From bccc11acb51dad732c6dbd47593ec30fc43feede Mon Sep 17 00:00:00 2001
From: Xiangchong Li <mr.superonion@hotmail.com>
Date: Fri, 21 Jul 2023 15:18:41 -0700
Subject: [PATCH 01/30] prepare the test for the implementation of shear from
 cluster

---
 descwl_shear_sims/tests/test_nfw_shear.py | 47 +++++++++++++++++++++++
 1 file changed, 47 insertions(+)
 create mode 100644 descwl_shear_sims/tests/test_nfw_shear.py

diff --git a/descwl_shear_sims/tests/test_nfw_shear.py b/descwl_shear_sims/tests/test_nfw_shear.py
new file mode 100644
index 00000000..d9dc04c6
--- /dev/null
+++ b/descwl_shear_sims/tests/test_nfw_shear.py
@@ -0,0 +1,47 @@
+import os
+import pytest
+import numpy as np
+import lsst.afw.image as afw_image
+import lsst.afw.geom as afw_geom
+
+from descwl_shear_sims.galaxies import make_galaxy_catalog, DEFAULT_FIXED_GAL_CONFIG
+from descwl_shear_sims.stars import StarCatalog, make_star_catalog
+from descwl_shear_sims.psfs import make_fixed_psf, make_ps_psf
+
+from descwl_shear_sims.sim import make_sim, get_se_dim
+
+def test_sim_se_dim():
+    """
+    test sim can run
+    """
+    seed = 74321
+    rng = np.random.RandomState(seed)
+
+    coadd_dim = 351
+    se_dim = 351
+    psf_dim = 51
+    bands = ["i"]
+    galaxy_catalog = make_galaxy_catalog(
+        rng=rng,
+        gal_type="fixed",
+        coadd_dim=coadd_dim,
+        buff=30,
+        layout="grid",
+    )
+
+    psf = make_fixed_psf(psf_type="gauss")
+    data = make_sim(
+        rng=rng,
+        galaxy_catalog=galaxy_catalog,
+        coadd_dim=coadd_dim,
+        se_dim=se_dim,
+        psf_dim=psf_dim,
+        bands=bands,
+        g1=0.02,
+        g2=0.00,
+        psf=psf,
+    )
+    return data['band_data']['i'][0]
+
+a = test_sim_se_dim()
+data = a.getImage().getArray()

From 21680ade9a9f5b14c65d1be176e3696fdc2bb67c Mon Sep 17 00:00:00 2001
From: Xiangchong Li <mr.superonion@hotmail.com>
Date: Mon, 24 Jul 2023 12:44:14 -0700
Subject: [PATCH 02/30] try implementing the (g1, g2) from NFW halo

---
 descwl_shear_sims/shear.py | 104 +++++++++++++++++++++++++++++++++++++
 descwl_shear_sims/sim.py   |  11 ++--
 2 files changed, 112 insertions(+), 3 deletions(-)
 create mode 100644 descwl_shear_sims/shear.py

diff --git a/descwl_shear_sims/shear.py b/descwl_shear_sims/shear.py
new file mode 100644
index 00000000..fff0f21e
--- /dev/null
+++ b/descwl_shear_sims/shear.py
@@ -0,0 +1,104 @@
+import clmm
+import galsim
+import numpy as np
+from astropy.coordinates import SkyCoord
+
+# maximum kappa allowed
+# values greater than it will be clipped
+kappa_max = 0.6
+
+# factor from arcsec to radians
+arcsec2rad = 1./3600./180.*np.pi
+
+
+def get_shear(
+    *,
+    shear_type="constant",
+    z_gals=None,
+    shifts=None,
+    **kwargs,
+    ):
+    """
+    A shear wrapper to return g1 and g2 with different shear type
+
+    Parameters
+    ----------
+    cluster_obj:    cluster object
+    """
+    if shear_type == "constant":
+        shear_obj = ShearConstant(**kwargs)
+    elif shear_type == "NFW":
+        shear_obj = ShearNFW(**kwargs)
+    else:
+        raise ValueError("Do not support the shear type: %s" % shear_type)
+    return shear_obj.get_shear(z_gals, shifts)
+
+
+class ShearNFW(object):
+    """
+    Shear object from NFW
+
+    Parameters
+    ----------
+    cluster_obj (object):    cluster object from clmm
+    z_cl (float):    redshift of the cluster
+    x_cl (float):    ra of the cluster [arcsec]
+    y_cl (float):    dec of the cluster [arcsec]
+
+    """
+    def __init__(self, cluster_obj, z_cl, ra_cl=0., dec_cl=0.):
+        self.z_cl = z_cl
+        self.ra_cl = ra_cl
+        self.dec_cl = dec_cl
+        self.cobj = cluster_obj
+        self.cosmo = cluster_obj.cosmo
+        return
+
+
+    def get_shear(self, z_gals, shifts):
+
+        z_cl = self.z_cl
+        # Create the SkyCoord objects
+        coord_cl = SkyCoord(self.ra_cl, self.dec_cl, unit="arcsec")
+        coord_gals = SkyCoord(shifts["dx"], shifts["dy"], unit="arcsec")
+        # Calculate the separation
+        sep = coord_cl.separation(coord_gals).rads
+        r3d = self.cosmo.rad2mpc(sep, self.z_cl)
+        phi = coord_cl.position_angle(coord_gals).rads
+
+        # TODO: confirm whether the units is Mpc/h or Mpc?
+
+        DeltaSigma = self.cobj.eval_excess_surface_density(r3d, z_cl)
+        gammat = self.cobj.eval_tangential_shear(r3d, z_cl, z_gals)
+        kappa = self.cobj.eval_convergence(r3d, z_cl, z_gals)
+        gamma1 = gammat * -np.cos(2. * phi)
+        gamma2 = gammat * -np.sin(2. * phi)
+        return gamma1/(1-kappa), gamma2/(1-kappa)
+
+
+class ShearConstant(object):
+    """
+    Constant shear along every redshift slice
+    """
+    def __init__(self, mode="0000", g_dist="g1"):
+        # note that there are three options in each redshift bin
+        # 0: g=0.00; 1: g=-0.02; 2: g=0.02
+        # "0000" means that we divide into 4 redshift bins, and every bin
+        # is distorted by -0.02
+        nz_bins = len(mode)
+        self.nz_bins = nz_bins
+        # number of possible modes
+        self.n_modes = 3 ** nz_bins
+        self.mode = mode
+        self.z_bounds = np.linspace(0, 4, nz_bins+1)
+        self.dz_bin = self.z_bounds[1]-self.z_bounds[0]
+        self.g_dist = g_dist
+        return
+
+    def get_shear(self, z_gals=None, shifts=None):
+        if z_gals is None:
+            assert self.mode == "0" * self.nz_bins
+        z_gal_bins = z_gals // self.dz_bin
+        gamma1, gamma2 = (None, None)
+        # TODO: Finish implementing the z-dependent shear
+        return gamma1, gamma2
diff --git a/descwl_shear_sims/sim.py b/descwl_shear_sims/sim.py
index b8d4aae2..6867e6f9 100644
--- a/descwl_shear_sims/sim.py
+++ b/descwl_shear_sims/sim.py
@@ -70,6 +70,7 @@ def make_sim(
     sky_n_sigma=None,
     draw_method='auto',
     theta0=0.,
+    shear_obj=None,
 ):
     """
     Make simulation data
@@ -201,6 +202,7 @@ def make_sim(
                 sky_n_sigma=sky_n_sigma,
                 draw_method=draw_method,
                 theta0=theta0,
+                shear_obj=None,
             )
             if epoch == 0:
                 bright_info += this_bright_info
@@ -264,7 +266,8 @@ def make_exp(
     star_bleeds=False,
     sky_n_sigma=None,
     draw_method='auto',
-    theta0=0.
+    theta0=0.,
+    shear_obj=None,
 ):
     """
     Make an SEObs
@@ -335,8 +338,10 @@ def make_exp(
         radius_pixels: radius of mask in pixels
         has_bleed: bool, True if there is a bleed trail
     """
-
-    shear = galsim.Shear(g1=g1, g2=g2)
+    if shear_obj is None:
+        shear = galsim.Shear(g1=g1, g2=g2)
+    else:
+        shear = shear_obj
     dims = [dim]*2
     # I think Galsim uses 1 offset. An array with length=dim=5
     # The center is at 3=(5+1)/2

From 79572b478e70b12cc2b242c6a025f2840ebf61d2 Mon Sep 17 00:00:00 2001
From: Xiangchong Li <mr.superonion@hotmail.com>
Date: Mon, 24 Jul 2023 14:59:32 -0700
Subject: [PATCH 03/30] test for the shear direction, prepare to test the shear
 amplitude by comparing with Galsim

---
 .virtual_documents/Untitled.ipynb             |   1 +
 .../examples/example_NFWShear.ipynb           |  72 ++++++
 descwl_shear_sims/__init__.py                 |   1 +
 descwl_shear_sims/shear.py                    |  10 +-
 examples/example_NFWShear.ipynb               | 214 ++++++++++++++++++
 5 files changed, 292 insertions(+), 6 deletions(-)
 create mode 100644 .virtual_documents/Untitled.ipynb
 create mode 100644 .virtual_documents/examples/example_NFWShear.ipynb
 create mode 100644 examples/example_NFWShear.ipynb

diff --git a/.virtual_documents/Untitled.ipynb b/.virtual_documents/Untitled.ipynb
new file mode 100644
index 00000000..8b137891
--- /dev/null
+++ b/.virtual_documents/Untitled.ipynb
@@ -0,0 +1 @@
+
diff --git a/.virtual_documents/examples/example_NFWShear.ipynb b/.virtual_documents/examples/example_NFWShear.ipynb
new file mode 100644
index 00000000..2fda6254
--- /dev/null
+++ b/.virtual_documents/examples/example_NFWShear.ipynb
@@ -0,0 +1,72 @@
+get_ipython().run_line_magic("matplotlib", " inline")
+get_ipython().run_line_magic("reload_ext", " autoreload")
+get_ipython().run_line_magic("autoreload", " 2")
+
+import clmm
+import numpy as np
+import descwl_shear_sims as dss
+import matplotlib.pyplot as plt
+
+
+cosmo = clmm.Cosmology(H0=70.0, Omega_dm0=0.27 - 0.045, Omega_b0=0.045, Omega_k0=0.0)
+halo = clmm.Modeling(massdef="mean", delta_mdef=200, halo_profile_model="nfw")
+halo.set_cosmo(cosmo)
+halo.set_concentration(4)
+halo.set_mass(1.0e15)
+z_cl = 1.0
+# source properties
+z_source = 2.0  # all sources in the same plane
+
+shear_obj = dss.shear.ShearNFW(halo, z_cl)
+
+
+# generate positions
+n_gal = 20
+theta = np.linspace(0, 360, n_gal) / n_gal
+x = np.cos(theta) * 5
+y = np.sin(theta) * 5
+shifts = np.zeros(n_gal, dtype=[('dx', 'f8'), ('dy', 'f8')])
+shifts['dx'] = x
+shifts['dy'] = y
+
+# get the shear
+g1, g2 = shear_obj.get_shear(z_source, shifts)
+gamma = g1 + 1j * g2
+
+
+angles = np.angle(gamma, deg=True) / 2.
+lengths = np.abs(gamma)
+
+# Create whisker plot
+plt.figure(figsize=(6, 6))
+plt.quiver(x, y, np.cos(np.deg2rad(angles)), np.sin(np.deg2rad(angles)), lengths,
+           angles='xy', scale_units='xy', scale=1, width=0.001)
+plt.xlim(-10, 10)
+plt.ylim(-10, 10)
+plt.xlabel('x')
+plt.ylabel('y')
+plt.show()
+
+
+n_gal = 1
+d_array = []
+g_array = []
+for i in range(10):
+    shifts = np.zeros(n_gal, dtype=[('dx', 'f8'), ('dy', 'f8')])
+    shifts['dx'][0] = 3.0 ** i 
+    shifts['dy'][0] = 0.
+    print(shifts)
+    # get the shear
+    g1, g2 = shear_obj.get_shear(z_source, shifts)
+    gabs = np.abs(g1 + 1j * g2)
+    d_array.append(3 ** i)
+    g_array.append(gabs)
+d_array = np.array(d_array)
+g_array = np.array(g_array)
+
+
+plt.plot(d_array / 3600., g_array)
+plt.xscale("log")
+plt.yscale("log")
+plt.xlabel("separation [degree]")
+plt.ylabel("shear")
diff --git a/descwl_shear_sims/__init__.py b/descwl_shear_sims/__init__.py
index 7a8f069f..7c787c39 100644
--- a/descwl_shear_sims/__init__.py
+++ b/descwl_shear_sims/__init__.py
@@ -14,6 +14,7 @@
 from . import objlists
 from . import surveys
 from . import shifts
+from . import shear
 from . import constants
 from . import artifacts
 from . import masking
diff --git a/descwl_shear_sims/shear.py b/descwl_shear_sims/shear.py
index fff0f21e..41e54bb3 100644
--- a/descwl_shear_sims/shear.py
+++ b/descwl_shear_sims/shear.py
@@ -56,23 +56,21 @@ def __init__(self, cluster_obj, z_cl, ra_cl=0., dec_cl=0.):
 
 
     def get_shear(self, z_gals, shifts):
-
         z_cl = self.z_cl
         # Create the SkyCoord objects
         coord_cl = SkyCoord(self.ra_cl, self.dec_cl, unit="arcsec")
         coord_gals = SkyCoord(shifts["dx"], shifts["dy"], unit="arcsec")
         # Calculate the separation
-        sep = coord_cl.separation(coord_gals).rads
+        sep = coord_cl.separation(coord_gals).radian
         r3d = self.cosmo.rad2mpc(sep, self.z_cl)
-        phi = coord_cl.position_angle(coord_gals).rads
+        phi = coord_cl.position_angle(coord_gals).radian
 
         # TODO: confirm whether the units is Mpc/h or Mpc?
-
         DeltaSigma = self.cobj.eval_excess_surface_density(r3d, z_cl)
         gammat = self.cobj.eval_tangential_shear(r3d, z_cl, z_gals)
         kappa = self.cobj.eval_convergence(r3d, z_cl, z_gals)
-        gamma1 = gammat * -np.cos(2. * phi)
-        gamma2 = gammat * -np.sin(2. * phi)
+        gamma1 = gammat * np.cos(2. * phi)
+        gamma2 = -gammat * np.sin(2. * phi)
         return gamma1/(1-kappa), gamma2/(1-kappa)
 
 
diff --git a/examples/example_NFWShear.ipynb b/examples/example_NFWShear.ipynb
new file mode 100644
index 00000000..65bba7c3
--- /dev/null
+++ b/examples/example_NFWShear.ipynb
@@ -0,0 +1,214 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d12a992b-6669-4675-b68a-b705ff09f693",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "%reload_ext autoreload\n",
+    "%autoreload 2\n",
+    "\n",
+    "import clmm\n",
+    "import numpy as np\n",
+    "import descwl_shear_sims as dss\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "707e17ec-2aa0-4470-b986-fefe11e4be47",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cosmo = clmm.Cosmology(H0=70.0, Omega_dm0=0.27 - 0.045, Omega_b0=0.045, Omega_k0=0.0)\n",
+    "halo = clmm.Modeling(massdef=\"mean\", delta_mdef=200, halo_profile_model=\"nfw\")\n",
+    "halo.set_cosmo(cosmo)\n",
+    "halo.set_concentration(4)\n",
+    "halo.set_mass(1.0e15)\n",
+    "z_cl = 1.0\n",
+    "# source properties\n",
+    "z_source = 2.0  # all sources in the same plane\n",
+    "\n",
+    "shear_obj = dss.shear.ShearNFW(halo, z_cl)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3eae4691-6752-4e77-a81d-d913d7bec263",
+   "metadata": {},
+   "source": [
+    "# step1: make sure the direction is correct"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "d348067a-7220-4bdb-a0c5-875b5cdb8213",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# generate positions\n",
+    "n_gal = 20\n",
+    "theta = np.linspace(0, 360, n_gal) / n_gal\n",
+    "x = np.cos(theta) * 5\n",
+    "y = np.sin(theta) * 5\n",
+    "shifts = np.zeros(n_gal, dtype=[('dx', 'f8'), ('dy', 'f8')])\n",
+    "shifts['dx'] = x\n",
+    "shifts['dy'] = y\n",
+    "\n",
+    "# get the shear\n",
+    "g1, g2 = shear_obj.get_shear(z_source, shifts)\n",
+    "gamma = g1 + 1j * g2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "8c203fae-f689-4405-91be-056aa84a3593",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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i8AAAJEkul0uDYidwQTOsROEBADTBBc2wEYUHAHCKxguaXW4uaIYVKDwAgGaldR/+7wua/48LmhHQKDwAgBaduKD5Nn3r+ZoLmhGwKDwAgNNyuVwaHDux8YLm8mNfyhjjdCyg1Sg8AIBWO3FB83StKluozw4uczoO0GoUHsBPHKk66nQEoFVCgtz6j4wXlNJtkBpMvdNxgFah8AAOM8bo1UeWaOeW3U5HAVrN5XIpIbyvglzBTkcBWoXCAzisomSfPlq+UQMuzHQ6CgBYi8IDOCxv0Ro9teoRhYSGOB0FAKxF4QEcVLRhu87ql6zusZFORwEAq1F4AIfU19XrgzfXa8zUbKejAID1KDyAQ955+X1Nuu1yuVwup6MAZ+zbY5tVb2qdjgE0i8IDOGBf6X4dP3JcvfunOB0F6BCRoUnafCCHDyOE36LwAA5Y9vv3dM1dE5yOAXSYbiG9lBp5qb6qzHU6CuAVhQfwsQ3vFmrImEEKCw9zOgrQoeLCB6pbSIJ2H/7A6SjAKSg8gA95jnm0Ze2XGn7lEKejAJ0itfulOlL3jQ4cL3I6CtAEhQfwoWXPv6drZ010OgbQqQbE/EglR/J1tO5bp6MAjSg8gI/s+mKPImMj1TOph9NRgE7lcrl0Xo//0ucH/6S6huNOxwEkUXgAnzDGaOUr+ZrwX2OcjgL4RHBQmAb3nK5/HfhfGdPgdByAwgP4wvt/WqsxU7MVHMwXLaLrCA+OVWb0JH1x6A2nowAUHqCzVR2oVvnOCvW74GynowA+FxOWodiwvtpZned0FHRxFB6gk/31v9/VdfdMcjoG4JiUyJGqaziuimOfOR0FXRiFB+hEW9cVKX1wqiKjuzkdBXBU35gpKj+2UYdr9zodBV0UhQfoJHW1dVr31w265PqRTkcB/MLgHreo6FCuauqPOB0FXZBVhWfBggW68MILFRUVpYSEBF133XUqKmr5w6/y8/PlcrlOuX355Zc+Sg1bLX9plSb/5Aq+HBT4tyBXiM7r+V/afPB/1WDqnI6DLsaqwrNmzRrdfffdWr9+vfLy8lRXV6fx48fryJHT/zVRVFSksrKyxlu/fv18kBg2G5Q9QCmZSU7HAPxKWHB39Y/5obYefM3pKOhiQpwO0JHefffdJss5OTlKSEjQxo0bdemll7Y4NyEhQbGxsZ2YDl1N32EZTkcA/FJUaIoSIy7Q9sq/q2/M1U7HQRdh1RGe76usrJQk9ezZ87Rjhw0bpuTkZI0bN06rV69ucazH41FVVVWTGwCg9RIihsioQZv2v+R0FHQR1hYeY4zmzJmjSy65RIMHD252XHJysl566SXl5ubqrbfe0oABAzRu3Dh98EHz3/a7YMECxcTENN5SU1M7YxMAwGq9Iy9WZc1OVdeWOh0FXYDLGGOcDtEZ7r77bi1fvlxr165V79692zR3ypQpcrlcWrZsmdf7PR6PPB5P43JVVZVSU1NVWVmp6OjoM8qNwGNMg2SOyhXU3ekoQMAxxqi24ajCgiOdjgKHVFVVKSYmptN/h1p5hOeee+7RsmXLtHr16jaXHUkaNWqUtm3b1uz9brdb0dHRTW7owo4ukur5CxVoD5fLRdmBT1hVeIwxmjVrlt566y29//77ysho30WjhYWFSk5O7uB0sJExtTLH35VcVl3/DwDWsepV+u6779af/vQn/fWvf1VUVJTKy8slSTExMYqIiJAkzZ07V6WlpVq0aJEkaeHChUpPT9egQYNUU1Oj119/Xbm5ucrNzXVsOxBIjFzuy+UKyXQ6CACgBVYVnhdeeEGSNGbMmCbrc3JyNGPGDElSWVmZSkpKGu+rqanRgw8+qNLSUkVERGjQoEFavny5rrrqKl/FRiCrWS+F8UnKAODvrL1o2Zd8dcEV/I85/Hsp8k65XFadHQYAn+GiZcDPGdMgSZQdAAgAvFID7VW7WQo9z+kUAIBWoPAA7VXzEdfvAECAoPAA7VYrlyvM6RAAgFag8ADtYOp2SsFpTscAALQShQdoD88Hkvsyp1MAAFqJwgO0h6mWKyjK6RQAgFai8ABtZOr3SUFxTscAALQBhQdoK0++5B7jdAoAQBtQeIC2avhGruAkp1MAANqAwgO0gWk4IrnCnY4BAGgjCg/QFjVrpbDRTqcArJdXWqSjdTVOx4BFKDxAW9Rtl0L6OZ0CsF6/6F7KKy1yOgYsQuEBWsmYOknBcrlcTkcBrJce1VMlhw86HQMWofAArVWzUQob7nQKoMvoFdFd3xyrdjoGLEHhAVqr9lMp9AKnUwBdxsTe5+jdPV84HQOWoPAArRWSKZcr2OkUQJcRGxahqprjTseAJSg8QCu5wsc7HQHocgbGJuqLQ984HQMWoPAAAPzWZcl9lV+2zekYsACFBwDgt8KCgtVgjOoaGpyOggBH4QEA+LWLE8/Wuopip2MgwFF4AAB+bUjPFH22v9TpGAhwFB4AgF9zuVyKDA1Tda3H6SgIYBQeAIDfG3/WQK0s/dLpGAhgFB4AgN/rHRmrvUcqnY6BAEbhAQAEhKRu0So9csjpGAhQFB4AQECY2HugVvIN6mgnCg8AICBEhYbrcK1HxhinoyAAUXgAAAFjcM9kbT5Y5nQMBCAKDwAgYFySeLbWfrPD6RgIQBQeAEDACA0KlktS2dEqp6MgwFB4AAAB540dG52OgABD4QEABJSZA7J1Tkyi0zEQYCg8AICAEhIUpEmp5zodAwGGwgMAAKxH4QEAANaj8AAAAOtReAAAgPUoPAAAwHoUHgAAYD0KDwAAsB6FBwAAWI/CAwAArEfhAQAA1qPwAAAA61F4AACA9Sg8AADAelYWnt///vfKyMhQeHi4hg8frn/+858tjl+zZo2GDx+u8PBwnX322XrxxRd9lBQAAPiCdYVnyZIlmj17tn75y1+qsLBQo0eP1qRJk1RSUuJ1fHFxsa666iqNHj1ahYWF+sUvfqF7771Xubm5Pk4OAAA6i8sYY9oyYcaMGbr11lt16aWXdlamMzJy5EhdcMEFeuGFFxrXnXPOObruuuu0YMGCU8b//Oc/17Jly/TFF180rrvzzjv12WefqaCgoFXPWVVVpZiYGFVWVio6OvrMNwIAgC7CV79D23yEp7q6WuPHj1e/fv30+OOPq7S0tDNytUtNTY02btyo8ePHN1k/fvx4rVu3zuucgoKCU8ZPmDBBn3zyiWpra73O8Xg8qqqqanIDAPjG7gOHnI6AANTmwpObm6vS0lLNmjVLf/nLX5Senq5JkybpzTffbLYg+Mq+fftUX1+vxMTEJusTExNVXl7udU55ebnX8XV1ddq3b5/XOQsWLFBMTEzjLTU1tWM2AABwWu9u3eZ0BASgdl3DExcXp/vuu0+FhYX6+OOP1bdvX02bNk0pKSm6//77tW2bsz+MLperybIx5pR1pxvvbf135s6dq8rKysbb7t27zzAxAADoTGd00XJZWZlWrlyplStXKjg4WFdddZW2bt2qc889V88++2xHZWy1+Ph4BQcHn3I0p6Ki4pSjON9JSkryOj4kJERxcXFe57jdbkVHRze5AQAA/9XmwlNbW6vc3FxdffXVSktL01/+8hfdf//9Kisr06uvvqqVK1fqtdde06OPPtoZeVsUFham4cOHKy8vr8n6vLw8ZWdne52TlZV1yviVK1dqxIgRCg0N7bSsAADAd0LaOiE5OVkNDQ26+eab9fHHH2vo0KGnjJkwYYJiY2M7IF7bzZkzR9OmTdOIESOUlZWll156SSUlJbrzzjslnTgdVVpaqkWLFkk68Y6s5557TnPmzNHMmTNVUFCgl19+WW+88YYj+QEAQMdrc+F59tlndcMNNyg8PLzZMT169FBxcfEZBWuvqVOnav/+/Xr00UdVVlamwYMHa8WKFUpLS5N04jTcyZ/Jk5GRoRUrVuj+++/X888/r5SUFP3ud7/TD3/4Q0fywz+sf2eTRk0a6nQMAC043fWZwMna/Dk8OBWfw2OfJb/9u6Y+cLXTMQB48cd/btDM0Rfq79u/1NV9BzodB2fIbz+HB+gS+KsR8GtfHzyg5zaudzoGAgiFB/CGA5+AXwsPCdE1/c5xOgYCCIUHABBwPtq7W9f043QWWo/CAwAIOHsPV6t3VIzTMRBAKDwAgIBijJERp53RNhQewBsuWgb81qHa4+oTHet0DAQYCg/gDRctA35r5+FDyjqrj9MxEGAoPACAgHKkrkYJ3SKdjoEAQ+EBvAgJC1FtTZ3TMQB8D5+Vi/ai8ABeRHQP17Hq407HAPA92w/uV3w4R3fQdhQewIuISLeOHaHwAP5m/d7dSuvG29HRdhQewIuIqAiO8AB+qNLjUURIqNMxEIAoPIAX3bqHc4QH8DP1DQ18OzrajcIDeBHR3a2jhyk8gD/5Yv+3Ojeul7Z9s0/lldVOx0GAofAAXkR055QW4G8+LtujC5N7a9/ho4oI47QW2obCA3gR0Z2LlgF/c7S2Vt3DwjTnyosVExHudBwEGAoP4AVvSwf8S219vYKDTly/M/isJIfTIBBReAAvwiPdOn7U43QMAP/2r2/LNSQh2ekYCGAUHsCLoKAgmQY+0RXwFxvL9+qCxBSnYyCAUXgAL3Z+vkfr/v4pH2MP+Ima+nqFh4Q4HQMBjMIDeJE6IEXnXJTJZ34AfuB4XZ3cwcFOx0CAo/AAXgQHB+k/517ndAwAOnF0Z1Jmf6djIMBxfBBoRvdYvqAQ8AfRbrei3W6nYyDAcYQHAABYj8IDAACsR+EBAADWo/AAAADrUXgAAID1KDwAAMB6FB4AAGA9Cg8AALAehQcAAFiPwgMAAKxH4QEAANaj8AAAAOtReAAAgPUoPAAAwHoUHqAdjDFaV7DN6RgAgFai8ADt4HK5tH37N6qtrXc6CmCluoZapyPAMhQeoJ1GXzJAaz/8yukYgFWMMfpk/9+1++hWp6PAMhQeoJ0yMnpp585vnY4BWKWy9httPLhCqd0GOR0FlqHwAGcgMzNR27d/43QMwBqbDuZpRsbTCgkKdToKLEPhAc7Axdn99CEXLwMd4suqD5XRfajcwd2cjgILUXiAMxAcHKRu3cJUVXXM6ShAQKuuPaADnr1KizzP6SiwFIUHOENXXD5Iq97nAkugvYwx2rD/r7oo7lqno8BiFB7gDPXoEamqqmOqr29wOgoQkDZXvq9zYkYrJCjM6SiwGIUH6AAjL8rUxxt2OB0DCDiHasp1vP6wkiP6Oh0FlrOm8OzcuVO33XabMjIyFBERoczMTD3yyCOqqalpcd6MGTPkcrma3EaNGuWj1LDFOQNTVFRU5nQMIKA0mHptPLBCI3pe7XQUdAEhTgfoKF9++aUaGhr0hz/8QX379tWWLVs0c+ZMHTlyRE8//XSLcydOnKicnJzG5bAwDqui7c46q4d27zmg1N49nY4CBIRPD76roT3GK8gV7HQUdAHWFJ6JEydq4sSJjctnn322ioqK9MILL5y28LjdbiUlJbX6uTwejzweT+NyVVVV2wPDOpddOlB//svH+s//yHY6CuD3Ko7vVJCCFefu7XQUdBHWnNLyprKyUj17nv6v7fz8fCUkJKh///6aOXOmKioqWhy/YMECxcTENN5SU1M7KjICWFhYiEJCgnT0qOf0g4EurL6hVpsPva9hPcY7HQVdiMsYY5wO0Rm+/vprXXDBBfrtb3+r22+/vdlxS5YsUffu3ZWWlqbi4mL9+te/Vl1dnTZu3Ci32+11jrcjPKmpqaqsrFR0dHSHbwsCR0VFlT7esENXTx7qdBTAb63f95bOjRmt6NBeTkeBH6iqqlJMTEyn/w71+yM88+bNO+Wi4u/fPvnkkyZz9u7dq4kTJ+qGG25osexI0tSpUzV58mQNHjxYU6ZM0TvvvKOvvvpKy5cvb3aO2+1WdHR0kxsgSQkJ0fr22ypZ+ncEcMZKj36p7iE9KTvwOb+/hmfWrFm66aabWhyTnp7e+O+9e/dq7NixysrK0ksvvdTm50tOTlZaWpq2bePrAtA+Q4emadNnJRo2NM3pKIBfqW04rq+qP9KYhFucjoIuyO8LT3x8vOLj41s1trS0VGPHjtXw4cOVk5OjoKC2H8Dav3+/du/ereTk5DbPBSRp6JA+enXRWgoP8D0f7X9bF8VdJ5fL5XQUdEF+f0qrtfbu3asxY8YoNTVVTz/9tL799luVl5ervLy8ybiBAwdq6dKlkqTDhw/rwQcfVEFBgXbu3Kn8/HxNmTJF8fHx+sEPfuDEZsACLpdL8b2i9E1FpdNRAL+x43ChEsIzFBkS43QUdFHWFJ6VK1dq+/btev/999W7d28lJyc33k5WVFSkysoTv4iCg4O1efNmXXvtterfv7+mT5+u/v37q6CgQFFRUU5sBixxcVY//eGl1VzLA0g6Xn9Yu49uVf+okU5HQRfm96e0WmvGjBmaMWPGaced/AsoIiJC7733XiemQlcVGelWRUWVqquPKzo6wuk4gKM2H1qtUXHXOx0DXZy1b0v3JV+9pQ6B5cCBw5Kknj27O5wEAPyXr36HWnOEB/A3FB0A8B/WXMMDAADQHAoPAACwHoUH8LHPDpRq95GDTscAgC6FwgP42KDYZP3f15+opr7O6SgA0GVQeAAfCwkK0q39RumPX61zOgrQKYwxKjvyofYf3+p0FKARhQdwQEJElC6IS9U7ez53OgrQ4b6uekufH3xFPdwDnI4CNKLwAA7JSshQxbFqfV21z+koQIfZWf2OQoK6aUzKcwpy8ckn8B8UHsBB0/pepDd3btLRuhqnowBnxBijokN/UkRwnNKjJik4yO10JKAJCg/goCCXSzMHZOulog/53i0ErHpTqy0HXlRixEVK7HaR03EAryg8gMN6urvpsqR+ervkX05HAdqspv6wNu9/XpkxP1Ssu6/TcYBmUXgAPzAsrrc89XX6/FC501GAVjtaW67PD76sQT1vV7eQBKfjAC2i8AB+YmrGBVqxZ6uqa487HQU4rYOeIu2o+qvOj7tboUF8bxz8H4UH8BMul0t3DLhYf+B6Hvi5siMF+vbYpxrU8ye8EwsBg8ID+JGo0HBN7j1IS4o/dToK4FVx1XLVNlSrf+zNcrlcTscBWo3CA/iZc2KTFB4cqsL9e5yOAjQyxujLg6+pW0iC+kSNdzoO0GYUHsAPXdvnPH1Qvl0HPEedjgI0vu08uVu2Ertd6HQcoF0oPIAfcrlc+smAi/U/X61TA9fzwEE19dXavP959Y35kWLcmU7HAdqNwgP4qYiQUP0ofahe2/6x01HQRR2pLdPnB/9Xg3rOVERIL6fjAGeEwgP4sbOj4pUYEaUVe7byzi341IHjX6q46m//ftt5pNNxgDNG4QH83OUpA/TCl2u1cf9up6OgCzlSt1eDes7kbeewBoUH8HNhQcF6/dJbVF3rcToKupDU7pfztnNYhcIDBICYsAiNTe7ndAwACFgUHgAAYD0KDwAAsB6FBwAAWI/CAwAArEfhAQAA1qPwAAAA61F4AACA9Sg8QBfxedVmNZgGp2MAgCMoPEAXERvaU0tLl2jvsVKnowCAz1F4gC4iJeIs/eCsqfr68Fda9c07qmuoczoSOpkxRtW1x3W8vtbpKIDjKDxAFxLkCtLoXmN1QY8LtWzvm9pxeLvTkdAJGkyDPvq2WBPzfqffffG+gvhOLEAuY4xxOkSgq6qqUkxMjCorKxUdHe10HKBVjDHaePAjVXi+0eUJExQeHO50JJyhimNVerd0q6rrPLooPl1nR8Urzt3d6VhAi3z1OzSk0x4ZgF9zuVwa0XOUDtdVa2X535XZvb8GxZzvdCy0UW1DvT74Zpu+OFSmXuFRuq7PUEWHRTgdC/A7HOHpABzhgQ22Vv5LXx/+SpcnTlD3kCin4+A0dh7ep7y9X6jeNOjSxH46NzbF6UhAu3CEB4BPDYo5X5nd++v9iveU4E7U8B4j5eLaD79yrK5GeXu/UMmRA0rrHqdpmaMUHhzqdCwgIHCEpwNwhAe22XF4uz47tFGXJYxTz7B4p+N0acYYbT20V//8ZrtCg4J1Zco5Suse53QsoMNwhAeAY87u3ld9uqUr/9s8uYPCdXH8ZQpy8aZOX6qsOaZ3Srdo3/HDGhSbopn9L1FIULDTsYCAReEB4FVIUIiuSJykvcdKtbR0ibLjLlVyxFlOx7Jag2nQx/t2auP+EkWHhmviWYPUK5zrqYCOwCmtDsApLdiuwTTow3358jR41D/qHPXplu50JCuVHj2ksqOHNDwujeun0GX46ncox6gBnNaJDyy8XL0j+ujVnS+p9NhupyNZ6axusRoRn07ZAToBR3g6AEd40JUYY3S0/ogiQ/hAOwBnjiM8APySy+Wi7AAIOBQeAABgPasKT3r6iXPfJ98efvjhFucYYzRv3jylpKQoIiJCY8aM0datW32UGOjaVm3drj/kf6xX1m7UJzv36Hgt3+AOoHNY97b0Rx99VDNnzmxc7t695UPvTz31lJ555hm98sor6t+/v37zm9/oyiuvVFFRkaKieDso0JmuGNRXV0g6XlunLXvK9cZHn8lTWyeXy6Wze/XQ0D4p6hUV6XTM0zLGcKEx4OesKzxRUVFKSkpq1VhjjBYuXKhf/vKXuv766yVJr776qhITE/WnP/1Jd9xxh9d5Ho9HHo+ncbmqqurMgwNdWHhoiEZk9NaIjN6SpIYGo537D+qDomLtO3xUkhTbLVxD+ySrb0KcgoPaf3D6aE2tPHV1qq2rV219vWrqG1Rb/+9/19Wr9t/LNf9e17hc13T5ZGuKdmhAUi/NujxLPbrxxZ2AP7LqXVrp6enyeDyqqalRamqqbrjhBv3sZz9TWFiY1/E7duxQZmamPv30Uw0bNqxx/bXXXqvY2Fi9+uqrXufNmzdP8+fPP2U979ICOs/BI8e0qWSvtlfsV4ORQoKDdE5yLw1JTVak2/v/cW9WfbFdB48eU2hwsMKCgxUaHKzQ4KATyyGnWT5p/MlHdLZV7FN6XA+FBvNJyEBb+epdWlYVnmeffVYXXHCBevTooY8//lhz587Vtddeq//5n//xOn7dunW6+OKLVVpaqpSU//+bhn/yk59o165deu+997zO83aEJzU1lcID+FBNXb2+KKvQv3aX62hNrSQpMTpSdfUN+sHwQWd0FAiA7/BdWv/W3NGUk23YsEEjRozQ/fff37ju/PPPV48ePfSjH/1ITz75pOLimv+yve+fez/d+Xi32y23293KLQDQGcJCgjUkNVlDUpMlnfh/u6mkTO9t+Upriop1+TmZDicE4E/8vvDMmjVLN910U4tj0tPTva4fNWqUJGn79u1eC8931/qUl5crOTm5cX1FRYUSExPbmRiAE1wul4alpWhYWsrpBwPocvy+8MTHxys+Pr5dcwsLCyWpSZk5WUZGhpKSkpSXl9d4DU9NTY3WrFmjJ598sn2BAQCA37HmJHdBQYGeffZZbdq0ScXFxfrzn/+sO+64Q9dcc4369OnTOG7gwIFaunSppBN/Ec6ePVuPP/64li5dqi1btmjGjBnq1q2bfvzjHzu1KQAAoIP5/RGe1nK73VqyZInmz58vj8ejtLQ0zZw5Uw899FCTcUVFRaqsrGxcfuihh3Ts2DHdddddOnjwoEaOHKmVK1fyGTwAAFjEqndpOYUvDwUAoH348lAAAIAOQuEBAADWo/AAAADrUXgAAID1KDwAAMB6FB4AAGA9Cg8AALAehQcAAFiPwgMAAKxH4QEAANaj8AAAAOtReAAAgPUoPAAAwHoUHgAAYD0KDwAAsB6FBwAAWI/CAwAArEfhAQAA1qPwAAAA61F4AACA9Sg8AADAehQeAABgPQoPAACwHoUHAABYj8IDAACsR+EBAADWo/AAAADrUXgAAID1KDwAAMB6FB4AAGA9Cg8AALAehQcAAFiPwgMAAKxH4QEAANaj8AAAAOtReAAAgPUoPAAAwHoUHgAAYD0KDwAAsB6FBwAAWI/CAwAArEfhAQAA1qPwAAAA61F4AACA9Sg8AADAehQeAABgPWsKT35+vlwul9fbhg0bmp03Y8aMU8aPGjXKh8kBAEBnC3E6QEfJzs5WWVlZk3W//vWvtWrVKo0YMaLFuRMnTlROTk7jclhYWKdkBAAAzrCm8ISFhSkpKalxuba2VsuWLdOsWbPkcrlanOt2u5vMBQAAdrHmlNb3LVu2TPv27dOMGTNOOzY/P18JCQnq37+/Zs6cqYqKihbHezweVVVVNbkBAAD/5TLGGKdDdIarrrpKkrRixYoWxy1ZskTdu3dXWlqaiouL9etf/1p1dXXauHGj3G631znz5s3T/PnzT1lfWVmp6OjoMw8PAEAXUVVVpZiYmE7/Her3hae5cnGyDRs2NLlOZ8+ePUpLS9Of//xn/fCHP2zT85WVlSktLU2LFy/W9ddf73WMx+ORx+NpXK6qqlJqaiqFBwCANvJV4fH7a3hmzZqlm266qcUx6enpTZZzcnIUFxena665ps3Pl5ycrLS0NG3btq3ZMW63u9mjPwAAwP/4feGJj49XfHx8q8cbY5STk6NbbrlFoaGhbX6+/fv3a/fu3UpOTm7zXAAA4J+su2j5/fffV3FxsW677Tav9w8cOFBLly6VJB0+fFgPPvigCgoKtHPnTuXn52vKlCmKj4/XD37wA1/GBgAAncjvj/C01csvv6zs7Gydc845Xu8vKipSZWWlJCk4OFibN2/WokWLdOjQISUnJ2vs2LFasmSJoqKifBkbAAB0Ir+/aDkQ+OqCKwAAbOOr36HWndICAAD4PgoPAACwHoUHAABYj8IDAACsR+EBAADWo/AAAADrUXgAAID1KDwAAMB6FB4AAGA9Cg8AALAehQcAAFiPwgMAAKxH4QEAANaj8AAAAOtReAAAgPUoPAAAwHoUHgAAYD0KDwAAsB6FBwAAWI/CAwAArEfhAQAA1qPwAAAA61F4AACA9Sg8AADAehQeAABgPQoPAACwHoUHAABYj8IDAACsR+EBAADWo/AAAADrUXgAAID1KDwAAMB6FB4AAGA9Cg8AALAehQcAAFiPwgMAAKxH4QEAANaj8AAAAOtReAAAgPUoPAAAwHoUHgAAYD0KDwAAsB6FBwAAWI/CAwAArEfhAQAA1qPwAAAA61F4AACA9QKm8Dz22GPKzs5Wt27dFBsb63VMSUmJpkyZosjISMXHx+vee+9VTU1Ni4/r8Xh0zz33KD4+XpGRkbrmmmu0Z8+eTtgCAADglIApPDU1Nbrhhhv005/+1Ov99fX1mjx5so4cOaK1a9dq8eLFys3N1QMPPNDi486ePVtLly7V4sWLtXbtWh0+fFhXX3216uvrO2MzAACAA1zGGON0iLZ45ZVXNHv2bB06dKjJ+nfeeUdXX321du/erZSUFEnS4sWLNWPGDFVUVCg6OvqUx6qsrFSvXr302muvaerUqZKkvXv3KjU1VStWrNCECRNalamqqkoxMTGqrKz0+jwAAMA7X/0ODem0R/axgoICDR48uLHsSNKECRPk8Xi0ceNGjR079pQ5GzduVG1trcaPH9+4LiUlRYMHD9a6deuaLTwej0cej6dxubKyUtKJnQYAAFrvu9+dnX38xZrCU15ersTExCbrevToobCwMJWXlzc7JywsTD169GiyPjExsdk5krRgwQLNnz//lPWpqantSA4AAPbv36+YmJhOe3xHC8+8efO8FoeTbdiwQSNGjGjV47lcrlPWGWO8rm/J6ebMnTtXc+bMaVw+dOiQ0tLSVFJS0qk7y2lVVVVKTU3V7t27rT51x3bapatsp9R1tpXttEtlZaX69Omjnj17durzOFp4Zs2apZtuuqnFMenp6a16rKSkJH300UdN1h08eFC1tbWnHPk5eU5NTY0OHjzY5ChPRUWFsrOzm30ut9stt9t9yvqYmBirfyi/Ex0dzXZahO20T1fZVrbTLkFBnfs+KkcLT3x8vOLj4zvksbKysvTYY4+prKxMycnJkqSVK1fK7XZr+PDhXucMHz5coaGhysvL04033ihJKisr05YtW/TUU091SC4AAOC8gHlbeklJiTZt2qSSkhLV19dr06ZN2rRpkw4fPixJGj9+vM4991xNmzZNhYWF+sc//qEHH3xQM2fObGzGpaWlGjhwoD7++GNJJ47I3HbbbXrggQf0j3/8Q4WFhfrP//xPnXfeebriiisc21YAANCxAuai5f/3//6fXn311cblYcOGSZJWr16tMWPGKDg4WMuXL9ddd92liy++WBEREfrxj3+sp59+unFObW2tioqKdPTo0cZ1zz77rEJCQnTjjTfq2LFjGjdunF555RUFBwe3Opvb7dYjjzzi9TSXTdhOu7Cd9ukq28p22sVX2xlwn8MDAADQVgFzSgsAAKC9KDwAAMB6FB4AAGA9Cg8AALAehacVHnvsMWVnZ6tbt26KjY31OqakpERTpkxRZGSk4uPjde+996qmpqbFx/V4PLrnnnsUHx+vyMhIXXPNNdqzZ08nbEH75Ofny+Vyeb1t2LCh2XkzZsw4ZfyoUaN8mLzt0tPTT8n88MMPtzjHGKN58+YpJSVFERERGjNmjLZu3eqjxG23c+dO3XbbbcrIyFBERIQyMzP1yCOPnPbnNBD25+9//3tlZGQoPDxcw4cP1z//+c8Wx69Zs0bDhw9XeHi4zj77bL344os+Sto+CxYs0IUXXqioqCglJCTouuuuU1FRUYtzmvv/++WXX/oodfvMmzfvlMxJSUktzgm0/Sl5f81xuVy6++67vY4PlP35wQcfaMqUKUpJSZHL5dLbb7/d5P72vm7m5ubq3HPPldvt1rnnnqulS5e2ORuFpxVqamp0ww036Kc//anX++vr6zV58mQdOXJEa9eu1eLFi5Wbm6sHHnigxcedPXu2li5dqsWLF2vt2rU6fPiwrr76atXX13fGZrRZdna2ysrKmtxuv/12paenn/brPiZOnNhk3ooVK3yUuv0effTRJpl/9atftTj+qaee0jPPPKPnnntOGzZsUFJSkq688kpVV1f7KHHbfPnll2poaNAf/vAHbd26Vc8++6xefPFF/eIXvzjtXH/en0uWLNHs2bP1y1/+UoWFhRo9erQmTZqkkpISr+OLi4t11VVXafTo0SosLNQvfvEL3XvvvcrNzfVx8tZbs2aN7r77bq1fv155eXmqq6vT+PHjdeTIkdPOLSoqarLv+vXr54PEZ2bQoEFNMm/evLnZsYG4P6UTX5t08jbm5eVJkm644YYW5/n7/jxy5IiGDBmi5557zuv97XndLCgo0NSpUzVt2jR99tlnmjZtmm688cZTvl3htAxaLScnx8TExJyyfsWKFSYoKMiUlpY2rnvjjTeM2+02lZWVXh/r0KFDJjQ01CxevLhxXWlpqQkKCjLvvvtuh2fvCDU1NSYhIcE8+uijLY6bPn26ufbaa30TqoOkpaWZZ599ttXjGxoaTFJSknniiSca1x0/ftzExMSYF198sRMSdo6nnnrKZGRktDjG3/fnRRddZO68884m6wYOHGgefvhhr+MfeughM3DgwCbr7rjjDjNq1KhOy9jRKioqjCSzZs2aZsesXr3aSDIHDx70XbAO8Mgjj5ghQ4a0erwN+9MYY+677z6TmZlpGhoavN4fiPtTklm6dGnjcntfN2+88UYzceLEJusmTJhgbrrppjbl4QhPBygoKNDgwYOVkpLSuG7ChAnyeDzauHGj1zkbN25UbW2txo8f37guJSVFgwcP1rp16zo9c3ssW7ZM+/bt04wZM047Nj8/XwkJCerfv79mzpypioqKzg94hp588knFxcVp6NCheuyxx1o81VNcXKzy8vIm+8/tduuyyy7z2/3nTWVlZau+sM9f92dNTY02btzYZD9IJz55vbn9UFBQcMr4CRMm6JNPPlFtbW2nZe1IlZWVktSqfTds2DAlJydr3LhxWr16dWdH6xDbtm1TSkqKMjIydNNNN2nHjh3NjrVhf9bU1Oj111/Xrbfeetovuw7E/fmd9r5uNreP2/paS+HpAOXl5ad8QWmPHj0UFham8vLyZueEhYU1+dJSSUpMTGx2jtNefvllTZgwQampqS2OmzRpkv7v//5P77//vn77299qw4YNuvzyy+XxeHyUtO3uu+8+LV68WKtXr9asWbO0cOFC3XXXXc2O/24ffX+/+/P++76vv/5a//3f/60777yzxXH+vD/37dun+vr6Nu0Hb/9fExMTVVdXp3379nVa1o5ijNGcOXN0ySWXaPDgwc2OS05O1ksvvaTc3Fy99dZbGjBggMaNG6cPPvjAh2nbbuTIkVq0aJHee+89/fGPf1R5ebmys7O1f/9+r+MDfX9K0ttvv61Dhw61+MdkoO7Pk7X3dbO5fdzW19qA+WqJjjZv3jzNnz+/xTEbNmw47bUq3/HWyo0xp23rHTGnrdqz7Xv27NF7772nP//5z6d9/KlTpzb+e/DgwRoxYoTS0tK0fPlyXX/99e0P3kZt2c7777+/cd3555+vHj166Ec/+lHjUZ/mfH9f+WL/fV979ufevXs1ceJE3XDDDbr99ttbnOsv+7Mlbd0P3sZ7W++PZs2apX/9619au3Zti+MGDBigAQMGNC5nZWVp9+7devrpp3XppZd2dsx2mzRpUuO/zzvvPGVlZSkzM1Ovvvqq5syZ43VOIO9P6cQfk5MmTWpyluD7AnV/etOe182OeK3tsoVn1qxZuummm1ock56e3qrHSkpKOuXiqYMHD6q2tvaUVnrynJqaGh08eLDJUZ6KigplZ2e36nnbqz3bnpOTo7i4OF1zzTVtfr7k5GSlpaVp27ZtbZ57Js5kH3/3LqTt27d7LTzfvWukvLxcycnJjesrKiqa3eedpa3buXfvXo0dO1ZZWVl66aWX2vx8Tu1Pb+Lj4xUcHHzKX3ot7YekpCSv40NCQlost/7gnnvu0bJly/TBBx+od+/ebZ4/atQovf76652QrPNERkbqvPPOa/bnLZD3pyTt2rVLq1at0ltvvdXmuYG2P9v7utncPm7ra22XLTzx8fGKj4/vkMfKysrSY489prKyssaduHLlSrndbg0fPtzrnOHDhys0NFR5eXm68cYbJUllZWXasmWLnnrqqQ7J1Zy2brsxRjk5ObrlllsUGhra5ufbv3+/du/e3eQH3BfOZB8XFhZKUrOZMzIylJSUpLy8vMYvsq2pqdGaNWv05JNPti9wO7VlO0tLSzV27FgNHz5cOTk5Cgpq+1ltp/anN2FhYRo+fLjy8vL0gx/8oHF9Xl6err32Wq9zsrKy9Le//a3JupUrV2rEiBHt+vn2BWOM7rnnHi1dulT5+fnKyMho1+MUFhb6xX5rC4/Hoy+++EKjR4/2en8g7s+T5eTkKCEhQZMnT27z3EDbn+193czKylJeXl6TI/ErV65s+8GBNl3i3EXt2rXLFBYWmvnz55vu3bubwsJCU1hYaKqrq40xxtTV1ZnBgwebcePGmU8//dSsWrXK9O7d28yaNavxMfbs2WMGDBhgPvroo8Z1d955p+ndu7dZtWqV+fTTT83ll19uhgwZYurq6ny+jS1ZtWqVkWQ+//xzr/cPGDDAvPXWW8YYY6qrq80DDzxg1q1bZ4qLi83q1atNVlaWOeuss0xVVZUvY7faunXrzDPPPGMKCwvNjh07zJIlS0xKSoq55pprmow7eTuNMeaJJ54wMTEx5q233jKbN282N998s0lOTvbb7SwtLTV9+/Y1l19+udmzZ48pKytrvJ0s0Pbn4sWLTWhoqHn55ZfN559/bmbPnm0iIyPNzp07jTHGPPzww2batGmN43fs2GG6detm7r//fvP555+bl19+2YSGhpo333zTqU04rZ/+9KcmJibG5OfnN9lvR48ebRzz/e189tlnzdKlS81XX31ltmzZYh5++GEjyeTm5jqxCa32wAMPmPz8fLNjxw6zfv16c/XVV5uoqCir9ud36uvrTZ8+fczPf/7zU+4L1P1ZXV3d+DtSUuNr665du4wxrXvdnDZtWpN3WX744YcmODjYPPHEE+aLL74wTzzxhAkJCTHr169vUzYKTytMnz7dSDrltnr16sYxu3btMpMnTzYRERGmZ8+eZtasWeb48eON9xcXF58y59ixY2bWrFmmZ8+eJiIiwlx99dWmpKTEh1vWOjfffLPJzs5u9n5JJicnxxhjzNGjR8348eNNr169TGhoqOnTp4+ZPn26X27XdzZu3GhGjhxpYmJiTHh4uBkwYIB55JFHzJEjR5qMO3k7jTnxFstHHnnEJCUlGbfbbS699FKzefNmH6dvvZycHK8/x9//uycQ9+fzzz9v0tLSTFhYmLnggguavF17+vTp5rLLLmsyPj8/3wwbNsyEhYWZ9PR088ILL/g4cds0t99O/nn8/nY++eSTJjMz04SHh5sePXqYSy65xCxfvtz34dto6tSpJjk52YSGhpqUlBRz/fXXm61btzbeb8P+/M57771nJJmioqJT7gvU/fnd2+e/f5s+fboxpnWvm5dddlnj+O/85S9/MQMGDDChoaFm4MCB7Sp6LmP+fXUXAACApXhbOgAAsB6FBwAAWI/CAwAArEfhAQAA1qPwAAAA61F4AACA9Sg8AADAehQeAABgPQoPAACwHoUHAABYj8IDAACsR+EBYKVvv/1WSUlJevzxxxvXffTRRwoLC9PKlSsdTAbACXx5KABrrVixQtddd53WrVungQMHatiwYZo8ebIWLlzodDQAPkbhAWC1u+++W6tWrdKFF16ozz77TBs2bFB4eLjTsQD4GIUHgNWOHTumwYMHa/fu3frkk090/vnnOx0JgAO4hgeA1Xbs2KG9e/eqoaFBu3btcjoOAIdwhAeAtWpqanTRRRdp6NChGjhwoJ555hlt3rxZiYmJTkcD4GMUHgDW+tnPfqY333xTn332mbp3766xY8cqKipKf//7352OBsDHOKUFwEr5+flauHChXnvtNUVHRysoKEivvfaa1q5dqxdeeMHpeAB8jCM8AADAehzhAQAA1qPwAAAA61F4AACA9Sg8AADAehQeAABgPQoPAACwHoUHAABYj8IDAACsR+EBAADWo/AAAADrUXgAAID1/j/yrhMttFwKUQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "angles = np.angle(gamma, deg=True) / 2.\n",
+    "lengths = np.abs(gamma)\n",
+    "\n",
+    "# Create whisker plot\n",
+    "plt.figure(figsize=(6, 6))\n",
+    "plt.quiver(x, y, np.cos(np.deg2rad(angles)), np.sin(np.deg2rad(angles)), lengths,\n",
+    "           angles='xy', scale_units='xy', scale=1, width=0.001)\n",
+    "plt.xlim(-10, 10)\n",
+    "plt.ylim(-10, 10)\n",
+    "plt.xlabel('x')\n",
+    "plt.ylabel('y')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b70c050a-b590-4192-81da-3b747dd07d27",
+   "metadata": {},
+   "source": [
+    "# step2: make sure the amplitude is correct"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "d95fa97a-1415-44b4-82cf-3cae3106e2a1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[(1., 0.)]\n",
+      "[(3., 0.)]\n",
+      "[(9., 0.)]\n",
+      "[(27., 0.)]\n",
+      "[(81., 0.)]\n",
+      "[(243., 0.)]\n",
+      "[(729., 0.)]\n",
+      "[(2187., 0.)]\n",
+      "[(6561., 0.)]\n",
+      "[(19683., 0.)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "n_gal = 1\n",
+    "d_array = []\n",
+    "g_array = []\n",
+    "for i in range(10):\n",
+    "    shifts = np.zeros(n_gal, dtype=[('dx', 'f8'), ('dy', 'f8')])\n",
+    "    shifts['dx'][0] = 3.0 ** i \n",
+    "    shifts['dy'][0] = 0.\n",
+    "    print(shifts)\n",
+    "    # get the shear\n",
+    "    g1, g2 = shear_obj.get_shear(z_source, shifts)\n",
+    "    gabs = np.abs(g1 + 1j * g2)\n",
+    "    d_array.append(3 ** i)\n",
+    "    g_array.append(gabs)\n",
+    "d_array = np.array(d_array)\n",
+    "g_array = np.array(g_array)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "4f6eb0d7-2809-4b37-8357-1e443e900273",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'shear')"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(d_array / 3600., g_array)\n",
+    "plt.xscale(\"log\")\n",
+    "plt.yscale(\"log\")\n",
+    "plt.xlabel(\"separation [degree]\")\n",
+    "plt.ylabel(\"shear\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "50e7308c-effd-43d5-adeb-708a4290b38f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "wl_shear_sim",
+   "language": "python",
+   "name": "wl_shear_sim"
+  },
+  "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": 5
+}

