diff --git a/csiborgtools/__init__.py b/csiborgtools/__init__.py
index 27ca05c..ac1d08d 100644
--- a/csiborgtools/__init__.py
+++ b/csiborgtools/__init__.py
@@ -19,8 +19,9 @@ from .utils import (center_of_mass, delta2ncells, number_counts,
                     binned_statistic, cosine_similarity, fprint,                # noqa
                     hms_to_degrees, dms_to_degrees, great_circle_distance,      # noqa
                     radec_to_cartesian, cartesian_to_radec,                     # noqa
-                    thin_samples_by_acl, numpyro_gof)                           # noqa
-from .params import paths_glamdring, simname2boxsize, simname2Omega_m           # noqa
+                    thin_samples_by_acl, numpyro_gof, radec_to_galactic)        # noqa
+from .params import (paths_glamdring, simname2boxsize, simname2Omega_m,         # noqa
+                     snap2redshift)                                             # noqa
 
 
 ###############################################################################
diff --git a/csiborgtools/flow/flow_model.py b/csiborgtools/flow/flow_model.py
index 5163d4b..2d0dd54 100644
--- a/csiborgtools/flow/flow_model.py
+++ b/csiborgtools/flow/flow_model.py
@@ -26,8 +26,6 @@ from warnings import catch_warnings, simplefilter, warn
 import numpy as np
 import numpyro
 import numpyro.distributions as dist
-from astropy import units as u
-from astropy.coordinates import SkyCoord
 from astropy.cosmology import FlatLambdaCDM
 from h5py import File
 from jax import jit
@@ -43,6 +41,7 @@ from sklearn.model_selection import KFold
 from tqdm import trange
 
 from ..params import simname2Omega_m
+from ..utils import radec_to_galactic
 
 SPEED_OF_LIGHT = 299792.458  # km / s
 H0 = 100                     # km / s / Mpc
@@ -53,24 +52,6 @@ def t():
     return datetime.now().strftime("%H:%M:%S")
 
 
-def radec_to_galactic(ra, dec):
-    """
-    Convert right ascension and declination to galactic coordinates (all in
-    degrees.)
-
-    Parameters
-    ----------
-    ra, dec : float or 1-dimensional array
-        Right ascension and declination in degrees.
-
-    Returns
-    -------
-    l, b : float or 1-dimensional array
-    """
-    c = SkyCoord(ra=ra*u.degree, dec=dec*u.degree, frame='icrs')
-    return c.galactic.l.degree, c.galactic.b.degree
-
-
 ###############################################################################
 #                             Data loader                                     #
 ###############################################################################
diff --git a/csiborgtools/params.py b/csiborgtools/params.py
index 87592cb..2442473 100644
--- a/csiborgtools/params.py
+++ b/csiborgtools/params.py
@@ -16,6 +16,62 @@
 Various user parameters for CSiBORGTools.
 """
 
+CB2_REDSHIFT = [69.0000210000063, 40.250007218751264, 28.24050991940438,
+                21.6470609550175, 17.480001404480106, 14.608109099433955,
+                12.508772664512199, 10.90721705951751, 9.64516173673259,
+                8.625000360937513, 7.7832702592057235, 7.0769233254437935,
+                6.475728365821477, 5.95783150553419, 5.50704240932355,
+                5.111111246913583, 4.760598622974984, 4.448113312911626,
+                4.1677853285437605, 3.914893700679041, 3.685598452365574,
+                3.476744253718227, 3.285714346938776, 3.1103203402819117,
+                2.9487179993425383, 2.7993421515051513, 2.6608558268213116,
+                2.5321101306287352, 2.4121122957547967, 2.3000000330000008,
+                2.1950207773798662, 2.096514773533915, 2.003901196522936,
+                1.9166666909722223, 1.8343558508261513, 1.7565632668759008,
+                1.6829268488994646, 1.613122190273029, 1.5468577900064306,
+                1.4838709837669097, 1.4239244641145379, 1.366803292753544,
+                1.3123123255056859, 1.2602739849878026, 1.210526327423823,
+                1.162921359250726, 1.117323566656109, 1.0736086272735772,
+                1.0316622782422846, 0.9913793189283591, 0.9526627299814432,
+                0.9154228931957131, 0.8795768989699038, 0.8450479301016136,
+                0.8117647122768166, 0.7796610229819017, 0.7486752517178681,
+                0.7187500053710938, 0.6898317534223188, 0.6618705083794834,
+                0.6348195374209455, 0.6086351017498701, 0.5832762206018658,
+                0.5587044572276223, 0.5348837244997295, 0.5117801080759505,
+                0.48936170529651424, 0.46759847820604516, 0.4464621192761633,
+                0.42592592856652933, 0.4059647012034677, 0.3865546241790834,
+                0.3676731815824261, 0.34929906746973005, 0.3314121056648591,
+                0.31399317585528075, 0.2970241454144613, 0.28048780643961924,
+                0.2643678175452504, 0.2486486499985392, 0.23331553782343795,
+                0.21835443153641232, 0.20375195520916023, 0.18949536658248856,
+                0.17557251998135315, 0.1619718318042056, 0.14868224838055033,
+                0.13569321600925854, 0.122994653006949, 0.11057692361085425,
+                0.09843081359419292, 0.08654750746436402, 0.0749185671253807,
+                0.06353591189600438, 0.05239179978414388, 0.04147880992632613,
+                0.03078982610853953, 0.020318021291547472,
+                0.010056843069963017, 0.0]
+
+
+def snap2redshift(snapnum, simname):
+    """
+    Convert a snapshot number to redshift.
+
+    Parameters
+    ----------
+    snapnum : int
+        Snapshot number.
+    simname : str
+        Simulation name.
+
+    Returns
+    -------
+    float
+    """
+    if "csiborg2_" in simname:
+        return CB2_REDSHIFT[snapnum]
+    else:
+        raise ValueError(f"Unknown simname: {simname}")
+
 
 def simname2boxsize(simname):
     """
@@ -65,6 +121,7 @@ def simname2Omega_m(simname):
     d = {"csiborg1": 0.307,
          "csiborg2_main": 0.3111,
          "csiborg2_random": 0.3111,
+         "csiborg2_varysmall": 0.3111,
          "borg1": 0.307,
          "Carrick2015": 0.3,
          }
diff --git a/csiborgtools/read/catalogue.py b/csiborgtools/read/catalogue.py
index 14733b1..6490fda 100644
--- a/csiborgtools/read/catalogue.py
+++ b/csiborgtools/read/catalogue.py
@@ -23,6 +23,7 @@ from functools import lru_cache
 from gc import collect
 from itertools import product
 from math import floor
+from astropy.cosmology import FlatLambdaCDM
 
 import numpy
 from h5py import File
@@ -30,7 +31,8 @@ from sklearn.neighbors import NearestNeighbors
 
 from ..params import paths_glamdring
 from ..utils import (cartesian_to_radec, great_circle_distance, number_counts,
-                     periodic_distance_two_points, real2redshift)
+                     periodic_distance_two_points, real2redshift,
+                     radec_to_galactic)
 from .paths import Paths
 from .snapshot import is_instance_of_base_snapshot_subclass
 
@@ -46,6 +48,7 @@ class BaseCatalogue(ABC):
     """
     _properties = ["cartesian_pos",
                    "spherical_pos",
+                   "galactic_pos",
                    "dist",
                    "cartesian_redshiftspace_pos",
                    "spherical_redshiftspace_pos",
@@ -596,6 +599,9 @@ class BaseCatalogue(ABC):
             elif key == "spherical_pos":
                 out = cartesian_to_radec(
                     self["__cartesian_pos"] - self.observer_location)
+            elif key == "galactic_pos":
+                out = self["__spherical_pos"]
+                out[:, 1], out[:, 2] = radec_to_galactic(out[:, 1], out[:, 2])
             elif key == "dist":
                 out = numpy.linalg.norm(
                     self["__cartesian_pos"] - self.observer_location, axis=1)
@@ -985,7 +991,6 @@ class CSiBORG2MergerTreeReader:
         group2treeid : dict
             Dictionary with group number as key and tree ID as value.
         """
-        print("Creating group to tree ID mapping...")
         with File(self.paths.trees(self.nsim, self.simname), 'r') as f:
             groupnr = f["TreeHalos/GroupNr"][:]
             snapnum = f["TreeHalos/SnapNum"][:]
@@ -1020,6 +1025,11 @@ class CSiBORG2MergerTreeReader:
                         "TreeNextProgenitor",
                         "TreeProgenitor",
                         "SubhaloMass",
+                        "Group_M_Crit200",
+                        "SubhaloSpin",
+                        "SubhaloVmax",
+                        "SubhaloHalfmassRad",
+                        "SubhaloVmaxRad",
                         ]
 
         with File(self.paths.trees(self.nsim, self.simname), 'r') as f:
@@ -1082,44 +1092,58 @@ class CSiBORG2MergerTreeReader:
 
         n = first_fof  # Index of the current main progenitor
 
-        time, redshift, main_progenitor_mass = [], [], []
-        max_next_progenitor_mass, total_next_progenitor_mass = [], []
+        snap_num, redshift, main_progenitor_mass, group_m200c = [], [], [], []
+        main_progenitor_vmax, main_progenitor_spin = [], []
+        main_progenitor_vmaxrad, main_progenitor_halfmassrad = [], []
         while True:
-            # First off attempt to find the next progenitors of the current
-            # halo. Deal with the main progenitor later.
-            next_prog = tree["TreeNextProgenitor"][n]
-            if next_prog != -1:
-                minors = []
-                while True:
-                    minors.append(tree["SubhaloMass"][next_prog])
+            # NOTE: 'Minors' are ignored. This is only relevant if we wanted
+            # to find the other subhaloes in the current FoF group.
 
-                    next_prog = tree["TreeNextProgenitor"][next_prog]
+            # # First off attempt to find the next progenitors of the current
+            # # halo. Deal with the main progenitor later.
+            # next_prog = tree["TreeNextProgenitor"][n]
+            # if next_prog != -1:
+            #     minors = []
+            #     while True:
+            #         minors.append(tree["SubhaloMass"][next_prog])
 
-                    if next_prog == -1:
-                        break
-            else:
-                # Fiducially set it to zero.
-                minors = [0]
+            #         next_prog = tree["TreeNextProgenitor"][next_prog]
+
+            #         if next_prog == -1:
+            #             break
+            # else:
+            #     # Fiducially set it to zero.
+            #     minors = [0]
 
             # Update data with information from the current main progenitor.
             major = tree["SubhaloMass"][n]
             main_progenitor_mass.append(major)
-            max_next_progenitor_mass.append(max(minors))
-            total_next_progenitor_mass.append(sum(minors))
+            group_m200c.append(tree["Group_M_Crit200"][n])
+            main_progenitor_vmax.append(tree["SubhaloVmax"][n])
+            main_progenitor_spin.append(tree["SubhaloSpin"][n])
+            main_progenitor_vmaxrad.append(tree["SubhaloVmaxRad"][n])
+            main_progenitor_halfmassrad.append(tree["SubhaloHalfmassRad"][n])
+            snap_num.append(tree["SnapNum"][n])
             redshift.append(tree["Redshift"][tree["SnapNum"][n]])
-            time.append(tree["Time"][tree["SnapNum"][n]])
 
-            # Update n to the next main progenitor.
+            # Update `n` to the next main progenitor.
             n = tree["TreeMainProgenitor"][n]
 
             if n == -1:
                 break
 
-        return {"Time": numpy.array(time),
+        # For calculating age of the Universe at each redshift.
+        cosmo = FlatLambdaCDM(H0=67.66, Om0=0.3111)
+
+        return {"SnapNum": numpy.array(snap_num, dtype=numpy.int32),
+                "Age": numpy.array(cosmo.age(redshift).value),
                 "Redshift": numpy.array(redshift),
+                "Group_M_Crit200": numpy.array(group_m200c) * 1e10,
                 "MainProgenitorMass": numpy.array(main_progenitor_mass) * 1e10,
-                "MaxNextProgenitorMass": numpy.array(max_next_progenitor_mass) * 1e10,  # noqa
-                "TotalNextProgenitorMass": numpy.array(total_next_progenitor_mass) * 1e10,  # noqa
+                "MainProgenitorVmax": numpy.array(main_progenitor_vmax),
+                "MainProgenitorSpin": numpy.array(main_progenitor_spin),
+                "MainProgenitorVmaxRad": numpy.array(main_progenitor_vmaxrad),
+                "MainProgenitorHalfmassRad": numpy.array(main_progenitor_halfmassrad),      # noqa
                 }
 
 
@@ -1148,7 +1172,6 @@ class CSiBORG2SUBFINDCatalogue(BaseCatalogue):
     """
     def __init__(self, nsim, nsnap, kind, paths=None,
                  bounds=None, flip_xz=True, cache_maxsize=64):
-        # TODO: finish all this!
         super().__init__()
         super().init_with_snapshot(
             f"csiborg2_{kind}", nsim, nsnap, paths, None, bounds,
@@ -1156,7 +1179,8 @@ class CSiBORG2SUBFINDCatalogue(BaseCatalogue):
             cache_maxsize)
 
         self._custom_keys = ["SubhaloSpin", "SubhaloVelDisp", "Central",
-                             "ParentMass"]
+                             "SubhaloVmax", "SubhaloVmaxRad",
+                             "SubhaloHalfmassRad", "ParentMass"]
 
     @property
     def kind(self):
@@ -1235,6 +1259,18 @@ class CSiBORG2SUBFINDCatalogue(BaseCatalogue):
     def SubhaloVelDisp(self):
         return self._read_subfind_catalogue("SubhaloVelDisp")
 
+    @property
+    def SubhaloVmax(self):
+        return self._read_subfind_catalogue("SubhaloVmax")
+
+    @property
+    def SubhaloVmaxRad(self):
+        return self._read_subfind_catalogue("SubhaloVmaxRad")
+
+    @property
+    def SubhaloHalfmassRad(self):
+        return self._read_subfind_catalogue("SubhaloHalfmassRad")
+
     @property
     def SubhaloContamination(self):
         mass_type = self._read_subfind_catalogue("SubhaloMassType")
diff --git a/csiborgtools/read/paths.py b/csiborgtools/read/paths.py
index 1e6f7b3..bf4a8cc 100644
--- a/csiborgtools/read/paths.py
+++ b/csiborgtools/read/paths.py
@@ -301,7 +301,7 @@ class Paths:
             return join(self.csiborg2_main_srcdir, f"chain_{nsim}", "output",
                         "trees.hdf5")
         elif simname == "csiborg2_random":
-            return join(self.csiborg2_ranodm_srcdir, f"chain_{nsim}", "output",
+            return join(self.csiborg2_random_srcdir, f"chain_{nsim}", "output",
                         "trees.hdf5")
         elif simname == "csiborg2_varysmall":
             return join(self.csiborg2_varysmall_srcdir,
@@ -351,6 +351,26 @@ class Paths:
             fname = fname.replace("overlap", "overlap_smoothed")
         return join(fdir, fname)
 
+    def random_mah(self, simname, nsim):
+        """
+        Path to the files containing MAHs from random simulations.
+
+        Parameters
+        ----------
+        simname : str
+            Simulation name.
+        nsim0 : int
+            IC realisation index of the simulation.
+
+        Returns
+        -------
+        str
+        """
+        fdir = join(self.postdir, "random_mah")
+        try_create_directory(fdir)
+
+        return join(fdir, f"random_mah_{simname}_{nsim}.hdf5")
+
     def match_max(self, simname, nsim0, nsimx, min_logmass, mult):
         """
         Path to the files containing matching based on [1].
diff --git a/csiborgtools/summary/overlap_summary.py b/csiborgtools/summary/overlap_summary.py
index 0898ed3..9eb9358 100644
--- a/csiborgtools/summary/overlap_summary.py
+++ b/csiborgtools/summary/overlap_summary.py
@@ -82,9 +82,9 @@ class PairOverlap:
         self._cat0 = cat0
         self._catx = catx
         self._paths = cat0.paths
-        self.load(cat0, catx, min_logmass, maxdist)
+        self._load(cat0, catx, min_logmass, maxdist)
 
-    def load(self, cat0, catx, paths, min_logmass, maxdist=None):
+    def _load(self, cat0, catx, min_logmass, maxdist=None):
         r"""
         Load overlap calculation results. Matches the results back to the two
         catalogues in question.
@@ -107,14 +107,13 @@ class PairOverlap:
         """
         nsim0 = cat0.nsim
         nsimx = catx.nsim
-        paths = cat0.paths
 
         # We first load in the output files. We need to find the right
         # combination of the reference and cross simulation.
-        fname = paths.overlap(cat0.simname, nsim0, nsimx, min_logmass,
-                              smoothed=False)
-        fname_inv = paths.overlap(cat0.simname, nsimx, nsim0, min_logmass,
-                                  smoothed=False)
+        fname = self._paths.overlap(cat0.simname, nsim0, nsimx, min_logmass,
+                                    smoothed=False)
+        fname_inv = self._paths.overlap(cat0.simname, nsimx, nsim0,
+                                        min_logmass, smoothed=False)
         if isfile(fname):
             data_ngp = numpy.load(fname, allow_pickle=True)
             to_invert = False
@@ -125,8 +124,8 @@ class PairOverlap:
         else:
             raise FileNotFoundError(f"No file found for {nsim0} and {nsimx}.")
 
-        fname_smooth = paths.overlap(cat0.simname, cat0.nsim, catx.nsim,
-                                     min_logmass, smoothed=True)
+        fname_smooth = self._paths.overlap(cat0.simname, cat0.nsim, catx.nsim,
+                                           min_logmass, smoothed=True)
         data_smooth = numpy.load(fname_smooth, allow_pickle=True)
 
         # Create mapping from halo indices to array positions in the catalogue.
@@ -609,6 +608,7 @@ class NPairsOverlap:
 
         self._pairs = pairs
 
+    @lru_cache()
     def max_overlap(self, min_overlap, from_smoothed, verbose=True):
         """
         Calculate maximum overlap of each halo in the reference simulation with
@@ -644,6 +644,7 @@ class NPairsOverlap:
                                        for y_ in pair.overlap(from_smoothed)])
         return numpy.vstack(out).T
 
+    @lru_cache()
     def max_overlap_key(self, key, min_overlap, from_smoothed, verbose=True):
         """
         Calculate maximum overlap mass of each halo in the reference
@@ -674,6 +675,7 @@ class NPairsOverlap:
 
         return numpy.vstack(out).T
 
+    @lru_cache()
     def summed_overlap(self, from_smoothed, verbose=True):
         """
         Calculate summed overlap of each halo in the reference simulation with
diff --git a/csiborgtools/utils.py b/csiborgtools/utils.py
index c606a44..157991a 100644
--- a/csiborgtools/utils.py
+++ b/csiborgtools/utils.py
@@ -19,6 +19,8 @@ from copy import deepcopy
 from datetime import datetime
 
 import numpy as np
+from astropy import units as u
+from astropy.coordinates import SkyCoord
 from numba import jit
 from numpyro.infer import util
 from scipy.stats import multivariate_normal
@@ -154,6 +156,24 @@ def radec_to_cartesian(X):
         ]).T
 
