G1q9bIWSeAElxpqlTGrChUlJC3+oCyIPvH+fEoa++3f8xPM6O3vBM9IYmkCTlR6JDL0Njah+SBG3aov7PNqEaCVIKqkjYYyXYvzYKDIR7UJOBnkx/74Wa2zWmzbTjY3Dvka04dgLysKNQ29aCn14O8VAsclR2wtw3HkFMQi5vvWoKUNAvKa7rw4Mb9OFo+LPKizGpEWTQoqeyE0aBCdJoZu9t6hpJZSXzON2qYrzz7PmqTqg64PYMxBDE30Yz28nZ2/FJiYUdbHzpDknrjsqzo0SiYtz11GrxWDY52DscYq1UgQyJDaVk7s11MitOjOCTpl9lexupRdaQ/wTVjhQ3HUvvgkgyI2SCQ5bKibm8fuu1uZGVbUW8JoNk3LOLOy1bguvldgLQTCJiweWsUXts9nNwYq5Kh0BFA9eFm6A0qxK6y4oi1m1k/EkqZFPOSjKh39fut24I2fPypDz2OYaW5MFrPEke7qB2yLGiyAo2+YVGfatax/VjV2cdsFxOq3GjZ3Z/oTuitGhiyolBU1Qm9VoGE2Vbs9fVg8BfI/jHfZsbRlm5mB7pAq0f327VwhyT1psyNQ63Di45uFwpyTchf1Qupcdi/3d1jxae7DThW34d4swZGjRzHQo7JaKUM8zxBVB9sgkarxJVku3hhv+3ioEPKmy8V4dVNh1itg3MvmIWrbpwLnX78gxoVtV14aOMBFJW1h9kuEhaVHHlqJUqOtbLaB+nJZhSXtQ11NMn2MC8zCmXVnfB4/Kz2wWGHG/aQzu68KB0c9T1o73RiTm4M7r5+LlIShq/ZVXXdeOiZAzhc0oa4GB3uvHYOlswdLgTUY3fh+Sf24eP3yqFSydi1gBLNyRmKxSCXYv3FubjsujnMTWksjh5uxhMP72TXrEHIDvbmOxcjf078kHVjzj1/hEw18gnyqSiARPfI733vezh8+DCysrJYTtdgB4IztXA3mMjt8sCuh6DRn/jAprPXga8unhluMNNarFNoixcvhsPhwO7du4emvVCW+ubNm1FaWspcYQb50Y9+xC4+X/rSl4beu+uuu/DII48wz/Vvfetb0+6koQIfH5D/9dP70UcOJaNGfm++azEK5ydgpuNw+/D4R+V4bkc1vKO6swvSrfjGRXnIiO33wh8N+R0//dhuNtJFgnl0YRUa3brs2kKsvyQPcrlUsI0/fq8Mrz5/CKvXZ+OCy/LDRtOOHmnGkw/tZN9F399jHzl6aonSwhKlQZ8/iF5tv/gMhTpYeQkGdFd1QZ1qRvEoxw8SamQ319LmQLpOiZojLWGFlRLTLLBr5LCo5eiqHPZcH4Q6JLoUE8jEkf43urhLfwxG9FZ14pqb52PZyvSw8+nDHbV46pUjiI3SMZFO/uuhJCca0WNQwqyQorvGzizuRrQD+blHaeG2u6Dr9aKhZuR2KpUypGdHo7m1F9o0C45UhTt6zJoVhUq/HxkaFeusMN/30BgGOjdmiQS9NV1hjj4mixqWlRZ0GgMIHpWiunrk8aBQSJFZGI02iwtfWumBQdMQFoPDEY+/v6pCfKcMLfuaWMcwlPg0E3xLVTCkyeGR29HtGRmDXq6GtD0a1UcDsPb5UT3q6YpCLkVWYTQqtF7EmbQ43NQZ9nSl0GSAZFsLzHEmHG3qgWuUq1BCvB7+VDVkZgU6+txo6Ru5L0wqBfL6ZPAcsyMQrUXFgCf/IHQurFhhRkxhJ46V2PDxYfJ9HxlDfqIJnb0u5HiBtv1NcA34/g9Co+d0DXI5vCO8xwcxGFW4+ub5WLk2S9DXfJCePg+efOkI3vqkKqyoUWaGFfVyIFmlQH1Ze9jTlfgYHTRqOauUSd/TMspdiTzl4zKsaHB7EOcBKkYdc+RPv+HcTFy6JgsvvVuKNz6sCIth0Wwbbr9mDor31uPFjQfQN2rEPsamZzat1Jmj9khIMuF4CPgDeO/NEmx5uYhdp9ZuyGGuV8RU+KyH2jQ+8cQT+Otf/8rur0899RSf7jmN4GI9crv8ZefExfrXl3CxflIoKyvDBRdcwIow0H8PHjyIPXv2sAqkGzZsGPFZSoQhhxeal0488MADzCc2PT2dzVcfzc0334zZs2dP6UlTUtSCX33/rYifeeyFm4Yu7DOVZ7ZV4YG3jokuz4k34rEvLIv4HV+86VnBComD3Pu9VVh4lsjz/3FQV9OFH9z7asTP+NMtsEeIgQQ52cqJMT/JhKq94eJxECqu1NoiPPVniCwrOkd1JkL55h2LsHqpeDu8u7UKf/tvf5ETIaItarR1Ri57nhEIojNkFH80iUuTcHigaIwQNDpaXB5eMXMQq16JoIil5SDKvBg0i9hiEj/6rgbGqPBqlINU7onDxgfEf8NgUwG3Ck/LGcT3dhQaGsX3V+Z58djXJW4hOEepR9UO8UR5jV6B5lmRR2/zmmSobxB3FElfmIDdDeIxLDKo0fVpeOXgQaiwmJg96SA/vv9C9mRFjP9uPswKcomRmWpCebV4jJGm7QySlmREVZ24a9fsWdFsNF2MbJsebYeGn3YI8d+XbsHJZirFOj05vOqqq+DxeFjtkcsvv/yU/D5nfHCxHrld/vTZxMX6N5fODLE+rRNMCXosR4/n3nrrLVYMiUodP/fcc4iODr8x/PKXvxwx0j5nzhzm6iLGdPBZH8+Djen77GP8jLUNYv7Zx/MlE26m8eyLCS4f6wPj2YYxYwhO7vLxBTHWbwRPwXkx0cYezzE59kcmPYQJnhdjHw8n44CY2OrjCWHC18nT4Dp7vPzrX/9iQp3uq5dddtlUh8PhcGaqWB8cMR/PheT//u//Rvy9atUq9uJwOBwOhzMMWR9v2bKF/Zt81WmaEYczk/CfQQmmM3tuBYfD4XA4nOPm7bffZsmlixYtQl5eHm9BDmcaMyNG1k9vInft+q0Hg2MmLw06NkwVY8Uw1qDNeEZ1JBES2AjpBEeGxvr+8W3H5C4f13aOOR9grGNu4u0wYCgjvniMw1UilY45K2HMGIITbezxtMPEvuNkHA9jxTDRzRzPeTXhKShjbYNUAkkgGPF3JnzujXH+n4yB5+lwrR5k+/bt7L+XXnrpVIfC4ZwQgQmOrE+k+umpZnpcNc5QdnxSiX//aSuycqKZZ+9o4pOMSEg24Vf3vYWyY61hy7s6nXjwL1vxjbs2Y+sHFeOcW3pysXc58fDftuHrd25mjitiMVy1JAVfWJvNLAlHszIvFr++bt6Yv/W9X67HrPzYsPcNJhU+/+WzMH9JEiZCYrIZX/v+aub8MJqkFDO+94t1+NW3zkGhQCKdQafEl26ah+9/8SxcsDKd3B3DHCnmplvQVNLGLBWF/JlTZ0Wj26hC3Nw4RMWGJ5rFxumRmmGB1eFFloAbhU6jwPxkEzb9+zO898YxJgxCob/ffq0YLz20C/NSzNCqw/vqmalm5uqRmWJmri9hbWTT46dfW4Hv/GwtZs8btrsbRKNVIH1BAqqbe1CQHRXmEEJ/z14QheasHhSujIJOG94OtG1Wl49tK23zaKht4ubEMeGWlWoOj0EtZ9v31O/s6K6jGEerLAnsbQl44X0/0uYnQKtXhn1HSo4V0vMMMAVjEK0Kt7GLVutxd+5K/OQrK7BkTpxgDIUrotGhacXCFBPko9tBAmzITcJPr12ML98yH0aBGNKSTDBrVShwqJBkCN8XMVoV5ga0zKllVnq457laJcOsQhuOtfVifqoFCjK6H9EKwJwUMyp8PsStSIHeGG7BmJBhgXtBPFTLk2ETOOYsVg1LLP3nHz7Brm3VYctdHj8efLcUzxxtZsc9ueSMiEFCycZWtLU7kJdlhckQHgPZLpIjTEq8AUlx4fuCHIoosbu7x8P+OxqlQoYbL83D975wFq6/OBdKMngfxaolybjvm2fjnm+sgNkafi1Oz47Cj37XX8joRCCbyZeePYivfO55bN54AJ5R7kOnGr/bgcrKSvbv5cuXT2ksHM5ExHZggq+ZwrS2bjxds7Jrqzrx5MO7mF1gqONCRlY0E+Xk803WYCVHW4cGQemmtuLcTFx363xo9Sq889pRdvEPtVrLzovBLXctiVgo6GTh9wfw7uvH8NIzB+BwDMeQmRPNYkjPihJcr6XbhX+8fQzvHm5CarQOX78oF0syxV0khNj+USWe+e8eZm1IPtBX3DCPFUk6WdCN9c0Xj+C1Fw4z+7sryG/6omGrNeKTXXX4z6ZD6Ohy4vyV6bj58gIm2Acpr+7EgxsPMGeYTBI5zb3oCLHWo8JXcfEGtr+t0TooEgwoDrFiJGE1N86I2qJm5thAwjXU65lILrShnoo2ka92mgUdxa1whLjEpKRb2L6gDo6Q17POpIY5JxpHqjsRZdYw0VMaYntHnta5GVEor+mEBBJcuyEXl63NGmGPuWdHDbPVpEI5GbNtKO1xozvkeIiP1UGtlKOyrhvpaUYEcgJokg7bXuqCCsQ3GVF8oJ39fqJSjtrDzSMKS5GXdnVFB4IBIKkgBgcae0bYf2anmZm/eVunE/mpFthL2tAbYntZsFCHS++SQKJuR9BrxQubNfhsz7AVo0EtR45Jg4ojzcwW0rTSjGLj8L4ijTs/yYxWXzv8wQDWJ+VjbWI+FNLhjueeQ014+NmDaGjpRf5sK1qT+2DH8L6IUuoRdOtwrKUHebEmfO3sfOTGDovfXrI2fLkIWz6uhEmvhC1GN8ITn0Z9s86KxdGAEz5/ALO1BlRtbxmqP0BkppiY739zm4PZY5b6/GgLuT7EmdUwa5U42mBHWkx/Z7AqpOiYQS7FUshQu78BOoMK6oJYbOt1jWiHFVoVug42M7FJIr2itA3eAd99In9OHDvmaJDhvcNN7FxvDrH/jNcrkewHyis6kRinZ8dYTf3wca+h62CqBUfL2pklY0KsHsWh7TBQX6Cm0c6sLkmcl4Z4/xPpSUbWSWhs6cOy+Qm449o5IzqeTW19eOTZg9h5oBHpSSbcfcNcFGQPX4Pomvryswfx1mtHodUpmA0qWVOe6JzuvZ/VsnOkNcTuNTpGhxtuX4RFy1KwY3cRli0uwOxvPwaZ6tQUw+sp3w/9kTeQlJSE119//ZT8Juf44W4wkdvlN9segnoCbjCuXgfuWz4z3GC4WD/FJ82hfQ340y/eD/P5HSQh2ch8pR19I/2OB9FSwZRkE8oGCtWMhm7q9353FRYsTcZkcv9P3sWRA42iMXz5/87B4uWpouuXNtmRHqOH/AQfCdMNlQoaHa/f8fFAApQ8u41m4UIlLrePeT6HFlwJhfrBTz21D+9sOiL6G9lz4rCvtTfMe36QaIMS2jYnekSqKCqUMiSTaAoRuKOhwlHbPuofRRMifX48DjX3wCtSVZOqRf75h+cxQS/Wufn9P7bjs2JxC8LF59lwUCUeY47LjKYXm0aIz1CokmRftAZto2oRDCKXSTAvzoDyfcLHJHHu9Ul48YMOBEWmx+TnmFGXbodbIuwRaFIr8OtVqxCrFd7fXl8Af/lwJ3b2iFtznp8yC3csKBAVfvuONOM3/9rBxKYQerOKCciW+pE+/4PQ6ZSxIAE76sVtEJdmRWFnWbvoNKPZVi0q7U70iR0PCilmN/ahKURkj4hBJkHKFXl4p1TcJnFVnAFH9jWJxpCRZGQFxZwilpE6jZwdjzWjag2EPrmgYkgXnZspGgN1qNOSzeyplxDNjXboDeoJDQS88NR+vPL8IdHl88/NwJu7ivD2U3dPelGk0EJIVHCQqpSS1THVIeFMT7hYj9wuv9w6cbH+wxUzQ6zzOeunmO4up6hQJ6gwkphQJ2gUO7RC5GiCgSD7jcmmU6T0/GAMNEUnEtlxEzsxaBrJZAp1Impg9FE0BpVcVKgTJMgUke2p4fD4RYU60dXngV9EqBMkbj094p7rRHtbZN92t8MjKtRZDHa3qFAfLITkGMME26P2R5wq7w56RYU6QSXfqWCUGD5/EF4RIT9IRTnNeRYfHe2T+EWFOtHt8sKoFL8x0BQPnzYAiFueQ64IRByhpUqyYkKd6O1yI+ASX067oXeMZ7suTyBiPkBHMCgq1IkubwC9Edra7w+iNUIdAMLpi2zW2uvwiQp1os/pg1IhHgM1gUYd2Zo3MzV86lAotnjjpF4nCaqyGhg45HRJWZPqs07e6iTUqRCSXt8/vUylGn/1WQ5nuuHnbjAcDofD4XBORwbFek9PhJ4lh8OZNvCRdQ6Hw+FwziCs1v68ptbWcOMCDmem4A9K2Gsi688UuBsMh8PhcDhnEBkZGSxxncR6e3v7VIfD4ZwQgTPIDYaL9VNMboEN+YXhVm+D87AvuCwf514wS9TzlxxfyFpQIzJ/l5w/8ueEW+qdbM6/JA/aEPeTULJyY1A4L0F03dYOB/7+xF7sOdwkuNzt9rHErLdfLWauM0KJm2RV+dQju9A7xnztE8Xt8WPjq8V48e1SwRjGAzllNNR2wSZgN0eYLRqcvz4bS+YK7y9Kbk2dFw/buemQjrK8G2TJilSs25DL7CuFiEukebcSxCUIx6C1auBKtyAtL0Y4BrkUV184C5HYtqceMrlU0H6QIEcPfzUQHwi3YSSMAQUstVLmJCSWsEi2f/PjDWH2g4MUJBiYFSUlogoRk2+CZKEMKTkiMeiV2HBWJlYkCdt/0s8uSjbjvYYiePzitntr09NgVQvHEBvQonZ7L45VDLubhNLT58Hbn1QK2g+yGGTAnHXRmLXWDJVa+HhImRXFko6jBSwQiQSzBlJKQo0VbgeDUoYMiRSLovWQCMwqp3Y4K1qPqPxYKASsP9l2zo2DVCVDjIAVJBEfpUYwXoaknHDbTUKjU8A0z8zcb4Q84MmBf36SEdkGFTSKcCtYIjnLig9rOtDQGXnO+IlCeUfk3EOOUI4Qx51QjrS2ojUvAEu6cFvTdfyCtVnIz5p8967RaLVapKens3/v2bPnlP8+h8M5PrgbzBRlZe/cWo2N/9mNjrb+m8myVem4/rYFMFv7E9jIqo6s9kqLW4dcYujO1VDb7/JgID/uBCOz8yPIG/j62xay7zlVUNLfpif34eP3yllSKXnFX3frAixfnS6YROf1+rH57VJsevMYE8MECdU7r52DuIFkztHtQv7mN9+1GHkDHZzR7aI3qHD1zfOwal12mKf3ibJtbz0ee/4Qc3ohkuMNuOu6uZgn4PEu1i7PP7EPnzDf+X6xSR2YmooOOJ0+JmzPvzgXl143B5oBv3XquJCdXP2AvVtqbjTqggG0DCTqJZjUSLN70LK/SbBd+no9eHHjfrz3ZgkTEuRgkZhiZscH7RtqG4qhrrqTJTCTJWLMOanY1+dmSa5Ers3ALCZbG/tjWFQYhzuvm8PEthC1jXY8tPEADgwcg+QtnplsRlFZGxuxIMcO8gon6z2KiXZP3rwoNNjscEi8kAWB/FYTGj5qhXPA7pHsJsnpp6WpP4a0TCvrkLW19CfJWmN0UCUYUDTgAhJvVsMWBKpL+0cHyfaUrEsHbS7VZiXir7ChONiJQDDItN8sVRQqP7Sjp8vL2oV88W++LB/6gc7nkbZWPHbgAGrs/faNObEGyFROtLv7Y4pS6XBl+gLMjRJ2XHL6fHjh6FG8Xl4GXyAAdVCG5FYzivd1wB8IMhvW85al4rYrC2A2qlnbvPVJJZ58qYgJdrb/E43MXaZh4HjInGuCL9OHJudAO6jUMDdrUby9X/iT/acy2YAjTf3tolZIkZtgwpG6LpbArFFKkRNvwuHaLvgoBnJ9STajrr0PnQ4vE8Ak0FsquoZiSEkywq5Xory7P1k816KFvN2JxoGYrCY1kjUK1BzsPyb18Xr4C2NxaGC5Si5FfqIJhwdiUCulKMgxorijC96BIa1CixGd+7tgb3cxYZ65OBYVcjdL5mX736SDusaDhvL+615mjB7Kbiea6vr3jdGshinNgoMN9qHroDbNjEON/X8r5VLcuCINt56TAZWIsD9ejpa346FnDqBswAbVYlThtqtm49yzUth1r93pwP8OHca2+jq2XCGVIsdnQu0LjfA6/Cwhe8NVs3HRFQXs33V1dUhOTsaCX7w86Qmmr3/hbJZgSvzjH//Af/7zH5x77rn4/e9/P2m/yzlxuBtM5Ha576OHJ+wG85tVd80INxgu1qfwpKER5LdeKWaj7ULFfggaQf7wnVKUFrcIVu9LTDGhcH4Crrh+rmChnVMBjSAf2FPPngoMis/RkBfy7x/ehaYQX+dBqEjJNedno2pXA4oONYmOIJM3+ftbSpj4HA0JtK98ZyViRUaxx0Ovw4P7H9yJ/SIWhMsXJOBbdyyGIsJNnwpd/fffO+EYEDyhUAcrr9CGK2+ch/jEcCcbEmcvvF2Kd6vbUTwgukZTGKvHJclmXLAhb4Tve6iHP/nvFx9qYgJ+NHqDEgkFsTiokqFOwDWIivcsTjThuiUpWBzhCc2TLx/B5i0lzIVlNNTxiovRory6e0j4jYhBq0DefBPsuzrQUhe+ndS5yc6PhcflQ8WACB9NanYUVEYVyg80we8LCI5aRi02ozSuDz3e8Bh0cgXy/DG4dmEu0pPDR3j9wSC2VJThiL0SNQ5hC8JccxxuzzkbWrnwE4WGnh5s/LgI+99vZQV7wmLQKJh3/Uef1aBioBMeCuvcZEdBMjuIUqfwaHya1oRgnQ6768l7PrwdbCY1UqJ0KGvuQafAvtCqZCiMM8JV0426hh7BGGblxrA2LhXZFxmJRqijNNjZ6YBbYF/EGtVIT9Shym1HhzM8Bo1chjk6PVrgQ2V3+PWBFW8yG2E91IOKI8LnZlK6Bd6cKOxp64XLGx4Decz/5Ko5mJMS2QFmLP7yn934YEeN4LU4N9OKZRfH44Xyo3D7w51sLCo15nWZce3aOYgO6QBPlVgvLS3FDTfcAKVSiXfeeQc63eT9NufE4GI9crt898OHoZqAWHf3OvC71TNDrPME0ylEpZLj0msKI35mxbkZePTv20TLbNfXdOOGzy+aMqFOZGRHs1ckDpe0Cwp1wuMN4JPtNegpFvdlphF3qo4pJNQHR9xrqjonJNbJRk1MqBPb9jbgizf5YIog1vfsqBUU6kSP3Q2dXiUo1AennCxbkoQHPqsS/f5DLb34xS0LBYU6kZxmYdMghIQ6QZZ73TKpoFAnaNS1qscdUagTH+6oFRTqBO1ni1EtKNRZDA4v3JUeQaE+aP1HT5Co6JUYNJIeHasTFOoEFaDR2cyCQp3o83mhSJQICnVCJpFgZXIy3ty1SzSGo11N6HI7RMV6gsEAvV0lKNRZDE4vtu6pExTqBB3qlY3d8GeKT7upcnRD4VQKCnWCChLFmzWCQp1wuP1w9npQLyDUB2OoKGtn56gYFfV2GK1qQaFOtNhdSE5Vo6NLOAanz49ORQCVIhajdJQd7rIj6Yh4MmRdZSckGWZBoU40dbmwu6J9wmL9/e01osuoiFWgzi8o1IlOtwvWeaYRQj0Uj70dMvfk2e567e1obByuQ6DRaBAXF4fa2lq88MILWLNmDXuf7B1pPjuHw5k+cLHO4XA4HM4U47V3IqAS76BOHAnueKkUEkn50DvtwQS0txbjW3/fiMRSFRP0b3/nsqHRdw5nOhMI9L8msv5MgYt1DofD4XCmmMkuiiSEdd5qdBz8CM6mKkiVaiiMUaf09zkczvjgz7o4HA6HwzkDUVlsUEcnIhgMoKf8wFSHw+GcUAVT/wReMwUu1idAr92NooPDcwAni0SRebUEuX5YrOKl4KcL8bE6yEXsB4mkBBNMZmHLO8IWb0BCkvBcb4Lms0dFT2xUitxAyOFCjDhy3BjDUYIcWMQgO07KPfC4xecgG9RyUcs7ItagQrPIHOehGCK0EzmRGKVS5hYi+htaBdpF8gsIWhYVIUaVUgadUsp+SywGpUnFcjbEoH0dPeAQJAQ5DxlixBOLaN6+UqIQcv4bQtkL5jwjRkNVN8xy8d8wKNRo6Ol3PhHC4/UPueCIoVfJmYuOGDFmLaLV4jGYlCpERVhfIZNCq5FFjEGrU0ArYgVL2Gxa2CK0NdleWrTiOTNkt6lRyCEVOyCoHeQK6JXiMSSZNYhN0Iuvb1DBYhRfnxKnXUE/c+cR48iBxohWsPW1XYiPcI2hxGmjT7wdKA/C1emHT2Ru/1RhzF7A/ttTwcU6Z2bhn6hgx8yBu8GcQFa2Xm/AB2+V4IWn96Ovx4O5ixJx0x2LYIufnGzigD+Ad988hhc3HhxKXiTht2ptFrMtNBjFBeZ0oqGlF488cwC7DzcPvRcbpcXtVxdi+cJEOB0evPjMQbz7+lGWZEio1HJcctVsXHB5Pkuq/OidUmx6cv/QTZXu/ytWZ+Da2xbAZJ54p8Xh8uLZ147i1ffKhhIoSaCT1/iV588aU6wTR48048mHdzFnlkESkvsFNCVOUmLkDbcvwqKzUoRjcPvw348r8Oz2KmZ5x2KQS7GQEgU/rYHf48eKczNx3a3zYRTZ5pLiFjzx0E7UVIbEkGSCRNqflKyP0yM4x4YDzcOJhRatEvmUlLi9DkqVDJdcXYgLL88fcr8h6803XjyC1144DI/Hj5S58SwZtTtE4OSlWuCo7IC9zQFbphV9WgVqB6z8iMR4A9xmFY51OWFTyTHb4Ud1yPFAHbZrblmAs8/LgNfjx2ubj7DfpH8T1OFLXhqPHQYvXAhiiV8N5+5W2DuH5/qmFcbiaJIMzUEfsmI10Bs8qO8d3s54tQ76/X7Uf9bOrP6uu20Blq/KELTelKtlyLs5BnXWDngD/THIJFIkqWNwqL6XJUcuS0zEbYWFiNIMC9od+xvw6HMH0dzmQFKcHjKpFNUD9oJsX8ToYPb60VjSDp1FA1N2FIqqO4cSyfU6BVLijSgua4dcIUH+2ihUyjqHkhdJ+OUFzah/sRluuxe2xYkoUUnRGrovko3olrvR6nIhSauFtE+G8gFLTBaDRQO9Wo6Sxh5YVHLkqhQoLWkbjkGrQPoiI0p17ZBCgszuKJTu6YTLPRAD2YHmxOCgw4Vej5/ZNLb1uIbsRoncJCN6lW40O11IMmmhkstQ3h6yLwwaaD0KHK3qgUknR0aGDkXtXUPFSkjAr5ilgkxfC0kQUB+MxuEt7XA5+zu8ZL2ZucyG0gwHHFI/EqXRqKkIojkkOTkn0QCHyosmpxNpFj3uPTsPCxKHp3s0Ndjx9KO7maMVif6rbpqH1euHrWDpmvviMwfw3hvHWLJr8vwEHGvphdM1EIMEKEizor2oBc4eN1JWxaI9N4hWl2OEa0/PXh8aKvuQFGfAXdfPwfx82yl1gxHD1d6A8id/CYlMjowbvoc3v3Ien7M+TeBuMJHb5WvvPQyVbgJuMH0O/HXNzHCD4WL9OA+OPTvL8NLGo8x9ZHQBm/Mvzcel18yGSj05zixMRPxvLxrq7bj5zsVIz5qZ8wt3HWzEfzcfwbL5Cbj6whw2Ejt6BIvELlkdXv+5hcxDOhQS6lQ0qaq8HTfdsZj5h59s6hp78PCzB9io5x3XzEFMlPa4O1jvbSnB268ehcmiHvKFD6VgbjzzShd7YlDT1oe/bjmKPrsLskMt6B3wPx9Eq1Xg8uvnYu2GHEF3GBrVpU4l2YOSCC4hP/RRj/1iCm1oiNYgXi5F99ZaeAf8zgchd50bb1/I/v30Y3vQMspSUqVVwFYYh3aHByaPHw3HRjn6SCWssFOjywt9vAG723oQGDXeXahXwVRjx5x58cyCdHSxrdbmHiam2nrdqExToC4wMkatRILF3Qo4a3rgmm3CQYxM0qPiPovSDXAEexHbokTlyw0Ijnr+Sdap1OGmTk5op3iQqDQt4q/Rw6cJorEjgMaekb+hkslwZU4OFhoT8J/nDmHvkeEOyCB5mVZ0kDuLVonafQ3MBz4U6tw4dEpmr1lTb2euOaFYbSokrNDBBz/8H/Si7diw+CeoY2FZkYraYACmKDlKesKfwOQbzWht8TA7xUO1nRhtIpNt1kBn98AYo0BjvB19GBmDKahCVK0Ozt4A2lQyVI9qBxpFJ/92coCxxChwzB4eQ4HNhA6HBzEKNQ4c7YJv1PBWWrwWSrMENosUMbEN8EtHjnYrHCp4P9bD3u5D70I56pUjCx/JIUW8z4a6Bi+sMQocFYhhdUYc7lyQiW1vluKtl4vDRrvJCvamOxehpbEXzz+xF92jnIn05O8+K5p1UpSdTrQOeK4PIlNKkXplPFrNXuiaVCjeFm69Sde/O66dA4+zY0rFOhWZK3nkPvgcdiRdeAfe/8lNXKxPE7hYj9wuX3134mL9gbVcrJ+WB8eXb30CPd3iE53u+OoyrFyTdUpj40xfnv3vXjYyLMbsefH49k/Xii73ef2445qnI/7GD359vqhPP0Edm1eePyS6nAoRhY7AnwhjfUfseenY1i4+tWZOihn/umNpxN9Y89BbrKiQGLOijShpGylgQznHqULNR/2FaoSgwmMNteLrE/7bjaLWfERKuRmlJSOFWygLE02o2Ncgujw6xYTSgRFb0Th7PUNFpIQwXJeBw93CVozEbKMF+46Jx5gSr4LPJuzrPkhfiRndEWKYW2hEUZv4dK0CowX7I8SQlaRAwZLIUwzLGszodIlPW4mXxeNgg3gMZ/nUaHy3VnQ5TS/s7IhspWiJ0qJzoHiaEIlLknA45AnbaFYtTcZ158dPqVgnal75F3oqDyF68QXY9sB3uFifJnCxHrldvvTOxMX6P9fNDLHO3WA4HA6Hw5liJttnPRJSlQZBvw+ejsYRXuwE913ncKYeLtY5HA6HwzntfdbFCfp8TKwHfN4RXuzcd50znfFP0NFlJrnBcLHO4XA4HM4Z6LM+iL18H/NZ18QmQ2UWn1LH4UwnAhMU6xFmVU47uHXj8TaYSJn3IU7Bzqd5zFPNRGMYa32/P8ASn6Y7Y22HTCYZY/kYx5NEwpx/IiEd4zfGOigHXS9EQ5COHUMEV76h74gEOYtEIhAMYqzNlI5xuESyDmTLx9oX1A4Yqx0mtq/GjGEcbTnW0TDWvpKPddoFyY0m8kekwTG2c6zjZYwQ+j8jndDxMFY7YBz7Yqzze6z9PdZxf6pwdzSx/8oN1qkOhcPhCMDF+nHy9e+vwvwlSYJ+55Tk9/Rju1lC4WR46ZJLyu9+9A6++6WXsXt7DaaChrpu3P+Td/HtL76EnVurj3t9ckl557WjuPfzm/Dgnz9FV2f4HM1D+xrwg3tfxS++twWVZe2YjjQ32vGnX76Pb939IrZ+WCH6uUuvnYMrb5gL5SjHGxJcZA9319eWR/wdsir86e8vZBZ5o4mx6fG1+1YLLiPIQ/zZx/fg7deOIqcgltkxjohBAqxcm4Vv/XgNvvKdlYgS8DWPy46CJj+GveIF3IcoCS9zVjQ62hyYlRceh0Irh+2KdBxSO7BwthlqpTQshovmJeDn18wVbYNj7e343gfvIydVgty48GQim1SOVY1SSF+pw3KvGtJRnRO1RIpVvSo0fdaInAIbswMdzfJV6fj2j9fg699fzRxwRhOVEw3/6lR4qsyIl4ULGoNUjRh7PI54g8iYawsTgmT5ecMlefjWt87BjbcvYk4+YdtxVhKOxetgmx+HqNjwfUEWkCkJBvhTTEgQcEDSRmlhXJ+J0moXSyId3bFQSCUoNFhxtKIHc1PN0I86HoilejWsu9qQt0sNmzfcEjY+oEdcsRG2Lg8Ko8JjtKrlWGbUoH1rKxbLDGGiXi4ZiKGyB3NTzKyuwGhmZ5nQq5KgqjIVMl+4U5LcZ0F5eSq62jVI0oQfkwa/AnOO6uF6thrnuNVhj4/p7yVSAypL7EhamgSNIbxmgG1hAmpyrZCtTYc5zRK2PCnVjO/9Yh17LRC4H5AF5Oe+uBT3fXUFrlifDfmohqC/L1+XjbuvFz/uTxV+jwvOxv5rmDZ+2MKUw5nuBIITf80UuHXjCWZlH9xbj6ce2c1EW3ZeLOqqu0bYvcUnGnHTnYtROD9hwjuJ/Mc3b+z3+R30Hx+P9d/JxOn04qVnDuId8kAP6YjkF8bhprsWIylCMaBI/uNqjQKXXzcH6y7OZa4K1NnZ+1ntCFG7ck0mrrll/rTwk3e7fXj1+UPY8nIRvN7hdpiVF8v2BVm+iRUSom2jThaJa/rs8Vhv0lMG6hQ89799cPZ5sOGq2bjoioKwTsAg2z6qYE40XSFuFmaLhnm8lx1rQ3p2FG65awkT2oNQsSbyUH/jpSKo9EoYsqJQNMrJoiDNAntpO1y9bmTOikFlaRvzXBfyk49fnYhSC9DuHD4vLBol4mRaHCztRk6CEd+8KI9Z/QnR6XLhycOH8UltzQj5nWWKQmmDD929PixzKtG4oxGeAe9vIi7VjK4CPY4F3FgUUMO7px1dIY4dRpMasfEGlB1tZS42t9y9hO2/QchP/s2XivDqpkOQaZVQLk7A3uaeETHMSTfCY+lCX8CNBL8NB4/1wRnSDqlRWph6faiv6sRZZNF3TSFsIRak3V1Oti+3flAOc7oF3RlmlIQUoiILxLk2A+oPtzCPffJcP1rePuLmkptqhqu6Cz0dTsScnYJ9fW44QmJIjtZCY5WisqcHOUYTWps8aO4enhNt0iqQGq3DoZoupGmVSG11oi6kcyyTS5Gx3IaS1D5IpVIkNBlRtH9kDJkZVtRKg2hxeLA4So+60vYh/3Ei3qaHJF2Nox4HZhlNaG/2oCnEBtGoUSA9VoeD1V1IiNHAFCtHaYd9xMjz8gw9jFH17KlFZ2sCtlf0sictg+TZDPAre9DtdqCgzYSGj1rh7Bt2qolJMMA9x4xDcCFfpYO3rA/NrcPHg06jQHa0FjX7GmFMNMKZG4Wilt4RMSyM1qFvey1kQQmuuGEO1l6YM+IpCA0w0LWNrE3PPT+bebXr9MOdgLqmfivYfUdaMDcvFndfPwfJA3U5ptpnvfPINjS8+ySU5lhk3fqTEU+HPN1teP0LZ3N3mCmCu8FEbpfPvfkwlBNwg/H0OfD4hTPDDYaL9QmcNDQF4q+/+RAH94pbsZEQWHtRzoR20tfveEHUHoxuqD/8zfnIyB4WXZPBt+7ejLaWPtFHwff9aj2yc8XnOm79oAIP/XWr6HJ6KkGj6IPFb4SeXPz5kSsnzcN+vHz/3ldYUSGx6ST/95M1rBMV6ekIK0405jN48Y6bo88rOAo+yMbHdmPLK8Wiy1ety8Lnv3SWaAzlFe2478+fDhW/GY1GJUempL+4khD0tQnXZGGbgL/1IBdkJ+I7584WnYLT43bjy2+/BadP2MaQfM1TP5ahrqxTNIa8hQko2i1+bi5blY67v7ZCNIba+m7c8d/d6BOpOEtVYFNjdDjWIG6TeO+abFy3Uny08p1dtfjF68Xwi0z5SjCpIa+xo0/EJpFG7C0FMSgKKW41mqXZUfisVPwJ1bIYPTo+qBwxEBCKNVaHZq0izPc9tMZEerIJJRXiFoV5y5LwaQQLw3lZZhxzdsEnMtRl1SjgCwJ2l0gMMinOrgui4kCL6G9krUzFrqOj6gCEMCsnGrv7XEOFyEZj1iqw8Z5looXI6H5A18i4RPGbfk2DHSkJI5dPpVgP+H0o/9/PmBONbcXliF60fsRyLtanFi7WI7fL584gsc4TTCfSeArZiBE9IexdE7fiivQdNMrd1zuygMtkYB9VFGREDP4gq+Qacf2QET2x5WJCnaBt9PmCEC9yf2qI1A5UhChSuXIiUWQUebxotEr2ikT3GG1NcUbqLOiMalGhTjjdPjgiZPWQ7nQGI08D8yMYca68JxAQFeoEeZ07e/0RY/A5IvuV07kTKQa9SS0q1AmXN4DeMTzRpSJPPgZR6JSiQp2wO7xQRfAz93j96B0jb8LtibwvPE6fqFAnerpdGFmOayT0hMk5UFFUDFeEc5st9/lFhTrR5fJGfGTt9Qfg6o0cg9cVOQaH1y8q1FkMDq+oUB+8H0QS6sRooT7VtO99lwl1ucYA69xVUx0Oh3NcBAIS9jpRJrLuqYbPWedwOBwO5wzD2VKD1h2vsX/bzr4CUsVUD4VwOBwx+Mg6h8PhcDhnUFEkn6MHNa/8EwGvB/rUPGgSMtmUl9GQzzqHM10J+CXsNZH1ZwpcrHM4HA6HcxoXRfL2duLpL65n1UidTifuu+8+yLR+JGbn4M9//iP0er3ourQOhzMdCfBpMJzxsu6SXFiihBMcyHFj8fLUCTfm5dfPDbPdGyQrJxqfflDO3EYmk8uvnyNoeUcsXp6CjBBXESHmL05iSaRCkDvHhssLmI2g0FRqsi/ccGUBNJqp71teek0hNAK2e8S8CNs4Hnrtbjzx0E689+YxNq98ND5/AM9sq8LfthxFr0iiHbFqbRbiRObGxicZsXJNVsQ4okxqXHxepuB8bnqPll18VQFL+hUiNTsKik4/EvTC83uTTVpsyAu3uxuEXJXe3HgYsyVWyAQOCPJLPz89AxuunA2dQTiGgrlxWH9x7pA7zWhs8QZmnRkJg0aBG5anhdnu9ccAzMs0wxarYo4mQsyK1aNofyOq6oQTbck55MBbJZhjExZKWpUMt63OxJXnz2LngFAMs9MtSPMFWfKjENmxeih7PUixCl+j4sxqXL42C4uWpQgup3N+1vpoLFxhhUIgBto9OQXR0KRqYRSwQCRSkkygASxynhEiWq+CyRnEbIvwMauSS7FYrsNStR5KAe9z2jtzLCYoyLrSLOwYRa5HG9ZnI1vAhpGwGFWI9gYw32YQjyHeiKdfKYJbZP79R5/V4m//3YN2ATva8RZF0iXPmpSXNiGTiW6r1Yr7778flZWViIqKwoMPPoicnBzm9iL2IicgDocztXA3mJOQlU1+1q88fwhvvVLM/NUNJhWuuXn+gPg8OY9ZSIxv/M9u7NpWMyQ2lCr5kA0iiflLri7EhZfnQ6GInNR2onS09eGZx/fgs0/7/dXJ1YQsCCO5n0SyFCQXmTUX5uCKG+ZCq+sXXeUlbXji4Z2oHHCvKFyQgJvvWDxm4taphLzhaRu2f1TBEhltCQbcdMdizF2YeELfR8L8g7dK8MLT+4cSdckCktp20FJwT0U7/vRGMaoGOmUWnRJfWJuNDfMTBY8xcqYgRxg6Lt0uH7PIvOy6Qqy/OE9Q+AlRWdeNhzYewJHS/sfjs2dFM1/otAGrUEoK3vTkPnz8XjmCgSAsZBWYaMSRxn5nEtq/GctsKPL2wen1Q6OQ4dYFmbh6Thpz7xCyp/zkvXI8/8S+oYTk6FwjZKt1qHT22/nlRkXhjrlzkWYyD3VwNj21Dx++U8ZiIJecGz6/cKiTTMW1yG6UbEedDi87Ty69uhAXHMd5UtXaiz+/cRS7K/qPyQwS14YAavv694VOLkea0oBDFd0sWTRKr0S6Qo7KotahDs5FqzNw46V50GuVIywyB5OqY+fFocqoRMPAdq+fE48vr89B9IAArm/uwUPP9Fv/ESlxBmh63Gip7D//VXoFdMuSsae1j8Vg1SmRoVKgcuDzZIGaUWhDkd3JkmLJDvKmFem45Zx0qAba4ciBRmY/SLUUiOzFMeg9y4FedX9MBrcOvn0GHDvWxf5OIkvEFCXKPf3iVCeVocCrRumhNvgDQZgMKljTTNjd1jcsqlPMqGztg93pZcfAPJsetYdahtohOc+KrlgJ6nv6v7PQaEDgk2b0NvZ/hzHZgMDyGBzp7j/GUoxa6Bt8qCvpj0mjlGF2lA6VR5pZAjENBFxz63ycc14mO0/oGHvn0yo88VIRunvc7FwoSDah8UATvAPJwtEFsWiI0aBmQHTPjjfCVd095MgVG6XF7dcUYvmCRMHzRK2S4dqLcnHZumzBDs5oToUbDE1x2XTbQvz+97/Hnj17oNPp8M9//hMFBQWT8nuckwN3g4ncLtdv/s+E3WCeufLzM8INhov1k3jSNNXb8dnWKqy9KFd01HGi0A2Vii4VHWxEQMDkgQq6UIEbMb/vk0HxoSbUVnfivAtyxi38RnduqDASFZdKSrWIija9UYUFS5IxXSk92oLyY23MmpOcIE6EtpZe/O23H6G6okNw+fLV6ahL0OP9I82Cy/OTTPjN9fOHRN1oOtod+PjdMqxelwWzyOjqWHy0s9/3fpXIviDLzRdePIwdlR3wkL/eKIxWNQpXJ+KOc3IQrRMe+ezqcLB2oM6aEKnr47D24hysTBEeAa4qb2fnxroNuawTOxryNf/grVIm2iLZXkbiw6JmbDpQhcNdnYIlNhO1WiT4VKjc1yToEkXi9fo1WXj/xSNoE3gSJlNIkXReBq68KBfzBM4LYsf+Bmx57Sgq99QLFqalAj7qghhUHGxmNQFGQ08i8hcn4Y7zZyHBEn480GDDlteLsE9SjeYo4ScCMZ3R6GvRYbfHjoBAQyQr1EjxKLGzvQ+OkFoEg1AhpIJ4I9pLO9Ah0A7UuZm1PA6o7kHzHuHj3nZWHIJxOpRsb2Id5tEkmDVYnhONq68fHggIpdfhwdObj6BoaxU6G8O9bqhzE7cqDb0eH2pKhc/NeXmxSEk04rX3ywWfhCXE6vG9Lywd6txOpVh3Nlcjv+YtlJeXQ6vVMqE+e/bsSfktzsmDi/XI7XLtpscnLNafu/pzM0KsT/28gtMIGv297No5k/obNIr9339/JijUBx+tV5S2TapYzyuMY68ThUZ5L7mmUHQ5jYDRU4npDvnKR/KWHw+N9XZRoU7s2FqNknzxKUZFdd2o73CIinVrlJYVnZoIYiJ9ECru1CmRCAp1wt7hQpJbISrUiZamXlGhTlS/3YTld68RXZ6WGcVeYpjMmgm3w+p8G3677aCgUCfqHQ7E2v2idq40krt7V52gUCf83gCCJe2Y90VhoU6cNS8B/73/Y0GhTnRVdcIWrRUU6gQ9uYkNQlCoE9T5XrguCVv2HRSNodXShto2maBQJ2q9LmhVakGhTvS4fAj0eASFOkHCt+tQJ9wRvOGbdzQBWVZBoU40dDmRnG8TFOoEPeHIjTNgq4BQJ+gpjeNwC2oiPBjdX9yC2qYeQaHOYmjpRWlV55hifbKhUfXaV/8NqcrNpr789a9/5UKdwzlBbrzxRng84VbV5557Lr785S9jsuBincPhcDic09Sesealf7AE05j8dDz88MNIT0+f6rA4nJNCkBJMJ+DoQusfL5s3b2bC/I477hjxflpaGiYTLtY5HA6HwznN6K0uQu3rDyPgdUNlicOf/vQnLtQ5pxWBKXKDyczMxNVXX41TCRfrHA6Hw+GcRnQe2YbG955GMBiALmkWbCuvZlNgOBzOzIR7Mh0nRWXi8yidTi9276gRncN4skhJF5+PrlbLmVPMRBMnKVn2dGffzlr09rinNAay/TQYxSsHpqZZkCli7UeQ80iUyHz1U0lGSr87i9g86LHKrJssGvYSIznNImglOUiP3YX9u+sw2WRFi59beoUcqgjl6MkBktxKFArxyy5ZD1JScCRSIuSjUGJ7eopJ0AKVoCaUOryic9rZd8hVsKjEk7Zi1QZkRUVoB6Uc2XEG0RioHaz+INQR2iEtxYToWPFkS7rGpUeYC65VyxEXYX26Rnd2OETtaImUVDPiROwmCVoWKQba1z0tveO+H1BRJJpfPpGXu6sVjR88g/q3HkfARwWPChB/7vUIuCbX2pfDmcqR9cAEXifC9u3bcdttt+GWW27BL37xC5a4PdlwN5jjzD6+4LYncO7ybHz+6kLEDLhrkHvJtg8r8dz/9jJbv7RMK265awmycmMmbcft2laNjf/ZM8JffdnKNFx720KWVHgikCPHM8ySsJIJrPMvzWO+4pQQejpBCZ1kUVdS3AK9QYWrbprHPLcjicHJpK/Xjc1PH8D7W0qGbuwk4K8esP+kdzbvrMGjH5Sx5DyCvL+vWZqKz6/KhC6C4DiVHChuYfaCtQPWjcTC2Tbcdd1cJETocAzidHiYxeI7bxxjtnuD4pOsPddcMAtSAbvHgD+A994sweaNB5g/e+5sG26+czET95MBnetvHK3Hw5+VoMvVn2RER02B0YKy6l62f8jbXNHmRHNIhzfHpkewzYHWxh7WQaNzNDSh1hqtg8msZs46JCAvuXo2LrwsX9BliGL49P0KPP/EXnR3uYbcS8hf/+qb58FgVKO4vB0PPr0fFbXDji4Ul7TDgZaGHlijtbj+cwux9GzheZYevw9v1x3Bu/XF8AX794VKKscFybNxbkIO5FIZ3i5pwIM7jqHd4R5qhwtzk3D30lkwa5Q4XNuFP79RjKMNw+0wmzzVq7vRXGdnnTNjmhkHQ9op1BaRLC5f33wEr794ZMjacbBtLris33qTnIoe33QI7YPtIAHOW5aCW6+cDYtRLToY8cRDu9h1gKwdSfiXHm0N2RfDbePx+vHi26XY9OaxIX91lVKGq8lydn02lAoZPt1dh8eeP4S2EH/1uYlGdFV2wt7lYsn+t9y9WDQZfdANJueeP0KmEu/siRU5GnrP68Xf/vY3vPfee+zv6667jomJQZ90+iz3TJ9ZcDeYyO1y2ZNPQKE9cTcYr8OBl2++BbW1tSPcYFQqFXsJERcXx6bALFiwAJ2dnXjyySdRVFSEf//73/j85z+PyYKL9RMQ6wqlll2wr7kwBwuzo/D0Y3tQFnKxZw0rIdu9DFx76wKYI4wYTgTm17z5CA7trcd1n1uI3ALbCX0P2bW9/WoxXn7uELNVDMVs1eD62xZi2aqZn5REonjTk/vx4dulYaNdo33Np4Kaqk48/eguJCabceWNc6HTj7xYdPZ58OC7JWjuduHrF+YiNWZsAXyqIV9zsrL7YEcNbrw0H0uOw4N/kIbabjz16C5msXjNLfOZ+BTi6JFm1ukarDUwCHW61lw4C1feOE/UDWSi9Li9eHxXGfZVd8DZEUBN28jRcCrmNC/BCG91N2z+ICpHXR8Gjzka4TabNSgraYVvlHsKicgb71iEeYuSRDs3Lz5zkAn8G29fxFx5QqFj/O1PKvH6O2WIohiK+z3XQ8krtOHmu5YgSeTJSJurFy9U7oFapsBlqfNgHjXi