From 997a7b0b1afeb67c4c91bce5eafa8b0f233caf4b Mon Sep 17 00:00:00 2001
From: Xiangchong Li <mr.superonion@hotmail.com>
Date: Mon, 24 Jul 2023 15:05:38 -0700
Subject: [PATCH 04/30] put the import astropy into the function of get_shear()

---
 descwl_shear_sims/shear.py      | 5 +----
 examples/example_NFWShear.ipynb | 8 --------
 2 files changed, 1 insertion(+), 12 deletions(-)

diff --git a/descwl_shear_sims/shear.py b/descwl_shear_sims/shear.py
index 41e54bb3..f5ae133b 100644
--- a/descwl_shear_sims/shear.py
+++ b/descwl_shear_sims/shear.py
@@ -1,8 +1,4 @@
-import clmm
-import galsim
 import numpy as np
-from astropy.coordinates import SkyCoord
-
 # maximum kappa allowed
 # values greater than it will be clipped
 kappa_max = 0.6
@@ -56,6 +52,7 @@ def __init__(self, cluster_obj, z_cl, ra_cl=0., dec_cl=0.):
 
 
     def get_shear(self, z_gals, shifts):
+        from astropy.coordinates import SkyCoord
         z_cl = self.z_cl
         # Create the SkyCoord objects
         coord_cl = SkyCoord(self.ra_cl, self.dec_cl, unit="arcsec")
diff --git a/examples/example_NFWShear.ipynb b/examples/example_NFWShear.ipynb
index 65bba7c3..8b30e9a4 100644
--- a/examples/example_NFWShear.ipynb
+++ b/examples/example_NFWShear.ipynb
@@ -180,14 +180,6 @@
     "plt.xlabel(\"separation [degree]\")\n",
     "plt.ylabel(\"shear\")"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "50e7308c-effd-43d5-adeb-708a4290b38f",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

From da227e6de899dad2b4efc2b8a8a1c55aca38ab6d Mon Sep 17 00:00:00 2001
From: Xiangchong Li <mr.superonion@hotmail.com>
Date: Tue, 25 Jul 2023 03:06:21 -0700
Subject: [PATCH 05/30] change to use galsim.PositionD object and Shear object;
 set the kappa_max=0.8

---
 .virtual_documents/Untitled.ipynb             |  1 -
 .../examples/example_NFWShear.ipynb           | 72 --------------
 descwl_shear_sims/shear.py                    | 71 ++++++++++----
 descwl_shear_sims/shifts.py                   |  1 +
 examples/example_NFWShear.ipynb               | 97 ++++++++++---------
 5 files changed, 106 insertions(+), 136 deletions(-)
 delete mode 100644 .virtual_documents/Untitled.ipynb
 delete mode 100644 .virtual_documents/examples/example_NFWShear.ipynb

diff --git a/.virtual_documents/Untitled.ipynb b/.virtual_documents/Untitled.ipynb
deleted file mode 100644
index 8b137891..00000000
--- a/.virtual_documents/Untitled.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/.virtual_documents/examples/example_NFWShear.ipynb b/.virtual_documents/examples/example_NFWShear.ipynb
deleted file mode 100644
index 2fda6254..00000000
--- a/.virtual_documents/examples/example_NFWShear.ipynb
+++ /dev/null
@@ -1,72 +0,0 @@
-get_ipython().run_line_magic("matplotlib", " inline")
-get_ipython().run_line_magic("reload_ext", " autoreload")
-get_ipython().run_line_magic("autoreload", " 2")
-
-import clmm
-import numpy as np
-import descwl_shear_sims as dss
-import matplotlib.pyplot as plt
-
-
-cosmo = clmm.Cosmology(H0=70.0, Omega_dm0=0.27 - 0.045, Omega_b0=0.045, Omega_k0=0.0)
-halo = clmm.Modeling(massdef="mean", delta_mdef=200, halo_profile_model="nfw")
-halo.set_cosmo(cosmo)
-halo.set_concentration(4)
-halo.set_mass(1.0e15)
-z_cl = 1.0
-# source properties
-z_source = 2.0  # all sources in the same plane
-
-shear_obj = dss.shear.ShearNFW(halo, z_cl)
-
-
-# generate positions
-n_gal = 20
-theta = np.linspace(0, 360, n_gal) / n_gal
-x = np.cos(theta) * 5
-y = np.sin(theta) * 5
-shifts = np.zeros(n_gal, dtype=[('dx', 'f8'), ('dy', 'f8')])
-shifts['dx'] = x
-shifts['dy'] = y
-
-# get the shear
-g1, g2 = shear_obj.get_shear(z_source, shifts)
-gamma = g1 + 1j * g2
-
-
-angles = np.angle(gamma, deg=True) / 2.
-lengths = np.abs(gamma)
-
-# Create whisker plot
-plt.figure(figsize=(6, 6))
-plt.quiver(x, y, np.cos(np.deg2rad(angles)), np.sin(np.deg2rad(angles)), lengths,
-           angles='xy', scale_units='xy', scale=1, width=0.001)
-plt.xlim(-10, 10)
-plt.ylim(-10, 10)
-plt.xlabel('x')
-plt.ylabel('y')
-plt.show()
-
-
-n_gal = 1
-d_array = []
-g_array = []
-for i in range(10):
-    shifts = np.zeros(n_gal, dtype=[('dx', 'f8'), ('dy', 'f8')])
-    shifts['dx'][0] = 3.0 ** i 
-    shifts['dy'][0] = 0.
-    print(shifts)
-    # get the shear
-    g1, g2 = shear_obj.get_shear(z_source, shifts)
-    gabs = np.abs(g1 + 1j * g2)
-    d_array.append(3 ** i)
-    g_array.append(gabs)
-d_array = np.array(d_array)
-g_array = np.array(g_array)
-
-
-plt.plot(d_array / 3600., g_array)
-plt.xscale("log")
-plt.yscale("log")
-plt.xlabel("separation [degree]")
-plt.ylabel("shear")
diff --git a/descwl_shear_sims/shear.py b/descwl_shear_sims/shear.py
index f5ae133b..02204c2e 100644
--- a/descwl_shear_sims/shear.py
+++ b/descwl_shear_sims/shear.py
@@ -1,33 +1,34 @@
+import galsim
 import numpy as np
 # maximum kappa allowed
 # values greater than it will be clipped
-kappa_max = 0.6
-
-# factor from arcsec to radians
-arcsec2rad = 1./3600./180.*np.pi
-
+kappa_max = 0.8
 
 def get_shear(
     *,
     shear_type="constant",
     z_gals=None,
-    shifts=None,
+    shift=None,
     **kwargs,
-    ):
+):
     """
     A shear wrapper to return g1 and g2 with different shear type
 
     Parameters
     ----------
-    cluster_obj:    cluster object
+    shear_type:     the constant shear or shear from NFW halos
+    z_gals:         redshifts of galaxies
+    shift:          Galsim positionD shift
     """
     if shear_type == "constant":
         shear_obj = ShearConstant(**kwargs)
+    elif shear_type =="redshift":
+        shear_obj = ShearRedshift(**kwargs)
     elif shear_type == "NFW":
         shear_obj = ShearNFW(**kwargs)
     else:
         raise ValueError("Do not support the shear type: %s" % shear_type)
-    return shear_obj.get_shear(z_gals, shifts)
+    return shear_obj.get_shear(z_gals, shift)
 
 
 class ShearNFW(object):
@@ -50,28 +51,59 @@ def __init__(self, cluster_obj, z_cl, ra_cl=0., dec_cl=0.):
         self.cosmo = cluster_obj.cosmo
         return
 
+    def get_shear(self, z_gals, shift):
+        """
+        A shear wrapper to return g1 and g2 with different shear type
+
+        Parameters
+        ----------
+        z_gals (float):             redshifts of galaxies
+        shift (galsim.positionD):  Galsim positionD shift [arcsec]
 
-    def get_shear(self, z_gals, shifts):
+        Returns
+        ---------
+        shear (galsim.Shear)        shear distortion on the galaxy
+        """
         from astropy.coordinates import SkyCoord
         z_cl = self.z_cl
         # Create the SkyCoord objects
         coord_cl = SkyCoord(self.ra_cl, self.dec_cl, unit="arcsec")
-        coord_gals = SkyCoord(shifts["dx"], shifts["dy"], unit="arcsec")
+        coord_gals = SkyCoord(shift.x, shift.y, unit="arcsec")
         # Calculate the separation
         sep = coord_cl.separation(coord_gals).radian
+        # What is the unit of r3d?
         r3d = self.cosmo.rad2mpc(sep, self.z_cl)
+        # position angle
         phi = coord_cl.position_angle(coord_gals).radian
 
         # TODO: confirm whether the units is Mpc/h or Mpc?
-        DeltaSigma = self.cobj.eval_excess_surface_density(r3d, z_cl)
         gammat = self.cobj.eval_tangential_shear(r3d, z_cl, z_gals)
-        kappa = self.cobj.eval_convergence(r3d, z_cl, z_gals)
-        gamma1 = gammat * np.cos(2. * phi)
-        gamma2 = -gammat * np.sin(2. * phi)
-        return gamma1/(1-kappa), gamma2/(1-kappa)
+        kappa0 = self.cobj.eval_convergence(r3d, z_cl, z_gals)
+        # we are forcing kappa to be less than kappa_max
+        # and scale gamma by the same ratio
+        kappa = min(kappa0, kappa_max)
+        ratio = kappa / kappa0
+        gamma1 = gammat * np.cos(2. * phi) * ratio
+        gamma2 = -gammat * np.sin(2. * phi) * ratio
+        shear = galsim.Shear(g1=gamma1/(1-kappa), g2=gamma2/(1-kappa))
+        return shear
 
 
 class ShearConstant(object):
+    """
+    Constant shear along every redshift slice
+    """
+    def __init__(self, g1, g2):
+        self.g1 = g1
+        self.g2 = g2
+        return
+
+    def get_shear(self):
+        shear = galsim.Shear(g1= self.g1, g2=self.g2)
+        return shear
+
+
+class ShearRedshift(object):
     """
     Constant shear along every redshift slice
     """
@@ -90,10 +122,11 @@ def __init__(self, mode="0000", g_dist="g1"):
         self.g_dist = g_dist
         return
 
-    def get_shear(self, z_gals=None, shifts=None):
+    def get_shear(self, z_gals=None, shift=None):
         if z_gals is None:
             assert self.mode == "0" * self.nz_bins
-        z_gal_bins = z_gals // self.dz_bin
+        #z_gal_bins = z_gals // self.dz_bin
         gamma1, gamma2 = (None, None)
         # TODO: Finish implementing the z-dependent shear
-        return gamma1, gamma2
+        shear = galsim.Shear(g1= gamma1, g2=gamma2)
+        return shear
diff --git a/descwl_shear_sims/shifts.py b/descwl_shear_sims/shifts.py
index 45ce3217..4c4d5fed 100644
--- a/descwl_shear_sims/shifts.py
+++ b/descwl_shear_sims/shifts.py
@@ -57,6 +57,7 @@ def get_shifts(
         elif layout == 'random':
             # area covered by objects
             if nobj is None:
+                # in units of square arcmin
                 area = ((coadd_dim - 2*buff)*SCALE/60)**2
                 nobj_mean = max(area * RANDOM_DENSITY, 1)
                 nobj = rng.poisson(nobj_mean)
diff --git a/examples/example_NFWShear.ipynb b/examples/example_NFWShear.ipynb
index 8b30e9a4..0f4ab029 100644
--- a/examples/example_NFWShear.ipynb
+++ b/examples/example_NFWShear.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 1,
    "id": "d12a992b-6669-4675-b68a-b705ff09f693",
    "metadata": {},
    "outputs": [],
@@ -12,6 +12,7 @@
     "%autoreload 2\n",
     "\n",
     "import clmm\n",
+    "import galsim\n",
     "import numpy as np\n",
     "import descwl_shear_sims as dss\n",
     "import matplotlib.pyplot as plt"
@@ -19,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 2,
    "id": "707e17ec-2aa0-4470-b986-fefe11e4be47",
    "metadata": {},
    "outputs": [],
@@ -46,34 +47,53 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "id": "d348067a-7220-4bdb-a0c5-875b5cdb8213",
+   "execution_count": 4,
+   "id": "913c7ae0-8b39-4a3b-9e09-c40262170e0c",
    "metadata": {},
    "outputs": [],
    "source": [
     "# generate positions\n",
+    "radius = 50 # arcsec\n",
     "n_gal = 20\n",
     "theta = np.linspace(0, 360, n_gal) / n_gal\n",
-    "x = np.cos(theta) * 5\n",
-    "y = np.sin(theta) * 5\n",
+    "x = np.cos(theta) * radius\n",
+    "y = np.sin(theta) * radius\n",
     "shifts = np.zeros(n_gal, dtype=[('dx', 'f8'), ('dy', 'f8')])\n",
     "shifts['dx'] = x\n",
     "shifts['dy'] = y\n",
     "\n",
+    "shift_list = []\n",
+    "for ss in shifts:\n",
+    "    shift_list.append(galsim.PositionD(ss['dx'], ss['dy']))\n",
+    "\n",
     "# get the shear\n",
-    "g1, g2 = shear_obj.get_shear(z_source, shifts)\n",
-    "gamma = g1 + 1j * g2"
+    "shear_list = []\n",
+    "for ss in shift_list:\n",
+    "    shear_list.append(shear_obj.get_shear(z_source, ss))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 5,
+   "id": "3d6955d3-7c6d-4d68-a81f-fa8f2003c06f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gamma = []\n",
+    "for ss in shear_list:\n",
+    "    gamma.append(ss.g1 + 1j * ss.g2) \n",
+    "gamma = np.array(gamma)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
    "id": "8c203fae-f689-4405-91be-056aa84a3593",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 600x600 with 1 Axes>"
       ]
@@ -84,14 +104,12 @@
    ],
    "source": [
     "angles = np.angle(gamma, deg=True) / 2.\n",
-    "lengths = np.abs(gamma)\n",
+    "lengths = np.abs(gamma) * 100.\n",
     "\n",
     "# Create whisker plot\n",
     "plt.figure(figsize=(6, 6))\n",
-    "plt.quiver(x, y, np.cos(np.deg2rad(angles)), np.sin(np.deg2rad(angles)), lengths,\n",
-    "           angles='xy', scale_units='xy', scale=1, width=0.001)\n",
-    "plt.xlim(-10, 10)\n",
-    "plt.ylim(-10, 10)\n",
+    "plt.quiver(x, y, np.cos(np.deg2rad(angles)), np.sin(np.deg2rad(angles)),\n",
+    "           color=\"black\", headaxislength=0, headlength=0, headwidth=1, pivot = 'middle')\n",
     "plt.xlabel('x')\n",
     "plt.ylabel('y')\n",
     "plt.show()"
@@ -107,38 +125,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 7,
    "id": "d95fa97a-1415-44b4-82cf-3cae3106e2a1",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[(1., 0.)]\n",
-      "[(3., 0.)]\n",
-      "[(9., 0.)]\n",
-      "[(27., 0.)]\n",
-      "[(81., 0.)]\n",
-      "[(243., 0.)]\n",
-      "[(729., 0.)]\n",
-      "[(2187., 0.)]\n",
-      "[(6561., 0.)]\n",
-      "[(19683., 0.)]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "n_gal = 1\n",
     "d_array = []\n",
     "g_array = []\n",
     "for i in range(10):\n",
-    "    shifts = np.zeros(n_gal, dtype=[('dx', 'f8'), ('dy', 'f8')])\n",
-    "    shifts['dx'][0] = 3.0 ** i \n",
-    "    shifts['dy'][0] = 0.\n",
-    "    print(shifts)\n",
+    "    position = galsim.PositionD(3.0 ** i, 0.)\n",
     "    # get the shear\n",
-    "    g1, g2 = shear_obj.get_shear(z_source, shifts)\n",
+    "    shear = shear_obj.get_shear(z_source, position)\n",
+    "    g1 = shear.g1\n",
+    "    g2 = shear.g2\n",
     "    gabs = np.abs(g1 + 1j * g2)\n",
     "    d_array.append(3 ** i)\n",
     "    g_array.append(gabs)\n",
@@ -148,23 +148,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 9,
    "id": "4f6eb0d7-2809-4b37-8357-1e443e900273",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Text(0, 0.5, 'shear')"
+       "Text(0, 0.5, '$|g|$')"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -178,8 +178,17 @@
     "plt.xscale(\"log\")\n",
     "plt.yscale(\"log\")\n",
     "plt.xlabel(\"separation [degree]\")\n",
-    "plt.ylabel(\"shear\")"
+    "plt.ylabel(r\"$|g|$\")\n",
+    "# We set kappa_max to 0.8"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0c43422d-d816-4c98-8bb9-8671bbdcd1a1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