 
+def radec_to_galactic(ra, dec):
+    """
+    Convert right ascension and declination to galactic coordinates (all in
+    degrees.)
+
+    Parameters
+    ----------
+    ra, dec : float or 1-dimensional array
+        Right ascension and declination in degrees.
+
+    Returns
+    -------
+    l, b : float or 1-dimensional array
+    """
+    c = SkyCoord(ra=ra*u.degree, dec=dec*u.degree, frame='icrs')
+    return c.galactic.l.degree, c.galactic.b.degree
+
+
 @jit(nopython=True, fastmath=True, boundscheck=False)
 def great_circle_distance(x1, x2):
     """
diff --git a/notebooks/MAH/mah.ipynb b/notebooks/MAH/mah.ipynb
new file mode 100644
index 0000000..6e9927d
--- /dev/null
+++ b/notebooks/MAH/mah.ipynb
@@ -0,0 +1,899 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Constraining MAH in $\\texttt{BORG}$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.cm import ScalarMappable\n",
+    "from matplotlib.colors import Normalize\n",
+    "from cache_to_disk import delete_disk_caches_for_function\n",
+    "from scipy.stats import norm\n",
+    "from scipy.signal import savgol_filter\n",
+    "\n",
+    "from mah import *\n",
+    "\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## $\\texttt{MDPL2}$ to $\\texttt{CB2}$ MAH comparison"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "min_age = 2.8\n",
+    "pmin, pmax = 5, 95\n",
+    "bounds = [14.5, 14.7]\n",
+    "mass_norm = 10**np.mean(bounds)\n",
+    "alphas = {3: 0.25, 2: 0.5, 1: 0.75}\n",
+    "xrange_cb2, random_mah_cb2 = extract_mah(\"csiborg2_random\", bounds, \"MainProgenitorMass\", min_age=2.8)\n",
+    "xrange_mdpl2, random_mah_mdpl2 = extract_mah_mdpl2(bounds, min_age=2.8)\n",
+    "\n",
+    "random_mah_cb2 /= random_mah_cb2[:, -1].reshape(-1, 1)\n",
+    "random_mah_mdpl2 /= random_mah_mdpl2[:, -1].reshape(-1, 1)\n",
+    "# random_mah_cb2 /= mass_norm\n",
+    "# random_mah_mdpl2 /= mass_norm\n",
+    "\n",
+    "\n",
+    "\n",
+    "plt.figure()\n",
+    "cols = plt.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]\n",
+    "for n in [3, 2, 1]:\n",
+    "    pmin, pmax = norm.cdf(-n) * 100 , norm.cdf(n) * 100\n",
+    "    ylow, yhigh = np.percentile(random_mah_mdpl2, [pmin, pmax], axis=0)\n",
+    "    plt.fill_between(xrange_mdpl2, ylow, yhigh, alpha=alphas[n], color=cols[0],\n",
+    "                     facecolor=cols[0], label=f\"MDPL2 Random ({len(random_mah_mdpl2)})\" if n == 1 else None)\n",
+    "\n",
+    "\n",
+    "pmin, pmax = norm.cdf(-1) * 100 , norm.cdf(1) * 100\n",
+    "ylow, yhigh = np.percentile(random_mah_cb2, [pmin, pmax], axis=0)\n",
+    "plt.plot(xrange_cb2, ylow, c=cols[1], ls=\"--\", label=rf\"$1\\sigma$ CB2 Random ({len(random_mah_cb2)})\", zorder=1)\n",
+    "plt.plot(xrange_cb2, yhigh, c=cols[1], ls=\"--\", zorder=1)\n",
+    "\n",
+    "pmin, pmax = norm.cdf(-2) * 100 , norm.cdf(2) * 100\n",
+    "ylow, yhigh = np.percentile(random_mah_cb2, [pmin, pmax], axis=0)\n",
+    "plt.plot(xrange_cb2, ylow, c=cols[1], ls=\"dotted\", label=rf\"$2\\sigma$ CB2 Random ({len(random_mah_cb2)})\", zorder=1)\n",
+    "plt.plot(xrange_cb2, yhigh, c=cols[1], ls=\"dotted\", zorder=1)\n",
+    "\n",
+    "# plt.fill_between(xrange_mdpl2,\n",
+    "#                  np.ones_like(xrange_mdpl2) * 10**bounds[0] / mass_norm,\n",
+    "#                  np.ones_like(xrange_mdpl2) * 10**bounds[1] / mass_norm, alpha=0.25,\n",
+    "#                  label=r\"$z = 0$ selection\", color=\"gray\", zorder=0,\n",
+    "#                  hatch=\"///\")\n",
+    "\n",
+    "# They appear to be plotting the min-max\n",
+    "plt.plot(RANDOM_MAH_Sorce_Virgo_UPPER[:, 0],\n",
+    "         RANDOM_MAH_Sorce_Virgo_UPPER[:, 1], c=\"red\", ls=\"--\", label=\"Sorce+2016 Virgo randoms\")\n",
+    "plt.plot(RANDOM_MAH_SORCE_Virgo_LOWER[:, 0],\n",
+    "         RANDOM_MAH_SORCE_Virgo_LOWER[:, 1], c=\"red\", ls=\"--\")\n",
+    "\n",
+    "\n",
+    "plt.yscale(\"log\")\n",
+    "plt.legend(loc=\"lower right\")\n",
+    "plt.xlabel(r\"$\\mathrm{Age} ~ [\\mathrm{Gyr}]$\")\n",
+    "plt.ylabel(r\"$M_{\\rm main}(t) / M_{\\rm main}(z = 0)$\")\n",
+    "plt.xlim(xrange_mdpl2.min(), xrange_mdpl2.max())\n",
+    "plt.ylim(1e-2, 1.01)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(\"../../plots/MAH_MDPL2_CB2_comparison_VIRGO.png\", dpi=450)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## MAH of specific clusters in $\\texttt{CB}$\n",
+    "\n",
+    "### Summary plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nsim0 = 17417\n",
+    "simname = \"csiborg2_main\"\n",
+    "min_logmass = 12.25\n",
+    "delete_cache = False\n",
+    "\n",
+    "if delete_cache:\n",
+    "    delete_disk_caches_for_function(\"load_data\")\n",
+    "cat0, catxs, merger_trees, overlaps = load_data(nsim0, simname, min_logmass)\n",
+    "nsimxs = [cat.nsim for cat in catxs]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "catx = cat0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pos = catx[\"spherical_pos\"]\n",
+    "\n",
+    "ra, dec = pos[:, 1], pos[:, 2]\n",
+    "l, b = csiborgtools.flow.radec_to_galactic(pos[:, 1], pos[:, 2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "70.92198581560284"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "50 / 0.705"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "79.66499999999999"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "113 * 0.705"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mask = (pos[:, 0] > 83.5) & (catx[\"totmass\"] > 1e14) & (pos[:, 0] < 84) & (catx[\"totmass\"] < 4e14)\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.scatter(ra[mask], dec[mask], s=20, c=np.log10(catx[\"totmass\"][mask]))\n",
+    "plt.axvline(csiborgtools.hms_to_degrees(11, 44), zorder=0, ls=\"--\", color=\"red\", alpha=0.5)\n",
+    "plt.axhline(csiborgtools.dms_to_degrees(19, 45), zorder=0, ls=\"--\", color=\"red\", alpha=0.5)\n",
+    "# plt.axvline(285, zorder=0)\n",
+    "# plt.axhline(74, zorder=0)\n",
+    "plt.xlim(0, 360,)\n",
+    "plt.ylim(-90, 90)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([1.1764427e+14], dtype=float32),\n",
+       " array([83.93529855]),\n",
+       " array([184.62410722]),\n",
+       " array([315], dtype=int32))"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cat0[\"totmass\"][mask], cat0[\"dist\"][mask], pos[:, 1][mask], cat0[\"index\"][mask]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "24"
+      ]
+     },
+     "execution_count": 120,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mask = (cat0[\"dist\"] < 100) & (cat0[\"totmass\"] > 2e14)\n",
+    "mask.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([1.3149970e+15, 4.9394832e+14, 3.9707187e+14, 3.6969589e+14,\n",
+       "        3.5838989e+14, 3.4054645e+14, 3.2880589e+14, 3.2858516e+14,\n",
+       "        3.1561675e+14, 3.0209864e+14, 2.9957615e+14, 2.9370144e+14,\n",
+       "        2.8257113e+14, 2.7131389e+14, 2.5883651e+14, 2.5817466e+14,\n",
+       "        2.3839617e+14, 2.3532438e+14, 2.2714606e+14, 2.2489048e+14,\n",
+       "        2.1780584e+14, 2.1020473e+14, 2.1004685e+14, 2.0899980e+14],\n",
+       "       dtype=float32),\n",
+       " array([  2,  23,  31,  38,  39,  44,  49,  50,  56,  62,  65,  66,  69,\n",
+       "         75,  82,  83,  97, 100, 108, 109, 114, 122, 124, 125], dtype=int32))"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cat0[\"totmass\"][mask], cat0[\"index\"][mask]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.25819606, 0.43737528, 0.51124454, 0.46384627, 0.26452401,\n",
+       "       0.32776746, 0.3836844 , 0.56561893, 0.22747411, 0.57970226,\n",
+       "       0.19699863, 0.55168611, 0.38103878, 0.19168761, 0.34359506,\n",
+       "       0.39914343, 0.74848348, 0.65658271, 0.61172086])"
+      ]
+     },
+     "execution_count": 133,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "overlaps.max_overlap(0, True)[108]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Cross main progenitors:   0%|          | 0/19 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Cross main progenitors: 100%|██████████| 19/19 [00:06<00:00,  2.98it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Appending main progenitor for 17417.\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = extract_main_progenitor_maxoverlap(108, overlaps, merger_trees)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mean = 14.2311, std = 0.1513\n",
+      "Found 2629 haloes in CB2 random.\n",
+      "Found 18572 haloes in MDPL2.\n"
+     ]
+    }
+   ],
+   "source": [
+    "logmp = np.log10(np.array([data[nsim][\"MainProgenitorMass\"][0] for nsim in data.keys()]))\n",
+    "\n",
+    "mean, std = np.mean(logmp), np.std(logmp)\n",
+    "print(f\"mean = {mean:.4f}, std = {std:.4f}\")\n",
+    "\n",
+    "bounds = [mean - 1 * std, mean + 1 * std]\n",
+    "\n",
+    "xrange_cb2, random_mah_cb2 = extract_mah(\"csiborg2_random\", bounds, \"MainProgenitorMass\", min_age=2.8)\n",
+    "xrange_mdpl2, random_mah_mdpl2 = extract_mah_mdpl2(bounds, min_age=2.8)\n",
+    "\n",
+    "print(f\"Found {len(random_mah_cb2)} haloes in CB2 random.\")\n",
+    "print(f\"Found {len(random_mah_mdpl2)} haloes in MDPL2.\")\n",
+    "\n",
+    "age, mah = summarize_extracted_mah(simname, data, nsim0, nsimxs, \"MainProgenitorMass\", min_age=2.8)\n",
+    "\n",
+    "\n",
+    "random_mah_cb2 /= random_mah_cb2[:, -1].reshape(-1, 1)\n",
+    "random_mah_mdpl2 /= random_mah_mdpl2[:, -1].reshape(-1, 1)\n",
+    "mah /= mah[:, -1].reshape(-1, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure()\n",
+    "# plt.hist(logmp, bins=\"auto\")\n",
+    "plt.scatter(np.arange(len(logmp)), logmp)\n",
+    "plt.xlabel(\"Simulation index\")\n",
+    "plt.ylabel(r\"$\\log M ~ [M_\\odot / h]$\")\n",
+    "# plt.axhline(mean, c=\"red\", ls=\"--\")\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(\"../../plots/COMA_MASS.png\", dpi=450)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pmin, pmax = 0.15, 100 - 0.15\n",
+    "# norm_mass = 10**np.nanmean(np.log10(mah[:, -1]))\n",
+    "norm_mass = 1\n",
+    "alphas = {3: 0.25, 2: 0.5, 1: 0.75}\n",
+    "\n",
+    "\n",
+    "plt.figure(figsize=(6, 4))\n",
+    "cols = plt.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]\n",
+    "lw = plt.rcParams[\"lines.linewidth\"]\n",
+    "\n",
+    "for n in [3, 2, 1]:\n",
+    "    pmin, pmax = norm.cdf(-n) * 100 , norm.cdf(n) * 100\n",
+    "    ylow, yhigh = np.percentile(random_mah_mdpl2 / norm_mass, [pmin, pmax], axis=0)\n",
+    "\n",
+    "    ylow = savgol_filter(ylow, 5, 1, mode=\"interp\")\n",
+    "    yhigh = savgol_filter(yhigh, 5, 1, mode=\"interp\")\n",
+    "    plt.fill_between(xrange_mdpl2, ylow, yhigh, alpha=alphas[n], color=cols[0],\n",
+    "                     facecolor=cols[0], zorder=0, edgecolor=\"blue\",\n",
+    "                     label=f\"MDPL2 Random ({len(random_mah_mdpl2)})\" if n == 1 else None)\n",
+    "    \n",
+    "    # pmin, pmax = norm.cdf(-n) * 100 , norm.cdf(n) * 100\n",
+    "    # ylow, yhigh = np.percentile(random_mah_cb2 / norm_mass, [pmin, pmax], axis=0)\n",
+    "    # ylow = savgol_filter(ylow, 5, 1, mode=\"interp\")\n",
+    "    # yhigh = savgol_filter(yhigh, 5, 1, mode=\"interp\")\n",
+    "    # plt.fill_between(xrange_cb2, ylow, yhigh, alpha=alphas[n], color=cols[0],\n",
+    "    #                  facecolor=cols[0], zorder=0, edgecolor=\"blue\",\n",
+    "    #                  label=f\"CB2 Random ({len(random_mah_cb2)})\" if n == 1 else None)\n",
+    "\n",
+    "    if n == 3:\n",
+    "        continue\n",
+    "\n",
+    "    ylow, yhigh = np.percentile(mah / norm_mass, [pmin, pmax], axis=0)\n",
+    "    ylow = savgol_filter(ylow, 5, 1, mode=\"interp\")\n",
+    "    yhigh = savgol_filter(yhigh, 5, 1, mode=\"interp\")\n",
+    "    plt.fill_between(age, ylow, yhigh, alpha=alphas[n] / 2,\n",
+    "                     label=\"Coma\" if n == 1 else None, zorder=1,\n",
+    "                     color=cols[1], edgecolor=\"red\")\n",
+    "\n",
+    "# pmin, pmax = norm.cdf(-1) * 100 , norm.cdf(1) * 100\n",
+    "# ylow, yhigh = np.percentile(random_mah_cb2 / norm_mass, [pmin, pmax], axis=0)\n",
+    "# plt.plot(xrange_cb2, ylow, c=cols[2], ls=\"--\", lw=1.5 * lw,\n",
+    "#          label=rf\"$1\\sigma$ CB2 Random ({len(random_mah_cb2)})\")\n",
+    "# plt.plot(xrange_cb2, yhigh, c=cols[2], ls=\"--\", lw=1.5 * lw)\n",
+    "\n",
+    "# for i in range(len(mah)):\n",
+    "    # plt.plot(age, mah[i] / norm_mass, c=\"black\", lw=lw/4, alpha=0.5, zorder=4)\n",
+    "\n",
+    "# plt.plot(RANDOM_MAH_Sorce_Virgo_UPPER[:, 0],\n",
+    "        #  RANDOM_MAH_Sorce_Virgo_UPPER[:, 1], c=\"red\", ls=\"--\", label=\"Sorce+2016 Virgo randoms\")\n",
+    "# plt.plot(RANDOM_MAH_SORCE_Virgo_LOWER[:, 0],\n",
+    "        #  RANDOM_MAH_SORCE_Virgo_LOWER[:, 1], c=\"red\", ls=\"--\")\n",
+    "\n",
+    "\n",
+    "\n",
+    "plt.yscale(\"log\")\n",
+    "plt.legend(loc=\"lower right\")\n",
+    "\n",
+    "plt.xlabel(r\"$\\mathrm{Age} ~ [\\mathrm{Gyr}]$\")\n",
+    "plt.ylabel(r\"$M_{\\rm main}(t) / M_{\\rm main}(z = 0)$\")\n",
+    "plt.xlim(xrange_mdpl2.min(), xrange_mdpl2.max())\n",
+    "plt.ylim(0.01, 1)\n",
+    "plt.tight_layout()\n",
+    "# plt.savefig(\"../../plots/coma_MAIN.png\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_3803914/1215582327.py:31: RuntimeWarning: All-NaN slice encountered\n",
+      "  mu = np.nanmedian(random_mah, axis=0)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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88grupcFgMBieS9d1kuUIkeIE8dIMkeIME7kJQoUpskoUXShQUiygOhAVB3LZhqlowpG1IGWyiEoehz2F05fB7S9gr1AQJZ1C1kQ+baOQdlPKVlKYcTPc7yIXs5Oe1sjNFMnOFMhHiqiajupS0VaraB0C+hod3SuAWUYpWNHz0ByJszg5TZWSxlfK4MrmkPpBTgjICRDTOkJeRyopmJUSlnIJa7GMtVjGki9jyquYCieemlYBfg94AcLhMzqAM7y0XlMB3Mc//nHe9773HXeesi1bthw3TZPBYDAYXh66rpNTUyTLUeKlGcLFcaazI8zkR0koM8i6lUTOQihnIlUwQ96MnLAjT0pU5KI0tk3h8Izg8OZxB0o46xTyGZls0kou6SCf9BAZbGD6oJ9ywY5SMgECpaxCbrpAdqZAeqZAIZtCldNoQRulehv5NwbQW8DuKVCZTVHTm6b+QJr6B1LURWLUpCIE4glc0TzC88zbULRJlG0yik1EsYmULBK5gIWw2UnWbCVrslCwmig7JFS3gFCtYwmWSZtUHtwc4777osyEyyxeYudQU9MZPVmr0YT60npNBXAXXnghjz766Cu9GwaDwfC6p+s6sdIUw5lu9o5tIaQNU5CSiJoJqWSFrJn8tEpmqkx2WiU5pdO3uBa5VKa+bxyTomIyydS3F+k4d4ZgY4LJvlryyToSwx6mD7ooZB3oqgSAUlTJh4vkwkVykQKZZJpSSUcBVIeJYouN4koXQoMPu1fHIRZZODVN2/gRFj4RZsEPw9RORLEUy0cdR8kukaizMdHhIVxTR7jGRaLKgTNQALfOlM2L1VamYDOTspiZzngJRX0kUk5SKQeKKuFw5wn6ErRXzNDgmKE8UyQ9LDIzbCI/VWbqyTiPPjpC/qmaObPdjuhbi8qZPdu+iog6D0dwalO5v/68agK4xx9/nG9/+9vs2rWLqakp/vCHP3DNNdcctcwdd9zBt7/9baanp1m+fDn/+Z//ybp16563bEEQ2LBhA6Io8olPfOKMTxBsMBgMr0ahwhg96Z2MZrsZy/UgamYK4wIT++NkR3QKYR20MlBGlmVsNhs2mwOXzcaRtQGqNThLKSEsWowgqiw6fxuuiigThxaxddP55GNQTJQpJMtkknmyqRTFvIYigGKWKNfZKC+wIp7vwFoBVpeKZFWQrArVsQyd+/tZ/MgUC3umaO0LYS7NhgaKLDLZ5mHXxQ2E6t1obQLZJjM9gRpiZg+CKiOURHRFwiSWubR9O3vyLaRLNmbCfnJZE4lJG5GIF48zS2MgQkv9CB4tQWm8wOiBHCN9SfYMRhkfjlMqHj8k8QWDrD5rPZ5gJYHKymPyMBsMz/aqCeCy2SzLly/nfe97H29729uOef/Xv/41t9xyC3fddRdnnXUWt912G5dffjk9PT1UVVWdtOxNmzZRV1fH1NQUl156KV1dXSxbtuylOhTD8xAE4bgBusFgOLPous5UYZDu1HYOJbdR1oosdZ/FIsvZaE/W8Mj9T5BMJmlpaaGm0gmVx5ahAo8ELLgUnXXJEug6hXiWZVduJjsFf/yHTpSySNkeIeexkPdbKNQ5sK6TcAR1LC4Vm01Bsytoso4vkqZ9MEzdnhhVM3FqZuI094QJTOXmtjnV6GbHZc0MtVcz1lRDOFCFqJkRixKepkmsgSQ7Di0jHZKYbcnV0QWwlOBNTbvYlFtELmNDiplpVIo0eSYpZ0bZ3RPm4P4MW8NFQuESqfTx+7pJkojNbUN2uNAdbkSPF8+qlXhaW0kBiVyevHjmNxvq8zSIQTcGMRzXqyaAu/LKK7nyyitP+P53vvMdPvCBD/B3f/d3ANx1113ce++9/PCHP+Qzn/nMScuuq6sDoKamhquuuordu3efNIArFosUi8W5f6dSqdM5lDPCe9/73rm+grIsU19fzzve8Q6++tWvGrP9GwyGEyppRQYz++lJ7aAnvQub5GCRay2XOP+WwrTAeM84v3j0zwwPD1NdXU1nZ+cJ00qpwMMBCx5FZ22yhAAMPTjEVV89TC7hYXfv2ST/0UTSoeE2lXBaytjtCpDDlpOwZkSaxmJ0HhqgdXCcxuEwlRPpo7aR9lgZW+hj+4WLGW+sYzLYgFL2IuVmb3/pIsxkdEJunca2UQLuOPfvW0kpYUMq6dTYMjR7ErQE4phaMwzKDq53jSCXMmyfzPLgxhx3PZIhEi0ec3wAksWCJRjE2tiAt66eqvo6gj4fbsCp6dg1DZuuIwFkZ4PMsbExKioq5uXzMrx2vWoCuJMplUrs2rWLz372s3OviaLIpZdeytatW0+6bjabRdM0XC4XmUyGhx9+mHe+850nXefWW2/ln//5n+dl31/NrrjiCn70ox9RLpfZtWsXN954I4Ig8M1vfvOV3jWDwfAqUNZKxEszREtTRIuTDGUPMprtpsbSRmW5lc7Q1YSGEjx85BDh8KOkUik0TcPpdNLZ2YkkSScsW2E2ePMpGmuSZQR0PJXDvPW2vUzmGrnPshDlvCy2pImarIA9LWHJW3GqWdom+mnvHWDR3jECk1kAMnYLI41Bdl+9gLEVLsJ1XtJyEJJ+lKSFmCgSsQgkdIGcDUpuUEUBvSgiZEQuqOnhYv8gkdEaPtm2mwXrotS7UoSzTgYTXv44LdP9ywkiO+J8fyDE5IRy1PHIZjOVCxbgWbSQYmUlHqeLFoeDOknCpWkclVehfPS6r1XGIIaX1hkRwEUiEVRVJRgMHvV6MBjkyJEjc/++9NJL2bdvH9lslvr6en7zm98QDAZ561vfCoCqqnzgAx9g7dq1J93eZz/7WW655Za5f6dSKRoaGubxiF4dLBYL1dXVADQ0NHDppZfy4IMP8s1vfpNoNMrNN9/M448/Tjwep62tjc997nNcf/31c+tfeOGFLFu2DKvVyve//33MZjMf/vCH+cpXvjK3TF9fHzfddBNPPvkkra2t/Md//Mcx+3HgwAE+/vGPs3XrVux2O9deey3f+c535kYNv/e97yWRSLBu3Tr+4z/+g2KxyC233MLnPvc5PvvZz/KDH/wAu93O1772tbkaWoPBcOpUXSVWnGKmOEqoMMpMYZRQcZRUOYpLDGBXPYh5G0LMgbBvCTuGJkilBigUCoiiiNPpxO12U1NTgyw//21FATZWWKgoaaxKlXFXRGhZtwfVXOChdAeHIs1Yw1ZWzqiYnGVkR5TO7kN0be2nZW8EWdFQRYGepka2XLGWnguqGVhvpZi1UZxwk0nZicfNJBApaSLYQJB0ZHRMqo6tpONXVTqcCc5pG2Lt0n6C1jyTET/Tpiyh8Qoe2tfOyIzM9ugejmw5wMSe8NEHIQjYgkH8Lc3UL15CXXMzDknCpmlUqCpzvde0E08Z8lqn6iKqPg+DGIyM7cd1RgRwp+qhhx467uv79u07rXIsFgsWi2U+dumMcfDgQbZs2UJTUxMwm0B99erVfPrTn8btdnPvvfdyww030NbWdtTAkZ/85CfccsstbN++na1bt/Le976Xc889l8suuwxN03jb295GMBhk+/btJJNJPvGJTxy13Ww2y+WXX8769evZsWMHoVCI97///dx88838+Mc/nlvu4Ycfpr6+nscff5zNmzdz0003sWXLFi644AK2b9/Or3/9az70oQ9x2WWXUV9f/3KcMoPhjJUpJ+hJ72Qi389kfoBwcRyPqYIqayNBSyPtplXktrkY29nPSDxJNhulXC6j6zpWqxW32019fT1Wq/WEzaMns8djwlvWOUeL07JhH67aEFvibez7bQWTFS2cpeTx1YZpUHo45/e9dGwaR1Y0Ii4Xjy9byWB7G7tXNjAalEkpJtJJB6WtElg0JIuK7NDQVZWKcpkFZYXKAsgaBDxx9HIvocgQ5VIcbUrn/m6Bn//MRXgiQD5fIlMaJVMeoKBlSY5MopZmAzBBgOYltVx9ZY506g0EA61H3yc07XUdrBlefmdEAFdRUYEkSczMzBz1+szMzFwN0quFruvkcrnnX/AlYLfbT+ties899+B0OlEUhWKxiCiK3H777cBsv8FPfvKTc8t+9KMf5YEHHuDuu+8+KoBbtmwZX/7ylwFYsGABt99+Oxs3buSyyy7joYce4siRIzzwwAPU1tYC8PWvf/2ovo6//OUvKRQK/PSnP8XhcABw++23c/XVV/PNb35zrtbV7/fz3e9+F1EUWbRoEd/61rfI5XJ87nOfA2ZrTb/xjW+wadMm3vWud72Q02cwvKaVtALdye3sSzzGeL6Pha41NNoXscp3MUFrMybRjK7r7Nq1i7vvvpu+vj78fj8Oh4PKykrMZvMLCtaeK2XSKVakeY+vm5qFA+xNNnHk0IWEN9nID2a4+EMD+Iph3vzNA3RtG6JgNnNf50oeWb6KXa0tJE0SJlsZi6OEJurYdZFOS54qFexpmI6ZGCqbWFqeoZCbxmrqZSo5xZGeOHsPFMjlj1edEwYGj7u/lVUVXHJVJRuuCNBSJZLPOti7c8mLPg+vBxoC2jxMI6JhVMEdzxkRwJnNZlavXs3GjRvnRi5qmsbGjRu5+eabX7Lt3nHHHdxxxx2o6qnPQpPL5V6xCYMzmcxcEHQqLrroIu68806y2Sz//u//jizLXHvttcBsc/PXv/517r77biYmJiiVShSLRex2+1FlPHcwSE1NDaFQCIDu7m4aGhrmgjeA9evXH7V8d3c3y5cvP2q/zz33XDRNo6enZy6A6+joQBSfuRAEg0E6Ozvn/i1JEoFAYG7bBoMBVF1hILOPfYnH6UvvptnRwTL3hawpvg2v5Mdn92Gz2QCIx+P8/ve/5+GHH0bXdTo6Ok6pOfS4BA2rI4fVlcHqzM4+uzLYPSksnhQXFGbTSf164HzcR/wMKm58Y72s+1iWS/5rkA1/2oOoafxk/QXcdfGlmII6ruoUdRXDVJdNWCacBGbMlCamCcUmCGVj9GcSJJJJYrEsyakMG/PHrw2z2szU1dXhrfIiWHWw6ORtOiarht0FFqsF0WnCFfdRpTbQssDOujc9QPeji6ld/RD/d7fxA/FUGX3gXlqvmgAuk8nQ398/9++hoSH27t2L3++nsbGRW265hRtvvJE1a9awbt06brvtNrLZ7Eva5+kjH/kIH/nIR0ilUnheg+lMHA4H7e3tAPzwhz9k+fLl/OAHP+Cmm27i29/+Nv/xH//BbbfdRldXFw6Hg0984hOUSqWjynjuPEWCIKC9BM0Ix9vOy7Vtg+FMUlCz9KZ3cyT1JP2ZvQStjSzzbuCq6psY6Rvndz/9Hd3d3ZjNZqxWK16vl9raWsbGxujv76ehoQG/339MuYKkYrYWkM0lJFMZ2Vye/dtcxmwtYnFksTqzWJxZLPY85YKFQsZJIe2gkHESH69hV99Sdgg+/A0Jakas+GasPOaws9g/TeuiGB+4bhONkQg72lu5830XUz4HFpkHMefBMpEh92iYiaEZRnoiDB+OU8ic+Me1KILP68Zd4cdX56Z6cYCaFT68S6xg0xCzJrSMheGCE2vaRHNcoGxTSXbEqNhWgyzPXl8WrXuYkYNL6ejaz+F9KyiXXl/dawyvXq+aAG7nzp1cdNFFc/9+ehDBjTfeyI9//GOuu+46wuEwX/rSl5ienmbFihXcf//9xwxseKXZ7XYymcwrtu0XShRFPve5z3HLLbfw7ne/m82bN/OWt7yFv/mbvwFmazx7e3tZunTpKZe5ZMkSxsbGmJqaoqamBoBt27Yds8yPf/xjstnsXC3c5s2b55pKDQbD88sqSbpTT3I4uY2xXA8tzk6WuNfxxtr345A9RKNRfvOL3/PII4+Qz+dpampC0zTy+TwzMzOMjIxgMpno6OjAZBawOlNYXRls7jROfxxnIIbdk6ZctKCUTCglM2rJNPt32YxSMJMKVxAeaqKQdVDM2NHUo28vCvDXWjOt9WGCo1YiIReHzBaC2Tjv+d3DnLV1HwNOM99/30IOt7gpJraQ+WGGqZ404z1ZsonSMcdtMpmoqamhOuhgxdI8SxcVyZRbmDK3YFtohroCQlFGjFsR0xbEtBm2WpgpWRkymQnJMmvyBRoVhUJljmRnjMCTQeTsbPAWqB/H7kkxfXgR3o59PLHxspfj43zNmL9BDEYT6vG8agK4Cy+8EP15PqSbb775JW0ynQ+CIJxWM+aryTve8Q4+9alPcccdd7BgwQJ++9vfsmXLFnw+H9/5zneYmZk5rQDu0ksvZeHChdx44418+9vfJpVK8fnPf/6oZd7znvfw5S9/mRtvvJGvfOUrhMNhPvrRj3LDDTe86oJzg+HVJF6aoSe1i8OprUwXhlngWkWn/Xw2mG9AK+kUZ4r0jQwRDoe59957GRkZoa6ujpaWFgAEUaWmuYwrUMYRSGFzpbG5D2K25SnlbOTTTgppJ+lIgKmedrJx7zFB2anKm1X6akusbRnBWSyTc+aR01Os/usA8UfCfLCsMwGQKcEPe49bhiAIBAIBamtrqa+vp76+ntZmNxefdYjFrSP8sm8N3SUPclUOZ8iGNO1A6q1BLJjQgYgkMWgyMWGSqTCptJTLrM8XEAWd5JI4heos/p1BTCnL3PlZuG4XfU+uZtWaHezfvRr1BR7/qSiXy8RiMWKxGIqizA0oO5PN9oF78c2f81HGa9GrJoB7NXohfeDOZLIsc/PNN/Otb32LPXv2MDg4yOWXX47dbueDH/wg11xzDclk8pTLE0WRP/zhD9x0002sW7eO5uZmvvvd73LFFVfMLWO323nggQf4+Mc/ztq1a4+aRsRgMDwjr2YZzOxnILOPgcw+FL3MAudK1riuJB8V2bdpP9/b82tyuRyqqqIoCqqqIspF6ltMXHKNE6d3ApunG7snhdWVpZB2kIn6SUf9REfryKdcFLN2dO3E87edmI7kLCB7MojOLMlchOlohPB0lNhYlvA9JaITRVLTeZTS8a+pFpsN0W/HZ3HgsThwuVwEg0Gqq6upfFZqKbOpzDnLu1nXtYmHh1u5e3wlSd1KxYwT054aBGV2/7OCQJ/FzLBJxqVptJTLrCoU5qb4UOxlIqtCSFkTlU/UIirPHHfD0iPkUi6sUhmzucRQ38IXcE5OTNM0UqkUiUSCdDqNJEn4/X7OP/98urq66OjomNftGV57BP35qr0Mc33gkskkbrd77vVCocDQ0BAtLS1G9gLDS8b4nr0+6bpOqDhGd3I7R1I7iBQnqJZbqdJb8RYbELNW+nr72LVrF6FQCF3XCQQCOBx2fNVZqltDVDZNYvemyadc5JJu8kk3ueRTf6fcaMoL+w0vmBRkTwaTN0vZEmc6PMn0WITRQ1lGu3NERtMnDNIABKANaKgLULiqiby7k9WWBoSgyFRAY03f8fdLFDVWLenngtUH6Zmp5J5UAxmnypEdTbRGLQQkFR2YliR6LGaSosiCUpnWchnrU7c6TdYoVGfJ12Ype4q4u/3Yxp0Iz6rlsblSrH3j/ez6y2VcdNmD7N2xjvGR5hd0rp5N0zQikQjhcBhd13G5XNTU1NDV1cXChQtZuHAhLpfrRW/nuU50D3upPL293+xbjN31Qn4MHC2XVnnH8iMv2/6fKYwaOIPBYHgVKBQKjI6OMjIywlC0m4nmbWi6SmnIRq5fIj9upa84Srk8MDeYSDZpNC80s+QsCU9lCodvEoc/QTlvJTpWx+DOVaRmKtFfYD8kQVKRPVkkdw7NnUf0ZLF6M0Qn4jz2xwT7Ns4QGz1+rbwoS3iDfqp9VVT4Azh8PpILZb75u4dYPZTiR198E3svCLJrSz3nqwJ+NDYHFRZNHH9fW+qmuer8HWQKFv57/xpCNQW0mJNtjzaySi7illSOmE30ms04NY1FpRK1yuz4RU3UyFfnyNdmKQYKWMM2HCMuLOEggnZ085wgqnRe+ASD+5ZRHZyhVLQwPvLCmzN1XSeTyTA9PU2hUCAQCHDJJZfQ2dlJa2sr1dXVR42wN8yPO+64g29/+9tMT0+zfPly/vM///OoKbCe67bbbuPOO+9kdHSUiooK3v72t3PrrbfO/Wj+yle+ckyGpkWLFh2VTODlZgRwBoPB8ApQVZXR0VH6+vro6emhp6eHeDxOUcvT+gGdxMMmij1WTCYZs9mMw2bCVgGVDUkq6jN4a8I4fAlySRfZmI9MzEdkuIFM3IdSfIEjJQUNUyCFpTqOqTqO7M2QTjuYnDYzOaAwuitE/+YB4mNHZyWwBVw4F1bgag1SGayhU6qh1hqYC0xK6PQsTvLN239L12CSH33+PHZ3trBrUzWde0P4l3tI2XRKJp1A6uiAyiSXuXT9Xpa2jvKb3Ss5UgGCDcqPtrA96mORvUC/1cSEyUZTucxF2RwuXUe1KuRr8hQq8xQr8lhiVmwTDrx7KxHVEwdMC9bspph1MNXbxpvfeTebH7kYTrEPlqIoZLPZuUexWETXdex2OwsWLOC8885jxYoVr5s8p6/UIIZf//rX3HLLLdx1112cddZZ3HbbbVx++eX09PRQVVV1zPK//OUv+cxnPsMPf/hDzjnnHHp7e3nve9+LIAhHdefp6Og4KmHAC55mZ54YAZzBYDC8TIrFIv39/XR3d7Nz504mJyfJZrPIsozX66W+vg7zBWGElERtuRbb8gwOXwJXxQSe6hAOb5J0NEByuorBHStJRQLo6uk3UenoqLJOwaahBTLYq2O4vUkcrjSxuJmN92fo2RojOZYiG4pTTB49ObkgidR0NVO5finSygX4zC7aZgSqc8eGOsXKNC7rET7zg0N0bZ7gr29ZzyP2y+jps9H8+14aL58drDQc1GieEY9qymypm+aNFzzJdNTHv20/j2xTCsuBILFxD5slO9aGIuPWMvWmFItNRXSrSsmqEHIoaJKGJWzDOm3Huz9wVP+2E6loGKOycYztf34jizoOEY8GCE3XHLNcuVwml8uRy+XIZrMUCgVgtt+vw+HA6XTS3t5OU1MT1dXV1NbW0tbW9rqradMQX5GJfL/zne/wgQ98YG6asbvuuot7772XH/7wh3zmM585ZvktW7Zw7rnn8u53vxuA5uZmrr/+erZv337UcrIsv6qSBxgBnMFgMLwECoUCU1NTTE9PMzU1xdDQEMPDw0QiEUqlEk6nk8rKSlpaWhAEAdlcwtQ5SimYZYkSxtOxhXLRSjbmJRPzMbRrBelwAE2V0dEpm3XKNg1NKqNJOqrEU886uqijC6ALs6+VzTqKSaNs1iibdWy2LPXWBLXWFLKgMhH2cd+9ZXofniG0qw+1WD7meJxOJ1XBIJUrOimtW4butLMgq9A2qWB5zv1V1zUqisMsHt3Dyv8ZomJqNuH8k2vX8uuVV3IEG+eF40xpGia7TFnSmfZpLBmbvSVVeJNcun4P1RVx/rK7k91OK1pzimzIw2RTAXFJmrWmMlJRxpyVkfIyYl5GilsQCw7kvIyUlY8KBp+P3ZNkyTnb2P/IBqzmIkuX7eOh+9401wQaj8dJpVLA7MThdrsdp9NJU1PTXKBWWVlJVVUVFRUVr3jtzGvR0+f/acdLe1kqldi1axef/exn514TRZFLL72UrVu3Hrfcc845h5///Oc8+eSTrFu3jsHBQe677z5uuOGGo5br6+ujtrYWq9XK+vXrufXWW2lsbJynozt9xjfsJF5vo1ANBsMLo+s6yWSS0dFRRkdH6enpYWBggHQ6TT6fB2Yzyjx9w7darZjtObzVITzBbtxVEcp2hQOFBmomRIbDiwmnfeQFE2WLRsmiUfJrlGpzlCwaiknDVBIxlUQkVUBUQVSFp/4WEDUQdAF0kEsitqyAVVeRrZMk0wOEp9I83CMzPqyQC2UoRMOUi8W54/F6vSxdupSamhoCgQDmCj99FS5GbRLugsrCrEIgVDgqPJLKJWqiI7SM9dG17whVE2kADjY1sOMN59Ld0UGv18c+2cYlpSzhvXGqlvvQ0RmvKdNQKuNbPMP69kGWV06zMVHL7ZEuhCYdXS8xPBFETVvJxiysyqt4SsJpBWgn+OTw1UzTsOQI3qow/buWE54w8+a338vjDzey+YlpNG1yLtjesGEDjY2NVFZWUlFRQSAQOGZCccMzVF1A1echE8NTZTQ0NBz1+pe//GW+8pWvHPVaJBJBVdVjpqEKBoMn7K/27ne/m0gkwnnnnYeu6yiKwoc//OG5VI0AZ511Fj/+8Y9ZtGgRU1NT/PM//zPnn38+Bw8efEkGnpwKI4A7iVPNxGAM5DW8lIzv16tLuVwmEokQCoWYmZlhcnKSwcFBpqamSCaTqKqK2WzG4/FQXV09l6PY4sjiqZ3GXbsHT1UY2VQmnPIznvEzEV6KrSLLRNbLLosdc6WIyaNiKuqYiiLmoogzKWMuzP4tl58/eCkUCoyPjzGV7mc6Os7UYJT4VPGEyzscDjo6Oujq6qKurg5BEEjIAvtdJkJmiaXZMm9OZrB7Mti9cWpjU9RMh6gai1HZkyJwMINUnv2uJjptHLqulpE3+BFrJERxmiXCFPWqmfNKVrKqhZpLwepSsZpKXGQusMhSwCupDBXMPBB3ECqreC057EqZyphCvVZgRnfR7FcxaSKqKqEp8uyzKqEpTz2rErouYLblsTjyWGw5TNYiJnPpqewRxdlnUwGzLUepJLL3iQoO71iEUi7x3g8+zPSUh+nxC7jyyhZaW1tpbW2lrq4OSXrxIypfT1RE1HloQlWfakIdGxs7ahTqc2vfXqhHH32Ur3/96/zXf/0XZ511Fv39/Xz84x/na1/7Gl/84hcBjsrhvWzZMs466yyampq4++67uemmm+ZlP06XEcC9CE//8srlcnP5BA2G+fb0iEPj5vHyKRaLjI+PMzo6SiQSIR6Pz02ymslkyOfzZDIZdF1HEATsdjsul4sFCxbMXRcUWaPgLWJp6aOxfgS/M8loPkB/2k94ZDXZhAdLQcJcEHAumkQY89C6rxZRe2E1Fvl8niO9vfSPjTM+NkYqHILjBP9+v5/Kykq8Xu9Rj6pgALs7j8WfIuPrZ1spSDRv4x1s4rx4H4GRLN7ePPYjJWxjZYSnitYFiDU72H9lI/2LazjQ3MJhuZacXceZLWKdLGK1l7DaSsglsCsq1lSOgFhkub1Ihy9BVLGw53ArPePVFESJVFMWxaqhDLiYTptIudI0ulI0104gyQqSrCJKzzwkWXnqbw1RUhEEnVLBSjFrp5i3Ui5YUUpmsgkrqaSd6ckUxbwTSXAjqnW0tbXzN+9ppLHtr9gcKWr8d3PNVb4X/P0xvDTcbvfzTiNSUVGBJEnMzMwc9frMzMwJ+6998Ytf5IYbbuD9738/AF1dXWSzWT74wQ/y+c9//rh9F71eLwsXLjwqBejLzQjgXgRJkvB6vXMJ1J/+pW0wzBdN0wiHw9jtdqNPzUvk6bm5ZmZmmJiYoLe3l97eXhKJxFzzpyRJmM1mLBYLJpMJr9dLXV3d3GeiihpafRxLcASHL4HbnSJgTuOUC4STASYGWjnS24hUmO2XVQlUPr399jC6pCLurZtt9jxNeU3j0Z172P3IRpRC/qj3ArUO6ipaqKmpm005VV2N3SFi96RweJI4fAkcgQkcgUNYpTypAzZGHqtkwaEM7xn7Kw2DMUzlZ/ILp30WRtsCDK+poK+6ht6KevqddSiaHbelhLMyhVyZxe2O4sqZSKXspENO/CmdxWKOSmcUZ36aBcvy1NRlOTjQyGfGljM+GESMyjiWzBBoizB9sJbyoB+vqFEpKlhDFYSA0FP7USqViEajxOPxuS4uTw8E8fl8R82XqOs6qqoSjUYJhUI4HA6WL1/Phqs30NLSgtfroFDeTiZ/H7nCJhqq7kOWjOBtPmi6iDYPo1C102iFMJvNrF69mo0bN3LNNdfMrq9pbNy48YSZnHK53DFB2tM/mE/UApLJZBgYGDimn9zLybgjvEhPR/RPB3EGw3wTRZHGxkbjx8E8UBSF6elpJiYmmJqaYnR0lKGhIZLJJNlsFkVR5gKB2tpabDbb8c+7oOH0x7C2zOCsnaHSE0NVJZIpL9mYh/xQK/1hH/mka24OtuNdbPVAFn1hCPGRBacUvGlAGpE4EglBpH9omMG//B/50DQAlVU+li5vYNElIq2LK3DFazFbS9jdKeyefhyeXZhsBfIpF+m0i0JYx3V3jKYH49QPRLEXZ2t7FVFkrKaC7Re3M9npZrzFz0Srl7TfhqCBoIqIZRFBFWhTQmhmFWQNT0wmlZaozkKXK8SiujjBjiQuR55w3EHfkIuBTA0P7Qmy574geWRKq9N0mrP4rppACjuwbWzBX5bBXETTNAqFApFcjnw+Ty6Xo1QqIcsyFRUVXHjhhXR0dFAul+np6aG7u5vR0dG5WmuYvQFLkkRVlYN3vmsNq1bXUVGpoqp/oaiOMDy9GZNcj8N6OfWVf0CWjBR+82W+m1BP1S233MKNN97ImjVrWLduHbfddhvZbHZuVOrf/u3fUldXx6233grA1VdfzXe+8x1Wrlw514T6xS9+kauvvnoukPvkJz/J1VdfTVNTE5OTk3z5y19GkiSuv/76F318L5QRwJ3EqQxiEASBmpoaqqqqKJePHbllMLxYZrP5dTf9wHzJZrMMDg4yPDzM0NAQ/f39c8EazHaDcDgcuN1uampqjuqQ/vRIT03U0CUNlzeBPziDPxjCF4iSUq1MZ/xMjTcw9ug61JTztPZNt5bR1o0g7mhEyJuPtwSCPUfIDFO6iUjZSlmXcGtTSNFexrYf5NCmKQBcHom//WiQs69tIG8xYyvq2NJFCt5x0mUL/RkPkwNNpGbMNI9PsnpqkFUHD9PePTuf21jAx5auxRQXuimssZJfL+LylQloCjUpC4GUhSUZECfzWMwlLNYSJksJk7OM2VzGYSlS4cyiNouMJ92MR70MRr1s6wsyMupgetSKWZCoqjHjtoJD0DhXLDPQmsbjDeHwK0jbq8gMlolmJ8lkMmiahiAIWK1WbDYbfr+fZcuW0dDQQHt7O+3t7djt9rmzdcklF5HLhxgbO0wk2ovFNo7VNobJMowoTSOIRUxSD7JcT6lchyzX4zRdRqXnK5jk+tP8Zhleza677jrC4TBf+tKXmJ6eZsWKFdx///1zAxtGR0ePuqZ+4QtfQBAEvvCFLzAxMUFlZSVXX301//qv/zq3zPj4ONdffz3RaJTKykrOO+88tm3bRmVl5THbf7kYqbROwcudhsRgMLwwqqoyMTFBf38/hw8f5vDhw0SjUVRVRZZl3G43LpcLh8Nx3KBYE3TSXoVERYl0ZYEl7gkabVHqHFEKqpmJdIDJdAWpySpc426s+dPpl6hjtuWxuTJYnWliizN4lBINQmKuD5eGznjax0C0ip5wLYmMlUCmm9LgIDP9EYZ7Y0Qjz8zJJggCa9at4o0fbKdydYT4RBVHetoYLzmJSCKrQxNcOdrN0qkhGgen8Ixn5tYtNlspvsGL+kYX0goZu0kho5qIq2ZSBSuZSR+ZhAtB0BAtZSRzmYImEi9YiBcs5Epm1JKJmGpmuGQnFrLhGShAqIgynsVhBn+7E2+rE9l29HnSBZ3SgijZxTGU3RbCD2YQdAGPx4Pf76e1tZX6+nqqqqqorKzE7/dis5fRtBnK6jiKMk5ZnUBRxykr4yjqOKoWRRDsSKIHSQxgNi3BYurEYurAbGpDEisRhNfnD6FXKpXWf+9ejc354uuJ8hmFD63aZdyDn8OogTMYDGesfD7P2NgYY2NjDAwMcOTIEaLRKJlMBlmW8fl8tLa2nnS0miJrJANl4hVl0r4y9rREVVLnTb7dUJYIHWhm3+Q6ijkHABae6b92Yjo2VwZ3ZQR3RRSXP4YrEENTJQoZB/1aBbJmRhzysiW+gLG8k6mynbQqYw2PU+g/QnxgJxMjoxSLx44c9VdWUtFYx6V/W8f5l6bIZZIkpyppL+Z4c/bP1O+Zwr01jjQ12yqgWUUSS90c3tDOdGs1E4uriFZZyHrLCAkrbLWR9pRRnAqWA0HEaSeTqsyQYiaribhFDY9awposICcKCJEcxUSZ0VoX0QU+2h4cps4tY6+y4Gi24jjbc0zTc7lcolAokC/nkS9NUJQEpg44WDGe5pLr62lrd1FRqSLJMTTtMTQ9jaZlULU4M6koYtqOJFVikuqR5XpMUj0WUwcmuQFZqkOWqhCE49VkGgyvTUYAZzAYzhiFQoGRkRGGh4c5cuTI3GCDQqGAJEm4XC4CgQDNzc0n7DNYNmtkXQpZl0rGWybnVHHHTPjDJlqOOHDYM3Rd9hiR0XqGdq7g+dIo6YKGWJHGHozi8cXweeL43HF0BGJpL7GCm+FCPdHRhRQwIcgl7PY8T+5bRCLpxOPJ4avI0uAIs//nW9l894GjyjdZLfibmrG3NGNqamDdKhPXLBnjkpYBzEI/6YMSwl/HsGxK4NqdRFR0FJPIkYV1PLJ+IcOLGsm2eJGr8pQDRQRFwByzYh62YkJDrU2jtmQw9wbQtwY4XLYyqcgESkVqRkOoAwnKaQXZIWGutGDxmrE0O9hd7yVst3J5MYPv7bNNkJJcJlARweFMYbbGMVvi2B0pvL4CXl8JRZS4L9VBQMxxtmMQSZCxXFaPxSQiy9JTgVgXkuhDFFyIohNJ9CJJlYiCMdL/TDN/mRhenzWnz8cI4AwGw6taKpXiwIED7N27l+7ubmKxGIVCAbPZ/FT6qXqsVuuJAzaTRixYIukvk3MpCJqAIy1hT8tUj9hwJWTEpwYQuKvCLL3oCUb2djHVs+CYssy2PLa6ELbqKHZvEpczjcucQ9AhlXcSz3gYidewd2Qphbx9rrO/UpAZyjhJ2lTWnHOY0c0L6chK+MggRSAxWOT3v7qXidEJAOqWtOLpbMG/rppFK02sqIiy0hpjmWMzmaxEcmuR8TtLVD0eJzgxOzt9PGDnvou6+MvSTvasaKS6Lk2lJYdckrDGypinnJgPBxCLEkpLnFLXFGrKijjiIby9gYGUhVJRxz0QpnYohtNnxt1kx3VpEIvLhI6OgEBJgAdtdkro/K15lEBFGI93muq6Gaqqk4RmbETDVjIpB5l0AK18Lg7TMlJyPY8If6bDs5IFzjX8y6F7+Ok5/2T073wNm79cqMZ35HiMAM5gMLzq5PN5Dh8+zJ49e9i1axfhcBhRFPH5fDQ0NJw0YAPQRJ14RYlodYmsS8EfNhMct2BPOzCVj38zqGwZpv2sXRx5Yj3xiVokUwlfzQxufwxnIIqrMoZoUoiXHWSSXtKRAOHDCyhM+ynnbDy7ps701KME9GBhWDCxQM5xzrpupL21LE48femV6O3t5Q9//COFfB7JamXR31zD2y70cXZgms6aAWoq4oT3WUn8RSO0I0Hj/hlq8hqaAMMLq3jobcu5t6OL/XV1tJYU2rIKl40ICMPHJkxXvXlyZ4+CJlA6VElUAc1TwLFikGWSgskKkk0AWQexiC6lKIs6ZUFHACRdwywovFvKEbSkMBXKlGOQD1nZuW0pmtJBTXUbNTU1LF5cjerPMlzsYVfmMLHS47wh+HZWeC/i77b+Nx9d9BYjeDMYXgQjgDMYDK84TdOYnJxkYGCAnp4eDh48yMzMDKqqUlFRweLFi09pHry8XWWmoUC8soQrIVM5aaE96pyrYTuR6rP30dAyyO59q3H503QteRh/MEQyEiBacnHE6iU52Qw9VVhnHCfNglAGJjAxKphIIdKql7nCGkNcO4ow4UGY8KKqKiNjY2w/fIjeHTsBWLTEzc9ur2PZwr1Mhf1MzATYtrWdRT/fxorN+6gD8j4T3ataOVLdyZ+WLWFPlYf6gkp7TuFtU6Wn9uqZoEhHR3eVUP05Sq1xNFcJTYNSzkTWmcE8LeDLWrDLQQQVHPYsXlcCnzeO3xfD74pgMRfJpjxMpn3sz1YSynpJh0x0l50EWgJ46h1kAglsXQ7qbE6cpjIHcxt5rDhDU3Ih7c4O3l7/AepsLWi6zsd3/ZSr6lawIbjkeT9Pw5lNQ0B70enOmJcyXouMAO4kjFyoBsNLJx6PMzAwQF9fH/v27WN6epp0Oo0oini93ucdfPA0HZ2UT2GmoUDBoVI1bqVrmwdZef7aHU1Wqb10Ow0VUxQyNs5avZVw3stAsYJHY42UdBPWuB3nLje21In3RQWmkBkRTMSQqEVhqV7EjwJNMYpt48Q3mRjfFOPI4OOMDQ+hFJ+Zq+zdb2/k7W87m92Hg/xlkxdVE/BqQ7z7R38iOJ5iz2WLGPgbDz2Rcxks+XjSY6axoPDWmTymE8wjoFRkKS0NodjK6CaNVNhObqudZjXJ6qYYFbUZTLVlTHIZs7WIxxunmLcRiwVIRAOMHFnE3ug59Oe97LLZMCkamRI0HNpLe7CG6978Zs4//3zMZjO6rhMvR5jKjxAvR1jlO596eyuScPTo0/+v+x58Zgd/17rheT8bw5nPaEJ9aRnTiJwCYxoRg+H0KYpCPp+nWCzOPfL5PCMjIxw4cICBgQHi8TiapuFyufD5fLjd7lOesFhHJxosMd1UQFQF6iZlGgoFXM40dmcWuyOD1ZGl5FDIWgSygomCYqaomimpMpqusygwht+UQShIlGJeEjNVZJIeclknmayDQsmCqB5/qhANmH46aBOhvSpGtS2PWcjQu2+A0Z5JolMpYiNp0pH8Mev7vCYWL2qibcFZdNXUUhGJUBEOUxGN4C1OsuDABIpJ5sHPrWD0kgDjm5ax2eKmKAqsjxfxKc9cuk2mEp7KMLaaGEIgQ9hqJotMvRzDp+cJFAsEA0k0TSIR8xOP+UknvZRKZpSyiWzJwmTST0yxkxMFsqJIThSIo6OWFVxTYWIVQZaHp3jjuedwxRVX4Pf7T+lzKmsqh5LjPDR1kIPJMf573U1YJCMB/MvplZpG5N93njNv04j845otxj34OYwaOIPBMC9UVWV8fJyhoSH6+vo4dOgQuVwORVFQVRVFUVAUhXK5jNVqxefzsWjRotNMEaZjd6Yp1qfoqZLxyHne5ehnsXcCqUthIl3JUL6SIcVFXPCREyuwFATMIRNyXsYkKAS9YRZWjuF0p9BUiVRfPYWkF5OpjNudoqZmCrsjg8OZQZRU8jk7+ZyDfM5GLmunJ1rD3niQgYyPNm+EixaMUPCWmN4+w+b/nWTfozOUCsfW2vt9MksX2ljU3EhbsZ41iSJNExPU/f4POHK5o5aNVTkZWlvDk19swOQzMZ22sTFo4h012znbOY6imIhrdmJmE0W7SlKwMK5asRRUhLwF2aLhzJUQEx6UYoDeuJ8nY34K+dm+ehoQkiUmTDKTsowqgFPSsGhlhGwGLZlCyudpkWS0YC3hlgX8f+tWsHbpUgKBwMk/IV2nPz3Dk9EBnowOcCg5zgJXNWsDbfz76huM4O11ZP4yMRg1cMdjBHAGg+EFebrf2vDwMP39/Rw4cIBwOEw2m0UURTweDzabbS6PqyRJyLKMLMvPW8smCBoOZwa3J4HbO/vweBKknBr3JZeSL1s5NzeNELZzSFjKFkcHOU8ZsSxhidgwJSxYk2acWRMCOi5/nKrmEWraBlEREU0Kg7tWMH5oMZykecZkKmGz57Dacoxj4sl0JQ5ziXWVo5yzuJ/eXI77fzTCnntHSSUKc+vV1lm49A0ulneKrF5kotUkYv1FAfOWPO4dhxC0QwDkPDamFlSTaPCQbDcTP0cl1WxFcUgE1AKtUoyv7bgGPWfl2lIck1rJ5ionmYCCjoZlUiWxSyQxYcJxjkixS2N6pgLbNi8NBZHcs06zCkzJMiNmmWlZxl8u409nWBSOoMTjKIoyl/Ggo7MLd2MTO3JFYmWFH7/talwnaM4uqGVGsmH2xEbYHR9mX3yEoNXNukA772k+l2W+RqxG0Pa6pOkC2gvI73u8cgzHMgI4g8FwSnRdJxqN0t/fT39/P/v37ycUCpHJZBAEAZfLRWVlJS0tLaedt1U2laioDFEZnKEiOENF1QyqKpNKeEkkvBzK1tBdWkwyLlMZlnFYSuyvFKAaLBELlgkrlfutSMXZS5rFkSVQO4K/dhp/zRSlooVUzE9JM5GJ+unfvppSzv48ewWFspmepIMjKQteFLqcSRyeBPvGh9n1nSEGd03OLet0Wrh0Qy3verubqy4ukcmYiG2zYvt2nNonBpA0jWiLl4Ermkl1OcmusKC36ORtOhHJSlaWsRQEbBEr2rCTiZSXbxTaaaDAQl+EWH0KTVAp7DQRu0+EhEzF1SYsF+fxyzDSE6R8r5cOUcEs6CCAAkyaZEZMMiFZpqJUwj4+QdvkFGZZQqz3UVzmRa2px2dzUen0k1Nl/jCTwJye4NKWBby7tYFtsV4ixTSRYppwMU2kkCJcTBMtZpBFkXqbnxX+Zq6uW8kXO6/BZTLmbDMYXmpGH7hTYPSBM7xelctlhoeH5wYaDAwMkEwm0XUdl8uF1+vF5XK94OkgrLYcq87aRmPzEPGYn0goSHimmvBMkLhuJVJTJFJTxKyCXS5hthWwxi24EhKuLNhQsDpy2JwZrK7M7LMziyQpxKaqiU3WEJusIdA8Rv3SHvq3ryY62jC3fR0oIJBCJIdIAYGCIFJEoAAI3jzNFXEq3VHGR4aZ2jzFwYcniISeyY7whgvt3Pg2P+cKTpL7JcpHStT0JwjORJE0DU0QONjZyRPnnUe4qgoAyZHH3jaJrXWaYtFCSLQhbm3BFLOjA1NWgeHmHG3+MFZngdgRJ9N7bBSLNiqXlfG2pXFUZMhEnYz1VBOeclNpVXHIGjqQEwWSskhaFKlSFQKZLKWxcTRVo7quDvtZjeyzhagwuelwN1Eh+tgyOsJULsnCah/VfjslSsSKGeyyhQqLiwqLi0rrU88WN5VWF36zE1k8nXRihlfCK9UH7hs7NmCdhz5whYzCZ9Y+ZtyDn8OogTMYDHOe3Y+tt7eXQ4cOEY1GyeVyWK1WAoEANTU1SNKLvWnrtC7oZeW67QwPtPO7X9xAuWxGRycZKDO9qEDBmaBKyrDOPUO9lsUn57BZ8mQ9EkemGuiZqmcwUo05r1GTzlI5U8Kvaog5G4WsA10XKSFgqw5R13mEP/z5TUTyTvKCSB6BPCJlBKxouNFw6BpWdCr0Mq7aOIp/kMGto+z530l69oUpFLS5vXe7LFxw3kIubGnnnH2TdH56P678OABFs5mZYJDdq1YxHQwy2NpK3O8HQcdSE8HePokpkCI/HGR4tJmMAwI7gpRLMkfcUGxO0FQTpj5mpncgSGHcSmtNggVvzGAOhFEiDrLjXvq2NhMuWAiaSzQ4y8RlkSnJhKKDvVzGlCngLCskBIGUIOFauBRf0EN/MIYspOksd+FU7WglkZxF5FNrL2VVXe1p154aDIZXhhHAGQwGYrEYjz/+OJs2bSIUCpHNZpEkCY/HQ01NDXa7fd5u7C53knXnPY7ZlmfPvpXokk7Tut0MWh10C35ccoGLXMO0W0Jk027Ck9UcmOygL1bFZMmOpAtU6SpVusI5KJSRiRCgR5CIIFF+as4oTRAIWDPcfMEW7tt0PsmcAzcaQRRs6NjQeHbPLF3QiVtH6R7bw+DvRhk8GD9qv70eBy2tC1nY1sabdJ31e/bQ9pc/ALCzvZ3+ZcuYqa8n4fWiP+tcye4srpYBbE0zqHkLuf5aotsXE1sZRSiJlHcE2WJT8HaGqK+IExn3cGD7IlpUhdXNYbTVo0hhO8lhH31bWokULciyjsmioVZqjEsylWUFd2gG9/gETkHAbrfjcDiorq6msbGRrmVd7Hem+NXoNj658HLeWLvCCNQMLzlNF9HmYQqQ+SjjtcgI4AyG17FwOMxjjz3Gww8/zNTUFF6v9wX3Yzs+HYs9h8OXxOWPUtcyjNOXoKyJpApWLDURnlTrGaQar5SnOZfHMeWgd+I87i+6mUZGQyCIQrWu0EGO53aHN6PjoEyTPpu4/enxn5KgseyCR4j1tVAxUUEFz8y7lk6nGUvMkCyHiSfjJBIJotMhJnuTc8sIAjQ11dHWtpjVwSDnJJO0DQ/Tct99OHI5clYrvzj/fLasXUOFy/2cqUZ1LHURnEtGkexF8iNBYo8tQ0k6KTlLDK1MEp6uIidotHeN0eHLMzNSSe90Cw3eFCtXDSJYyoyOVTDwcBf5hAXJpOOwqNTYSnh0Daem4UyXiI2Nk0wmqa2t5eLrrmPJkiUEAgH8fj+yLDOQnuGrB/5AUHXz03P+ngqLax4+V4Ph+akIqPMwCe98lPFaZARwJ2FM5Gt4LdJ1ndHRUbZu3cqjjz5KKBQiEAjQ2dn5optGBVHFFYjhDYZmH5VhdATUghm3M00+5WT/4+cyVvAz3lQmbhdw5qF90I49VElal9ghWMgh0q6XWECO2V5hp+7pI2heeQBdFxjZ2wmAJqoMx7rZ+viT9O8ZO+66ogiL2mvoDLZzhc3GinCYll278CUSwGzz6HBzM5s7O7lrzWqWqyp1yrOuD6KGrWkGx5JRdEUie7iJwkQF6AIJWaC3WmfYbqMpXaa9ehjJpBEreIhnJKoXhHFmMiTCLib2NJEMOVAUkYWmIq2WFJLA7KgEZTbV2NTUFJFslvr6eq699lrOPfdcfD7fUcfz5/Hd3Nn3EP9vyVVcUt15WufRYDC8uhmDGE6BMYjB8FqQTCbZu3cvW7du5ciRIySTSSorK6murn7hOSkFDW8whL9mGm8whDsQJZd2kZypIhGqQlAEulbswSQr7Ni+niGTk1BrlqRuxpGRaOh2YsuayCJwQLASR6JLL1CH8qJ+c/vrJ1iwfge7/nIpeXeew0P72faHA0z3PFXDJoDf58fn89FUK7EhGEI6UsFlqRLLZmawFmcHKSiSxHh9PYOtrQy2tDBZW8sBm41hk4kLcjlcug6iiuzKY6mO41g0hpKykzncRD7kJSOLjFslBhwymrXM4qZxKs1pSjmJomTG486RnvaSG/cQnXSTLppxiBoBUaVSUqgQVZ6uCFVVlUgkQjgcxmQy0dTUxIUXXsg555xzzHVJ13V+MvgEf5nay3+uuZEqq+dFnE3Dme6VGsTwz9svnbdBDF8+6yHjHvwcRg2cwfAaNzo6ysaNG3nyyScJh8PIskx1dTVNTU0vsJlUxxWIUt06TLBlhFLBQnSijpGDS0mGKkEXcNRPMGXR2Zuo40+PNGCWBawtcWxyiWzeTvNBF/mMhX5BIiZIZBDp0IusI/+ip+w0u9L4zj7An7cG2Xfwzxy8d5xUZHaiXJPJxIoVK7hs6VLOjkZZMnyEtl39CDkNSBD3eulvb2e8ro6J+nqmqqtRTM802g5bBKTGEO90RDC7s8juHKKtRDFrI5Zys33fKsayXrIyuNuzBL0JfJ405zvySIJGLmlGkwSQTCQHKhgY8ePVwC8qdEkKLlsJXddIJpMUCgVGnspgUS6X0XWdiooKrrjiCtatW8eSJUswmY6dX03TNf79yF/oTk7yvbM+gNuY0sPwClGZn+ZPow3s+IwAzmB4jQqHwzz44INs3LiReDxOVVUVS5YsOe1mUrM1j9OXwOmP4/TF8VSFEQSYHmxmz18vIZvwzi7ni5GsC3MoXYGQrKWFIh2mMpEFcTIhN/FDQeJdcVIDXiazFvyCSkBXaaCMG+1FBW46Oro/Q8zaT/eBHg6+eZLQUHrufafdzhVtbXxAllk1OEjFjh0AaC6J0OpaNlWuY7ClhazTObeOBijC7FQjsieLqX2KFU3TlGIuwtEA09MVjIy4iZRsBIpQacph82Xoqu7HEciilgV0USAfkimUTZjtKskpL4z48GTMLJEUzKajU2xpmsaRI0dwOp3YbDaqqqqorKycqyldvnz586awur3nr4xmo9y+9r3GBLoGw2uYEcAZDK8x6XSaRx55hPvvv5+pqSmqq6vp6uo65do2iz2Lr2YGX800vuoZZHOJTMxHJu4lEapirHsx6agfENAFjWx1lD7VRlF1sqiksCarI+atqGaZ8IZxGnYEMSespHw5xgSVs2NlRJQXdYy6rjM2NsbYzAih7DjhmTDhoRRK8Znf6iZB4EKHg3drGu/K5bAeOIAmCEzU1fHohg3YrpCouLjAN/56BVMmmZQoUhQFioKAIgiYRIXFNRMsaRrFZimwb6qRA7svIFOwYsprWCIlLGNZWn0z1J6TxuzVSO+TKGdlch4zgg4mVDJlD6aDLlqiMoGnPwP52OPXdZ3e3l6qq6v5h3/4B9rb208zzRj83/hunowO8L2zPmAEb4ZXnDEK9aVlBHAGw2tEKpViy5YtPPjggwwNDeHz+ejq6jqF/m06Tl+CquYRqppHMVsLxKeDxKeqGT2wlGzSA89pBlGAkDtPL1aW2oucnS9RigTRY1Vzy+SaUtgmnZgTVgAmm/LUDtsQX0STiqIoHDx4kG3btjEzM3PM+xbgAuDdwFt1HVnTmKqpYWcwyEh9Pbva2xh3OlHtRX7w1t/xgYfeRNxswo6GT1AoCQKivUBT8xgL6saZTPrY19vKoUiQmu4klaMxtK4qBJtAbVua5itnKBVMjI02YDdl8V2QRhR0MhMulDEv7riVBbo6O3L2JIet6zr9/f14vV4++MEPsnjx4tM+N7tjQ3yv/2F+cPYHsMnm017fYDCcWYwAzmA4w8ViMZ544gk2btzI+Pg4LpfrhP2jnqHj8CYItswGbZKsEBpu5PCm9aQigRPmBy0iMGLRGZVFzmsY5x1ZifBgC8XnTqIhamSb0lRsqQEg41Yom3W8kdOvFdJ1nWQyyf49e9j15JOkCrM5R+3AVcAyYIkJait8WCrqmagO0lddwy31tYx5PGQkibQooIlgR8OMzi2r9vDYRBM9RR+qG/xlgUZbkvbGYSqrIkzt9LL72zXkRkSGrvFRU84iLDYRuLhAp38AyZ9DL8qUSyY0u0p95xRaWULvDlAx5KJmrrbz1HrvjIyMYLVaef/7309n5+mPFh3PxfjCvt/w/616tzFgwfCqoeoi6jzUns1HGa9FRgBnMJyhdF3nvvvu495772V6ehqv18vSpUtP2uwmW4rUtg9Qu2AAyVQmNNxI96b1JMMVnKyKKINAj2AhJAu8qeNJ3qQqDB7qJKwcPyDL1WUxxy3Iudn3p5ry1IxYEU6yjadzrY6OjhIKhYjH46QiEWKJBCXtmSwIdcBNTisbWheinO/CdHWEXw9dwf5MLUlJwKHrWNHQRJ2iLJCVdURdx6doOBWdRY4YlzcN8LVfXMWFE+O4amfwdiUweRQm7vdx8PBCbLU2qt5XJN2Qp8bWg9mkgC6gF2XImtHGPZRdJXCUyAz5cfd7qVHE2RGjp1nBOD4+jqZp3HTTTaxZs+b0VgbS5QK37PoZ/7j4SpZ66k97fYPhpaIjoM3DIAbdmAfuuIwAzmA4Qz388MP84he/wGaz0dHRcdLBCe6KCHWLe6lqHCMyXseRretIzFRxsmhDA8JIDAhmkkh0yBm+/NZfsnfbOfSONp9wPR2dbGsS7/4KAHIOhbxDpT10bLNeJBKhp6eH0dFRxsbGyOfzxywDs3O7LXFbuPDiBrJr3sBf3fVkFu1jXX0/t++4lhnBg+ACs6aBpmNWNPwljUBOw1fWMOtQTJVJjWd534e3sGW/h6pVe7FcppLp91MYXEiy4Ka8MonpTWkEW5ySIpGKuXEOefFOWRDMOqWaNMWaDCVfnlhPFcEJBwuePoWneI/RdZ1cLkc0GiWVSmGz2bjhhhs477zzTq2AZ1E0lc/u/RWX1XRxWU3Xaa9vMBjOXEYAZzCcgXbv3j0XvDU0NJxgKZ2K+gmaug5hceSY6FnAlt+/hXLBesJyswhMITMlmEggEkClSS9TS46LL/kLkyMtjJ8keAMoVuYRFBFT3ALAVHOB6lErgv5UiitgIpXg8Ucfo3/vPnjWVJRW4CxgJWCv8JNrbSa2bCG+6wSKqp0nj7SREkRu7NxMqzfE1r9cxrqcGZuWQ9ZBLakkh3JkQwVKqTLRVJmIquNeVKD57WnOuilOiyfNI0+uIjdUxWDcR8wMrrOHcXoiqGWZzLSbyHAlk0UHl9nCKNUZMueHKasisXEv7GqkNimzWNSft19bqVSi+NRUIIVCgXw+T6FQwG63EwwGueSSS+jo6GD58uUvaEqXfz/yF9wmG+9vu+i01zUYXmpGE+pLywjgDIYzzMDAAD/4wQ8oFAosWLDgmPcFQSPYMkxT16HZTAQHOggNN6Ef5yKoAREkJgUTU8hI6NSgsEQv4kedm9qjY/lezOYie3ese979y7YmcQ56EBAo2FQyHgVfr5ODTpmhYo7BBx8huWkH+lMZTi4HLgPWmM0429sZXryY/vZ2cjYbmqtM1TmDjIQChHvr6FKKXLr+ccyWEofuuxh3yUQ+WmCmP01yKAuAp9mBu8GO2W3C4pZwLp7G2TlB8kAzq4QwP9q8nt8MLCGHSGNNlOZVwwgFCdNDrbizZnKyTLJSYf3SbhISJEb8mB6vIpiXWCQ+1ZQrnnj+80KhwMTEBNlsFovFgsViwWq14vP5WLRoER0dHSxcuJDm5ubn6ad4YmVN5T97HuBQcpy71t1k5DU1vCppuoCmv/jv5nyU8VpkBHAnYaTSMrzahEIh/ud//odQKMTSpUuPuXH76yZZsGYXSslM/85VRCdqeXY1kQakEYkgMSWYiD9Vy1ajKyymiPU4aauqqqdY3HmA+//0VjTt5HPIlV0lFIeCOGNnxCoxvSBPLGTj4MQwyq6DjO/eh6LMTqHxBuCfBQF7Zye7V67ksaYmNFEkIwgMWkzEAkXWrugn01NDe7+XLnOKjosfp1ywcuDBC1EKAr1/GMPkkPEtcBJc6UOwzE4MHBZFCpLOwlWH0Xwp7n7sPC6sHaeoieweaGJd9SS0xRDcReRhL7Z9QdAF+q0CxWXTLPclyB6ooWrSQfXTp0/UTnjcMDt9y8TEBLqu09raykUXXURTUxNutxuPx4PNZpuXQGsqn+Cze39Fi6OSO9e9z5guxGB4nTICuJP4yEc+wkc+8pG5tCAGwyspnU7zve99j76+vmOCN4c3wYK1u7C50vTvXEV4tAEQ0IAZZCYEmRgSRQTcaPhRWfycWrbjsVjznHvRw2x74gKymRMnQdeBmEmkvwbCfS2kXSp1Dz5K4bu7ODidIvesZtKzgS/YbNjOOotNq1eTcTpRgFGTzJBNRnbnaHfEWNo+ibSrAce0G6srTcfFj5OYDjLw5CqUgk7Pb8cIrq+g3O5hXJTYLcrkEPHrCtXWLOvP2UMpbSPy0Cou8KRY3TnAz6KtVFzRjaqLSCE71p015JN2+jSJfHucuqXTqENefLtbCKjSUU2kuq5TLBbJZDIUCgVKpRKlUglFUdB1HZvNxooVK7joootYsWIFFovlBX7SJ/ZE6Ai3Hvo//n7BpVxdv2reyzcY5pOK+DxXmFMvx3AsI4AzGM4AiqLws5/9jJ07d7J48eK5kaZma56WlfupahpleH8n40cWomkSCUSGBTPjyARQadTLdFDEdpqJ4des38zIYAsTz+n3VhAhapKImUSiZpGQWcSlaXj1Gdq++3/89WA3vc8aOVoty5zr87G2sZG6hQvZuaiZgkch7lbJuWMIrgKVzgyNtiJC2oKQtCFsbkFI2PHVTbLovG2M7Otk6sgClILGwXunKFzTQr/PTpWmUqUrtJbzOHUNc2UMx7m9JGYqyesmeEMfNqlItGwhKstYj3ixDPgYzlsZU2VqWyLUdU4SL9gRNjURTM3WaOm6TiwWI5FIkM/n0XUdi8WC0+nE7Xbj8/nw+2fzqbpcLtra2li8ePELzyt7EoqmcmffQzweOsJ319xIuys479swGOab0YT60jICOIPhVU7Xdf785z/z8MMP09raOlezY7blOest9zAz2MxDv7+GyZKdGUEmLEg40GjWy3RS4IU2sFUGp6mqnubPv7kOHZgxiwzZZcatErLO7CjPssrCbJnL+0JMPPp//ObQGKGnYsRqp5X2NV0s7uykpsMN/jwZf45D3gK62Es2b0XKWAhkTDgnrIgJH6SfGewAOg1dh6hd0kv3o+eRnKkiXtTZNqVTfMciFgtl1pQyyIAu6KgVWZT2CIXqDImSF8UhoAllrvcNExAUfrRzLaaeSnrzdiKqyJKWMOcsm0TKmhk42IQlaiNQLpPP55meniaVSuH3+1mwYAHt7e3U1dVRU1NDdXU1LpfrZel3puka++Kj3NH7IDU2Lz9Z/2Hs8vzX7BkMhjOPEcAZDK9y27dv5/e//z2VlZW4XC40IIWI5s7zo8cvo2eqHhM6QUGhWS+xBnUe/mPrrD57Kw89eR6bHQ5GbRLeskZrXmF1soT5qSDNMjlJ4k9/4s5QiLGn1qx02lh//Trq/64avzTb606PqwhxOwdH6ynvt7Mgo+PXTjxDlCiXWXTedqzOLHvveQORvJO9goVQWWOJr8gi0uAooXmKFKoyKMEsogpeZ5JY0oM+UokzJ3Djmp2oUTPf/cvlHMzbUVWNGssoq84OI5ph6rcaxbECfV2NtI0McDCVQpZlmpqaeOc738nKlSupqal52QcJTORi/Hb0SR6cPkCtzcc7Gs/iDTWnng7NYHg10BBfZJbjZ8oxHMsI4AyGV7GhoSF+9rOfURIlksEGDgomsoh4UFlsL1KbN9GoZ5jvxEmNbX38YXop92aX0KWUWTFTwvJU0KbrOlODg4w88ACPhsMUn1rH47NwzoeXsOKiRZQ1O5mEGXG/H/ImQGCnzUJJEDgnVzhx4CYpVC8YpKGrm/hkkE2/uZBdeTOFWpVmrZ8V61XEoELeqiBmTQgZMwoC6BpN7hDh3YtwDFRht5R491WPMB538qk/b0DLF6goHqBhg4K1XaAhspxFwipKF5bIZLN8KzTD+qVL8Xm9rF27lqVLl77gEaIvRl96mp8MPs6e2AjvbDqLH579QSOzgsFgOC4jgDMYXqWGJqf4/I9+zhFHBaaqFhr0Mmv1PC40JEnl/PWPs+V3b6E8z9tNWjV+bqrHOWbmncNxArEY04kEQ5EIY/EQA6NDjMZyc8vXNXhYfP1CVrasxR4KIGwWGVqRonrUhpCfDYIOWsykJJGLMvm54E3XdJS8SjmvoitF6rsGaD9vkOiQm79+ZxGD1QFsZyfoCiYxlSVMYSdS1I04ZEHImClW5EmsiOBPQr0tQuLxLvRpL+XSBNe9dRdbJ2q5/d7lLLSGab3CSq5Bost2Lm9ueQ8OyzMDMgZjMTo2PsSn3vHOeT6Tp25vfIQfDTzGWC7K37acz5c634ZZMi7PhjObqguo89B/bT7KeC0yrhAGw6tESVHYNzHN9uExtg2N0T8+gRSOck7ATYWeOWpZb/UMmYSHcvHEk/KerqQs0OMwEfaWuU48wMB/jHJfdzf7dJ3Cc5a1AWvaW1h04SX4V3rItqVxbK0EoGzWyDtU3HGZclahP64wHc/QPJHmSKyErukgCAgimJ0CXddM0/WWUUbHqrh3/zpKVeD4cJ7qaAnvtB1Lbwti/pnaMB2d9MIE+foM1f02qlb2ENu8lOGxAFOFGLe/fy87h9pJlt7EjR8OMyKEWOpdzobKTxKwVB1z3AdCM3QGX75BAbquM5GPcyQ5SXdqgp3RIRRd5e9aN3BR9VIkwWguMrw2GIMYXlpGAGcwvEI0XefQ1AxP9I+wbXiMqWSK5fU1LA14WRCbILv7CdpaW3HKx97QK+oniI7Xveh9KAowZJfpt8uoAlQN9dF0z6/40iM58s8asGozi7RUOmm1+Wn0V+G+4AIsDgf56izJjijefRUUU2WyUwVCTQXYqnLopyMoTplQnYtON3iutCMtFtECs02okrmIw5Mir5i5L7MSIaCjKBbsg07c2634tGOPWzWrJFaGQBdoGJZwr+xhy2Mr6R+rwpmf5K73b8NkX02hczGTmc2cHbiM6yo+iFN2n/AcHJwJ0fUSB3CarvGrkW08ETpCX3qagMXJEncdi921fGrpG+nw1Bv92wwGw2kxAjiD4WWk6TqbBka471APO0cnWFAZ4IL2Zr76xkto8ns5cOAAP/vZz+jv76e9vR2Hw3GcUnQq6sfZ//CFp799ICsJxEwiA3aZkFrCebgP/cgAke5u9kZjc8s2uhwsuX4JbRdXEQgvQow8EwSpZpVoxwwlU5H0NyByJITJJWOrs1NcY0Ie9xH+fyZMthLLfXGs1SlKJYnMlBdh0kTX4m5slhIHexdRjHnx5QUsiZMnuy95C8RXhbCMOnHaE5gWR9h2/1k4o1A/tImvfyGC6nfxk6zMhZVLeWfDP2AWn3/E5oGZaa5ftuy0z+WpSpXzfHHfb5AEkX9YeBkLXNXG5LuG1wVdF9HmIQ3W8bLIGIwAzmB4WWSKJf647zC/2rWfhVUVXLuig6++8RLMT83nVi6X+fOf/8wf/vAH8vk8nZ2dJ0xO7/AmEUSdTNz7vNvVgSGbxBGniYwkoJUV9L4hCod6yfUOEp2YRH/WJLsW4FK/h4YPriXwVg9Cb5BcbyURTSIviuR0HaUqjrczSmrMRjoVwPQeHautjGwrIjvSNDkL0DJDVc6CkjeRibiIbK7CK+U5a9k+6msmGdrXxVRPG/5TvDAXKnPEl0fI7K2ifWkfFh2Sf11JMJ5nWh/i8/8aQbZpxByf5jMNFyIJp3Zp03SdsWSSZu/zn8vTVdZUdkYH+ebhP/OW+tXc2Ho+otE8angdURFQT5Yw+DTKMRzLCOAMhpfQUDTOL3fu47G+Ia7qWMQP3vM2gm7n3PuqqnLkyBH++te/snnzZgKBAI2NjSdtTgvUTxAZr+OkmdSBhCywzWuhHI3hfOIg8d5+hoeG5lJZPa0NWG83seY8C+lLLiN/sURsrIJNjzaSUGT85jQeRwJPVYqalhyipKNlTHgqiwhulULJRLhkZTrtwGctUlKgqdc5V5tmdaVpXL6NQP0EE92L2LX1TajlUxs3m9UEpoMF5GVR0vsDnH/2XkqjlST2NBAxj2G6OM/ft8do99hxVP6OVunETaXHMxyP0+T1Is5T82VOKbI10sejM91sj/SzwF3N5zrfwrpA27yUbzAYDE8zAjiDYZ4l8gXuO9TDn/Z1IwjwzlVdfPKS87DIz/x3SyaT7Nq1i8cff5ze3l5KpRLNzc04nc6TlDyron6CkYNLT/h+XoRDThMDKEh330P35q1oz86KwGwC+XMdJnwXNjL99laSK02MqU7KikQ5a8FckWVl1T4Es46ekNB1GcWtI/RVERuqYEY2E5IlNAEqFZXmUpklisrhtXkqux0ICNi9CeqX9hBoHGfyyAJ2/P5qlNLJAzdNh7gmEVJlJlUZb2OcYNcMzSEV/7l7iG5vYSyaw3pBPy7dxA3VZjp9ZsSKXyOIJw7eCkqZkqLith496GM+BjCky3kemelm4/RBDifHWRto48LgEv5p6dW4TPM3yMRgONNo+vwMQNBOL4HM64YRwBkM80DXdZ4YGOG3ew7QG4py5dKFfPOay2kO+I5aLpvNct999/Hoo48yMzODxWKhrq7uBH3djiWbi7gCMeJT1Ue9XhZg1CoxYJdJSmB+bAdj9/6VbDYLwBqrlesKBd4AmFfV8eDKc7hnwVL8DTOcu/wQEwUXDPixjFrI7M4QH1WpXFiDbZVMoitOMWVlYHstKdVEraxSrSgsKxSxPav5NW9X0UWNBv8U9ef2YvOkmO5tZ8cf3oRSPHFftIwmMqHIhDWZjCbiE1UqJYVVi6bRF4RpExJQqXNgow/L6iTurI2LHSu4rG0TkmxB8P4YQfSfsPzxZJIP/fn/SBeLfGDNGt7dtQzpqXRXL3QAQ1Yp8thMNw9OH+BQcpzzKhfxrqb1rAm0YBKNy6rBAKDNUx+4+Sjjtci40hgML0JJUfjzwR5+sn03bRV+/mbtStY01R23Sa6vr49f/OIX7N+/n0AgcFRO01MVqJskMV2FpsrowKRFYsAuMW2RqCuo1HQPM/Kne5icnASg3mHlzlyBSySVHdcu4WdLN7DR2kalprImOIx/2RDTM0ES/1NFZFcOyVKiZq0f91ssxDtjxO1ZJvpr8YWtrCopeLXScRtuZXOJqtVHOLd2CK3CymT3IsLDDeja8fvxaTpMqjKD5dmJeBvlMsvMBVyChiBApiWJedEE9bYYMzu9jKV0/GstbBCu5vL1U8ilnyE4bwb73yIIx98GwI6JCf7prw/wlYsuoitYzTefeJxrD/0vX734EpZVV3MgNM0Vi9vYExsmXEwRL2WJl7LEilkSpSxppYCOjq7PTl8CoOoaY9koZ1cu4NqGdXxr5fVG0GYwGF52xlXnJO644w7uuOMOVFV9pXfF8CqTKhT43537+f3eQ1ywoIU73vlmGnzHnzG/XC7zwAMP8Mc//pFkMsnixYsxm19Y7oSK+gnGxxo55JQ54jDhUTQWZBXOiRXZu2sXf7z/L2iqhsUm8ymniS+G8/SvbORT17+JzeVmGjWFy8oZ7EuHkBZHSd8vceRHLgJLzdS+M0DILTO8IIEnOEV5yE/1TgdtKkDpuPsjmUs0LT9I9YJBBvMVHNy8ntL4sXOtPS2nCQwqZsYVE0FJYZmlgFfUnlWgitg5TnXbFJY87PyBE/sqC43rHXxs4VvxaneB5kAI/A5Bbj7pufrdoUP8984d/M+b38KCQACAr1xyEb/r38NHN/8vFodK1JbkX45M0uDwE7R68Jkd+MwOmh2V+MwO3CbbXF8+QWDu7yZHBRZjJKnBcFIaAidOmHd65RiOJejPHoJmOK5UKoXH4yGZTOJ2n14nacNrS7pQ5Cfb9/B/B7q5dkUH71q9DI/txP2cJicn+cUvfsH27dvx+XzU1ta+oPm+VCDqVsgsnGbXRDvNOZUlGQWnqqMoCvc+cA97d+4D4MIqFz+LZvFLMv921Zv5y5q1NGgKbWoJqzyDvn6CQCADW21snT6LkMPMjCxRUxmnvmUG87Sdih4vUvnENVugU9U6TMuavcTG6+juXUJPi0bHjuMHsTFVoq9sJqVLtMlFGuUy8tOnQdAwVyaxtUxjaQiT0SxEnnDAcJYN74TFVVmarWkEMYBgvxHs70E4yWhOVdP49qZN7JuZ5vY3volQOc5joW62RwYYy0Xp8NSz3NvIZKRMNq1x62WXn/bnYTCcSV7ue9jT27vhkesxO198or9SpsTPLvpf4x78HEYNnMFwCjLFEj9/ci+/33eIa1d08IcPvgfHSWrRNE1jy5Yt/OpXv2JycpL29nbsdvtpbbMkwLhVYtQmM2MWWeCdZkFO561Thbn/uIlShN//8n8ZG4khAl8H/l8ozY7FS7jtiisxedxcV91D+6pdKJrOQXM9rnSJQ70t7Mgtptqu0GzKsXDJDIg63u1VmNInnzvN7k2w4OydiCaFw4+cTzpcwVRbDv/M0ZeTsg5TqomBshmLrLA0EKfKnUF2FpBdeSRnDtmVR3YUcOdELEkdr1SiphCn+eoCRdFEiEaafB9FtJ6LINU+7zlLF4t84v57EaxFOhbbuGH77QStHi6oWsxnOq6m3VX9TKaD9tP6OAwGw2kyUmm9tIwAzmA4ibF4krt3H+CB7j7esmwJv//Au3FaTh7gpFIp7r77bjZu3IjJZKKzsxNRfP5OuIoAIbPIjGW2T1taEqgrqLTlFK6rOELTwl523HMFOmAp5NH33s8vHjpARNXxA98MVGBet45vLV1CyelEEgQytmGWnLeV4XwlMyYv8V1NTI9V4VM1rpLSZBYkyNVncPT4sI05MVlKWPwxzNYisqWIyVrEYs9jcWSxOHJYnTlEWWFkTxeTPe2gi+joxIIlFu92kdFEplSZhFmhumWa9qYpVnszSCYFJWtFzVhRszacCjSUFWpLCdorE6SQmfA4ycaamHQ08kA2SoNrPW+tu+mU5057fLKfz237I6Ijz+qKZrq89fz9wksJWJ5/ZK/BYJh/xiCGl5YRwBkMz6FqGo/1DfG/u/YTyeS4bnUXv//Ae3Banr8poLu7m5///OccPnyYxsZGfD7fSZdPSwJ9Dpkpi0RWEqgsaQSLKmfFi/gUHQGwuVMsWrWHXX+5jKbBMVbs2cGmQ938o6qjAE0OJ29/29sYb22hBMxIEqPDOZq1I7z5//XRr9QwE6+hOqKzZt0THMqcT1i2EV8awxK20rrfTkX1CP5lE9g9KYpZB+WC5amHlVLeSmy8lmLWQTFrp5i1HzUz+pRLo5yX2FEw09o2xfLWCZzuLMWJCgo99UTjTrSCiWAgyfL2YRa1H8ZqLnKgx8m+QzUcGL2WfQvGqXUEiDtGWepu4Z2VH6HW1vy857uglnl4+hA/6nuC4XSUt7Ss4SMdF+Ezn9qoXoPBYDhTGQGcwfCUbKnE7/Yc4n937WdZbZCPXHA2y+uqT7nP2rZt2/j+979PKpVi6dKlmEzH7+SuA1MWkW6niaQssjBb5rx4EfdTAduzCYJGx/mbSf+hjrf/z/9RNzzMP4jwg6f6/S/q6GDttW/jsN3GjC4i743i3hlmxVvTnPfeYY6ojYgJKzy+gGkdcoJMx5WPcCTWyNK0TuWiGVgoEB2rY3j3MpIzVSccOfpcOU3goGqmpquf8z0hXG/LzQZthxsJzfjQVQGLHGF52z5WdUzgsJXZvMvDXf/bDBVLaVnTiLxI5UD2MIqap8N3HusDH8Nl8h6zraJaZm98hL70NGO5KGPZKGO5GDmlSED0kQyb+f3ln6DhJcioYDAYXhiNeUpmbwxiOC4jgDO87oXSGX6xYx/3Herhqo5F/PRv306l8/RqcHbu3Mn3v/99SqUSS5YsOSboKwuzU36MWyUmrRKessbSrEJd4eRJYjpqtrH884M0PBhhyCKzPGjnyEwOBIHgG6/EvmED4WQR+4MTNA6kqerwsORTEvXnD7O30MwiOUT3I6soVuZJL0gQEawUt53DgsZ+UvEKDu5fTC7h4fmyOjybokNP2ULYV+LcC3dQYcqR29FOaMoPmgjouG19rF/ew7LFWfYdqWLj9rUo5tUU2mIUlvbit5Yw23TqbG3sSnfz6cX/SrWt5qjtjOdibAn3sjncy6HkOJ2eepZ66ljubeLqulXUWn3c+tgm8gWFH7/tcuwnCJgNBsMrQ5+nUajH/rQ1gBHAGV7HVE3jp9v38KvdB/ibtcv504duwG4+/SBg3759fP/736dQKNDW1jYXvJUE6LfLjNhk0rJAbVGlrqCyJlnC8jxjv635PBfu/CvrntiHrsKdG+r51P4Y2ZkcZquVa976VmotdYT/t49SWqF6jR/fuQEcvhS15+9jf76J9fYh+resIr4yiupQcPX4sIRslBA41Fd3ysdX0iGhSbMPVSImCDSvmOKqJUcQonZSm9egKzKiqLKopY/VSw/icamMRTYQ0T/EsvNasZb3ct/UL1noWsbfVv8H7qdq2fYn9lJrq6faVsN0PsHu2DC7YkPsig0hCSLnVi7k+uZzWOlrOmraDlXT+OQD9+M0W/jmGy6ft1RYBoPBcKYwAjjD69JoLMFn/++vtFX6/3/27js8qjJt4PBveknvvdNSSCEhofcuKKiICva6C66urrvqrrr21d1VV8X62StWEEF6rwFCIJDee51MJjOT6ef7IxqNSWgG67mva65dzrznPWdOTPLkLc/DZzdeeUbr2/qTn5/PK6+8QkdHB8OGDUMikdCmkFLoJqdOLSOmy0Fmhw1f+5n9HWqzWEg+eIgLDuzDw2Ihd1wY90R7sfmjQlwuF77evkyMmoFjvwJ9uJGQTD/cgrrTmMhVVoZM28uxrgjidFY6LD7kDxXQ1CnwORqI5AymMpzfBGs6lwydsztok0sEvKUuvGQOAiMNhCU0kOxXjflkJObCCDQqG6OSCklPKKK5Tcrh/FQSRt1ObGYULdYG3mp4EoBrou8iQvtdTVCL086XdesxOzy4cMd/kUuljPKNJsMvlluGTiNQ3X9Kku8Hbw9NmyYGbyLRL5RLGKQpVHEXar/EAE70u+ISBD48fIx3s3P5x5ypTIiLOue+ioqKeOmll9DpdAwbPpxKrZyT7goECYwwOsjS2075DeYEGuQyKuUKbBYbl+3ewzV7d+NhtVA3yptXrxrFS+/WUfZBPgBRfrGMDhlPZGYI7iHqXtO0EomL4VP2kG8PRVXhhX1EBQf1MXgf80fVpjnF8/iu9miLU45ZkOAjdeIrczJEYcNb6kRQuGgOs9IcZiXE0UWSTzX6XUn442TG9H3ER9dystWdtypj6PAMRZoukOt6GvdSL7wVvozzn02a93gkEglWp5119bnsaiqgwFBJtGc1c4Ju5M/DEwhQnz6/kxi8iUS/HuIu1PNLDOBEvxtNBiP3frmRAHc3Pr7h8j6Fzc9UR0cHmzZtYtOmTeg7OvBITWSttwoPh8AYvQ1/u2vAc80SCTUKOeVyBR3I0FhsLN+0hSv37UFrtdKY7EfxXUGsNMfx2d+zMeiMyGQyRnplMG7qGAISvfvtNzYzhyqVJ45GT2RxbZS2huO/NxxpP8l4LYKEGoeCZqecTpcUb6mTQJmDUaou3CQukAhYtC46ve1U+jgweNvxbVYyIsed2KmFhLZqmD9jD15aMwc6/fjiQBJahjE6YQyJsSPxVPiilbn3Sf9xuK2cx0+uIdk7kiVRYzE4Ami2RrAoYswZPXcxeBOJRKLviAGc6Hdh/cki/rd9H3dNn8Cs+KHn1IfNZmPPnj189dVXVFZWIouJoDI1DrkgMK7dRkA/gZsTaJHLqFPIqZXJsbskuFwS/B1OFtdWsOLTjwlqaaE2IZj8v6nZ2BHIB/9uJ//kNhDAU+tFVsBk0i9OQuXZ//q8kOElmELNGDqCsAWa8TSoUO2I6yn7BCAI0OaSUWZX0uGSESm3kai04PVN7VEBgQ5fO+UhNgzedhQ2KR56OT7NSkY1OBgRVcWIS8oIczdS0eXGhgI/CvPSGBs3k1vGjCU8PHzA3boGexf/K9zAcX01D4y8mFSfKARB4OH8t7gq6vozfv4PbNuKm1IpBm8i0a+EOIV6fokBnOg3ranTyFObd2OwWHj3msUEepx9UldBEMjLy2P16tUcz8vDEhJAy9TRuKQS0jtsBNt6B252oF4hp1ohp0khx8fuxOWUIDglxDttDHFYmZSdzczNm3Eo5Bx+OI71PlLeedREWUl2Tz9RnnFMyZxO1LiBU5l4BTfhllxOsTmMTg0MtVho3p7eE7wJAjQ45RTYVWgkAnEKG4HSLr7tzqJx0hpipS3Yhtosxb9BRVSRFrlDitTNwsUz9hAd0EqR1YMip5xnPxiKsjmeeXPmceXyDDw8PE753LY2nuSZwq+5MHwU749bjlLW/SOnylyJS3AR4xZ7Rl+DT0+epM5g4I1FF4vBm0gkEiEGcKLfILPNzpaiUtYcK6DZaOKqzFQWpyWdUw3S5uZm1qxZw85du2jw1qKbPAoNkNppJ9j63cYEO1CjVFClkKOTyvB3OFHbBXztLrokMkY4rUQ77ciAi778krTcXOqHBHLyGXduv09PQV4bAFKplOHjoxnml8CwkES0gSrUHkZ8QhvxDmnCM7AFkOC0y3Ha5Tjc7ByzRNGBkixXA6XbJvBtSpBOl5RcmxoZkKUy4y7t3vrqlAq0B9poDbFiVbvwb1Ax/Kg7CqUDl48ZR0oXLv9OrgkuwWWT8chX45Cpa9CoVUyKvoZZN886ZeAG0GIx8GT+WlqtnTyXcTVxHkG93t/buovx/pPO6GtS1NrKS4ey+fiyJWLwJhL9iojF7M8vMYAT/Sa4BIGDFTWsySvgSHUdU4fFcuf08SQEB55T4Ga1Wtm5cydfrF1LntROe1Y8AS4JEzrs+H0zVeoE6hRyKpQKWqUyvO1OZHYJCsAuSPF3ORjqsuAjfPfjJ+HkSdJycymdFUHxo+7ct6KBgjw9Ko2EeZf5MG1mCDXx8Qyrs6NWHcMntAmpzEl7fRC62lAqDqciCBKkcjtCsBHTsHZMLgWzFSUcXTcTl0uGySWh1K6ixSVnpLKLIJkTQSLQ4WOnNdiGwdeOR6eUQJOA1mnBFaXHkWTFZVIgbdegbVewNKIcXZmC/7wWgmeIgfTrBWb5PElsRPwpn5sgCKytO8rLJVtYFjOBJVFjvqs9+g2by8aR9kP8M/Gx034dTDYbt69fx5MzZ+F3lrVkRSLRz0ucQj2/xABO9KtW2dbOF8fy2VBQQkJwAAuTE3h0wUzkZ1B7dCClpaW8+dGH7OhsoXV4MFF2yGy34+nsHsHqkEo5oVRSK1Ogcgq4HBLcBBfeLoEgl50gl4O+q9UE3C0dzP/6K4yRaor+6cNTD1ZyLNuAQqFgSuRcvMp96HBKcLOaiPIwopIJOGvDkLpk+CjtqCIbUcTVYpYLGNyd6J1KWp0qUpUFrPvyAopNHtQ55UiBSLmdkUojUgl0etkpSzKitknwFmwEqA3INCDTaZG2q1HUeCHtUNFltKDXV3HDDQXU1rjx5fZM5l0wCtXoPYwMvpJYn1MHbw1d7Tx2Yg0OwcmrWTcSrvXtt93R9iMMcR+Kp6L/NCE9T0wQ+PvWLVySkEhG2JnnrROJRKLfAzGAE/0qFTW18sy2PejMXVycmsjH11+Ol+bcdpV+y2az8dWGr3nu6H7qfN0ZpvRjos6O2tVd/qpeLuOEUokeOTInxFtsBLkc+AnOb76RBFRuZtx923Hzbcfdrx13Hz0KjQWZzEHiHxrRdFk4+GQ0z/5Lx66vDcikEh68ZRiLL28hIKiKDQ5/Al12ortcdJo0WOUurDYZHUYtFnc7Vt9O3KxytF0yotxNzNRWIxdkDF28kbo2H9p1XrTpvGlo8aGpw5POcBOlMXbCVR24WSTIGzyQF0YgNap6PrfD4aCzM5/M5BZmTTLQ0TWK4GF/44nZcdRaD5DTtot47wUDPjeX4OLT6mzeLNvJzUOnsTA845SjnntbdzE9aNZpvx4f5uVhstm4MT39zL+IIpHoF0McgTu/xABO9KtSpzfw3I59lLS0cee0CYyPjTynKdIfqq6u5vmPP2SttItAqZTFbXaUSOiUSihVKShTKHA5JeCUkOawEPvNeja50oZ3aCO+YfX4hDUgkQgYdT4YdT60lEdRoUvFatYwrnEnQRtLOHHBCP7znowt604ikcCzj8QQnzScwyU+6I64UTWmBc3WWHZbv/vWFHzMuFLrwKlCunk4HQEWusI7udptD099OR1nUxDBXp0E+bUT5Kdn2PBKpk7IQSp1cczkR02jP6254XS0eOP8Xp1TjaqLiMATpMXXEhlqx2DNwjPsBnw03TnbnC4bB2tfYnLwX5FK+q+P2mIx8I9jn6CVK3l73K0DJt+F7kBvT+tOGi31JHqOPOXX42RzM28ezeHTJZcPytdXJBL99MQA7vwSAzjRr4LZZud/2/exu6ySP07K4omLBqd8ktlsZvuOHTybvYsqHy3j2h34SlUUqRRUKxUIAqjsAoJdQqLDRpzThlzmJHhYBYFxFbj76ulo9kdXG0pNXgJdBg++X1dUqbAxLXkbY58+RHukktcDm/jszXYAFi2cSattHLtzuts6/E1IjEqk3wRvgsqOkNSAEGhEkheCUONNZawRSYiZGwIPUHwoA3lzEHIJtBs8aJXJORoqIJjckeSm0RVtYpzDQIp7K+GzynDTWOk0a2g3uGGxOIgO01NY7ku7dSFhPssJ8Qrs9WxO6r/ARxVDqDat32e3r6WEx06s5oa4ySyKGH3KQKvMWMpH1e+ilbnx52F/Qy4d+EdPl93OXRu+5t+zZuN1jrn6RCKR6LdODOBEv3hOl4u7Pl9PfHAAq29ZhlLW/2jQmWgyG1lXXkSHpYvSmioKy8upFxxotSqGWuQc99QgFwQibA5iu2yUSFUEOR0kOM2oFHZC4ksISyjGpPOmJi8BfUMQLmfvbyNBEJCYDIxOKGDazHJc19XzWruTeywyOr4J3qZOncrI5HG9P2doJ/J6DwQEhLg2hBFNSCp8kW4ajt6moDy8i8CIDq7yOUxzaSxVpcO6z/OwYE1owe5pQ1Luh0unpSHGgle1H4WV4RQCLpeLTkMLElcd/r4WAv21HCq6kmkzLiYiIqLPc7I4Oziq+4CLIp/v857d5eSlki3sairg2fSrGOoZPODz1tv0fF63itLOEhZHXE6qd/ppR9Se2L2LC4YNJzUk5JTtRCLRL5s4And+iQGc6Bfvyc27CPXy5LbJY895Ok0QBL4ozee/R/YwyTuQmvwi6qqrMXl5IfX2RYOAl83OCIsZk0TKcbkarcTFdJsJD7mdiLR8QoeX0N4QzIktkzHpfHBYndg67FgNXdBm4qYD6wjuascVrcQ7U6BTpeLdj1w8u9VEAUCXDV9fX2bMmEF8fO8NAQICjtBO1AfDcE0oB5mAdMcQXEYV+XYVxsAuouKbuVBeiKXdiwMlKbQN76Qj2IpdBoLLDbnRA4W3BIXWinerguBqNVarlbq6OoxGI76+viQlTSMjI4OkpCS8vb0HfF5HWt8mzmMq3srInmMuwUV2WzmvlGwlws2Pd8b9Aa1cNWAfx/RHea/qLSYHTGVZ1HUopaevN7u9opyC5hYeuGzqaduKRKJfNoHBSQEi/Phb+U36TQVwixYtYseOHUyfPp1PP/2013tms5n4+HgWL17Mf/7zn5/pDkVn673sXKp0elYuufCcg7cms5H79mzC6nBwvcyHbZ98RaW3Fx0R0cTanQR2WmmXyKiWKilQqPETHKTaLQQKTtTueuIn76Gj3o11D6bTWijHbuoAOpCpZCg95Wg8ZNxR8DXx+lqc47TYKy20v9fFcgE+++YetBoNkyZPJiMjA1k/I4guH0v3T6lxFUhKApAUB9DulJNj0xDubyA6o5bhbV0cDQrgiCMKWWoHHuouIus0uJX6ILNJe1VeMJlMFNUU4XK5iImJYfr06aSmphIYGNjn2j+kt9VQ2rmFy6LfAaCxS8/auhzW1uYQrvXl6tiJTA1KGPB8p+BkTd1n5LQf5rahdxKpPbN6s21mMw/v2MFbiy7+UbuIRSKR6PfgNxXA3X777Vx//fW8/fbbfd577LHHGDPmzGouin4ZthaV8cXxfN65+tJz/oX+VXkhT2Tv4rohSTTt3s9rdXUYhg8j1CmgsQhUS5QYZU6CXQ5GO7rwFFzoCg20FRjwHN3I6BUV5H4aS21eDBo/FdEzVSjcZD3BpFTq5JoDHxFVW03d9UP4JGk+Deb1vPRUBZ0GOzKJhImjRpE1YwbqAdZzCQoH9lG1yGROXLtjadJ5UOdQYBSkjPLuoDOrAVOHG9l+Mnz17kR6t6Gt80BZFILE1vtb2Ol0UlRUhFwuJzExkenTpzNq1KgBr92fgy0vk+S9hL0ttXxZt4ayzmbmh6XxUub1hA2QGuRbepue18pfxE3uxn3xD6KVu53RNQVB4L4tm/nD6EyiTjEyKBKJfj1+zinUlStX8u9//5vGxkZSUlJ4/vnnyczMHLD9s88+y0svvUR1dTX+/v5ceumlPPHEE71+dp5tn+fbbyqAmzJlCjt27OhzvKSkhMLCQhYsWMCJEyd++hsTnRW708nKXQfZVVrBS0suwk15+qm3HzLbbTy4fxsFbc1MUgfy6sbdtMukqHxDUTlA6XIQ7rIz1tXFt+NhgkugamsTguBg9iP1+Ec3UbBjOna5L0F91vELDIuu48KSDbh/XUdR5gjeDJjNnnXvsndrd1WFwMBAFi5cSMgp1nIJ7lYcE8qxy6QUbRuOrs2DIJmDGIUVaZiZ2vhOsGqZFlCEl8bEyY54lLsikZr7PhNBECgsLCQmJoalS5eSlJTU72jfgPciCGS3bqW8M49ni7SM9JGyJGoMY/yH9knG258iQwFvVLzK9KBZzAyac1Yjph+fOIFEImFxYuIZnyMSiUT9WbVqFXfeeScvv/wyWVlZPPvss8yePZuioqJ+ZyE++OAD7rnnHt544w3GjRtHcXEx1157LRKJhKeffvqc+vwp/GLmKXbt2sWCBQsIDQ1FIpGwevXqPm1WrlxJdHQ0arWarKwssrOz+3bUj7/85S888cQTg3zHovOhsq2dZW99gs3h4KPrlhDkefa1SzeUFTPhw9fYWVKBrrqTz4tKcBkNTOsycaHNyEU2I5kOC6EuR0/wZu9yUPBxDd7RsOjZfDTeFnK+mo1R19+Ik8DciYeZb9mG9ol6KqOieGp4Bq+89iJ7t7YhkUiYNGkSN9988ymDN6e7BeuEckoLQ3BY5QwzSZmh6SQi3EDTOB0NIzoJ0MN875Ok+1ZSsmMM6kNhAwZvJSUlBAUFccstt5CSknJGwZtLcJHXXs3/Cjdw+Z7H2Nf8LFr5XD6YcAdPpV3B+IDhpw3eXIKLrxvW8kblq9wYeyuzgueeVfBWpdfz8uFDPD5jppgyRCT6Dfl2BG4wXmfj6aef5qabbuK6664jISGBl19+Ga1WyxtvvNFv+3379jF+/HiuvPJKoqOjmTVrFldccUWvGONs+/wp/GJG4EwmEykpKVx//fVcfPHFfd4/1+h3zZo1DBs2jGHDhrFv377z+RFEP4IgCHyWe5LX9x/hwbnTGBPTd2fkqThcLt7POcbLxw6io4vLY0dSWlNHoaGSjMpqhoSHM1BsYGqyUP51A4lLtGQtzaalIorKoyNhgMW3Y1MKGKovR317LW0+PtwWF8f699/H5RIICPBn4cJFhIaGnvJ+9W42lBMraD0aQYynBUmDOxY/GwVxZqQuCb4SC2qJFb+QDkb7lbHlq/lIdQMHs9XV1Wg0Gm688Ubi4uJOeW1BEMjRVbK16QQ7mgoI1fgwOcCDeSE5pPlexUify84okLI4u8hpP8zulh0opEr+Hv/P01ZX6M9/9+3lL+PG46vRnPW5IpHol2uwp1ANBkOv4yqVCpWq90Yqm83GkSNHuPfee3uOSaVSZsyYwf79+/vtf9y4cbz33ntkZ2eTmZlJeXk569ev56qrrjrnPn8Kv5gAbu7cucydO3fA978f/QK8/PLLrFu3jjfeeIN77rlnwPMOHDjARx99xCeffILRaMRut+Pp6ckDDzww4DlWqxWr1drz7x/+RyMaXHank4fWb0PfZeHD65bgfZYVFQ5W1fDAlq0YVBbiwwO5a9QE7li7BldZBfHVNcTFxvYJSARBoLOmi8YcHU6bi6zbVCTP20tlTgqNJQMHQImRFUzZuxPZ/9polyuY4+9PzrZtAIxMTmD+BRehHGDK1ypIaHLKaXe3MnRSKa6j4YS3qzGPbKK504uuIWZCq5S4RrTi9LDhVu7OtJg8ivJG0qHzH/CeGhsbsdvt3HTTTaSmpp7yWTlcTv5TsI48fQ0XR4zmhrip6Kz72df8AlOC7yHKfewpz3cJLgoN+exv28sJw3HiPRKZF3IhiV4jkZ7BNOsPlba1UdrWxrNz5531uSKR6PflhymPHnzwQf75z3/2Otba2orT6SQoKKjX8aCgIAoLC/vt98orr6S1tZUJEyYgCAIOh4Nbb72V++6775z7/Cn8YgK4U/kx0e8TTzzRM3361ltvceLEiVMGb9+e89BDD/34GxedVkeXhT998hWp4SE8PH/GWSXnbTWZeGTzDo7pG+jS2PlH1lTcpUpuXv05YSVlSKtrCHSPpPFIO1K5BKlcilQhwaq301ZowD1EQ8TEACIzGojLOkDRrrG01w885ZlmPMGCp75CWmRle1gYSy0WGoqLkcnhggWTSB05pU+gaHRJqXPIqXcqcCIh0k9PwpRiJIWBOEKNmDIa6LBq8KpyI9SnE1NGCw67Ar9tEYyILEOltHIyNw0QkKrtyLQWHEYNgq272qpOp0On07F06VImTZp0yudltFu4N/cjvJRa3hxzCwqplEOtb1DWuZUFEc/iq4oZ8NxmSxP72nazv20vvko/xvlN4IrIZWe8SWEgzx88wPLMrEFJyiwSiX5ZBnsErqamBk9Pz57jPxx9O1c7duzg8ccf58UXXyQrK4vS0lJuv/12HnnkEe6///5Bucb58KsI4M40+p0xYwbHjh3DZDIRHh7OJ598wtixpx5R6M+9997LnXfe2fNvg8HQb7JT0Y9TrdOz4pO1XDdmFItSzm7xemFzC39YvQanp8CoqFAeGDuV1QWFvHBsHyNyi2jZV4tCr6U1uAOvKDdcDgGXw4WjS0DhLidxaRRSuZTgoaVEpZ4gb+NUTO0+/V7LzWRi1t7NJB84hs1NxcNZWfw3Nxer1YqPn5xrb5qJp+q7nUiCAI1OOaUOJXYgxsvEaJ82FEGduKL02KwKGNpOZ6cb+sZAIkwOSKvB1uaGw6LEf18wvmFN+KQXcaQhBr/ZR5C5dyE4pbjMKmTuXbhsCrqa1ZjKbcyZPZqMKVEYHc24yf37LXvV0NXOHUfeZXpQEjcNmYpDsLC5/nG6nHoWRr6ERu7d5xyX4CKn/TDbm7fQam1hjN947hz2N4LUAyfuPRtlOh3FbW08I46+iUS/SYIgQRiEAO7bPjw9PXsFcP3x9/dHJpPR1NTU63hTUxPBwf3/7Lr//vu56qqruPHGGwEYOXIkJpOJm2++mb///e/n1OdP4VcRwJ2pLVu2nPL9a6+99oz66W9eXTS4jtc1cvfqDTxywQwyo8PP6txtpeXcv20Ldjcnj4+dyYyoIawtyOe9d77Ga30R9Z1mYsaF4z/CE6l84Gm9oCHlRKWe4PjGaXQZ+v5QCGloIOvgQZJOnEDudFI7JZLrbOFs+WYt5YiR7tz653G0V3UHbzYBKh1KKu1KfGQORsY2oUhqApcEaZccwdeIvNQfV70n5bE2lDInYX6tyFxukBOCK7WeuA4X2rnZuMus6JoDMJSF4ejU4jRqEOzffrsKWCStGIQqUsfHEpEg43Db/9Fpb8Ti7EAr98VDEYK3IgJvVRRtVhWvlmazJDKBEZ5GDre9QaVxLwHq4UwPfQCZRNHrcwuCQF7HMVbXfYZGpmFOyAUkep7bFOmpiKNvIpFosCmVStLT09m6dSsLFy4EuivRbN26lRUrVvR7jtlsRvqDVFXfbgITBOGc+vwp/CoCuF9q9Cs6NwaLhXvWbOT5xfMZFjjw2q4fEgSBtw4d5f/yDiP3lvLOrEsJkqh4+M6VbP3yCFq1CW2SkqSE+NPuwAyMrSB61PG+wZsgMKKoiHH79hFZU4NLJsF5gSe7shK56e0Wiou7g7fZF4Vx/Z9iyN+dBYDFJWG31Y0ImZ2J4c0II5tAkKA6FIZUEHCNq0B6KAKTUUNZaifeKjMBZhfao4Fow1tQTypDYlZg0Ycgb1GSNCqbr7Yv7lOmC8BoNFFe3sCsWfO5ZtJ1vf7YcAp2TPYWDPZ69LZqsluzqe8qYW6IBrXUQoslEHd5IBl+1xHtPqHXlK9LcFFgOMna+i9wCS4uDV9CvGfiedkZWqbTUdTayn9nzxn0vkUi0S+DC8mgVGI42z7uvPNOrrnmGjIyMsjMzOTZZ5/FZDL1rKG/+uqrCQsL61letWDBAp5++mnS0tJ6plDvv/9+FixY0PO75HR9/hx+FQHczxX9rly5kpUrV+J0Os/bNX5vBEHgofXbuCoz9ayCN7vTyYObtrG7pYLwIE9ennEhX//va9a8tZHSFE+Cx7sYGRJzRglrA2KqiB2dy/GNU+nq+C548zQYuGDdOoYXF2N0c6PyiuH4LBf4x7thvP+fXNrb25HLZSy/J4oJ06PI3z0eBCkWQcIeqxvJfno8Rzbg8rShOhmArN4DfMy4xlUiyQmn2cdJfUIHYQ4zIdUKPIL0KCdU0eZwo/PocGQlgchkDqZf8glH9o/rN3gzmUyUlZUxffp0rr322j4jxTKJAk9lKDKpHy8Ul9JhH8bjKffjoxp4rVqTpZF9rXs4qNuHt8KHOcHzSfFOO68pPV44eIA/ZmYiEysuiES/WT9XIt8lS5bQ0tLCAw88QGNjI6mpqWzYsKFnGVZ1dXWvEbd//OMfSCQS/vGPf1BXV0dAQAALFizgscceO+M+fw4SQRB+EWXGjEYjpaWlAKSlpfH0008zdepUfH19iYyMZNWqVVxzzTW88sorPdHvxx9/TGFh4Xl/gAaDAS8vLzo6Ok47/y46tTXHC/g6v5iXzqI0lt3p5E9rviLX3MC06Fj+ljyO/938KrWGGvZkeZFgtZKoPLMpb3dfHUkzd5C36bs1bxJBIP3wYWZu2YLCbid7ahY+9wt8vrWF/76ko61ND4C3twcPrwzF13sIJdnpgAQLcNLPTtzwBhQeVhQlfigqfEDmQohvQohsx1ntQ3UgdDkVxNcJBEXVIlHaMZaGURckoKrzxL28O/3GyLQj+Aa0sHNT35Eps9lMSUkJkydP5uabb0ar1fb7GQs76rn/+CdMCYrn1qEz+s3jZnKYOKw7yP62Pejtesb4jWOs34RBW992KuU6HX/8ai3rll0lBnAi0U/gp/4d9u31slb/Cbnbj1+O5DBZObjwOfF38A/8YkbgDh8+zNSp3xWw/nYTwTXXXMNbb731i4x+RWenpr2DF3cf5MNrzyzPGHTnd/vL2g20CWZmxw3lep8RPLjgSbTJSvbHezBEIjnj4A3AN7ye5rLonuBNYzaz5OOPia6qoj4kmNI/jyTHeoJHL9HRputOJaPRaJg4JZlb/mGgvTaJkuwEXCoHlig9XdEdxBkVuFd4I2v06K5nGtaBK7kemjwwGrXU+ipwa5cyTtqKJrGVzuMxmCuC0Y1uRt6mwK28+weSm7uB4Ul5bFjdNw9ic3MzjY2NTJgwgZtuuqnf4M3qtPNa6XY2NRznb4kLGB8wvN9ncLBtP6tq3mekVzILwy5lmMeIQV/fdiovHDzIHzOzxOBNJPqNG+xNDKLefjEB3JQpUzjdYOCKFSt+1gWDonPncLn46+oN/H32FHzd+h856u+cv6z9Gj83DUd0dSyrCeWFB/6P4AsCecfWzjA7pEjObgDZM6iFhqIhPf+etHs30VVV5C5JQ3qriX/8fR+795kACAiWc+m1/sxZ5I+7l4H8g6NpcPjgGF+N08tCU5Uvmj1RhJhloHKArwlXfBMonbgOR1AXb6JdoiGx00R4ZC2WmgBa1mfissnRp7QicUjxzPftKUKfPnY/RSeTMHZ+9xem0+mkrKwMuVzO5ZdfzoUXXtjvNHFuexWP5n1Bul8MH4xfgbuibxtBEFjXsIYDbfv424h//CSjbd9nsFh47cgRitta+ffs2T/ptUUikei35hcTwP0SiWvgBocgCDy5eRdJoUFMGhJ9RucYLBbuWb+ZME8PYkO8yT5qobK6hMilMbzYUE1il4VExVnWSJW48Axoo2j3WNTuRkLcqhidm03XFC2Hx9Zzx/xWTCYnCoWcmTOnkjkmBYVaILfATFdwF/Z4C/ImE8Yqb9oUTiICO1CPa8OltYFDCiYlQp03TQ4N1tR2IhV6MiQmBLkC3Y5kHHoPAMxRBpxaB34Hg3qCt9Dwanx8dezdNr3ndk0mE6WlpcTGxrJ06VLS0vquSzM7rLxQvJkDrSX8PXEh6X7953Kzu+y8W/UGrdZW7ol/AHf52ZcoO1dmu523jh5lVV4el41MYtVlS8TRN5Hod+DnLGb/eyAGcKewfPlyli9f3jOfLzp7TpeLh9Zvo8tu5/ELZ53ROdnVtdy/YQtXpacyxTOQeavf5W8R6XTEtPN8WQnJ7R3Ea88ugaxMYSNkaPcay9Hzv0YmcxD7pAGzxcWl7Uo239G9wzk8PJyFCxfiE+mBJUqPMbIDqVGJvM4DZbMWc2gn7skdeNV5oaj2AaMKzEokTimdw1tQjWhgpKYDXFJsJSHoa6JxtLvzbVkuAQFTlAHv4wFIXN1BjFTmIGPsPo7sH4vzm40LjY2NtLa2MmnSJJYtW0ZAQECfz5Snr+Efxz5mWlAi749fjkbWf0Brchh5qfR5fJQ+/HnYX1FIFf22G2xddjsf5eXxdu5R5g8fzpdLl+J1BptMRCLRb4M4hXp+iQGc6LyxO538bfVGvLVqnrxgzmnzfdmdTp7fc4Bd5ZW8cPECdAcrue+ZN0i8OJZOiYHni0tIbWxiuHf/CXc9/FrxCmhDKncg+95L62XA07+NLpMbXR0eFO/NJN6zgcjPPuUCdyWbj+mRSqVMnjmJzMsTcUV20qXVIa/2Qp0diiSqHWdiPU21vsjzg/Fs0yD9/rZ2hQ3plBIifdrpbPGj0RyMYmccEmffUSaHpw0koNB/F2wljDyOweBFbXUULperZ8r0qquuYt68eSgUffO0fVJ9kHcr9vBw8qWk+UYP+ExbrS08V/Jf0n0yuTB00U9SLL7DYuHdY7l8cuIkc4YO4ZMllxPg9uMqNohEIpGoNzGAE50XFruD2z/9ihFBAdwxddxpA4dafQe3r1nP6IgwVl21BIVUykv//h+OuxMZYYH/lRSSWtvAcD+/fs4WiBp5ksiEQlqqw3E65DgdcuxWFRajG211obQ3BDN0fDYd9YFMSS4h8atcHjcKbMSGTCFjydOzCB3tDfV2lAUBSI0KGN6Ma2wLdcVB1K4ZRarEjlYq9LquMrIF98xi2gzeNOYNxxneiWZXVL/BG4A5woim1h0JEtw9O0hKOUp4VBUbvlyIzWanuLiY0NBQrr76ajIyMvqc3+W08fiJNTRZOnhz7C34qzwGfKaVpgpeKn2OBWGLmOB/6jJbg6HZaOSNnBy+Linh0sRE1iy9Em+1WKBeJPq9EgZpClUcgeufGMCJzovHN+1gZGgwKyaPOW3bg9W1/P3rzTwwcyqTYqMBOLL5GEEzhnKwrYXcBgMJ9Y39Bm9ylZXEiXtRKG1kr52L1TzQSI+Ad2AzyR4WGiwKNr7cyoPfvDP7pgnECklIv9aA3IUwogkhS4euNJDja0YxAidjZTa+H4NK1TY8sgpx+ZkoLR2Ke7sSx7A2NLujkNj7TyIsSAS6QkxEHvEiY/I2QiOrKSlIYO2nl9HSbKeiooCUlBRuuOGGfku3NXbp+fORd8nyH8KDIy9GLh04WfFxfS7vVL3BddE3keg1csB2g6HOYODF7Gz21VRzdUoqX199NVrFTzNNKxKJfrkEuksLDkY/or7EAO4UxE0M5+ZYbQMFDS08eP2007b9KPc47x7J5bXFC4nx/W5qdMMb2zg8W0tbaydDG5sZ6ePb51wP3zaSp+2kuSqS0iNpCK6BAxqlTwddci8+kgcS/mIJf+uyIwDp6emMDpiC0CZAaAfOlHrMDV7kfJmOnwMmK6zI+/njTzmyinZPgbbsFLwFB7akJjS7o5BaBg5cLIEmZHqBaZPXcPK4L++9lUqXWYEglKFQKJg1axbLli3Dw6PvqFqxoYG7ct7nD0NnMC8sdcBrCILAlqaNbG3exO1D/0KENnLAtoPBZLNx7Refc2N6Og9OnYryNBUwRCKRSDQ4xADuFMRNDGfP6XLx8IbtPDh32il3GjpcLh7fspMqvZ4Pl16G5/cWt7c3d1BkaCHX4s2QuhbSvbz7nO/mrSd15naKszNoqoge8DourQ17nA5HXCtGpwrJuhBeWPUFeiAsLIzps+fQrHKiTKtForVRtGsEKp2WdIUND6Wr3z7bgqyERehwHIvFx+HEmtqEZk8kUnP/mwgcDgdNTU3YhxmZ4l+Nvm0kwX43c/VV3TV3lUolWq2W+Ph45PK+35L7W0p45MQXPJR8KaP9Ygf8rBanhXcq30Bvb+fe+AfwUvR9boPtX7t3szA+niVJ53eUTyQS/fq4kPTstP+x/Yj6EgM40aD66MhxRoYGkRw2cI6xNrOZP69ZT3xgAK9eelGvQM8lCNx//2scHKMhqLaFce59R6M0Hp2kztxKyaFR/QZvgkTAGWzEHt2Oy9tCgmBiilsVGw5Gs+GFT8lzuXBXqQi98BoKg5zEjymlqzAYTbkfmQAqS7/3LUgEauK6MPpZSZBaMRnVWNIaUe+LQGrsm0zYbrdTXV2N2WwmODoQzTA5WQExDI94G+kppj+/b03NYV4v28lzGdcwxGPgpNVNlkZeKnueER7xXB9zM3Lp+f/W3l1VSX5LMw9+LwG3SCQSfUvchXp+iQGcaNC0GE28c/AoH99wxYBtTjQ2cdfaDfxxXCYXJcb3eq/W0MHtX62l9WARrsRIprdq+mx+UGlNpM3aQuXxJBrLe49GuVR2HNF67NF6pCYl8nIf0jrrSR/eyKfZdh7+8y6Mre3IgcmXXknymA5kw5uR7o1F3XHqxfZ2hYuyRCNyu5SR1S7sfmosoxpQH4hAZuidGsNut1NbW4vJZCI2NpZZs2bREb2GFoed4ZFvIZWcPngTBIGXS7ayu7mQ18fcRIB64PIxx/W5vFv1JheHX8ZYv/Gn7XswdFgsPLhtG/930ULkYk43kUgk+smJAZxo0Dy1eRe3TszES9N/rq/P807yyoHDPL1gLonBgUD3iFtOQz1riwrZUVFB5IFyOsd4kNwloPhBYKBQW0ibvZW6omHUFX1XJkpQOLENbcMRrUdW44ltdzS1enf0EoE/T9vMsr95sXVtPg47RAD3JMTTuFCOENyGdMcQJF2nTghs0TgpTu0koE6Fv8OGPL0KM3LUeyOQtX8X+LlcLqqrq+ns7CQ6Opply5Yxfvx4rM4PeLq8hatj/opUcvo8aK3WTp44uQar08FrY27CTd5/qTCX4OKr+tUc0O3jtqF3EqmNOm3fg+Wh7du5OjWVWN++axNFIpEIuhPwSsREvueNGMCJBsXesioaOjpZmJzQ5z1BEHhy+24Kmlv4cOll+Go1lLfrWHUij01lpcT5+jFvyFC8TuTz9a4yum5LI7619zSmTGEjbeZWWqoiqDqR2N2v1IU9th370Dao86T66xHUmLS4SV2Ey+wsHrqfi6+uZ/f+EwCMCwpibVMTn10Xg+BjRrpzyIA7Rr9lV7goTukkRAduQxqwO6Ro7QKm41F9greCggKCg4O5/PLLmTBhAu7u7nSa11Gkew2XdDxDPNJOeS1BEFhXn8uLxZtZFj2eJdFj+y1ED9Bp7+SNilcAuC/+nz9pZYUNJSU0mYxcnTrnJ7umSCQSiXoTA7hTEHehnplOi5VHN+7g5csv6jPl6XS5eGDjVqwOB69ftojqDj0P79pOUWsr16Sm8sXl3bnCcnJyWLV9L4ZxkcRbnMh/sG88euRJTB1elOWkItC9xs06sglBr6Fyy3DqO9yIVdiYqjGikEBJ8XGWv7QBXbsThVLKFTck88/NzchcGmqHBCHdG95TCaE/AgK2QBPFIyz4qM24ywQUeUHIWtUoL6lG3+rd0/bb4C08PJzly5czZEh3rdUO43u0GZ6kQriR8f6xp8yF19il5/GTa7A47bySeQMRbv3lu+tWbizltfKXGO8/iXkhC85rIfr2ri6KWlup7+ykvtNAvaGTPdVVfLj4stMmZhaJRL9vgjBIaUTEPCL9EgO4UxB3oZ6ZxzftZGlGClG+3r2O251O/rpuIx4qFTeMyeDuTRsoaG1hReYY/jtrTs/mBafTycaNGzE2SOiY6st0nbVXPwqVhdDhJXzy+UW0K8EnrR6pxk75/jhMLe4MU9hI0BiRSqCrq4u1X39NXl4eAMHBwdz5TChyZRhRL+dycPIoJLkDp9YQJAL2Ya1Yo/XUWL3wNMgJyw7pSQ+i8OvAadQg2Lu/dVwuF/n5+URGRrJ8+XLi4uIQBAGd4d8YzJ8S4v8JRwpe5L74ZQNe88vaI7xUvIUbh0xlUUTGgAGZIAhsa97MxsZ1XBt9EwleSaf+wpwDm9NJTn09u6uq2F1VhcVhJzEwkDBPT8I8PBkZFMRtY8YQ0k+qE5FIJPo+cRPD+SUGcKIfZUdxOXV6A48tmNnruNXhYPnna3HgotLcTv72Zq5OSeM/3wvcvnX8+HGOHTtGW2woQ61OlD/4aysiKZ/c0jicMUbCYnQ4CwJQVniTCUg1pp52lZWVfP7553R2diKVwszpmWQsGk1XYhXpT+uQCpAfkzLgZ3Gp7VhG14NdSmNjIBqrnMhCba9t8MqADmzN3kB34FlQUEBUVBTLly8nNjYWQbDR1H43NnsBEYFrOdlZT5g6HF9l/yNqBR11vFG2kzfG3kKIxnvAezPYDbxT+TpdTjP3xj+Ij3Jw1565BIHP8/N54eABRgYFMSEqipeSFxDmOfDmCZFIJBL9fMQATnTO9F0W/rV5F/+3dBFSiQSHy0VBSwsHa2t442AOXU47i1MSWZw4kqH9lsD63uhbjUD7ZQFM6eg9+iZXdRE0tJTDpePw9exCvTUGiU3OD9MC5eXlsXr1alwuF+Fhbjx6XwLlhhkIowsZateRuL0Og4cHtf1UOABwBJiwptcjL/WlXqYCpUB0kbZPDiNlgJ6uymBsNhtFRUVER0ezYsUKYmJicLo6aWy7CZASHvA5SLRsbPw/pgbO6PeagiDwTOHX/Gn47FMGbyc6jvNO5RtMDpjK3PMwZXqyuZl/bttGiKcHH112GcH9pG4RiUSisyWOwJ1fYgAnOmePbdjOdWNGEeThziuHD/HOsVyG+/tjtzhIDAxk5cL5KPtJTPt9x48fJ2dvLl0KX4IRUH8vd64ggDqhmJzGYajd7ah3RfVZtyYIAvv372fz5s0ApCQPZ+vH8O66Cbgyqgl2mZDkeRNVtY+DmZkIP1i3JUgE7MNbsUfpUWWHUhMkYFcIDDnh3s/uKQFlQAdVm4OpKCogOTmZ66+/nvAIH9o7X0Jv/D+0qskE+jyJRKJgW9NmFBIFGT6Z/X723S1FOAQXU4P6bvwAsLtsfF77CXkdx7g17jZi3eNO+SzPVofFwtP79pJdV8cDU6YwNuL8Vm0QiUS/L+Iu1PNLTOAkOifbi8vRmboYERbAJR9/SF2nga+XXc3CIfE47AIvnEHw5nK52PD1BjoPOfGaGoyP47u5U7sAhXKB6GHV6DQa1Nlh/QZvGzdu7AnesrKyePGpOMrrYtAFWFG5mwiTduDxWXce7/zExN7Xd7PRNbEKp08Xmu3RVAULOOQCQ/Lckbr6/sCQeRqxGqGhqp0FCxZw511L0Xq9RWVDFhbbUYJ9XyXI92kkEgXNlibWN6zl6ujr+9284HA5ea5wA38ePqff9xstDTxR8DBmp5l/JDw0qMGb1eFg1Yk8LvrgfSK8vPjyyqVi8CYSiUS/MuIInOicfHWiEI2Hgnu3buaRqTNIDw0lt76BF/cd5P2li1GdJniD7tG3/e/nEjrKjzpPBYE2J4IAtU4F+TYVV0zeRY45FlVOaJ8yVS6Xiy+++IITJ7pThMycOZM5s1JIi1/Hi+tmQGYdI7UVFKyfxpKTn2Pw8KDmm+lTAQFHtB5bfAvKggCkVV6UJ5qQCBLiTrgj7eevPbPZjE1bgKzBnT/88XpGJO2gtfNJPLSXEhH0NUp59Hf3Jrh4u/J1FoQtwk/l3+9nX1N7hCEewYz06Rs4NVuaeKb4KRaFXcqYQUrMa3HY2VlZydclJRyuq2NSVDSrLltCkPtPl35EJBL9voi7UM8vMYA7BTGNSP/Mdjubi8tYOj6Fp+fPQymTUaPv4K9fbeSFRfPx02pP24fT6eTDFz/BaXYRkRlMvkJCmBF2W7SoJAKzAhswRTqRlngjb+q7Jmvnzp2cOHECqVTKRRddRGpqEnPG7+FYcQz6pBZi5S3UHU5G1iAhqqqKQ6NHI0gkuDR2rKkNCAoXml3ROBxyykaakNslxBS4IUGCMqgdp1GN06TBYDBQV1eHRCJh7Ex30uPSCdc+hNOVQFTwXuSyvgHajpatyCVyJvlP6fezmxxWXi/bwatZN/Z5r92m49nif3Nx2GVk+Y09/RfjFARB4HhTIx8cP86+mhomREayKD6ep2bNFovOi0Si8647gBuMNXCDcDO/QWIAdwpiGpG+7E4nt65dg5tCwd8mTgKgwdDJTZ+s5qFZ0xgW0P+I0/c1NzfzzuvvceyLIuIv7x6B0slk5BvlpCkt+GutCFMqQa9Bdjy0z/nFxcXs2rULgIsuuohRoxJYPGsncpmLLQ0xuIe3oGpVU1UWTUbBYaSCwImkeGwjWrDHtKMo8cNV60V1lBW9v4nAWjUhVerusssyJ97jTmI3S9n7nBtKmRvp6elMmz6Rk54P4qN8H1/Pf+KhWdTv1GeLtZn19Wu5J/7+ft+vN7fzyIkvmBOSTLi2907STruBZ4qfYnbwvB8VvLWYTGwqLeWjE3l4qlRcOTKZR6bPEIM2kUgk+g0RAzjRGXMJAn/dvBE/pRa/6O7pyGajkRs+/oL7pk9mbPSp11G5XC4OHDjAe//3AWVrG4mdHYKXnyc1Tjl2JExRm1F4WLBOqGKIupWGjeP67AJtb2/niy++AGD06NFkZozginnbMJrVrDo0CueYKpIUDeRtnoXc4WTMgQN0eLtTdK0NqV6JYlc0tZE2DKM6CapVM/KANzLnd9fQDq2jo1JNV6eTaX/UMsn/PqJjTFTp7gGji2HBG1DI+waV3c/nm6nT0IX4qwJ6vecUXHxcdYB3K/Zw85BpXBSe3ut9s8PEsyX/YZzfRCYHTjuzL8g3uux2DtXVsae6ij1VVQjAlOgYnpt3ATE+PmfVl0gkEg0WcRfq+SUGcKIzIggCj+/eiVQiYVRQCGabnTaTmRtWfcGfJ41jUmz0Kc83mUy8//77bP5sG6ZjLpKXxOHmr6HJKec4KgIcTuS+ZrqyahkitCHND8Jl710D1G638/HHH2OxWAgPD+fSRRO46sLN1DX781VOMq5JZcRr6ijbloXTrmDazq3463S8f/9EVDnddUtrY81IBEg+4N13o4LMiXpoFcff9eXiOdfQFbGaNvlDqHUFWOWLCHOXDhi8ddo72dD4FVKkTAyY0uu98s5mHj7xOf4qD94ee2ufwvRWp5XnS58h0TOJOSEX9Nu/3tLFB8fz+Dz/JE5BwEOpwkOlxOlyUdfZyejQMCZERXHDqHRxXZtIJBL9DogBnOiMvHgom/L2dl6ZfyH/XL+NSUOiuf7jL7h1XCYzhw055bmCILBmzRrWvr4BeYuWUdfEINfIaHHKOGZVE+drwellxDK8Br8yLTHJjRwoyuzTx/r162lsbESr1fL4/WlcPHsjh/KGs/3kcFyTy4lwGjAXRNLZ6k9gUyPj9++lIDOcYtlkZO0ynDKBlhArSYe8+t1l6gwopqNGzkUzLidzbAmNhgMc7gohMvh5OkwHCdEM63NPxcYidrdsJ99wknSfDK6PuaVXnrbPqrN5o2wnd8bPZXpw38oJVqeVl8qeI1wTwaKwxX3eL9fpeONoDnuqqrgkIZFVly3BXamk02bDaLPiEiDG2/uUZbpEIpHo5yB88xqMfkR9iQGc6JRcgsCTe3ZR1NbKixdciEImI6+ukRMtTVw7ehQXxA8/bR8nT57k8+e/QtnpRuLSGKQyKXqnlKNWDePVJhojDQTHNaHeF0HShJ2UH03G5ey9XuvQoUPk5uYikUh487kIxmS08O6X02no9MQ1uRR1vYaI4UVk52eB4GT+ts9xKGV8Pf5iJEJ3X81hFnxalChsfbPntLQ1MmxeMxGGeYxMegGLPZyhwWvwtRvZXP9PJBIpI30uBboDtwNte/m68StUUjWTAqawLOo61DJ1rz6P6ip5u3w37437Iz4qtz7X7LQbeL70GaK00VwReVWvIOxQXR0vHcqmxWTi2tQ07p88pdfOXpVcjv8ZbBYRiUSin4s4hXp+iQGcaEB2p5N7t27G6XLx6oKFKGUy2kxmKnTt3H/BVBaN7D8B7fcZjUZeffINzHV2Rl83tCdIKXOoGKnsQjGiBa8YA137oxnqVY9M7qCxPKZXH2VlZWzcuAGAh/8WiNo9gze+iEOQu3BNLENS683QkGJqTsTjcklJb9xEZHEL6+fOxuDZvQbMJRVoDreQ1mRFM1yHqTgMhO5Arq2tDWVEHV7yQFITPqJeuAitbAHB8mhCFDJG+S3jSNvb+CijqTXX8EH1O0iA62NuJtottt/P3W4z8eDxT3k05bJ+g7dmSxPPlfyX8f6TmBN8Qc9zKW1r499796Dr6uLPY8cxNiJCHF0TiUQiUR9iACfql0sQuO3rdYR7enLfxMlIJRJMNhvXf/QZsf6+XJYy8rR9CILAh2+uonhDNSlXx/UEIoIALU4pCRl1OPy6OHRiKJM7HcRNzaXk0KiewAqgpaWFTz75BJdLYNEFgSg8ryGnQNPdz4gmJAY17o1KPJPbKNg1FnV0EbP/dZiasDAOZXRPw0rkDiRpNUwJqUei0SA4Zagjm9HvS8Cil1PfUMvcW2wke1XjUv+Jz2qOEarexPvVbzHGbzxj/SawMHIin9V+TLZuP4vCL2OM77gBAyuX4OLB459yaWQWyf3keSs3lvJy2QssCl/M2G/yvDUbjfzvwH6O1Ndz1/jxzIiNEwM3kUj06ybOoZ5XYgAn6tcbR3PQyOX8feJkJBIJXXY7t3y6hhgfX4afQaoQgNycY6x+YhPhM33ReGh6jhsEKbGJDeBpRb0nko4AJeEhVTjtctpqw3ramc1mPvzwQ6xWK+MztWSNX0qX9ZvgTeZCiGpHun0okWMOUNUQgWlCNZf9dR9ym4u1CxYgSKWowlvwyiyk2e5B64EEZHU+gIB2WB1+s45QuFbL7GsleKkNDPH7Lyur9nFx2GLG+U+k1drCvtbd/K/kP1icXWT6juXBxMdxk/cdUfu+9yr2IkXCspjeSXgFQWBL00Y2N33NdTE3E++ZSKfVyv8dOcyXhYXclDGah6ZNRy4VC6SIRKLfgEGaQkWcQu2XGMCdwu81kW9eUxMfn8zjs8uuQCKRIAgCd63dwKTYaOp1BhKCA0/bR2dnJ/++4QWUcQLhw3rv3GxwyAkMb0eZH0CXS47WKeAb1kRrbRjfVql3OBysWrWK9vZ2wkPV/Pm2aeSVfrd7U4jUIZiVyMaU4RXeRF7eGNJXNxN/qIZdEyfSHBSE3NOE1+giKnMTqfVQMKyuOyGwTObE2+zCo8HCqEvM2KVyRgfcxW69GU+FF2P9JgDgrwrgwrCLmR+6EIPdgLfS+7Sf+1h7FZ9WH+SdcX/stZnB5DDyZsVrWJwW7ov/JyqJO68ePsQHx49zcUICXy27Cjel8hQ9i0QikUj0HTGAO4XfYyJfk83GXzZt4N8zZ+Oh6k7jseZkAQA3ZWVw2esfsWLSmFP20d7ezn1LH6bDpiNl4og+77dIJYR7WpDqNHQoJXg5BLyDmik7kgZ0j1StW7eO6upqNBoFq16LZuuRVABcSgeOGB2ShEacbe4kqBuoz43HIzuACz5ZRaufH7smTUIid+Az8QSGo3FUBkhJ1hmJH1VEYHAjfgHNtLf5kHtESbB1AclTxiNVDGFny9P8I+GRPlOXUon0jII3vc3M/cc+5ZGUxXgrv9tgUGYs5f/KX2KM33iyvGaw6lgRH+UdZ/bQoXxxxZX4aDSn6FUkEol+ncRSWueXGMCJevnnju0sHBFPSnAIAE2dRl7Ye5CPll2Gw+VC39WFv/vAU4gNDQ08dMsTVBXWMfLSISh/MKpkF0AT1ImsTYtEkNAhl+LttOPuo6ejpXtq9uDBgz07Tt96LoKq5nHY5QK25HocIUYUbWpkJiUeuX74zTtK9vaxzNq+BXeTic8uuQSHXIb3mJNYG73RBurw81UxPfQ4LY0hFJ1MorkxmLzjVURGRnLVFXegcVfzWP4/uSLyKjwVnv19rFMyOax8VZfDR5X7uSwqixSfKOD7U6YbSFFdwPojZp7Xfcai+ARWXbaEQDFfm0gk+g0Td6GeX2IAJ+qxvqSYBmMnT86cBXQHIP/YsIU/TxqHv5sbRU2txAX4DXh+WVkZ/7rzaWpzG0m9chgqtapPm2annJCQFmRN3UFgh1xKrLuOzjZfXE45ZWVlbNq0CYBbr09iVKoX72wNomtSFfI6D9w2x+FKr0FSHEB4QjH1RUMJqGlj9KFD5CUlURETg9uIamQaC0MEHWu0kfge92Vj/cU996DT6ZDJZCxZsgRPT08+qfmQKLcY0nwyzup51ZjaWFV1gM2NeUwOjOc/o5YS5xEEgMPl4L2qt8ltLSKvYBiOAAfXpqWRFR6BVNycIBKJRKIfSQzgRAA4XC6e2b+P1y9a2BNgfJ6Xj1ouZ96I7gS2+Y1NJA6w/q2iooLH7nqKxiM6Rl0dj0LZ/39ajU4FQ0MMyPeHA9ChkBDt20h7YxA6nY5PP/0UQRC4ZEEAD98t5YNdyXRNqkJZ5I+i0gdBawM/E/LcYALHVXH487ksW78Ku0LBppkzUQa14zaihlhjFw0eKjpbvAmt9+i5vs1mo6amhgsuvAB7lIV/FTyC1WXh7uF/P8WzcXKwrZRqUxt15nbqunTUmXVYnHYujczik4m346n4bhrU5DDy34KnKWkxMkQ6ky8um4inWj1g/yKRSPSbJEgGZwOCOALXLzGAEwGwprCAtJAQIr28ge76mi/uO8jHV13esybsZEMzY2P6psWwWq28+MSr1B9oI/26EQMGb4IAnRo7MomAxNg9tWqQS4kNraY8ewiffPweFouF0akarlwylRc2RmLKrEeZF4SirntqU4htRVLlQ3BMFe11wSQcKCGypoaNM2diClLiP/YI0Y5OVBKB9boMIsrceuqpOhwOimqKiL0sisIRx7C2G1kQupB4z8ReGw6+71BbOf8p+IogtRfxnmEM8wxhWnACgl2GsctFclBQr+CtxljLv/KforXNh7uTbycjLKzffkUikUgk+jHEAE6Ew+XilSOHeG3Bwp5jnx0/ycxhQ/Bz+24x/smGZm4c13ea8e3n3yNvTQkjr4xBoRr4PymDICUotANZc3dQ5fjmjyr/gGae/6SMxqZ2/P00TJ1zI4cUDhxZdaiOhCJv7l4r9l3qkCGEzs6heksK12z+iOaAALInjsJvyjHCZB3I9Wq+KBiHPMSGp14BgE1ppTakEv+5nqT4JzMvekGfgvPf19Cl59nCr6kytfLX+AWk+3UnF67Ut/PCgYPkNTUxMiiIf+3ehcVuJz4gkPDALkpdmwkTxvP0zGW9KieIRCLR7424ieH8En/DiPiquJCUoGCivL2B7oDu3SO5vH3FpT1tnC4XzZ1Ggjx6L7zf/uUuvnxmM2FzvHHzOnV+tAaHnICQDuR13X0Y5FKiVAbq91jYtbcRmUzK/FsW4prfhqTZHe3WWCS27/4TFSLaoc0NX28dWGHSmwdxM5v55PJF+M06RqhHK5IaL/YeGEddpoHoQi1Wvy5MYZ2YfY14Vfjwp4g/MzRy6ID36BRcvFu+h4+rD3Bt7CQeS7kMuVRGlV7PCwcPcryxkT9kZvLkrFnIvsnX1mBu5t2Kd6nrquHK0JuYEZE5YP8ikUj0uyEm8j2vziqA+/LLL8/6AjNnzkQjpkn4xXK6XLx06BAvz7+w59jXhcWMCg8l+HvBWqVOT4yfb68UG0VHSll5xxso0uyER59uqlCg0SUjPMCILLd7ob/Wz8CL47Yw84IWANIuGkHATAWq7GBk7ZofnC0gxLUiPRZGxJBskm+uJ/JkA/vHZWK5tYVwzxY686I4UTeM5hQ9UpUTY1YDCoMSw3EjXo2+3LH8DuIi4/remSAgkUhosRj4x7FP8FRoeH/8chx2WJV3gvUlxbSZzdw6ejRPzJzZk2jX4XKwvXkLGxvXMy1oJn8O+hMKqeKsnr9IJBKJROfirAK4hQsXnlXnEomEkpISYmP7rxcp+vl9VVxEYmAgMT7dNUMFQeD1g0f474Vze7XLb2gmMeS7DQxV+TU8fu0zOOM7GZ409LRlny6cup8rI9v4sjOci2btxGDUMiS2jr++aKe0zILGS8WEmePR7AjpWbPWi08XSAV8m41Mf+kQXvkWtsycStnjUtRuRvbWJKEPlSH3aKdNoSa6TItnvS8lhaV4e3vzh9v+gDYwkPePHeNoQwNNJiMtJhOdVitIJDjVZlw+7UQSTpg2hOVr1tFqNjN7yFD+Pmky8QEBvT5jlamSNyteJUgdzL3xD+CnOrPqFCKRSPR7IaYROb/Oegq1sbGRwMDTZ+IH8PDwOH0j0c/G6XLx0uFsXrxgQc+xPRVVhHp5Eufn26vtyYYm0iK6Kyps/2gvq19cj3SkBX83X9Sn2WEZ5NfOkKh6/n54AiEqKzntfnh7mHh1czQfvPoGABNnj8erMXTAPgR3K/55dq5/9V3cWq18cNs0mm+WYBaUtFdGIa32IahFQ0uQHQ8vB161WoqKi5EFBuI1