7vD48L895Tjc3IkvL8tFns0cFsNr++rxwscVSOt0oepguBVjUroFfToFlixJxlUX5LBrayitzb2sxgQNIJCIpo5NKC63D8++fhRHStpw+7VzkCvy5GF0fYQRMaT2x012slSvQjXK/rO13YFHnz/E/n3HNYWIGTUgQvvx+TePYffuOmh7vagfdUzS/WDZqgxcd1v4/eBErBtpFP31L5zNihYRvb29+M53voOdO3cyQU4VSq+44opxfRdn+sLF+qkR66P5yU9+gp/+9KcQorW1FTExwwOxfr8f69evxyeffIKjR48iIyMDkwEX6yco1gcp1CtRVzHywhzKZdcWMtEwnSkvacXPv7Ml4mceevaGsBvYTOPt14rx1CO7RZcnppjw679dekpj4pw43/nSS2huGB7FH83nvrgU554/a1Kb+OZ/bEVli7D9H7FWJkfV3gbR5RmzolBRIj61TqtV4F9PXz+hGJ9+bDcr2CbG3EWJ+OYPz8NkQk8daZRcjNnz4vHtn66d1Bg2P72fDUiIQU9l7vvl+gn9xm9/9DaKDwl7wxP0pJKe5J1Msd7R0cEs40pLS5mH+m9/+1ssX758QtvBmR5wsR65XS7575MTFuuv3nbzcY2sC7FlyxZceOGF+Mtf/oKvfe1rmAxmtvricDgcDucMpbm5GV/84hdRU1PDEkhpGkxOTs5Uh8XhzCiMRuOEiiLR1BiCOs6TBRfrHA6Hw+HMMBoaGvDzn/8cjY2NbD76v/71LyQlCU+Z4nBORwJTZN04mr1797L/ZmVNXjFHLtY5HA6Hw5lBeHs68d3vfpdNB0hJSWFC3WY7sZwlDmemEjjFYv3dd99lT7Dmz58/9N7hw4fxwx/+kJ2HV155Jc5o68ann36azcGji9Hs2bPxy1/+UrDc62StHwm5gENFKDL59G9i2RjbQAl7EjEfthnEWNspl4c7b3Bm7v48FeeefAwHobFiGPP6EcHe8OQd99KTkuw+EU5GDBPlZMQwVlufjMuoz2FH/ZbH0N7ezqomUlVSLtQ5nMmHpsp89atfRXZ2NpujvnTpUibcaUSdXJh0uvHlnJyWI+s0Yf9b3/oWm4t31VVX4eDBg8yOav/+/di0adOkry9GQqwed143B7lpFrzw1H588HYpgiEOIxqtApdfPwfrNuRiupOWGYVv/3QNnnxkFxrrRvqr5xTEMrcI5SiHhpkIJRvSzXTTk/vQY3cLOvdwZg7f+dlaPPv4Hmz/uGrE+2THd82t83HOeZmTHsMfb16If75TgrcONoxwGDFrFbhn7SycPzuOJXe+uukQPO5+679BEb/+4lxcdt0cHNrbwNxOOkY5ysxbnISb7lg04RivYs5CSrzy/CG4B6w/WQwyCdZuyMUV18854e8m554nH9nJnIyY1eiarBEWqOTARNfHj94tY9cSskt0OX0jnXsuysEV18/FZEGWnmTt+cFbJQMxdI5wvaIYzj0/Oyzx80T40v+d038/eGuk4xTZ36ZmWJgNpcPhxZU3zD0hpyK/x4XqFx9gnuyxBRn45z//yYsdcc5Y/H4JpH7JhNY/HpYsWYJPP/0U1dXVzPmFbHTz8vKQmpqKyWZau8HQZH3Kkr/xxhvZ6MEg5GtJgvudd97B2rVrJ219oezjy+9+GjdevgCXr8uGImQkhm5CTzy8k1k4rjg3E9feOh+mCAVSpiPMwvG1Yrz87CHW2bj+cwtw1jkz37JxNExAPH0AH2wpYX7ckTyQOdOfQQvHhtourCH/6xMUQhPhUE0n/vRGMcqbe3H5omTceV4WjCH1CageAtVFoPoIBXPjmU1oQkhBHbL+I0G/5aUiWGN0uOmOxZi7sN+W72TR0dbH6ih89kkV8gvjcPPdi5lN6IlABeCYJ/7rR4c88Yn07ChWY4JsJD96p5QJ19COMXWkYuMN7DpJVrPUDpPpif/Je+V4/ol9sHf3+7AP1jCISzCi7FgrO+/p/I9UaO5EqKnsYF7u5OlO9TaoyNzodiBb0nPWZKK+vp7dp2Z/+zHIIhSjomqkVJW0t7oIUrkSH7zwPyxevPikxs2ZXnA3mMjtsvbBZ6DQTMANxunAu/dcj+7u7gklmJ4KprVYf+KJJ3DrrbcyW5zzzz9/6H2HwwGz2cyWPfLII5O2vtDBUVndhLQU8bmBzY09E64gOtWQH7FGI4dKfXoVQxpNa3MPomL0U1YMiXPyoOJI7W0OxIyj+NJkQSOpTd1OJFjEbx7NjXbY4sVvCiTqqTiSUDGkk8XJuEZ97ysvhz2FC31SNWdhIg7srhddn0ayP/fFszCZPPXILrz92lHR5SvXZeGOLy+b1Bge/cd2fPxOmejydRfnYvUF8aJFkUKLH9EgE03plMvlzJ5xzZo1vMjRaQ4X65HbZe0ZJNan9TQYmqpC5Ofnj3ifvGTpscOBAwcmdX0hrGOMls90oU5MVhGn6UaMbebvK04/VN10KoU6i0EqiSjUiUhCnaBCUJPNybhG9fYMjxKPhoZ/Ii0f/MxkM2YMowqjTQZj/UZojLqkrDCfdfJTJ6He1NTEpm0qFAr8+Mc/xrp16yYtZg5nphCcYIIprT9TmNZiva2tvxy3xRL+mNRqtTKP2cla3+12s1doT47D4XA4nIng93vhD/TP23e6+tDTI17Ya7A6KQl0egh+2WWX4dJLeeE2Dmc6WTfiTBfrgzN0hJxI6L2xZvBMZP3f/OY3+NnPfnYCUXM4HA6HI8yh0ldx8NhL7N/PvAF88/uRW4ryrWhgiaqVklkCh8M585jWYn1wRJzmE422xKH3hEbMT9b69913H775zW+OGFmneYUcDofD4ZwohdmXID/zQvbvJWen4oLLU8Kmag7ibK7GO5+9w6a/0OARTeHkcDj9BPwS9jpRJrLuqWbqzW0jQJ7oRElJyYj3ySO9qqpqaPlkrK9SqYZK0E60FC3n1HN4fwMe+O1HKC/pnwo1HaFEv3/98RN8+kH5mE+JTmcoqfLBP3+KD98eaXfHmX54vX68/NxB5iKj1QknoC9enooNVxaMcLsJJT7RiOYGO/bvrjuxGHwBbHrzGP751D7Ye8XnpZ934SwkpQi73ZADDSW5Tjb0G6PdbmQyBZQKDTIy43HxFfNhMAjnEAQDAbRsf5X9+5JLLsG8eRO3luRwTsdpMIEJvGYK03pkfcOGDSzz/dVXX8Xq1auH3n/rrbfgcrlwxRVXTOr6nJlHa3Mv86zes6OW/b3nsxrmt33NrQuYXdp0INSmz+sNYMcnVcyX+da7lyA14+RayE1nPB4/3njxCF5/4TD797aPKplgJyu9zFkxUx0eZxT7dtbi6cf2oKWpf4613qDErPxYlBS3AEEgIdmEW+5ajPw58Wz53IVJePf1o3jp2YNwOrzQ6hVISrGg9GgrGuvtKD7cjLmLEplN5XiTXvccasLDzx5EQ0sv+3vr7nrcdFkeLliZEebqRLaMP//zBrz3xjG8uPEA8zcnv3my9lxzwSyWlDzZ0HH88z9ehPe2lLAY+no90GoV/TFclMPqPtTVCXda7KV74W5vgCYlhhVi4XA4Zy7T2rqR+M53vsMKGr3wwgtMfNMo+UUXXYS4uDh8/PHHI6yr6L3CwkLmn34i60eCWyhNf6joydOP7mbCbzTku33HV5dh0VkpmEoqy9rxt99+GFYAh5BIJbjw0jxc97mFON2hIjp/+82HrHM1GkoxOe+CWbj1nqVTEhtnJH5/AH/99Yc4sEfYijE+yYizV2fgwisKBCt4khXspif2Ye/OWiZWR6NQSFlBsvWX5Ik2Pd2mfvWP7dh5sElweXqyCT/+ynJEiThZ2buc+OT9CqxcmwmDcWo67T12Fz5+txznnJcBY4irGIl1mmK54BcvD7nB0Kh6+ZO/gKutHj/7xhfw7W9/e0pi5kwtXHdEbpcVf9oEuebEHbR8zj5s/ebVM8K6cVpPgyHIT/aHP/wh7rzzTqjVaixYsAArV65ko+WjhXZXV1dYZv3xrM+Z2Rzc2yAo1AerGB49JHyjP9ViXUioD9q87d15YlMDZhq1lZ2CQp2g4YM9n/U/GeFMPT6vX1SoE+S3njcnTlCoD1rBGs1qQaFO0NOlSN9P+P1BUaFOVNZ2o7mtT3Q5iWOamjNVQp2g36YYQoV6KFSVlKwa6dW+/wMm1CUyOS6//PJTHiuHw5leTOtpMAQJahLb9HI6qViPuAc4ZczLZLITXp/D4XA4nKnAa+9EQOVihZDyug/AEKPHTTfdhMzMTL5DOBwBuHXjNGUsoU2PRSayPofD4XA4U8FgUaTu0n1sagw5v9ATYf4EmMMRhrvBcDgcDofDOeV0F21n/12/fj0r3sfhcDh80jbntCEjK4olaQohl0uRmhGFqYbs7MgNQgxrtBZNDeLVcutru3DkQCNmOrYEA3QGpejyrDPADYZsKj/7tAr2btcJf0drcw/275rcPAeZXBbRpSg6RgezJbL/d0q6hSWSCkEJxRnZ0RHXJ6eXrFSzeAwWDawiyaUzCb/HhZ6qQ+zf11577VSHw+FMawLBCVo3BmeOdeO0d4OZLvCs7JkBJXA+8fBOlB8b9lcvnJ+Am+9cjLjE6ZHt3d3lxHP/24etzF+9/72oGB2zlqT4qWNx/mV5uOyaQqjU/cLe6fBg88YDzIaOku0WnpWMGz6/CDE2PWYqvXY3Nj21Dx++U8aSa4noWB1uuH3RlLv2TDZlx1rxxEM7UVXewZyKrrhhDtZemDNuO0GP24fXXjiMN8j+0+NHwdw43HznEmafOFkdC7LVpP3V19OfKKpQynDRFQW4+MoCKFVjpz+R5eNTj+4e0blIy7TilruWICs3ZlwxvLu1Cv978QjsA8mqCrkUl6/LxrUX5UA1jhimI6FuMD2Vh1H35qNYPS8Hr7/+umD1bc6ZA9cdkdtl4a9fgnzAQelE8Ln6sOf7l88INxgu1scJP2lmDtT//PT9Cry/5RguuboQC5YmT1vB9sx/9jAXjbJjLfD5RvabrVFaXP/5hfC4/Xj+ib3o7ho5AqsksXRlAS6+ajYUipGJ1TOJqvJ2bPzPHuQW2JhbxniE30yFLASf/e9ebP2wYqijNkhSqhm33L2EtUMkdm+vwcbHdqOtdaT7iUwuxboNubji+jlQa8Sf3kyE3h43XnhqP7NjvPH2hYixjc8fPRRyfnn52YM4Z00mVq3LDvNHHzOGPg+eeqUIre0O3HHtHMTHztwO62ixXrflMfRUHOJ2jRwG1x3C2LlY54jBTxrOZHBoXwP+8LP3RJdLZVROOfLDrx/8+nxWnIYz/dnychHrmESaLvKLP18c8Tu+eOMzrMCPGF/97iosWnZ6P5k4nRgU63O+/zTKn/gZAj4vPnr5GSxevHiqQ+NMMVx3RG6XBSG1CU4Ev6sPe3902YwYWT99h7A4HA6Hw5khdJfsZkJdobcgISFhqsPhcKY9gYAEkoBkQuvPFLhY53A4HA5nilmlbcP+GD2uuupSxMfHT3U4HA5nGsHFOofD4XA4U0xZWRkUCgUuuugi7q3O4YyHQLD/daJMZN1TDBfrHM5UnoDyyO4flDjqkwTg9wXEv0PEEo8z/ZCPkQhMDivj+w7xOeuTnWwc8Afg9QUiuq84nV5oJinJ9XSF5s2azWbMnTt3qkPhcGYE0kCQvU6UQReymQAX6xzOFJJXGIf/+8kaPPnwrjB/9flLknDTHYvg8wXw1CO7WTJqKEkpZtx81+IxPao504c1F86CUiXD8//bN8JfneoDrF6fjatvnDfmd/z0Dxdh4392Y9e2mhHvk40nHS9zFyVisjh6pJkdq309blz3uQU465z0Ectbm3vx9GO7cXhfAzZcNZtZO5JrEWd85Ofns9F1DofDCYVbN44TnpXNmUx8Xj/eevUoXnnuIMxWLW66cxHmLBgpuvbsqGFOIiSUrrhhLtZclMNsHzkzD0efBy+Sb/6bx5CeHY1b714SsfCQEFQc68lHdqGtpZfZd5IwnqxR9Y52B555fA8++6RqxPu5s22shoEt3oDXNx/B6y8eYb7voR2IG29fNG3tU6eTG0xBQQHuvvtu3HvvvVMdEmeawHVH5HZZ+P0XJ+wGs+fXV8wINxgu1scJP2k4p6pgkk6nFJ0u4fH44XJ6WQElzsyno60PlijtCRe/8fsD6LG7YZ7E6p10zH3l1ufgdvkEl5NPekZ2FMpCCpGN5tZ7lmDNhTmTFuPpItb/9re/4bzzzpvqkDjTBK47IrfLwjNIrPNpMBzONMJkjiy6aEoBn1Zw+mCNPvEbDUFPViZTqBPBQEBUqA9WFo3k+044x1jO6Sc7O5s3BYczTiQTnLNO166ZAhfrHA6Hw+FMMTKZjPurczjHgTQ4MbFO688U+IRXDofD4XCmGJvNxgQ7h8PhjIaPrHM4HA6HM8VkZWVNdQgczoybBiOZwMj6RNY91fCRdQ6Hw+GIolTJcd1tC6BWh4/tyGQSrL8kF1fdOJc5vwgxd2Eilp6dylt4DHjVUg7n+JD66RWcwGvmtDgfWedwOByOKORUQ7aQy1al49n/7sX2jyrZ+/mFcbjprsXM75+YszAJb7x4BK+/cJg5yMTGGXDj7Qsxfwm3bRwPVuvxWXdyOJwzBy7WORwOhzMmFqsWX/jG2Tjv/FnMYnTx8pGj5eRSdPl1c3D2uRk4sLseK9dlTXo11dMJLtY5nFNbwVQ6g6bBcLHO4XA4nHEzKz824vLoWD0r2MU5PiwWC28yDuc4kJ5BYp3PWedwOBwOZ4qhIi8cDocjBB9Z53A4HA5nitFoJre4FYdzuiE5g9xguFjncDgcDmeK4WKdwzk+pHwaDIfD4XA4nFOFSqXijc3hcAThI+scDofD4UwxWq12qkPgcGYU0gG/9ImsP1PgCaYcDofD4UwxfGSdw+GIwUfWORwOh8OZBsWnOBzO+JEGJ2jdGJw5I+tcrHM4HA6Hw+FwZhaBibnB0PozBT4NhsPhcDgcDofDmabwkXUOh8PhcDgczoxCegZZN3KxzuFwOBwOh8OZUUi5GwyHw+FwOBwOh8OZavjIOofD4XA4HA5nRiGZYILphJJTTzFcrHM4HA6Hw+FwZhTSQIC9JrL+TIG7wXA4HA6Hw+FwONMUPrLO4XA4HA6Hw5lRSLkbDIfD4XA4HA6HMz2RcjcYDofD4XA4HA6HM9XwaTAcDofD4XA4nBmFJDhBN5jgzHGD4QmmHA6Hw+FwOBzONIWPrHM4HA6Hw+FwZhRSnmDK4XA4HA6Hw+FMT6RnkFjn02A4HA6Hw+FwOJxpCp8Gw+FwOBwOh8OZedaN0gmMrPtnzsg6F+scDofD4XA4nJlFABNyg6H1Zwp8GgyHw+FwOBwOhzNN4SPr4yQ44Mdpt9snc39wOBwO5wxi8J7C7y0csWNjUH9wRuL3OOCbwMi63+fETEES5EfBuKirq0NycvJk7w8Oh8PhcDicIWpra5GUlMRbZACXy4X09HQ0NTVhosTFxaGyshJqtRrTGS7Wx0kgEEBDQwMMBgMkEglOxx48dUboomA0GnE6w7f19ITv19OT032/dnZ2Ii0tDVVVVbBYLDjTOd339/FAY6k9PT1ISEiAVMpnLY8W7B6PBxNFqVROe6FO8Gkw44ROlDOhZ0sXxzPlAsm39fSE79fTk9N9v5JQP52373g53ff3eDGZTFMdwrRErVbPCJF9suBdNQ6Hw+FwOBwOZ5rCxTqHw+FwOBwOhzNN4WKdw1CpVPjJT37C/nu6w7f19ITv19OT032/nu7bd7zw9uBwwuEJphwOh8PhcDgczjSFj6xzOBwOh8PhcDjTFC7WORwOh8PhcDicaQoX6xwOh8PhcDgczjSFi/UzpHjAoUOHUFJSckJliye6/qnE4XDg4MGDKC0tPaFYGxsbcfjwYfT29mK609fXhwMHDqCsrGxC33Ps2DHs2LED9fX1mK7Q/qBtraioOOHvaGtrY9/R3d2N6QwVQdm/fz+rqneiRXboHKAiOz6fD9P9GP7ss8/Y9eVEoH1JbUXbOlMLAM3k+MdDV1cXOx7b29vH/Kzf70dxcTG7Bo9V8OZ4PsvhzHiCnNOa+++/P6jX64O5ubnBhISEYEpKSvDtt98+ZeufSn75y18GdTpdMC8vLxgfHx9MT08Pfvjhh2Ou19XVFfzRj34UTEpKCkZFRQVnz54dVKlUwQ0bNgSPHTsWnG4EAoHgD37wg6BWqw0WFBQEbTZbcNasWcHt27cf93cVFxcHNRoN9WqCv/jFL4LTDb/fH/z2t7/NYqT9EhMTw/bvrl27xv0dRUVFwfPOOy9oMBiC8+bNCyYmJgavvfbaYHNzc3A64fP5gl/72teCarU6WFhYGIyOjmb/3b9//7jWp8+tXr16aH06B+h4/t3vfseOmenEa6+9FrzmmmvY/lQqlcG5c+ce1/perzf4pS99iW3rnDlzglarNTh//vzg4cOHgzMBiv+rX/3q0L6i/URtcPDgweDpwgMPPMD2DV2TaRvp/KNtfO+99wQ/v3nz5mBcXBy7x2RnZwfNZnPwwQcfnPBnOZzTAS7WT2MefvhhJsKeeeaZIeHzla98hd0gSKRN9vqn+sYgkUiCL7744lCsd955J7tRlJWVRVz3k08+YTeShx56iN1EidraWnaDoRtCR0dHcDrxm9/8JiiTyYJvvfUW+5tivvHGG4MmkylYV1c37u+h9RYtWsQE7HQV6z/5yU+CCoUi+MEHH7C/PR5P8KqrrmLibDxi++jRo0GLxcKEXV9f39D7mzZtOi7Bfyr47ne/yzqJW7duZX+7XK7gxRdfHIyNjQ22t7dHXNfpdLJjNSsrK9jQ0MDeI4H+85//nO3bRx55JDjdtvXZZ59l+yQzM/O4xTp1aqizOrgPafvXr1/PBhS6u7uD051vfetb7Dq6Y8eOoX194YUXsn3Y2dkZnOnQtUUqlbL9PLg/ent7g1dffXVQLpcHP/vssxGfp4EGev/73//+UMeSxDcdu4PX9BP5LIdzusDF+mkKXcRopJhGFEOhmyMJ01tuuWVS1z/VI5IkaC666KIR79NNgkZk77rrrojrU8dDaESLRm/oBvDYY48Fpwt0UzcajWxkOJSWlhZ2A/vGN74x7u/68Y9/zEY233333Wkp1unmToLs1ltvHfE+dUhICNx3331jfse5554bXLBgAeu8TWdIoJFQv/vuu0e8X1FRwfbNz372s4jr7969W/BzJGKpE3v99dcHpyvHK9YHj/V777037DymNqAnCdMZ6nhRB5Q6kKGUlJSw+H/1q18FZzp0TaYO8WioI0nbOPo4p2s3PSEcHCwZZOHChWzQ5EQ/y+GcLvA566cpNAe0rq4O69atG/G+VqvF2WefjTfeeGNS1z+V7NmzBy0tLWGxGo1GnHXWWWPGmpubi8LCwrD3dTod+29HRwemC1u3bmXzXEdva0xMDObPnz/u/bJr1y78+te/xt/+9jdERUVhOvLRRx+xHITR25qYmIiCgoIxt7W8vBwffPABbr75Zvb30aNH2Zx1p9OJ6cb7778Pt9sdtq3p6enIzs4ec1tTUlKgVCrZORsK5SHQoAx9x+nCO++8w+bij24rOo+Tk5On1bVJiHfffRderzcsftpHtL+ne/zjQSaT4aqrrhrXNZX2JbXJ6tWrIZfLR3x+/fr1Q/ei4/0sh3M6wcX6aQolDRIZGRlhy+g9SvahhLvJWv9UMlasJFhOJGH0P//5D/vvsmXLMF0Ya1sp2ZQSryJBYvWWW27BBRdcgOuvvx7TlbG2lcR3JHbu3Mn+S4maJGZpey+++GJYrVb84Ac/QCAQwOmyrdRZ++c//4mNGzeyapgk/p999llcccUVWLlyJb71rW/hdGGibTXVzPT4J4LQNZXOT0oQFWsPYrBNjuezHM7pxMiuKee0YdDxQq/Xhy0zGAxDn4mOjp6U9U8l441VaLkYzz33HJ555hls2LABy5cvx3RhrG0loU4dE5PJJPod3/nOd5jrDY1QTWfG2lYaiaaXWJn21tZW9t8HHngATz75JG666Sb2Nz1N+NrXvgaNRoMf/vCHmAnbSk9TaIRcIpGIfgd1RD799FP87ne/wwsvvMA602q1Gr/85S8jHg8zjbHaarq7/cz0+E+UoqIidr6lpaXh7rvvPu7r9/F+lsM5neAj66cpCoWC/VdolHXQzm3wM5Ox/qnkZMf6ySef4HOf+xxycnLw+OOPYzox0W0lgf6Pf/wD999/P5KSkjCdGc+2jn4UPvpRPHHeeecNCXXi3nvvxbx58/CXv/xl2liRjrWttJ2RhDrZPS5duhR79+5l03/Izq6pqQlf//rXcdlll+HRRx/F6cJYbTVdrkuna/wnAk1NoYEPOoY3bdo0QmwfzzVtJt2XOJyTCRfrpylxcXEjRhdDoRE3qVTKHp1P1vqnkrFipbm8NPVhPNDUCbqp0Lzo9957b1o8OTiebaV5+pRXIMbPf/5z9rh4zpw5zFudXoMe13RDpb9pFHcmbCsdf4OCXIj4+Hj237lz54YtI7FOU7nG4/08HbZ1cLkY9BSouroaP/vZz9ixOwiJdZrf/9vf/hanCxNtq6lmpsd/vDQ3N2Pt2rVs2958800sXLjwuNoj9DPH81kO53SCi/XTFEo2JEFNxShGQ0U4Zs+ezaYBTNb6p5LBi79YrCTMIo3ADrJv3z6cf/75TARSYmKo6JkuLFq0KOK2Di4XY9asWawD8o1vfIMJOXr9/ve/Z8tef/119vdEiyxN9rbSaDglii5evDji+oPLhTof9B6N8kXq2EyHbaURROpMjbWtJIjEhAq9R6PspwtibUVJmzTVYqy2mq7x08jwkSNHpn38xwMJ6DVr1rCBAEqcXbFiRdhnbDYbe8ondk2jqVx0vznez3I4pxVTbUfDmTzIt5d8h0P9pfft28ess/7whz+E2Z6Rf21o8ZTjWX+qIYu+1NRUZm04CG0Pxfr3v/99xGePHDkSVkCIiqlQEZqMjIxgTU1NcDqzePFiVqQq1LrsnXfeYdv6+OOPj/jsoUOHgjt37oz4fYP7dLpZNxJkxUaFVcgKbpBXX311hP//IAcOHGAWhqGsXbuWFUEiC8NQm0TyXl+5cmVwukDnHRW2on0beg4+//zzbFtfeumlsH22d+/eob9feeUVQds/sgmkbV2xYkVwplo30nbS9g5Cx0JaWlrw7LPPHvG5J554grXBli1bgtMZshGlbV62bNmIfb1x40YWPxWMOh2g84xqOFCti48++ijiZ8mGlWpHhNbEoPWp2NHNN998wp/lcE4XuFg/jaHqm1QZb82aNewGRkVISIyeddZZI0Qtcf7557MbRaioOZ71pxoS23SxvuCCC1ixoKeffppVtyNBRoV0Qlm1ahXb1kGqq6tZMRK6qTz33HNMyIe+aPl0Ys+ePayq7GWXXcZEOokUqlZJ2z7aT5y8h2m7ZqpYp/Ynr3UqpkJ+8NQZIW/4Sy+9NKwqJ1VzpeM1FDqG6fO0z0nwvvDCC6wQFBVVmm7VIknQkNf6DTfcwKo8PvrooyxOqvQ5GhJ71AkZhPY7ncO0PlXjpbYi8UdVPWn/DxZami6Qf/zg+UX1HKgKpdj5RttJ2xsKHfdU+ZQ8+N9//31W0IzO/+lU/yESFDPFT+KS9jUVoKNOFe370wG3283uE4O+96OvqTRgMromBlUopurEdI6+/vrrrINJx8boQm/H81kO53RBQv831aP7nMmDbAv/9Kc/sSkeNHeb5g5+5StfYY8LQ6FpEdu3b8fHH3/MPne8608HampqWKw0RYLiI9/dL3/5yyO2h/jSl77EEvFofjZB20wOKWJQcuJXv/pVTCcoiZASJGmKBHkXX3TRRbjnnnvCpvt8/vOfZ3OZycpPjNLSUmbl+IUvfIEl1k5Hq7u//vWvbIoDJaaR68ldd90VNl+d9hPNQd+yZcuI98n5htqK/PjpckfTEGh/TscEW9pGcqsh+znKP7j00ktx++23sylpoVxzzTXMCeeVV14Zeo+2jZL3aDoTnQvkAEPz1el8nW7zeCnBefPmzeM636gNyPHn+eefH/E5mgrx97//HSUlJWxbyabytttui5iIO52gJGByKqJ9TfFffvnl7Pwbva9nIuSjTtckMSiP5MEHHxzxHrlY0XlO9RVo+teSJUvwzW9+UzA36ng+y+GcDnCxzuFwOBwOh8PhTFNmfheew+FwOBwOh8M5TeFincPhcDgcDofDmaZwsc7hcDgcDofD4UxTuFjncDgcDofD4XCmKVysczgcDofD4XA40xQu1jkcDofD4XA4nGkKF+v/3969hET1hnEcf/6mqBRooIZYYloLzU0oaF6g6EJJCElJipILi0AsQ4gWuRAXYriQooXgIgLBSJLceAGhRLwkgmmGYGhU4qYsEi+Jl3heOIOi0Vya/5zy+4FZvGfOe+bMEeE37zznGQAAAMCmCOsAAACATRHWAQAAAJsirAPwWG1trfmZ9189Hj16xFUGdoDz589v+t/ftWuX7N+/Xy5cuCCvXr3y6bklJyfLqVOnfHoOgDsI6wD+mL6+PllfX9/yKCoq4ioDf4kHDx5IeHi4rK2tmXF+fr7k5OQ4PT8wMNDxv//jxw/p7OyUb9++SWZmpoyMjHjxzIF/E2EdAAA4tLe3y9mzZ8XPz09WV1elo6NDzp0759YV8vf3l4SEBPPt2/LysjQ3N3OlARcR1gEAgKEr4S9evJCsrCwz7u/vl9nZWRPe/4SgoKBN49DQULl+/fqW/S5evCiHDh3atC0xMdGU2bx9+1ZOnz4te/bskeLiYvPcy5cv5eTJkxIWFiYhISGSmpoqjY2NZnVf6fahoSHp6upylOhoeY7ld/MBXyKsAwCwg3369MkRYDVMLywsmNIXHWdkZJh9oqOjzbisrMylY6+srMj4+Ljcvn1b9u7dK3l5eR6d69evX6W8vFxqampkcnJSzpw5I+/fvzcr//Hx8TI6OiozMzPy8OFD843AxMSEmff582dJSkoygdwq0dH3rZyZD/iSv09fHcA/5dixY9tun5qakpiYmP/9fAD8nq4wWyvIt27dkt7eXhkYGDBjK+Deu3fPpdV5DfYb7du3T54+fSoHDx706E+iK/0a/g8fPmzGubm58uTJE1lcXJQbN25IZGSk47wfP37s1DH1vXoyH/A2VtYBeP0GU4I68Hfo6elxrKbrCrveEJqenu7SMTbeYKo17x8+fJBLly6Z0prW1laPzk9Xv62gvrE8Ruvrr127Jm1tbTI/P+/SMT2dD3gbYR0AAJhwPjw87Ajrg4ODpowlLS3N/ZDh5ycHDhyQ+/fvmxV8Z8toflUrHhUVtWXbkSNHpKWlRZaWlkxNu9bB6wcMZ1fGPZ0PeBthHQCAHUpbKlr16rt37zbhXNs06vj48eNmn4iICDO2buZ0h87XG0a1JG7jyrXezDk3N7dl/+np6W2PExAQsO327OxsUyLz5csXef78uWk9eeXKFad/48HT+YA3EdYBANihdBXZKlnRMhD90SBrrC0XKysrHeOGhga3X0d7tr97986E8+DgYMf2uLg4efPmzZagriv87r4fLbd59uyZKcfp7u52PKcfRrSe3t35gK8Q1gEAgKO/utI6c22R6G5/dYuG/I8fP8rNmzdN9xbt5KKlMZarV6+auvi6ujr5/v27vH79WkpKSkzrRGfV19eb42vZjq7S67cF2s1Fg/mJEyc21abrBwP90LCxzMbZ+YCvENYB/NFuMBt/atx63Llzh6sM2NjY2JgJ6FZY1xsttRREu6K4yuoGow8N5snJySaEazeYioqKTftevnxZqqqqTLcZ7Rijobm6utr0O3dWQUGBuem0tLTU1LTHxsZKU1OT6ZNeWFjo2O/u3buSkpIiR48eNedl9Vl3dj7gK/+t0/EfAAAAsCVW1gEAAACbIqwDAAAANkVYBwAAAGyKsA4AAADYFGEdAAAAsCnCOgAAAGBThHUAAADApgjrAAAAgE0R1gEAAACbIqwDAAAANkVYBwAAAGyKsA4AAADYFGEdAAAAEHv6CTyztwZ/uYhCAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 700x700 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "g = alex_jointplot(ds)\n",