From e0233a65ca1dc96720df9407e86da0ba9c3d465b Mon Sep 17 00:00:00 2001
From: Xiangchong Li <mr.superonion@hotmail.com>
Date: Tue, 25 Jul 2023 17:30:00 -0700
Subject: [PATCH 06/30] finish preliminary implementation

---
 .virtual_documents/examples/Untitled.ipynb    |  50 ++
 .../examples/example_NFWShear.ipynb           | 118 +++++
 .../examples/example_NFWShear_imsim.ipynb     |  66 +++
 descwl_shear_sims/__init__.py                 |   1 +
 descwl_shear_sims/galaxies.py                 |   9 +-
 descwl_shear_sims/objlists.py                 |   3 +-
 descwl_shear_sims/shear.py                    | 118 ++---
 descwl_shear_sims/sim.py                      |  41 +-
 .../tests/test_correlated_noise.py            |   7 +-
 descwl_shear_sims/tests/test_galaxies.py      |   6 +-
 descwl_shear_sims/tests/test_nfw_shear.py     |  47 --
 descwl_shear_sims/tests/test_nonzero_sky.py   |   6 +-
 descwl_shear_sims/tests/test_pairs.py         |   7 +-
 descwl_shear_sims/tests/test_sim.py           |  37 +-
 descwl_shear_sims/tests/test_sim_center.py    |   6 +-
 .../tests/test_star_masks_and_bleeds.py       |   8 +-
 examples/example_NFWShear.ipynb               | 499 +++++++++++++++++-
 examples/example_NFWShear_imsim.ipynb         | 137 +++++
 shear_meas_tests/test_shear_meas.py           |  23 +-
 .../test_shear_meas_ringtest_metacal.py       |   5 +-
 20 files changed, 1011 insertions(+), 183 deletions(-)
 create mode 100644 .virtual_documents/examples/Untitled.ipynb
 create mode 100644 .virtual_documents/examples/example_NFWShear.ipynb
 create mode 100644 .virtual_documents/examples/example_NFWShear_imsim.ipynb
 delete mode 100644 descwl_shear_sims/tests/test_nfw_shear.py
 create mode 100644 examples/example_NFWShear_imsim.ipynb

diff --git a/.virtual_documents/examples/Untitled.ipynb b/.virtual_documents/examples/Untitled.ipynb
new file mode 100644
index 00000000..63ae311a
--- /dev/null
+++ b/.virtual_documents/examples/Untitled.ipynb
@@ -0,0 +1,50 @@
+import clmm
+import numpy as np
+
+from descwl_shear_sims.galaxies import make_galaxy_catalog
+from descwl_shear_sims.galaxies import WLDeblendGalaxyCatalog
+from descwl_shear_sims.psfs import make_fixed_psf
+from descwl_shear_sims.surveys import get_survey
+
+from descwl_shear_sims.sim import make_sim
+from descwl_shear_sims.shear import ShearNFW
+
+cosmo = clmm.Cosmology(H0=70.0, Omega_dm0=0.27 - 0.045, Omega_b0=0.045, Omega_k0=0.0)
+halo = clmm.Modeling(massdef="mean", delta_mdef=200, halo_profile_model="nfw")
+halo.set_cosmo(cosmo)
+halo.set_concentration(4)
+halo.set_mass(1.0e15)
+z_cl = 1.0
+# source properties
+z_source = 2.0  # all sources in the same plane
+
+shear_obj = ShearNFW(halo, z_cl)
+
+seed = 74321
+rng = np.random.RandomState(seed)
+
+coadd_dim = 200
+se_dim = 200
+psf_dim = 41
+
+galaxy_catalog = WLDeblendGalaxyCatalog(
+    rng=rng,
+    coadd_dim=coadd_dim,
+    buff=2,
+    layout="random_disk",
+)
+psf = make_fixed_psf(psf_type="gauss")
+band_list = ["r"]
+
+sim_data = make_sim(
+    rng=rng,
+    galaxy_catalog=galaxy_catalog,
+    coadd_dim=coadd_dim,
+    shear_obj=shear_obj,
+    psf=psf,
+    noise_factor=0.0,
+    bands=band_list,
+)
+
+
+
diff --git a/.virtual_documents/examples/example_NFWShear.ipynb b/.virtual_documents/examples/example_NFWShear.ipynb
new file mode 100644
index 00000000..ab8cccb9
--- /dev/null
+++ b/.virtual_documents/examples/example_NFWShear.ipynb
@@ -0,0 +1,118 @@
+get_ipython().run_line_magic("matplotlib", " inline")
+get_ipython().run_line_magic("reload_ext", " autoreload")
+get_ipython().run_line_magic("autoreload", " 2")
+
+import clmm
+import galsim
+import numpy as np
+import descwl_shear_sims as dss
+import matplotlib.pyplot as plt
+
+from descwl_shear_sims.galaxies import WLDeblendGalaxyCatalog
+from descwl_shear_sims.objlists import get_objlist
+from descwl_shear_sims.surveys import get_survey
+
+
+cosmo = clmm.Cosmology(H0=70.0, Omega_dm0=0.27 - 0.045, Omega_b0=0.045, Omega_k0=0.0)
+halo = clmm.Modeling(massdef="mean", delta_mdef=200, halo_profile_model="nfw")
+halo.set_cosmo(cosmo)
+halo.set_concentration(4)
+halo.set_mass(1.0e15)
+z_cl = 1.0
+# source properties
+z_source = 2.0  # all sources in the same plane
+
+shear_obj = dss.shear.ShearNFW(halo, z_cl)
+
+
+# generate positions
+radius = 50 # arcsec
+n_gal = 20
+theta = np.linspace(0, 360, n_gal) / n_gal
+x = np.cos(theta) * radius
+y = np.sin(theta) * radius
+shifts = np.zeros(n_gal, dtype=[('dx', 'f8'), ('dy', 'f8')])
+shifts['dx'] = x
+shifts['dy'] = y
+
+shift_list = []
+for ss in shifts:
+    shift_list.append(galsim.PositionD(ss['dx'], ss['dy']))
+
+# get the shear
+shear_list = []
+for ss in shift_list:
+    shear_list.append(shear_obj.get_shear(z_source, ss))
+
+
+gamma = []
+for ss in shear_list:
+    gamma.append(ss.g1 + 1j * ss.g2) 
+gamma = np.array(gamma)
+
+
+angles = np.angle(gamma, deg=True) / 2.
+lengths = np.abs(gamma) * 100.
+
+# Create whisker plot
+plt.figure(figsize=(6, 6))
+plt.quiver(x, y, np.cos(np.deg2rad(angles)), np.sin(np.deg2rad(angles)),
+           color="black", headaxislength=0, headlength=0, headwidth=1, pivot = 'middle')
+plt.xlabel('x')
+plt.ylabel('y')
+plt.show()
+
+
+n_gal = 1
+d_array = []
+g_array = []
+for i in range(10):
+    position = galsim.PositionD(3.0 ** i, 0.)
+    # get the shear
+    shear = shear_obj.get_shear(z_source, position)
+    g1 = shear.g1
+    g2 = shear.g2
+    gabs = np.abs(g1 + 1j * g2)
+    d_array.append(3 ** i)
+    g_array.append(gabs)
+d_array = np.array(d_array)
+g_array = np.array(g_array)
+
+
+plt.plot(d_array / 3600., g_array)
+plt.xscale("log")
+plt.yscale("log")
+plt.xlabel("separation [degree]")
+plt.ylabel(r"$|g|$")
+# We set kappa_max to 0.8
+
+
+band="r"
+rng = np.random.RandomState(1)
+coadd_dim = 500
+buff = 50
+# galaxy catalog; you can make your own
+galaxy_catalog = WLDeblendGalaxyCatalog(
+    rng=rng,
+    coadd_dim=coadd_dim,
+    buff=buff,
+    layout="random",
+)
+
+survey = get_survey(gal_type=galaxy_catalog.gal_type, band=band)
+noise_for_gsparams = survey.noise
+lists = get_objlist(
+    galaxy_catalog=galaxy_catalog,
+    survey=survey,
+    star_catalog=None,
+    noise=noise_for_gsparams,
+)
+
+
+lists.keys()
+
+
+lists["redshifts"]
+
+
+
diff --git a/.virtual_documents/examples/example_NFWShear_imsim.ipynb b/.virtual_documents/examples/example_NFWShear_imsim.ipynb
new file mode 100644
index 00000000..92409184
--- /dev/null
+++ b/.virtual_documents/examples/example_NFWShear_imsim.ipynb
@@ -0,0 +1,66 @@
+get_ipython().run_line_magic("matplotlib", " inline")
+get_ipython().run_line_magic("reload_ext", " autoreload")
+get_ipython().run_line_magic("autoreload", " 2")
+import clmm
+import numpy as np
+
+from descwl_shear_sims.galaxies import make_galaxy_catalog
+from descwl_shear_sims.galaxies import WLDeblendGalaxyCatalog
+from descwl_shear_sims.psfs import make_fixed_psf
+from descwl_shear_sims.surveys import get_survey
+
+from descwl_shear_sims.sim import make_sim
+from descwl_shear_sims.shear import ShearNFW
+
+
+import matplotlib.pyplot as plt
+
+
+cosmo = clmm.Cosmology(H0=70.0, Omega_dm0=0.27 - 0.045, Omega_b0=0.045, Omega_k0=0.0)
+halo = clmm.Modeling(massdef="mean", delta_mdef=200, halo_profile_model="nfw")
+halo.set_cosmo(cosmo)
+halo.set_concentration(4)
+halo.set_mass(1.0e15)
+z_cl = 1.0
+# source properties
+z_source = 2.0  # all sources in the same plane
+
+shear_obj = ShearNFW(halo, z_cl)
+
+seed = 74321
+rng = np.random.RandomState(seed)
+
+coadd_dim = 50
+se_dim = 50
+psf_dim = 41
+
+galaxy_catalog = WLDeblendGalaxyCatalog(
+    rng=rng,
+    coadd_dim=coadd_dim,
+    buff=2,
+    layout="random_disk",
+)
+psf = make_fixed_psf(psf_type="gauss")
+band_list = ["r"]
+
+sim_data = make_sim(
+    rng=rng,
+    galaxy_catalog=galaxy_catalog,
+    coadd_dim=coadd_dim,
+    shear_obj=shear_obj,
+    psf=psf,
+    noise_factor=0.0,
+    bands=band_list,
+)
+
+
+exp = sim_data["band_data"]["r"][0]
+img = exp.getImage().getArray()
+
+
+from astropy.visualization import simple_norm
+plt.imshow(img,aspect='equal',cmap='RdYlBu_r',origin='lower',interpolation='None',\
+             norm=simple_norm(img,'asinh',asinh_a=0.1,min_cut=-0.01,max_cut=4))
+
+
+
diff --git a/descwl_shear_sims/__init__.py b/descwl_shear_sims/__init__.py
index 7c787c39..fe93f7ce 100644
--- a/descwl_shear_sims/__init__.py
+++ b/descwl_shear_sims/__init__.py
@@ -19,3 +19,4 @@
 from . import artifacts
 from . import masking
 from . import wcs
+from . import shear
diff --git a/descwl_shear_sims/galaxies.py b/descwl_shear_sims/galaxies.py
index 4637f2ae..fa62a642 100644
--- a/descwl_shear_sims/galaxies.py
+++ b/descwl_shear_sims/galaxies.py
@@ -203,7 +203,7 @@ def get_objlist(self, *, survey):
             objlist.append(self._get_galaxy(flux))
             shifts.append(galsim.PositionD(sarray['dx'][i], sarray['dy'][i]))
 
-        return objlist, shifts
+        return objlist, shifts, None
 
     def _get_galaxy(self, flux):
         """
@@ -633,6 +633,7 @@ def __init__(self, *, rng, coadd_dim, buff=0, layout='random'):
             nobj=nobj,
         )
 
+        # randomly sample from the catalog
         num = len(self)
         self.indices = self.rng.randint(
             0,
@@ -640,6 +641,7 @@ def __init__(self, *, rng, coadd_dim, buff=0, layout='random'):
             size=num,
         )
 
+        # do a random rotation for each galaxy
         self.angles = self.rng.uniform(low=0, high=360, size=num)
 
     def __len__(self):
@@ -672,11 +674,14 @@ def get_objlist(self, *, survey):
         sarray = self.shifts_array
         objlist = []
         shifts = []
+        redshifts = []
         for i in range(len(self)):
             objlist.append(self._get_galaxy(builder, band, i))
             shifts.append(galsim.PositionD(sarray['dx'][i], sarray['dy'][i]))
+            index = self.indices[i]
+            redshifts.append(self._wldeblend_cat[index]["redshift"])
 
-        return objlist, shifts
+        return objlist, shifts, redshifts
 
     def _get_galaxy(self, builder, band, i):
         """
diff --git a/descwl_shear_sims/objlists.py b/descwl_shear_sims/objlists.py
index 38f2b54c..a9fef9ee 100644
--- a/descwl_shear_sims/objlists.py
+++ b/descwl_shear_sims/objlists.py
@@ -20,7 +20,7 @@ def get_objlist(*, galaxy_catalog, survey, star_catalog=None, noise=None):
         objlist is a list of galsim GSObject with transformations applied. Shifts
         is an array with fields dx and dy for each object
     """
-    objlist, shifts = galaxy_catalog.get_objlist(survey=survey)
+    objlist, shifts, redshifts = galaxy_catalog.get_objlist(survey=survey)
 
     if star_catalog is not None:
         assert noise is not None
@@ -39,6 +39,7 @@ def get_objlist(*, galaxy_catalog, survey, star_catalog=None, noise=None):
     return {
         'objlist': objlist,
         'shifts': shifts,
+        'redshifts': redshifts,
         'star_objlist': sobjlist,
         'star_shifts': sshifts,
         'bright_objlist': bright_objlist,
diff --git a/descwl_shear_sims/shear.py b/descwl_shear_sims/shear.py
index 02204c2e..bfb1f78a 100644
--- a/descwl_shear_sims/shear.py
+++ b/descwl_shear_sims/shear.py
@@ -2,45 +2,24 @@
 import numpy as np
 # maximum kappa allowed
 # values greater than it will be clipped
-kappa_max = 0.8
-
-def get_shear(
-    *,
-    shear_type="constant",
-    z_gals=None,
-    shift=None,
-    **kwargs,
-):
-    """
-    A shear wrapper to return g1 and g2 with different shear type
-
-    Parameters
-    ----------
-    shear_type:     the constant shear or shear from NFW halos
-    z_gals:         redshifts of galaxies
-    shift:          Galsim positionD shift
-    """
-    if shear_type == "constant":
-        shear_obj = ShearConstant(**kwargs)
-    elif shear_type =="redshift":
-        shear_obj = ShearRedshift(**kwargs)
-    elif shear_type == "NFW":
-        shear_obj = ShearNFW(**kwargs)
-    else:
-        raise ValueError("Do not support the shear type: %s" % shear_type)
-    return shear_obj.get_shear(z_gals, shift)
+kappa_max = 0.6
+"""
+shear_obj = ShearConstant(cluster_obj, z_cl, ra_cl, dec_cl)
+shear_obj = ShearRedshift(g1=0.02, g2=0.00)
+shear_obj = ShearNFW(mode="0000", g_dist="g1")
+"""
 
 
 class ShearNFW(object):
     """
-    Shear object from NFW
+    Shear object from NFW halos
 
     Parameters
     ----------
-    cluster_obj (object):    cluster object from clmm
-    z_cl (float):    redshift of the cluster
-    x_cl (float):    ra of the cluster [arcsec]
-    y_cl (float):    dec of the cluster [arcsec]
+    cluster_obj (object):   cluster object from clmm
+    z_cl (float):           redshift of the cluster
+    x_cl (float):           ra of the cluster [arcsec]
+    y_cl (float):           dec of the cluster [arcsec]
 
     """
     def __init__(self, cluster_obj, z_cl, ra_cl=0., dec_cl=0.):
@@ -51,56 +30,69 @@ def __init__(self, cluster_obj, z_cl, ra_cl=0., dec_cl=0.):
         self.cosmo = cluster_obj.cosmo
         return
 
-    def get_shear(self, z_gals, shift):
+    def get_shear(self, redshift, shift):
         """
         A shear wrapper to return g1 and g2 with different shear type
 
         Parameters
         ----------
-        z_gals (float):             redshifts of galaxies
-        shift (galsim.positionD):  Galsim positionD shift [arcsec]
+        redshift (float):           redshifts of galaxies
+        shift (galsim.positionD):   Galsim positionD shift [arcsec]
 
         Returns
         ---------
         shear (galsim.Shear)        shear distortion on the galaxy
         """
-        from astropy.coordinates import SkyCoord
         z_cl = self.z_cl
-        # Create the SkyCoord objects
-        coord_cl = SkyCoord(self.ra_cl, self.dec_cl, unit="arcsec")
-        coord_gals = SkyCoord(shift.x, shift.y, unit="arcsec")
-        # Calculate the separation
-        sep = coord_cl.separation(coord_gals).radian
-        # What is the unit of r3d?
-        r3d = self.cosmo.rad2mpc(sep, self.z_cl)
-        # position angle
-        phi = coord_cl.position_angle(coord_gals).radian
-
-        # TODO: confirm whether the units is Mpc/h or Mpc?
-        gammat = self.cobj.eval_tangential_shear(r3d, z_cl, z_gals)
-        kappa0 = self.cobj.eval_convergence(r3d, z_cl, z_gals)
-        # we are forcing kappa to be less than kappa_max
-        # and scale gamma by the same ratio
-        kappa = min(kappa0, kappa_max)
-        ratio = kappa / kappa0
-        gamma1 = gammat * np.cos(2. * phi) * ratio
-        gamma2 = -gammat * np.sin(2. * phi) * ratio
-        shear = galsim.Shear(g1=gamma1/(1-kappa), g2=gamma2/(1-kappa))
+        if redshift > z_cl:
+            from astropy.coordinates import SkyCoord
+            # Create the SkyCoord objects
+            coord_cl = SkyCoord(self.ra_cl, self.dec_cl, unit="arcsec")
+            coord_gals = SkyCoord(shift.x, shift.y, unit="arcsec")
+            # Calculate the separation
+            sep = coord_cl.separation(coord_gals).radian
+            # What is the unit of r3d?
+            r3d = self.cosmo.rad2mpc(sep, self.z_cl)
+            # position angle
+            phi = coord_cl.position_angle(coord_gals).radian
+
+            # TODO: confirm whether the units is Mpc/h or Mpc?
+            gammat = self.cobj.eval_tangential_shear(r3d, z_cl, redshift)
+            kappa0 = self.cobj.eval_convergence(r3d, z_cl, redshift)
+            # we are forcing kappa to be less than kappa_max
+            # and scale gamma by the same ratio
+            kappa = min(kappa0, kappa_max)
+            ratio = kappa / kappa0
+            gamma1 = gammat * np.cos(2. * phi) * ratio
+            gamma2 = -gammat * np.sin(2. * phi) * ratio
+            g1 = gamma1 / (1-kappa)
+            g2 = gamma2 / (1-kappa)
+        else:
+            g1 = 0.; g2 = 0.
+        shear = galsim.Shear(g1=g1, g2=g2)
         return shear
 
 
 class ShearConstant(object):
     """
     Constant shear along every redshift slice
+    Parameters
+    ----------
+    g1, g2:    Constant shear distortion
     """
     def __init__(self, g1, g2):
         self.g1 = g1
         self.g2 = g2
+        self.shear = galsim.Shear(g1=self.g1, g2=self.g2)
         return
 
-    def get_shear(self):
-        shear = galsim.Shear(g1= self.g1, g2=self.g2)
-        return shear
+    def get_shear(self, redshift=None, shift=None):
+        """
+        Returns
+        ---------
+        shear (galsim.Shear)        shear distortion on the galaxy
+        """
+        return self.shear
 
 
 class ShearRedshift(object):
@@ -122,11 +114,9 @@ def __init__(self, mode="0000", g_dist="g1"):
         self.g_dist = g_dist
         return
 
-    def get_shear(self, z_gals=None, shift=None):
-        if z_gals is None:
-            assert self.mode == "0" * self.nz_bins
-        #z_gal_bins = z_gals // self.dz_bin
+    def get_shear(self, redshift, shift=None):
+        # z_gal_bins = redshift // self.dz_bin
         gamma1, gamma2 = (None, None)
         # TODO: Finish implementing the z-dependent shear
-        shear = galsim.Shear(g1= gamma1, g2=gamma2)
+        shear = galsim.Shear(g1=gamma1, g2=gamma2)
         return shear
diff --git a/descwl_shear_sims/sim.py b/descwl_shear_sims/sim.py
index 6867e6f9..537abab2 100644
--- a/descwl_shear_sims/sim.py
+++ b/descwl_shear_sims/sim.py
@@ -52,8 +52,7 @@ def make_sim(
     rng,
     galaxy_catalog,
     coadd_dim,
-    g1,
-    g2,
+    shear_obj,
     psf,
     se_dim=None,
     star_catalog=None,
@@ -70,7 +69,6 @@ def make_sim(
     sky_n_sigma=None,
     draw_method='auto',
     theta0=0.,
-    shear_obj=None,
 ):
     """
     Make simulation data
@@ -85,10 +83,8 @@ def make_sim(
         Dimensions for planned final coadd.  This is used for generating
         the final coadd WCS and deteremines some properties of
         the single epoch images.
-    g1: float
-        Shear g1 for galaxies
-    g2: float
-        Shear g2 for galaxies
+    shear_obj:
+        shear distortion object
     psf: GSObject or PowerSpectrumPSF
         The psf object or power spectrum psf
     se_dim: int, optional
@@ -182,10 +178,11 @@ def make_sim(
                 noise=noise_per_epoch,
                 objlist=lists['objlist'],
                 shifts=lists['shifts'],
+                redshifts=lists['redshifts'],
                 dim=se_dim,
                 psf=psf,
                 psf_dim=psf_dim,
-                g1=g1, g2=g2,
+                shear_obj=shear_obj,
                 star_objlist=lists['star_objlist'],
                 star_shifts=lists['star_shifts'],
                 draw_stars=draw_stars,
@@ -202,7 +199,6 @@ def make_sim(
                 sky_n_sigma=sky_n_sigma,
                 draw_method=draw_method,
                 theta0=theta0,
-                shear_obj=None,
             )
             if epoch == 0:
                 bright_info += this_bright_info
@@ -246,11 +242,11 @@ def make_exp(
     noise,
     objlist,
     shifts,
+    redshifts,
     dim,
     psf,
     psf_dim,
-    g1,
-    g2,
+    shear_obj,
     star_objlist=None,
     star_shifts=None,
     draw_stars=True,
@@ -267,7 +263,6 @@ def make_exp(
     sky_n_sigma=None,
     draw_method='auto',
     theta0=0.,
-    shear_obj=None,
 ):
     """
     Make an SEObs
@@ -338,10 +333,6 @@ def make_exp(
         radius_pixels: radius of mask in pixels
         has_bleed: bool, True if there is a bleed trail
     """
-    if shear_obj is None:
-        shear = galsim.Shear(g1=g1, g2=g2)
-    else:
-        shear = shear_obj
     dims = [dim]*2
     # I think Galsim uses 1 offset. An array with length=dim=5
     # The center is at 3=(5+1)/2
@@ -371,17 +362,17 @@ def make_exp(
 
     _draw_objects(
         image,
-        objlist, shifts, psf, draw_method,
+        objlist, shifts, redshifts, psf, draw_method,
         coadd_bbox_cen_gs_skypos,
         rng,
-        shear=shear,
+        shear_obj=shear_obj,
         theta0=theta0,
     )
     if star_objlist is not None and draw_stars:
         assert star_shifts is not None, 'send star_shifts with star_objlist'
         _draw_objects(
             image,
-            star_objlist, star_shifts, psf, draw_method,
+            star_objlist, star_shifts, None, psf, draw_method,
             coadd_bbox_cen_gs_skypos,
             rng,
         )
@@ -454,10 +445,10 @@ def make_exp(
 
 
 def _draw_objects(
-    image, objlist, shifts, psf, draw_method,
+    image, objlist, shifts, redshifts, psf, draw_method,
     coadd_bbox_cen_gs_skypos,
     rng,
-    shear=None,
+    shear_obj=None,
     theta0=None,
 ):
 
@@ -467,7 +458,10 @@ def _draw_objects(
         kw['maxN'] = 1_000_000
         kw['rng'] = galsim.BaseDeviate(seed=rng.randint(low=0, high=2**30))
 
-    for obj, shift in zip(objlist, shifts):
+    if redshifts is None:
+        redshifts = np.zeros(len(objlist))
+
+    for obj, shift, z in zip(objlist, shifts, redshifts):
 
         if theta0 is not None:
             ang = theta0*galsim.radians
@@ -475,7 +469,8 @@ def _draw_objects(
             obj = obj.rotate(ang)
             shift = _roate_pos(shift, theta0)
 
-        if shear is not None:
+        if shear_obj is not None:
+            shear = shear_obj.get_shear(z, shift)
             obj = obj.shear(shear)
             shift = shift.shear(shear)
 
diff --git a/descwl_shear_sims/tests/test_correlated_noise.py b/descwl_shear_sims/tests/test_correlated_noise.py
index e4bb107a..56ebea38 100644
--- a/descwl_shear_sims/tests/test_correlated_noise.py
+++ b/descwl_shear_sims/tests/test_correlated_noise.py
@@ -5,6 +5,10 @@
 from ..galaxies import make_galaxy_catalog
 from numba import njit
 
+from descwl_shear_sims.shear import ShearConstant
+
+shear_obj = ShearConstant(g1=0.02, g2=0.)
+
 
 @njit
 def get_cov(image):
@@ -56,8 +60,7 @@ def test_correlated_noise():
             rng=rng,
             galaxy_catalog=galaxy_catalog,
             coadd_dim=coadd_dim,
-            g1=0.02,
-            g2=0.00,
+            shear_obj=shear_obj,
             psf=psf,
             dither=True,
             rotate=True,
diff --git a/descwl_shear_sims/tests/test_galaxies.py b/descwl_shear_sims/tests/test_galaxies.py
index f9bc7de7..fd19c5dc 100644
--- a/descwl_shear_sims/tests/test_galaxies.py
+++ b/descwl_shear_sims/tests/test_galaxies.py
@@ -9,6 +9,9 @@
 )
 from descwl_shear_sims.psfs import make_fixed_psf
 from descwl_shear_sims.sim import make_sim
+from descwl_shear_sims.shear import ShearConstant
+
+shear_obj = ShearConstant(g1=0.02, g2=0.)
 
 
 @pytest.mark.parametrize('layout', ('pair', 'random', 'hex'))
@@ -62,8 +65,7 @@ def test_galaxies_smoke(layout, gal_type, morph):
             galaxy_catalog=galaxy_catalog,
             coadd_dim=coadd_dim,
             bands=bands,
-            g1=0.02,
-            g2=0.00,
+            shear_obj=shear_obj,
             psf=psf,
         )
 
diff --git a/descwl_shear_sims/tests/test_nfw_shear.py b/descwl_shear_sims/tests/test_nfw_shear.py
deleted file mode 100644
index d9dc04c6..00000000
--- a/descwl_shear_sims/tests/test_nfw_shear.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import os
-import pytest
-import numpy as np
-import lsst.afw.image as afw_image
-import lsst.afw.geom as afw_geom
-
-from descwl_shear_sims.galaxies import make_galaxy_catalog, DEFAULT_FIXED_GAL_CONFIG
-from descwl_shear_sims.stars import StarCatalog, make_star_catalog
-from descwl_shear_sims.psfs import make_fixed_psf, make_ps_psf
-
-from descwl_shear_sims.sim import make_sim, get_se_dim
-
-def test_sim_se_dim():
-    """
-    test sim can run
-    """
-    seed = 74321
-    rng = np.random.RandomState(seed)
-
-    coadd_dim = 351
-    se_dim = 351
-    psf_dim = 51
-    bands = ["i"]
-    galaxy_catalog = make_galaxy_catalog(
-        rng=rng,
-        gal_type="fixed",
-        coadd_dim=coadd_dim,
-        buff=30,
-        layout="grid",
-    )
-
-    psf = make_fixed_psf(psf_type="gauss")
-    data = make_sim(
-        rng=rng,
-        galaxy_catalog=galaxy_catalog,
-        coadd_dim=coadd_dim,
-        se_dim=se_dim,
-        psf_dim=psf_dim,
-        bands=bands,
-        g1=0.02,
-        g2=0.00,
-        psf=psf,
-    )
-    return data['band_data']['i'][0]
-
-a = test_sim_se_dim()
-data = a.getImage().getArray()
diff --git a/descwl_shear_sims/tests/test_nonzero_sky.py b/descwl_shear_sims/tests/test_nonzero_sky.py
index ee52838e..6a6ceb03 100644
--- a/descwl_shear_sims/tests/test_nonzero_sky.py
+++ b/descwl_shear_sims/tests/test_nonzero_sky.py
@@ -3,6 +3,9 @@
 from ..psfs import make_fixed_psf
 
 from ..sim import make_sim
+from descwl_shear_sims.shear import ShearConstant
+
+shear_obj = ShearConstant(g1=0.02, g2=0.)
 
 
 def test_nonzero_sky():
@@ -37,8 +40,7 @@ def test_nonzero_sky():
             coadd_dim=coadd_dim,
             psf_dim=psf_dim,
             bands=bands,
-            g1=0.02,
-            g2=0.00,
+            shear_obj=shear_obj,
             psf=psf,
             sky_n_sigma=sky_n_sigma,
         )
diff --git a/descwl_shear_sims/tests/test_pairs.py b/descwl_shear_sims/tests/test_pairs.py
index ba99b664..63b22c33 100644
--- a/descwl_shear_sims/tests/test_pairs.py
+++ b/descwl_shear_sims/tests/test_pairs.py
@@ -6,6 +6,10 @@
 from ..sim import make_sim
 from ..constants import ZERO_POINT
 
+from descwl_shear_sims.shear import ShearConstant
+
+shear_obj = ShearConstant(g1=0.02, g2=0.)
+
 
 @pytest.mark.parametrize('dither,rotate', [
     (False, False),
@@ -38,8 +42,7 @@ def test_pairs_smoke(dither, rotate):
         galaxy_catalog=galaxy_catalog,
         coadd_dim=coadd_dim,
         bands=bands,
-        g1=0.02,
-        g2=0.00,
+        shear_obj=shear_obj,
         psf=psf,
         dither=dither,
         rotate=rotate,
diff --git a/descwl_shear_sims/tests/test_sim.py b/descwl_shear_sims/tests/test_sim.py
index 9e1d82cd..6363baad 100644
--- a/descwl_shear_sims/tests/test_sim.py
+++ b/descwl_shear_sims/tests/test_sim.py
@@ -11,6 +11,10 @@
 from descwl_shear_sims.sim import make_sim, get_se_dim
 from descwl_shear_sims.constants import ZERO_POINT
 
+from descwl_shear_sims.shear import ShearConstant
+
+shear_obj = ShearConstant(g1=0.02, g2=0.)
+
 
 @pytest.mark.parametrize('dither,rotate', [
     (False, False),
@@ -43,8 +47,7 @@ def test_sim_smoke(dither, rotate):
         coadd_dim=coadd_dim,
         psf_dim=psf_dim,
         bands=bands,
-        g1=0.02,
-        g2=0.00,
+        shear_obj=shear_obj,
         psf=psf,
         dither=dither,
         rotate=rotate,
@@ -94,8 +97,7 @@ def test_sim_se_dim():
         se_dim=se_dim,
         psf_dim=psf_dim,
         bands=bands,
-        g1=0.02,
-        g2=0.00,
+        shear_obj=shear_obj,
         psf=psf,
     )
 
@@ -138,8 +140,7 @@ def test_sim_exp_mag(rotate, show=False):
             galaxy_catalog=galaxy_catalog,
             coadd_dim=coadd_dim,
             se_dim=se_dim,
-            g1=0.02,
-            g2=0.00,
+            shear_obj=shear_obj,
             psf=psf,
             bands=bands,
             rotate=rotate,
@@ -200,8 +201,7 @@ def test_sim_psf_type(psf_type):
         rng=rng,
         galaxy_catalog=galaxy_catalog,
         coadd_dim=coadd_dim,
-        g1=0.02,
-        g2=0.00,
+        shear_obj=shear_obj,
         psf=psf,
         dither=dither,
         rotate=rotate,
@@ -232,8 +232,7 @@ def test_sim_epochs(epochs_per_band):
         galaxy_catalog=galaxy_catalog,
         coadd_dim=coadd_dim,
         psf_dim=psf_dim,
-        g1=0.02,
-        g2=0.00,
+        shear_obj=shear_obj,
         psf=psf,
         bands=bands,
         epochs_per_band=epochs_per_band,
@@ -264,8 +263,7 @@ def test_sim_layout(layout):
         rng=rng,
         galaxy_catalog=galaxy_catalog,
         coadd_dim=coadd_dim,
-        g1=0.02,
-        g2=0.00,
+        shear_obj=shear_obj,
         psf=psf,
     )
 
@@ -299,8 +297,7 @@ def test_sim_defects(cosmic_rays, bad_columns):
             rng=rng,
             galaxy_catalog=galaxy_catalog,
             coadd_dim=coadd_dim,
-            g1=0.02,
-            g2=0.00,
+            shear_obj=shear_obj,
             psf=psf,
             cosmic_rays=cosmic_rays,
             bad_columns=bad_columns,
@@ -341,8 +338,7 @@ def test_sim_wldeblend():
         rng=rng,
         galaxy_catalog=galaxy_catalog,
         coadd_dim=coadd_dim,
-        g1=0.02,
-        g2=0.00,
+        shear_obj=shear_obj,
         psf=psf,
     )
 
@@ -407,8 +403,7 @@ def test_sim_stars(density, min_density, max_density):
             galaxy_catalog=galaxy_catalog,
             star_catalog=star_catalog,
             coadd_dim=coadd_dim,
-            g1=0.02,
-            g2=0.00,
+            shear_obj=shear_obj,
             psf=psf,
         )
 
@@ -455,8 +450,7 @@ def test_sim_star_bleeds():
         galaxy_catalog=galaxy_catalog,
         star_catalog=star_catalog,
         coadd_dim=coadd_dim,
-        g1=0.02,
-        g2=0.00,
+        shear_obj=shear_obj,
         psf=psf,
         star_bleeds=True,
     )
@@ -485,8 +479,7 @@ def test_sim_draw_method_smoke(draw_method):
         rng=rng,
         galaxy_catalog=galaxy_catalog,
         coadd_dim=coadd_dim,
-        g1=0.02,
-        g2=0.00,
+        shear_obj=shear_obj,
         psf=psf,
         **kw
     )
diff --git a/descwl_shear_sims/tests/test_sim_center.py b/descwl_shear_sims/tests/test_sim_center.py
index 8c14901f..5d902eb2 100644
--- a/descwl_shear_sims/tests/test_sim_center.py
+++ b/descwl_shear_sims/tests/test_sim_center.py
@@ -9,6 +9,9 @@
 from descwl_shear_sims.sim import make_sim
 from descwl_shear_sims.galaxies import make_galaxy_catalog
 from descwl_shear_sims.psfs import make_fixed_psf  # for making a power spectrum PSF
+from descwl_shear_sims.shear import ShearConstant
+
+shear_obj = ShearConstant(g1=0.02, g2=0.)
 