ZTL3HM3BmXOE6bGxLEpIIMTdHX+tliZbO1/UHuZQWzl/GXYNSpeKhk4ji5OSGOHv329gWtJZxKvlL7I06hpSvUed8fMWiUSi3x1x+vO8OasA7pprrjmr6dBly5bh6Xn2iVF/KX7rpbS+Li1hhH8AsT7fBWuvHDjEX6ZM6NVOEAR2l1VyTXoKz/3xNZRqBaNvSuCzz/NJHpp82utMzsjj05yRKHysdOUHUNDWvTFiTfZmjHo7foG+ZA4bN+D5LpmLkPZSrn5kC0qng7cfn4r1AhcBLhvVX6fiZu6e6nVKBRqiLATnerLVZKQjJZmYoGASYmK4LTaOME9PjHYLu5oL2VhzkOy2MgLVnkwIGME74/6ARta9MzYtZODPkt9xgjcrX+OW2OUM8Rh22s8uEolEItH5cFYB3JtvvnlWnb/00ktn1f6X5rdcSsslCKzMPsjz8y7oOXasvhFBgNTQ3hFMTk09QSoNTy/5Hxctn0PilGH8/e9/J+AH04r9CfbTER7cwupNkxk2rxCprvsPgEafUpo3HwRg1vTZyGSyvveocGKKNpB27DCXP3yALi8l21cmQ7oVPzMUrp+K09n9n7BZIqEw1obBoKGiS0qEQsHjM2YyKaN712xZZxNPnNzO9qZ8xvsPY2LgCO6Mv6BXyavTydXn8EHV26wYcgdRbjFnfJ5IJBL9HolTqOeXuAv1d2pDaQlD/fwY4vtdZYU3D+VwY1Z6r3ZOp5Nn3t+A9Hgzf37lFkLjgtmwYQONjY0kJiae9jqTMvJYdWQkMQHGnvJZpvAmNr6+C8HhZERCKEOH9t4V6lQ7MMZ0YA02cMUTR5m4MY+a4f7sez4GU6QSdYuak1vGIyChSSajSKnEGmLCO8gMhU7C/e0kpyRz2K2ZwwXrKTTU02Y1sjgyixUTZ+OhOPukutm6A3xWs4rbh91NmCb8rM8XiUSi3x1xF+p5NagBnMlkori4mIiICPz9xUXdv1Tfjr49M+e7jQrNRiMFzS08/b3NC5Una3j+rjcpn+DL9nf+gkqhwGKxsHXrVjw8PPodNfu+YH8d4cGtrNs4leHjK6BSy76iHex9/iBmnQWkEqbPmNMzimf3sGGM02P1t+BRrmXFDbuIK6skNyWZLS+G4uVnRF7pQ/H+UbRLpRzUqJHLnHhF6lD5WbCcdBJoVDBt+mRCQ8P49rt+YuAIMnxjTjtaOJCSziI+q13FncP/RpA6+Jz6EIlEIpFoMA1aAPf444+zc+dOMjMzKSoqwt3dnZUrV4opRH6BNpWVEuPjwzC/74LsVbl5LElJ6imjlbM1j1VPfkHKvbPxt1lQKbrTY+Tk5FBZWcmQIUNOe53JGXm8nzOSuDFVnFxfwa63jtCpNwLg7ufF+FvS8FdE4FI46YjXYfO34FbuiVeeP7M2bCWurJJdS0Zx4m5fVBorPjYbOftHcUytokEuI9zLRGd4FxKFDc1HLQxXB/DHP97K8OHDB+1Z2Vw23ql8g6ujrheDN5FIJDorkm9eg9GP6IcGJYB788030el0bNy4sefYJ598wh133MErr7wyGJcQDRLhm9G3f8+a3XPM5nSy+kQBn197JQD71hzi69e38s/P7+bWz9dx7+zJALhcLrZt24ZMJkOl6luo/vtCAtoIDGzjeHsc9dvLWffsTgA8vD2YOH4S9ReOYnZwPo11JgxJbWjq3AncEYbEJSW6ooLx+/dRNj2QI1fHYdwdjmN6GdpmBevc3Yiz2wgLMmHztOMtsSF5r54onzCWL19+RoHl2fiqfjWx7kNI9Bo5qP2KRCKRSPRj9F8A8iytWrWKe+65B4Drr7+epqYmFi9ezKFDhwaje9Eg2lJeRrinJyP8vysjtbm4lLFREXip1Wx9fzdb3t/F/R/fSZvDRqfVyoig7rYFBQUUFBQQHn76NWCZKQU81xiPq9HMpuf2A5AxOZU/Lf8T7pPHkBDUhEkrp3N4Oz6Hg/As9EXikqLu6uLitZ9h8lexdu4COrITsFlUKCRODtbHMdlmomNIF3K5E7XGAqvqifWP5Lbbbhv04K3KVMGBtn1cFnHFoPYrEolEvwvCIL5EfQxKAKdQKLDZbACMHz8erbZ7Z5/L5RqM7kWDaOWhg9yWNabXsfeO5LIsPZVtH+zmyOZj/P2DO1Cqlaw+VsCilISedjt37sRqteLu7v7DbnvRarqo9HfRonNn0yP7sVgshMQFMmfifFwKOSd9JFwQdxRdcyABu8NQdnwzmie4WLjrYzzbTay5ZAH6znDMEgnVUR0oJQ7cW7zJibcT2iEg+JmRftnCEP8obrvttkFPFu1wOXi78nWWRC7FTX7qzysSiUSifogB3Hk1KAHcjTfeyF//+lecTic33HADHh4ePPPMM8ydO/f0J4t+MuXt7cgkUhICvkvEnN/UjEQiwcvoYuNb27nztVuRybs3J2wsKOaCpBEAHDx4kP379xMScookaUCrU4Z7fBWHuvwoXllFZUUlCrWci+cuRiqTkhPuIi2sAlNNKJ7Fvki+3R4udTHGuJERByrZP2E0RcEJHAsV2DxMID6uEgUODsZKGFktwRRuQrZDT4QsgD/84Q9ERUUN+rPa2LieQHUQ6T6jB71vkUgkEv3+bN26lfnz5xMXF0dcXBzz589ny5Yt59zfoARwF110EfPmzWP69Olcc801zJo1i46ODh599NHB6F40SLaUlzIjtnct0PdzjrE0LZn//fE1bnvhRuSK7mWRDR2daBVKfLUaCgsLe3IA+vn59ekXwClAjlVNvkOJe1w7pt02dmzeAcCseTPwDfSlIVlHlUzF5VHZtBz/LgmuMqiduOSdTH05h8agQF6eO4f1SQI1HhISmwWCVUZkdimTTspoizUhye0ksF3LLbfccl7KtNV31bG9ZQtXRF416H2LRCLR74YgGbzXr9yLL77InDlz8PDw4Pbbb+f222/H09OTefPmsXLlynPqc9B2oV555ZVceeWV6HQ6fHx8zjllg+j82VxWxhMzZvb8W99l4WB1LfG5nYxfmEn4sO9KWe0tr2JcbCR1dXW89tprtLe3M2LEiH77FQTIsWnQSFxclVJIgUHNh4/tw+l0MjQlluSsVFoz6imqCWOCpBlrqy+2Li1StQ2P1FI8lK3MWZaHxAYrbllCZaKL2Fo5CS3fXEDlxNWkoWa4CWetGa8SuPGPN5KQkNDv/fwYRYYC3qr8PxaHX46XwnvQ+xeJRKLfC0Hofg1GP792jz/+OM888wwrVqzoOfanP/2J8ePH8/jjj7N8+fKz7vOsR+AeffRR1q9fT1NTU7/v+/r6isHbL1Cr2YzeYiHue2WzPj1+gom+wZQeKmX+LTN7td9XXk1yoC+vvvoqlZWVDBs2bMCva6lDiVOARIUVfVQLX/zxIC3NrWi8NIxdNpfq9Ha6SgNoN2uZH3+Q+uI4tEPq8J+bjVQvY9Z1Jfg26nlg+cW0JnkyNV/WE7x5euvpEhS0Wd0xOyxoD3Rx/XXXkZ6e3u+9nCuby8aq6vd5u/J1rom+kSy/gUt7iUQikUh0NvR6PXPmzOlz/NsZy3Nx1iNwDzzwQM8v8uDgYEaNGkV6enrP/4aFhZ3TjYjOr20V5UyL+S6ZbbVez0dHjzP8syoeefeOXsGZSxA4Xt9IWHURx48fJz4+fsCkvY0OOdUOBZPVJrRhjbx2Xw5FuU1I1UqiblvGiRB/ZMVKcEmZ7GpD4m6FkfWoLSraN6VwyevriKyu5Ykb51GcMoxJ+VKk3xsuDwqup8mloFqlQrqpjSuWXM348eMH9dlUmsp5o+JVhrgP5f7ER9DIxNyFIpFI9KOJlRh6XHjhhXzxxRfcfffdvY6vWbOG+fPnn1OfZx3AjR49moaGBq677jr8/f3Jycnh888/5/HHH8fpdBIQEMCoUaNYv379Od2Q6PzYUl7GTaO664LanE7u/PJr0iudXLj8AnyDfXq1LWhsJkSjIndnDtHR0SiVyn777HRJOWZTM1FtQoqLj75YT8H2RiQKOZl/W0Rmmhyv4y6kThsyty5Cph+m0elN57E4rHW+zP9qHSOKinj10okczhrFyCopkh8kbAyMqKVK8MV81Ma0jEnMnj17UEd4G7rqeaHkWa6Ovp5k79RB61ckEol+9wZr/dpvYA1cQkICjz32GDt27GDs2LEAHDhwgL1793LXXXfx3HPP9bT905/+dEZ9nnUAd/DgQd566y3uu+8+Ro8ezdNPP01cXBxWq5Xc3FxycnI4evTo2XYrOo+67HYKWloY9c0O0qd37mWY2hNtZQPj/5XZp/2esiq0Bh1tXV14eXn126ddgANWLRmqLjQSF+v2fU7ulhokMimj77iQzDRfvI8GIEGC3NOE79RcvDUGytZPxGrwYtKunWTk5PDFjDTWzpxCem1/s/kCFl8LFrucuPYwrvzzlact33W2PqtdxfzQi8TgTSQSiUTnzeuvv46Pjw/5+fnk5+f3HPf29ub111/v+bdEIjl/ARzAtddey6WXXsrDDz/MqFGj+OMf/8j9999PVlYWWVlZ59LlL9LKlStZuXIlTqfz576VH2VPdRVjIyKQSaVsLy0np7aemI9KufnlW/ptvy2/GEnhyVOmDDlq0zBEbsVP5qS8q5AjW04ikUDG8gvInBKA96Hu4E3mYcZ36jEkdV4o/cx0GbwYkZ/PtB072J0xlJUXL2BCff9/XWk9DZQ7vBFcEq678nq8vb0H43H0KDQU0GJtZmLAlEHtVyQSiUQgEbpfg9HPr11FRcWg93nOaUTc3d156qmnOHz4MCdOnGDIkCG88847g3lvP7vly5eTn5//q68osbm8jJmxcdidTh7bupNLXH4kj48nMDKgT9suu53q1jasrc34+vr20xu0OWWYXVKi5XYc/kaOlx0BIGZSPKPnB+NzJBCJ0B28+U3NpfN4NFHhFRyvHUqHrI6LvlxNeUQA91+1hHFNAw+NN0e34S01o0U7qPVNAVyCi09qP+SS8CXIJIM7qicSiUQi0fn2o9KIOBwOrFYrV1xxBU1NTVx33XXMnz9/wF/8op+e0+Vif00ND02ZxsHqWhL8/Tn43G7+ten+fttvOXYSZYeOoMDAfteaCQLk2tSkK7tw+poxja6i7O9VAETOHopfdhBSpxSZe3fw1pEXgzq0iTqplsJmb5599i0cUjl/vfI6xrTLkQ6wOrVRpqfNR0WW3EmgOnLwHsg3DrTtw03mxkivlEHvWyQSiUSImxh+oLa2li+//JLq6uqe6lXfevrpp8+6v7MO4P71r3+Rl5dHXl4ehYWFqNVqkpOTyczM5JZbbhlwzZTo55HT0EB8QAAahYINhcUoDjVw+b2LUKoU/bZfffAIsuZ6AiP6nz6tdCjwkTpx9zdimVRF27pGjAYHCm93MtyGItXJkHsb8Z18jJryaEoCFdwUXcyBL2fz0PNf4K3v5MHrriVBrUE6QHKfOn0jrfPgxpAjGKUeeCtD+213rqxOK2vqP2PFkDvElDcikUh0voibGHps3bqVCy+8kNjYWAoLC0lKSqKyshJBEBg1atQ59XnWAdx9991HdHQ011xzDVdccQXDhg07/Umin82msu7qCw6Xi+2FZcyttzJ6Tlq/bQ0GA7lNLSTKJUilfWfXbQIU21VM9urAMqEaWaMbbTsLAAgZn4Cbzh2Ffwc+E06QVx1HucOXPwRso+ZIClM+3kdcVRXvzZuHIjyi3+sLgkB5VQX2S5TMDa4kOehm9rXvwlMxuCO6m5s2EO+RQIR28EtwiUQikUj0Q/feey9/+ctfeOihh/Dw8OCzzz4jMDCQpUuX9psf7kyc9Rq4iRMn0tbWxkMPPUR6ejrjx4/ntttu48033+TYsWO/+gX/vyV2p5NNZaXMGTKUnfmlyCv03P6/GwZs//WOnThtNiKDAvt9v8CmJk5twTm+e8p0SKWNrbt0AIwdkYQqpA2fiXkUFwynxOLPpZQjc4HtawWTs7PJTkmhNCOj374FQaCgoADNfBnDQvUsG/YEDokDB954Kvov33UuOux6tjdv4aKwSwatT5FIJBL1Qyxm36OgoICrr74aALlcTldXF+7u7jz88MM8+eST59TnWQdwO3fupKOjg6KiIl577TXGjx9PQUEBd911F2lpabi7u5OZ2Tc1heint72ygozQUDxVKp59Zz1XTsnEw8e937Z6vZ5Pdu/H12pGLu87MGtwSWkRZISNrQCpgLLcm6723VhtApowf0ZP7MIrq5DqQ0kc1ngxvs5CXMpx8ranc9Har9B7eLBtzhwYYMqysbGR8LkyCBe4M/5utOrRGKwnsAmqQR2B29Cwjgn+k/BRius0RSKR6LwSA7gebm5uPeveQkJCKCsr63mvtbX1nPo8512oQ4cO5fLLL+epp55iy5Yt6HQ6ysrKeOedd5gxY8a5disaRB+fPMHixCQ2vbeLBm8JVy+YOGDbbdu2UWuxE+uh7fOeU4AjVjXpo8sRpC4ElZPRynY+WqMHYPrFXuCSUb85nb1+bqSVyYhPOEFV8TCivzpOWHs7X19wARa1ut9rOxwOvIeV0zjMg/vir8Vbm4XRVojBmotVkOIpH5wRuA67nmzdfmYGzx2U/kQikUj0y7Ry5Uqio6NRq9VkZWWRnZ09YNspU6YgkUj6vC644IKeNtdee22f989m6nPMmDHs2bMHgHnz5nHXXXfx2GOPcf311zNmzJhz+oxnFcAdP34cl8s14PsxMTEsXryYxx9/HICTJ0/icDjO6cZEP06T0Uilvp0ou5IPVu9k7IhYNIr+Ny40NjayadMmXJ7e+Eh7/6kjCHDEqmFofAMqPzPyBk/k7WoSQo5y4JABgAjNbDqOx3IoQk5Mo5RAi4PAmCpydvhx+d695CUlUXSKNCBB4XvpGufPZcGTifaagiAIFLc9Soz3CjodHYM2Arep8WvG+0/CXd7/KKRIJBKJBtHPNAK3atUq7rzzTh588EFycnJISUlh9uzZNDc399v+888/p6Ghoed14sQJZDIZixcv7tVuzpw5vdp9+OGHZ3xPTz/9dE+e3Iceeojp06ezatUqoqOjeyXyPRtntYkhLS2NxsZGAgL65g/rz9ixY8nNzSU2Nvacbk507j4vyGfRiATef+QzfC4eyayEgQOojRs30tjUhCNkCFrB3uu9QrsKlacFn/gm1NtisIypJYUO3lnTiSCAR1wUbt7+7B3ixNsoIaZJStDwMkprg/nbe2uxqNVsGPCvFIGktH10hljpssRwYewVADSb1mN3dRDsvhizYy1u8h+/s9lg7+BA217+mfj4j+5LJBKJRGfgZ9qF+vTTT3PTTTdx3XXXAfDyyy+zbt063njjDe65554+7X+Y+uyjjz5Cq9X2CeBUKhXBwcFnefPdvh8Hubm58fLLL59TP993VgGcIAjcf//9aLV9p9n688M8J6KfhiAIfF6Qz2uz5/Ni3SZyO1Q8FhvTb9uKigp27tyJX3gELQi9KpHWOuQ0OWVkjq5EURCAoHGAyskFkUXce4sZgOjRI9kX7yC+RkZ4mxQQ8B1RiuafAuEtLXy2aBEmN7c+15VIXIyZuBOZpokP9LN4JP16pBIpDpeJ0vanSAj4N11OE1q5B9JBSLS7qfFrxvpNxEPh+aP7EolEItFPz2Aw9Pq3SqVCpVL1Omaz2Thy5Aj33ntvzzGpVMqMGTPYv3//GV3n9ddf5/LLL8ftB7+7duzYQWBgID4+PkybNo1HH30UP7/B22R3ts4qgJs0aRJFRUVn3H7s2LFoNJqzvinRj3Oovo4ILy9KNp0kZEESScEKtMq+06eCILBu3Tr0ej3+ETF4890OYrsAJ2xqJg+rQ5ALyCu9sY6qZ5imjexjAjUlBpDJcJ81lLEFctyt3aGfPrgDpcvK1E3FVERHkzdyZJ/rSiQuJkzfglTSxYsHx5KUOZREvyEAVHW8gpdqFD7qTOrMpYOy/q3TbmB/2x4eFEffRCKR6Ccz2KW0IiJ6p6B68MEH+ec//9nrWGtrK06nk6CgoF7Hg4KCKCwsPO21srOzOXHiRJ9pzTlz5nDxxRcTExNDWVkZ9913H3PnzmX//v0D1uj28fE541yjOp3ujNp931kFcDt27DjrC4h+ep+cPMFliUns/POn2G5IZd6IuH7bnTx5koMHDxIZGUmDRIaX8N36xlK7ihitGVdSM+p9EaB04gwzMM+nliue7B5Z9UodwfRqD2TfDG83yGQMHVGM2wcatBYLR9LT+9l1KpA1YRcqpZXnXovDscSbO0Z1b60226uo6/yIzNAvATA42gZl/dvmpg2M8RuPpzj6JhKJRL9aNTU1eHp+93P8h6Nvg+H1119n5MiRfbJpXH755T3/f+TIkSQnJxMXF8eOHTuYPn16v309++yzg35/3/ejSmmJfnksDjv7a2u4Y1gae73U5DQ18uiCWX3aORwOvvrqKywWC97e3hRKZMQK3YGZXYAqh5xJYyqQ1XoiM6hxJNcTpurEZJJx4Kt6AMYkj+oJ3mrkck56SFgSWkHEehsWlYrCPhsXBNIyD+Ltp+OtV1JxTnTnAv/x+Kq717iV6B4n0vN61PLuNQYGu+5H54DrtHeyt3UXDyY+9qP6EYlEItFZGuRSWp6enr0CuP74+/sjk8loamrqdbypqem069dMJhMfffQRDz/88GlvKTY2Fn9/f0pLSwcM4K655hqg+/ftBx98wOzZs/uMDP4Y55xGRPTLdKiujtGhYez8YC/2aVEsTklC2c/w7uHDh8nNzSU6OhqADqR40T0CV+5SMGpiKbjZUBYEoAhtxR7bzgxlK098IcVmsiL39SY9IoZOiYRtWg2FSiXLQk5gOuFLXFEFJxITcfxg12tiSi5hkVV89PYodB4S3KJ8uDZ5EQCt5u2Y7eVEeF7X095gb8PjR47AbWxc983om1jiTSQSiX7rlEol6enpbN26teeYy+Vi69atjB079pTnfvLJJ1itVpYtW3ba69TW1tLW1kZISP9lJ79PLpdz6623YrFYTv8BzoIYwP3G7KyqZFJUFAe35HLIpufy1L5r0CwWC2vXrkUqleLm5oYLsCFBhYBNbcdzainuTtDsi8BzZDnC6Gp8jVJ8VFbWftgCQHBWOgVqFVvctMTZ7Mwwm4gbWozXBy6kgkBuaur3rigwPDGPofH5fPbBOOrbLEjnBnF3ynXIpTKcri6KdY8yzPcfyKTfDYkb7G14ys89gGuztnKgbR9zQxaccx8ikUgk+nW58847ee2113j77bcpKCjgD3/4AyaTqWdX6tVXX91rk8O3Xn/9dRYuXNhnY4LRaOTuu+/mwIEDVFZWsnXrVi666CKGDBnC7Nmzz+ieMjMzOXr06I//cN8jTqH+xuyuqmKa0xfn2HAmx8Xg1U/y3N27d1NUVMTQoUMBMErBTebAHtyBOaEFZ2Eg7joZPtOPYreo6LC7cWNgKW8eCKQ1/zhIJKizsrBJJMw3mlAAfuF1SCVOhu8up83Xl9rwcABkMgejx+/BP7CJ1asmU1LSge+NCUyNGctI7+82LngoE/HTTsbiNFFrLqHaXES5MY8M35nn/CzW1H/G9KBZYt43kUgk+hlIGKRNDGfZfsmSJbS0tPDAAw/Q2NhIamoqGzZs6Jm+rK6u7lPvu6ioiD179rBp06Y+/clkMo4fP87bb7+NXq8nNDSUWbNm8cgjj5zxOrw//vGP3HXXXdTW1pKent5nh2tycvJZfkoxgPtNqTMYUMnlHFy1n5KRCu7O6Fu0vqOjg/Xr1+Pu7g7pekzhBppavHBv9sIeaKRgXwyT/ZrxnlGGMS8GnbuE4eoWrFIZr33SAYAmcTgTFGqiLFYApHIHw8ccom1VLEHNx9g6bRpIJLi5dzJxxmbMRne++Gg2BfnVDL0yA1mwJ5dFzUIQBOrNRzjUthqFagE7iv+Ewd5GmHYoEdphLApfQaQ2/pyeRY25mqLOQpZFXXf6xiKRSCQafD9THjiAFStWsGLFin7f629D5vDhwxGE/qNNjUbDxo0bz/oevu/bTRB/+tOfeo5JJBIEQUAikZxTHflBDeD27t1LZWVlrxv5tnir6PzbVVXJGI8AjnVUMyQkiSgf7z5ttm7dSlVVFfFjhmALr0OzPQaLVYs/AvXVasaPzcfTpxPd1jSsghyGV3CRbzmPNMdTv/krAALGZRBl++5rHJt6jI5mf2I21SMAx5KT8Q9sYtLMTRSdSOLYkSTyT+aTPCeTyvAuno1fwcmOvWxseBu7S0eoOpEYbSLj/C8hUB0xKHnfPq/9mAWhi1BKlT+6L5FIJBKJfoyKiopB73PQArgrrriCxsZG0tLSenKinGn+E9Hg2FlVgc+HpZimhHNb5qg+73d1dbFz5058fHxwxutRlPoitcrpkMgJdlnxGFOAmwtaN6YjSMExrpqZHvW8WphGTm4dDoMJtbs78UOHgbE7gPPwbSNkSDmHPp3LvBMvURETg9lfw9Sp6zi8bxxV5bEUFeUzNGk41SkW/jx8KZ4KNzY3vsuMwHHYLJ8zOvRZpJL+y3ydiwLDSdptOsb5TRi0PkUikUh0lgZ5F+qvWVRU1KD3OWgB3LFjx8jPzx+s7kRnyeZ0ciK7lHFaOYJGzqiw0D5tCgoKaGxsJCYpAmdgPapj3esBOpDSpbEwIqQN3doxOL1sWDLqGK4yYGryZLfKi+pPtwMQkDWKSHv3d5NE4mLEuIOUHkkjJq8GN7OZ3NRU0kZn09IUTHVFHBUV5fj5+yNbEMK0wCGk+gznRMc+vJX+mMxvkhTwzKAGby7BxWe1q7g4/DKkEnGPjkgkEol+Gd59911efvllKioq2L9/P1FRUTz77LPExMRw0UUXnXV/g/YbLjMz86yqNJwPixYtwsfHh0svvbTnmF6vJyMjg9TUVJKSknjttdd+xjs8f3Lq6/BbV4V1Zgw3ZKb3O/p59OhRnE4nkkQj8gofJA4ZTrr/uPEaUYOxNARrXDuWrFoi6pUs8Knm6ZZkLJvyqcmtBkAxKZMga3e6kfD4IpwOOU2F0Uzbtg2jmxttUzyJiK7g8P5xNDc343K5SFw6BpcKroyag82pY0fT6wRKTuKrmYC3OmNQn8Mh3UFUUjUjvVIGtV+RSCQSnaWfqZj9L9FLL73EnXfeybx589Dr9T1Lzby9vc854e+gBXC5ubmkpKSQkpJCZmYmo0eP7pPJ+Hy7/fbbeeedd3od8/DwYNeuXeTm5nLw4EEef/xx2traftL7+il8/MZmoicOo1CvY+awIX3e7+zs5MiRI/iH+2APN6As9wHAgBR3pYXY2HoMChXOADOa7dEsSDjJq6UpOKvb2ffCTgBGTZlEqI8vMkDtZiQmOY/C/VlkZh8ioLWV7bOmkD5jP9l7J6Brc9Dc3MykS2ZwSF7K8tgMTjbfxobKeZgdBsYE3sEIv0cG9RnYXXbW1H3GJeFLxOl7kUgk+pl9W0prMF6/ds8//zyvvfYaf//733uV3srIyCAvL++c+hy0KdQ1a9YMVlfnbMqUKX12l8hkMrRaLQBWqxVBEAbcafJrZbPaOfFRNln/vZSlgf7IpX3j8hMnTtDS0kLsJX5Ia7yQ2Lq/9HpkDI2rpLPeH0ewCe3mOBJiq7DKJKzvCKT83+9i77ITFRVF0LzpeFm6x+xGjD9ATcEIJHUypuzcSW1YGJKbLLS1BFBTGU1FxQnGTBzLkfA6/hizkFr93YR7Xo3JEsEU7zQC3KYO+nPY2bKNKLdoYt37Lx0mEolEItHPoaKigrS0vpkhVCoVJpPpnPoctBG4qKiofl9nateuXSxYsIDQ0FAkEgmrV6/u02blypVER0ejVqvJysoiOzv7jPrW6/WkpKQQHh7O3Xffjb+//xnf16/Bls/348oIZm91DZckJ/bb5siRI0hVEpyxBhQl3yXHbZRKSBxWSadEg6LYH4UgMDkzj1crR6J7fhvNjU1ovNRccskl1LupCLM4iUzKRyZ3UHk8iRlbt6KyWtm/NJPIIRUc3j+euro6AgICsE3yJN13BKGKXNyUQ/HUzKPKXESqz5RBfwZdzi42Nq5nYdilp28sEolEovNPnELtERMTQ25ubp/jGzZsID7+3NJl/egA7qqrrgLomTL99nW2U6gmk4mUlBRWrlzZ7/urVq3izjvv5MEHHyQnJ4eUlBRmz55Nc3Pzafv29vbm2LFjVFRU8MEHH/SpkfZrt+6dbXhOiuOC+GG4KfumzWhra+PYsWMEjXFH3uyG1PLdpgG/mHo6271xeNhRVHiTkFJCucmLTRtaKd6TAxKYfdssJN7d9edCfVqISsrn5K4JhNbUk5aby9FRqcQsKyTnwFg69BLa29uJWpRME3qWRU6mquNV4nz+woG29aT7zEAp7Ztc+Mfa1Pg1qd5pBKlPXetOJBKJRD8RMYDrceedd7J8+XJWrVqFIAhkZ2fz2GOPce+99/LXv/71nPr80VOoTz31FACffvrpj+pn7ty5zJ07d8D3n376aW666aaeUhgvv/wy69at44033uCee+45o2sEBQWRkpLC7t27e210+CGr1YrVau35t8FgOMNP8dPTt3Sg7+qi3tLFVemp/bbJy8tDp9MRm6xFcey70cdWl5TU+DJ0Rn+UhX6olTampZxk+fEJVL7xPgCZF2cwJCaO2jYZEXYbSdP2UHRgNNZON+Z9/SEWlYq62wLxNLVTURpLaUUBnpfG0eDXyQOJN1PX+Qr+2pkoZOEcbd/O8qHPDPozMNg72NWynfsTBndNnUgkEolEg+HGG29Eo9Hwj3/8A7PZzJVXXkloaCj/+9//epL8nq0fPQL3bSHXqKgoQkNDcTgcmEymntdgsNlsHDlyhBkzZvQck0qlzJgxg/3795/y3KamJjo7O4HuKgS7du1i+PDhpzzniSeewMvLq+cVERHx4z/EebL9w720jQsmMSiQQPe+JaO+jfTdYuVIkCBt0/S81xnajsGuwSUDebUXo8flsbclnJMfV2HqNOLp6UnadckoOlTUqmWMH3IcXUMQzZXRpObmElZfz765WcRMKObQ3gk0dbbhuDiA8NgI/p32Z9ykRhqMnxPr/ScOtK1nqMcoPH9kcfr+rGv4kgn+k/FWeg963yKRSCQ6N+Imht6WLl1KSUkJRqORxsZGamtrueGGG865v0FbA/fhhx+SmppKWloa11xzDampqdx4442D0ndraytOp7Onjtm3goKCaGxs7Pn3jBkzWLx4MevXryc8PJz9+/dTVVXFxIkTSUlJYeLEidx2222MHNm3wPv33XvvvXR0dPS8ampqBuVznA/71x6mzk/GlWn911FraGigsLAQv3EqFOU+SL6pKmcWBIanltDp0qLMDyRpSBUJYfU8VxNPxdp9AIwbPw7B14m0U0m7xsXIkHKKs0cjdTqZun07zQEBqP7aSf7xFBqtKmqmCCRpYnk4cwUqmZJy/bOEeyzjWEcOh3WbmB50xaB//hZrM0faDzE7eN6g9y0SiUSiH+HbUlqD8fqVe/TRR3uqMWi1WgIDA390n4O2C/WJJ57g0KFDjBs3jkOHDpGbm8sTTzwxWN2fkS1btvR7vL+Fg6eiUqnOuEDtz6niRDXaCG9cAkyKje63TWlpKUZnB75hKuQnvHqOu0bUYbarcTkUBJpdzJl9mD8VTcL8SSmGDgPu7u6MnJSEyaynw9vKcP8WCnZPwOWQM6K4AM/OTo4vjUehNZNTlkhhip7IIi1/vfFmJBIJButx2i0HaVNlcaT9S26IfQRv5Y//D/aH1tR9zqygOWjl2kHvWyQSiUSiwfDJJ5/w4IMPkpWVxbJly7jssst+9IbKQRuBU6lUPek67HY7qampnDx5clD69vf3RyaT9dl80NTURHDw73fR+ua3d9AxLoQYPx8Usv7rh5aWluKZIUNR44nE2f3llrqbCYuvolOuRsgPYPGsvbxTnkh5iy8nN+wFYNy4ceDvQmWQ0RnVRohBirG9O3dcxpEjdGnUeP+xiW2HJlKQ1Ilmn4kbJl2Op6cnTpeV/Ja/08Z0jun3cGPso+cleKsxV1NqLGZq4IzTNxaJRCLRT0vcxNDj2LFjHD9+nClTpvCf//yH0NBQLrjgAj744APMZvM59TloAVxwcDB6vZ4FCxYwd+5clixZMmhrx5RKJenp6WzdurXnmMvlYuvWrYwdO3ZQrtGflStXkpCQwOjRo8/bNc6V0+Gk8FApuU4DM4f3TdwL4HQ6OZl/As9RUhQVPt8cFdBkFlGtC6Gl0Ydl8SdoM2t5wzgEt9UnaW9vR6vVkp6ejt3LRlpgCSf0obhVdv+l4KPTMaSsDN1FnuTUJ3EwQgFbWpgTO5H09HQAyvVP0+T0p87SyfWxj+JxHta9AXxZ/wUXhFyIQixYLxKJRKJfuMTERB5//HHKy8vZvn070dHR3HHHHec8EDVoU6hr164F4JFHHmHHjh0YDAbmzJlzxucbjUZKS0t7/l1RUUFubi6+vr5ERkZy5513cs0115CRkUFmZibPPvssJpOpZ1fq+bB8+XKWL1+OwWDAy8vr9Cf8hHK2HGfE9EQ2GZu4ML7/TRl1dXV0+jTjYVAiNXZPCWviGjDKZUgkAjFdNoYn1bPsxEy0DUqyd+wGYOzYsSiUCrwiqhDkVjCr0bi6d+WOz+8eocueFs8XkjiUOzqIlIRx8cUXI5FIaOvaQ5NpPXrXVKYGzcdN7nlePn+1uYo6cw23xC4/L/2LRCKR6McZrA0Iv5VNDN/n5uaGRqNBqVT2bLQ8W+el2veUKVO48MILUfaTk2wghw8fJi0trSdT8Z133klaWhoPPPAAAEuWLOE///kPDzzwAKmpqeTm5rJhw4Y+Gxt+Lza/uxPnmDBkCinR3j79tqmoLEedYUNdEQCARGlHk1yBXqah80g4yzMO8UreaFotWpQ7T9LW1oZarWb06NE4onWMCihh0+GJhJm7a5/GRBSTmpNLQ3wgH4Sm4XsMZBVWFi9ejI+PDzanjoLWexnu9xi15jJi3PpPKjwY1tavZm7IfOTSQfsbRCQSiUSDSZxC7aWiooLHHnuMxMREMjIyOHr0KA899FCvzZhnY9B++5WVlfH8889TVVXVU6QV4Msvvzyj86dMmXLaElcrVqxgxYoVP+o+fwuMehOmDjPZdVUMC/QfsO7nYf1OnE6QN3enF3FPqKLG5Ie90heZxIVWYWedNABzg4aK7TsAGDNmDDIfKWGjTtBWE0aZw4tRFhsJyblkVR5A3u5i/SVjkFsk6PZVM2fOHDIzMxEEgaK2Bwhym4tZCMBfFYZa5nZePr84+iYSiUSiX5MxY8aQnZ1NSkoK1113HVdccQVhYWE/qs9BC+AWLVrEihUrWLJkCdJ+anH+Gq1cuZKVK1f2Ckh/CXas2sfIizPYba7momH9l+AwWg3UBxRi+1yLxE2CVGNFGdOEzRKIb7Evgall7O8IwadWhfnIcVpaWlCpVGRmZdKVXsswTSP7Ds+nw11CmMxEYupRJHcrMLpp+XphBLKvmomNjeWSSy5BIpHQYFyN2V5Fgv9/2dWymlj3/tOaDAZx9E0kEol+BQYrh9tvYARu+vTpvPnmmwQEdM+IDUZJz0GLtLRaLTfffDNjx44lKyur5/Vrtnz5cvLz8zl06NDPfSu97P78APqhnvh7upEywOLHNRXv0lUEni4/oHv0rbE5EHODJ1akTIipIqclhGaTiqqt2wDIysrCMdJConc1TaUxlDs8CbE6GR6fT/vOQCLLa9k1NQl5rYDaLGPp0qX4+/tjdTRTqnuSeP8nkElVlJuOn7cA7tvRt7F+E85L/yKRSCQaJOIUKtBdj729vZ2JEycSFBREUFAQ/v7+rFixAr1ef879DtoQxr333svf/vY3ZsyY0SuH2qRJkwbrEiKgpqiOgDA/CnVtGOwWkvtZA9hirSe/6xAtW2wkxLkjc+tCE9VMS00smmYtHRoHI/xbeW/LJFoKCmhrakKpVJI2OxVimol2b+LA5gXUqmVE2mwMSziJ46buzQirLxmBbWMTl15yOWlpad9Mnf6TUI/FeKqSsLksNHRVEqk9dbWLcyWOvolEIpHo10Kn0zF27Fjq6upYunRpT+H6/Px83nrrLbZu3cq+ffvw8el/LfupDNpvwY0bN7Jjxw5KS0t7plAlEokYwA2yTW/vZNa1U/hr0SEsUgeBbn3LZ31Z/y7etVFgzUcikeCeVElHUSiaSBNeuYEwooGC1gAqrVp0mzYDkDYrBcukTiY7m6gtHIbVoqHeW8ZlgcXoG33IOJjPsdQomkqdTBiVxYIFC5BIJDQZ12F2VJIY0F3jtMpUQJgmDoV08BMhi2vfRCKR6FdksEbPfsUjcA8//DBKpZKysrI+my4ffvhhZs2axcMPP8wzz5x9nfBBC+C2bNlCUVHRgAvqRT+e0+nk5L5CrnjwUswn9pIU2Xf0rcOuo9ZcTscOLZ6ensg9TahC28jfmYHav5FOmYyZobXUlYZyrLQUfUMDCrWcpKviiT7uRuC0JvbvHkOnTILaJTAq+SgNr0ejseSwdUIcMSZ/rlpxFSqVCpuzjWLdYyQHvojsm4Ct3Hj+pk/F0TeRSCT69RDTiMDq1at55ZVX+s2YERwczFNPPcWtt956TgHcoK2By8zMpKysbLC6+0X4pSXyPbr1BGlTR1Kq0+GpUZMc1Hf9W7mxgGBJFK0tbXj/f3t3Hl5XXSd+/H3uvi/Z9ybd93QvbUEKFDoVQXBERAXEkRmZdAQ6OogKzCiCOo8Mg1Yq/FRkGEdcABeQrUKRpXsLTZMmXbLv6703d7/3nN8fgZSY7tzbm5TP63nOw5Nzz/l+P9+kpJ9+V48H+8wWggdKGcqKYO6ys68oyfnFLexoLqH3lRcAWHBJJcU15Uyf9w5N1bNJxEx0WPRMN/WTVPXkvNlHUqdQo+by2c98dmTTwfq+b1Pg+Dhuy4L31Z+eBK4t3EprqFnmvgkhhJgwOjo6mDPn+FtqzZ0794y3EUlZAlddXc3cuXOprKxk2bJlLF26lGXLlqWq+IwYb4sY3nh6Gxdeu5ID3T2Ek3FWlJSMeeZIsBaLz00wGMThsGMu6Ke/KQ93oR99v42pzn76QzZ2KAfxN3ZgMBq4cNZFZBd3YHf7aK2dCUCHWceFU/ZR+848ph2so3Z6Ph9Z/lGWLFkCQE/wJQKxGiZ7vjxSdygRYCDeTZF1Ssrbvqt/O+dlr5LeNyGEEBNGTk4OjY2Nx/28oaGBrKwzO63otP82vPfee1m0aBGLFy8e1SX4+9///owCEKeu/XAnpTOKeOSFWgLxKJUFhWOeORKsxXakFJ1Oh8EZBQWOBO0UO1vo1xm5OKeVd0JZ7P1tEwBz58zF4bQwfdlmDu5YjJrUowH9do25njaqf1NK9oCfLRcs5KqrrkJRFDRN48jgQ0zL+gZ6nXWk7oZgNZNss9Arxz6X9YN4x7eXz5bdmPJyhRBCiHRZu3Yt3/jGN3jppZfGHG4QjUa56667TuvUqvc77QTu7rvvHpnnVlBQMJLMvfffD7oxnTi2SCiKyWpCURR2traxYtIkdH8z3zCY8BOI+2jdHsfj8WDKGyDa5SGUG0HfZyUyrZeV+W38vz8vpmf7KwAsWrSIkpl1RIbs9LYM9+iFHVFKXT72vHk+pft3ATDzC7fhcg2vRB2IbEUjQbb1glH1p2v4dCDWjy/uY5K9IuVlCyGESBNZxMC3vvUtlixZwrRp06iqqmLmzJlomkZtbS0//vGPiUaj/M///M8ZlX3aCdzSpUvp6OjgpptuIicnh927d/PUU09x3333kUwmyc3NZdGiRTz33HNnFJA4tkO7jzBt4WQ0TaPd5+drl1ww5pmGYB15WgmHew9SXFyMOb+R/o4scosGiVlV6LfinhLj1691o8bj5OTkMHlqNuXzt7L7z5cCwwmhflorpRE7ne3ZrG06Qk9xLrOu/PhIPa3+xylxXo+ijB6BPxLcx7LsdSlv+zu+t5nnno9OOTc2iBZCiA8DWcQAJSUlvPXWW/zzP/8zd95558iJU4qicOmll/KjH/2I0tLSMyr7tBO4bdu28dhjj/H1r3+dpUuX8sADDzBlyhSi0Sh79+5l9+7d7Nmz54yCEcdXu+0QM5dPo2lgkAQqK0rLxjxzZKgW44CDSCSC1WrBlDdI3c4ZFM87RKzLyWyjn0MtBbTs2A3AwoULmbL4bTqPVBD0eQAomdTIi0kXOY3Z9Bzex/SmXkJf/peRXtdQvJnB6G7m5P5gVN390Q4iyRB55jP7g3gi7wzu4YKc1SkvVwghhEi3iooK/vznPzMwMMDBgwcBmDp16hnPfXvPGXVpfP7zn6e+vp4ZM2awaNEi7rzzTpLJJMuXL+eWW27hkUce+UBBjRfjaRVq3Y6DzFw2lT8dqKPQ5cRsGJt7HwnW0lczhMlkwuCMoGmgTvED0FFXzOriZn77lp1wSws6nY6Vq0vJLW2jYe/wsKfRFGXRijfY31eMy59kfrIbANsnrxmpo9X/PxQ5PoFeZxu5p2kaz3X8jJU5V6R8G5loMsrhoUPMch1/FY8QQohx6kN+CsP7eb1eli1bxrJlyz5w8gYfYBWqw+Hg+9//Pjt37qS6upqpU6fy+OOPf+CAxpPxtAo10D+EK9vJK0caWFY6dvVpOBmiN9pJ0952srKyMOUP0t3jJa+iH2OLiy70LMjr4pfPdgAwc+ZMFl10gCN75pOIDU+snD3/bXY2zMIV1dEX7mdZSzvJLC+sWAFAQh2iM/h7il2fG1X3fv+b+OJ9rMy5MuXtPhCoYbJjKmZ96jcGFkIIkUZylFZafaBJRYlEgmg0ynXXXUdJSQk33XQT/f39qYpNvKu/cwBvvgdN0zjY08vHZo49pqoxWIc3mc9A/yAejwdj3iD9ihmLqpFsczEvr5OmTiftO/YBsPIjU7G5/bQfOrrlR3ZOD1vbp1EYTeKbGmHB7lb0H7sC3u3t6xh6Cq9lOVbD0YUq4eQQf27/OR8vviU9q08H9zDfXZnycoUQQoiJ7LTnwH33u99l37597Nu3jwMHDmCxWJg/fz7Lli3jn/7pn3C73emI80PtwLZDzFg6lf093WgqLCwqGvNMQ7AWfa+NZDKJ0WjAkD+AGslDl1Q40O/l+sW1PPwbhWQ4jMvl4uIrw7QemIamHk263N5BDsU9TLGGWLGzHlM8AZ/+NACaptLqf4KZOd8ZVe+Lnf/DHPdKSmzTUt5uVVPZ53uby4s+fvKHhRBCjCuyiCG9TjuB+/rXv055eTk33ngj1113HdOnT09HXOJ9arcdZOXHl/LH+nrsJhM2k3HMM4eHaul8ZwiHwwGOMKpewxwyom930qwYWWhr5uZftAKweMlc8qe0sPWpo0OeRmMMnTFGr85AbuEgn7xjL7FZszC9uz9NX3gLep0Nj3nJyDuNwRoOBfawftp/p6XdLaEmnAYXWabstJQvhBAijWQbkbQ67SHUCy64gL6+Pv7jP/6DxYsXs2rVKv7lX/6Fn//857z99tskk8l0xPmhdvjtRqYsKOfNpmZm5eWO+TymRukMN9NZ3U9WVhZDJQMEkhZ0jhidTTnMyO/mm/c04+8awOVycfX12fQ2lxKLHN2E1+UZYE/7ZAqz/Fzyu2qyhkIYvvlNUBRULUGjbxOlrhtHFikk1Di/b/sxHyv6R8x665iYUuHtwb3M9yxMS9lCCCHERHbaCdyWLVvw+XzU1dXx6KOPsmrVKmpra/nXf/1XFi5ciMPhmPBHaI0nyWSSZCKJ0WSgecDH4pKxw6fdkTascRcB3xB2hxPX5C7Cg24MnQ6aElb0h7fw62d8KDodf//3VzNzaSMttaPn0bm9A7zeNxW7I8hVz+wmUFCA7tprAWjy/QQdJgrswz12SS3Jcx3/jwJLBTNcS8bEkwpxNcbW/jdY5F2clvKFEEKk13tDqKm4xFhnfLDktGnTmDZtGp9+d44UDJ/ptXPnznNmH7iNGzeycePGjPYqthxop2xGMV3BIQwozM7PG/PMQLyXhA90Oh0dej2VjiFaE06MR7x09vTw1k+HT1NYcvHFLPmIQmTITqBv9LCk0z3IYaWEv/9NHUWBIVpv34BTr8cX2UOr/wmWFj2FougJJnz8uvkHGHVmPll6W9ra/WLnn5lin0qpbVLa6hBCCCEmqpSeDF5RUUFFRQXXXHPNyR+eAKqqqqiqqsLv92dsccaBbQeZuXwaNT09JBIaS47RA9cb6mSwPYDH48FXECCSNEHMQE+fhZbfbiIaUfHMnMLalSspnfUiLe8eWP9+h01WzEqMK37/FoN2O9m3305CHWJ/71eZkX0PFkMhbeHD/Krpeyz0XsLqvGvSdjJCX7SXV7o3883Z/5GW8oUQQpwFMgcureRsonFuOIGbyrbmFlxmMy6LZcwzR7oOMtQZxpBdQGl5J4NRJ8YGL+8cbiTY1Y3Xa2Du319LTmEXZluYnqaxpyW8qRaxenMLpX191Kxbh9Xjob7/O3gty8iz/x3vDP6VJxrv5WNFN3Nx/rVpPdbqN63/x2UF6/CYvGmrQwghRJrJPnBpJQncONd2uJOiKQVsa25lWdnYDXwB2gabiQ0maVYcFOT1EdPpMLS6aKivA2Dh+ZOYarEwZfEejuyZj6aN/rHHzHFCRj3X/u8OAiYT2he/SCTRTm/oZaZlfZ3GYA3Pd/ycL0y+lxmu9J5KUeOrpiPczsV5l6a1HiGEEGIikwRuHPP3BXBmOVAUhYa+AS6fNXYD32AwyGCsFxNeQsYEmFQUn4VwQmGwvgaAoYqlLChqQG9I0tlQPqaMw+VR1v2lhkktbbwwfz5TKitpD/yWfPvlhJJRftP8AJ8svY1cc/GYd1MpoSb4VcsTXFv2OQy6lI7uCyGEOMtkEUN6SQI3ju37ay3zzp+FPxolGk+yYtLYoc/9+/ejWuP4DUUsmNSOP2nF0OVgf3s3sUAAm02HsWA20xe+zeHdlfA3vW8qGkFnlJsefZMuh4O6devIzcumY+i35Nk/ya+avs/KnCuY7Jif9vZu7n6RImsJs+XcUyGEmPhkCDWtpJtjHHtnSw2XfX41L9QfxGuzYNKPPapq995d6BZCc8jB8vJqOlUTlh47tfU7AShbVMalkw6TiBvpbRk7BNuao/Kp3+zE1R1m03nnserii+mPvIZZn8+b/VvxmPLScsZpY/AIuwZ2oGmgoaKhsa3vLb4+699TXpcQQghxrpEeuHGsobqZinllvHzwMHMKxm4f4vP5eLt+N2rYQK4hgTlvABUFZcDC4OFqAOIzF3De/D0c3rUQUMaUEbX1cO2TO2ic7MV30UWcd955tAd+TZHzU7w9uIXLi24e2bw3VaLJKD85vBGLzkKWKYsccy555ny+NGU92WY5dUEIIc4J0gOXVtIDN04FBoawuazodDqqO7q4ZdXYzZGrq6vxJwdwR9xMK+whGLNB1Ehzf4CBth4UHVx8kZGwz8VgV/6Y94OWJDf93xZM8ST/d94c1n30o2DoxxfdzWTvt9DxJ+wGV8rb9sf2p5njnidnnAohxDlMzkJNL+mBO4GNGzcye/Zsli5N78rLY6l+/QBzV80kHI8zEA5zyZTJY57ZuXMnRo+eoaCZotJu/EkrugErBw4fAMA7u5iPzjlM4ztzx7yroaFLNLLm5QMMftpGbPqFLFu2bGTxwkB8gJw0LFpoCTWzvX8rVxefG3sFCiGEEJkgCdwJVFVVUVNTw44dO8563e9sqaFy9Ry2Nbei1+socDhHfd7T00N1dTWWIhdayIi1uJeIXo+hy85g814ACpaXYdEnGOwaPfwaNSc5OC/Ajf+7mZDVxMEv5LJ27ccwGHV0DP2WIse19MbayTaP3TT4VKiaypaeV9jc9SKqpo66/0TTY3yy5FrsBvsZlS2EEGKCkCHUtJIEbpw68k4jkysn8ecD9ZR4XWPmoe3fv5/+/n5UrxEvcTAkQa8ROaLnSG0XABevsdDdUM775771FEapXeJn5j4fsw90sP38YoKWYpYsWUJfeAtmfT5O8yx6o21n1AM3GBvgoYM/YEf/Vqp97/CDuvvpjfYA8FrPK1j1VpZmnXfG3xchhBBCyBy4cSnoD2GxW9Dr9WxrbuWC6aPPA9U0bXj41GgkaU1QYB3C15eF5tFxsPEI8biGvSCLtQt6qH1+3sh7fXlROksjzNzlYulf9wHQfYmTWdlLMBgMdA88R4Hj6uFno+1UelafVty7+nfwq5YnuDR/LWvy/w4Fhdd6XuG7td9mbcFHeaHzOf5t5jdSvihCCCHE+CNz4NJLErhxaP8bdcxZOYNIPEEgGmVx8eiesO7ubmpra3HmFmC0D+K0DdDuy4MgHDm4G4CylSXEokaCg8PHUYVtSVqmhZi524Ulomdm3QEacrLIWWWntOQCNE1jILyVCs+tAO/2wJ36EOrLXS/wWs8r3DrtXymxlY3cvzDvYma6ZvN440+5rODvyLOMXUwhhBDiHCRnoaaVDKGOQ+9s2c/8C2dT292DwaBjdm7uqM/379/P4OAgIWcuTkcQvSlBzKqidpk4sq8BgNWXm+k+Ug5AUq9xaO4QZfV2LGE97sFBKto6eGNSGYVFUaymmQTjB9EpZqyGUlQtyUCsmyxTwSnFu63vTV7pfpkN0782Knl7T76lgK/O/AaXFXz0g31jhBBCCAFIAjcuHX67kWmLJvNOewcxLclkb9bIZ+8fPu1Ch8sYJtycC7Y49a/6iIbjeL1mPnZ+gMFD5WhoNM4I4u43ktVjAmBm3fAZqc1zZ2Ew9WI0TmEg8hZe63koioIv3ovT6MWgM5401mrfO/yu9dfcOu0reEyetHw/hBBCTECyiCGtJIEbZzRNQ02q6A16tjQ0MiMvB4Pu6I+ps7OTuro6svLy0WwJnPoIwZ4sNKBm+3Dv28ILCuiNeIgEHfQURYlZVEoOW0fKmFFbS6fHxYJPrcCoL0anWBkIv4XXsgIYHj7NNp18+PTI0GEea/x/VE29TYZGhRBCjKKk8BJjSQI3zvS29ZNdPNzjVt3ZzeUzRx9g/97wacSRQ0VBH3pFJWyGeEyh7cDw/m/n/Z2dQ23lJHUa7RVhKmrs6LTh/wWsoRDlzc1smT2D+Qs9mIzTULUEA5EdeC3Dq0N7o+0nXYHaEW5n0+GH+GLFLUyyl6f4uyCEEEKIE5EEbpxp2NdMxdxJIwsYPjpt+shnmqaxY8cOTCYTnaqJSWWdBH0uYiUB2l4LkggO4XHruPDCBANHyuguieDuM2GJHD1DdVp9PTpN4/DypVhtnZiM0wlEq7EYCjEbhufancoCht+1PskVRVcz0zUrPd8IIYQQE5sMoaaVJHDjTMO+4fNPXz1yBJvZSLbNNvJZR0cHBw8eJDcvj35Vjyevn1CPF80dpea5bgD+7mIH1eFSLAEzXaURihoto8qftm8fAYuZgo9fTSxxEJNh+vD8N8vRvdmGe+COn8AF4n6OBA+zPHtlilsvhBDiXPHeNiKpuMRYksCdQCaO0mqobqJiXhl/OlDP3ILR88pqa2vx+XzgzKbE60NnSOLTGyAJjW/XA7BunZ2/9k7HnxfF3WfC/L7eN2M8zvSmJv46fzZrFy0lnmjFaCilP/IWXuuKkef6YiceQt3Rv40FnkWYdKYUt14IIYQQp0ISuBPIxFFaA52DZBV42N3WzqXTpoz6rK6uDr1eT6dqZEZFO0NhG/HcCL3bIkQHBzCbdcxbkUWi23vM3reF27djTib5y9IFFDlcJNU+UBwEovvwWpYBEFMjhJNDOA1ZHM/WvjdYkb0q9Y0XQghx7pAh1LSSBG4cScQT6PQ6gvE4g6Ew66Yfnf8WiUSoqanB4/HQlTSQnTuIXzFhTEDtSx0AXHihnYPRYhyG+JjeN29/P2tefZUDRYV0r70URVFIqn0E423YjdMw6IbPWu2LdpBtKjruaQkd4XYCiQBTHNPS+J0QQgghxIlIAjeOtB3soGRaEa80HMGsN5BtPzr/rbm5mYGBAeyeLJIomL0B/IoJA3BweyMAH/s7B3vCeYRyoqN63xRN48o//hFUlcdv/jyLiyehamE0LcJgtBqvdfT8txMdYr+1703Oy16JTpE/OkIIIU5Cet/SRv4WHkcaq1uomFfGH2oPMDVn9BDmkSNHCIfD+IwOiixhdOY4SVVHMDiEr2N4AcOlF7nx6QxMqreP6n1btGsXFY2N/Hr2bJrnTGd+bgHJZD96nZfByNaR/d/gvflvx07gVE1le/9bnCfDp0IIIU5CFjGklyRw40jDvmbK55ayp62D88vLR31WW1uLyWSiSzUyKXuQcNKEOa7Q8aYfgKLJVqbkqww1ZI+cuADg8vm47KWXaMrN5Y3zzqNNjVOZW/Du/Dcvwfgh3OaFI8/3RtvIOc4mvgeH6nEZ3eRbTu2ILSGEEEKkhyRw40hjTQtqkQOjomNhceHI/WAwSH19PR6vl76kHm/+AGHNiF6Fpp2DAHjnldAXs5DbYh9V5kf//GcMiQQPLVxIxYJKFJ2OLIuNpNpPTDPjMs9Hrzs63Dq8B9yxV6Bu63uD82TrECGEEKdCFjGklSRw40h4KMLuwW5QYU5B3sj9xsZGBgYGUNw5uHQq+uJB4kNWFINGS00XANNn5dASceKIHC3PEokwo66OnQsX0uR0olWUcEFxOQDJZB9hLTlq+FTTNPqOMwcupsbYO7iHJd7l6Wm8EEKIc0omh1A3btxIeXk5FouF5cuXs3379uM+u3r1ahRFGXNdfvnlI89omsbdd99NYWEhVquVNWvWcPDgTaFZ/wAAQFVJREFUwTP5tqSMJHDjRHgojNVu5tWGBmzG0Rv4NjQ0EI/H6dfZKNDHMLiDJIfMxNQofc3DK1DnzvfS53OivO/UuNLmZhRgb04OWVlZNBvgIyXlACSSvQSTg2RZj85nCyZ8GHRmLPqjdb/n7cE9THFMw2l0pucbIIQQQqTAk08+yYYNG7jnnnvYvXs3lZWVrF27lu7u7mM+/9RTT9HR0TFyVVdXo9frueaaa0ae+f73v89DDz3Epk2b2LZtG3a7nbVr1xKJRI5Z5tkgCdw40bi/ldJZJbzT3slFkytGfbZv3z7MZjPdSQNZeQGshhgxC3TtCqAlVaxZNhbNSDDUOzq5Km9qAmC72cy8+fPY29/FeYWlAAQTh9E0cJrmjjzfe4IFDDv7t7M8a8UxPxNCCCHGyNAQ6gMPPMDNN9/MTTfdxOzZs9m0aRM2m42f/exnx3w+KyuLgoKCkeull17CZrONJHCapvHggw/yzW9+k49//OPMnz+fxx9/nPb2dp555pnTCy6FJIEbJxqrm9GXurEoRs6fXD5y3+fz0dDQgNvrJawpGCb3o8b1JJ0JWrb2A+CZNIkSR4Bwl2tUmZOamujKzcVnNJI9czolTjd24/ACh8HoATzmmaP2ezvRAobmUCNTHFNT3GohhBDnqkwMocZiMXbt2sWaNWtG7ul0OtasWcNbb711SmX89Kc/5dOf/jR2+/Cc8oaGBjo7O0eV6Xa7Wb58+SmXmQ6SwI0TTftbaHdpxOIJlpYeXUTQ0NAwfHyWKwevMY6uwEfcN/yHqql2eP6bYfIUJpmHGBw8msCZYjGK2ts5kJdHVlYW3Q4LH3l3/huAP95ClmXJyNfRZJhq3xvkWcrGxBZKhIioETxGb6qbLYQQQpwSv98/6opGo2Oe6e3tJZlMkp8/+ijK/Px8Ojs7T1rH9u3bqa6u5otf/OLIvffeO9My00USuHGi9WAHu1QfRS4XdtPRbUAaGxtJJBIM6szklQ7iiKmEVDNqTKOrfjiB884txGOMMhA4ugK1tKUFnaax0+Fg2bJl7BzoGpn/Fo63kFCjeMyLAeiJtvGTw3eQYypiadbaMbG1hVsotpYc93QGIYQQYowUD6GWlpbidrtHrvvvvz/lIf/0pz9l3rx5LFu2LOVlp5oh0wGIYbFonMZBHzctWDTqfk1NDRaLhe6kgSmTfNiTSfosegLVfhLBGEaTiVWVCl1BB6p6dPPeSY2NABwqLOTmZcv4f/vfZE728L8eekIvYdMbMehzqfFt5Y/tP2FtwY0s8K4+Zmyt4RZKrWN75oQQQoizpaWlBZfr6EiT2Wwe80xOTg56vZ6urq5R97u6uigoOPEepsFgkF/96ld861vfGnX/vfe6urooLDy6xVdXVxcLFiw43WakjPTAjQNBf4iIAax6I6sqjiZKgUCApqYmXG43EXMCnDGMlggxM9S9PQCAp6yUT0+tZ/uhyaPKLG9qot3pJL+yEr/HztL8YnTv9qD1hF7EQpStAzt5vvMxbii/+7jJG0BrqIViW2nK2y2EEOIcluIeOJfLNeo6VgJnMplYvHgxmzdvHrmnqiqbN29mxYoTL8T7zW9+QzQa5XOf+9yo+xUVFRQUFIwq0+/3s23btpOWmU6SwJ3Axo0bmT17NkuXLk1rPc21bcQLbERiCeYVHv0XQlNTEz6fD707m/yKAWwdViJWDTRo3D+8gCFvRhELvN28vf/oAgNDPE5RWxv7s7O58MILeb2jmQveHT6NJroJxg4RTcbY2v8X/mnK9yi0jl71+rdawy2UWCWBE0IIceoytQ/chg0bePTRR/nFL35BbW0tt9xyC8FgkJtuugmAG264gTvvvHPMez/96U+56qqryM7OHt0OReG2227j3nvv5Q9/+AP79u3jhhtuoKioiKuuuupMvz0fmAyhnkBVVRVVVVX4/X7cbnfa6mmqaaWzwMi0nCwMuqM5dXNzM7FYDJ/OgntSB65DNvwJO1rQRG/18MTJKy9J8mxvOQwdnTdX0tqKQVVpmzKFTyxezHdf+h3/MHd4wUJvaDNey1K2D9Zzfs7V2A0nbpeqqXRE2imyHvt0BiGEEGI8ufbaa+np6eHuu++ms7OTBQsW8Pzzz48sQmhubkanG91/VVdXx+uvv86LL754zDL/7d/+jWAwyD/+4z8yODjI+eefz/PPP4/FYjnm82eDJHDjQHNNC+1euOl924fA8B8ok8mE3x2jIKnDZA8TQk9vvUq4O4CiU/iXdf38a/1Spr9vA9/35r95r7qKsEFHUtMotA/vEdcdeglNN4veRBvLstedNLbeaA8eoweTznTSZ4UQQogRqToG6wzKWL9+PevXrz/mZ6+++uqYezNmzEDTjl+Roih861vfGjM/LpMkgRsHmuraiVxkZ1be0eOzIpEIhw4dwuPxEJ/kx9FqJ1beiTGh0XxgeDfp8ske6oeKGfI5RpVXcuQI3Q4H8z76UV5vaxxZfRpL9uKLvE193MoSh4LxFJKylnCzDJ8KIYQ4bYqmoZwgKTqdcsRYMgduHPAPhTDqDZR4jg5nNjc3Mzg4iMHrwV3iw9buIOpOoKDRUjc8fHrp+Xr+2DADR/ho75s+kWBSRwfdM2ZQUVHB1o5WVhYOL4xoDzxFXL+CqBpmpu3U9nRrC7VQLAmcEEIIMa5IApdh4aEwcQNoqkaJ++jy6KamJiKRCKFSDbXPis4ZBp3GkGqkt2Y4gZsyN5v6sHdUApff3IwpmcSydi2KolDT182cnHw0TaUt8GtqQnEu8E7HYMg5pfhawy2U2mQLESGEEKcpQ0dpfVjIEGqGNR9oRyt0oNfpMBuO/jgOHz6MXq8nURbA1uRCm96OOaLR1WHCf6QPgE73IkIWcEaOJnA5NTUAFH3608TVJD3hIAU2B/2RN+hL2LDoPZSYdKCMXmVzPK1h6YETQghx+s5kBenxyhFjSQ9chjXtb6E/30i+4+gpCvF4nNraWty5LoxZEbw9JmI5EQiaaK4eQFM1ysuMtA9NYciiYYsMv6dpGhUtLYSysrDNncuRwX6meLJQFIX2wJO0JwpZmXMlqjaAXnfyBC6cDBNKhMgyZaWr+UIIIYQ4A5LAZVhTTSsdLpiSfTRJamtrY2BgAOs0K0PddgzuKIpBJZI00fbu/Lf5lS6MEROmBOi14R64kN/PzP5+EitXgqKwv6+bWVm5RBPdNA/tZCiZZLb7PJJq3yklcO3hVjlCSwghxJmRIdS0kgQuw5pqW4k6TUzOOprANTU1EQwG0UpVQp0OtGk9WGMqEbtK3/7hBM5RMQmXMY7jfcOnppoarIkEjnXD24PU9vcwJzuPjqHf0a3O4Lzsj6FX9CSTfej1J0/gWkMtlMgJDEIIIcS4Iwlchg0GgjhtVko8RxcwHDx4EJ1ORywvjNptJV4QxNBpI5BUGKgdTuCsnlkErdrIAoZkMsnUjg4AdKtXA1Db381MbzYNvt/SEY2zOOvS4WfVPvS6kw+LtsoWIkIIIc5Qpk5i+LCQBC6DIqEoMT2YdHpK391CJB6PU11djbvchRrX47Qk0BmSxHtdtNcESUTiuFw68qhgyMpIAtfb28sCvx81OxtmzULTNA4O9JFtOUBr3Ml8z2qs+uF5dqc6hNoqW4gIIYQ4UzKEmlaSwGVQy4E2KLSTSKiUvHtUV0tLC319fVinm0l02cmZ0o2dGDHFQGt1DwCz57oxKXqGLBqOdxcw9HZ3M6u3F91HPgKKQnc4SJbFSnfoD7TGrKzI+RgAmqaSVAdOOoSqaiptkTaKrSXp+wYIIYQQ4oxIApdBTTWtBHJMhONx8p3Dpyk0NDQQCoXQSpIEu+w4KvowdVuJeGIM7BseIi2aWgjA0LtDqLFYjNKhISzBIHzkIwDU9vUwKzuXWv87FFunkm0efkdVB1AUKzrlxOe3dUU6yTJmYdab09V8IYQQ5zAZQk0vSeAyqLW+nQ4n2IxGdO+u9Kyrq0Nv0RN3x7BkD2EzRnDH4wScCfrfTeC8+ZNJ6DRUBUxJBb/fz+JgcLjQdxO4mv5uKrNVWmJWVuR8YqTOpNp/SsOnzaFGJtnLU9tgIYQQHx4yhJpWksBlUNOBNhI5Vsq8HmD4/NOamho8s5yY/EayZ3STYwhQUtRE18AQ4f4gBoNCYVY5QxZGVqD6fD4qfT5wOqGyEoDavm7yHHXENAtTHPNH6jzV+W9NoUbKbOUpb7MQQgghPjhJ4DKop3eQ3Gz3yBFazc3NDAwMYJ5qwmyJofXpMaCh02l0Vg+fvlA6xYMx7h4ZPgUIh0JMbmuDVatArwegpr+H/mQ10x3T0Cn6kToTp7iFSHOwSRI4IYQQH4gMn6aPJHAZoqoqoVgcp9E8sgK1oaGBcCSMWhoCU4I8NQBxPdvap0PT8PCpqbAINWlg0KHhDA+Xkx8MYhsYGBk+jSTiRBJRmiP9LPZ+dFS9p7KFiKqptIab5QxUIYQQZ07TUneJMSSBy5C+9gESHjMGRRlZgVpTU4N1qhmzOYa2p5C8LB/RiIl9yRycLc0AuIpyGLJodHpVSnp1DAUCfKylZbjQdxO4uoFelhYF0KFS7jx/VL1JtQ/DSYZQe6LduIxuLPoTL3QQQgghRGZIApchLXXthLNNRONJSj1ugsEg9fX1OJda0AHBDhdOV5COsINPlx6itnZ4v5CSSW72lSeZ2aLHGYxy/VNPsa6mBu2yy2D5cgBq+noo8hxmii13zDFYSbUPnf7EPXBNoUYmyfCpEEKID0BWoabXOZfAXX311Xi9Xj75yU+O3GtpaWH16tXMnj2b+fPn85vf/CaDEQ5rq2+nz6UwGA5T6nHR2NjIQHgAfWkCfY+NuD6J3pjEntBxnqmB9vY4AHkVHjQFFu/v4p9+8hMWNjez66MfRXnuOTAYAKjp7yCi62Wee9WYepPJk69ClflvQgghxPh2ziVwt956K48//vioewaDgQcffJCamhpefPFFbrvtNoLvbbuRIUdqW9EKbQxFY7gsFhobG9FPB3NUh77bjsXjJxgz87lJh/jvbTkAmNxu/KVm5h9W+Mz//QpzNMq9K1fSd+utI4sXAFrC7+DURyl2XDim3lNZhdocaqRMthARQgjxQcg2Iml1ziVwq1evxul0jrpXWFjIggULACgoKCAnJ4f+/v4MRHfU4QOtlE0rxmu1AnDgwAEslUZ0hiSGXjsO7yAORYfDGGfLO8PDoI7iApJt+SzcfwS3389zF17I/rIySkuPHncVScSxWw9QaAxiN04dU+/J5sBpmkZzqIlSqyxgEEIIceYUNXWXGGtcJXCvvfYaV1xxBUVFRSiKwjPPPDPmmY0bN1JeXo7FYmH58uVs3779tOrYtWsXyWRyVNKTCYFgiDync2T+28GBegwWHehVlIAJj2eQCkuEnb48AnXDyaZlRjaTO3Qs3LOHmNHIG8XFuFyuUW15sm4P2Q4f0x2zx8x/g3eHUE+wjUhvrAeHwYHNYEt9o4UQQgiREuMqgQsGg1RWVrJx48Zjfv7kk0+yYcMG7rnnHnbv3k1lZSVr166lu7v7lMrv7+/nhhtu4JFHHkll2KctFo0TJondYKLE46KpqYlwQRDrkBFDrw0FhVxvP9OsQV4ZKqC33g9AVkkeWb4QM+rq2D9nDj2RCGVlZTgcw8dwJVSVpxv/QpbRSK5t+Zh6NS2OepKTGJqCsoGvEEKIFJAh1LQaVwncunXruPfee7n66quP+fkDDzzAzTffzE033cTs2bPZtGkTNpuNn/3sZyctOxqNctVVV/G1r32NlStXpjr009JxuJNo1vAWIsWu4QROydIwWjT0vXY0DWYV9BHTdOyPuBlo6QWgwpPP/LffRq+q7F64kHA4zIwZM0bK/XNDPVOzY3gNfjyWZWPqDUVfx2ScgU7nOG5scoSWEEKIVJBVqOk1rhK4E4nFYuzatYs1a9aM3NPpdKxZs4a33nrrhO9qmsbnP/95Lr74Yq6//vqT1hWNRvH7/aOuVGqt72DQY0BVIc/h4MCBAxhy9eCMDq9ANcQps8WoC9sJtwZRo1EUg55phmwW7dlDb3Y2jUVF6HQ6ysrKRtq46Z3tlHl78egjOIzTx9QbCP0eh+3KE8Y2fITWpJS2VwghhBCpNWESuN7eXpLJJPn5+aPu5+fn09nZOfL1mjVruOaaa3juuecoKSnhrbfe4o033uDJJ5/kmWeeYcGCBSxYsIB9+/Ydt677778ft9s9cqV6vtzhmhZ0RQ76QyFcBj11h+vQ2xme/zZkwuL1kauH/SEXnW8Pr5a1F2czq6mT3N5edi9cyFAwiMPhGInt9bYmsq1mBuKtzMv6HIoy+keralGC4RdwWo+fwGmaRkuoWYZQhRBCfHByEkNaGTIdQKq9/PLLx7yvqqe+jOXOO+9kw4YNI1/7/f6UJnEHqhupuLSYrkCQmG+QgN6PN25E6bejoDB7ShMDcSMdmhXfgUEAcgrzWbRnD0mdjrcrK/EPDJCVlTWS0D78zjY+MztObSjGJNdnxtQZimzBaJiM0XD8dvTH+rDoLdgN9pS1VQghxIdTqoY/ZQj12CZMD1xOTg56vZ6urq5R97u6uigoKEhpXWazGZfLNepKpfbmHmZOK6M3GMTX2YHqSaIYNOpas4lrMK+sndqwnWDSTKxpeIHGJE8ec/bv5+C0aQQdDgKBANOnT0en07G/r4uheIyw9gJltpnodeYxdQ6F/4DzJMOnzSFZwCCEEEJMBBMmgTOZTCxevJjNmzeP3FNVlc2bN7NixYoMRnb6QrEY03NziCdVjhw8iKVUj8EcJ97iplrRUe4d4kDSTjRsJtQ1fIh9JUbMsRgHpw7v7aaqKhUVFQA8XrOXf5jtoTPSx2z35fT5/hNVC4/Up2phguGXcFg/dsx4NE2jLnCAzV0vUW6rSHPrhRBCfCjIKtS0GldDqENDQxw6dGjk64aGBvbu3UtWVhZlZWVs2LCBG2+8kSVLlrBs2TIefPBBgsEgN910U1ri2bhxIxs3biSZTKaszFAgTFivUexwYTUaqN9fj/3vdQQ67UzRJTi/cj+9SegP2/F1mxjq6wNgeSgEQFN5ObFYDIPBQHFxMYORMK+3NfLpqXvZ1++kyGymv/+/SKp95Hm/O1xn5FVMxpkYDcWjYvHH/bzR+xpv9L6GTW9jVe6FrMgee/yWEEIIIcaXcZXA7dy5k4suumjk6/fmod1444089thjXHvttfT09HD33XfT2dnJggULeP7558csbEiVqqoqqqqq8Pv9uN3ulJTZ3dxL0KXHYTTi1OsY9A2Q71Vp2pPHLH2StZW1HFY1DDqV5poYaBpWu41FHR0EbTZ6s7Px9/XhdDopLi7m1/XVXDfDRldkP07DPHTJauyWdQQjmwmEnsVpu5xA6PfHHD59+PBDFFmK+NKU9ZTY5OQFIYQQqSNz4NJrXCVwq1evRjvJapP169ezfv36sxRR6jUd7kCXY2MwHMGiqqiTYqiakcFuJwvn1BLSdDQE7ZhMcdr2DW9fkp+bx6SWFhoqKkBRCAQClJWV4XS5+L+6vdy34iUCylrKjV7C0W04rJfhdf4jHX3/gNk4nVDkL+R6/mNUHMFEkM5wO1+ZcSd6RX+sUIUQQogzl6oVpLIK9ZgmzBy4c8XB+hayS7PpDgZRwiHcy/WoQ2bimo4V8+rYH7dwsK8Qiz5GT20jALOzs7FGIjRNGt6fLRgMMmPGDLa0NbKmtBW9LoYvaaHMNotIbBsW83Ks5uW4Hf9Aa8/VmI3zMOhH91IeCNQw3TlLkjchhBBiApIE7gQ2btzI7NmzWbp0acrKbDnSRUF5Ht2BIJGhNswFoOu1MzOvhyxvgM6YgbZgDrEYDB48DMAlRiMATZMmoWkamqZRVlbGL2u3saLgZaZnfZOm0AGKzTbAhFE/nOhlOb+M2bQAl/26MXHU+KqZ7Z6bsnYJIYQQ7ycnMaSXJHAnUFVVRU1NDTt27EhZmZ1N3ZRNzqe5txdrfidJvwmd38g/rtrJ1r4CrFqMqGKiblcENRzBarWyxucjbLHQlZdHOBzGarWC180kxwvk2ZaRVIrR0DCp9VjNy0YOsVcUPcU5T+Cyf3JUDJqmUeOvZo5LEjghhBBpIqtQ00oSuLPM1xdgUnEezb192EuDqJqJZdkdFLn9/EXLwmUdArPKvtcGAZgyeTKTm5tpLitD0+nw+/04nU7eDNZyXuE7TM36Ktv6nmOh52Ii8e1YzWMPsf9b3dEu9IqeHHNumlsrhBBCiHSQBO4siyaSFLtc9MW6MFhVHI4QH5tfzU9r5mPutqBqCgZzjINv9QBQWVCAIxik6d0zTwOBACWTJpHkaYodn0ZTXLw9uIXzsj9KOLoNq+nkCdx+/z5mS++bEEKINJIh1PSSBO4sSsQTRLUkRU4XelcH0SMKNxYc5JV3ZtJtNOKJJGjy5aD6AnTX9wOwRjf8I3pvAUMkEiFSXsRsbw9FzkvY0fcCs1zLMStDqGoAk3HmSeOQ+W9CCCHSTtVSd4kxJIE7gVQvYuhp7SPqMpBtNpOV18tcdwgbKr/fXonFG8ZqiNAcyKFtezsABQUFLOzqImY00lFYiKqqKIrCPt0QbnMvVsN0tvY9x8qcKwnHtmM1Lx1ziP3fSqgJDg0dZIZzVkraJIQQQoizTxK4E0j1IobOxh4SXgv1LbXY7FFWlfbx15ZJhNCht8Ww2IfoD7lp2jp8fNaUKVOY1NRES2kpql7P0NAQ+hwvLnsnTtNU9gd2UGSdTJ6llHB0ePuQkzkSPESxtRir3pqSNgkhhBDHJIsY0koSuLPo8MFWnEVutna9Tl+bjak5gzT35KC5oqgBM07PIANJK0feGj7A/jyPB4/PNzJ8GggE6J9UyMcmKzhN83mj5xlW5XwcgMgpzn+r8VfL/DchhBBigpME7ixqPNxBblkOLRxA3w8qCn0BOzp3FHwmvO5BWo/0ExqMYzEa+eZf/kLIamXfvHkABEJBOrKdTPX04lNzMerMlNvnEE+0E0+2YDHNP2kMNT5J4IQQQqSfQooWMWS6IePUuDpK61zX1tBF4dJpRCyDzDYkaQx40CX0GDxhnFGVYMxCw3N7AFinqpg1jcdvuIEBrxeAbo+dKSYbwXg1ewIaa/JvJJ44RFvv9Xgd/4SimE5Yf1OwgUAiwCR7RdrbKoQQ4kNOjtJKK+mBO4v62gfIKdIR9etZXNLPkSE3SlKH2RMmSxdm5ztm2v9SB8C/KQpPfO5zdBQWAhBXoH1KMVcUODgcMZBjLmOSKU5rz9V4nf9MtvvfTlr/U62/4cqiq9GdZKGDEEIIIcY3+Zv8BFK9CjUSi2NQAoSDeuYU93Mk5IYEWD0hbOYQTz3SDhpcBXRcfjltxcXA8PzN1z0msjr7mVTcQ2vczUXeKXQO/BP5WT/C47jhpHXX+KrxJ3wsz16ZkrYIIYQQJyL7wKWXJHAnkMpVqKqqEkkmSMT70eI6su0hWqJ2FINGMqHH52+m+tU2AP4daHl3416AaoeBuKYyubOfat1ulrgmEx76D4qyf4HdsvrkdWsqT7X9mk8Uf0p634QQQpwdsgo1reRv87PE1+MnZtMTjfXhNcdo6ckmrlNQLXHCA1b+9MRB0OCSfCvTLcaReW/tZh2H7EbmN3eQd5GKThdliqkbu/VyrOYlp1T3zoHtWHRW5rpPvshBCCGEEOOfLGI4S7qaeoh4jAxFeylzhmjtzEWzqCSscRLNffz1hQAocI8BWovz0BQFDdhXnOTvPAfRL+6HLhszLEcgEScn57VTqjehJvh92+/4h4ovjRxyL4QQQqSbomkoKViAkIoyzkXSA3eWtB3pQsm2ElL8zPYM0NaZg6bXUB0xYs3DQ6feKQWs6gjTXFAEQNCo8ZG5NZiHTLQ8Yqa0pxS7Lk6e+070+qxTqvcv3S9SYi1jsmNK2tomhBBCjKGm8BJjSAJ3lhw62IazwIVmSzDX0zfcA2dQUVxR4v3DG/cWO8zoVGjJKQEgUBQk7Lehq88jPBhneuUBLDoLLvt1p1Tni51/ZkvPK3yq9NSeF0IIIcTEIEOoZ0ljfRvOhTZ0TiCmJxIzoek1MMQZ6hsAYOq7P43uwgIAlEIfiQ43AHMre7Fmd5DruPmk552qmsrvWp+kLlDLHTO/icvoTlu7hBBCiGORIdT0kgTuBDZu3MjGjRtJJpMfuKzOhm7MF09CU3Xo4no0NDRFwxrSaG+OAzA/FiVu0uHLycGAhjPXh7Yvj0mT65h1XjMRtYBi180nrKcn2s3Trb8hmAzylRl3YpEzT4UQQohzjiRwJ1BVVUVVVRV+vx+3+8x7sTRNI5pIMBDpxxvRiJsNaHoNRVPwqmFaGhMALBoM0VHmwaDXkcgOEoqaWF50mNlLd9CTtDDV9D2M+rFxaJrGroHtvNbzKp2RdlblXMjlhVdi0MmPVwghRIakagsQ6YA7Jvkb/izo6xgglG8hog9jjSSJ2YzD898AB4MM9kQBWN4e4MAFM1GAcKkff6+bRSv/REvYybY/zeT8WxYcs/zN3S/yVt8bXFF0FfPclegV/VlrmxBCCHFMcpRWWskihrOgsbqF9ml2ctU+7JpCLD7cAwcaqr8LAJPZSH5Co76wGA0NQ4EPR1ChJ2Gj5q0igj0VOByOMWW3hlp4ofM51k+9jQWeRZK8CSGEEB8CksCdBY3VzYTsBqz4caAbTuAMKjZDhJ62EAAFDisKUJ9TCs4oqg4qvY0kdbBzcxZFRUVj9nGLqzF+2rCJa0s/i9d0atuKCCGEEGeDHKWVXpLAnQVHqptJWvSYbAnsaMTiBlRTkjyTn33VZgAq9HpUHRxylaEV+mgZyGLB5DqCfV5i0TjF756L+n5Pt/2WMtsklmQtO9tNEkIIIU7svSHUVFxiDJkDdxa0NnahzMzC5NBwohKPG0g6YmTpghxpGO5VmxONMlBuRzOYSZT6CLSWkJysY6AjH0VRyM7OpinYyOGhgwAEk0PsHdzNXbO/ncmmCSGEECIDJIFLM1VViSQSKIDeBU4tQSxmRLXHceqidLVFAFjsC7H/2hnklvQSR0eeGiYUszDY7UVRAmRlZfHzxkeZ5piGWTe8NcgtU76MVbYJEUIIMQ4p6vCVinLEWJLApVl3cy9qng0DGgaXgisQJxC3k7THcOgjDHYGACi2GXjt8tnk5rdQs38aa8r3gClOT4sdqzUBbo3YUJTPlN0oZ5oKIYQQH3IyB+4ENm7cyOzZs1m6dOkZl9G0v4VIsR2rEgMVrIY4naqZpDWOWQsT6fED0PmpqcS8OuJdbppjDhYUHyYeNTPYp2K1WmnWN7LQu1iSNyGEEBODzIFLK0ngTqCqqoqamhp27NhxxmU0VLcwmGXAYQiiDenQmZLUKFYUe5S2lgSapmEFmv+hHKMzSry6gLhBJRa04+/JIRwO43a7qQ6+wyLvmSeSQgghxFmlpfASY0gCl2aN+5vpdYJNF4SQHp0xgT2qw2sKMvibMAA5Ljs2d5L2llziSSP5lgChmIVATzZDQ0OUzizBFx+kwj45w60RQgghxHggCVya9XcMEjUqWExhlIgRgymBUVFxECP050EADJMKMOsTtHXmEDWpTPZ2gimBr9uLqqqYZxpZ4FmE7iSH2AshhBDjxXuH2afiOl0bN26kvLwci8XC8uXL2b59+wmfHxwcpKqqisLCQsxmM9OnT+e5554b+fzf//3fURRl1DVz5szTjiuVZBFDGmmaRlRNYtUZMJki6CMmjIYEBn0SuxKlsTsGgKPEg0GXpKsri7ziLnKVCBbXEK1HdLjdbnqcXXzEe22GWyOEEEKchgwdpfXkk0+yYcMGNm3axPLly3nwwQdZu3YtdXV15OXljXk+Fotx6aWXkpeXx29/+1uKi4tpamrC4/GMem7OnDm8/PLLI18bDJlNoSSBS6OgLwRWA0bAZIlhjDgxGxMoxiTuoSBNseG10Z6pFlRVh5Y0EHFE8cQ1QoMueroGmbFgOv3JXqY5p2e2MUIIIcQE8MADD3DzzTdz0003AbBp0yaeffZZfvazn/G1r31tzPM/+9nP6O/v580338RoNAJQXl4+5jmDwUBBQUFaYz8dMiaXRn3t/WheC6gagQOg67djNcbpN0Nhu49D7z6XM9VIPGbEpkvQhxmHquLvySYcDpO92EulZ4GccSqEEGJi0QA1BddpdMDFYjF27drFmjVrRu7pdDrWrFnDW2+9dcx3/vCHP7BixQqqqqrIz89n7ty53HfffSSTyVHPHTx4kKKiIiZPnsxnP/tZmpubTz2wNJAELo162/qJOg0kEirRQyqhsBmbIU7UmsR22MeRd58rnGwgFrCS5fDTHvSS5Rmkr92BzWbDlzXAQu+SjLZDCCGEOF2pngPn9/tHXdFodEydvb29JJNJ8vPzR93Pz8+ns7PzmHEeOXKE3/72tySTSZ577jnuuusufvCDH3DvvfeOPLN8+XIee+wxnn/+eR5++GEaGhq44IILCAQCKfyOnR5J4NKot62fgF0hmkxiSMTx6xUshiQmR5iXXgwQA7w5Hoon6Yn47ORk9WNKgCNrgKZ6yC/Mp1NtZ6ZzVqabIoQQQmRUaWkpbrd75Lr//vtTUq6qquTl5fHII4+wePFirr32Wr7xjW+wadOmkWfWrVvHNddcw/z581m7di3PPfccg4OD/PrXv05JDGdC5sClUW9bP31mFTWpYdLriZhVkqqCWY3xxM7hI7TmXjYPnaYjGDVhdISxt+sxWSN0t0e56MqZtJiOYNSZMtwSIYQQ4jRppGgRw/B/WlpacLlcI7fNZvOYR3NyctDr9XR1dY2639XVddz5a4WFhRiNRvT6o1OVZs2aRWdnJ7FYDJNp7N/BHo+H6dOnc+jQoTGfnS3SA5dGvW39DNl16FQVk8lEzKQSSRjo3tVLXUDFosCcj1egT8BgwkAkasOTSKIzJIjHjHgmuym2lmS6GUIIIUTGuVyuUdexEjiTycTixYvZvHnzyD1VVdm8eTMrVqw4ZrmrVq3i0KFDqOrRQ1fr6+spLCw8ZvIGMDQ0xOHDhyksLPyArTpzksCdwAc9SqulqYu84mz08eEMPmFOEknq2fG74dlvl+R6sBYnMWgaHUNOhqIO8nVhYmEDWd5slGxNEjghhBATU4aO0tqwYQOPPvoov/jFL6itreWWW24hGAyOrEq94YYbuPPOO0eev+WWW+jv7+fWW2+lvr6eZ599lvvuu4+qqqqRZ77yla+wZcsWGhsbefPNN7n66qvR6/Vcd911qflenQEZQj2Bqqoqqqqq8Pv9uN3u035/IBCkxOuh++ARTGYzemOS5k6VPa92A/CRmZMYsIfRkWR6Vhdvds1isq6PUEBh2bJl9CR7WG499r8YhBBCiHFNBVJxfLd68kfe79prr6Wnp4e7776bzs5OFixYwPPPPz+ysKG5uRmd7mj/VWlpKS+88AK333478+fPp7i4mFtvvZU77rhj5JnW1lauu+46+vr6yM3N5fzzz2fr1q3k5uamoIFnRhK4NApEYxTpDOhjMRJWKx5djF/+up9kUmMl4J1eRK+mYDTH+MikWv7YVUlioBk1YeeKK67gh10/kB44IYQQ4jStX7+e9evXH/OzV199dcy9FStWsHXr1uOW96tf/SpVoaWMJHBpkognCCbjGDUNfTRKxGUjRzfAn949Pusfgb5yK7GYkahixJLQo2kaeiVAtqcMT7abwZYBcsyZy+6FEEKIM3Wmx2AdqxwxlsyBS5PejgFCNoVoJIoSCRM0m4n391NfF0avwBWAb5qJZMyAEtdzoLMM/WCA6bOKKMybRmekk3xLgZx/KoQQYmLK0By4DwvpgUuT/QeacRa46QuG0MdjDBr0RPcOL15Y4jFgTuiIlqhEgwbcjjA7O4pwxZPMXzgFs9FBW7hFhk+FEEIIcUySwKXJO3VNlJTl0xMKY0zEadfribzdCMAa1UpniRm9LY5+EIyGOFviZVzkduDyRDDqsmgPtlEkCZwQQoiJKkOH2X9YyPhcmhw63Mb0acUMRqMoapJ4JMrh/cOrT6+JROgpdhGJm3CaorzSNZ3Cdxr52Mx5xNUBjHoPbeFW6YETQggxcckQalpJApcmrU1dVM4sxxeLk7RaiR+oIxFXKSm2UhmN01/qIJg0gjXOxa5D5IcSTJkyhVhyAJPOS3u4jSJrcaabIYQQQohxSBK4NNA0DX+nj/kzy4klEsTdbnz7awG4YIYHgIEyOyHVSJnFh9qrkZuby6RJk4irA6jYiCTDeIzeDLZCCCGE+ADUFF5iDEng0qDF78MS0YibNEgkCNlt9NcfAOCiHAcAAxU2giErJkuUtkMG5s2bh9FoJJ4coC8epchajKKkYgdEIYQQQpxrJIFLg+rubhxGEzsPN2ALBenpHyARCuJw6lmWNAIwWG4mELCCXqO53saMGTPQNI242k93dEgWMAghhJjQ3tsHLhWXGEtWoabBgvwCipxO9ra2oQsGiHV1AjB3ng1XZ4SwzUjAZsPQpWFUkkTDBUydOhVVC6NpKu3hHlnAIIQQYmKTVahpJT1waeBS9eTkuKnt6UVBhfZ2AGbMsGDvCjKQ7yAaM2NR4ihxjZKSMvLy8oipAxj1XtoisoBBCCGEEMcnCVwa9LX3k1OURaN/iKRBT6RjuAduznQL9p4hBortBBMm3IYI8QBUVlaiKArx5AB6nZu2cCtltvLMNkIIIYT4IFQtdZcYQxK4E9i4cSOzZ89m6dKlp/Veb1s/riIPsUSCqMNBoKsLgHlTjNh9QQZKrASSZqxakvYjDqZOnQpAXB0gmIClWcux6C0pb48QQghx1sg+cGklCdwJVFVVUVNTw44dO07rvWmLJ1N46UzskRABNCLBIVCg0jb8+WCZlaBiIMvuI9jpprh4eLg0muijKzbE6txLUt0UIYQQQpxDJIFLA1eWk8PBQYzBIRKdw71vrnwX3v44AH0FDojryM0fJNGbjcfjAaAxuA+jzkOhtShToQshhBApkqreN+mBOxZZhZomb7e2oYSDqJ3D89+83gJMbQMA9BQ4capx4lEdee5y9Ho9AA1D7zDZPjtjMQshhBApI6tQ00p64NLkQE8fyUiE+LtbiBTk5qO0JADoyHeTZQzS02ShtLQUgNZQC0nVR7FtVsZiFkIIIcTEID1waRBNJAjF45gNesLvDqGWFmWhNiYIuy34rVaKND9tR8zkLsoF4NBQPcUWD2Z9ViZDF0IIIVJDTdHwp6xCPSbpgUuD+p5eshWImYwjK1CnlHihOcFAkY2Qqifb6WPwiIHs7GwAVuddgtdoxqiX80+FEEIIcWKSwKVBq8+PIxIiHIuTiMVQ9HqKi+woLTEGS23oFY1w1IQpaiInJ2fkvbg6gFF64IQQQpwLNDV1lxhDErg0uGzaFHLbWxjyDQJgy87DFg+i8yUZyHfgNoVo68zGYbGM9MABxJMDmHTSAyeEEOIcIPvApZUkcGkQiUQIBoP0H2kAwJlbgKGlCYBer4sc4xCtHdl4PR7sdjvAuwfZD2LQeTIVthBCCCEmCFnEkGL9/f3ccsstPP3008Tjw/u+ZecX4uprBaDLaiXbFKSjzcXFpfkoigJAQg2gU0zodeaMxS6EEEKkjCxiSCtJ4FLM7XazZcsW4vE4Rq+HmbPmM3XBErLr6wHoMJmwDNqIBzSK5h/dsDf+7kH2QgghxDlB9oFLK0ngUkyv1/Nf//VfPP6739G1cCHTgjqIBskZiqHpoHnQjun5hRgSwb+Z/9aPUea/CSGEEOIUyBy4NLjyyitxFRdjVZOENYVEwEeWP0qswIw+7qJ3KIhJTRxjBaokcEIIIc4RGilaxJDphoxPksClgV6vJ242YYzGCGs64kM+3AMRIqUWinLK6AkEsSnKqB64WHJAeuCEEEKcO2QValpJApcGFouFoukzSAwOElYV9LEI5s4gsTI3lZWVhDVwGHRkZR3d8y2uDmCSPeCEEEIIcQpkDlyaeEtKyKmrozkWx2Wz0P3bS/DZHEz1TMXwxi4KPA5MJtPI80OxA7jM8zMYsRBCCJFCqgqkYBNeVTbyPRbpgUsTn5rkgoULMQV6yc3LI7nAjG7WdKZMmYJitVNRkD/ybCzZS1/oVfLtH8tgxEIIIYSYKCSBS5OuoSHWXXAB5zlVrrwsSa6uEbv1PJxOJ3qHg+llpSPPtgWeJNd+mQyhCiGEOHfIHLi0kiHUNOkeGmJqYR7fuFWP27IVY/b/UmScC4DZ4eSjl64BQNXitAV+RWXeI5kMVwghhEgt2Qcurc65Hrirr74ar9fLJz/5yVO6nw7Dx2KpGHVBsr1JjHm/R3k3eVM1DZ2ioNfrAegJvYTVUIrTPCvtcQkhhBDi3HDOJXC33norjz/++CnfT4eBSASv1Yqiy0LneRBFf3S/t2giwZXzjiZrrf7HKXVdf1biEkIIIc4aVUvdJcY45xK41atX43Q6T/l+OnQGAhQ4HMf8zGo08i+rVwDgj1YTSbSTY1tzVuISQgghzhZNU1N2ibHGVQL32muvccUVV1BUVISiKDzzzDNjntm4cSPl5eVYLBaWL1/O9u3bz36gJ9E5NET+cRK49wvE9lPiugGdYjwLUQkhhBDiXDGuErhgMEhlZSUbN2485udPPvkkGzZs4J577mH37t1UVlaydu1auru7z3KkJxZNJpiadfIVpcXOa5nk/uJZiEgIIYQ4y7QUDZ/KIoZjGlerUNetW8e6deuO+/kDDzzAzTffzE033QTApk2bePbZZ/nZz37G1772tbMV5kmtmzY90yEIIYQQmaVppOQgU0ngjmlc9cCdSCwWY9euXaxZc3S+mE6nY82aNbz11lsprSsajeL3+0ddQgghhBDjxYRJ4Hp7e0kmk+Tn54+6n5+fT2dn58jXa9as4ZprruG5556jpKRkJLk73v1juf/++3G73SNXaWnpcZ8VQgghxDGoauouMca4GkJNhZdffvm07h/LnXfeyYYNG0a+9vv9ksQJIYQQYtyYMAlcTk4Oer2erq6uUfe7urooKChIaV1msxmz2ZzSMoUQQogPFZkDl1YTZgjVZDKxePFiNm/ePHJPVVU2b97MihUrMhiZEEIIIf6Wpqopu8RY46oHbmhoiEOHDo183dDQwN69e8nKyqKsrIwNGzZw4403smTJEpYtW8aDDz5IMBgcWZWaahs3bmTjxo0kk8m0lC+EEEIIcSbGVQK3c+dOLrroopGv35uHduONN/LYY49x7bXX0tPTw913301nZycLFizg+eefH7OwIVWqqqqoqqrC7/fjdrvTUocQQghxTpIh1LQaVwnc6tWr0U7yg1q/fj3r168/SxEJIYQQ4oyoGiiSwKXLhJkDJ4QQQgghho2rHrjxRubACSGEEGdI04AULECQHrhjkh64E6iqqqKmpoYdO3ZkOhQhhBBiQtFULWWXGEsSOCGEEEKICUaGUIUQQgiReppKaoZQZR+4Y5EeOCGEEEKICUZ64E5AFjEIIYQQZ0ZTNbQUbCNysu3FPqykB+4EZBGDEEIIcYY0NXWXGEN64E7Be9m/3+/PcCRCCCHE6Xnv766z3ZOVIJ6SgxgSxD94IecgSeBOQV9fHwClpaUZjkQIIYQ4M4FA4KwcC2kymSgoKOD1zudSVmZBQQEmkyll5Z0LFE0Gl09qcHAQr9dLc3Pzh+JMVL/fT2lpKS0tLbhcrkyHk3bS3nObtPfcJu09OU3TCAQCFBUVodOdnZlTkUiEWCyWsvJMJhMWiyVl5Z0LpAfuFLz3B97tdn8ofkG8x+VySXvPYdLec5u099x2uu09250PFotFEq40k0UMQgghhBATjCRwQgghhBATjCRwp8BsNnPPPfdgNpszHcpZIe09t0l7z23S3nPbh6294vhkEYMQQgghxAQjPXBCCCGEEBOMJHBCCCGEEBOMJHBCCCGEEBOMJHAnsXHjRsrLy7FYLCxfvpzt27dnOqS0uP/++1m6dClOp5O8vDyuuuoq6urqMh3WWfPd734XRVG47bbbMh1K2rS1tfG5z32O7OxsrFYr8+bNY+fOnZkOKy2SySR33XUXFRUVWK1WpkyZwre//e1z5lDs1157jSuuuIKioiIUReGZZ54Z9bmmadx9990UFhZitVpZs2YNBw8ezEywKXCi9sbjce644w7mzZuH3W6nqKiIG264gfb29swF/AGd7Of7fl/60pdQFIUHH3zwrMUnxgdJ4E7gySefZMOGDdxzzz3s3r2byspK1q5dS3d3d6ZDS7ktW7ZQVVXF1q1beemll4jH41x22WUEg8FMh5Z2O3bs4Cc/+Qnz58/PdChpMzAwwKpVqzAajfz5z3+mpqaGH/zgB3i93kyHlhbf+973ePjhh/nRj35EbW0t3/ve9/j+97/PD3/4w0yHlhLBYJDKyko2btx4zM+///3v89BDD7Fp0ya2bduG3W5n7dq1RCKRsxxpapyovaFQiN27d3PXXXexe/dunnrqKerq6rjyyiszEGlqnOzn+56nn36arVu3UlRUdJYiE+OKJo5r2bJlWlVV1cjXyWRSKyoq0u6///4MRnV2dHd3a4C2ZcuWTIeSVoFAQJs2bZr20ksvaRdeeKF26623ZjqktLjjjju0888/P9NhnDWXX3659oUvfGHUvU984hPaZz/72QxFlD6A9vTTT498raqqVlBQoP3nf/7nyL3BwUHNbDZr//d//5eBCFPrb9t7LNu3b9cAramp6ewElUbHa29ra6tWXFysVVdXa5MmTdL+67/+66zHJjJLeuCOIxaLsWvXLtasWTNyT6fTsWbNGt56660MRnZ2+Hw+ALKysjIcSXpVVVVx+eWXj/o5n4v+8Ic/sGTJEq655hry8vJYuHAhjz76aKbDSpuVK1eyefNm6uvrAXj77bd5/fXXWbduXYYjS7+GhgY6OztH/Zl2u90sX778Q/G7C4Z/fymKgsfjyXQoaaGqKtdffz1f/epXmTNnTqbDERkiZ6EeR29vL8lkkvz8/FH38/PzOXDgQIaiOjtUVeW2225j1apVzJ07N9PhpM2vfvUrdu/ezY4dOzIdStodOXKEhx9+mA0bNvD1r3+dHTt28OUvfxmTycSNN96Y6fBS7mtf+xp+v5+ZM2ei1+tJJpN85zvf4bOf/WymQ0u7zs5OgGP+7nrvs3NZJBLhjjvu4Lrrrjtnz0b93ve+h8Fg4Mtf/nKmQxEZJAmcGKOqqorq6mpef/31TIeSNi0tLdx666289NJLH4oDl1VVZcmSJdx3330ALFy4kOrqajZt2nROJnC//vWv+d///V9++ctfMmfOHPbu3cttt91GUVHROdleMSwej/OpT30KTdN4+OGHMx1OWuzatYv//u//Zvfu3SiKkulwRAbJEOpx5OTkoNfr6erqGnW/q6uLgoKCDEWVfuvXr+dPf/oTr7zyCiUlJZkOJ2127dpFd3c3ixYtwmAwYDAY2LJlCw899BAGg4FkMpnpEFOqsLCQ2bNnj7o3a9YsmpubMxRRen31q1/la1/7Gp/+9KeZN28e119/Pbfffjv3339/pkNLu/d+P33Yfne9l7w1NTXx0ksvnbO9b3/961/p7u6mrKxs5HdXU1MT//qv/0p5eXmmwxNnkSRwx2EymVi8eDGbN28euaeqKps3b2bFihUZjCw9NE1j/fr1PP300/zlL3+hoqIi0yGl1SWXXMK+ffvYu3fvyLVkyRI++9nPsnfvXvR6faZDTKlVq1aN2Ramvr6eSZMmZSii9AqFQuh0o3+96fV6VFXNUERnT0VFBQUFBaN+d/n9frZt23ZO/u6Co8nbwYMHefnll8nOzs50SGlz/fXX884774z63VVUVMRXv/pVXnjhhUyHJ84iGUI9gQ0bNnDjjTeyZMkSli1bxoMPPkgwGOSmm27KdGgpV1VVxS9/+Ut+//vf43Q6R+bKuN1urFZrhqNLPafTOWZ+n91uJzs7+5yc93f77bezcuVK7rvvPj71qU+xfft2HnnkER555JFMh5YWV1xxBd/5zncoKytjzpw57NmzhwceeIAvfOELmQ4tJYaGhjh06NDI1w0NDezdu5esrCzKysq47bbbuPfee5k2bRoVFRXcddddFBUVcdVVV2Uu6A/gRO0tLCzkk5/8JLt37+ZPf/oTyWRy5PdXVlYWJpMpU2GfsZP9fP82QTUajRQUFDBjxoyzHarIpEwvgx3vfvjDH2plZWWayWTSli1bpm3dujXTIaUFcMzr5z//eaZDO2vO5W1ENE3T/vjHP2pz587VzGazNnPmTO2RRx7JdEhp4/f7tVtvvVUrKyvTLBaLNnnyZO0b3/iGFo1GMx1aSrzyyivH/P/1xhtv1DRteCuRu+66S8vPz9fMZrN2ySWXaHV1dZkN+gM4UXsbGhqO+/vrlVdeyXToZ+RkP9+/JduIfDgpmnaObE0uhBBCCPEhIXPghBBCCCEmGEnghBBCCCEmGEnghBBCCCEmGEnghBBCCCEmGEnghBBCCCEmGEnghBBCCCEmGEnghBBCCCEmGEnghBBCCCEmGEnghBBCCCEmGEnghBBCCCEmGEnghBApt3r1ahRFQVEU9u7de1br/vznPz9S9zPPPHNW6xZCiLNFEjghxGm7/fbb+cQnPnHCZ26++WY6OjqYO3fuqPudnZ3ceuutTJ06FYvFQn5+PqtWreLhhx8mFAp94Nj++7//m46Ojg9cjhBCjGeGTAcghJh4tm/fzuWXX37CZ2w2GwUFBaPuHTlyhFWrVuHxeLjvvvuYN28eZrOZffv28cgjj1BcXMyVV155RjHFYjFMJhNutxu3231GZQghxEQhCZwQ4pTFYjHsdjuJRII333yTb3zjGyxfvpytW7ee0vv//M//jMFgYOfOndjt9pH7kydP5uMf/ziapgHw+OOPc/vtt9Pe3o7ZbB557qqrrsLpdPI///M/rF69mrlz52IwGHjiiSeYN28er7zySmobLIQQ45QMoQohTpnBYOCNN94AYO/evXR0dPD888+f0rt9fX28+OKLVFVVjUre3k9RFACuueYakskkf/jDH0Y+6+7u5tlnn+ULX/jCyL1f/OIXmEwm3njjDTZt2nSmzRJCiAlHEjghxCnT6XS0t7eTnZ1NZWUlBQUFeDyeU3r30KFDaJrGjBkzRt3PycnB4XDgcDi44447ALBarXzmM5/h5z//+chzTzzxBGVlZaxevXrk3rRp0/j+97/PjBkzxpQrhBDnMknghBCnZc+ePVRWVqasvO3bt7N3717mzJlDNBoduX/zzTfz4osv0tbWBsBjjz02ssL0PYsXL05ZHEIIMZHIHDghxGnZu3fvGSVwU6dORVEU6urqRt2fPHkyMNzr9n4LFy6ksrKSxx9/nMsuu4z9+/fz7LPPjnrmeEOxQghxrpMeOCHEadm3bx8LFiw47feys7O59NJL+dGPfkQwGDyld774xS/y2GOP8fOf/5w1a9ZQWlp62vUKIcS5SBI4IcRpUVWVuro62tvb8fl8p/Xuj3/8YxKJBEuWLOHJJ5+ktraWuro6nnjiCQ4cOIBerx/1/Gc+8xlaW1t59NFHRy1eEEKIDztJ4IQQp+Xee+/lscceo7i4mHvvvfe03p0yZQp79uxhzZo13HnnnVRWVrJkyRJ++MMf8pWvfIVvf/vbo553u938/d//PQ6Hg6uuuiqFrRBCiIlN0d7beEkIIVJk9erVLFiwgAcffPADl3XJJZcwZ84cHnroodN6T1EUnn76aUn8hBDnJOmBE0KkxY9//GMcDgf79u07o/cHBgZ4+umnefXVV6mqqjrl9770pS/hcDjOqE4hhJgopAdOCJFybW1thMNhAMrKyjCZTKddRnl5OQMDA9x111185StfOeX3uru78fv9ABQWFspKVSHEOUkSOCGEEEKICUaGUIUQQgghJhhJ4IQQQgghJhhJ4IQQQgghJhhJ4IQQQgghJhhJ4IQQQgghJhhJ4IQQQgghJhhJ4IQQQgghJhhJ4IQQQgghJhhJ4IQQQgghJhhJ4IQQQgghJhhJ4IQQQgghJpj/D3+0GKPsFgNxAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "cmap = plt.cm.viridis\n",
+    "\n",
+    "x = np.asarray([data[nsimx][\"Overlap\"] for nsimx in nsimxs if nsimx in data])\n",
+    "w = x - x.min()\n",
+    "w /= w.max()\n",
+    "\n",
+    "norm = Normalize(vmin=x.min(), vmax=x.max())\n",
+    "sm = ScalarMappable(cmap=cmap, norm=norm)\n",
+    "sm.set_array(x)\n",
+    "\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "lw = plt.rcParams[\"lines.linewidth\"] / 2\n",
+    "d = data[nsim0]\n",
+    "ax.plot(d[\"Age\"], d[\"MainProgenitorMass\"], color=\"red\", lw=1.5*lw,\n",
+    "        label=\"Reference halo\", zorder=1)\n",
+    "\n",
+    "i = 0\n",
+    "for nsimx in nsimxs:\n",
+    "    try:\n",
+    "        d = data[nsimx]\n",
+    "    except KeyError:\n",
+    "        continue\n",
+    "\n",
+    "    ax.plot(d[\"Age\"], d[\"MainProgenitorMass\"], color=cmap(norm(x[i])),\n",
+    "            zorder=0, lw=lw * (x[i] / x.max()))\n",
+    "\n",
+    "    i += 1\n",
+    "\n",
+    "\n",
+    "mu = np.nanmedian(random_mah, axis=0)\n",
+    "ymin = np.nanpercentile(random_mah, 16, axis=0)\n",
+    "ymax = np.nanpercentile(random_mah, 84, axis=0)\n",
+    "ax.plot(xrange, mu, color=\"black\", label=\"Random\")\n",
+    "ax.fill_between(xrange, ymin, ymax, color=\"black\", alpha=0.5, zorder=-1)\n",
+    "\n",
+    "cbar = fig.colorbar(sm, ax=ax, orientation='vertical')\n",
+    "cbar.set_label('Overlap')\n",
+    "\n",
+    "ax.legend()\n",
+    "ax.set_yscale(\"log\")\n",
+    "ax.set_xlabel(r\"$t ~ [\\mathrm{Gyr}]$\")\n",
+    "ax.set_ylabel(r\"$M_{\\rm main} ~ [M_{\\odot}]$\")\n",
+    "ax.set_xlim(0)\n",
+    "plt.tight_layout()\n",
+    "# plt.savefig(\"../../plots/example_mah.png\")\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nsim0 = 1\n",
+    "simname = \"csiborg2_random\"\n",
+    "kind = simname.split(\"_\")[-1]\n",
+    "min_logmass = 12.25\n",
+    "\n",
+    "# NOTE: These can possibly be pickled to avoid doing this long process every\n",
+    "# single time.\n",
+    "\n",
+    "cat = csiborgtools.read.CSiBORG2Catalogue(\n",
+    "    nsim0, 99, kind, bounds={\"totmass\": (1e13, None), \"dist\": (0, 135)})\n",
+    "# merger_reader = csiborgtools.read.CSiBORG2MergerTreeReader(nsim0, kind)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = np.array([0.01428571, 0.02424242, 0.03419913, 0.04415584, 0.05411255,\n",
+    "       0.06406926, 0.07402597, 0.08398268, 0.09393939, 0.1038961 ,\n",
+    "       0.11385281, 0.12380952, 0.13376623, 0.14372294, 0.15367965,\n",
+    "       0.16363636, 0.17359307, 0.18354978, 0.19350649, 0.2034632 ,\n",
+    "       0.21341991, 0.22337662, 0.23333333, 0.24329004, 0.25324675,\n",
+    "       0.26320346, 0.27316017, 0.28311688, 0.29307359, 0.3030303 ,\n",
+    "       0.31298701, 0.32294372, 0.33290043, 0.34285714, 0.35281385,\n",
+    "       0.36277056, 0.37272727, 0.38268398, 0.39264069, 0.4025974 ,\n",
+    "       0.41255411, 0.42251082, 0.43246753, 0.44242424, 0.45238095,\n",
+    "       0.46233766, 0.47229437, 0.48225108, 0.49220779, 0.5021645 ,\n",
+    "       0.51212121, 0.52207792, 0.53203463, 0.54199134, 0.55194805,\n",
+    "       0.56190476, 0.57186147, 0.58181818, 0.59177489, 0.6017316 ,\n",
+    "       0.61168831, 0.62164502, 0.63160173, 0.64155844, 0.65151515,\n",
+    "       0.66147186, 0.67142857, 0.68138528, 0.69134199, 0.7012987 ,\n",
+    "       0.71125541, 0.72121212, 0.73116883, 0.74112554, 0.75108225,\n",
+    "       0.76103896, 0.77099567, 0.78095238, 0.79090909, 0.8008658 ,\n",
+    "       0.81082251, 0.82077922, 0.83073593, 0.84069264, 0.85064935,\n",
+    "       0.86060606, 0.87056277, 0.88051948, 0.89047619, 0.9004329 ,\n",
+    "       0.91038961, 0.92034632, 0.93030303, 0.94025974, 0.95021645,\n",
+    "       0.96017316, 0.97012987, 0.98008658, 0.99004329, 1.        ])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from h5py import File"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f = File(\"/mnt/extraspace/rstiskalek/csiborg_postprocessing/random_mah/random_mah_csiborg2_random_1.hdf5\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([          nan,           nan,           nan,           nan,\n",
+       "                 nan,           nan,           nan,           nan,\n",
+       "                 nan,           nan,           nan,           nan,\n",
+       "                 nan, 9.6507642e+10, 3.0197550e+11, 3.2376760e+11,\n",
+       "       7.0668498e+11, 7.7206113e+11, 9.3394495e+11, 1.5814801e+12,\n",
+       "       1.9799633e+12, 2.8672110e+12, 3.4369173e+12, 4.2152047e+12,\n",
+       "       4.0595470e+12, 6.7960056e+12, 8.0506048e+12, 9.8562317e+12,\n",
+       "       1.5811687e+13, 2.1929025e+13, 2.7361473e+13, 3.6311776e+13,\n",
+       "       4.0548774e+13, 4.6049712e+13, 5.1784131e+13, 5.8592594e+13,\n",
+       "       6.2322144e+13, 6.8669858e+13, 7.1316036e+13, 1.2149067e+14,\n",
+       "       1.4343837e+14, 1.5714247e+14, 1.6746878e+14, 1.8382215e+14,\n",
+       "       1.8707539e+14, 1.8938536e+14, 1.9296859e+14, 1.9942214e+14,\n",
+       "       2.0502892e+14, 2.0806736e+14, 2.1257209e+14, 2.3088363e+14,\n",
+       "       2.4707200e+14, 2.5878680e+14, 2.6422236e+14, 2.7754664e+14,\n",
+       "       2.8651874e+14, 2.9785370e+14, 3.0968989e+14, 3.1373079e+14,\n",
+       "       3.1944651e+14, 3.2246628e+14, 3.2166309e+14, 3.2575374e+14,\n",
+       "       3.2939614e+14, 3.3505896e+14, 3.4118875e+14, 3.4710996e+14,\n",
+       "       3.5778809e+14, 3.6322364e+14, 3.7472670e+14, 3.8465764e+14,\n",
+       "       4.0075266e+14, 4.1667327e+14, 4.3123039e+14, 4.3992541e+14,\n",
+       "       4.6098277e+14, 4.7190678e+14, 4.9967297e+14, 5.0868244e+14,\n",
+       "       5.2763220e+14, 5.5073174e+14, 5.9031232e+14, 5.9930933e+14,\n",
+       "       6.1641921e+14, 6.2424880e+14, 6.3106035e+14, 6.4602838e+14,\n",
+       "       6.5189973e+14, 6.5782102e+14, 6.6686783e+14, 6.7132272e+14,\n",
+       "       6.7323109e+14, 6.7524221e+14, 6.7571224e+14, 6.7606094e+14,\n",
+       "       6.8144662e+14, 6.8422983e+14, 6.8603237e+14, 6.8886215e+14],\n",
+       "      dtype=float32)"
+      ]
+     },
+     "execution_count": 168,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f[\"MainProgenitorMass\"][0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[69.0000210000063, 40.250007218751264, 28.24050991940438, 21.6470609550175, 17.480001404480106, 14.608109099433955, 12.508772664512199, 10.90721705951751, 9.64516173673259, 8.625000360937513, 7.7832702592057235, 7.0769233254437935, 6.475728365821477, 5.95783150553419, 5.50704240932355, 5.111111246913583, 4.760598622974984, 4.448113312911626, 4.1677853285437605, 3.914893700679041, 3.685598452365574, 3.476744253718227, 3.285714346938776, 3.1103203402819117, 2.9487179993425383, 2.7993421515051513, 2.6608558268213116, 2.5321101306287352, 2.4121122957547967, 2.3000000330000008, 2.1950207773798662, 2.096514773533915, 2.003901196522936, 1.9166666909722223, 1.8343558508261513, 1.7565632668759008, 1.6829268488994646, 1.613122190273029, 1.5468577900064306, 1.4838709837669097, 1.4239244641145379, 1.366803292753544, 1.3123123255056859, 1.2602739849878026, 1.210526327423823, 1.162921359250726, 1.117323566656109, 1.0736086272735772, 1.0316622782422846, 0.9913793189283591, 0.9526627299814432, 0.9154228931957131, 0.8795768989699038, 0.8450479301016136, 0.8117647122768166, 0.7796610229819017, 0.7486752517178681, 0.7187500053710938, 0.6898317534223188, 0.6618705083794834, 0.6348195374209455, 0.6086351017498701, 0.5832762206018658, 0.5587044572276223, 0.5348837244997295, 0.5117801080759505, 0.48936170529651424, 0.46759847820604516, 0.4464621192761633, 0.42592592856652933, 0.4059647012034677, 0.3865546241790834, 0.3676731815824261, 0.34929906746973005, 0.3314121056648591, 0.31399317585528075, 0.2970241454144613, 0.28048780643961924, 0.2643678175452504, 0.2486486499985392, 0.23331553782343795, 0.21835443153641232, 0.20375195520916023, 0.18949536658248856, 0.17557251998135315, 0.1619718318042056, 0.14868224838055033, 0.13569321600925854, 0.122994653006949, 0.11057692361085425, 0.09843081359419292, 0.08654750746436402, 0.0749185671253807, 0.06353591189600438, 0.05239179978414388, 0.04147880992632613, 0.03078982610853953, 0.020318021291547472, 0.010056843069963017, 0.0]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(list(1 / a - 1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "17"
+      ]
+     },
+     "execution_count": 142,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sum(cat[\"totmass\"] > 5e14)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d1 = merger_reader.main_progenitor(10000)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([99, 98, 97, 96, 95, 94, 93, 92, 91, 90, 89, 88, 87, 86, 85, 84, 83,\n",
+       "       82, 81, 80, 79, 78, 77, 76, 75, 74, 73, 72, 71, 70, 69, 68, 67, 66,\n",
+       "       65, 64, 63, 62, 61, 60, 59, 58, 57, 56, 55, 54, 53, 52, 51, 50, 49,\n",
+       "       48, 47, 46, 45, 44, 43, 42, 41, 40, 39, 38, 37, 36, 35, 34, 33, 32,\n",
+       "       31, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17],\n",
+       "      dtype=int32)"
+      ]
+     },
+     "execution_count": 132,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d1[\"SnapNum\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8156"
+      ]
+     },
+     "execution_count": 127,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sum(cat[\"totmass\"] > 1e13)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "33.333333333333336"
+      ]
+     },
+     "execution_count": 128,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "250e-3 * 8000 / 60"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure()\n",
+    "plt.plot(d1[\"Age\"], d1[\"MainProgenitorMass\"])\n",
+    "plt.plot(d2[\"Age\"], d2[\"MainProgenitorMass\"])\n",
+    "plt.plot(d3[\"Age\"], d3[\"MainProgenitorMass\"])\n",
+    "plt.yscale(\"log\")\n",
+    "\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1.4623428e+15, 7.4588315e+14, 7.1264091e+14, ..., 9.9620782e+10,\n",
+       "       9.9620782e+10, 9.9620782e+10], dtype=float32)"
+      ]
+     },
+     "execution_count": 110,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cat[\"totmass\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv_csiborg",
+   "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": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/MAH/mah.py b/notebooks/MAH/mah.py
new file mode 100644
index 0000000..7eb2a70
--- /dev/null
+++ b/notebooks/MAH/mah.py
@@ -0,0 +1,221 @@
+# Copyright (C) 2024 Richard Stiskalek
+# This program is free software; you can redistribute it and/or modify it
+# under the terms of the GNU General Public License as published by the
+# Free Software Foundation; either version 3 of the License, or (at your
+# option) any later version.
+#
+# This program is distributed in the hope that it will be useful, but
+# WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General
+# Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along
+# with this program; if not, write to the Free Software Foundation, Inc.,
+# 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
+"""Script to help with `mah.py`."""
+from datetime import datetime
+
+import csiborgtools
+import numpy as np
+from astropy.cosmology import FlatLambdaCDM
+from h5py import File
+from tqdm import tqdm, trange
+from cache_to_disk import cache_to_disk
+from os.path import join
+
+
+RANDOM_MAH_Sorce_Virgo_UPPER = np.array(
+    [[2.18554217, 0.16246594],
+     [2.93253012, 0.17284951],
+     [3.2939759, 0.34169001],
+     [3.75180723, 0.42006683],
+     [4.28192771, 0.44691426],
+     [4.61927711, 0.53819753],
+     [5.34216867, 0.58454257],
+     [5.89638554, 0.68954882],
+     [6.23373494, 0.73361948],
+     [6.45060241, 0.81341823],
+     [7.05301205, 0.92071572],
+     [7.82409639, 0.92071572],
+     [8.28192771, 0.95953933],
+     [8.61927711, 0.97956078],
+     [9.70361446, 1.],
+     [11.17349398, 1.],
+     [13.07710843, 1.],
+     [13.82409639, 1.]]
+    )
+
+RANDOM_MAH_SORCE_Virgo_LOWER = np.array(
+    [[3.36626506e+00, 1.00000000e-02],
+     [3.75180723e+00, 1.10877404e-02],
+     [3.99277108e+00, 1.04216677e-02],
+     [4.30602410e+00, 1.15552746e-02],
+     [4.61927711e+00, 1.67577322e-02],
+     [4.98072289e+00, 2.14703224e-02],
+     [5.39036145e+00, 3.82789169e-02],
+     [5.89638554e+00, 5.00670000e-02],
+     [6.30602410e+00, 5.11116827e-02],
+     [7.29397590e+00, 5.32668971e-02],
+     [7.77590361e+00, 5.55129899e-02],
+     [8.11325301e+00, 6.68516464e-02],
+     [8.57108434e+00, 8.56515893e-02],
+     [9.60722892e+00, 1.32152759e-01],
+     [1.04265060e+01, 1.46527548e-01],
+     [1.07638554e+01, 1.49584947e-01],
+     [1.11493976e+01, 1.72849513e-01],
+     [1.18240964e+01, 2.16931625e-01],
+     [1.21855422e+01, 2.45546942e-01],
+     [1.25951807e+01, 3.48819614e-01],
+     [1.30771084e+01, 5.27197199e-01],
+     [1.36795181e+01, 8.83462949e-01],
+     [1.38000000e+01, 1.00000000e+00]]
+    )
+
+
+def t():
+    return datetime.now()
+
+
+@cache_to_disk(90)
+def load_data(nsim0, simname, min_logmass):
+    """
+    Load the reference catalogue, the cross catalogues, the merger trees and
+    the overlap reader (in this order).
+    """
+    paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
+    nsims = paths.get_ics(simname)
+    if "csiborg2_" in simname:
+        kind = simname.split("_")[-1]
+        print(f"{t()}: loading {len(nsims)} halo catalogues.")
+        cat0 = csiborgtools.read.CSiBORG2Catalogue(nsim0, 99, kind)
+        catxs = [csiborgtools.read.CSiBORG2Catalogue(n, 99, kind)
+                 for n in nsims if n != nsim0]
+
+        print(f"{t()}: loading {len(nsims)} merger trees.")
+        merger_trees = {}
+        for nsim in tqdm(nsims):
+            merger_trees[nsim] = csiborgtools.read.CSiBORG2MergerTreeReader(
+                nsim, kind)
+    else:
+        raise ValueError(f"Unknown simname: {simname}")
+
+    overlaps = csiborgtools.summary.NPairsOverlap(cat0, catxs, min_logmass)
+
+    return cat0, catxs, merger_trees, overlaps
+
+
+def extract_main_progenitor_maxoverlap(group_nr, overlaps, merger_trees):
+    """
+    Follow the main progenitor of a reference group and its maximum overlap
+    group in the cross catalogues.
+    """
+    min_overlap = 0
+
+    # NOTE these can be all cached in the overlap object.
+    max_overlaps = overlaps.max_overlap(0, True)[group_nr]
+    if np.sum(max_overlaps > 0) == 0:
+        raise ValueError(f"No overlaps for group {group_nr}.")
+
+    max_overlap_indxs = overlaps.max_overlap_key(
+        "index", min_overlap, True)[group_nr]
+
+    out = {}
+    for i in trange(len(overlaps), desc="Cross main progenitors"):
+        nsimx = overlaps[i].catx().nsim
+        group_nr_cross = max_overlap_indxs[i]
+
+        if np.isnan(group_nr_cross):
+            continue
+
+        x = merger_trees[nsimx].main_progenitor(int(group_nr_cross))
+        x["Overlap"] = max_overlaps[i]
+
+        out[nsimx] = x
+
+    nsim0 = overlaps.cat0().nsim
+    print(f"Appending main progenitor for {nsim0}.")
+    out[nsim0] = merger_trees[nsim0].main_progenitor(group_nr)
+
+    return out
+
+
+def summarize_extracted_mah(simname, data, nsim0, nsimxs, key,
+                            min_age=0, include_nsim0=True):
+    """
+    Turn the dictionaries of extracted MAHs into a single array.
+    """
+    if "csiborg2_" in simname:
+        nsnap = 100
+    else:
+        raise ValueError(f"Unknown simname: {simname}")
+
+    X = []
+    for nsimx in nsimxs + [nsim0] if include_nsim0 else nsimxs:
+        try:
+            d = data[nsimx]
+        except KeyError:
+            continue
+
+        x = np.full(nsnap, np.nan, dtype=np.float32)
+        x[d["SnapNum"]] = d[key]
+
+        X.append(x)
+
+    cosmo = FlatLambdaCDM(H0=67.76, Om0=csiborgtools.simname2Omega_m(simname))
+    zs = [csiborgtools.snap2redshift(i, simname) for i in range(nsnap)]
+    age = cosmo.age(zs).value
+
+    mask = age > min_age
+    return age[mask], np.vstack(X)[:, mask]
+
+
+def extract_mah(simname, logmass_bounds, key, min_age=0):
+    """
+    Extract the random MAHs for a given simulation and mass range and key.
+    Keys are for example: "MainProgenitorMass" or "GroupMass"
+    """
+    paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
+    nsims = paths.get_ics(simname)
+
+    X = []
+    for i, nsim in enumerate(nsims):
+        with File(paths.random_mah(simname, nsim), 'r') as f:
+            mah = f[key][:]
+            final_mass = mah[:, -1]
+
+            # Select the mass range
+            mask = final_mass >= 10**logmass_bounds[0]
+            mask &= final_mass < 10**logmass_bounds[1]
+
+            X.append(mah[mask])
+
+            if i == 0:
+                redshift = f["Redshift"][:]
+
+    X = np.vstack(X)
+
+    cosmo = FlatLambdaCDM(H0=67.76, Om0=csiborgtools.simname2Omega_m(simname))
+    age = cosmo.age(redshift).value
+
+    mask = age > min_age
+    return age[mask], X[:, mask]
+
+
+def extract_mah_mdpl2(logmass_bounds, min_age=1.5):
+    """
+    MAH extraction for the MDPL2 simulation. Data comes from
+    `https://arxiv.org/abs/2105.05859`
+    """
+    fdir = "/mnt/extraspace/rstiskalek/catalogs/"
+
+    age = np.genfromtxt(join(fdir, "mdpl2_cosmic_time.txt"))
+    with File(join(fdir, "diffmah_mdpl2.h5"), 'r') as f:
+        log_mp = f["logmp_sim"][:]
+        log_mah_sim = f["log_mah_sim"][...]
+
+    xmin, xmax = logmass_bounds
+    ks = np.where((log_mp > xmin) & (log_mp < xmax))[0]
+    X = 10**log_mah_sim[ks]
+
+    mask = age > min_age
+    return age[mask], X[:, mask]
diff --git a/notebooks/diagnostic/hmf.ipynb b/notebooks/diagnostic/hmf.ipynb
new file mode 100644
index 0000000..0fed805
--- /dev/null
+++ b/notebooks/diagnostic/hmf.ipynb
@@ -0,0 +1,192 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import csiborgtools\n",
+    "\n",
+    "from hmf import *\n",
+    "\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bin_edges = np.arange(12, 15.2, 0.25)\n",
+    "xbin = (bin_edges[1:] + bin_edges[:-1]) / 2\n",
+    "bin_edges = 10**bin_edges\n",
+    "xbin = 10**xbin"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 20/20 [00:08<00:00,  2.36it/s]\n",
+      "100%|██████████| 20/20 [00:08<00:00,  2.41it/s]\n",
+      "100%|██████████| 20/20 [00:07<00:00,  2.64it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "hmf_cb2_main = calculate_hmf(\"csiborg2_main\", bin_edges)\n",
+    "hmf_cb2_random = calculate_hmf(\"csiborg2_random\", bin_edges)\n",
+    "hmf_cb2_varysmall = calculate_hmf(\"csiborg2_varysmall\", bin_edges)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## CB2_varysmall HMF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "k = np.where(paths.get_ics(\"csiborg2_main\") == 16417)[0][0]\n",
+    "\n",
+    "fig, axs = plt.subplots(1, 2, figsize=(10, 4))\n",
+    "lw = plt.rcParams['lines.linewidth']\n",
+    "fig.subplots_adjust(hspace=0)\n",
+    "axs[0].plot(xbin, hmf_cb2_main[k], zorder=1, color=\"red\", label=\"CB2 Main\")\n",
+    "for i in range(len(hmf_cb2_varysmall)):\n",
+    "    axs[0].plot(xbin, hmf_cb2_varysmall[i], lw=lw/2, color=\"black\", zorder=0,\n",
+    "                label=\"CB2 Varysmall\" if i == 0 else None)\n",
+    "\n",
+    "    axs[1].plot(xbin, hmf_cb2_varysmall[i] / hmf_cb2_main[k], lw=lw/3, color=\"black\", zorder=0)\n",
+    "\n",
+    "\n",
+    "xmin, xmax = xbin.min(), xbin.max()\n",
+    "for i in range(2):\n",
+    "    axs[i].set_xscale(\"log\")\n",
+    "    axs[i].set_xlabel(r\"$M_{\\mathrm{FoF}}$ $[M_{\\odot}]$\")\n",
+    "    axs[i].set_xlim(xmin, xmax)\n",
+    "\n",
+    "axs[0].legend()\n",
+    "axs[0].set_ylabel(r\"HMF $[1 / (h^{-3} \\mathrm{Mpc}^3~\\mathrm{dex})]$\")\n",
+    "axs[1].set_ylabel(r\"$\\mathrm{HMF}_{\\mathrm{Varysmall}}~/~\\mathrm{HMF}_{\\mathrm{Main}}$\")\n",
+    "axs[0].set_yscale(\"log\")\n",
+    "axs[1].set_ylim(0.9, 1.1)\n",
+    "axs[1].axhline(1, color=\"red\", lw=lw, zorder=0)\n",
+    "\n",
+    "fig.tight_layout()\n",
+    "fig.savefig(\"../../plots/varysmall_hmf.png\", dpi=450)\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure()\n",
+    "\n",
+    "ylow, yhigh = np.percentile(hmf_cb2_main, [16, 84], axis=0)\n",
+    "plt.fill_between(xbin, ylow, yhigh, alpha=0.75, label=\"CB2 Main\")\n",
+    "\n",
+    "ylow, yhigh = np.percentile(hmf_cb2_random, [16, 84], axis=0)\n",
+    "plt.fill_between(xbin, ylow, yhigh, alpha=0.75, label=\"Random\")\n",
+    "\n",
+    "plt.yscale(\"log\")\n",
+    "plt.xscale(\"log\")\n",
+    "plt.xlabel(r\"$M_{\\rm FoF} ~ [M_\\odot / h]$\")\n",
+    "plt.ylabel(r\"HMF $[1 / (h^{-3} \\mathrm{Mpc}^3~\\mathrm{dex})]$\")\n",
+    "plt.legend()\n",
+    "plt.xlim(xbin.min(), xbin.max())\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(\"../../plots/hmf_comparison.png\", dpi=450)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv_csiborg",
+   "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": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/diagnostic/hmf.py b/notebooks/diagnostic/hmf.py
new file mode 100644
index 0000000..e485eb6
--- /dev/null
+++ b/notebooks/diagnostic/hmf.py
@@ -0,0 +1,48 @@
+# Copyright (C) 2024 Richard Stiskalek
+# This program is free software; you can redistribute it and/or modify it
+# under the terms of the GNU General Public License as published by the
+# Free Software Foundation; either version 3 of the License, or (at your
+# option) any later version.
+#
+# This program is distributed in the hope that it will be useful, but
+# WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General
+# Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along
+# with this program; if not, write to the Free Software Foundation, Inc.,
+# 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
+"""Script to help with `hmf.py`."""
+
+import csiborgtools
+import numpy as np
+from tqdm import tqdm
+
+
+def calculate_hmf(simname, bin_edges, halofinder="FOF", max_distance=135):
+    """
+    Calculate the halo mass function for a given simulation from catalogues.
+    """
+    paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
+    nsims = paths.get_ics(simname)
+    bounds = {"dist": (0, max_distance)}
+
+    hmf = np.full((len(nsims), len(bin_edges) - 1), np.nan)
+    volume = 4 / 3 * np.pi * max_distance**3
+    for i, nsim in enumerate(tqdm(nsims)):
+        if "csiborg2_" in simname:
+            kind = simname.split("_")[-1]
+            if halofinder == "FOF":
+                cat = csiborgtools.read.CSiBORG2Catalogue(
+                    nsim, 99, kind, bounds=bounds)
+            elif halofinder == "SUBFIND":
+                cat = csiborgtools.read.CSiBORG2SUBFINDCatalogue(
+                    nsim, 99, kind, kind, bounds=bounds)
+            else:
+                raise ValueError(f"Unknown halofinder: {halofinder}")
+        else:
+            raise ValueError(f"Unknown simname: {simname}")
+
+        hmf[i] = cat.halo_mass_function(bin_edges, volume, "totmass")[1]
+
+    return hmf
diff --git a/notebooks/plot_correlation.ipynb b/notebooks/diagnostic/plot_correlation.ipynb
similarity index 100%
rename from notebooks/plot_correlation.ipynb
rename to notebooks/diagnostic/plot_correlation.ipynb
diff --git a/notebooks/powerspectrum_H0.ipynb b/notebooks/diagnostic/powerspectrum_H0.ipynb
similarity index 100%
rename from notebooks/powerspectrum_H0.ipynb
rename to notebooks/diagnostic/powerspectrum_H0.ipynb
diff --git a/notebooks/field_sample.ipynb b/notebooks/field_sample.ipynb
index a86d01e..b29f09a 100644
--- a/notebooks/field_sample.ipynb
+++ b/notebooks/field_sample.ipynb
@@ -38,7 +38,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -66,7 +66,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -81,7 +81,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/scripts_independent/PV_process.ipynb b/notebooks/flow/PV_process.ipynb
similarity index 100%
rename from scripts_independent/PV_process.ipynb
rename to notebooks/flow/PV_process.ipynb
diff --git a/notebooks/field_velocity_fof_sph.ipynb b/notebooks/flow/field_velocity_fof_sph.ipynb
similarity index 100%
rename from notebooks/field_velocity_fof_sph.ipynb
rename to notebooks/flow/field_velocity_fof_sph.ipynb
diff --git a/notebooks/flow_bulk.ipynb b/notebooks/flow/flow_bulk.ipynb
similarity index 100%
rename from notebooks/flow_bulk.ipynb
rename to notebooks/flow/flow_bulk.ipynb
diff --git a/notebooks/flow_bulk.py b/notebooks/flow/flow_bulk.py
similarity index 96%
rename from notebooks/flow_bulk.py
rename to notebooks/flow/flow_bulk.py
index 6b99610..fcb5a1c 100644
--- a/notebooks/flow_bulk.py
+++ b/notebooks/flow/flow_bulk.py
@@ -60,6 +60,6 @@ def read_enclosed_flow(simname):
 