     "g.axes[1].grid(False)\n",
@@ -319,9 +633,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'matplotlib.figure.Figure'>\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x700 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "g = alex_jointplot(ds)\n",
     "g.axes[1].grid(False)\n",
@@ -335,9 +667,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'matplotlib.figure.Figure'>\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x700 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "g = alex_jointplot(ds)\n",
     "g.axes[1].grid(False, axis='x')\n",
@@ -365,9 +715,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'matplotlib.figure.Figure'>\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x700 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "alex_jointplot(ds, marginal_kws={'show_kde': False});"
    ]
@@ -381,66 +749,81 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from ipywidgets import widgets, interact, interactive, fixed\n",
-    "from IPython.display import display, display_png, display_svg, clear_output\n",
-    "from IPython.core.pylabtools import print_figure"
+    "# from ipywidgets import widgets, interact, interactive, fixed\n",
+    "# from IPython.display import display, display_png, display_svg, clear_output\n",
+    "# from IPython.core.pylabtools import print_figure"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
-    "cmaps = ['viridis', 'plasma', 'inferno', 'magma',\n",
-    "         'afmhot', 'Blues', 'BuGn', 'BuPu', 'GnBu', 'YlGnBu',\n",
-    "         'coolwarm', 'RdYlBu', 'RdYlGn', 'Spectral',]# 'icefire']  uncomment if using seaborn 0.8"
+    "# cmaps = ['viridis', 'plasma', 'inferno', 'magma',\n",
+    "#          'afmhot', 'Blues', 'BuGn', 'BuPu', 'GnBu', 'YlGnBu',\n",
+    "#          'coolwarm', 'RdYlBu', 'RdYlGn', 'Spectral',]# 'icefire']  uncomment if using seaborn 0.8"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@interact(overlay = widgets.RadioButtons(options=['fit model', 'KDE'], value='KDE'),\n",
-    "          binwidth = widgets.FloatText(value=0.03, min=0.01, max=1),\n",
-    "          bandwidth = widgets.FloatText(value=0.03, min=0.01, max=1),\n",
-    "          gridsize = (10, 100),\n",
-    "          min_size=(10, 500, 5),\n",
-    "          cmap=widgets.Dropdown(value='Spectral', options=cmaps),\n",
-    "          reverse_cmap = True,\n",
-    "          vmax_fret = True,\n",
-    "          )\n",
-    "def plot_(min_size=50, overlay='KDE', binwidth=0.03, bandwidth=0.03, \n",
-    "          gridsize=50, cmap='Spectral', reverse_cmap=False, \n",
-    "          vmax_fret=True):\n",
-    "    dx = d.select_bursts(select_bursts.size, add_naa=True, th1=min_size)\n",
-    "    bext.bursts_fitter(dx, 'E', binwidth=binwidth, bandwidth=bandwidth, \n",
-    "                       model=mfit.factory_three_gaussians())\n",
-    "    bext.bursts_fitter(dx, 'S', binwidth=binwidth, bandwidth=bandwidth, \n",
-    "                       model=mfit.factory_two_gaussians())    \n",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "30be0341925e410fad4310508c2b4cf1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=50, description='min_size', max=500, min=10, step=5), RadioButtons(descr…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# @interact(overlay = widgets.RadioButtons(options=['fit model', 'KDE'], value='KDE'),\n",
+    "#           binwidth = widgets.FloatText(value=0.03, min=0.01, max=1),\n",
+    "#           bandwidth = widgets.FloatText(value=0.03, min=0.01, max=1),\n",
+    "#           gridsize = (10, 100),\n",
+    "#           min_size=(10, 500, 5),\n",
+    "#           cmap=widgets.Dropdown(value='Spectral', options=cmaps),\n",
+    "#           reverse_cmap = True,\n",
+    "#           vmax_fret = True,\n",
+    "#           )\n",
+    "# def plot_(min_size=50, overlay='KDE', binwidth=0.03, bandwidth=0.03, \n",
+    "#           gridsize=50, cmap='Spectral', reverse_cmap=False, \n",
+    "#           vmax_fret=True):\n",
+    "#     dx = d.select_bursts(select_bursts.size, add_naa=True, th1=min_size)\n",
+    "#     bext.bursts_fitter(dx, 'E', binwidth=binwidth, bandwidth=bandwidth, \n",
+    "#                        model=mfit.factory_three_gaussians())\n",
+    "#     bext.bursts_fitter(dx, 'S', binwidth=binwidth, bandwidth=bandwidth, \n",
+    "#                        model=mfit.factory_two_gaussians())    \n",
     "    \n",
-    "    if reverse_cmap: cmap += '_r'\n",
+    "#     if reverse_cmap: cmap += '_r'\n",
     "\n",
-    "    if binwidth < 0.01: binwidth = 0.01\n",
-    "    if bandwidth < 0.01: bandwidth = 0.01\n",
-    "    if overlay == 'fit model':\n",
-    "        marginal_kws = dict(binwidth=binwidth, show_model=True, pdf=True, \n",
-    "                            show_kde=False)\n",
-    "    else:\n",
-    "        marginal_kws = dict(binwidth=binwidth, show_kde=True, \n",
-    "                            bandwidth=bandwidth)\n",
-    "    alex_jointplot(dx, cmap=cmap, gridsize=gridsize, vmax_fret=vmax_fret, \n",
-    "                   marginal_kws=marginal_kws,)\n",
+    "#     if binwidth < 0.01: binwidth = 0.01\n",
+    "#     if bandwidth < 0.01: bandwidth = 0.01\n",
+    "#     if overlay == 'fit model':\n",
+    "#         marginal_kws = dict(binwidth=binwidth, show_model=True, pdf=True, \n",
+    "#                             show_kde=False)\n",
+    "#     else:\n",
+    "#         marginal_kws = dict(binwidth=binwidth, show_kde=True, \n",
+    "#                             bandwidth=bandwidth)\n",
+    "#     alex_jointplot(dx, cmap=cmap, gridsize=gridsize, vmax_fret=vmax_fret, \n",
+    "#                    marginal_kws=marginal_kws,)\n",
     "    \n",
-    "    fig = gcf()\n",
-    "    plt.close()\n",
-    "    display(fig)"
+    "#     fig = gcf()\n",
+    "#     plt.close()\n",
+    "#     display(fig)"
    ]
   }
  ],
@@ -460,7 +843,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.14.7"
   },
   "toc": {
    "colors": {
diff --git a/tests/test_exp_fitting.py b/tests/test_exp_fitting.py
index 2d647a3..ac81d1f 100644
--- a/tests/test_exp_fitting.py
+++ b/tests/test_exp_fitting.py
@@ -44,8 +44,8 @@ def test_expon_fit_hist(sample):
     lambda_fit, resid, x_resid, size = expon_fit_hist(sample, s_min=sample_min, bins=bins)
     tau_fit = 1./lambda_fit
     relative_error = np.abs(tau_fit-sample_tau)/sample_tau
-    print('\n [expon_fit_hist] Fit (tau): %.2f  - Relative error: %.2f %%' % \
-            (tau_fit, relative_error*100))
+    # print('\n [expon_fit_hist] Fit (tau): %.2f  - Relative error: %.2f %%' % \
+    #         (tau_fit, relative_error*100)) # note that in numpy 2, this fails because array to scalar problem
     assert relative_error < max_relative_error
 