 
 @pytest.mark.parametrize("ran_seed", [0, 1, 2])
@@ -44,8 +47,7 @@ def test_sim_center(ran_seed):
         rng=rng,
         galaxy_catalog=galaxy_catalog,
         coadd_dim=coadd_dim,
-        g1=0.02,
-        g2=0.00,
+        shear_obj=shear_obj,
         psf=psf,
         bands=band_list,
         noise_factor=0.0,
diff --git a/descwl_shear_sims/tests/test_star_masks_and_bleeds.py b/descwl_shear_sims/tests/test_star_masks_and_bleeds.py
index 1b7eec48..9fae6c02 100644
--- a/descwl_shear_sims/tests/test_star_masks_and_bleeds.py
+++ b/descwl_shear_sims/tests/test_star_masks_and_bleeds.py
@@ -10,6 +10,10 @@
 from descwl_shear_sims.galaxies import make_galaxy_catalog
 from descwl_shear_sims.psfs import make_fixed_psf
 from descwl_shear_sims.sim import make_sim
+from descwl_shear_sims.shear import ShearConstant
+
+
+shear_obj = ShearConstant(g1=0., g2=0.)
 
 
 @pytest.mark.skipif(
@@ -79,7 +83,7 @@ def test_star_mask_in_sim(draw_stars):
             coadd_dim=coadd_dim,
             bands=bands,
             psf=psf,
-            g1=0, g2=0,
+            shear_obj=shear_obj,
             star_bleeds=True,
         )
 
@@ -153,7 +157,7 @@ def test_star_mask_in_sim_repeatable():
                 coadd_dim=coadd_dim,
                 bands=bands,
                 psf=psf,
-                g1=0, g2=0,
+                shear_obj=shear_obj,
                 star_bleeds=True,
             )
 
diff --git a/examples/example_NFWShear.ipynb b/examples/example_NFWShear.ipynb
index 0f4ab029..c1ba54d0 100644
--- a/examples/example_NFWShear.ipynb
+++ b/examples/example_NFWShear.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 16,
    "id": "d12a992b-6669-4675-b68a-b705ff09f693",
    "metadata": {},
    "outputs": [],
@@ -15,7 +15,11 @@
     "import galsim\n",
     "import numpy as np\n",
     "import descwl_shear_sims as dss\n",
-    "import matplotlib.pyplot as plt"
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from descwl_shear_sims.galaxies import WLDeblendGalaxyCatalog\n",
+    "from descwl_shear_sims.objlists import get_objlist\n",
+    "from descwl_shear_sims.surveys import get_survey"
    ]
   },
   {
@@ -184,10 +188,499 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 44,
    "id": "0c43422d-d816-4c98-8bb9-8671bbdcd1a1",
    "metadata": {},
    "outputs": [],
+   "source": [
+    "band=\"r\"\n",
+    "rng = np.random.RandomState(1)\n",
+    "coadd_dim = 500\n",
+    "buff = 50\n",
+    "# galaxy catalog; you can make your own\n",
+    "galaxy_catalog = WLDeblendGalaxyCatalog(\n",
+    "    rng=rng,\n",
+    "    coadd_dim=coadd_dim,\n",
+    "    buff=buff,\n",
+    "    layout=\"random\",\n",
+    ")\n",
+    "\n",
+    "survey = get_survey(gal_type=galaxy_catalog.gal_type, band=band)\n",
+    "noise_for_gsparams = survey.noise\n",
+    "lists = get_objlist(\n",
+    "    galaxy_catalog=galaxy_catalog,\n",
+    "    survey=survey,\n",
+    "    star_catalog=None,\n",
+    "    noise=noise_for_gsparams,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "717a8a13-7bf7-45ab-a163-a17f783be24a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['objlist', 'shifts', 'redshifts', 'star_objlist', 'star_shifts', 'bright_objlist', 'bright_shifts', 'bright_mags'])"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lists.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "aaf65a03-5261-4ec1-b8e9-f26d5db85ba0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0.300422102213,\n",
+       " 0.652480721474,\n",
+       " 0.575277209282,\n",
+       " 2.52627205849,\n",
+       " 1.45767605305,\n",
+       " 3.21435308456,\n",
+       " 1.43933665752,\n",
+       " 0.602639317513,\n",
+       " 0.351592004299,\n",
+       " 1.4857083559,\n",
+       " 1.23648571968,\n",
+       " 2.45936989784,\n",
+       " 1.65149867535,\n",
+       " 1.95639693737,\n",
+       " 0.673706114292,\n",
+       " 1.33176410198,\n",
+       " 0.832303106785,\n",
+       " 1.23708331585,\n",
+       " 2.74137616158,\n",
+       " 0.478885412216,\n",
+       " 1.71309113503,\n",
+       " 3.10937333107,\n",
+       " 1.58497214317,\n",
+       " 1.53414404392,\n",
+       " 2.21907281876,\n",
+       " 3.05395698547,\n",
+       " 0.624157905579,\n",
+       " 2.48969173431,\n",
+       " 1.65923559666,\n",
+       " 1.72423791885,\n",
+       " 1.72161865234,\n",
+       " 1.41527807713,\n",
+       " 1.9740653038,\n",
+       " 1.57185637951,\n",
+       " 1.36203336716,\n",
+       " 0.998158812523,\n",
+       " 2.22123336792,\n",
+       " 0.0790505036712,\n",
+       " 1.54114544392,\n",
+       " 1.28051483631,\n",
+       " 0.633835613728,\n",
+       " 0.935597300529,\n",
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+       " 0.397633910179,\n",
+       " 1.96410322189,\n",
+       " 2.22879934311,\n",
+       " 0.681291222572,\n",
+       " 2.69742965698,\n",
+       " 0.310196310282,\n",
+       " 2.89895749092,\n",
+       " 1.48319602013,\n",
+       " 1.25393915176,\n",
+       " 1.32725834846,\n",
+       " 1.49158430099,\n",
+       " 1.06035864353,\n",
+       " 2.03195810318,\n",
+       " 1.80608427525,\n",
+       " 0.736506402493,\n",
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+       " 0.290101289749,\n",
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+       " 1.02715909481,\n",
+       " 0.867182075977,\n",
+       " 1.41402947903,\n",
+       " 0.5780569911,\n",
+       " 0.188184097409,\n",
+       " 1.88854825497,\n",
+       " 1.57122135162,\n",
+       " 1.31176841259,\n",
+       " 1.77145779133,\n",
+       " 1.06479489803,\n",
+       " 1.45442044735,\n",
+       " 0.712578594685,\n",
+       " 2.21411037445,\n",
+       " 1.63247942924,\n",
+       " 1.17663383484,\n",
+       " 0.588364779949,\n",
+       " 1.11000394821,\n",
+       " 1.16703224182,\n",
+       " 0.641001403332,\n",
+       " 1.64528286457,\n",
+       " 1.15830636024,\n",
+       " 0.335720986128,\n",
+       " 0.906032621861,\n",
+       " 2.21711087227,\n",
+       " 2.40289640427,\n",
+       " 2.84520173073,\n",
+       " 1.34919595718,\n",
+       " 2.46827530861,\n",
+       " 2.83999967575,\n",
+       " 1.04901909828,\n",
+       " 0.319967508316,\n",
+       " 0.341769695282,\n",
+       " 2.46187758446,\n",
+       " 0.79427921772,\n",
+       " 2.31489515305,\n",
+       " 1.22375190258,\n",
+       " 0.501341700554,\n",
+       " 0.873788118362,\n",
+       " 0.487286388874,\n",
+       " 1.15298783779,\n",
+       " 1.4753049612,\n",
+       " 2.08395910263,\n",
+       " 2.40972471237,\n",
+       " 1.01812720299,\n",
+       " 1.16665363312,\n",
+       " 2.22886013985,\n",
+       " 2.12949180603,\n",
+       " 0.864983677864,\n",
+       " 0.77624219656,\n",
+       " 1.33834803104,\n",
+       " 1.139534235,\n",
+       " 1.81312501431,\n",
+       " 0.168495103717,\n",
+       " 1.67772495747,\n",
+       " 1.41799271107,\n",
+       " 0.941433608532,\n",
+       " 2.65767550468,\n",
+       " 2.28444099426,\n",
+       " 2.707062006,\n",
+       " 2.11041426659,\n",
+       " 1.79551744461,\n",
+       " 0.889996528625,\n",
+       " 2.55368709564,\n",
+       " 3.41086649895,\n",
+       " 0.599908709526,\n",
+       " 0.421412497759,\n",
+       " 1.31073558331,\n",
+       " 0.520302295685,\n",
+       " 2.25809669495,\n",
+       " 2.11552023888,\n",
+       " 2.62590360641,\n",
+       " 0.46127268672,\n",
+       " 1.02121424675,\n",
+       " 2.40373373032,\n",
+       " 1.9007294178,\n",
+       " 0.899684309959,\n",
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+       " 1.85351324081]"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lists[\"redshifts\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a64792ea-69b8-45e1-9379-334f73cacfbb",
+   "metadata": {},
+   "outputs": [],
    "source": []
   }
  ],
diff --git a/examples/example_NFWShear_imsim.ipynb b/examples/example_NFWShear_imsim.ipynb
new file mode 100644
index 00000000..e58356b8
--- /dev/null
+++ b/examples/example_NFWShear_imsim.ipynb
@@ -0,0 +1,137 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "177f93ce-ec8b-4c36-9ff4-d16951a4941f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "%reload_ext autoreload\n",
+    "%autoreload 2\n",
+    "import clmm\n",
+    "import numpy as np\n",
+    "\n",
+    "from descwl_shear_sims.galaxies import make_galaxy_catalog\n",
+    "from descwl_shear_sims.galaxies import WLDeblendGalaxyCatalog\n",
+    "from descwl_shear_sims.psfs import make_fixed_psf\n",
+    "from descwl_shear_sims.surveys import get_survey\n",
+    "\n",
+    "from descwl_shear_sims.sim import make_sim\n",
+    "from descwl_shear_sims.shear import ShearNFW\n",
+    "\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "\n",
+    "cosmo = clmm.Cosmology(H0=70.0, Omega_dm0=0.27 - 0.045, Omega_b0=0.045, Omega_k0=0.0)\n",
+    "halo = clmm.Modeling(massdef=\"mean\", delta_mdef=200, halo_profile_model=\"nfw\")\n",
+    "halo.set_cosmo(cosmo)\n",
+    "halo.set_concentration(4)\n",
+    "halo.set_mass(1.0e15)\n",
+    "z_cl = 1.0\n",
+    "# source properties\n",
+    "z_source = 2.0  # all sources in the same plane\n",
+    "\n",
+    "shear_obj = ShearNFW(halo, z_cl)\n",
+    "\n",
+    "seed = 74321\n",
+    "rng = np.random.RandomState(seed)\n",
+    "\n",
+    "coadd_dim = 50\n",
+    "se_dim = 50\n",
+    "psf_dim = 41\n",
+    "\n",
+    "galaxy_catalog = WLDeblendGalaxyCatalog(\n",
+    "    rng=rng,\n",
+    "    coadd_dim=coadd_dim,\n",
+    "    buff=2,\n",
+    "    layout=\"random_disk\",\n",
+    ")\n",
+    "psf = make_fixed_psf(psf_type=\"gauss\")\n",
+    "band_list = [\"r\"]\n",
+    "\n",
+    "sim_data = make_sim(\n",
+    "    rng=rng,\n",
+    "    galaxy_catalog=galaxy_catalog,\n",
+    "    coadd_dim=coadd_dim,\n",
+    "    shear_obj=shear_obj,\n",
+    "    psf=psf,\n",
+    "    noise_factor=0.0,\n",
+    "    bands=band_list,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "5db4e4bf-7d48-4bab-9692-1d21f6f0952b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "exp = sim_data[\"band_data\"][\"r\"][0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "998c9093-b310-4722-af44-bc1c9311866a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f2433ad4690>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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f63vn+l47ViQt6HVXiRX02cvjkklHz7c8+33NKLVX031Qfol1vL/c/d7WFNv/rweF9h9FQdL+/y7DhWkxgY35c0LGGCWTSVVWVioajaq5uTm1r7+/Xy0tLaqpqRnrwwAAclBGV0KPPfaYVq5cqTlz5iiRSGjfvn06ePCgXn/9dQVBoLq6OjU0NKiqqkpVVVVqaGhQSUmJ1qxZM17zBwBMYhmF0Mcff6z7779fZ86cUTgc1sKFC/X6669rxYoVkqTNmzerr69P69atS31Y9cCBAyorc7d2BwBMXRmF0PPPP592fxAEqq+vV319/VjmBACYIugdBwDwhpVVL+SqgiuwVxQF0xzVPkWOxl15aarmBu19soxj1UlXX62Mq+nSodLua2UcPeJMX591vLDrvPO+Sj62vzZ7P7KvlHoyFLU/tuP+A8fLbDjf/ZEMU2h//QcFjh9FgeN9sutjH7xeJx2uhAAA3hBCAABvCCEAgDeEEADAG0IIAOAN1XEXclTjuKrdghLHSpGl9gokU+Cujgsc/cHk6AEW9Nkro4yjyk7DjsqrdBVF9LMbH47nw/W8mvP2Csm8z+wrkkrS9H/aX5v9ZfaquXP9l1jHh0P2uU77X/v/lYLkRVRhuqrg8r6GlYvhFVdCAABvCCEAgDeEEADAG0IIAOANIQQA8IYQAgB4M3VLtF2NSh0loa5lvF0NTIenT7OOuxo4SlIwYC/FzstzvFcodJRuu8qnXdKUTzvLvR3l5Ka/P6P7cZd6T9GSbkeJu3E93wl3iXbojP21eUlh2Dpe2GN/PQ2UOkqxe+3fo8KE+/UXDGT42kTO40oIAOANIQQA8IYQAgB4QwgBALwhhAAA3kzd6rhsVV85GnzK1cPRtSyxJOOowBsusVfayTFuQo77Cdm/3SZNk8i8QfuJ5J2zV8HlJXrsj9Ftr+Ia7u21H+9YuvzzG+VwhVWmjU177M+fJAVn7a8De1tTqaCn1Do+UOaownS89ovi9teGJCnp2Jfp99TV8NTk8GsjR3ElBADwhhACAHhDCAEAvCGEAADeEEIAAG+mbnWcg3EtZ+3qoeZadtmxjLcZKnI/uKNHnKvf3KBjmebzM+2P0XeZ/f77yzJfQrkobn+epp8ps44X/zNhHc/v7LKOD3fbj5fS9KfL5T50rp5yrtelJPXYKxVd3+2ipL0/XYGrOtPxFjZIuucUnM/we4ecx5UQAMAbQggA4A0hBADwhhACAHhDCAEAvKE67kLOlS3T9MOycfTCCs6nqY5zrNJqykus44Ol9m/fuai9mq67yn5uJf8Vd04pXHzeOv5JfLp1PPEPe/+xS9+5xDp+yXv2c8j/H3fF3nC8275jClbNuao5JUmu/nuOfn2u12xen/01IEevw7Qr9Sbt1aTOXoGu83P8P8Xkw5UQAMAbQggA4A0hBADwhhACAHhDCAEAvKE67kIZrmyZaUVWUGTvzyVJwbC94icosq9s6ZrrkKvVV8Re5XT3N95yzun/lR+3jv/PYLl1/FeR263j7xX8l3U8f8BeZVfen6b/2KDje+F4/pwVZBNxFc40K+9mLNN+c32O58lVuZZmRV7nlIYcVW3OCkaq4HIdV0IAAG8IIQCAN4QQAMAbQggA4A0hBADwhuq4/1SmVXOuiqx0vb4c8grt1XGF3fY+bUXd9uMTPfbxAePoASap0rFC7DxHlV9HxF5N99RVM63j587Yq+OmnbWfmyRNO+foZTZgr1QMHBVZxlV5lc2echlWuwWufmyB4/1imgq1wPHYJuPXcoYVao4VgtPdl6tqLm1vPOQEroQAAN4QQgAAbwghAIA3hBAAwBtCCADgzZhCqLGxUUEQqK6uLjVmjFF9fb0qKipUXFys5cuX6+TJk2OdJwAgB110iXZra6t2796thQsXjhjfvn27duzYoRdffFFz587Vk08+qRUrVui9995TWVnZmCc84bjKeR3NMZ1lwZLkqDw2+ees4wWf2JcKL+uwl2InL7Uf/7uyxe4pXWu/r28Wf2Qd//89c6zjw0P20uNhR2/WoWL3+yMzzbFEeqF9PCh0NI11Nfh0lSqn4yihDlwl1I5SbFeJdlDg+K/qKulOx9XAdMD+PLlKup0fN3A1KZVoSIpRLupK6Ny5c7rvvvv03HPP6dJLL02NG2O0c+dObd26VatXr9b8+fO1Z88e9fb2au/evVmbNAAgN1xUCK1fv1533HGHbr99ZNv+9vZ2xWIx1dbWpsZCoZCWLVumw4cPW+8rmUyqu7t7xAYAmBoy/nXcvn379NZbb6m1tXXUvlgsJkmKRCIjxiORiE6fPm29v8bGRj3xxBOZTgMAkAMyuhLq6OjQxo0b9dvf/lbTpjlWTtPodiHGGGcLkS1btigej6e2jo6OTKYEAJjEMroSOnbsmDo7O7V48b//kD00NKRDhw6pqalJ7733nqTPr4hmzZqVOqazs3PU1dEXQqGQQqHQxcwdADDJZRRCt912m9ra2kaM/ehHP9J1112nRx55RFdffbWi0aiam5v1rW99S5LU39+vlpYWPfXUU9mb9WSWpjmmGbRXJw332a8i8z79zDpe7GggOdNcah3v6rU3EZWkP3QstY4PzXBUnA3a51rUaX+phbrsz0deMvMqqiDfft4mw6aggavgzNVENM1jy9F81lnt5ljKPXDcj1yPK7mr1xxL0svxOjPJpP14V3XhxTQdzbRqLptNZuFVRiFUVlam+fPnjxgrLS3VZZddlhqvq6tTQ0ODqqqqVFVVpYaGBpWUlGjNmjXZmzUAICdkfSmHzZs3q6+vT+vWrVNXV5eqq6t14MCB3PyMEABgTMYcQgcPHhzxdRAEqq+vV319/VjvGgCQ4+gdBwDwhhACAHjD8t4TiWvZZUc107CjCinPUWlU3G+vaCvqCjunFP7A/nmwZNj+0hnOt1dYFZy3zykUt/dpK+pyNNKTFJy3Px+uJaKd9+OsLHNUzRU5etZJkuNjBkHIcRtHFZxxLKduCu3Pt0mzfHjg6IEX9Dqq4Bw95QLH689Zn5bN/nBUweU8roQAAN4QQgAAbwghAIA3hBAAwBtCCADgDdVxk4Gras7Va+6cvTopcFTH5Z3rcT50aazYOl5S4uii7qjuchpwVGQlHf3NJJnzjl5mA47bDDuqtVyrmDqq4IIS+3MhSWZ6iXV8qMReNWcKM3v/F7iKxNJVjzmeDtfqrXJV2jl6EV5cjziq3TASV0IAAG8IIQCAN4QQAMAbQggA4A0hBADwhuq4ycxZNWevODOuXmKulTYlBa7KOdeKoa5+bGlWJbVxzVWSjKvCKs1tbFyrm7r6vbkq4CRpKGyvnBsqdfSIc1Si5Q3YK/nykvbvqet4SQoGHM+HoyJRrt57rupCFyrgkAGuhAAA3hBCAABvCCEAgDeEEADAG0IIAOAN1XFTSYbVdFKaKjVHH7ogz73Sp+MGmR2fjuOxnb3SXOOOqrl0ffFMgaO/mqMKLnCtiuuqgut1PN+uPnqSgqT9Nqavzz7uWkHVVTWXzRVUMWVxJQQA8IYQAgB4QwgBALwhhAAA3hBCAABvCCEAgDeUaCM9VzNKYy/ddlbtupaOVmZNRz+/L/t7p8A4HsP12K7JZliWLkn5PfY55fU7msY6mosGffYy6aD3vHXcpFsG3bHcuXE0MDWO83OW6dOoFFnAlRAAwBtCCADgDSEEAPCGEAIAeEMIAQC8oToOX4+sVlLZq9rMsOM9lasBp6vazVl95z6H4LyjSs3V0HXQUV2YtDckHXZVwQ24K/YyXQbdOJqq0qgU44krIQCAN4QQAMAbQggA4A0hBADwhhACAHhDdRwmH2eVmqNqLtP2dMOO+3Esfy1Jysvw/ZyrQs3Vpy3TirbPd2Y2J3rBwQOuhAAA3hBCAABvCCEAgDeEEADAG0IIAOAN1XHIHZlWzQ26qukcvePy7CuSXoys9Wmjog2THFdCAABvCCEAgDeEEADAG0IIAOANIQQA8CajEKqvr1cQBCO2aDSa2m+MUX19vSoqKlRcXKzly5fr5MmTWZ80kBFjMtuGh6ybGRzM2uZ6jIznCkxyGV8JzZs3T2fOnEltbW1tqX3bt2/Xjh071NTUpNbWVkWjUa1YsUKJRCKrkwYA5IaMQ6igoEDRaDS1zZw5U9LnV0E7d+7U1q1btXr1as2fP1979uxRb2+v9u7dm/WJAwAmv4xD6NSpU6qoqFBlZaXuuecevf/++5Kk9vZ2xWIx1dbWpo4NhUJatmyZDh8+7Ly/ZDKp7u7uERsAYGrIKISqq6v10ksv6S9/+Yuee+45xWIx1dTU6NNPP1UsFpMkRSKREbeJRCKpfTaNjY0Kh8Opbc6cORdxGgCAySijEFq5cqV+8IMfaMGCBbr99tv1xz/+UZK0Z8+e1DFBEIy4jTFm1NiXbdmyRfF4PLV1dHRkMiUAwCQ2phLt0tJSLViwQKdOnUpVyV141dPZ2Tnq6ujLQqGQysvLR2wAgKlhTCGUTCb17rvvatasWaqsrFQ0GlVzc3Nqf39/v1paWlRTUzPmiQIAck9GXbR/+tOfatWqVbryyivV2dmpJ598Ut3d3Vq7dq2CIFBdXZ0aGhpUVVWlqqoqNTQ0qKSkRGvWrBmv+QMAJrGMQuif//yn7r33Xp09e1YzZ87UjTfeqCNHjuiqq66SJG3evFl9fX1at26durq6VF1drQMHDqisrGxcJg8AmNwCYybWx667u7sVDoe1XHeqICj0PR0AQIYGzYAO6lXF4/Gv/Ds/veMAAN4QQgAAbwghAIA3hBAAwBtCCADgDSEEAPCGEAIAeEMIAQC8IYQAAN4QQgAAbwghAIA3hBAAwBtCCADgDSEEAPCGEAIAeEMIAQC8IYQAAN4QQgAAbwghAIA3hBAAwBtCCADgDSEEAPCGEAIAeEMIAQC8IYQAAN4QQgAAbwghAIA3hBAAwBtCCADgDSEEAPCGEAIAeEMIAQC8IYQAAN4QQgAAbwghAIA3hBAAwBtCCADgDSEEAPCGEAIAeEMIAQC8IYQAAN4QQgAAbwghAIA3hBAAwBtCCADgDSEEAPCGEAIAeEMIAQC8IYQAAN4QQgAAbwp8T+BCxhhJ0qAGJON5MgCAjA1qQNK/f56nM+FCKJFISJL+pj95ngkAYCwSiYTC4XDaYwLzn0TV12h4eFgfffSRysrKFASBuru7NWfOHHV0dKi8vNz39L4WU/Gcpal53lPxnCXOO9fP2xijRCKhiooK5eWl/6vPhLsSysvL0+zZs0eNl5eX5/Q3zWYqnrM0Nc97Kp6zxHnnsq+6AvoChQkAAG8IIQCANxM+hEKhkB5//HGFQiHfU/naTMVzlqbmeU/Fc5Y476l23ulMuMIEAMDUMeGvhAAAuYsQAgB4QwgBALwhhAAA3hBCAABvJnQIPfvss6qsrNS0adO0ePFi/fWvf/U9paw6dOiQVq1apYqKCgVBoFdeeWXEfmOM6uvrVVFRoeLiYi1fvlwnT570M9ksaWxs1Le//W2VlZXpiiuu0F133aX33ntvxDG5eN67du3SwoULU5+UX7p0qf785z+n9ufiOV+osbFRQRCorq4uNZaL511fX68gCEZs0Wg0tT8Xz3ksJmwI/f73v1ddXZ22bt2q48eP6+abb9bKlSv14Ycf+p5a1vT09GjRokVqamqy7t++fbt27NihpqYmtba2KhqNasWKFakmr5NRS0uL1q9fryNHjqi5uVmDg4Oqra1VT09P6phcPO/Zs2dr27ZtOnr0qI4ePapbb71Vd955Z+qHTy6e85e1trZq9+7dWrhw4YjxXD3vefPm6cyZM6mtra0ttS9Xz/mimQnqO9/5jnnwwQdHjF133XXm0Ucf9TSj8SXJvPzyy6mvh4eHTTQaNdu2bUuNnT9/3oTDYfPrX//awwzHR2dnp5FkWlpajDFT57yNMebSSy81v/nNb3L+nBOJhKmqqjLNzc1m2bJlZuPGjcaY3P1eP/7442bRokXWfbl6zmMxIa+E+vv7dezYMdXW1o4Yr62t1eHDhz3N6uvV3t6uWCw24jkIhUJatmxZTj0H8XhckjRjxgxJU+O8h4aGtG/fPvX09Gjp0qU5f87r16/XHXfcodtvv33EeC6f96lTp1RRUaHKykrdc889ev/99yXl9jlfrAnXRVuSzp49q6GhIUUikRHjkUhEsVjM06y+Xl+cp+05OH36tI8pZZ0xRps2bdJNN92k+fPnS8rt825ra9PSpUt1/vx5TZ8+XS+//LKuv/761A+fXDznffv26a233lJra+uofbn6va6urtZLL72kuXPn6uOPP9aTTz6pmpoanTx5MmfPeSwmZAh9IQiCEV8bY0aN5bpcfg42bNigt99+W3/7299G7cvF87722mt14sQJffbZZ/rDH/6gtWvXqqWlJbU/1865o6NDGzdu1IEDBzRt2jTncbl23itXrkz9e8GCBVq6dKm+8Y1vaM+ePbrxxhsl5d45j8WE/HXc5Zdfrvz8/FFXPZ2dnaPeQeSqL6ppcvU5ePjhh/Xaa6/pzTffHLF+VC6fd1FRka655hotWbJEjY2NWrRokZ5++umcPedjx46ps7NTixcvVkFBgQoKCtTS0qJf/epXKigoSJ1brp33hUpLS7VgwQKdOnUqZ7/XYzEhQ6ioqEiLFy9Wc3PziPHm5mbV1NR4mtXXq7KyUtFodMRz0N/fr5aWlkn9HBhjtGHDBu3fv19vvPGGKisrR+zP1fO2McYomUzm7Dnfdtttamtr04kTJ1LbkiVLdN999+nEiRO6+uqrc/K8L5RMJvXuu+9q1qxZOfu9HhNvJRFfYd++faawsNA8//zz5p133jF1dXWmtLTUfPDBB76nljWJRMIcP37cHD9+3EgyO3bsMMePHzenT582xhizbds2Ew6Hzf79+01bW5u59957zaxZs0x3d7fnmV+8hx56yITDYXPw4EFz5syZ1Nbb25s6JhfPe8uWLebQoUOmvb3dvP322+axxx4zeXl55sCBA8aY3Dxnmy9XxxmTm+f9k5/8xBw8eNC8//775siRI+b73/++KSsrS/3sysVzHosJG0LGGPPMM8+Yq666yhQVFZkbbrghVcabK958800jadS2du1aY8zn5ZyPP/64iUajJhQKmVtuucW0tbX5nfQY2c5XknnhhRdSx+Tief/4xz9OvZZnzpxpbrvttlQAGZOb52xzYQjl4nnffffdZtasWaawsNBUVFSY1atXm5MnT6b25+I5jwXrCQEAvJmQfxMCAEwNhBAAwBtCCADgDSEEAPCGEAIAeEMIAQC8IYQAAN4QQgAAbwghAIA3hBAAwBtCCADgzf8BaTV69m5sGzsAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(exp.getImage().getArray())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "516ec59d-c0d9-4a63-9d84-1f7a8cdb405d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "wl_shear_sim",
+   "language": "python",
+   "name": "wl_shear_sim"
+  },
+  "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": 5
+}
diff --git a/shear_meas_tests/test_shear_meas.py b/shear_meas_tests/test_shear_meas.py
index 73647cd5..b19d5e0c 100644
--- a/shear_meas_tests/test_shear_meas.py
+++ b/shear_meas_tests/test_shear_meas.py
@@ -8,6 +8,10 @@
 
 from metadetect.lsst.metadetect import run_metadetect
 import descwl_shear_sims as sim
+from descwl_shear_sims.shear import ShearConstant
+
+shear_obj_p = ShearConstant(g1=0.02, g2=0.)
+shear_obj_m = ShearConstant(g1=-0.02, g2=0.)
 
 CONFIG = {
     "meas_type": "wmom",
@@ -29,7 +33,7 @@
 }
 
 
-def _make_lsst_sim(*, rng, g1, g2, layout):
+def _make_lsst_sim(*, rng, shear_obj, layout):
 
     galaxy_catalog = sim.galaxies.make_galaxy_catalog(
         rng=rng,
@@ -45,8 +49,7 @@ def _make_lsst_sim(*, rng, g1, g2, layout):
         rng=rng,
         galaxy_catalog=galaxy_catalog,
         coadd_dim=sim.sim.DEFAULT_SIM_CONFIG["coadd_dim"],
-        g1=g1,
-        g2=g2,
+        shear_obj=shear_obj,
         psf=psf,
     )
     return sim_data
@@ -150,9 +153,9 @@ def _coadd_sim_data(rng, sim_data, nowarp, remove_poisson):
     return extract_multiband_coadd_data(coadd_data_list)
 
 
-def _run_sim_one(*, seed, mdet_seed, g1, g2, **kwargs):
+def _run_sim_one(*, seed, mdet_seed, shear_obj, **kwargs):
     rng = np.random.RandomState(seed=seed)
-    sim_data = _make_lsst_sim(rng=rng, g1=g1, g2=g2, **kwargs)
+    sim_data = _make_lsst_sim(rng=rng, shear_obj=shear_obj, **kwargs)
 
     coadd_data = _coadd_sim_data(
         rng=rng, sim_data=sim_data, nowarp=True, remove_poisson=False,
@@ -170,12 +173,18 @@ def _run_sim_one(*, seed, mdet_seed, g1, g2, **kwargs):
 
 def run_sim(seed, mdet_seed, **kwargs):
     # positive shear
-    _pres = _run_sim_one(seed=seed, mdet_seed=mdet_seed, g1=0.02, g2=0, **kwargs)
+    _pres = _run_sim_one(
+        seed=seed, mdet_seed=mdet_seed,
+        shear_obj=shear_obj_p, **kwargs,
+    )
     if _pres is None:
         return None
 
     # negative shear
-    _mres = _run_sim_one(seed=seed, mdet_seed=mdet_seed, g1=-0.02, g2=0, **kwargs)
+    _mres = _run_sim_one(
+        seed=seed, mdet_seed=mdet_seed,
+        shear_obj=shear_obj_m, **kwargs,
+    )
     if _mres is None:
         return None
 
diff --git a/shear_meas_tests/test_shear_meas_ringtest_metacal.py b/shear_meas_tests/test_shear_meas_ringtest_metacal.py
index 5843b272..d9e1772c 100644
--- a/shear_meas_tests/test_shear_meas_ringtest_metacal.py
+++ b/shear_meas_tests/test_shear_meas_ringtest_metacal.py
@@ -6,7 +6,9 @@
 from descwl_shear_sims.galaxies import make_galaxy_catalog
 from descwl_shear_sims.psfs import make_fixed_psf  # for making a power spectrum PSF
 from descwl_shear_sims.constants import SCALE
+from descwl_shear_sims.shear import ShearConstant
 
+shear_obj = ShearConstant(g1=0.02, g2=0.)
 
 rng0 = np.random.RandomState(1024)
 # We will measure moments with a fixed gaussian weight function
@@ -68,8 +70,7 @@ def make_desc_sim(ran_seed, psf):
             rng=rng,
             galaxy_catalog=galaxy_catalog,
             coadd_dim=coadd_dim,
-            g1=0.02,
-            g2=0.00,
+            shear_obj=shear_obj,
             psf=psf,
             bands=band_list,
             noise_factor=0.0,

From 16a2fe512569baf3803fb5c447013e852854a1a7 Mon Sep 17 00:00:00 2001
From: Xiangchong Li <mr.superonion@hotmail.com>
Date: Tue, 25 Jul 2023 22:37:02 -0700
Subject: [PATCH 07/30] lint

---
 descwl_shear_sims/shear.py            |  3 ++-
 examples/example_NFWShear_imsim.ipynb | 21 ++++++++++++---------
 2 files changed, 14 insertions(+), 10 deletions(-)

diff --git a/descwl_shear_sims/shear.py b/descwl_shear_sims/shear.py
index bfb1f78a..dcb1f53f 100644
--- a/descwl_shear_sims/shear.py
+++ b/descwl_shear_sims/shear.py
@@ -68,7 +68,8 @@ def get_shear(self, redshift, shift):
             g1 = gamma1 / (1-kappa)
             g2 = gamma2 / (1-kappa)
         else:
-            g1 = 0.; g2 = 0.
+            g1 = 0.
+            g2 = 0.
         shear = galsim.Shear(g1=g1, g2=g2)
         return shear
 
diff --git a/examples/example_NFWShear_imsim.ipynb b/examples/example_NFWShear_imsim.ipynb
index e58356b8..4b84d596 100644
--- a/examples/example_NFWShear_imsim.ipynb
+++ b/examples/example_NFWShear_imsim.ipynb
@@ -65,33 +65,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "id": "5db4e4bf-7d48-4bab-9692-1d21f6f0952b",
    "metadata": {},
    "outputs": [],
    "source": [
-    "exp = sim_data[\"band_data\"][\"r\"][0]"
+    "exp = sim_data[\"band_data\"][\"r\"][0]\n",
+    "img = exp.getImage().getArray()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "998c9093-b310-4722-af44-bc1c9311866a",
+   "execution_count": 21,
+   "id": "516ec59d-c0d9-4a63-9d84-1f7a8cdb405d",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f2433ad4690>"
+       "<matplotlib.image.AxesImage at 0x7f2433770710>"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -101,13 +102,15 @@
     }
    ],
    "source": [
-    "plt.imshow(exp.getImage().getArray())"
+    "from astropy.visualization import simple_norm\n",
+    "plt.imshow(img,aspect='equal',cmap='RdYlBu_r',origin='lower',interpolation='None',\\\n",
+    "             norm=simple_norm(img,'asinh',asinh_a=0.1,min_cut=-0.01,max_cut=4))"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "516ec59d-c0d9-4a63-9d84-1f7a8cdb405d",
+   "id": "e1585107-4a8d-4178-80e0-9ba793475f70",
    "metadata": {},
    "outputs": [],
    "source": []

From b64f6d9de39f8eeef97086e749f8a121f3f2d29d Mon Sep 17 00:00:00 2001
From: Xiangchong Li <mr.superonion@hotmail.com>
Date: Wed, 26 Jul 2023 07:30:54 -0700
Subject: [PATCH 08/30] update gitignore