     for n in range(nsim):
         V_n = csiborgtools.cartesian_to_radec(V[n])
-        l[n], b[n] = csiborgtools.flow.radec_to_galactic(V_n[:, 1], V_n[:, 2])
+        l[n], b[n] = csiborgtools.radec_to_galactic(V_n[:, 1], V_n[:, 2])
 
     return r, Vmag, l, b
diff --git a/notebooks/flow_calibration.ipynb b/notebooks/flow/flow_calibration.ipynb
similarity index 100%
rename from notebooks/flow_calibration.ipynb
rename to notebooks/flow/flow_calibration.ipynb
diff --git a/notebooks/flow_calibration.py b/notebooks/flow/flow_calibration.py
similarity index 99%
rename from notebooks/flow_calibration.py
rename to notebooks/flow/flow_calibration.py
index 2b93c10..88af6d7 100644
--- a/notebooks/flow_calibration.py
+++ b/notebooks/flow/flow_calibration.py
@@ -122,7 +122,7 @@ def read_samples(catalogue, simname, ksmooth, include_calibration=False,
     # Calculate direction in galactic coordinates of V_ext
     V = np.vstack([Vx, Vy, Vz]).T
     V = csiborgtools.cartesian_to_radec(V)
-    l, b = csiborgtools.flow.radec_to_galactic(V[:, 1], V[:, 2])
+    l, b = csiborgtools.radec_to_galactic(V[:, 1], V[:, 2])
 
     data = [alpha, beta, Vmag, l, b, sigma_v]
     names = ["alpha", "beta", "Vmag", "l", "b", "sigma_v"]
diff --git a/notebooks/flow_mapping.ipynb b/notebooks/flow/flow_mapping.ipynb
similarity index 100%
rename from notebooks/flow_mapping.ipynb
rename to notebooks/flow/flow_mapping.ipynb
diff --git a/notebooks/flow_mock_pv.ipynb b/notebooks/flow/flow_mock_pv.ipynb
similarity index 100%
rename from notebooks/flow_mock_pv.ipynb
rename to notebooks/flow/flow_mock_pv.ipynb
diff --git a/scripts_plots/plot_match.py b/notebooks/overlap/plot_match.py
similarity index 100%
rename from scripts_plots/plot_match.py
rename to notebooks/overlap/plot_match.py
diff --git a/notebooks/utils.py b/notebooks/utils.py
new file mode 100644
index 0000000..c0f40ff
--- /dev/null
+++ b/notebooks/utils.py
@@ -0,0 +1,132 @@
+# Copyright (C) 2023 Richard Stiskalek
+# This program is free software; you can redistribute it and/or modify it
+# under the terms of the GNU General Public License as published by the
+# Free Software Foundation; either version 3 of the License, or (at your
+# option) any later version.
+#
+# This program is distributed in the hope that it will be useful, but
+# WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General
+# Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along
+# with this program; if not, write to the Free Software Foundation, Inc.,
+# 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
+"""
+Various utility functions.
+"""
+
+import numpy
+from scipy.special import erf
+
+dpi = 600
+fout = "../plots/"
+mplstyle = ["science"]
+
+
+def latex_float(*floats, n=2):
+    """
+    Convert a float or a list of floats to a LaTeX string(s). Taken from [1].
+
+    Parameters
+    ----------
+    floats : float or list of floats
+        The float(s) to be converted.
+    n : int, optional
+        The number of significant figures to be used in the LaTeX string.
+
+    Returns
+    -------
+    latex_floats : str or list of str
+        The LaTeX string(s) representing the float(s).
+
+    References
+    ----------
+    [1] https://stackoverflow.com/questions/13490292/format-number-using-latex-notation-in-python  # noqa
+    """
+    latex_floats = [None] * len(floats)
+    for i, f in enumerate(floats):
+        float_str = "{0:.{1}g}".format(f, n)
+        if "e" in float_str:
+            base, exponent = float_str.split("e")
+            latex_floats[i] = r"{0} \times 10^{{{1}}}".format(base,
+                                                              int(exponent))
+        else:
+            latex_floats[i] = float_str
+
+    if len(floats) == 1:
+        return latex_floats[0]
+    return latex_floats
+
+
+def nan_weighted_average(arr, weights=None, axis=None):
+    if weights is None:
+        weights = numpy.ones_like(arr)
+
+    valid_entries = ~numpy.isnan(arr)
+
+    # Set NaN entries in arr to 0 for computation
+    arr = numpy.where(valid_entries, arr, 0)
+
+    # Set weights of NaN entries to 0
+    weights = numpy.where(valid_entries, weights, 0)
+
+    # Compute the weighted sum and the sum of weights along the axis
+    weighted_sum = numpy.sum(arr * weights, axis=axis)
+    sum_weights = numpy.sum(weights, axis=axis)
+
+    return weighted_sum / sum_weights
+
+
+def nan_weighted_std(arr, weights=None, axis=None, ddof=0):
+    if weights is None:
+        weights = numpy.ones_like(arr)
+
+    valid_entries = ~numpy.isnan(arr)
+
+    # Set NaN entries in arr to 0 for computation
+    arr = numpy.where(valid_entries, arr, 0)
+
+    # Set weights of NaN entries to 0
+    weights = numpy.where(valid_entries, weights, 0)
+
+    # Calculate weighted mean
+    weighted_mean = numpy.sum(
+        arr * weights, axis=axis) / numpy.sum(weights, axis=axis)
+
+    # Calculate the weighted variance
+    variance = numpy.sum(
+        weights * (arr - numpy.expand_dims(weighted_mean, axis))**2, axis=axis)
+    variance /= numpy.sum(weights, axis=axis) - ddof
+
+    return numpy.sqrt(variance)
+
+
+def compute_error_bars(x, y, xbins, sigma):
+    bin_indices = numpy.digitize(x, xbins)
+    y_medians = numpy.array([numpy.median(y[bin_indices == i])
+                             for i in range(1, len(xbins))])
+
+    lower_pct = 100 * 0.5 * (1 - erf(sigma / numpy.sqrt(2)))
+    upper_pct = 100 - lower_pct
+
+    y_lower = numpy.full(len(y_medians), numpy.nan)
+    y_upper = numpy.full(len(y_medians), numpy.nan)
+
+    for i in range(len(y_medians)):
+        if numpy.sum(bin_indices == i + 1) == 0:
+            continue
+
+        y_lower[i] = numpy.percentile(y[bin_indices == i + 1], lower_pct)
+        y_upper[i] = numpy.percentile(y[bin_indices == i + 1], upper_pct)
+
+    yerr = (y_medians - numpy.array(y_lower), numpy.array(y_upper) - y_medians)
+
+    return y_medians, yerr
+
+
+def normalize_hexbin(hb):
+    hexagon_counts = hb.get_array()
+    normalized_counts = hexagon_counts / hexagon_counts.sum()
+    hb.set_array(normalized_counts)
+    hb.set_clim(normalized_counts.min(), normalized_counts.max())
diff --git a/scripts/mah_random.py b/scripts/mah_random.py
new file mode 100644
index 0000000..4a850e0
--- /dev/null
+++ b/scripts/mah_random.py
@@ -0,0 +1,97 @@
+# Copyright (C) 2024 Richard Stiskalek
+# This program is free software; you can redistribute it and/or modify it
+# under the terms of the GNU General Public License as published by the
+# Free Software Foundation; either version 3 of the License, or (at your
+# option) any later version.
+#
+# This program is distributed in the hope that it will be useful, but
+# WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General
+# Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along
+# with this program; if not, write to the Free Software Foundation, Inc.,
+# 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
+"""
+Script to extract the mass accretion histories in random simulations. Follows
+the main progenitor of FoF haloes.
+"""
+from argparse import ArgumentParser
+import csiborgtools
+import numpy as np
+from h5py import File
+from mpi4py import MPI
+from taskmaster import work_delegation  # noqa
+from tqdm import trange
+from cache_to_disk import cache_to_disk
+
+
+@cache_to_disk(30)
+def load_data(nsim, simname, min_logmass):
+    """Load the data for a given simulation."""
+    bnd = {"totmass": (10**min_logmass, None), "dist": (None, 135)}
+    if "csiborg2_" in simname:
+        kind = simname.split("_")[-1]
+        cat = csiborgtools.read.CSiBORG2Catalogue(nsim, 99, kind, bounds=bnd)
+        merger_reader = csiborgtools.read.CSiBORG2MergerTreeReader(nsim, kind)
+    else:
+        raise ValueError(f"Unknown simname: {simname}")
+
+    return cat, merger_reader
+
+
+def main_progenitor_mah(cat, merger_reader, simname, verbose=True):
+    """Follow the main progenitor of each `z = 0` FoF halo."""
+    indxs = cat["index"]
+
+    # Main progenitor information as a function of time
+    shape = (len(cat), cat.nsnap + 1)
+    main_progenitor_mass = np.full(shape, np.nan, dtype=np.float32)
+    group_mass = np.full(shape, np.nan, dtype=np.float32)
+
+    for i in trange(len(cat), disable=not verbose, desc="Haloes"):
+        d = merger_reader.main_progenitor(indxs[i])
+
+        main_progenitor_mass[i, d["SnapNum"]] = d["MainProgenitorMass"]
+        group_mass[i, d["SnapNum"]] = d["Group_M_Crit200"]
+
+    return {"Redshift": [csiborgtools.snap2redshift(i, simname) for i in range(cat.nsnap + 1)],  # noqa
+            "MainProgenitorMass": main_progenitor_mass,
+            "GroupMass": group_mass,
+            "FinalGroupMass": cat["totmass"],
+            }
+
+
+def save_output(data, nsim, simname, verbose=True):
+    """Save the output to a file."""
+    paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
+
+    fname = paths.random_mah(simname, nsim)
+    if verbose:
+        print(f"Saving output to `{fname}`")
+
+    with File(fname, "w") as f:
+        for key, value in data.items():
+            f.create_dataset(key, data=value)
+
+
+if "__main__" == __name__:
+    parser = ArgumentParser(description="Extract the mass accretion history in random simulations.")  # noqa
+    parser.add_argument("--simname", help="Name of the simulation.", type=str,
+                        choices=["csiborg2_random"])
+    parser.add_argument("--min_logmass", type=float,
+                        help="Minimum log mass of the haloes.")
+    args = parser.parse_args()
+    COMM = MPI.COMM_WORLD
+
+    paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
+    nsims = paths.get_ics(args.simname)
+
+    def main(nsim):
+        verbose = COMM.Get_size() == 1
+        cat, merger_reader = load_data(nsim, args.simname, args.min_logmass)
+        data = main_progenitor_mah(cat, merger_reader, args.simname,
+                                   verbose=verbose)
+        save_output(data, nsim, args.simname, verbose=verbose)
+
+    work_delegation(main, list(nsims), MPI.COMM_WORLD)
diff --git a/scripts/mah_random.sh b/scripts/mah_random.sh
new file mode 100755
index 0000000..169c17d
--- /dev/null
+++ b/scripts/mah_random.sh
@@ -0,0 +1,23 @@
+memory=4
+on_login=${1}
+nthreads=5
+
+queue="berg"
+env="/mnt/users/rstiskalek/csiborgtools/venv_csiborg/bin/python"
+file="mah_random.py"
+
+min_logmass=13.0
+simname="csiborg2_random"
+
+
+pythoncm="$env $file --simname $simname --min_logmass $min_logmass"
+if [ $on_login -eq 1 ]; then
+    echo $pythoncm
+    $pythoncm
+else
+    cm="addqueue -q $queue -n $nthreads -m $memory $pythoncm"
+    echo "Submitting:"
+    echo $cm
+    echo
+    eval $cm
+fi
diff --git a/scripts/match_overlap_all.py b/scripts/match_overlap_all.py
index 60a3091..884c239 100644
--- a/scripts/match_overlap_all.py
+++ b/scripts/match_overlap_all.py
@@ -56,7 +56,8 @@ if __name__ == "__main__":
                         choices=["overlap", "max"], help="Kind of matching.")
     parser.add_argument("--simname", type=str, required=True,
                         help="Simulation name.",
-                        choices=["csiborg", "quijote"])
+                        choices=["csiborg1", "quijote", "csiborg2_main",
+                                 "csiborg2_random", "csiborg2_varysmall"])
     parser.add_argument("--nsim0", type=int, default=None,
                         help="Reference IC for Max's matching method.")
     parser.add_argument("--min_logmass", type=float, required=True,
diff --git a/scripts/match_overlap_all.sh b/scripts/match_overlap_all.sh
index b2c11db..7b0cf2a 100755
--- a/scripts/match_overlap_all.sh
+++ b/scripts/match_overlap_all.sh
@@ -1,16 +1,16 @@
 #!/bin/bash
-nthreads=11
-memory=4
-queue="cmb"
+nthreads=41
+memory=12
+queue="berg"
 env="/mnt/zfsusers/rstiskalek/csiborgtools/venv_csiborg/bin/python"
-file="match_all.py"
+file="match_overlap_all.py"
 