 def test_expon_fit_histw(sample):
@@ -55,8 +55,8 @@ def test_expon_fit_histw(sample):
                                 weights='hist_counts')
     tau_fit = 1./lambda_fit
     relative_error = np.abs(tau_fit-sample_tau)/sample_tau
-    print('\n [expon_fit_hist] Fit (tau): %.2f  - Relative error: %.2f %%' % \
-            (tau_fit, relative_error*100))
+    # print('\n [expon_fit_hist] Fit (tau): %.2f  - Relative error: %.2f %%' % \
+    #         (tau_fit, relative_error*100)) # note that in numpy 2, this fails because array to scalar problem
     assert relative_error < max_relative_error
 
 if __name__ == '__main__':

From 3503a1082e2198ecc1c821c6a3df53819028afa6 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Fri, 4 Sep 2026 11:31:32 +0200
Subject: [PATCH 134/142] Update tests.yml

---
 .github/workflows/tests.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 70b829f..2033751 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -115,7 +115,7 @@ jobs:
         uses: pypa/cibuildwheel@v4.2.0
         env:
           CIBW_SKIP: "pp* cp310-win_arm64"
-          CIBW_TEST_REQUIRES: pytest numpy matplotlib scipy pandas tables numba seaborn lmfit phconvert
+          CIBW_TEST_REQUIRES: pytest numpy matplotlib scipy pandas tables numba seaborn lmfit phconvert pooch
           CIBW_BEFORE_TEST: python -m pip install pytest
           CIBW_CACHE_PATH: $GITHUB_WORKSPACE/tests/data
           CIBW_TEST_COMMAND: python -m pytest {package}/tests/ 

From ba7de5a1f4fe5a4d9d4d28ad6e468c9cb3a4c195 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Fri, 4 Sep 2026 11:59:35 +0200
Subject: [PATCH 135/142] Update tests.yml

---
 .github/workflows/tests.yml | 14 --------------
 1 file changed, 14 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 2033751..128b3fa 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -78,20 +78,6 @@ jobs:
           cd ..
           cd tests
           python -m pytest
-      - name: Apple Silicon Oddities
-        if: matrix.os == 'macos-latest'
-        shell: bash -l {0}
-        run: |
-          brew install c-blosc hdf5
-          export HDF5_DIF=/opt/homebrew/opt/hdf5
-          export BLOSC_DIR=/opt/homebrew/opt/c-blosc
-      - name: Install Dependencies
-        shell: bash -l {0}
-        run: |
-          conda install -c conda-forge pip
-          python -m pip install setuptools build
-          conda install -c conda-forge pytest numpy numba scipy pandas matplotlib pytables phconvert
-
   build_wheels:
     name: Build wheels on ${{ matrix.os }}
     runs-on: ${{ matrix.os }}

From 31f4dc7d19630e408c781dc7dd55d0603ed2c55d Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Fri, 4 Sep 2026 12:04:17 +0200
Subject: [PATCH 136/142] Update tests.yml

---
 .github/workflows/tests.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index bb61dfe..c26fad1 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -99,10 +99,10 @@ jobs:
       - name: Build wheels
         uses: pypa/cibuildwheel@v3.3.1
         env:
-          CIBW_SKIP: "pp* cp310-win_arm64"
+          CIBW_SKIP: "cp310-win_arm64"
           CIBW_TEST_REQUIRES: pytest numpy matplotlib scipy pandas tables numba seaborn lmfit phconvert
           CIBW_BEFORE_TEST: python -m pip install pytest
-          CIBW_TEST_COMMAND: python -m pytest {package}/tests
+          CIBW_TEST_SKIP: "*win* cp315-macosx_arm64 cp315t-macosx_* cp315t-manylinux_aarch64 *musllinux* cp315t-manylinux_x86_64"
       
       - uses: actions/upload-artifact@v4
         with:

From 6696284428f0545b8ba7e41c34ccb49f3cdc2e52 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Sat, 21 Feb 2026 16:34:59 +0200
Subject: [PATCH 137/142] Update tests.yml

---
 .github/workflows/tests.yml | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index c26fad1..cae1248 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -27,6 +27,8 @@ jobs:
         run: |
           cd tests
           python fetch_files.py
+      - name: test move
+        run: cp tests/data/* *
       
   build:
     needs: download
@@ -93,7 +95,7 @@ jobs:
           fetch-depth: 0
       - uses: actions/cache/restore@v5
         with:
-          path: tests/data
+          path: ~/tests/data
           enableCrossOsArchive: true
           key: testfiles
       - name: Build wheels

From 823decab3a9a3faf4179fe3e2b31d7dba1ee6e19 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Fri, 4 Sep 2026 12:16:08 +0200
Subject: [PATCH 138/142] Finish rebasing and merge of github actions

---
 .github/workflows/tests.yml | 15 +++++++++++++++
 1 file changed, 15 insertions(+)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index cae1248..539bf3c 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -28,8 +28,23 @@ jobs:
           cd tests
           python fetch_files.py
       - name: test move
+<<<<<<< HEAD
         run: cp tests/data/* *
       
+=======
+        run: |
+          ls tests/data
+      - uses: actions/cache@v5
+        id: filecache
+        with:
+          path: ./tests/data
+          enableCrossOsArchive: true
+          key: testfiles
+      - name: Check Saved
+        shell: bash -l {0}
+        run: ls tests/data
+  
+>>>>>>> 3393dfc (Update tests.yml)
   build:
     needs: download
     runs-on: ${{matrix.os}}

From c46ab421705e480b0952231251f2c3dbed8687ae Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Fri, 4 Sep 2026 12:32:38 +0200
Subject: [PATCH 139/142] Fix broken tests.yaml

---
 .github/workflows/tests.yml | 21 ++++-----------------
 1 file changed, 4 insertions(+), 17 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 3f32548..ebd1119 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -7,12 +7,6 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v6
-      - uses: actions/cache@v5
-        id: filecache
-        with:
-          path: ./tests/data
-          enableCrossOsArchive: true
-          key: testfiles
       - uses: conda-incubator/setup-miniconda@v4
         with:
           auto-update-conda: true
@@ -33,10 +27,6 @@ jobs:
           path: ./tests/data
           enableCrossOsArchive: true
           key: testfiles
-      - name: Check Saved
-        shell: bash -l {0}
-        run: ls tests/data
-  
   build:
     needs: Download
     runs-on: ${{ matrix.os }}
@@ -66,10 +56,6 @@ jobs:
       - name: Apple Silicon Oddities
         if: matrix.os == 'macos-latest'
         shell: bash -l {0}
-        run: |
-          conda install pip -c conda-forge
-      - name: MacOS install hdf5 dependencies
-        if: runner.os == 'macOS'
         run: |
           brew install c-blosc hdf5
           export HDF5_DIF=/opt/homebrew/opt/hdf5
@@ -115,11 +101,12 @@ jobs:
         uses: pypa/cibuildwheel@v4.2.0
         env:
           CIBW_SKIP: "cp310-win_arm64"
-          CIBW_TEST_REQUIRES: pytest numpy matplotlib scipy pandas tables numba seaborn lmfit phconvert
+          CIBW_TEST_REQUIRES: pytest numpy matplotlib scipy pandas tables numba seaborn lmfit phconvert pooch
           CIBW_BEFORE_TEST: python -m pip install pytest
+          CIBW_CACHE_PATH: $GITHUB_WORKSPACE/tests/data
+          CIBW_TEST_COMMAND: python -m pytest {package}/tests/ 
           CIBW_TEST_SKIP: "*win* cp315-macosx_arm64 cp315t-macosx_* cp315t-manylinux_aarch64 *musllinux* cp315t-manylinux_x86_64"
       - uses: actions/upload-artifact@v4
         with:
           name: cibw-wheels-${{ matrix.os }}-${{ strategy.job-index }}
-          path: ./wheelhouse/*.whl
-
+          path: ./wheelhouse/*.whl
\ No newline at end of file

From 6d0b2161c5a8a9b5dac862cd1a8501120950485a Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Fri, 4 Sep 2026 13:43:13 +0200
Subject: [PATCH 140/142] Update tests.yml

---
 .github/workflows/tests.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index ebd1119..5fafa38 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -105,8 +105,8 @@ jobs:
           CIBW_BEFORE_TEST: python -m pip install pytest
           CIBW_CACHE_PATH: $GITHUB_WORKSPACE/tests/data
           CIBW_TEST_COMMAND: python -m pytest {package}/tests/ 
-          CIBW_TEST_SKIP: "*win* cp315-macosx_arm64 cp315t-macosx_* cp315t-manylinux_aarch64 *musllinux* cp315t-manylinux_x86_64"
+          CIBW_TEST_SKIP: "*win* cp*-macosx_arm64 cp315t-macosx_* cp315t-manylinux_aarch64 *musllinux* cp315t-manylinux_x86_64"
       - uses: actions/upload-artifact@v4
         with:
           name: cibw-wheels-${{ matrix.os }}-${{ strategy.job-index }}
-          path: ./wheelhouse/*.whl
\ No newline at end of file
+          path: ./wheelhouse/*.whl

From 009f4df9717d0e915f15a01f4b61f68733d40d3e Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Fri, 4 Sep 2026 14:20:42 +0200
Subject: [PATCH 141/142] Update tests.yml

---
 .github/workflows/tests.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 5fafa38..6e6e1ce 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -101,11 +101,11 @@ jobs:
         uses: pypa/cibuildwheel@v4.2.0
         env:
           CIBW_SKIP: "cp310-win_arm64"
-          CIBW_TEST_REQUIRES: pytest numpy matplotlib scipy pandas tables numba seaborn lmfit phconvert pooch
+          CIBW_TEST_REQUIRES: pytest numpy matplotlib scipy pandas tables seaborn lmfit phconvert pooch
           CIBW_BEFORE_TEST: python -m pip install pytest
           CIBW_CACHE_PATH: $GITHUB_WORKSPACE/tests/data
           CIBW_TEST_COMMAND: python -m pytest {package}/tests/ 
-          CIBW_TEST_SKIP: "*win* cp*-macosx_arm64 cp315t-macosx_* cp315t-manylinux_aarch64 *musllinux* cp315t-manylinux_x86_64"
+          CIBW_TEST_SKIP: "*win* cp315-macosx_arm64 cp315t-macosx_* cp315t-manylinux_aarch64 *musllinux* cp315t-manylinux_x86_64"
       - uses: actions/upload-artifact@v4
         with:
           name: cibw-wheels-${{ matrix.os }}-${{ strategy.job-index }}

From 250426b0e1dcb8add7f0469deb1ef61ebd861243 Mon Sep 17 00:00:00 2001
From: Paul Harris <harripd@gmail.com>
Date: Fri, 4 Sep 2026 16:03:19 +0200
Subject: [PATCH 142/142] Update tests.yml

---
 .github/workflows/tests.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 6e6e1ce..1410ca1 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -105,7 +105,7 @@ jobs:
           CIBW_BEFORE_TEST: python -m pip install pytest
           CIBW_CACHE_PATH: $GITHUB_WORKSPACE/tests/data
           CIBW_TEST_COMMAND: python -m pytest {package}/tests/ 
-          CIBW_TEST_SKIP: "*win* cp315-macosx_arm64 cp315t-macosx_* cp315t-manylinux_aarch64 *musllinux* cp315t-manylinux_x86_64"
+          CIBW_TEST_SKIP: "*win* cp315-macosx_arm64 cp315t-macosx_* cp315t-manylinux_aarch64 *musllinux* cp315t-manylinux_x86_64 cp313-manylinux_aarch64"
       - uses: actions/upload-artifact@v4
         with:
           name: cibw-wheels-${{ matrix.os }}-${{ strategy.job-index }}