-simname="quijote"
-min_logmass=13.25
-nsim0=0
-kind="max"
-mult=10
+simname=${1}
+min_logmass=12.25
 sigma=1
+kind="overlap"
+mult=10         # Only for Max's method
+nsim0=0         # Only for Max's method
 verbose="false"
 
 
diff --git a/scripts/match_overlap_single.sh b/scripts/match_overlap_single.sh
index f4aadc9..685089a 100755
--- a/scripts/match_overlap_single.sh
+++ b/scripts/match_overlap_single.sh
@@ -8,8 +8,8 @@ file="match_overlap_single.py"
 
 simname="csiborg2_main"
 kind="overlap"
-min_logmass=13.25
-mult=5
+min_logmass=12
+mult=5  # Only for Max's method
 sigma=1
 
 # sims=(7444 7468)
@@ -37,14 +37,14 @@ do
 
         pythoncm="$env $file --kind $kind --nsim0 $nsim0 --nsimx $nsimx  --simname $simname --min_logmass $min_logmass --sigma $sigma --mult $mult --verbose $verbose"
 
-        # $pythoncm
+        $pythoncm
 
-        cm="addqueue -q $queue -n 1x1 -m $memory $pythoncm"
-        echo "Submitting:"
-        echo $cm
-        echo
-        $cm
-        sleep 0.05
+        # cm="addqueue -q $queue -n 1x1 -m $memory $pythoncm"
+        # echo "Submitting:"
+        # echo $cm
+        # echo
+        # $cm
+        # sleep 0.05
 
     done
 done
diff --git a/scripts_plots/plot_vobs.py b/scripts_plots/plot_vobs.py
deleted file mode 100644
index 8f3a0af..0000000
--- a/scripts_plots/plot_vobs.py
+++ /dev/null
@@ -1,89 +0,0 @@
-# Copyright (C) 2023 Richard Stiskalek
-# This program is free software; you can redistribute it and/or modify it
-# under the terms of the GNU General Public License as published by the
-# Free Software Foundation; either version 3 of the License, or (at your
-# option) any later version.
-#
-# This program is distributed in the hope that it will be useful, but
-# WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General
-# Public License for more details.
-#
-# You should have received a copy of the GNU General Public License along
-# with this program; if not, write to the Free Software Foundation, Inc.,
-# 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-
-from os.path import join
-
-import matplotlib.pyplot as plt
-import numpy
-import scienceplots  # noqa
-from tqdm import tqdm
-
-import csiborgtools
-import plt_utils
-
-
-def observer_peculiar_velocity(MAS, grid, ext="png"):
-    paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
-    nsims = paths.get_ics("csiborg")
-
-    for n, nsim in enumerate(nsims):
-        fpath = paths.observer_peculiar_velocity(MAS, grid, nsim)
-        f = numpy.load(fpath)
-
-        if n == 0:
-            data = numpy.full((len(nsims), *f["observer_vp"].shape), numpy.nan)
-            smooth_scales = f["smooth_scales"]
-
-        data[n] = f["observer_vp"]
-
-    for n, smooth_scale in enumerate(tqdm(smooth_scales,
-                                          desc="Plotting smooth scales")):
-        with plt.style.context(plt_utils.mplstyle):
-            fig, axs = plt.subplots(ncols=3, figsize=(3.5 * 3, 2.625),
-                                    sharey=True)
-            fig.subplots_adjust(wspace=0)
-            for i, ax in enumerate(axs):
-                ax.hist(data[:, n, i], bins="auto", histtype="step")
-                mu, sigma = numpy.mean(data[:, n, i]), numpy.std(data[:, n, i])
-                ax.set_title(r"$V_{{\rm obs, i}} = {:.3f} \pm {:.3f} ~ \mathrm{{km}} / \mathrm{{s}}$".format(mu, sigma)) # noqa
-
-            axs[0].set_xlabel(r"$V_{\rm obs, x} ~ [\mathrm{km} / \mathrm{s}]$")
-            axs[1].set_xlabel(r"$V_{\rm obs, y} ~ [\mathrm{km} / \mathrm{s}]$")
-            axs[2].set_xlabel(r"$V_{\rm obs, z} ~ [\mathrm{km} / \mathrm{s}]$")
-            axs[0].set_ylabel(r"Counts")
-
-            fig.suptitle(r"$N_{{\rm grid}} = {}$, $\sigma_{{\rm smooth}} = {:.2f} ~ [\mathrm{{Mpc}} / h]$".format(grid, smooth_scale)) # noqa
-
-            fig.tight_layout(w_pad=0)
-            fout = join(plt_utils.fout,
-                        f"observer_vp_{grid}_{smooth_scale}.{ext}")
-            fig.savefig(fout, dpi=plt_utils.dpi, bbox_inches="tight")
-            plt.close()
-
-    with plt.style.context(plt_utils.mplstyle):
-        fig, axs = plt.subplots(ncols=3, figsize=(3.5 * 3, 2.625))
-        for i, ax in enumerate(axs):
-
-            ymu = numpy.mean(data[..., i], axis=0)
-            ystd = numpy.std(data[..., i], axis=0)
-            ylow, yupp = ymu - ystd, ymu + ystd
-            ax.plot(smooth_scales, ymu, c="k")
-            ax.fill_between(smooth_scales, ylow, yupp, color="k", alpha=0.2)
-
-            ax.set_xlabel(r"$\sigma_{{\rm smooth}} ~ [\mathrm{{Mpc}} / h]$")
-
-        axs[0].set_ylabel(r"$V_{\rm obs, x} ~ [\mathrm{km} / \mathrm{s}]$")
-        axs[1].set_ylabel(r"$V_{\rm obs, y} ~ [\mathrm{km} / \mathrm{s}]$")
-        axs[2].set_ylabel(r"$V_{\rm obs, z} ~ [\mathrm{km} / \mathrm{s}]$")
-        fig.suptitle(r"$N_{{\rm grid}} = {}$".format(grid))
-
-        fig.tight_layout(w_pad=0)
-        fout = join(plt_utils.fout, f"observer_vp_summary_{grid}.{ext}")
-        fig.savefig(fout, dpi=plt_utils.dpi, bbox_inches="tight")
-        plt.close()
-
-
-if __name__ == "__main__":
-    observer_peculiar_velocity("PCS", 512)
diff --git a/scripts_plots/radial_velocity.ipynb b/scripts_plots/radial_velocity.ipynb
deleted file mode 100644
index 419de96..0000000
--- a/scripts_plots/radial_velocity.ipynb
+++ /dev/null
@@ -1,626 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from os.path import join\n",
-    "\n",
-    "import csiborgtools\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy\n",
-    "import scienceplots  # noqa\n",
-    "from cache_to_disk import cache_to_disk, delete_disk_caches_for_function  # noqa\n",
-    "\n",
-    "import plt_utils\n",
-    "\n",
-    "%load_ext autoreload\n",
-    "%autoreload 2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Field evaluated at radial shells"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# TODO: This is a little dodgy\n",
-    "\n",
-    "def plot_field_shells(field, MAS, grid, to_save=True):\n",
-    "    folder = \"/mnt/extraspace/rstiskalek/csiborg_postprocessing/field_shells\"\n",
-    "\n",
-    "    # with plt.style.context(\"notebook\"):\n",
-    "    if True:\n",
-    "        cols = plt.rcParams['axes.prop_cycle'].by_key()['color']\n",
-    "        lw = plt.rcParams['lines.linewidth']\n",
-    "        plt.figure()\n",
-    "\n",
-    "        # CSiBORG2 main\n",
-    "        fname = join(folder, f\"csiborg2_main_{field}_{MAS}_{grid}.npz\")\n",
-    "        file = numpy.load(fname)\n",
-    "        dist, mean = file[\"distances\"], file[\"mean\"]\n",
-    "        mean /= dist\n",
-    "        mean /= 70\n",
-    "        for i in range(len(mean)):\n",
-    "            plt.plot(dist, mean[i], c=cols[0], label=\"CSiBORG\" if i == 0 else None)\n",
-    "        \n",
-    "        # # BORG2\n",
-    "        # fname = join(folder, f\"borg2_{field}_{MAS}_{grid}.npz\")\n",
-    "        # file = numpy.load(fname)\n",
-    "        # dist, mean = file[\"distances\"], file[\"mean\"]\n",
-    "        # for i in range(len(mean)):\n",
-    "        #     plt.plot(dist, mean[i], c=cols[2], label=\"BORG\" if i == 0 else None)\n",
-    "\n",
-    "        # # CSiBORG2 random\n",
-    "        # fname = join(folder, f\"csiborg2_random_{field}_{MAS}_{grid}.npz\")\n",
-    "        # file = numpy.load(fname)\n",
-    "        # dist, mean = file[\"distances\"], file[\"mean\"]\n",
-    "\n",
-    "        # mu = numpy.mean(mean, axis=0)\n",
-    "        # std = numpy.std(mean, axis=0)\n",
-    "\n",
-    "        # plt.fill_between(dist, mu - std, mu + std, alpha=1/3, color=cols[1])\n",
-    "\n",
-    "        # for i in range(len(mean)):\n",
-    "            # plt.plot(dist, mean[i], c=cols[1], label=\"Random\" if i == 0 else None, zorder=0, lw=lw/2)\n",
-    "\n",
-    "        # Plot settings\n",
-    "        plt.legend(loc=\"lower right\")\n",
-    "        plt.xlabel(r\"$r ~ [\\mathrm{Mpc} / h]$\")\n",
-    "\n",
-    "        if field == \"radvel\":\n",
-    "            plt.ylabel(r\"$\\langle v_r \\rangle ~ [\\mathrm{km} / s]$\")\n",
-    "            plt.axhline(0, c=\"k\", ls=\"--\",)\n",
-    "            plt.ylim(-0.1, 0.1)\n",
-    "            plt.xscale(\"log\")\n",
-    "        elif field == \"overdensity\":\n",
-    "            plt.ylim(-0.5, 0.5)\n",
-    "            plt.axhline(0, c=\"k\", ls=\"--\",)\n",
-    "            # plt.xlim(0, 200)\n",
-    "            plt.ylabel(r\"$\\langle \\delta_r \\rangle$\")\n",
-    "            # plt.axhline(-0.1, c=\"k\", ls=\"--\")\n",
-    "        elif field == \"density\":\n",
-    "            plt.axhline(277.5 * 0.304, c=\"k\", ls=\"--\",)\n",
-    "            plt.ylim(50, 100)\n",
-    "\n",
-    "        plt.xlim(0, dist.max())\n",
-    "        # plt.xlim(0, 100)\n",
-    "\n",
-    "        if to_save:\n",
-    "            fout = join(plt_utils.fout, f\"field_shells_{field}_{MAS}_{grid}.png\")\n",
-    "            print(f\"Saving to `{fout}`.\")\n",
-    "            plt.savefig(fout, dpi=plt_utils.dpi, bbox_inches=\"tight\")\n",
-    "\n",
-    "        plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 199,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_16251/4241837727.py:58: UserWarning: Attempt to set non-positive xlim on a log-scaled axis will be ignored.\n",
-      "  plt.xlim(0, dist.max())\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_field_shells(\"radvel\", \"SPH\", 1024, False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Enclosed mass "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'a' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[2], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43ma\u001b[49m\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'a' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_enclosed_overdensity(to_save=True):\n",
-    "    with plt.style.context(\"science\"):\n",
-    "    # if True:\n",
-    "        cols = plt.rcParams['axes.prop_cycle'].by_key()['color']\n",
-    "        fig, axs = plt.subplots(2, 1, sharex=True, figsize=(2 * 3.5, 2 * 1.5 * 2.625), gridspec_kw={\"height_ratios\": [1, 0.8]})\n",
-    "        fig.subplots_adjust(wspace=0, hspace=0)\n",
-    "\n",
-    "        # CSiBORG2 main\n",
-    "        d = numpy.load(\"/mnt/extraspace/rstiskalek/csiborg_postprocessing/field_shells/enclosed_mass_csiborg2_main.npz\")\n",
-    "        V = 4 / 3 * numpy.pi * d[\"distances\"]**3\n",
-    "        V35 = 4 / 3 * numpy.pi * 135**3\n",
-    "        rho_mean = 0.3111 * 277.53662724583074\n",
-    "        boxsize = csiborgtools.simname2boxsize(\"csiborg2_main\")\n",
-    "\n",
-    "        dist = d[\"distances\"]\n",
-    "        density = d[\"enclosed_mass\"] / V * 1e-9 / rho_mean - 1\n",
-    "\n",
-    "        density135 = d[\"mass135\"] / V35 * 1e-9 / rho_mean - 1\n",
-    "        densitytot = d[\"masstot\"] / boxsize**3 * 1e-9 / rho_mean - 1\n",
-    "\n",
-    "        print(f\"CSiBORG2_main overdensity within 135 Mpc / h: {numpy.mean(density135)} +- {numpy.std(density135)}\")\n",
-    "        print(f\"CSiBORG2_main density of the entire box:      {numpy.mean(densitytot)} +- {numpy.std(densitytot)}\")\n",
-    "        mu = numpy.mean(density, axis=0)\n",
-    "        y = numpy.copy(density)\n",
-    "        for i in range(len(density)):\n",
-    "            axs[0].plot(dist, density[i], c=cols[0], alpha=0.25, ls=\"dashed\")\n",
-    "            y[i] /= mu\n",
-    "            y[i] -= 1\n",
-    "        axs[0].plot(dist, mu, c=cols[0], label=\"CB2_main\")\n",
-    "        mu2, std2 = numpy.mean(y, axis=0), numpy.std(y, axis=0)\n",
-    "        axs[1].fill_between(dist, mu2 - std2, mu2 + std2, alpha=0.5, color=cols[0])\n",
-    "\n",
-    "        # CSiBORG2 varysmall\n",
-    "        d = numpy.load(\"/mnt/extraspace/rstiskalek/csiborg_postprocessing/field_shells/enclosed_mass_csiborg2_varysmall.npz\")\n",
-    "        V = 4 / 3 * numpy.pi * d[\"distances\"]**3\n",
-    "        V35 = 4 / 3 * numpy.pi * 135**3\n",
-    "        rho_mean = 0.3111 * 277.53662724583074\n",
-    "        boxsize = csiborgtools.simname2boxsize(\"csiborg2_varysmall\")\n",
-    "\n",
-    "        dist = d[\"distances\"]\n",
-    "        density = d[\"enclosed_mass\"] / V * 1e-9 / rho_mean - 1\n",
-    "\n",
-    "        density135 = d[\"mass135\"] / V35 * 1e-9 / rho_mean - 1\n",
-    "        densitytot = d[\"masstot\"] / boxsize**3 * 1e-9 / rho_mean - 1\n",
-    "\n",
-    "        print(f\"CSiBORG2_varysmall overdensity within 135 Mpc / h: {numpy.mean(density135)} +- {numpy.std(density135)}\")\n",
-    "        print(f\"CSiBORG2_varysmall density of the entire box:      {numpy.mean(densitytot)} +- {numpy.std(densitytot)}\")\n",
-    "        mu = numpy.mean(density, axis=0)\n",
-    "        y = numpy.copy(density)\n",
-    "        for i in range(len(density)):\n",
-    "            axs[0].plot(dist, density[i], c=cols[2], alpha=0.25, ls=\"dashed\")\n",
-    "            y[i] /= mu\n",
-    "            y[i] -= 1\n",
-    "        axs[0].plot(dist, mu, c=cols[2], label=\"CB2_varysmall\")\n",
-    "        mu2, std2 = numpy.mean(y, axis=0), numpy.std(y, axis=0)\n",
-    "        axs[1].fill_between(dist, mu2 - std2, mu2 + std2, alpha=0.5, color=cols[2])\n",
-    "\n",
-    "        # CSiBORG2 random\n",
-    "        d = numpy.load(\"/mnt/extraspace/rstiskalek/csiborg_postprocessing/field_shells/enclosed_mass_csiborg2_random.npz\")\n",
-    "        V = 4 / 3 * numpy.pi * d[\"distances\"]**3\n",
-    "        V35 = 4 / 3 * numpy.pi * 135**3\n",
-    "        rho_mean = 0.3111 * 277.53662724583074\n",
-    "        boxsize = csiborgtools.simname2boxsize(\"csiborg2_random\")\n",
-    "\n",
-    "        dist = d[\"distances\"]\n",
-    "        density = d[\"enclosed_mass\"] / V * 1e-9 / rho_mean - 1\n",
-    "\n",
-    "        density135 = d[\"mass135\"] / V35 * 1e-9 / rho_mean - 1\n",
-    "        densitytot = d[\"masstot\"] / boxsize**3 * 1e-9 / rho_mean - 1\n",
-    "\n",
-    "        print(f\"CSiBORG2_random overdensity within 135 Mpc / h: {numpy.mean(density135)} +- {numpy.std(density135)}\")\n",
-    "        print(f\"CSiBORG2_random density of the entire box:      {numpy.mean(densitytot)} +- {numpy.std(densitytot)}\")\n",
-    "        for i in range(len(density)):\n",
-    "            axs[0].plot(dist, density[i], c=cols[1], alpha=0.5, ls=\"dashed\", zorder=0)\n",
-    "        mu = numpy.mean(density, axis=0)\n",
-    "        std = numpy.std(density, axis=0)\n",
-    "        axs[0].plot(dist, mu, c=cols[1], label=\"CB2_random\", zorder=0)\n",
-    "        axs[0].fill_between(dist, mu - std, mu + std, alpha=1/3, color=cols[1], zorder=0)\n",
-    "\n",
-    "        # CSiBORG1\n",
-    "        d = numpy.load(\"/mnt/extraspace/rstiskalek/csiborg_postprocessing/field_shells/enclosed_mass_csiborg1.npz\")\n",
-    "        V = 4 / 3 * numpy.pi * d[\"distances\"]**3\n",
-    "        V35 = 4 / 3 * numpy.pi * 135**3\n",
-    "        rho_mean = 0.307 * 277.53662724583074\n",
-    "        boxsize = csiborgtools.simname2boxsize(\"csiborg1\")\n",
-    "\n",
-    "        dist = d[\"distances\"]\n",
-    "        density = d[\"enclosed_mass\"] / V * 1e-9 / rho_mean - 1\n",
-    "\n",
-    "        density135 = d[\"mass135\"] / V35 * 1e-9 / rho_mean - 1\n",
-    "        densitytot = d[\"masstot\"] / boxsize**3 * 1e-9 / rho_mean - 1\n",
-    "\n",
-    "        print(f\"CSiBORG1 overdensity within 135 Mpc / h:    {numpy.mean(density135)} +- {numpy.std(density135)}\")\n",
-    "        print(f\"CSiBORG1 density of the entire box:         {numpy.mean(densitytot)} +- {numpy.std(densitytot)}\")\n",
-    "        mu = numpy.mean(density, axis=0)\n",
-    "        y = numpy.copy(density)\n",
-    "        for i in range(len(density)):\n",
-    "            axs[0].plot(dist, density[i], c=cols[3], alpha=0.25, ls=\"dashed\", zorder=0.5)\n",
-    "            y[i] /= mu\n",
-    "            y[i] -= 1\n",
-    "        axs[0].plot(dist, mu, c=cols[3], label=\"CB1\", zorder=0.5)\n",
-    "        mu2, std2 = numpy.mean(y, axis=0), numpy.std(y, axis=0)\n",
-    "        axs[1].fill_between(dist, mu2 - std2, mu2 + std2, alpha=0.5, color=cols[3], zorder=0.5)\n",
-    "\n",
-    "        \n",
-    "        # BORG2\n",
-    "        d = numpy.load(\"/mnt/extraspace/rstiskalek/csiborg_postprocessing/field_shells/enclosed_mass_borg2.npz\")\n",
-    "        V = d[\"enclosed_volume\"]\n",
-    "        dist = d[\"distances\"]\n",
-    "        rho_mean = 0.3111 * 277.53662724583074\n",
-    "        density = d[\"enclosed_mass\"] / V / rho_mean - 1\n",
-    "\n",
-    "        mu = numpy.mean(density, axis=0)\n",
-    "        y = numpy.copy(density)\n",
-    "        for i in range(len(density)):\n",
-    "            axs[0].plot(dist, density[i], c=cols[4], alpha=0.25, ls=\"dashed\", zorder=0.5)\n",
-    "            y[i] /= mu\n",
-    "            y[i] -= 1\n",
-    "        axs[0].plot(dist, mu, c=cols[4], label=\"B2\", zorder=0.5)\n",
-    "        mu2, std2 = numpy.mean(y, axis=0), numpy.std(y, axis=0)\n",
-    "        axs[1].fill_between(dist, mu2 - std2, mu2 + std2, alpha=0.5, color=cols[4], zorder=0.5)\n",
-    "\n",
-    "        # BORG1\n",
-    "        d = numpy.load(\"/mnt/extraspace/rstiskalek/csiborg_postprocessing/field_shells/enclosed_mass_borg1.npz\")\n",
-    "        V = d[\"enclosed_volume\"]\n",
-    "        dist = d[\"distances\"]\n",
-    "        rho_mean = 0.307 * 277.53662724583074\n",
-    "        density = d[\"enclosed_mass\"] / V / rho_mean - 1\n",
-    "\n",
-    "        mu = numpy.mean(density, axis=0)\n",
-    "        y = numpy.copy(density)\n",
-    "        for i in range(len(density)):\n",
-    "            axs[0].plot(dist, density[i], c=cols[4], alpha=0.25, ls=\"dashed\", zorder=0.5)\n",
-    "            y[i] /= mu\n",
-    "            y[i] -= 1\n",
-    "        axs[0].plot(dist, mu, c=cols[4], label=\"B2\", zorder=0.5)\n",
-    "        mu2, std2 = numpy.mean(y, axis=0), numpy.std(y, axis=0)\n",
-    "        axs[1].fill_between(dist, mu2 - std2, mu2 + std2, alpha=0.5, color=cols[4], zorder=0.5)\n",
-    "\n",
-    "        # Plot settings\n",
-    "        axs[0].set_ylim(-0.15, 0.15)\n",
-    "        axs[1].set_ylim(-0.4, 0.4)\n",
-    "        axs[1].set_xlim(-1, dist.max())\n",
-    "        axs[1].set_xlabel(r\"$r ~ [\\mathrm{Mpc} / h]$\")\n",
-    "        axs[0].set_ylabel(r\"$\\delta_r$\")\n",
-    "        axs[1].set_ylabel(r\"$\\delta_r / \\langle \\delta_r \\rangle - 1$\")\n",
-    "\n",
-    "        axs[0].legend(fontsize=\"small\")\n",
-    "        for i in range(2):\n",
-    "            axs[i].axvline(135, c=\"k\", ls=\"--\")\n",
-    "        fig.tight_layout(h_pad=0)\n",
-    "\n",
-    "        if to_save:\n",
-    "            fout = join(plt_utils.fout, f\"enclosed_overdensity.png\")\n",
-    "            print(f\"Saving to `{fout}`.\")\n",
-    "            fig.savefig(fout, dpi=plt_utils.dpi, bbox_inches=\"tight\")\n",
-    "\n",
-    "        fig.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CSiBORG2_main overdensity within 135 Mpc / h: -0.04871875932547955 +- 0.00201984793460838\n",
-      "CSiBORG2_main density of the entire box:      -5.9114960471373655e-05 +- 2.4011781815055744e-07\n",
-      "CSiBORG2_varysmall overdensity within 135 Mpc / h: -0.050013774679444456 +- 7.933447782885623e-05\n",
-      "CSiBORG2_varysmall density of the entire box:      -5.8827166581804094e-05 +- 1.3328610811057487e-07\n",
-      "CSiBORG2_random overdensity within 135 Mpc / h: 0.006494700117062541 +- 0.03838291159994349\n",
-      "CSiBORG2_random density of the entire box:      -5.874905109742312e-05 +- 4.066696396103268e-07\n",
-      "CSiBORG1 overdensity within 135 Mpc / h:    -0.05300083843550532 +- 0.008948856840208618\n",
-      "CSiBORG1 density of the entire box:         0.0008964738425259703 +- 8.270101657242762e-07\n",
-      "Saving to `../plots/enclosed_overdensity.png`.\n"
-     ]
-    }
-   ],
-   "source": [
-    "plot_enclosed_overdensity(True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# CSiBORG1\n",
-    "d = numpy.load(\"/mnt/extraspace/rstiskalek/csiborg_postprocessing/field_shells/enclosed_mass_csiborg1.npz\")\n",
-    "V = 4 / 3 * numpy.pi * d[\"distances\"]**3\n",
-    "V35 = 4 / 3 * numpy.pi * 135**3\n",
-    "rho_mean = 0.307 * 277.53662724583074\n",
-    "boxsize = csiborgtools.simname2boxsize(\"csiborg1\")\n",
-    "\n",
-    "dist = d[\"distances\"]\n",
-    "density = d[\"enclosed_mass\"] / V * 1e-9 / rho_mean - 1\n",
-    "\n",
-    "density135 = d[\"mass135\"] / V35 * 1e-9 / rho_mean - 1\n",
-    "densitytot = d[\"masstot\"] / boxsize**3 * 1e-9 / rho_mean - 1\n",
-    "\n",
-    "\n",
-    "density135_csiborg1 = density135\n",
-    "\n",
-    "\n",
-    "# CSiBORG2 main\n",
-    "d = numpy.load(\"/mnt/extraspace/rstiskalek/csiborg_postprocessing/field_shells/enclosed_mass_csiborg2_main.npz\")\n",
-    "V = 4 / 3 * numpy.pi * d[\"distances\"]**3\n",
-    "V35 = 4 / 3 * numpy.pi * 135**3\n",
-    "rho_mean = 0.3111 * 277.53662724583074\n",
-    "boxsize = csiborgtools.simname2boxsize(\"csiborg2_main\")\n",
-    "\n",
-    "dist = d[\"distances\"]\n",
-    "density = d[\"enclosed_mass\"] / V * 1e-9 / rho_mean - 1\n",
-    "\n",
-    "density135 = d[\"mass135\"] / V35 * 1e-9 / rho_mean - 1\n",
-    "densitytot = d[\"masstot\"] / boxsize**3 * 1e-9 / rho_mean - 1\n",
-    "\n",
-    "density135_csiborg2 = density135"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0.008948856840208618, 0.00201984793460838)"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "numpy.std(density135_csiborg1), numpy.std(density135_csiborg2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([15517, 15617, 15717, 15817, 15917, 16017, 16117, 16217, 16317,\n",
-       "       16417, 16517, 16617, 16717, 16817, 16917, 17017, 17117, 17217,\n",
-       "       17317, 17417])"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)\n",
-    "paths.get_ics(\"csiborg2_main\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure()\n",
-    "plt.hist(density135_csiborg1, bins=\"auto\", histtype=\"step\", label=\"CB1\", density=True)\n",
-    "plt.hist(density135_csiborg2, bins=\"auto\", histtype=\"step\", label=\"CB2\", density=True)\n",
-    "\n",
-    "plt.legend()\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0.        , 0.        , 0.        ],\n",
-       "       [0.11111111, 0.11111111, 0.11111111],\n",
-       "       [0.22222222, 0.22222222, 0.22222222],\n",
-       "       [0.33333333, 0.33333333, 0.33333333],\n",
-       "       [0.44444444, 0.44444444, 0.44444444],\n",
-       "       [0.55555556, 0.55555556, 0.55555556],\n",
-       "       [0.66666667, 0.66666667, 0.66666667],\n",
-       "       [0.77777778, 0.77777778, 0.77777778],\n",
-       "       [0.88888889, 0.88888889, 0.88888889],\n",
-       "       [1.        , 1.        , 1.        ]])"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x = numpy.linspace(0, 1, 10)\n",
-    "numpy.vstack([x, x, x]).T"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "d = numpy.load(\"/mnt/extraspace/rstiskalek/csiborg_postprocessing/field_shells/enclosed_mass_borg2.npz\")\n",
-    "V = d[\"enclosed_volume\"]\n",
-    "dist = d[\"distances\"]\n",
-    "rho_mean = 0.3111 * 277.53662724583074\n",
-    "density = d[\"enclosed_mass\"] / V / rho_mean - 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Bulk flow"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 118,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def process_bulkflow_amplitude(cumulative_velocity, subtract_observer):\n",
-    "    if isinstance(subtract_observer, bool):\n",
-    "        if subtract_observer:\n",
-    "            subtract_observer = 0\n",
-    "        else:\n",
-    "            return numpy.linalg.norm(cumulative_velocity, axis=-1)\n",
-    "\n",
-    "    if not isinstance(subtract_observer, int):\n",
-    "        raise TypeError(\"Incorrect type for `subtract_observer`.\")\n",
-    "\n",
-    "    for i in range(len(cumulative_velocity)):\n",
-    "        for j in range(3):\n",
-    "            cumulative_velocity[i, :, j] -= cumulative_velocity[i, subtract_observer, j]\n",
-    "\n",
-    "    return numpy.linalg.norm(cumulative_velocity, axis=-1)\n",
-    "\n",
-    "\n",
-    "def plot_bulkflow_amplitude(subtract_observer=False, to_save=True):\n",
-    "    with plt.style.context(\"science\"):\n",
-    "    # if True:\n",
-    "        plt.figure()\n",
-    "\n",
-    "        # CSiBORG2 main\n",
-    "        d = numpy.load(\"/mnt/extraspace/rstiskalek/csiborg_postprocessing/field_shells/enclosed_mass_csiborg2_main.npz\")\n",
-    "        dist = d[\"distances\"]\n",
-    "        cumulative_velocity = d[\"cumulative_velocity\"]\n",
-    "        cumulative_velocity_amplitude = process_bulkflow_amplitude(cumulative_velocity, subtract_observer)\n",
-    "\n",
-    "        for i in range(len(cumulative_velocity_amplitude)):\n",
-    "            plt.plot(dist, cumulative_velocity_amplitude[i], c=\"C0\", alpha=0.25, ls=\"dashed\")\n",
-    "        plt.plot(dist, numpy.mean(cumulative_velocity_amplitude, axis=0), c=\"C0\", label=\"CSiBORG2\")\n",
-    "\n",
-    "\n",
-    "        plt.axvline(135, c=\"k\", ls=\"--\")\n",
-    "        plt.xlabel(r\"$r ~ [\\mathrm{Mpc} / h]$\")\n",
-    "        plt.ylabel(r\"$\\langle U \\rangle ~ [\\mathrm{km} / \\mathrm{s}]$\")\n",
-    "        plt.legend()\n",
-    "\n",
-    "        if to_save:\n",
-    "            fout = join(plt_utils.fout, f\"enclosed_flow.png\")\n",
-    "            print(f\"Saving to `{fout}`.\")\n",
-    "            plt.savefig(fout, dpi=plt_utils.dpi, bbox_inches=\"tight\")\n",
-    "\n",
-    "        plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 119,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving to `../plots/enclosed_flow.png`.\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 350x262.5 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_bulkflow_amplitude(False, True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "venv_csiborg",
-   "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": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}