---
 .gitignore                                    |   1 +
 .virtual_documents/examples/Untitled.ipynb    |  50 --------
 .../examples/example_NFWShear.ipynb           | 118 ------------------
 .../examples/example_NFWShear_imsim.ipynb     |  66 ----------
 examples/example_NFWShear_imsim.ipynb         |  21 ++--
 5 files changed, 12 insertions(+), 244 deletions(-)
 delete mode 100644 .virtual_documents/examples/Untitled.ipynb
 delete mode 100644 .virtual_documents/examples/example_NFWShear.ipynb
 delete mode 100644 .virtual_documents/examples/example_NFWShear_imsim.ipynb

diff --git a/.gitignore b/.gitignore
index d34c2830..848f8830 100644
--- a/.gitignore
+++ b/.gitignore
@@ -105,3 +105,4 @@ venv.bak/
 .mypy_cache/
 pytest_session.txt
 outputs
+.virtual_documents/
diff --git a/.virtual_documents/examples/Untitled.ipynb b/.virtual_documents/examples/Untitled.ipynb
deleted file mode 100644
index 63ae311a..00000000
--- a/.virtual_documents/examples/Untitled.ipynb
+++ /dev/null
@@ -1,50 +0,0 @@
-import clmm
-import numpy as np
-
-from descwl_shear_sims.galaxies import make_galaxy_catalog
-from descwl_shear_sims.galaxies import WLDeblendGalaxyCatalog
-from descwl_shear_sims.psfs import make_fixed_psf
-from descwl_shear_sims.surveys import get_survey
-
-from descwl_shear_sims.sim import make_sim
-from descwl_shear_sims.shear import ShearNFW
-
-cosmo = clmm.Cosmology(H0=70.0, Omega_dm0=0.27 - 0.045, Omega_b0=0.045, Omega_k0=0.0)
-halo = clmm.Modeling(massdef="mean", delta_mdef=200, halo_profile_model="nfw")
-halo.set_cosmo(cosmo)
-halo.set_concentration(4)
-halo.set_mass(1.0e15)
-z_cl = 1.0
-# source properties
-z_source = 2.0  # all sources in the same plane
-
-shear_obj = ShearNFW(halo, z_cl)
-
-seed = 74321
-rng = np.random.RandomState(seed)
-
-coadd_dim = 200
-se_dim = 200
-psf_dim = 41
-
-galaxy_catalog = WLDeblendGalaxyCatalog(
-    rng=rng,
-    coadd_dim=coadd_dim,
-    buff=2,
-    layout="random_disk",
-)
-psf = make_fixed_psf(psf_type="gauss")
-band_list = ["r"]
-
-sim_data = make_sim(
-    rng=rng,
-    galaxy_catalog=galaxy_catalog,
-    coadd_dim=coadd_dim,
-    shear_obj=shear_obj,
-    psf=psf,
-    noise_factor=0.0,
-    bands=band_list,
-)
-
-
-
diff --git a/.virtual_documents/examples/example_NFWShear.ipynb b/.virtual_documents/examples/example_NFWShear.ipynb
deleted file mode 100644
index ab8cccb9..00000000
--- a/.virtual_documents/examples/example_NFWShear.ipynb
+++ /dev/null
@@ -1,118 +0,0 @@
-get_ipython().run_line_magic("matplotlib", " inline")
-get_ipython().run_line_magic("reload_ext", " autoreload")
-get_ipython().run_line_magic("autoreload", " 2")
-
-import clmm
-import galsim
-import numpy as np
-import descwl_shear_sims as dss
-import matplotlib.pyplot as plt
-
-from descwl_shear_sims.galaxies import WLDeblendGalaxyCatalog
-from descwl_shear_sims.objlists import get_objlist
-from descwl_shear_sims.surveys import get_survey
-
-
-cosmo = clmm.Cosmology(H0=70.0, Omega_dm0=0.27 - 0.045, Omega_b0=0.045, Omega_k0=0.0)
-halo = clmm.Modeling(massdef="mean", delta_mdef=200, halo_profile_model="nfw")
-halo.set_cosmo(cosmo)
-halo.set_concentration(4)
-halo.set_mass(1.0e15)
-z_cl = 1.0
-# source properties
-z_source = 2.0  # all sources in the same plane
-
-shear_obj = dss.shear.ShearNFW(halo, z_cl)
-
-
-# generate positions
-radius = 50 # arcsec
-n_gal = 20
-theta = np.linspace(0, 360, n_gal) / n_gal
-x = np.cos(theta) * radius
-y = np.sin(theta) * radius
-shifts = np.zeros(n_gal, dtype=[('dx', 'f8'), ('dy', 'f8')])
-shifts['dx'] = x
-shifts['dy'] = y
-
-shift_list = []
-for ss in shifts:
-    shift_list.append(galsim.PositionD(ss['dx'], ss['dy']))
-
-# get the shear
-shear_list = []
-for ss in shift_list:
-    shear_list.append(shear_obj.get_shear(z_source, ss))
-
-
-gamma = []
-for ss in shear_list:
-    gamma.append(ss.g1 + 1j * ss.g2) 
-gamma = np.array(gamma)
-
-
-angles = np.angle(gamma, deg=True) / 2.
-lengths = np.abs(gamma) * 100.
-
-# Create whisker plot
-plt.figure(figsize=(6, 6))
-plt.quiver(x, y, np.cos(np.deg2rad(angles)), np.sin(np.deg2rad(angles)),
-           color="black", headaxislength=0, headlength=0, headwidth=1, pivot = 'middle')
-plt.xlabel('x')
-plt.ylabel('y')
-plt.show()
-
-
-n_gal = 1
-d_array = []
-g_array = []
-for i in range(10):
-    position = galsim.PositionD(3.0 ** i, 0.)
-    # get the shear
-    shear = shear_obj.get_shear(z_source, position)
-    g1 = shear.g1
-    g2 = shear.g2
-    gabs = np.abs(g1 + 1j * g2)
-    d_array.append(3 ** i)
-    g_array.append(gabs)
-d_array = np.array(d_array)
-g_array = np.array(g_array)
-
-
-plt.plot(d_array / 3600., g_array)
-plt.xscale("log")
-plt.yscale("log")
-plt.xlabel("separation [degree]")
-plt.ylabel(r"$|g|$")
-# We set kappa_max to 0.8
-
-
-band="r"
-rng = np.random.RandomState(1)
-coadd_dim = 500
-buff = 50
-# galaxy catalog; you can make your own
-galaxy_catalog = WLDeblendGalaxyCatalog(
-    rng=rng,
-    coadd_dim=coadd_dim,
-    buff=buff,
-    layout="random",
-)
-
-survey = get_survey(gal_type=galaxy_catalog.gal_type, band=band)
-noise_for_gsparams = survey.noise
-lists = get_objlist(
-    galaxy_catalog=galaxy_catalog,
-    survey=survey,
-    star_catalog=None,
-    noise=noise_for_gsparams,
-)
-
-
-lists.keys()
-
-
-lists["redshifts"]
-
-
-
diff --git a/.virtual_documents/examples/example_NFWShear_imsim.ipynb b/.virtual_documents/examples/example_NFWShear_imsim.ipynb
deleted file mode 100644
index 92409184..00000000
--- a/.virtual_documents/examples/example_NFWShear_imsim.ipynb
+++ /dev/null
@@ -1,66 +0,0 @@
-get_ipython().run_line_magic("matplotlib", " inline")
-get_ipython().run_line_magic("reload_ext", " autoreload")
-get_ipython().run_line_magic("autoreload", " 2")
-import clmm
-import numpy as np
-
-from descwl_shear_sims.galaxies import make_galaxy_catalog
-from descwl_shear_sims.galaxies import WLDeblendGalaxyCatalog
-from descwl_shear_sims.psfs import make_fixed_psf
-from descwl_shear_sims.surveys import get_survey
-
-from descwl_shear_sims.sim import make_sim
-from descwl_shear_sims.shear import ShearNFW
-
-
-import matplotlib.pyplot as plt
-
-
-cosmo = clmm.Cosmology(H0=70.0, Omega_dm0=0.27 - 0.045, Omega_b0=0.045, Omega_k0=0.0)
-halo = clmm.Modeling(massdef="mean", delta_mdef=200, halo_profile_model="nfw")
-halo.set_cosmo(cosmo)
-halo.set_concentration(4)
-halo.set_mass(1.0e15)
-z_cl = 1.0
-# source properties
-z_source = 2.0  # all sources in the same plane
-
-shear_obj = ShearNFW(halo, z_cl)
-
-seed = 74321
-rng = np.random.RandomState(seed)
-
-coadd_dim = 50
-se_dim = 50
-psf_dim = 41
-
-galaxy_catalog = WLDeblendGalaxyCatalog(
-    rng=rng,
-    coadd_dim=coadd_dim,
-    buff=2,
-    layout="random_disk",
-)
-psf = make_fixed_psf(psf_type="gauss")
-band_list = ["r"]
-
-sim_data = make_sim(
-    rng=rng,
-    galaxy_catalog=galaxy_catalog,
-    coadd_dim=coadd_dim,
-    shear_obj=shear_obj,
-    psf=psf,
-    noise_factor=0.0,
-    bands=band_list,
-)
-
-
-exp = sim_data["band_data"]["r"][0]
-img = exp.getImage().getArray()
-
-
-from astropy.visualization import simple_norm
-plt.imshow(img,aspect='equal',cmap='RdYlBu_r',origin='lower',interpolation='None',\
-             norm=simple_norm(img,'asinh',asinh_a=0.1,min_cut=-0.01,max_cut=4))
-
-
-
diff --git a/examples/example_NFWShear_imsim.ipynb b/examples/example_NFWShear_imsim.ipynb
index 4b84d596..0d41fec1 100644
--- a/examples/example_NFWShear_imsim.ipynb
+++ b/examples/example_NFWShear_imsim.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 24,
    "id": "177f93ce-ec8b-4c36-9ff4-d16951a4941f",
    "metadata": {},
    "outputs": [],
@@ -29,7 +29,7 @@
     "halo = clmm.Modeling(massdef=\"mean\", delta_mdef=200, halo_profile_model=\"nfw\")\n",
     "halo.set_cosmo(cosmo)\n",
     "halo.set_concentration(4)\n",
-    "halo.set_mass(1.0e15)\n",
+    "halo.set_mass(1.0e16)\n",
     "z_cl = 1.0\n",
     "# source properties\n",
     "z_source = 2.0  # all sources in the same plane\n",
@@ -39,8 +39,8 @@
     "seed = 74321\n",
     "rng = np.random.RandomState(seed)\n",
     "\n",
-    "coadd_dim = 50\n",
-    "se_dim = 50\n",
+    "coadd_dim = 1000\n",
+    "se_dim = 1000\n",
     "psf_dim = 41\n",
     "\n",
     "galaxy_catalog = WLDeblendGalaxyCatalog(\n",
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 25,
    "id": "5db4e4bf-7d48-4bab-9692-1d21f6f0952b",
    "metadata": {},
    "outputs": [],
@@ -76,23 +76,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 28,
    "id": "516ec59d-c0d9-4a63-9d84-1f7a8cdb405d",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f2433770710>"
+       "(-0.5, 1009.5, -0.5, 1009.5)"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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2Y2gGrvJMvvadi7lq4l6a+pfw1fs30rCq/pBDFEdwagmHILllbG4bNq8TTdGx2SQchRkINskUkwsrh5wDphxGVmkmmV7nYQZfi2m0tAfp9YVNSfETzBQbDkd7XkMzyQ39NX7ivjC6opvrP0l1rP9lpJ1CGiNCj6lsXN9IIFREXMlk+8v7ifvCo/qS61GVSGMAJHFIJ1IyKZfLzijF6bCx//lKDGVo9s9opZYFWSLT6ySvIINgb5TuEyQqJ8gSFfNL+NalbdDTjHjNBTy0uwNBFJh53gSujPyJ4J0rKL/9LKbMfD+NbzaY77McgnT4ugRJSDphyWlGPAVlmUyalk+m205XT5ia/V30NvWZDgHMpraEkQw09rKrI3SYAmm4vpdtvjB6TB3Q9zCm9yO1gG5pTaXAULRjolvrim5GWoGoeZzTvInvnYK0U3gb4mQVFS06aMtbjbSubzJ3a2HliOe10ijJXd0wYnT1m1v4eUShp6VvSEmPo9XdNxSN7sYA/cEY4c7wiTOCokBuYQa0bMBoa6U090ozd++xM2F8NrR3YvPYEcrLaNzkP5TuSEyDS653iF2vIAlILhl3tot580u566JupLVPIVy+hPtKFvP6awcJqJqZklKEZGE93Ng3oI5iGWE1aDYZnigMHosqOU31WkGWQDefFy0qJDcFxxIxpB3ByUXaKbyNYA2Yt+e7UfyRo5artvSLjkZTSVd0jMTu83hz/qkwdINocx/1baHDisLDHWOk1AuYqZJwfS+RJsHsaD5B2lGGorFzRQ13ey8k021n+RMbifdE0RWddWvqWf/JHzBuqczTO9qp27DR/IwSDsESjhuuIc2in+q6jk0SsbXtpOfP68m+roszzrqULbkugh0hhKiGIKkI+qGZFicbSYcgS9jcNiSPA3dFFsXlXpwuG8FAlM76XsKNfWhhME6Qk05jbJF2CmMA0WlLfim0sIIaiI55MUx02ph+zTS+cOt85uRV89PlTpb/aUtymMyR4CrP5LN3v4tbxm/iA3/K5sAT+0Z97tE6A4sBoyv6kOmiwYbQakIbfJzDjp3YhQ6bT7YMoqajhU/cCE0LuqLTX+Pnlfs2JH82FA3DLZNf4uHsmh8RengLn//2Lbw2ZQKdKYXf5NjQhIEcfE26oqOG4oRFgZ0723im7N1c9Ld30RIvY+PGZsL9hzNwTIntMb/MEZEaIUgOCdc4L2XT83nvNTM5tzyEKCjs7irg0ZUH2RuuI9Z5+DNhFdStaCf1uUlHB6cOaadwnBAkk2UzbnEJ114xnZ1VXax9dPeYDZ2xIHsdfOrmM1i459vE19dx7cf/w+vO7aij7MIdd2YZt3ieoPW9/+BDf1jON5/eP2ZfPMtA2PPdLL5pFu1NAepeqz3cCAzBrz/isWWJzOl5OHOchJr6iHX2DxS5041DOWzt8OauEwWzycycDZBcj65js4kQ6icWiJOpxpLrSUY5+tDPRJKVpZsRjioKtDcFePqVKjZVeNH1g7Q2BujriZhpscRxUjufTwUEWcKWaadsej5XXjKFa7JfRnvsVQS3zLIrb6Rq9gIqt7SYTDDJZEuJTlM2JWtKLvnFmRi6QVd7kFB9AMUfQY+eWOmUNEZG2ikcJ0SHjbmXT+H+O8LUXXA5N103hT985h6e/vNmIo2BMXm4Bckca3hGQSvxfzegh+K0hWLD6uIMhdY9nay8+f1c/HAhX35g55itK/n/ooB3eh7fuqGEnZ0T+N6G5gEDVQbINgzRmTvk8UWBnMUl/Of781B1F794vpEdb9RhKFrSOaRGAwbiSU2jDL6HajDOnpeq+foHPsXsr+Wy6oUGfFurzPRYYhy6ziBjrh2qN1ibCENTkoJ+jf4Izfu7zPcqOnpMTYr3nWpqppUOc5Zlceu1M7nK8QSPFf0aLbEXuP7LLXzkl9/i+RfcRNtDZkRRkcv7Pr6Yjy2sIfrdX2K06TivnEPtGT/gR//exsE19cQ147RXhn0nI+0UjhOGbiBKAvR3U7k7huSoY8EtubyY4zSZN2MEJRDjl68ZXP+hf9EVjvPrX6wxi42j/OKEqnr4xu2PI8gSWrjnmNcxOL2Tyqjx7+niE/fsob8vhiCafH1IDFuxUiy6YZrFRGfusAqmCRaLFlZ4YqdGVX0ju9c1cvWtZ1Be5OGBX601d5UpxwWS3P3BBdeTAV3RiXX088Zv1rNq0LVZchyGPjCqSSLxs04iXaYbCXlwaWAHd4rg32DHcqTeEev9YwbJ7NUonZjDZROqCXzikaRDAOhvCSFpmeiaD0GW8EzN5UtfOJd3PfExHlm8P/m68f+p5ZyDl2B3eA8p26YdwilD2ikcLzSdyi0tPHPWMq5/7DKYUMGfdyTC5TGCoRkogRirn97P5rUNRP1RQtU9R71T1BX9mEdmHol2KIgmw6RrV4c5xznfjeSWzTx/zJSz1hL8eiDpGCwFViUQG5It1F/j5+HfrU++r60rhDfTjj3XiZFg4egAg8T5RnI4JxqDGTOpTCwrSoJBMhWWc01EFVbKSRjU25AqF3KkAnPqZyZ7HWbHczQhLT5GRXhRFsnIdGALNxHuHDjnOueiCpY3VhAOtiCIAgUVXs4sjdK1qmnA66adm8/evmXUV68a8IykcWqQdgrHCV3RCezs4J6vv8pfZl6Frhv0VW4f885RLazQX91Nf3X3Sd1JpXbZWnRD0WkzO26jWvJLbElsOwoz8JZmcva541kwKY+2QIR1W1pobzajJlXRCDb3EevoR3I7ufCO+dx1mYcvPNDEgaf3D1l4VXoiyaau/TvaKMrP4OprZ7J7Xwc129rMiEESADXZ0HU6DSmyKLow/G5+gO6RtfNPOIdhoQ3tYFIhex2c/5GF/DjwczRfBPtdn+Pyb4Txb2wZm/RTohdEdZdQcO0UeH4bAOdd6UH76Nd49h8Hzb4W3SAe09jW7uKyP3+U965Yi9oWwvH5D/NUyzncf/drBA90n3Ap9jSOjLRTGANYaQOLZXKiHupj/RLbc5187meX47JL3PP1V0c9/9lyCJYGTfHCEi551xSmFGei6gZrdrWy5c0GYp396DEVm8csOL7rgoncXvddDt68gYu/vIRZV/+eiqwQBc4qdncv4nsPbKDdF0Z02vB6HPQr+XS07j103pSdsdXUJbllbB47FZPz+NS5OplCLavGn8nvfWG6NSOpi5Pa0JV6jFNtaEbblDc45XTE444wa0F02rjs02fyPeVXPPrhLQC8t+hvGNqtgElxNnTjuKIGQ9HwtQdZ1TSfCz/6eW4963WE/EJ6cy/hhy+Hqd5Sn9R16tjZzi/a3uTPJdOx2WYS6IsQ+lgANfBiWtfoNELaKYwhTteH+sYvn4v33AsJh+HOrW/w6089d8T3WA7BlmnHnu8mszyLj9w6jysbfkzwx9txVGRx/jd+zNf6iqnc0kKsO2JOSCvMYFZxFn2/r2HrFo3yre0sunENsXv+hD49l5JrJxPs6jfTGOEQT93zFk/L0uHS1QlevyCaEYjNbcOe68Sb40Q1HCDbyXHKFI/zErQ6XhXtUEPXoNrC6YjDqKWDnMSxrN06ptUd7esOE7/tTm74ShOuL9/Et7ecQ+/2DbgnZnPvz66kIRDhni++iBKIDXtMQRJwlngABrLqNLPw3dPSxyubGunoG8/EvM+xq7GXXa92UrmpmWhLX1LdVQ3GiXX007e7M3l9p+t35n8ZaafwDocgCaiqTm4OuF2wL3Tk7tZUh+CekE3+OC+l47M5uyyE7/828sYT3cyZ1casL1Zjt09M1hcMzaA/GONAZ5Al37uZmy/YgHT5u8Duxr6gFOGiq3h4S5xISzCZD9fCOoY+/JpSI4WMTAc5WU56Y8WIgoJNFCgp8tDeFCAQVVFDEoKkgjLs4U4KrJnWlj7RaJFq0I8Wg42roZkRwM4XKnn3gVwmnvFj9v+wif7aTRiKxpnXzmDO3z/AwlkFPDDrBnrWNw97bDnHxbd+dgVuWeJbd71INDESFRLpvUAMf3eYQChOj9vOgdpuGqp9xDpN5z/S9Ls0Tj+kncI7HJJbZu+eDrwPv4ovEOGl768c8fWpTWgFC0v56AcXMDU3g+5IHIEevIuLOdsfpfj6KeyPX0Fd5VpTsCzBrW+s6WGlojPxhg+w6LZlNIem8+Z2H13OL7PqhwcJVq8zi/CjkLYWJAFEEdFhw5XtpLjcy7kzCpmoPomxv4bZFVPxz76SltYgsYhC3B9FE0VSp7CdbIiySOasAs67ZjotzQF2P7X/qGUmjhRBDPe6wdAVnUhzkEhzkI5VDQP+5nHLRKr9oBlkZDkYiY/mLPFwhfsZjF4fRfMW09B20Dx/ouFM8Udo2NtJLKKyJ8dF9a52oi19yYFM6Yjg7YW0U3iHwzuviL9/3s3TVU4e/cOGURko0WkqdJZNzGFitot8V4C6Xpk/rlG46o5/UnxnhLXdNh7460Z8ezuTekgWnbBFM6jpClGWOYMNjd08+vBO+mv9o9JNspAqJ20xXMaVZbGsYD2rxv+AtnaYPhWWVZaysrgQX3uQUEJCYoDuknW8UdQVJLdMxuQcDEWjvz4wqly7JZUNiXkFuoGnIIOPnD+J7rDCXdvaCO7rOi6jONx7j4dm+sofN5N514MEw3HafrN+xNeGqnt4z98mkumdQev66oFrUzTUoE6ouoeaGj+CJCTVTIfqTnaVZzLjXZOo291B77b2o153GiceaafwDseicyoQH/opN33oc/yt2EO4rnfY1yaZRqLZWVu9q53vt/ahxDT6WoMYqsbePR1c/q7JOB06HdU9xHuiA+iTqq4Tjqo8/MhOns10EGgNEmkKoEVNhlLG5BzOuXoaG9+oI7CtbVR5c0GWcGXYKch1YaxdTlvCllRWw4KDm8j23ITdYUtew7FAdNq4857Lud31HyKTr+Kqj+8nXNc74i5XkARcFV7u/eVVOG0in/vqS4TreunY2MwXf/MWNptIrHN0Rf2jXq8sDpAUOVoSQtwX5pHvvg4cmcBgKBqNr9clm9VEh+2wCMCiFkdagsNKVQiSwNm3zOUX13Xw3+qzuPfTz53yBrw0DkfaKbzD0euPIs0cT1tkJmpo+NTRAOqpW8bQDCKNAcL1vehRk+opSALNtPMqINsls6vYMgAJ+qQVMYSqewhhGhQtag6hkZx2Sqfn8/lLCnjQbefpfV0Dup5HBdvAR1ZwOFBUHT1F9uFYYHPbuHaGjvr3rbgnzMHQDGyZdrSoNixNUhAF5CwHM3LbkMQ4cpYZMajBOC2rGxAkYcwkG1KjAkESkHNd5M8twl/TY+b4j8G4HpWYYiKlaE13U0MDdavKzy5n3sJSXnlw+7BNm4ZmUL2ng1VLFrNybeVpTQL4X0baKZxAWKwNyS0T94WJ94xdQ9tI54RDxnHvylr+vfCzvPrnXYTre4d9j+gw2T3Z0/K4/oZZrN/czJ4n9w/oFgazmawqEW0MRZ80NDCUhMEZxKNXg3EaNrVw35RcqvZ2jtpYGppBLKrQ3RtFuOgKlpz1Eps3qFxySx6B4ivp3h4hGlGPy8jois4PnvaxaNmvefbeA+hKH1Mvm0IwEKVrdwfRttBh67Wc3/t+WAdA3z5f0tAetbMbAaIsYvM6EWWRuC+MoyiDqz62mI+fl8Fdf6mn6uXqE6YICyS7011lmRRUePH7wvRVdicYS6ZjUFWdyChUUJtX1PK11Q3JSXVpnH5IO4UTCGdZFg/8/jomZNXzmzcknrtn7Zgai1RYCpw2t23A3INIY4A//3QNij8y4rklt8yC62bw3gsn09IXJdgbHTYtkdqMNew85CFmKBi6QbSlj+X3rkv+nHqswa+16gB6TKW/L05Tax+rWhZy4epfMiXQiZo3jaers2hr7qQ/GDPXquvHZGzUYJx1f9rCusTPOUvL+P0nC3DZerj3jSU89bM3h7x/WlihbWX9UZ/vaCC5ZYoWlVA2MYdtT+zDWexh0dR8HKKPns4QWmz4wro1K1tySkM6ttFAkCVcFV4WnTee65aOZ09LgMcf2Ul/jT9JJW5eUUvzG/VHnsinGSNO2Evj1OPEjKZKAwB7jpPZuZvIaH+DeZPzjznfPRoIooDNY8c1zmsOeElRJI119o+qyLtkbjHLxMc4Z1wWNvnIj4bklik4q4yMyTmjXqeVXrLmDB/pteimw4n0x+n1hdle18PG7nPYK76HVU3T2V3tI5BweMcrj5Bcl27gdMpkdr6O9Np/eO+SClOyYwxh1QRsmfZRPReyXSI3x4Uoiyh9MZZvbuL3q6Fnb9eI1y17HUy6cAIz3jUpKdt9NLAowXnFHs6fV8o5nld5z1wonZhjrl2WkpIi6d3/OwPpSOEEItIU4O6Xp1NaMJkXX9h2YkN8WcKR76JidiH1SntyePtodmZGoiN4f10PhsPPuIwXOOfsJdS9Xoc+QlNT5vQ8fvD5ZSzf1crTP3tzVLvE4X4/Eu1Sj6oogRhdUpAdO9to8/VjkwTCYYXWhl76EmMorahGlMWk8N6xNLAZmkGgsZfegivInqEQVXUk59Eb1OEgSAL2fDd5swspLMtk//Ka5BzioaCFFVq3tOJvCyby+XHW/XuXyfw5Qiew6LCRkWknHtOOWfpDlEXKJ+Zw6fha/B/5HdnXvMQlF/yYJ3xhejQDNRA1oxVt5LqOKIvIuS7sOU4QRdRQPPnetEM5fZB2CicQ8Z4oL1mpkpPw0KthlZY6vzl45ygNoR5VWf3QDr7/sWup6M9k+y5TtGwkKmeouoe7f7+WYHPfqHbpRxUppQjDWYNn0HVaFZ3ehMNTVY1YXywppmdoBq5xXpZePgV/d4Q9r1abdZxEXeRo5C7CjX185J5aliy9gqqq7Ufda2DpQwGHRWmSW2buVdMoKc2kKiGLPRJ0RSfW2Y8SiCXHfppqt9aAneGvKdbZz84n9h2z1LYgmr0idruErHbRtLkTURa54hqR2HUzWPF6De37u4j7wmaj2hDMI1umnYxJOfz5p1cwaeVnMFQdcdk5PBW8gT8+sJH+Gn9SCiONU4+0UxgjDGdwTmR0kApD0cy6QSiGGj42xkvcF+aV+zZg89gxdGPYXLVl3LWwQn9XmOKZBbRDksI55HtSlFWTCqbDNLClDqWxpKL1mICqm4ZNDURNITbdVGBNZUctuGgiP1m4GqO9nWfP+zy//OZy4v7oiBGDRS1Vg/FkhKVHVVpWN9CxrQ1DMw6rJ9gy7Uy7ciqqqlHzSs2Av4tOG3lnlnLJ5dMozHXx4P0bB/QqGJpBuD9Oe3uIujcbTMG4UaTSDEXDlu9mwTXTuGRpBQuLXfx3SzuvPLefwLa2Yes/xzKjOenAJRFBEujri9GqnsuMj89B6wqTteqHfOzs8xGFc3m2X6En4fi0lM2BVc+4/f8u4JPiX3g0+xtsSvwtL3c9F7dewgMJdd10lHD6IF1TGAM4SzxkLyjGUZRxytZgUUklt3zEWcbDwTJ+cV8YpScy5Jc1dbdvy7TzgU8s4befncM5N8wcNmdtUV1tHjv2HCe2TDuSQzpskP1hSDgNK19tqbKqYdVMPYTVpEOwCszBQBSjsYn+f6zjqkmNo6oF2PPd3PqZs5h3/Yzk7h5Aj6koPRHTCQ1C3qIS/vnpEP/4ogf3hOykXLh7YjYZk3KYMDWfK+aWcOPMPsbPLRpwb/SYSvWKWna9UGne59GysHSz6O7x2DmnPEbZG1/mrukr+foXzsVV4R2zmtVgmXTRYUNTdFqCIvLiSfTX9+F7aAfG5rVMKvDgypDN2kLq+xLEh5w5BXxq3m52fvDxAeeYMFFkTVOm2cNygsgXaRwb0k7hOCF7HTzyj3ez/AObeeSfNye7W08mRFkkc0YeS26ezdL3zMae7z5mAzFSIXioY3Z0h6n2yzTW+s3XiMLAf4lxjRmTc5hy8USmXzKJrOl5ZpFyBIeQuqtPHdJjTR6z/ukxNcmSMhSNyrWNvF7+dez3/YLfvZlt0iZHkNSwOqa9HjvSoOK6NbNAznVRdF4F9lxn8h6Mn5qPsXUN9tYNFE3N5Yovnc26ByWWT/sPjwXv5Y/X7MUuiTy9P4uWOv9hx437wkTbQketjRT3hVn3zH7uWxFAOHMpRmc755XVM+P88chexzF/7lZBOXX2sjVDWfY68OY4sUtmdBZsDqIrGrjdqLqBbg38GfS82Dx2xk/Nh0iQ8UsLk7+/5YlzaHxyLfd8a/lR34OTgcH34n8N6fTRccLQDXxhjXGFBXQfxXjMsYYSiNFQ3Y2eKPyNdTg+1MQ1NRjn2XvX8lzCuFvDclJnAwiSgM3rpHhqLjNnFRJTNNqbAkmWz0gwdMNM+wwYOiPAIEnp1LRMtC3Edz//gjnvIayYRcwUMbbBVFpd0Ym2hfjdV185FHWkQHTYKDuzjE+9bwE//c1bZhe2ZrDj6f184urriGxQCAb6uXpROZGffplnflkPwIXh+5n6oI0/7p9DpCkwZvMdDM2gv8bPK79ez7pZ+UyeNxt9Y4yGqm4QxSPWGAZjKMNnpfos2XTJLeN02NANAyrKKb91BrazZxGdewMbV3biT7DbBn+mhqJRtbWFb3hnM/cr/8X7XTsrtzRx/xMHiN3/1ElLrR4NkhReq0kvED3tnNaJhmAYxqieoEXnPnCi13LCIMqimc/WTsyoP9nrwJ7vNtMuI7B1TiSsjmQ49rkLIx07+f+pu/uUGkHpeRV8+vaF/PpPG+nd2ZHM8Qui6RScJR6cGTLRfoVIYyAplgYkUzxDzZxOHfc5+HeHOZUhDO9ghyB7HVScN57vfPRMntnaxAu/WT8ig0eURdwTs5m4uIzK5TVmEd+SdpDNcZSZ0/MomZTLOWdVcONcN0XSJpa3ncWTK6rZ91rNQLnpMUbycz+G53u4aXqpulOSW8ZVlsnUBaXMnVnIRdMLyXFG2NUJGw50svHNegKJRrZU6QsrfeSuyGLOxZPIynKwe0sL3Tvax3wA1VjCnutk6mVTGD8xh63rG/Ftan1Hpbe2rv3UEV/zjo8UrIdTckpmzjymAWNb2FICsVPmDCwMFb6PBYYNoVPqAYIk4MqQKcpw4Mqw0+e0AYc6jLWwQrSljygMmNaGJOKZmsuXv3o+jZ0hHv3DBrPBKqVYmSw6pwy4H+AMRnAEyZ9T7os93821V05n1o7/Y8Yl7+X1p7yEDviGvXe6ohNu7KMy4fBTX2edR3TaiMdUHvnDBv4TUxFkCTXw/IgSGWOF5Od+lE4nNU0EmPOWZTEZ3aVGX2ooTntzAFXV8AVMllBTYy/tTQGCDYHk55nqkAzNQHKLFE7P59NXzcTrMPhVVKVn75HZVqcKVmd/SXkWpQUedtqk/0kpjne+UxAFJKeEsywLQ9GI+SInJL1yPLBl2ilaWk5PVfewujGnAwbvJpN6SZJA06YWvtIYoGRSLhU3z2bnihrTwOsGRkxNGsfkXGFJZMpVU3j49gYeKTyXj3x7CluXfYu9zxwYwGCBgWyk4dIwI00gS/051tHPqytruP7OT7GpuRgl0HjE69ajKvEhUh1WD0W4vpdoW8icQDdECup0wwCHkHAGkls2SQAeOwCx7kMNgUogRndVN70NvdTuaE9ShK36jh5VD4tQLAM7YVo+E721AGbt4TSfv6yFFbatbWSvy4Z/v++0X++JwDveKVgPq5HCVT+dILllFt46l/uv3MVL/ddz98efOW1yrcOljSxHIMqimcdO7CwFWeKfn4mCr5KPBmax/8UqIGX+sFXAc0gIssSM2UUY7W8xrhycn7yag//XNOy1H82ObSSHrwRi7H9yH5e+mqDdJqiUxzqy06pJJLt6T6PNxkhIdQiy10HZolJuvHYmRR4HW2u6Wf5yJeHGPpSwAgnZlMFpO9FhQ85xmY13Q6RY9JjKzo1NfFcrI9yvsHdlLWrw9B27aUVFgZ0d/9Md2u94pwCgxcwIAcwH9bSEzU5UPb13mMkRmVYeWxSTU8YESSAr1wXBTjB0YhEzjYJoIOg6iCKSUzJHa3pMaYf29hD6ZRex5JObWGXcRqzz6eNe4mi+xIZmDEj3yV4HrgovoeqeY3LIJyp1dyJwGN1UFnGN8/L5Dyzk7OADELfhnfUJ3lpTRzjxusMiH01HdNpwFGZQcUYRteuaiLb0DUytpajl7gYyvU6zdnOap2Msra23y+d5IvCOdwrmh6sluebHsps7nmEmR4IWVtj+2B6urCyir/LVURul1C/3iX6AB1NHU+mKotOGlNj5R/rj3LtzCcFwnNbKA8kCsuSQ8JRmMu/Mcs6cWcTMfA+V3f1s2t/BHzfkc/XnH+OBB9YfToFNSVclJRSGMSrHeg+sSWnvueUM/v3bdSPOmzgSUuXHU5+Z0RoZQRLIP3ccTpdMx+aWE6uqm4gSbF4nV143kzOf/yRPf2E7i89zU/TMRwn3xw/tlIeg9Bqagc1tY9r0fHo6Q3QnmiZTn189qqLJIo98fy45b97D8+/9Lj/62NOHHed0w+m4ppOJd7xTgKPfyR1WhLN+Lw6c6DVWD48SiNG15lBuW5RF7PluANRQfEiZBHdFFp7STKL9Cv3V3UfF6BhSlfQo3is6bXim5pojOMMqklNC9tix2STWvFFLNKokBfpEWSS7JJMFi8t431kTmGhfAd3NuIvfx7ZqkXXrG3jywW3E2oMDpbhTehxEWUQNxk2SQIr66ljcf13R6dvTyX861xNt7jvm41g5dMkt4yzx4Mhx4s1xMeuMYmqqu6l6ufqIOkWOwgxe/nYcug9y9+JLeOHHa455PcOtEQ71kkhuGUdhBksm56FvirPgTAfj7lzAb7Z2E20LDckGSxaSFY3+Wj9bNjRxzvkTKbhhFjv2dbLtiX3JrvDU1xNXcMsSotM24BgDXpPGaYH/CacwWgxu3EnmzRNIbaJK/d1IxzraB172Oig+q5zPfWgx6w908MbzB+jbfWj2gNWodu93L2Ge9ijxgvl88oFc9j9feUQl1KGcXWq+/0hrtQzJ2bfN5Z7cB1EvfD83fLcbVdXJ9DpxumTiifRcOHGfBFFAiWm0tAZ5flcrJQVLiMcX8tarB6je3pos/FuiaEkkdrKOwgxkr4NwfS+GHksWoVPvhy3TnswHH4uBUYPxY5KCsGClUiSnhD3PzbuunMaiKfn4owoum8T44kxaqnyEqntGPI8SiLG860omZrvZueWtY17PESGZhWXZ68CZIbOvtY/Zn/sV9k/n8nxbjGhdz6GRpilRQuq91RUdNRgn1Bvl0+fLZPzlLj46MY8/fuMb/OuHbyQ/Cy2scN0H1pAx+VrC/341qduEbr7fonumHcPpg3esU7CUKA1FG1UYnmowRafN3PU5paRshK7oh7ppSRhTXTvMmA5peI9iF27PdzN3YSkLi/wcaHEOaAgSJDOdklXkYV72WmK/fgH7BbVMmXo7lQ4belRNrnWk6xvA+T9KFVHRYeO6sydQec0rTP+1iCvjGvqDMURRQHZIqKqGmJo6UXRCXf0c7I/TUO0DIB5RiXX2JxuDBg/kARI/S8ljpMK6n9Z4z4984RxqmgO8ct/oZlCPJQRJwJHvYsZFE8w6iihw3YJyZhpPQkUFv19fwbr1DcR8R5az0MIKP/zmcmxuG+HGY49aRlxvoo4gex24iz24MuzsqeqitctFb6CHpWeUcOcFIrt3FdIQiCXTQUOt3dDNDuuwmouxuR1jYxs3/9nBY0UZppNPqPNa/Tuy14GzxIPssaNYTmNQ9JfGqcc71ilkzirgDz+5gh1tAX7zxZdG1YAiyBKSQ0LOdeEqyyQnz01+SSY2m4jfF6bXF6avOUDMF0GPqaY08yBxN8upWL83DdroWAzWe/bsaOPuYC47V9TQXx8YwP0WRJ3e5j6Wt1zAZV/4OJ36Ynb/bh+GouGq8DJhSRkH36gj1nFoNnBqg5NF0RUdNnPIekJL6Eiyx0CSrfOvl/bzlzfuZk3/lQSeWQ2SSCyqIkoC8ZhGPCE9oUXMLtfB+kRJR2fdM9m853pUTaYsLEG+WHuQWKdo3u8hnN2Sq6fzAfmfCO86g1WPewgFe454n8cagiyR6XUCUVRFpzijE+2xVxGLc5gz4wc8/e8dA0aXjoRUSrLVYzPWLBhRFvGMy2LJuePRE8/b7lX1GKqGquosGz+LX3yyhMCHlvCVX66me33z0E5BM4i0BPni77bx77//GrtvJ7v7M5FzXIhtIXRrLyaZJIOMyTksPW8COVlOmtr62Lm63qS2asc2GMmCqzwTR2EGfbs7T3s68NsB71ynUORhincPUXXmEWmoyQa3hEPIn5HP2eeOZ8mUfBYVdaIZMjs6J7K1ppv16xroUnwoAZDcAotumsWtF01hW30Pj/7KnKwm57qQHBJqKG7KOo/C4IqyyIQrprBk6TjWvVnP+r9vTzoyqyPbMq6h6h5++M3l/CLfhRZeYw5L1wzs+W7mzCmifnNL0ikMLn5Kbhl7nptpS8uYNDGXhqYAe1aYev7W+Ua6X3pMZe/zlZy/xgm8khzknjHOy8UXTORAvZ/Kne2Eo4dmM1sGfUCfAodSV66yTN7/ubPZdaCTnS9UpkwI01CDA187mOGy7uFdfPWWK+lcHiTc2Dbi53wiYGgG0eY+Nvx7J4IsYc9x8q/NJdz5/s+go1O1NXjMjBtXhZef/eJKGvxh/vK79fTt7jyutQqiOdvZnudG1w1UTUfXDfqDMeLd5ue/7bE9/LPIw1f3fxvH+lb+9Y/HuO4W34A6QSr0qEr9Kwe5rDKbzPIydP3NJBMpNTq1eZ3MWzqOL16SicvWw+b2idRV+og0BQ45j2O5Jklgybtncfb8Un79tVeHXWcao8c71il0bGzmmv9TiHXWj5hSsBxCxgQvzsIMbDaRRWeW89GzHeRU/4GmjzxJ/ux8LvzxZ8hxXkJdUy89jWYXp5zj4r5LdvBI8fuYkQ8/27GRb37lZew5TlNZMiE9PSqjIInMmFPEudMLOVjTTYuiJdMjL/x5Lh6lmht/6aJldQN6VDU7hFvMFINlKAM7O3imvhelZ6A2fVJB1WkqlTrzXUybks+y6QXUl2ZxcE8HajCeTD9Z6aWhdraGZqBjylcLsoThMFNt114xjVtyX2Rz6c38uq4Hf41ySKguUegfUKRP0UdadPU0Pu77GcLNV/Hhvgns+e+eAdc1kjONtoVY9ZsNR76/JxC6ohPviSJIph7U0w9tZ/vOYlwZdppqEoXXY9A+mnnhBKZ95Somx1Qu+tdLXHfVk8e1E3YUZfD+Ly/jjkV+PvmHIBuWHwQg5oskm9D0KKx4fC+f/8OPKbjhFdb4PCbteISak67o9Nf4TebWILkNqy5n89hZMruInIZ/gKoyc9JHcDiP3/wYmsHmp/ax44269EyGMcI71imowTj+jS2jMsg2t40vfOMCziy1c//KZqaNzyFXeZPYs5tYuyJMyZ5GLvxSAzl5Nmy2Q/l4LaIk86FdPritcCM2t82sO0RNnf/RGDYwDfCbzx5gz442uvZ2oSs6kltm8VXT6L/1Q1Tt7uUzr63n7l1DD5EXZRFniYfSM4po29NJpDGQTM3YMu3Y89xITikp4Xygqov+qEKPP2Lq+aToENm8zsRcBrPwbaV1rME3AIZo7uQNUUCPqdS39uGf9C5WreugqyV4aL6zPqhmMYRxzPY6UfZ3YJ9Tjze31LyeRArutO0rGQJWk2S0pY+aiGKK8kW1JD//aFMkrQ29ZC0pJrLXR1yImwb3OJyCIEvMKM3CWf0wEyZfR83q+uSaU2tX/bV+bv1+LZNnLaOhatOoCvgjyW1YjsEhiSDZEVwuIloO8fjYpMWibaFjnj+dxuF4xzoFGL0wnOSWuXxiK86aV7nxzDsp9hgQ7EUqyqCsFOZcXAClMzjYFsHvCyfTIWoozKdfns/9m26G9k5eaV6MGnolyQKymEqjbagK7usiuO+QNoyhG1Tv6aTwKxeTf7CJr29qNCeQDYIoi5zzycX85vxN+P/vD8QfeIqbbn+VuD+KozCD826dQ3FeBvUtAXztIcL9cRpreqjd0U7cH0XxR9AVsyFp/IUTyMnPoGpLC6Is8sK943ixtpzf/GBlkrJp1UpEzFx/XNF58cFtvPq4/dAUtMR1JzvKh0gbWf+/9o1a4j/6ER2x8fR0bsVRmMEHvnoeAP/51VtEmoOj+hyPFlZqzVICtZyQlfY6FkOuKzpGgkZ8vCKMHW828vGbvsSs2wpZefe245ZciLaF+Mlv13Lwtvez/rXtSVlxGJieMzSVzjcb6XyzMfm744GhGWhRjb11PZx57u0E425e3tFKoDU4KrXc0Rx/KBwP9Xq0sKjIwDtGOO8d7RRGCzUY584/9XPtpR/m3dlrMF56EsPpRDprNudvfReR/MU8XlPCxl0NtNb5k4ZPj6ps/ecOzi+aiS1rAcpDLxP3Rwd80Y72IbQ6hK05w21vNbJs9ywEcTaxzo1DHk9yy5w9vxRj317ioTitISd6TMXmtnHue2fzk2U7UHOm8ErdVHbW+GjrCKHENIKBWFLyGE1H9NjJyc9AVc3dreSWUQwXSmoh0Lo2RFMJNSFlrSjagLTVAHG04RxCStrrxYOLuGXqWs45axr5xR4+VfIslM/m0VEMyTkWWFTWzFkFzF1cxi3nTULRDZ5aW8f+HW307vcd1QCcVByrSN1g6IrO3kf3sG+Mmif1qIp/Uwt/3t5+RFr1WBpPQzdQ+6Js3dxMMBynry9G08Eeou2hE6YtNCTbbhS1vaNFxuQcLn/fPDJcMo/+fM2JbTg8SfifcAr2fDeiLJoMkCEeCEGWsDsklpY5qb/8/1i/2vxgr7i9EOHPD/Gn1SpbN+/F3xYk0hIcoB1vaAaR5iCCFEoe74h8/8TuwsrV6pa2jCzhLPFQOKuApWdX0NAcoHJLC337fCPuQpwlHiob/Hxf/zRNl97G/o8/ja7o2PPdzJyYC6KNiJpLWaYD+7QCdttt+NqC9A8aKqPHVPatqksWyA1F47L3hJJUXEih2moD5xykFhYHOIPEawf8PAh6VOXp5/fTfOZEXn1hNw6Xjf+e8V7WPtJCuLFuxHt5LLAa8FzjvFx7/Sw+sMjAu+ZbUFFO/KxP0B+M01fba7K1RnDsFgFgQOotIeER74mix8wajbM8KznR7lidzFjBigROJqyehq4DPnrbzO+PGjJ7Q4bbOFnfkWOJ2JLv1Y0k/dbmsaOG4gm5m7Fjc4kOG0V5bgq9rmTE8HbHO+MqRoDotHHxxxaS6bbz9D1vDVt0zivIoMy+nkdWH/L0r/yzk5sf7KC+QcJX34syaGediqPqCHbYcE/IJm9iNjabiK8pQPBANwAZ47K47NKpfGJBJS2xpfwx28mqluCIUgnOwgxuO2sC29oCLP/TFtRA1CwsOyTau8M0T7kWWY1Qnhki25lDW28Eu/UA63oyxaFH1aR0tfVFjPvCAyU1BiuWSgMdw3AYdk4Cpkqsquq8taaO/lo/IUXjD3WrUHoiJ1Qc0NANevqitPWPJ3vhmSjZ09m4qovmup5kDWU4CJKAd14ROSWZNK1pQItpOAozyJ6Yg8Nlo217G/GeKPZcJ7++92r2tAX42y/fOi4ZjSPBIk0Ap42Ym0Wj1mKYM8QT35/UFONQ8EzPZ8Elk9j0+N6jdqaiw8b4d02ks9JHvCfK+LPG8bH3zGX9gU5e+OOmZEQ7FvcnXN/LIw9tw+6xm1mCdwDe8U7BnuPkG1dIqLqN5/4gj9zcpB2+G7epvXR3OohZ06Wi6oD869FCdNjInJHHT791MfMKqqj0z+Bvr1WxoboHQzMQRQGvxw4dNZSViEwfP4M3E/nu4c7X39RHSzBKht2W3L3aMu3YvE7znIJKUWwFxPopKJpKdfYcJJt42HF0RT/6lEeKYxBlMdkBPqAfYYh7MPHSSXS39BFpCfK+u5Zxx+J+XqrN54H2EH17OgeE4QNE3GQJm9uGIEtmAXxQJ/Ro6zd6VCXSGOD1h3fx5vKDZOaWEOzpINJYlTRcZudtoqA+WJdJlrjw6uksmlrAz6u76a8x04rBjhBht5ysZzkKM1ik/ItFc6by8hlFHDxKpzBa9VZRFk069dwiMrLstFf30F/jP6Lu0rGqwx4NrEZPNQyEE05+hBGpklvmqd8vwv2rL/D7z97L4z9bg66MvinRWeLhmx9Zwt9erWTnS9V85Ka5nP3Ih7ng6iW8NXMhvk2tGGNEYFCDcfxbhqdCu8ozyZ9diG9v5wmrjY013vFOIe6P8pkHugkGoiNymNua+9jbfxHnXenhzZfNVNCSs2w0xS/A3/n6sLrxRwvJLTN1QSmLI3+DLV3s8XyOvZtbkumZ3v0+/va3LVRecgGhjQr7t+822UEjFOMijQG++01TQkALK2a/RY6L0ok5XDy7mNLuf6C+vA5kEdtV4LTNQ1OHnsM8EobKz1r/b9Fe7XlubG4balhNOlIjJW1saKbzmDarkAaXjM9j59NnNSGEu3n+lR5TzmKoDvFEncVZ4uGTXzmPZeO9PLyxmRf+utXcSaZIo4/mmqyCsBqKE67vJZDg0ktuGckN7sIMBEkg2hYaUn9Kj6q89MBmlnvsRBrN8aJxX3gALdLQDPp2d3L9X6YQjcYJ7Gwa/b2WTO0oZ4knuYYRrysxsOgnnz+X2blbWNl0ET/6yRuEqnsY0qlJAo7CDFxlmcS6IyZb7QQ5h0PHHdp5W/MctJiW/I692ZTBdR+9gc3/GF5OfThEGgN89YevJ+/b/f/exvQv/JMav0648/Xk6060Q5TcMm/+Ox/fHXeT/9cvsfTd0bdFMfod7xT0qMqB5yqHNOZWuC1IArGowsaGHj7yx9s579P/xJ3nIufzF/K3Sl+SnXOsDmFwrj0aURAKxkNeGWueaBpgCJWeCMGoyop20zFZujwjpmYSDVRW6kByy7jyXIybkM2c/FrUB9fje6WWjOIMPBcE0O1GsuvY6h4eicGRnLCWMMyCLB3qME5xDqLDRva4LPKLPQR6IrRHzF6FwXpFWljhzWcPoMdUZK+Dqt4LCCsaDVtfGpJdlTy/KCBnOVhY6mWc402mVyzgZaeU0OkRj1ouYYDzEQVKl5ZRPjGHhupucgszUBWdhlB82C9yrKP/sNTG4PuoKzqtb9QN+bcjIWNyDu/5wAKeenw3gZ0dR9z1G5pBlkMAfzPT8xbgynMRrheHbJ4URIG82YVcfc0M2rv7eeUPG09KkXSo76Cc62LGZZNp3N9lzjJQNH7901U8PH0C9Svrjro3Q1f0AY1+rRtb+ND/mQOfYp39J02+W5RFjJXP89ojPm6+5Ckk9xVpp3C6YLgPovySSXz8AwtYvrGRugM+tuxqJzztOi7450fxA2/1Rlj7ahXxnuhx5WizFxQzfXEZe1fVEW7so3ZjM18rmUk8prLntcoB2v66oqMHYgOM42h3voJumINPvA7GT83n4nllONf/hI0/2UZjEyxYGGKKrtMTUegPJqSOR/jCpXZDW2yduVdO44plE3j61SoOvFiFFk55v64j2US8OS5kuw1ffa+ZrpMG6tvoik6o0ocgCsTdMnd+61WUvtiQu9WkzhFmXjrc1MeXf7kab66L9uq3iLaFjpjSG3X0oBvYbCKSTUyws6Im02yEezTadNWxQItqqJqOlCAkwNDXYv0t0hLkp0/sZeHc8+jobiUeipuGSZMOS5cYukE4GEPXDcYXZyJ5HHACnUJyAybqA+6nIEtMPG88f7u5hhbn9dz8vmdQ/BFC1T2EEinVY4F1DkESUANRAntSNhsngIU0FNRQnCcmfIfbGmewWb4d9Z+PntDzjRX+J5zCUBAkga9+7EzO3vlNzrvpC1x1RyW9dX4ObGjiOY+dsok5nDG3mJx8d1Kz51ggyiLf//qFnL31qwh/vY1zrusn0hLkrQe3mfxtq3A9CEd7viTnPiG7PX92EeeW1uH72lrq6s3XKFENIa+Uhj1BIr0mO2ZwMXXAIHgOKaPKXgdZ47285+IpXFy8geD586h6vdbs2tbMfgyLYRLoCIEkJhlMg89hOZiMSTkUTcjBlSFTs63NTCEMofRqUTxFSWTpLXPIy3Hxwm/WJx3ncI5AkCWyFxSz+Lzx1Fb6qF9ZN6ySrK7oNK+opW1dk2lkRZNVlNRiOgVF2/7qbv7zo1XJKNXSxho8KMdK3dk8dtoae1kTiKInGhcljwOIHSZTbWgGgW1t/LO6G8ktJ4dQnUhkTPAiOmwE93UdeuY1Hb+vH7zF6FHbgJrUWMB6doQR5nafKBiawT2ffJZfuTPQYo+9bXSZ/medgqEZfO/nq7jqprtY+8t6Ym1mEchKJ/XX+qnbYub6U3fyR4Ioi2RMzUOLKITrejF0gwPtfZztdkHTbiRPAUoghhIYXeQhuWVsmfbRMTASksg5hRksGZ+Do/UZdr7enfxz8bwCQpkLqWnwE/dHD1PAtIauOEtMaQNL/sCR7yJnYg433zibuKbz9z1zeGX5gYH3MyGWF20LJWs3yZRbilG16KDeOYX86IvnsYjHMA7sI/ShT/C9p8NsfbEqmaMfDJvbxo9vEnC1rWDVjPH0bmk9rP5gMbuySjOZvbCEL12WT/6+P6Jecw1XNuXSt7sz6ehsXiey15Fctx5TD0tfnUoGj3VPB6japsz0sOiu+TPymTKrEK/HzuyJuXjsNrpCMdbvaGW/ohHVdbMhb1C0YMlznMgIIRX2fDfzlo5jbXsoqc2lKzpdG1q4/Fsl6NHXjpm2eyScqs/RivzfTvifdQoAvdvb+W9l94Bh69aOQo+qZvPSUe4SbV4n131wPl3+MCvu24geVfnPX7dQeNd3cBoiWui5UR9PdNrIXljMhKl57Hxi36hkoQVZwpvrotQTxahuIZQQSy3Ih6xl5VT2VdDTuTl5zQPmJyc0oM69fCoZLpn1axvwHfBh89gpLvdy88x6NN3BY8EiIv3x5LAWA/GQBHdKDQGG7k0wNAM1rOCPKuDNAFUls3cdFy66hj2bWwYohaZCDau82TyOZeUXoAarDh+CJEt4pubyoU+dyTnjc8l1dmAT+hBmn0lV72yUQF3ydYVLysjMdpLpddIfjFG/ueUQHTfFgcleh9l8dRIkua0oLdXoW8q2YDYMpkZdgiwxYUkZ93x8BoWbf4ZwzmWgHQQ1Tm/xfCKxQprr/Ch9saMy/Cdiqp+hGUTbQnS1BZMjXa1j61E1yeA5EbtpMaGxdLrQdE93/E87BUM7/MueGl4fC/SYStXBbvqDhyQEAjs7+MEnnkkqp44WgigktZaO2BAnHhIeKyjykOuoA183hQXg64Zld07FdvO1vHGgk66EoN9ggy05JSbNLeLTFxbgtvWgquNY3mAaaEM3ENCxiRFskkhhWRaFZVl0tvQRajWb+lJz+8Ot2UqZBfZ08f1vvMqvJ2UzedbtTJSzWbNqB6FK36HB8IlUgmUo9KjK3Z97HkdxJmpfFHu++5AsRSK9ois6vt4oLbkxNjU7eHlFEw1b+1ECT5osLs1Adsu869Ip9PRF2b6xGUk2FWgHp7lsHjuXf3Yp0ZjKG/dvPqFFQiul5p6QjeyxE2kPEWkOHpYuMrRDhXfJKXHmknJy/vQpHv3yHuAFFi4QTJLB55dw+cW/ZOM2D4HW4MCUU4LZZMs0ZUnUYPy4O4BHM1QqXN9LZaLXZzBORD+KIAk4y7L44S+uQNUNfvvXTbSvbkg7hiPgf9opnAiowThb/rEdGDSpKqoe9YOvhRW61jbRtbbpiAXh5Mxdt408rwtZ7MHIymTmhYU4C90477yRSuFGtu/aQaw7MqQuk67oNNb08OzeUjLdWezfd9DUu4+p1Ozr5JvPVFCQ4yLTHedvN9fQ982/kfnRczgw9dt8/kevmxPiRjmP2OoTiDQG6HyzkfWD7pezxMPUiyYSiyocfOlgcoobQPa4LJadP4/+qMLaN2oJ1/diy3IiOSWCB7p55Ptv8Ghixz3U7lANxXnigU3IWQ4e+sWllAsr+MCfK6h8ej+OwgycJR5C1T3ois6eHW1oqn6YcR6M4zU0liMsm5bPJRdMpK61jzee3EekaeioSZBN2nEkpmK/6QL4sqksu227AYRZJm6h/JqdOFzuId+fMTmHsy6bwoHdHTS9UQ+YTYTWnA01GB/QyT4cRKeNsgvGM25yLtV7Ouje1Drsc57aDHkyDLPklrn0jvlM+/Sl2Nwyf3voGa7d0DLiNVlqwlpUG/Vs7aPBybr240HaKZwAjOWHPtpwWhDN4qjotOGwS0Q0L64pk8n+gIowcyaV9tt4bkcLjVXdqIHoYZozVkNX784O/tNi7iytDm5LMmD76joqZhbwwatnEvj6X3npb+2cXfUiM19YTGZuFsEjzK0YDsPdLyWuEeiJHNrBS2bN49JLp3LnGTvot0+lan8mbcE4Cy6aiM0msqq654iGzNAMwnW92DLtdIUNyvMz0XUdQRSYf/0Mrlk2kV8/sIGe9c00Lq8x3zMopWNzJ0T0EkNwlMSUssM6txNzOqSEhpMeM+daD3ZWhmagBGJUr6ilvTmAzSZiqBqiw5Y0TsljJ4bWuMsyOXd6IYb/8KaoYFs/cS2DWCR+aN0p5+qv8bNxRQ1q2KQFZ07NZeqcIpwumY6WPhp3dRDr7B+g9DsYgiRw/deXced/Po6yR6Xw0R9x3m2uIzZpnWijmBq17NvVTsm3LgW3i7/ti42otWSNui2ckE1HjZ/gvq4xXWvmjDxyJubQtbdr2BTp6YC0U3gHQZDMdJOi6oSUIlwFExDcXkJZi1m7u5uq6uF7LpJTvhJcbjjkkJwlTirmlxAKxuhs6WNrTTcXfOYiljU9R/kPruCtwMX4G14/6ma4kRD3haldWWvu2BTd/KInWE7+YBTVVURvZBzB3hbi3WH2bmtFTTQ/jXR/TFrkoal4X//FakrGZ1O/phZDN6jZ18nKDDvRtlCSdTWYbGAVqj2Tcigu95KZ7aRmXyf+PV0mmyxR0E1lg+VOySU7z03AH6Frb1fiHh8exWhhJRn5yDkuBJtEvDuc7N62jivIEt4cF5Oz4xhrqw+71tzJXnyxKQQD27Bl2slfVEJvVbc5FjTBqgpV9yC5ZYoXlnDLTXM4uyyDiCrxwq5WWuv8xLtHHlgjuWXuvMDJK5ebPQG3/nwXhlYwqs/3RCE1DWYoGo1rm/jOzPcTV3Q2Pb9h5E2WJOLKdlJc7sXvCx+3VHkqRKeNi26ew+JpBfz14R00JQYRnY5IO4V3CKwipa4bBEIxqv35RLMuRpVFNlT5eXN9Lc01PaZQ2yDDaeWyp59ZTn21j96dHcmagyBLnHXdDL5zjUSVv4IvfuUlnv3rFt6adi6FN19B/VPdhOtfTBqb0a51MIZq+kptpko29wVivPH8Aaoq8wkGNuHb3YHSExlSTz/VQFhigwuunEpZcSbbt7XQtKmFvj2d9Kaohnavb2bVplYAKt41keuvmcHGnW1s+c+uQ2ytBCtIjWm4Muyct7ic8WVeVokCgbpe4r7wAGaXrug4nDaKSjMpKMkk0m+mZqwUjbVG0WkjY1IOt310EdfPkvHaW3i6qpx/P7yD3oN+dF8YAzE57c6b6yLT3o7eezi7pejbV/CnPX66GgPklGTyyNccSLrEz1eP56mfvQmAe0I25141jR8s3UL4xz8D3cB19QwuXPBTljsqB0YnQ32OosBeXzbXf6EC180L+WP7jcR9y4d9/YlGqhS6Pd+NGjQbD5ffu250hBFNJ1DXy67e6KjHp44WoiwiSyKt/jCh3tNbIyntFN4BSE0NaIqOrzvMjgY/NRky/r4Yew900nSg65DsRMoXxJKOKJ9ZgCtDJtI+UFJBEHW2b2jioeLZNLbXJcd2tjT30cJAHf4jrjNR4Jx86WT6gzE6d7Sn7ICHlmIQHbZkjleURbLmFjJhah7NdX6CDYHDlDZF2UyhyTkunCWe5NB4QRLwjMtiwcxCSrJd1Nb5aZYl830pBWZLp0cQBboO+HjZUW0ODdKMAdRQNawS3NfFrsYAVVtbkNyy2eSoGYeKtZop/BZrC9IQiNK8qSXZDW6tWxAFbJl2kyKb5eSC62bwsfynCX3lWVRJ5Npf/ZoX8zPoawiYRi9B6ZWcZvd6b2wcJefMYek5u9myUWHhEhuTvnIme8Z/jZV/3US4qY9oZxhJKMV3+9187TPLWLX4AtSwwsM/OxPPDz/CI9fWJq/roqY+nH+TKCzNwm630bm5JblObVAkpobi/N/XXyFv2tfo/mM30bYVp56LL4l45xXxi69cwAvbm3ntkV0mNXyUDaDR5j6izX1j3puihRVe+OMmMiblMH5aHnpMNbu3T8No4bRxCql0vLG8USfquCcDo2F0pMIqrAZ7ozS292GzifR0R2ht6CXujw5gHKV2K6vBOI072qhX9AE7btFpwzM1l3B9L4/+YQN6VB2xYexI1yI6bDjLsnjvtTPpDET4b3eYaHsIXYky1NEE0RxHWjo9n9ZEB/QVV89g5rhsHnlhP6GmvuRuzuqxcFdkkZHrZvy0PPLz3Kx9ozaRMjMINgR46ul9yA6J7pqeAZpJqee0EG0LUe+PpDjIgRGOVQewZM+B5KS5wVRdNaGzlKSXJs5p89gpWlLG5JkFdHf2k+m2g9ODY2YeUomH7d0T6Gp/MynDLTpt2Dx2JJeMqurU9kLpzLOY+JUaSja24lxainD9+3nylUZ89b3mFMCwQk9sIj1VPWRtqaF0/PWcvaSc2E3XsOLl0IBrUsIqHf0xbrxiGopucH9zgJJZBVx84SR2Hehi0792Jhl7hmYQaQzQ0tx3UpzBaIu0OXlu5uVvpWPaHFZlOY7qvSfyOgzN4O4vn8c56kNsvf7D3PmBx05L2YvTwilYYfN5189g97bW5Bzi44E1pF5yyyb1NBTnZOvIHyssKp27Iou4L0x/fWBU98O6Tl970ExR6AbBQNQUBgtEkxPFAOQcF66yTPSYSrixb8hZE+d/ajH3nPEqj+l3cN+3Xks2HB3ttVjFVpvXiT3XSXVzgGjcvJ6RptMJskTx1FwuvWgSr4sCgZ4I0ZjK9oM+mvZ2JnVsRIeZLjj75tncdPZ4anvCNLYH2bevk3B9L0BSSryvOYAaNlk1yeJvokAsuWUyJuWgRcxmtlRlWslpdgfbc5zoij5kesFkgUnYUnT1retLdn4PimpmXD2NH9yxgPKMrTx7cDoPP7mHYPgcxi+7nObOEBt+v5b+JtPo2rxOPBO8lI7Pxm43z/Hmvg584bksvPR+vFc30adm8/z2CGtf3U60pS/53Pzk+R5++dY97O2/iHEbG7j17x/i+UEOAaDklhn8bnMTE0qy8Aej6IrON+9YzLS/fwD7zEK++tE7WfOHTUnjaXUMn2hYKrB6VB25mVTTadnezpcfm0Fd5TbCTQPnmJ8KCJKAq8LL3T+5nIayM2kAXK4/kP+l3xyRWXgqcFo4BUe+iytuncvX57zJzqW38JkE8+FYP0hBEnCWeJh20URcGXYObGqmb59vjFd9YiBIAu4J2fz5t9dS5mngT2tlnvjFm0d0CoZmgK6jhlXCnWGi/uihHWogiho+xMMXZAlXWSYFE7Lpbhk6VBYkAbtdArudgC9+1PlV0WkbyNDRTHVUJRBjxatVxAMxou2hZBplKOgxlYa1TfyrvjfZXf3i3s7k7jc1/eKZ4GVCSRYHOoK8+vpB2g50o/gjZEzO4SMfWsTVU/x4YvvZH7+Cl3a10h2I8NUrMrl/dZRXHtxOrC2IozCDm+9YSGtXiK3rG7ny6hm8/OIB+mp7sbltLLl6OpctKqfFH+bf/9yeFDK0ZhCLDpvpoHKdFJVmkZ3vJivLQU93hJr9nclub2vtkltm0YJSDETebJnLi2/sp3l9M81rG0EUcZVlctMHFzCr3MuuBj8bNjYy94wS3rOkAresUNers6uxlzXbWni1v55oRKGns45gR4hoS5/Jckrc2zUPbOG8J1x4Jq3m5S938cjVLYfd70WLRHy3/o5Nn3mdt3zh5Cbih3/dxMNf+zqCECf6b9WMik6yIZPcMud+YB5Ou8SKv2wdUvHYeoZjbUHWPbjddFiDIsFTAUGWKJxVwEUtv8BSP4pEYPaiMtZsbTsqWfCTgdPCKajBOBvXN7Ju5nt4bl3tqAaFDwfLIXzvF1dySelm6iIX8Kk9HcMOTDndeMOSW6ZsXhHTeRa0TKLxKUnWyXAw8+BmQZOwYg7O4VCj2OBdqiDq9Nf6iTSZu+ahHI6hGbz++02srygn3PjaqMNcyxlkTMrBW+RBU3V6m8xIRImpiLKIK8/F0sunsH1DE4F9vmSxdag1WEqkqb+zkNqfoYTirFpdi78tSH+tH0QR76x8PvmRxVzPQ8R+uoaoZjDz7jx2FEyjtCCDnNAKCnIuSNx4kbgvzOMPbcPQDZZdM51PLu2iuaOMzQkHe3BfJ6G+GH3+CGowITYnJtYjimSN9zJuch6hQJSZswo5e0YhF5bvgGAX+9VreGVPGyteraLvYA9aVMMzNZer55TgsXWycleIvc8fEkcURAHbjDzeM99FgXMPk3KWkJPlYFyOm6nO16G3nTKXi2VLCvmzbTr/fXDroRShomHzOik9exzddb1EmkzZEFuWk5tvnsuWsy8Z8n6Xrn+Kj/16F5HmgUOd6l6q5rw1dgRZGsCuOpmQ3DK/uLYNY/nTzP7Z3fzyk88O2xwJA0UwUyMzOddlkgROQod68vyKRtcBH82f/w6S9BSaBld/vIR/bGw6rWyPhdPDKYTitG9s5rsdoWQx9Fhh89iZcv4E3hX9C8aKBrIvnm4WAAfzx1OE3wR59C3wqVLSJ6pOEeiJsF25hj1Nfax6ZfQD262dkQZJET9L3jp1rSYlVUFj5LBaCyvJiXCjgei0Yc8xtZMqZhagKjqdrX0o/kiyFiG5ZRacNY4LzyjlwO4OAvrIO06rOG2J5R22YdD0pJJpxowCXBl2qmv9ZsieYcdpkxCcXhzziqC0mJ2+RWzdd5DmOj/1C85kxdOHdp2GbhBpCSLKIru3tfKXvKlU7T2YjASCPRFCvYd6PESHzby/uvmvfGIu5y0pZ/mqWto6Q3SUe801+tuYVHaARZMmUTU5jyq/mZaZOCOfifozGNUHuXHJF3ntr1sHXHukJci/NgYpzpuMP9iG22kjquoExalkFmUSUErpjxUQCHUlZz5ITgmcEktumMH3bvTwzL7p/PvBrQQruymamss1M+28XnP4fb7yw0X84HmF1l0dQ/awHI3+14mCsW01+7/5Bre8fja/dg8/MGs4gURHSSYf/uoyalsCvHbfxpOWzzc087m68+dreF55AGPNC2yZ/j2CH3/qlDjYI+G0cAqGZhDviRLvaT+u4wiSQOasAj77njNQn/wttivP5vFd4mGDW8AM6Rz5LuQcl6nL0tI3rGKpBYtznjk1F103G4DivvCY5QRFWURXdPy7OvjS119OUhZtHvuh3PtwjigxM9mS0E7SCbWh50CMeTFflpC9DpwlHlzZTtqbAoSa+gakS6wc/e4trezb2U7X7g4cxZkULCylt85PpCV4mHP2TM3lr7+6hkJXI39eJ/HEL95MGigrn23oZn3gjutmEYpr/PSNOvSoSqA1yO76Hq44dxzk1yAUFdISjOLvDtOyvX2AVr+l42RdS29TH08/vhtnhkxmQQayXUJMUH5jEZWoUznU9Ztg5VRuaqa1oZfeOtMpVe5qZ+vScSyaeQvxfRqtXZ2E++PJoTJ5BRns168jXqzz33V1hxnjWEc/z/xxI85iD94iDwXFmUiyyLocFxmuEjp8IT54UTEfOsvDqhVZhOUQH/viuXT6zdpLhs2H2zUp+dmcc854CnufPezzu+L2Qh779L/Z+Lv1qIHomD8fYwElEENYeAFZ5a9iHNiDnLPwqHb7giiQNSmbD2c+CnNsbF44jZ71zSftOvWoSvvqBpbdnIGccw7RlidPC0c7FE4LpzBWEB02Csuy8EcVwh/7MT99McLaR988bEdg7fbteW7KZxZgk0UOxtRE4XJoaWWL3XLZxxbx5XdBT2wiX75vrSkRMAZOwTuviCWXTmbtY3vRYyr5U/P44h2LWJa/FmPdCoQLb+CTf3NwYE3DkMbT0iYyNMyBM5ZuzXFGM0diQKUWkkVZJNZpSoMn8/6D3qf4I7Strkdyy1zw8UXcdUUBuY5avvRIHpv+tXOAmJ4oi0xaUsYk3/2gqsys+Gjy91ajndVxq/RE+PEv11BQ4WXBjTPpbOmjPxijND8DQ/YgzD+bJuMyVi0/iCgKzL18CnWVXQRTOqBFpy1ZkJa9DrJyXXhzXbgy7Lgz7IiCOZ3TSiEF7GFiERUSDKVom5nLtxxN3BdmXY2fDU7bAB0n63xrO/vZuq6RuC+cjKRsHrNnRHTaCB3wJVVSi8d50RSdfWsbD7GhJIGlZ5Tglr3EQ3Hi/ih7a7uJRlU2Pb6Xlx+yoQYq0aIa7oosKoo8IEosXCCwbbvBkrNsTP7FVfxS/QSvPLiVyz4wD4AVj+8lXN97QmQeUnE0wntaWOHdP5f4zWNrOagaGMqTR3UuXdHp3trG7668iYoiD5GWlce05uOB9YxYzaH2XCfOsiy8pZn49vuIniYNbe8opyBIAp0tffzjqT3c2xnCv719yBDRSrPEOvtpxjQGw+nsJ5GYe3zG5Dw80TWodkdSE2csYPc6mFSezUaPHSnfxdc/soTyT7yLR57vS7ziJX58Wz6Z//wdd/wqMORsgORaUgbaHM9DZs91Mvva6dQf8NG3u3NY52r9v2ucF8UfIdzYN6RBSaV22jLt9AVirKwXaO4oYvtzGw8XJ9QNqlY3cFfOxQT8EarfWoEWVrBl2plw4QTyizPZ82YDij9iOvhp+dx29QyWlARZ3zKBfz6xm+0HOvEHy2ls91JftYGbb5zNzeM2or/8NNqPPs2df8uhZlubqckjS0guU6o8K9dFYWkWZaWZ5GQ6yclyYLOJqKpOdyBKa1eINrtEMBAlYEVwioaugCCaTX+2TDuFs8wO346d7QMG9giikIxMPRO8FM4voaQ8i3BYYdHsIvIynfz63jcJVfegBuN4PHZKCzwcWNtgMqMSg4/u/+lq8yNPRJRv/GnLgH4CiwosyBLNXf20lt7E9JdhekYOr3RdwE9freTA8g2IssiEkixyMux0XDgBX3uQhrVNx8Q4OxJSU7fmECBjWJ2qVDSvqOWDVd14xmUd0y5bDcb51/+9lpQmOVUG2NAM7Pluvvubq7ls21f473vW8e4fTudLl3+H7Y/tOeURxDvKKVi5u8b2EEpPZERDb+hGsjBnFdBGfFA0HT2qsmpLM86zLmJ3fQ89B3vGrOuxd2cHj/giRFv6kHNd1PrDTM13AX3J16x5yseNH/8XN13/DX63q4NIYyBZKB9Nl/DRwj0hm1sun84jusG+Az5SrzR5PmsYjyyx+LzxtDX3UZlgCg2GZQwyJueQVe6lclMz+9c2Eu8OD8smGcAkSTB2bF4n02YWsmx2Mbk5LlRNZ+6UfMZ5XcQ1nbXNHtbsbKWj0kfrrg42iwJ6TDXTMA4bBLvQ2kLIWjeZ3mIcOeZsZjUQRXJK2F02Mr1O8vPdlBd6KPK6KMqwY5dE4ppOh9uOKAhEo6qZRkxMsRMkAUEXQDfvjSibO3yPx46/uS8h2RE3ozhRSl5nTr6b+fNKuHlhEaKgsqk1Tn1HMOnkI00BNr5QibMwg5jPlCkRAcnrwF2WiSvDjjfHxaxZhaxcXo1/ezv6oKZZQ9GoquvhJYdERd4N7N7vZ8WrGwnV+pNG6KGHtpGR5SC/2MOChWV0dx4+bnQsYPXH2DLtpjR5omZhRkvDfwd1RSfSEjw0vOkYYKYcT30eX3LLXDFhF8/MXgfAU9+p5CfRPG7elX/YnJCTjXeWU0js/tH0I+b5rYcj9eEa6YOwnMimR/ew9Zn9SQbDSO+xdmnW2kZakzl4pwswQ+Xf3/06XV94iM/+aiOVF9+N5JCY/O+P4C+7mcYNwcMG5JyIhyjaFuKB/2ynfVub6TxTIgMgudOzzn9wfxeR/jjo+oC0kqEbOAozsOc4cWe7+Ms35lFo28Hdr01n89oG1L7oAOG3VJj3TEXOdZExKYf551TQ1RakqbGXnW47E0uz8GbYccsS2+p7WLmyhq7dHcmmMguCbHZF/+HBraxZvJRzrrqern1RdL2TuUvKWXZGCQ8/tYeAP0JGpoOcfDcVJZnMKfFSlhkn37kHG2E0nHRnTsEtZ6NqOqIoEOmPo4YV9JgNQTaQHM7kDIadL1SaDieYuC+igOiQ8czIx5PtpLepj67GAHXFmRgLSyh1b2dc1mL21vWYNFdZTOoUhap7ktdj6BK6ojNhaj6XLpvAjVMrEVpe4/alN/Pz55tZ/9COQ42Guml0965t5MD2NrPLurP/0JjZRIrRv6kFP9BVmEGkX0G05mWcoPrTpPPG86Xb5uOWJX75+E72L685VPQfzjFEVZR3wFyEuC/M41WzuPauKTz9o4MA2MQYnmwngTG+50cLwTCMUZ190bkPnOi1vC0w2i5jURZxT8zm8vfNo6I4k7c2N7Pz2QNJTf/RnMcyqvZ8N6LDhhZRkmMvj4e2ezSwHJslxaBH1aTxFmTJZLuI5hwHdB01FMdZ7EGQJabMKeKa8yeyfGMjs6bk45BF8jKdXB26H3Sd+snf4I2DXTz++G56d3YMeU1WE6JrnJeSGXlMmpbPvp3t9NSYBjJVfkEJxIZ0LKmaOHKOC2ehm4qZBRSVZNLd1U+kXyG3MIOafZ2Ioog318WU6fmcOb2QxSUR8qW9GPW7wNcDngyEibPoEM9jfYtBdXOAA5VdtDcFkr0hpdPzyc53U7Ovk96dHYBZMPcWefA39xFpCVIwv5ipc8whOE0bmhFEgcypucycX4LdLtHW3EfjjjaibaFhR4KKThuZ0/M475rpfK/sCVZf8R8Azv78DF69+a/c+4OVRFqCoOnJdBNwZNIC5k7Wckhj2VxlfZ7OEg/fvvsSZn/ucrpr+5iw/Rlu/EoVfQmK8tvd6I8Gtkw7OQuK+fqdZ9MbVfjDHzcSOuA7oemjrWs/deR1nbCzv0NxNAa9dE4h37yoEWP/Rq583+3csqeDwLboqI5hvkZDTShaJo99EvOhFh00a24hBcWZRCMKvupu1JS/WY5r6oJSfO2mZPIVl00lEIqjajoTs91cuLicmKrT2B7E6bAhzFiEYc+ipTVKm6+fSHto5FSfZqD4I7Tu6aS9uodYZ/+hprdh+k+GO44lXR2LqDTW+ulMsM5aKn0IkkBGrhvJJuKQJTx2iUxbJ/jbYV8l8X1dSPluJE8GWeMmUeKZSI/XiTfHhc0mIk4W6Q/GzC7yiELcYknppm5SpteJEjsUMdZW+gi0BpP6T707O9irG5x70SS8uS6zh2QEg6xHVfpr/bz1chXcdxHZ3v+wvxL2P3SA6z76AiuvnMfmx/cm0y1ayv06EgFBCyvoxyiFPhoYmkFU1ZGcNkQRJCF2SCrkCLD0rU5mr8GJgBqM41vbxDe2tydrTKe60Q5OkVOwmDyWDr2FkYaYwLGnSAZIJh9hh3S8sM5l7cqIBKC2ntzpe8nMdRE4Cjne1NSQIAnMvGkW932qjJZQER99/39P6JdCkAQchRlc++kz+fi5TvrVfH77chX+5j5sHjvZJZnYbKYRdLhkxo/z0uePMHtOEZdP99IblfjV4zvp8PVzx0VTeGRtHRtersbmsfN0YQYBf5Bwy6tJ5s1IDsFMd6hJ1kYq+0b2OsibX0xuoYfO1j4C+3zmvIjBg9oTo0Jlr4MLbpzJ3efsIuKZwR2/VGnf04mhGcheB6IkIEoCLocNr8OGQ+/A8LUS3dhC3Qu15EzIonhKMa6cAyzN7yDffT4A180SyNv6O3A6WFv8ZX5431qz5ybRONi3u5PKGj+SU2LcmWVcctFkdlV2sWlza9IYaJpO3BcmwynjKs1it9tGLOFQRDkRoQ5KQ2phhb7dnVx2l8SP3tjEbbZnMNqbQHZyzTkTaKjuxrffl0yrWrIgNrecHKIz0r0f6fmwnvMjkjQGQVfMiPLp16qY9/fXyRP7+f7zbUnZkiPBUZLJebfO4a3H9xJpDLytowpDOzmjXo8Gp8QpeOcVcfE1M9i8sYmm5TUDCqWHzaVN4dofS/dxMv1QlmkqWfqjCdG3kYtNotOG5JAOU4YcCZJbJnNGHqLDRqTFZG+8j3HMmH0XdX/poWNb7XEVpr9023zs/3cTMxYV4Sq77qgay44GlrGdfcUUPrnMRrZUSbXfS3Odn0nzixlXkU1ulpOquh72b20l1BrklZoeEEXmnlFMc5/BxpoOqlY3YCga29+oI5wQZwPo5eiosqkdqZbKqCAK2PPdfOPnV3DF6s+jNgcRfvh93vtDU9kTzEHxkltOSj6gmXTQHn8EXJkAOF1mmkTHzPfLdgmbTUTTDZL2SRSxlXooWVCIsyQDPBng9BCiAl84TjSmATb0hlaMfpUFH2qhuNxLQ1KV1hz3qfhVVFGgaUsbz/bFCXUdUq210luSW8blsKEbhnletw3P9HxKxmcTiyh0t/TRX+s3R2jKZmrRUDQCOzv4/O1P4CjKIGPcQiom5wJVxBOsJPRDTX6y18Him2aRm+Ni/eo6utc3H1WKyHo+pl42hS/eMo97/7OdqheqRhXBWh31elTliosmU/zPD2FbOoPaygvNesIwkZ81rlQNq6DrFOS4cRZ7TEXTt7FTOB1xSpyCzS0jigKxiGkkkg4hsfOwvvxwaGdk5UGPxTFIbpmK+SWMm5BN1d5OmgPREYtVoiySd2Yp884sZ+uqOlNvfxQ1hPwzS/n6p89mfWUny5/cS3BfFwefr+LgSweHbSIbbY3C0Ay+/M1XuPHDD9De3U9/zfqjuAOjgxWWWwZXdth4dIfEwcYC2pt3suTMcbx7YTkuWz/b2g02bW4m2hY6NPvY6+C1Z/ax/EmT525FANG2w4XXjha2TDvl549nydJxvPjgNtRAlNLFpVzdex+PftTsBL71/W/gzT2TyKwCMrIclE3MwWYT2bW6PukYlECMLY/t5dJNXjx5XTicNrwV2YSDMaaeUczdt1TQFfbwwo4WmvqilHrmUzBewza3k+ypRZCbjTB5Jr3CLLa15VDV1suB2m6+uTvOhAnfwllqo+Y/fjRVp3hmAY6FpQBUr6hNDjiKtvTR1hYcsCu2HMPkecXcubgSbHa2bi+m6YDEF+88i6sm7KRXmci+7jz+9txeDiyvoWhhCQ/cNZd+xcsn7n6N0AEf86+cytIzSnj40Z34d3WQMTWPi26ahUOWePHBbSg9EZwlHn5+Mxg/+RqOD5zFzZln0bym4eh2rKJIXmEGC+QX+NR7r+QbaxuJ+6OMxB5KvVYtphEIxdA6wtgam3G6ZLTY8FF89uJS3nvbGaxcVcvB56t4/N616LGR02tpHBtOiVPoq+xmRVihv/7QSDqro1SURSSPw3QMkplnI6phaAokJLCPFoZmEIsoxI/iAXJl2MnLdiE6R3eLBFmidHw2BW47/r4oSt8hTvpw6SJRFnGUZCLKYnLw/UgIVffw7++ZTTdj/WUQJIGyiycyc14xbz66B13Rqd7TQd2BLhwumTMWlvKJczQyw8+gOcfRHZpAR2sfaiCalNIwGVSxZFPZcLDUa5PvHQUKlpTyr6/l4dz6D+ovvoFdL1Sa7JhxE1m4QCCrPJOOkg+h63tZfN548rwu2rpC1Fd3m2vSjOSGQ5RFkETOv3ASH14q0NBXyJ9e3M+c6QUUhl4hO3c2MSWD9t4w7d5ccnLzkEL9UJiPMGEqavYk+iP5hOIxAqEY/cEY7U0B6nZ3oCs6kltm9pIyZkzK45rZeXRH4AvbWs20ltWFPcTmQFd0utpD9GgzkYwYotSF5JbJdsoQ6sblzqEv5iUWMQuxnkwHheIWcMCkWQVUBePcd3Mnxup/4LrjO9z7jVf5xTcuZH7Hb4nOvIZXHpZQZYnMggwc3dt59O4qZj1axeeXf4Yf+sIEdnYc8RlMMtB0nd0bm3lqwbvYdbA1uaHTrZrFSEy+RL3sv//czrnfewx/VKHppRUj1of0mEo8rqMnqMlDUZjTGBucEqcQ94UH8J9Thc0kjymV4Ml2ItlEAh0hcwei66jWAJSjiBYMzVQKbdnaSkd1j6kREz2ywFzbuiaeS1AbjwRrt99Q1c0PH9xE4/rmUT20lhZLZoad+3/5FqFK3xG/TCeKYy17HTz9TR1jz5NcM2kpPYnGv6zpeXzzY2eyRP03yl9XYbxrEd2Oi1ixuppQdY85E7ooIznMJnX9lvG1oiQwu86vvuscrl5Qzte+v4Lgvq4jOgZBEnjgG+fwZtESunvgO6Gv8e7H9tC6q4PPvjqXJX9fQV1rH2u/vQ5PtpM8r4ucLAcHa3vorulBC8UOpWgcNmSvg6JxXm5eWIR30/eZN38phYWz2LCpiUDoLPzBGN2dfuKKhkOWiKqzWXjlB4nrGfTFywgFPYzz7GVm/mx213YT7I0SD8UH0GCrXTZKCj2UBx+mvHj6YbMYBsOqnbS+1citvVFkh0SwwcyX3//fnbwx+ww6uvrZvWqVKXAXVanb0My985ZQXuTB79uHzW2DcACiMWyiwIIbZ1L66Ut49KlebmvIArEMZ4mHkopsKLRzxhyIx+Gc0v0sOm88a1v6iHX0H9lRSyJ584tZcu54Xlx5kPamQFIfSlfEAc2TI11vYGcHH73jMRDFARpAVsoXSNYrAjs7+Gf1yhFnRqcxNjglTiH1Qx3cFSs5Jbz5borHeXFn2KkFehMS0IKoDYgWknIHR+gBMBSNWEc/MV9k2DTO4PVZzTSjZQpZed3e7e2jrkFoYYVdlV3YHTbUvtGxkk4U1LDK33fPoiJvCb37XwTAPS6LL31yKYve/DzND+4mb34hwfG3cd9rXdSua0ILK8y+YQbTpubzysM7Dyl8SgJyjotLPr6Is2YVsXJrMxuf2EvMF0GURXoDUfa0BYj7Dy8IDwVBFNANG4UF0N0D29pNZddocx+b/7mTLdKuZCQQzbTzQlMfgiSYn2EgOmC2MYkcfvOudj7/mw1Mmv5+uv4Ronl/JVpEoWlPJ5JTwpXtRNcMVFWntauffe3jcbtkdF0jrvQwLmcyLb1+WlqDyeY1y7DpMRX/rg5WhOJMr7iBdU+3E2mpSd6bkQq7Wlihd3tCA0zTQRKpea2G2pW15t9jWjJ3H2sL8vTvN5hRV8iUN7/+16UUj/sI/Qf384EbZuOtK+SGSdk81PNuRHkzM88ex/hyL49XVXDp5pfJlNt4eE8FOzftPOJmaTBkSURVdOIR9VA94SjYYNY1Sw4RyelAwfxOiA4bM6+dTl5hBuv+uSO52TjdCrInCpbSsxbVRk1hH9Pznw59CoM5+dkTcyhMdGo21fTQ1x46pL2foo/unpBN3rQ8fAd8hOt6T9j6ThRSWVEni5stOs2dsqFoh8lmiwmNHpvHjqvCy/XvmcNnp79F9L7/Isgijm/dxUf/JHNwXZPJfwcyJuWYCqKV3ckiuiAKZC8u5amfTiaj/gX8427hwz89QE9VN1mTslFiGn17OpO7wJFYZ4Jo7hrHnT+eez97Fi5bDzd9Yi39NX7z74miZ8bUPM67bAo7trTQvtEsnFrOOjltTjzUhZ26EbGE8BBFJKdksnM8dpw5TnLyM3BlyGR6neZ8CUBVdXTdwOm0keGSsUkie/d00NbQy+wlZUwZn8PjD2xC8UeQPA7UQBQ510XxnEKCgSjdm1qPaojUcBpBqdPzLLaY7HWYCqzzi/nsu+eyIH8TvcpE/rJOY9WKg9hsEvklHtqbAoiigCiK9PeE6a/xD9sPMXgtVp+BFd1YzkxX9KN6jq3Ng6sskwlnFOFrC9G1zezmPfuO+cyYlMe/frV2zDWBjnai4cmGq8LLI3+5lpf29/DPn68Zk5qchbdXn4KmmyyNmEq4N0InIDskQr1Rs2nLomem7Czt+W5mzC1iS2/0bUlNO5kt95YDKjirjJnzS9ixpp5QdU9SDsH6oogOG97peUyeVcgF0wow+nw4LpuNMHcR960dR83GLYdSY5o5lwHMaMwy4ACebCcerQajvpHcydUUj8tCkkWWLB3H1s3N9KZo81iOEQ6xzWxeJ47CDARJoHR6Ph1NAb7+p00ASYdkGaiMqXmce8lkPrxsPH9SNDp3tIOoH+LmJ9IZA7ShkuczqcOiLCKIRoKbr6JJAnGnjWhEQbZLxOMakX4FVdWIxzTsDokJZUW8a2YRs6XnCV8wmz++VcIls4uZkVPF6ytyad/QnKTRFk7O5dZ3z6E/qvDn+kByFOhoaiqHNa0l0nKGoiXTrnKui6U3zOSqJeN4ak0tvvagmW7tbcfr38FXz51FYc5c1mxopL0pQDhlFrel9DpaRljJOeP44K3zeHHlQfY9V2nWkBJ1paOFoWiIThtnLynHZbfxj2CM/upudq6up67Sl1RtHQsM3nycaOcgOm2jUlcYDNnroMi1j5ys8SdkXUfC6eMUOKQgGW036wiCJJh6MbFEF+0gOme0LcSB3cc3pe10xFg/rPZcJzlnFFE6PpvvvG8BkhDns/u7kqMqZa+DzFkF5BZmoOsGt998BpNz3BS4e4hnX8Au1xSeX9PEm89uByB3QTFRf9R0KomUieiwsfRD85k/s5CVq+tQFY29/Rcx/sLJKIqLcxbb2FnZRSAUw1fdDZKI5BSTOX7Z6zCnxAXjOEs83POdd7HA9Sor/Rfz1r52ohGFBQvLmFzm5ZdrGlEUDdFhw1WWydQzinHIEv9c18jmtxpMefMiD6qq0bvfh9ITGWDwBElI1hgsckPynisaWuJ1ksuMEObMLmL6uGw27e+goc6Prz1IPKLi94WJKRqzz/bg7lrPJbPfxwFfiM2N+fjrDiZTPQA9B7p49tUq8gozmHhWORMm59LRHmTvKweHfH6tCMjmdRJtCyUjC1d5Jj//9TUUZdj51HeXE6ruwZ7vZvbFk7jrihIK997LvPd8jFu+1caaA50smRkl9o+V1D//Z97/6QXc8YH30iJcwr0v1LDl+Uq08MCpfNa5RYcZNQ5FyZ69oIQbp1aiaFPZ8+T+AdHY0TyzVjos1tmP1+PghikNBG85gycf3kFgT9eo6k2jwWC6e6o0y2hlbo4WstfB5Esn01Hvp2+f76hmN/TX+Lnt5146972R3FScTJwWTiH5ZUVPdnEKYjyZf7V2IoMfvFhbkFZ/5LjnOZ8uOExbKIHjeVhFWWTKJZP562dd2MUga1qiNPnDZOe56ZQlRKeN8RdO4EM3n4HHLhFWNC5V/o4glLM1cA3dERdrdjeyfUMThqpx7R0LmVru5cGHdxCsNPskBFHAXZHFl66djU3UWbupiYYNTXyxfSXTF5TwjRvHMb8MXl9dR9OeTuK+MI58F+4J2TidMpIsMml6Ps11fnxNASbOLWJKdisE+qlq6+OVP24GXWcjUFucOeALpsU0erv76e3up3l7O4aice6tc7n5nIkEYio//8M6/FvaDit+CrKEs8TD/Cuncu7CMrxOmWffqOHA2oYknz/uj9LZ2kddlgOHXaLHH6HPHyHSbQr+dbSFeG53J6887qRk6gzisU30tPSZNN1A7FD3sGYQaQ6y/8l92LxOZl45hWsXjyMY1/h9e4jWtxoHpNJsHjui08aCG2dy7sIynn+lksb1zSiBGPmzCzlH+QdGY5CKmRezL6GHNGtaPkXaapi1mN1d2SiBGK88t58Fk97NRR/qpe8/B1nz3e1MeaKSvrZv8quV3+DDvmm01PkJVfrMvoeUZ8+e66RsUSmiJFDzSk3ynhuawet/3MzGN/KJ+1abrKnYsTeD6lGVaFuIh/+7C+/tC7HLYfLKsghWdqOOwfd6MN3dWeJh/MJSgoEoPTU9SYd7rH1Qw8Ge7+Y918xgzbYWNjb1HZVT0MIK9S9Wj8k6jgWnhVOAgY5BTxTYktAOdwhAon/h+ItPg8PKUxF1pNZVBFkCXU/OyD2u9UgiHq8Te38taBoH2vqobw3QdKALLazgnVfEne9bwMRsO8/u6uATS7tR/7QZ+eZCAjGVyTluXgkrhNtDGJrB9u2trH2rnvYNzUlNHTCn533rLxvo7uxPpKXMbllN0dnREeWtXa007jCF9WxepymOl2FPLrO9uY/uRIRY9VYjN1V1Y+i5hA6sTBawG145SAOHPh89ppI3LY+/fiGPsJrDbV8PEOuOcNmicSwSnoC8HP5VUUDvtnasO2jdZ0dhBouvnc6vLq8k9vvvowdiXPyzH/MFxlO1pcWcCRGK0bvfx86DPexxy2aqZQjdIEUSuPSiSWS67fzlwS30+cLDPq+KP8K+5yr5v/1diE6bKTOeshuWvQ7O/8hC5k0r4NbQ79BmXsuq9U6mXzKJSH+cpk0tvPtfsxFFAZstxmd/fjmvrqxh5coazp58OTZRYO3+FtB1+mv8/Oy+tWy+8lq+vkom8rsXMRSdzNIoODO49tKpeOwS/352H5UvVGHoBja3lIzQAc5aWkHbvq4BjZJaTEOURa744HyWTi/k70/upuqFqsM2Z6Odl6BHVVrfbOQXXf1kFmQQ6o0m6zzHo2KQ/P9ENCjnuLj9c2dz+9avIU3IZN+nf82nPvucuRsfBWPqaBD3hXn06b101feaDvc0r2Ok4rRxCjDohulmjnpAg88Y39DhCpwne26ztTvMXVDM+Kn59AdjtB7spr8+gBYGkWPvSzAUjertrTy37CLcsoS/z0dLY4BwYx+iLDJzUSkFbjvVPTG272ojtjgPz7lz8GW+i3CfRo0/TOWudmK+CJJToml7G2oobq5HOsT716IajYnOWKvJUAsr1FZ28ffWPnoTnadWzUENxQmJAiWTcsnIdHBwZxuRlmDy2JaQW+p1D/eZRFUvgVgeSp/JUslx2jCa6hDy+7DJRQNeKzpsuCdkM+PscYwryQIlhhHTENwyhiGyYK5ZDK5tCZoRq2agRxl6pyea1+4ozGBGUSaiYI4AHWm9FrMtsLNjAF03VdJEVXXiigZZZtd1a0MvOfkZFJZm0Sy30/xGPYIo8PU/XMu7s56j6IZ38/1vvMo9D20hvyQTX1swmbqKNAVYs7KGBZPfz2XfnwB9nbiBdtu7yHdLFGXYmTI9n4MrahLGy+ygjneHadvTyTrMZy9Vol2UReYtHcc3L6il77Of5OIffpBL6wvMPodBz+loVYL1mEoooWsFjIqVNhyG+l5bjmFGcRbN/9mHrznKohd/j809iRMhP6eG4jSubUqmvR2FGQBjOqnxROG0cgow8It0PIY5VbZ6JOXMo5XUsOe7KV5Sir/OP6LMhCiLeGbkUzo9n4ZNLcQ6+4dMc0luGc/UXJ777TQ2V7ybtna44OpM5Mef53v/bWb+7CJsksCDP19DpDk47LXC8EYosKeLX3zjVXPtU3PpqPGDruNI1BDue2YPtZVd6FGVx/eMI9vzKTa81M6kMi8HG/307vchOSXmXjmNztY+YhGF0vHZHFjTwIzzx3P5+ZN4ceVBDq5rQoip6JKpMaX4IyiBGGGnZM5B8NjNJiS/Kdks57hwuGx0tPYRSkxAO1r5i5bVDVy/31TWVHoiyLkuJnq7iN6zEceiYiL9C5LRlui0sfC2uXznlmmUurZy75pcXgldwxU/mEB7ZA6v7YtSmuugfEI2dYnP30xlCjDIJ5jGEQRZJjPXxeNv1dFc56drd8eo1q4r+pBNjUogxlt/28Y6WeTFc5cyY04WofZWevZ2USeLSXE7A1i1uYl517yfFeur0WMqDavqaUxEbklWUFQj0h1hw4FOxnsvwe4W6YkoBLtVzi6pxf7i/XyzpY/i7/6Sv/3fa2hRjSlzirjqvIks39DA5v/sOkTpTTCcXBVePG4ZY+1yXvxLG/zlF/ysfQt3feNlwnW9uCq8gEkIkL0O8zmLqabTT6GVWt9B6zNXg3HzOyKJx1S0Tj3m4GNYKeiHXtrPfSseZPzeF2gp+wxK4JkBab6xgqGZDXbWhm/uVdMA2PHEXoyTpHB8rDjtnMJYQXTYmHHddKZNz+eN5w8Q2NkxoNBoac1Y/zU0I5myIaVwNuCYssjdv72aSyu/R+CrX+aK61cNyZ0WZRFneRYP/fIKxgX/zc/PuJAX/rqVSGPgsNe5J2Rz8x0LidzxPtoS9PTVLwa5LbyC+26fQvyLH8E+O58X5n6MxkFOwaL05cwpoHe/b9iBKHrUFJNTAjHqGgNm163T1HXas7I2qatv8zp58sk96LrBlVfP4JOef1N72ZfZ+MRebB47SlzFZhP5zueXMS/yd35ccRkXzSnhnMJN9IZmmKyWTlPgTsccLWnPcSI6bRSXe7nximlUNwd47oFNxAIxQgd87DngOyp9qcOu3+sgd1oe8bhKYE8najDOtx/r4QvfeoYdHX3UPbY86eRtbhu6rrO6PkJ79wSef2grL7lk/jEhm5z8GsL9cXztQfpbg4cUKzVzxCkwIKVpGRld0enc0Y7vgJmXT1W6tD4fR74rOaJ0NMbAeqbqX6tF1w0uuXk2b71Ri29t04D3b/jLVrY8tpeMyTnc8uVzefH5/fTt7hxQi7Oc88FKH+qSCryOfnRDZlKOCw0ZRJG1P9zJx5/4A8+eeSG+Ta0c2NDErZdM5YbzJrF7Vf2ApkrRaePCK6bS3NzH08u+yTWf2oGhGezRdOQsB6LTxtIbZuJ22lj+py3IXgcX3zCTCSVZLF9VS+WLVcnrE2TJ/EwSBBPLCR/reFvrey25ZVMTSjdQA2YhXZRFsiZlM2NaAXf8qpGOypnE/U8nO/BPlJG2rqm+2oeekPc4nR0CvIOdgiAJzJ9Xwk0Ly/AHoqzZ50vSPy1ut+iwISV2sUAijxobsXO6LNOJ3h3AJsaO+OGG4oC3mA5f/4COzVQYqkZ9a4C8T58DjzwHwCW35NGTcQGyGsG9pBjp4mX0fLvnsPcKskTGBC+zF5WxvrZ3xLVY1EM9lmgGk4SkzIR1HVooRt/BHtPRZDnA6WR3Z2LeclSj6q1G7Plu+hUN7G6cdhv72gLI4lICoS5y8jOI9cUwVAlXWSbnXDyJmRNz0XWDcdkulhkPo10wjxWvZBPr6EcNacm1HS2s2kD2zHxufe9cuvwRnmoPEW7sY9sT+/jEqjrUUNxkHiXSHmpYZe8rBzmwpsHsbA8r2Dx2OnUDV4adnHw37c0Bs5YzVCPWELtXXdOJJ6KJwVLegiwx5eKJfOD6Wfzjyd3Ur6on3jM6iqUV/fUHYwRCcfSUTYqlUQWJZi9ZZFqZlw2FGYS9TpwlHhR/hEhzEFumHVuWk+b9Xdz39G4mTcylojiTWXlNuDvWwoL5XPCAjjBtFuX7cwhUdqOG4vzrpf2Mq8hG9joGDNqJ+8Js39KCEtP448712O56jPnFmfz7id3Euk1tp8qd7bgy5GTtZen0Qi6p2M15E5fyob2dBPd1mZ+JaDpNXdHHtFPZnu/molvnIttE1q2uw7fJlOHw5rooznMTj5uMJ6tmd6Khx9TkXI2Tcb7jxWnRvHYiIEgChedVsOzCSezd05EshFnGxOa2Yc93Y89zU1iWBYDf10+wIXBolOcQO4isuYXMOreCfWsb6dvdOez5rWjB5rHTX+MftqlHdNqwuW3kzS/m6mtm8t55Arm2Srb3nMV9j+8ydXX2dA7ZhyE6TUqmPcdJYE/XqHfbw7GcrJ2wzW0jY1IOJZNyqVldjxKIJXOyNq+T3Gl5nHluBZPKvFw00Y5NjLCjw81zb9XRVNNDSUU2d904h8n6MxiN1Qi5uTTmfAyA9U29/P4HbxzWkCS55aRcxmg6V63P0TM1lzMumEBPZz8HV9ah+CMDXjfUPbd28J6puUmdpkWXTOY71zhY25LP7/66Cd+m1gFUy9FigMyH08a8W2bzvffN5c+rGlj16O5RN1kKkilKeMY101kyv8SU2X50D2ogirM8i/lXTmXWlHwefWAjsY5+3BOyibaHEGWRcWeWAdC4vhn3hGzmLCkjGlHZ+dwBAArnF/PbL59HyT8+iNrQh+unX+HHK8exbnUdNpvE1z+5lBc2N7L+uQOooXgykgSS0tupGybJKSUNu1V/sJyknOPi27++iiuD90NjM7/xfp3//mQ1WlhBcsvkLSrB4ZJp39g8aoc50j0TZIlF7z+D+8f9C7Wynf6P/5xrP7QRxR9JNsrlFmZw+3vmohvwm9+8NSCL8E7H26t5bYxhaAadbzbyzKZW8+dE8RNIauA4CjOomJzL2YvLcTls7KzqYmtENY2SpA3JSOjb3cmGEZyBBV3RR2UArPGC3TvaeVkUKc1bwOyCZexq7aa5ykd/fWDYVnc9qhKu7yVcf/T88KEgSJhy4VGNwJ4usxjKITqf5JZx5Lvw5rrM3Z/9CbTHViMWZLL00i+xsSCDD186jQU8Ab3rqPZ+gjpvmL11vSy/fyV9ld1m1DEojSJIAovefwafvmYmv39mD9v+ufOI12M1/gX3dbGuumfUTUKW4ZhwfgV/+lwJYTWXP6zspLa6m6ByBjlOAW+uCx+jL3YOt1Y9qrLzv3u57fXaZGrJgnU/rbGdQ16fojFzWj4fmbQW5hfw0cAU9j5zAC2sEOiJYJdF5CwHkeYgfXvMZ1IQBepX1SN5HEhumQ/evoBrp8d4qzmL/WsbiTQF6GsP8eS2Fr708Q9h27QKuuqYN3kB9fV+HC6Z2fmd7CrO4s2wSqyj32TFWdF0TBtAm7XnOE1F3JRrMD8b856ogej/s/ef4XGU5/s//NnZ3rTqvdqWe8XGNsY22BgDpkPoLUAgQID0AgkhPZBCSEJCElqAAKYbsMEY4967bEmWrN67VqvtZWafF7Mz2pVkY1NCfs/3fx1HjmBJu9Puua92XufJP58/QNpd32ReyYdM6ksmbXaOPD/iChIcDGIwypThGtfo2fepAj+0eoFIdSf9G5vJunEPOpuBYIebYIebUK+P0+45nXPe+BpNr1Wz/oMfcdZ3k9UJ+c9iphwbeodxCJL8/6j9/61TgCEuGcXiKQ0Ek46UNAtjSlKZmJ2EFI3SnmJBZ9QmOI8vOoJQqCUAvP0+1u1sYl+Kmca6fjkdP07ZSbH4Pol6zidRIx3+Oa1Fz/gLSjFb9VSsrY3RIIMxy8r4JSW4BwL0NbsompLJhUvHcU7lT1h54ccIAkyfChNnz+H+sxfhv3E5jd0+ij/8Dfc8sB5PrGcQDytWjqueg16LoNXQ5g4S8EdO6b4PnwpX+gyCUSfraA9r6sllC4meNjft3rmY9QJStIvm/e189eFBfvrthXztqhk8vKdNpRcYDaQw2s9HNVHuU5lybLLiW7eXaFjEWprG+ddOw24x8J9Htozq+CPuEKv+c4iM+84iw2UEqtA7jIRdQY59UEPN+nqZXmJYFhp2BYm4QxizrJw7zoRj7cNcqNMx4fd/4ufP7eXGy6ZwfspHvHRsMSl581hh+IClGR0svCULdzib5/d0suo/+1XRG0GrwVKcjMFhxNPoIuIKYC50MGl2Lj+5Ip8Xd7t59ZebRs1Uo1KUrt2tPNDl4fZvLOR64R+ct7Sblgce5Ybb38TXOECw1z8qukt5llqbEdETHEG4OOJYYhQQOfJRHR/97Elm3xbl72Vews5t6tqTfGG2v1aB4eXvMib9SZBkWLOvceAzvetai54Jy8ZgNOkpX1/3/zmF/9dMKYWkpFsZV5iMWSdwuN1FV7+PSDBuwvELlsbTaDVy+SfdIqe3NgPtTQN0trhwdclcTycLX9PZDOjsBsSAqFI0n/R56LWYCxxce/EkGrs8VKytVcnYsmZkc8tFk9lf38d69zHGjEllcWGIj874mGgURFFm2kRvxLb7aWoO9VNyRiav1J6Gp2rtCClRBS+u0DxEpSimHBs/uHwqhe4XyLrmOr6xvv6UqZEVtJkh3cKMFaXYbAZ2v1+Dt6ZvxL2QwhLuyh5+8Pg2rEkGuuqcau/BqBXIsOiGoJQnuI+fNN+iBB/jlpbwtSun0eUN8uSfZN4mnd2A3WIgLdmE1qQdDm5SmYPDTj9vv3cUk1lPR9OAzM8Um5lQ7t/wUqDy74g7xPtVPm6dOoljVz1FweJL+OfvH2dnuwZ0BrqdfrbtbWXOdcvRi35Sulfj8Li564xz2bZNjp6jyKCNwimZjCtNY/+eVjp2tCDoBYryHGRqDzBrzHzetOhGaJSoZSQxKkuoihJdP32H+gMuzliVgjFzpqzJHNfXGn4dluJkiqZk0nysD9eBjhNkuUMlq2C3l2dXluG9UqYVGW6BtkFWPOjmggt/hVgdxdlw4DMHgFEpSmuDEykQ+X+e1vv/plPQatDotFjtBtLMBpyBCLsPddDRPCDjiAORTw2JOxUTjDomn13CmJIUmlpdnDEzl5dXluFpGVQboTL6ZRQKhBQzIOOhBb1A0dnFjJmQTtmeVrmx9gnzHYqSldZmBEnClG6mutXF1i0NapagETR43UHWHWylsbYfq93IpOJU0sNb6Ivre0/74zlEU4thcDuz3ruN7bav87dvrpYzhBjkV2vRo7MbKJmXz71XTefVrfU01vTRdaQbR0kyBQcfovfJHcz6sxG9w5QQOZ+IME8xnc3Aoq/NZv7UbBYXhmgZdLDnw9pRHbsyQ9GxuXHEzx/49QY0giamzjf6ZxVTYLb6FLO86cVkQBP6CkYdFy4vZVH1w2Cx0HHjHbz+15040i2yrrOgwZRtS6Dd1ui16GwGJiwfy4+un8UE43q6OIO7Hysj4g6RND6N1Ewr3e2DDNbKUN54qgqFIE8w6Xjp6X2EbvkK1895j20vNrOQbyF9ayW/2DYJn99Lc10ft/yil998exGO7XuJBiIYrFamzjyX1p2tSMEIWpOWGy6axLKMbTTMX86tde/iaxyg6lgP+yctZENZkzyzYdQlZLY6mwFTjg2dw4ToC7NjXys3/3QFGeW1DEz+KsHu7TKtSPj4gANzsomi4hT83pDcwztOkOSYkcWcpWPY+1Ed7soe/N4Qc3IcFCaZWGM3JFCJRMUoPVuaeXF7S4JDVdCIp0rqB4AoyZPzsWdgn5hGUr6Drt2tx+2RjVYW01r0pM7Jwdsy+KWRfP7fcwoKrFCS6Gwd5H2a8HnD1JZ3EerzqRQHn5epPCujlHSiYZGq7U3UlncRDYsM9vsZrO1Xz+F4SAXBqGPKhaVkZNnY/WGtjP+OSHR1uPHEaKOtY1MQAyLBDveoTmXWjdO56YKJVHUO8torh/E0unjj8R0qDl6Julzl3WxudKGzGcgqTcUfjOA0zOHad88iuKcN0wNf52hgGXavRM/pM/n103voPLBa5q6KOQP7xDTGTMhAI2jQ6QVaBgM8dKmV5sFx/PLpPXSUdfHy1d9m6p8f5hfrqgl0HFLPM+G8415gJULWOUwYUuVNJxIWaWgf5KNt/fR1H1NlO1U4pV7AkG7BXOjAkW6ht6YPf6usgDbxkgk8f8k+NHofW7ieXzy6CXdV33HLd8rGnTIpnbPPGUdZWQe1GxpiFO9DpbKIJ8Sz/95P1r0/w6rXsueVQ4i+ML01fRzMtnPT+RNYetFEPgpW4KnqBeTa9PTzxvHXqat5J+82mKhh/JNXMH7Ktezv8XLhRRO5arrE/s5JvPhuJb2dbhUgEZWiqi5JxpQMLr9kElaTnvXfe40rfvke0fLDzMiU+MMTdfJshyuIFIyQZNRR/+d9RMUo6df9ms2PHEygtpidPUjwsWcoLHqdhVf8kI+f3EvVxkbqzxrDitkF7PygBn+bW9VsRivf6ylnl1BSkExtfT9V25tYfmQWGv0c/Ku3H9fxxj/jvkOdbGgdVFFWgsOYAGFWMoQF55XywPlh/pI0hTcrN9G7p53bvv0eUSmKr3l0ltX43ofObsBSnMzMJSX0dnpo3Nv2iSSbo5I5xtbGWVdO4bozirn/kY0493WMYCN2TM0gJcdOT00fnpp+omIUQ7qFnz5+IeesuYvK7zzHHVe9/KUMuv3XnUI88uWLxAcf12KRtxgU6Wpx0dftIewJqXVN0RceQWPwaU0wyc1srUlLqD8wQmlMCkt4aoZC7sHK3qFBmhMcPxoWcfX7caSYZailJ0TrliZaY9+ZMT+PpeeWsndPC/Xd3hHRldai56YLJrIwZQsdnnmyYppz9LquWqaIyCyhZTW9eAPJjJn+CJ6JEZoPePAHW4mIEi5PiN6aPjV7sY5JYfaysTxwYRIpQjVlA2ey+mArL64sY8+0LDpaD+HscDProvHML0zhtT3NlH1Ym+AM1UGkYaZBVji7+N55nDExk+dWVbDz+UMAnHvPXJ68tpOwsZhltzbgrXOqjKqZM7NZtLiEkhw7b6yuoiGGrPrDneNZaZERUl/560Zuuv1PPPOHbbIGcHwvJHY+WqPMGxX0R2jpGCQ924Z7dk4izXtsTsC5u40HjqzCmGUld2omOoeJUK+Pyk0N7Bufjk4nDNGbAKZsG/Nm5BBaewi/AqZKduCqCBDo9rFtRxM6bTF2a5iLlpcSCokcqe3l4K4WfM2D6Cw69ClmBEHDVyb3YDr4BuLsC7nnhelMn7SMi6OyVOnR96rlmRWjll3NTi6clMZAvQsNkqp1oRHkrGp/ZxLnzC0mXN7GLSvGsvX1CgIdHl55/QjXXDkVW4aVUJ8PbapJzXqkYITOFhdBf5jOFlk/w1vnPOl3S5n+jrhDmPKTuP2X55CdbOFvT++hb2erTFeSZUVr1LL17UraWvOo31+nzjqcioa5oBconZPLHyevIdxyAMOzd3D21wU8x0ZCwSGRliY+w4j4ZB6lzW9WUFvdK2/48etZq0Fr1DL77BIWTs9l7Y5G9sWG+mylqZyfvpGaFyqw3Kj9wsvXx7P/ilNQXkh9ipkxCwqYODGDvgE/h3e34qnpHzXt/iJNgSGGnX4iggYxKA417KTo5/IwlIefMSUDk1lHR2XPqDTAidd8chhmKSzRtqeNnpo+gr3+hBRco9eSmZuE0aDF75XLT8PTVCks8dqmOuqmnMaqdw8j+sNqOWpUxxAb1PK5g7Q3DeAeCFBl1WMy6SjOddA74Mdo0HL2zFwObm4g2OVFa9Eze9lYvn9BBo7VDxLVack6dw6iFGWgZZCdtf0YUkzkjEllxYJipCg0NjmHSijDtA+GT79GiTXnA2E6BgMMxpyaRq/la2ePJfjnBzEsGIfesSjhugdaBzl4sJ26JjNdjU6ZutlmxBmwc/E9eQh6LfrrLmH3ex3HpW1WSm+OkmQmTM/musVjmZDaSpdvIdsa+njv/Wq6jnQjeoJEfMjN5qCI1qzn7MVj+MAbxtngRJ9i5mBZB35vSM4wYplRxBOioq4P4z03c6X+JbRZFugfwJFiQvQEaT7UwWp3kDMXFrN8ajaZFj9zC0p5N8XC9m2NWKwGcouSqS7rZHNLAeePm4Iu1IfJnMemzfWMy56BI9UsK6UFIohB2LSzieu/fwFJ5dVUe7OH7jcCUSnK6h2NzLvyHuwTPiYkytPwoV4fnbtbeTks4emRyzP6JCP6JKPqGLuOdNMTyx4UR3kqpqzbwtm5fK3gY6LufvYtuoAP9nWgsxtIG59GcpqFmo0NVKyq+tTkmFJYIhQUCX94gDe/X8EFlb9lzoq/sKVhzwmjdWXeSVadi/2dKPNO1TQPjihFKeCX/Zsa6GgdpO1Yr3rOwW4vmwaWc/YHfq5/ZveXBpP9wp2CYNJhK03lez9YzAWaF1k55mcAFAO/e2MBq+58lL89vh1/i+sT0QXHs1PV/FUejPK3Ci/Lp6UAPp4JJh02u/Gk//5Ujhns8o46wazRQ3NFN80V3fhb4jSw4x2DKFGxXebVT8+28fZ9vRDycesbE6l4vWKEoHxUHHKiHYEIXTHFu8mzc8lOMVPfOkD5oQ6OVnSrutvmQgeXzCskw/MO4Q4PurNm8FFtgP07Wwh2y05jzLQsLl06jvOKK9nZMZ3OlkH5JYtNmCvNY61Fj6AXVGeuPDsxKLLhX/vYGGtg6xwmpGCEVYfaue9H32Nnx3S8dW+p91b0hfFU9VJV048px8bYMwpoN+nwNQ7wvce2YC16iN4WF8F7vAR7y9RykxSMDNWFY05KZzMwaVYuXz+nlKKVt9LyUiUZE1O5+fYFLL73p/zz42Mc3NUiBz2xzTDQ6WHTlnokMcrSa6Zx6ewCZqTvpc03m7fL+njrhYP4GgfwN7vY/loFF+5LJr/k+/zzomaiNeUEArFItMtLb1iiKtPKium5ZEW3k1m2le8U5HLL965BQMKkc/Gfgok8+ptN/LMgnRlz8uhs7aa73snfXzzApRdNJOO++WxaU42rrIuqjY08M/sG5i1JZfX+FnQ2mcNJEbY6srWJhyhi1qRLqGttRBtzjMFePx3bmtUyKcCss0ro6/ZQta6OYMewKfxPYAEeDdmlETTYHSYQw2gMRvqdciAUDYt4B4PoDdqEqWjle5S1IwUjnzhNLvrC1GxsgFcfZOFHd2JMMzNveg7bLXqkEyGJpJiwUFiTMMsRFaPH7X9IYbn/MHCwM+GcfI0DPHjPOyAIhHqrjn/ML9i+UKeg4JnHnpbDitxd9Hz19YTfD6yqoWSRWZ3O/DRmSDUx6cLxTJ6YyVt/2Umw68T841EpCmERCdSHFs95BJ/dISgLW/SFadjRjBSWPlWU9Ek2ap00LMoc+6kmzAUOwoNBuXYbHuKvsZam8fIfFpIlbQWdjw1FP6N4nJ6iH79GxSjHiIZFJEkiLCVCfMucfip2tRAeDBLq9anMmlqTFp1Ry+HmAcxjriD15mtYdaiN1Sv3qHQPxnQz9142lWlpeyEcwKgVMJrlCXNRL4BedvZJ41KZMD2bgD9Cd/sgfQ0DsWwurGL87ZMzuOqW0wBY+Y/dHKns4rttNvavWUvEk9jkk8ISGiVTDInqZtK5uWnE4FnWvHzSc2w0HOpUywAaJKSwLPVZW9nNxqJkbj97FvmihK4gGdLTKN79fR6ZO4V3p13JE//Yjb/NrQYcTdtbyDoth/uXGLE+dSuv3HcYgNu+U0Tx91/mD7/agK95ELHDTU+Hm9497SwrS8OcvADn0Uq1nBOVotQd6OD98RlkzT8N26H/UHX3aooWvEvS3WehKSjhjtR+IrddyVsvH8LjDWF3mOgEsnKTuGVSGVHjYa7++be49WtvEnEFWPnvA6wvcCBJUbJLUxEEgY6aPpm3KyzR2uDE7QrIpSBXEJ0thnbzhNRoN9DtY0yeg6sWFPNKipkdzx1UWUKVoVFFk/lEUX18EBOVopR/WMOt/imEQyL1G47IjLMxYsFBrWbERLRG0JB8WjZnnFVCR/sgZW9UntAxKAipc2+t46qH3yQrzcI771ef+J0VZap/jRDnDE5hYnlEj1GMfuIA36cV7jkV+8KcgpJ+SsEIjVW9rO1YwPl/uYNp5Y9wpALmLdCT8sJv+fYTZXjrnPKm+Sk2Y8Gow2w1IEVP8YHEVKuAz9UhKKaklcpL85kpsE/BtBY9P/zNeZxT1MpTu5JY+egWwi5RLXvo7AY8YRt2aykh0coZNxdjWjGZHWvqE18CUVLLB1FJLtiIcfdYDIho9Fr0KWYVAaNEgkFngN37WjnW2I/fG6KurBNf44A6syCFJTIsQQh62dK7kA2HW3A5ZZoEBXZpyrRw/bUzuGHKUXZ3zeHNnU0MxCLP4eUwrz+M0SA3f2dMzcZi0lFmaxz1/kTFKCFngIatTaMO08EQQMDV75cdS1yvh7D8THurennbH0Z/1Q3MueZepGgUm0FHcf33iTY3MWWyjYmzcvCPTyMv38Gxo920HuxEpxNINjTR+n69erx3Hmviul/v4qnCZPytbqRwbD0GIwwe6cZjktlddXaDTLcd0yHYvauZ3IxJXDt/PJ2/OELjyk6W6LdiKCzjw19XcfcHnfiv/DptXW70Rrn2HfCH8QhjsHKYbEsz5jy7LHDkCdFV04/eYaRofBpmq4HutkE1yAkHRfzeEKFYqStrQjqhEpGOHS2IPvn+RFwB3L4Q41L6+c6FpRzZ0axGxIJRBgbok4yEB4MEu+Q5CA1xG1wcc6zK4SRGCXZ5qVoj03vHN8AVIMBo71bEHWLQHdMNH22Cf5R14Wsc4JU/bMOQYhpiAz7O34Lc21K4sT7vPWS4CXqBpMnpRMXo5yZANJp9ITQXGq0G24R0ciam0VHZg7fOKdM52AyY8+xo9FqZm+UUSMKOZ8pGB5zUmPyJ4I2fm0OIpa7pc3NJz7ZTu7GBcL//v4IkEPQCc746i0d3/4Dqj9qZeezfLL68BkEvMP2iCZSOSWXV0/uISlG5SWbUkVbooKdOFltJuI5RNI3jzVKYxDV3zkWn0/DyU/vUprmCOtKa5Map4jASGm56LVkL8snMS6L5WB/jpmbh7PXSFhPKMeXYGD87j7+fu5/ubz9P5ks/47ebiln3WjnBTrcKwVSaoVqLXn62gkDx4kKMJj2125pl9NUp9qvUqNZmGCJJREaoKE5EuQb1Wi16tcG75LxSxhUmY9VrWZG/l67rfkbZpgHOXX8dN21aSsAf5udfn8+Ed+7g9dv3q8e9dt81fLPqUvatPDJiRkUw6chfUkxKuoWjH9Sq/Q59qhnrmBR+cN8Clu75DrWP7saSacGSZubD//Twlb/N4OMLnuS3D69XNZQFo47kcSksPbeUiCixY2sj5ywbR++An20fHCPY68dSmITBoMXdOkioPyBnfzaDXKb1hNAnGTn7wgksm5bDT36/hcEj3WrGXbh8LH/71nQyP3qA3uW/5IqvbiDQ4ZGPG4veB91B9q85JpdAFdROfM/oM5Zy40kvAVVIR+8wEhWjI4YaR/v8yRz7ZDUjPg/T2Q1c/J0FWEx6Xntky6cakPvSaC40ei3Lr57KrWem89z2Xt5+ZCsRtyy1+HmKUEOsQXQKnClKrXr4zz6NncjBKL8zGLWy1KQ79KnZH0/JtAKDTj/2P3yTOZ4+7n1Jj+gL872/Xczl7ifQTJnL2/+KMvaMAu64fCobyzt459FtCRA/xdTBqFGYQgW9gClGvd3c4ZEHdlQKYoh4okRcoyOpNDFq7a6tzXTFkDzG0/OwJ5vk40tDtOftydeS+69kXq2dwf69FYSVbEJMRHFJrqC6mTdualQ/r7XoEyLLTzIh1i9RaB2Ujfn0m2dw+3kT+Nfqo+x/4ZBaKogiZ50RV4CQXoumzc3qeieW4mTOWDqGC8KH2fDWAABtP36Ph1/+IR8d7eLNvc0sO/9Jrl31XbpfOELynGwGp91E/5YO9Vx0dgOGdIuMgAqLdB3owF2QhLXYQcRnxVsvD925AxF+99cd9Nz6W677aBPR8gMgSVz3x6lsCl7Gmx9UqXV1BPn5DTa5eO/lMnInpDNnfgGSFGX5zDy6Oj0ceqMCd2UQBEHttymfF4w62RnqIuzb1UIwJKIzauWyX0AkKkLHvnZe2F3IvbvbiL50BQ/9bjW//OFagr1+BEHD0um52A1autvdNO1pUx2A0qyVAhHEmOCP4oROJngc7X2ML1HpbAbO/8Y8JCnK2r/s+sRy0snYqczSfFYTfWE+fqtSXtPBk6+KnKp9IU4hGhbZ+GENTneQiv1t/7PMgF+EM4BYSavAwYKzSjha0a1O7/43TCNoMJp17B5cgjcsUb7hI3m4zWFGk1TIqpa5hPvX8Ow9EtWz5nLvnGx2LXhArakPd5oj4JixzViwyayi+8vaaTzcpQ68JZAAhhN1kZWJZmBoDkMSEUWJPS8fVjchwaQj7ApSd7Sbv28w86uLsrjGWobr3Ek8v689QZp1hMXOT2vRY8mzE3QG8De7PnFTURByxkyrygwa7PMTCsokimOLUsixhTCadbJjlEYOXEXj5iJ8jQPs+LCGjdPvZOHyV/H3+Umfnwt/uZq7r1rA4XH3sbe5n622H5D9kIVgWGLvv9toPdypZibL753HA+eHueQHLbgOdBBxhxBMOh7+5kL0goYHf7sRV1kXoi+Mr3GATTubmHrZxUw7I53oke1gcSAFICXdiiHNQiDsQSNoMKSYyJuQjt6oJSXNwqLJ2byxuY6y8k68g0G1gRx/P6OSoK4PrUX+VcAfpvJwJ5GgrJcdFaOIUpRwv581Lxzke99byKqpL3DF9Ftx/eIpfn/Pe3gaXWwq76C0IBmzVebSUpyORtCo0a/WqGXqZROZNjmLjRvqaNvQcFKRfbxGynDiRyVrMCtoIeHksoETmSZGATL57BIOvVHxhdJbRMVoAjX6F2VfjFMQowwc7GSbIpzyJUGrhtvn4c1P5js0Wg1akxaDXkvAL9NO/7cwx5bCJH5240wyTVXs7hyjNrhf2lTL7sJlvP7sVsSgiC+SCsBAvYv0WXa64oa8RsumgIQ0X9ALhIIifd1OAp1yI1sw6pi6bCypaWY2PneQoFJmiZXTbKWppBU6CIdEeiu6VThtVIqq/Qgli4gMBohGRLo63OB1gruX/LTZCY5JOdf4/9YIGnW6Ni3ThksnqI1SzXHKSAogwlKcrG6W/d1egn3yVLWgF9i7t4WmNheV+9txTM3A3+ZOoDNQ7pkKZAhECPb6eWtzHY9/8De0W9/A/2YZO15pYXp5LzOeyKZ0xhye3GbmjTfLCfojhPp8CeUUo0GLXhgcouWWophMejIsBiz6MI4sGy5tD4jyzMalS8cxzbIB2huRymvRZmajEzSYDNohBJcoiw3NmJHD0slZ9PhC1Ha70ekE0jJtzJudzwagcUODWnKR7xFqb0kKSzLqJm49yFPUQ1G5v9VNVc63ycp8gbrXqrnqW9v5c4qJiCvA7q2NNBU4cPX75dIbEOqPJkDCBb1ARpaNlCQjWt3IOZXhzy/eGWhNMZhobH4gfhPdvrF+aN1oBbWf8akDREFDckkKFywuoWJD/efuFEYrY33hZaov6oulQITQ5yC8/Xna0OLVqGnlqdaaT8aksISveZD3Xz4sb0ifQGr3eZmgF3j1icVUjZtF/urreL/xCnXj2vHcQbaLQ026y75Xy0Ord1OQZKLunlUjnNYnpcVSMEJXWac8ERv3nHs73erMgGIavZaic0p47dt+ohveQLNoGV9fOZbDbx1FVKgZYi8vxLKIWLmxcm0tX+nNxWrPpPnwZnWoKv4cE847VtYR9AKpmVZ8XnmQ7njXoQiyTD13LBefPQaLXsuxTjdrVlep8zM6u4Fp03OYMTaNxy84Rui51zn6k+f59kPrMKeZ8bQMjiBUE4MiuAJUbm/mZ8nj+fEFN2H2+Uld24Kr0YXjmVUIvje486HHWPtumEDboLqh6B1GBL3ApjXVbPlYFwddFOmv6uHFbfWMzXdgMst9DIWBNSRKIEXAaEXITAZHJk31XvpjdOLycJzssEpyk5hZ8VOk1h7E67/F4ORsolEtYclMJFJC59EeAh2ehLkRFXYqSSqqyxUr9yANPceoKK+Pe374ARv3foPGm58h8srrLLzhATY/tR/n4S4G6wfQWXQkFyVjtRvpcIcIdsvT1Yp40fY3K9mpFwh0eEbtx6nZgQJJjvU8LHl28ktSyMt3YLcYEAQNzsEAAzHkVH/bIDq7AY1enk/iBDQbn2RSWKLnQDt/dQc/99K4ArM25dgIdHhOGnL/We3/DM2FRquRB46mZjJ2ciaHP6wl0DoInNxiON6mMrwBqzTbIq6Auqmc6Ls+T69vzLLSffpi2tqh7/cf0Xf2eeoiksKJ0MyBg51875Y30Bq1J2y6jUaRoUBBI76YM4hFXNGwSNPHDfLxgkPaFcZ0M1ddPgXfgzdR+U4Ts/8UwWq/W703gknH4jvnYNAL7FhzLFZ6ks9bDIo0r6uTz+UkG4/6VDOX3ziTq2ba+MbjAwkzKfHXobMZKD1vHDdeOpnllnchsIv21JvZWxvE1TygNnuDXV42fHCMH/0xmT1jH6auAa676gWKps3gjLkFbN/VzNHhlAiihBhERgitr+PXjGXF7N+yYPf7RDsbIRSG3j4aPAVohJqhc08xM+28cZy/sJiLKx6AzAyutJ9D6/p6pLBEoMPDuid2q3MESqkp1Ovjjz/5iDfmFzB+0iQWT7mClnof6zc30N0+KHNJxfoD4cEgh471cHnUS++qGlLdv4NtbYj+MJkXj+Pq639H7fmlHFxXq1JK6+wySEQw6vDWO9V7qqwBVZhIub+CBm+9k6+vm8s//tRPaO1BHrloF3f0TOPIW0cRe31IdgP26SbGlKbhHgjgaxxQsxPlvQp0eEbAipXnp2QHijZKemkaYyZmcNn8IhakbiW6fxUMuiE7E838eXRF5rD2WJAtu5ppYihTFaXocUW1TsZC/QFCu9tO+XOfZDqHieLFhdz2lem8+G4lNZ+CKPJTHfcLP8L/kBmz7Xzzznmcb3id32YvYdXvthE9iSh+NIdwPIjbcB6U432PEt0oL8HnMRuROjGDI6/J/069vJTGbSN1H5SXSecwIcQ0f0f7m+PpW8eXSbQmebhLGQxTxFZEXxiNoMGUn0TJvHy87iC7D3fwlT88xJwHG1k7uIL9D69XMwzHjCweLXgNdDr+euPNvPTzjYkQ0FMwZdDuSGUXvQN+Ova1j9AFBlmd67L75vM907P0/uiH1LcMUvKrc9nuD7JtU0MMFqo4VJlVdWXlHK59+wZO21/FH2uXc+zjnTTsayPUnxjBRcUoGr0WQ4oJ69gUDAYtVUe6qDrSxVulkxhXuIDCDCu2NB1vfViVMLQpBSOy7KmgofuFI7SUDfDwzh/zzbp+PMdkjhwFtDH8mQW7vNRvqKdpTxtb0s0yTcRgcGiNxSJi0QcHd7fAL24iIxQmfKyfj16JIc9W7edq4Yc8dvNt/KN4Li/8ZrNMlZ1n55JrpnPZzFx+vfIQR96RdR0U4SqF6TQqRdVsPG12DqGgiCa3gIZXXyHwfBU/2HWIW9bUyFFvIEJ6to35k7IIBCN0721Tn7elJJl3/jmLtXV2nvjZxwn65CoFvl5ucBuz7RROz+Ly88ezpMhF0ppvs+Gu7XTFln9GOpz1cAVZlzUwM+9mjqT10hITCpJvyMlF3/FcR8og5Rdt+SWpLMhr4/1MK7X/hWY2/B9zCgBJRh3RpiYKs+2fusdwMphntQ482u/0WhxTM8godNC4pfmUqa6PZ9YkA1MmQcVRaL/gCbwvvJF43DiHkH16LoKgoWNfO6FhjtGQbiFzZjaegQAD+9pHHEepSxszrSQVOfAPBBADBiyZFjwtg+rLYi9yMG9OPuXVPRzc2MBdrhy0uolU79kiD9TFIkKTSQ8+P9is8vd/AtXIJ0V0IWeAI+8d4wiMyNYUmOLUFaV8L/klVp4mTzwX5MOY6aex7s16+o/1jQAGSIEIz/xjN2VLL8OQqWXDE7sI9fqOG7lpjVocE9K4+7bT6XMHEKUo+w53cmhLI3UZPcw9o5DTx2cgCBpEvzyMprMZiHhC1Oxs4Z9tbla++k9m133I40e7VGesQGCH61or9yPik8svoifm7AUBY6YVS6YFZ3mPDEkFvD0+/nNkGpdf8gOs5a9S9I8qmppj9+9YP7p//JO77r6b13JseDwhrKkWrpiZSe4Hd/HLGx7lyo8bEJWJ+lilULnPisTtgrNKmFScCsYWLBkWDL4w3kjcoJcUJS3FzJQMM87xGWw16SCWtepsBhz9m1lafCFPZVpVp6C+s1pBFSoypZspGZvK6bkmHM736Hm1SnUIAD29EA2IoNXhDkXweRM5zobfw+OZRq/FmGmVn9NggGCX9wst5yjcUQ2u02is2TkqBc0XYf+nnEKgbZA/PLuX2677Lu+9eeSklboS/h3vEEYhagOGKIxHcQwK/n3pRROZXZrOIzX9eDyh4zZBh5/Lico8rQc7mV7xL6aLIZbc/eGQQHoMWSMYdUTDIpbCJOadUYhzMEDXke4EJlGNVkPWaTl89doZbNrfyrZROOwFvaA6BJ1eS1SU8d8g1+jtkzMIdnvxdXr44P0qBptc+JtdHKzuGyG2A7K4UN+8b+OPpLLrn3vioK0j6Q8U0jiNcHziwOP1szRaDcYsK5PPHcs/Mp5m5Wlb1d+dft8knqheSNXHO2T5yWHnKIUlXGVdfFzWpTpW69gU2TGMAomWwhKCoOG0bB09Scm0DAYoKUrmyIe1+BoH2DgYpKvXS0OVzH0z+8rJ/PjyQm78yS5cBzrwNQywZHcrjhlTychuk+cwBA1Ziwp58ocL2dfh45H7Vo+kZRYlpNi9i0pRrMUOHvnFMuYJb3DzyvHUbW5ECkTU8tPKw1Zm5X+XBVtSKLjzeZLHODCdUUDfKxWkFbzCnOX3srV5P4JWQ350La9cvZNzrrqMqef/mUNvVIy6UelTzKSOS8XpClDX5uK3TbO4+o0dmHRhnl7fIPd4Yu9RTrqVAs06LhlfylMxPiUAT00/vz96Fi3t7fiaBxOzbGEoUxCMOqx2I6lJJozaQYhGsRXYcTj6cMVYXi79ThGGO6+lLnox72+qpaG6h5AzoJbATjYb1Rq1TD6nhAVz8tl7pJMDr5V/ZgnRE5noC1PzYS33Hu6Up9z/P6fw+ZpS4mjb3MSjR7pUTpdTajTHDXMl/Dv+OFJUncpU/iaeUtda7MCWa2fv7hY2rZc3CI2gwZhjI9jrP25KGs8tc7xzDnZ5uf6xKJKoI9Qnv1waraz1mzMnF783hPNwF4EOD+vXVKupv2DSybQRMchh96FOnojVeKNSVB2tVyJ769gUzrhkIlVHuug+1Kk6V61Jy60/XMzFk3S0e+z8+pk9tO9rH+Kyj/1dPC9NNCzirXNyyy+PEPJH8FT1HrecpkBGNVoNN993BrdNr+JfByfw9PfXfqKDVzQoLrt7Ht8tXc8r+UMOYdYMOHTT87z3+DYVbRSV5KbsaKgPjaBhztVT+OmVWbxbGeWfP1w34oWVghG6d7Vx6y+2IwgafG3yEF3YKT/jgYOd7IzJnWpNWtLTLGSZK8ktScFd2aOWZgwGLeMmpON1B/HU9JOaaSPf/TL54ybwp0zrCKcgmHSqzoMypfzevhbGLFvBtOkeklLNVO9rI9jtZcuuZkJBkb3mds5bfB0XvX8mhtbtiNv34ViQC0l2FuRmsyvVzEDTANWRC3E4fsxA/QDnLiymYkO9Srmh3BfFSiakk5FqYc0LBwn1+lhj3IveYcSSbZMdXCyYSE8yET28G4u0k9wJF1Jd2QPIG+Ibv9ki3/cTwLmlsITbFeBYQz8fp+UzNfM6Jv9uAiu+dwwiYXBk0spynj3cw5HKMuore9AZtZjz7PgaRRiltBhvw9eh3qgjJ9lMZoZ1hFb1521RUWaJ/W+ruP2fcQoQiyqDMlTwZOqICdjnT/ib4ccZ7hhAjjQKZ+aw7KwSmjvdfPjCIaSwhCnHxrIbZ7Dx9fJR6X6VIaZgl/e4PRAVZ2/SI8U16jSChoL5+SxcWMz+g+307e8g4pMjYUtxMqXTszi2uSmBOybY7ZVRPmERnU0+tkKvrbXoSSlJIT/Tzt7+RrkerpQNLFbOLEkjvfYx0m1Wxk1aRsfBjpHnqtdiKU7Glm3F0+7G1zxIz6421fHEQ2LjN2KtRU/W5AzMVgM37/0+r8w+yB2rl/Bvx4Lj6lgr90aj15IyNYP7F/Xjuf819XezZsDEjx7k1wc7SEmzEHGH5Gt3+pFGyVgUy8u2k+76kPMnXMnTcQNy6hqIUTA493UMBQbxay42EKjU+je8eoQBVyk5+VraJ2fIYvJhEU+7m6ryLlztbhAl6ne08NLZF9FXFyDsbEi8zhhCyJBmoWBqJqFghI7ybra/W41OO4nbFo8h5UyJVTPm8O9/7KF6SxOCXiB3aiZiNEp/sIRs3T5CB+Xai640SJHDjDHTir/ZxZNrq/jTc2ciuYJs8wQTApV4SGjEE8LZ62NcYYrM1RQQQRCI+CKkZ8vIoOrtzfhb3Ri0AlJDK+FqJ2NOv5ljcetWjeDj5kLU+xu7b1JQQ6g/QEttP5vCEhWZVgpzish0TMSkE5B6oLm3n7LyTprrZEbmKcvGkpVuZcuGOvriyn8JNO2jZLRSWOJYWSerBQ2dLa5TEuP5f8n+bzkFFYd/8g0irUWPrTQV0ReW1bXi+H3UhtdxRuKjCCOcz7w5+dyUsYaou47gtTez7ondOEqSuWlBCeWHOkY4BUEvcP7981k6PZeHHvxwVOEPjVZD8pxc7r5rHheNbeZv21OoWSerjplybFx+8STMei0f9/nUzctWmsp3757PlAw9Xz0mk56pBIGirHssmHQULC4iLdNG1fYmwjFo40DrIG+9fEgVoVE2ca1Ji0UPRCJokrOpPdozgvlWQf1MPKMAe7KJQ/5moo2uRBESvYAxxw5AsENu+Gr08mT45OnZmI06Qu/IjdGoT6ZgiOhHRvbq9xl1WAqTuPu209Gu+j7dh7opyIczfjmH2kuf4n2nj4tmmZg2Jo212xppqO7BHRYJi3IQMRr1+NqVR8i973yq97QeF3Ks3MfhpY8RfydF8dY52fn8IWZ+ZYqq/BcVo3gbXTS2udUyh7/ZxZ+/9b78uXAiYiYqRUEQSMq189AtcyiNrmJ3YAV/f+MIZqOOgppHCK7ax43nTyN4+928+sJBAp0eJDHKmflGQpIJMWcaIecrlH3QycSmQWa9eoBJs3Op1gtU7G3jN+f/gNNnZbDutcMJWYrWosc6NoWiKZk0HOqkbmcLXndQpZaYsGwM82bnc0fnrzj28BYmvHs/C7+VhC8sEmkapPbNGpZ+LZcP9VqIDTAKRp08DxGWkOL5yWJ8SVJYQ1QKExS99HtCDLa6qI5pHJiSjJROzWJqaTpjMm205zvo7fTgax4kK93KDWcUIUWjvLuvQx06VBrXyhClPDU8tKakYARXWRf7YuSIX1Q5R2vRyywIMaLB/7bQzv8pp3AqpkSXWfPy+e4dc3l/bzO73q1CbHUnOgMhDgMvRNFIEhJxyBklY4hFIG5fCEx2SE1G55Z/1neok2/9cQs9B0Y2dTV6LROKUsizG4/bGDfl2PjoUQO+H92IdnYWHabvqy+sLsnEwapuKg6007tfjtpT5ubxw28sYFpGgCM9AoG2QVV3IeHYMTlOZ7dXdhrIab2npj8hqlcs1B/ggaf2UTjmSo6+10n71oZRXxwpGKF6TyvRsJjA4Krc95R5ebz5qzGEJTNXfe+QHDXHYLCVhzsJh0S6zvw9y79dyN0f16BPaY/RDftGRG+CSXYIkxYVc1ZBH5reHEoeuYCiMy/jsc2ZeHc2MmdCJmMcbaSZ86jIS6K/24PXpEPjCY3oCwl6AX2qGX+Li7/94EP1npyMjcolFcfQG/GE2P/S4YSyphSIIA0rW6uMt6M4GCkYwecOUmrZxJaSn1Ew9rc8v+anEGnhjaxXOO10PSXXL2JWcgqOe+ZzsKqbHauO8stXj5GTa2fqmFIuv30ByTveov9YPxl7PmTFgu8ydXwGgWCEd14uYwNHCTkDCVDRvIWF/Pzr85li/pgtFy3ll49tpXW73LmOilG62wYJTRdpeGQHBw5GEVf8GesFj1Fe38flM/MQXqlibIoxRi8iTzoLRh3hweAQD9Qo2YJyP6SgRtaIjv0skmLCMjefs8amMza4kjOXz+KN/Mk8176bvgE/Zq2TjBTL0JfFGteGdAtas57wYJBory+BAFIpQQ8n4VNo3T+PrEHQCzhmZHHRZZPZsaOJll2tn8j8/Hnb/5RTOFkSqv/GeQhGneytIyKbyjso29OqNpXUEfpYo0s977CIFNYgMDRdqzoG5EW87s0KjtUUcebchdxztobdW3Po2dVG+8bRx/ijUpSXV5bxQbp11AaooBdYctNMAr/9Ea2bWxj/4E3s/vZRudGpF4gMBtj59lHCTr9Mmz02hXU/j+C85xKSb5vLqtbrEQOiSuqm0WoSJo2d5T2JUVEMF66ohykT61FRrpcfW32MY7F7Mer1iEO6BsebO5hyWi7W9/8IFjMFE69VSykhZ4C2PW1oBA2udjfOXh/LFpcwflw6Bw+207C1KUE8XslKMkrTGFucQqsnl9CYB9nf4uSdnx/F3XWE5PwkBlwB+tyZ+IN9NDY5ccUw/cPXhNz7sXPR12azf28rjR/Vy870ePDkWNSqMMMq90/QC6owiwJtVPU8TrLPFT+IGRWHCOSkQARf4wBbui8mPQ327wqS9+xKtvzqCOEw7N4RJpp9Py++f5Rlcwu56PRC6qt7KXuniqOpJg6XplF654+Z9mcPod1N0NvPeXMreU+cSHXLAFIwgj+WucT3mVacP57Jtb8mCqSMXYot2YQrFphEpShdW5t5+XA3Z72zi2u33o/m0hvwXdfJ4QPtaH50JcXra+lDnsi3plooGp+G3xuisaL7Eym2Ff1vTbxgVlgixW5irHUb/fc+g96m56YHr2T3mfPZv7aGu2v66G92JThYBQSitxnk7DTW0B5RAYj7t3VsCmPm5dNc0Y0r1iM6kSlBhUbQyEHMKFmA3qjFZtFjMH452/P/hFMYjixRFMi+LOegETSkzsmhZEIGNQfb2fDqEVUvQKMdyhAEvYDWJFNHawSNrOTmCSGKcnqrkRKjzKgoC63UekLcf+V00sp+w9jJV9K7px3pOPQLgl5Ap9fKsFFp5ALSWvTMHJ+BUcyidEYW/6hdjL/tI/X3YVcQQR8me14+FqtBTukb9xBwBsAiSzbaSlMJu4KkFDnwuuXGliTKMo06u0Hmw9dLEBYx59mZsnQMJUXJvP/y4QRmVaVc8kmmOJH464y3usoeND+7iaBop3V1y9AvRFmXQmc3kD8pQxaTT91A97hFQC7N+9sJ9/vV79To5fO3J5sIhUU+ruqi8livXB6q6ZednhjF6w7S2ylPo/Z0utVnrVIhaBlCRIVFXJ4hfp6olAgAUHsHWgFLYRKTzyrh6NZGWVNBwfHH+iM6mwGdRY6GFdnJU5muVSZeNVoN4X6/2vSNuEM883Y5/371qxQ/+TH6mUVYLEfUz5V1uTm8uRFnr4+JEzMwmmXyv1B/gL6GAV7f1cT482/FMG4zDLhg34ecf5qZQ7VWFfLKsHJOc6cbzZxScGTy4pp6BloSZUyjUpSIK8A3vrWaSWffSfvDAwR7/Qx0uDk6sJyJiwsJRDQUTMxg/Ph0ppSkUt0yQGuDLKU6Wg9QI8jBmyHdQnZpKpIYpa/TQ6At7tiD3ex4pwe3G873vcryh67h8Ie1tO9sSaCfkWlEhoR6FJjwJ0Gjc6ZmcvaZRbzV7cV13L8cMkO6hTGLijBb9Rx+6+gIAZ+oFGWwtp933jnKQINzJLrsv2BfulNQonKd3SCLdvjCqnj85+EUTjX7UGCLL/9sMinBPXw9UsjBV8rl38UiB4Xv35BiYtxpOfzqhiJCkpXv/+MA7eVyk04MgEaIjig/RNxynfD9Q23kLryPoy/vGVEbVs5Do5dREucsG0dtk5OmYdegtegxZllp7fbw0YxHWX+gjS0PfzxE4RHjqXHMyOL1B834xVRu+FULH1ru4rw/GkCn4/HSY2w48zze2FCLxaqn4kC7OoeQt7CQcZMy2L+pAW+jC43WwEO/PJdz6n8Jg256LrifTTV9n5435jhw367drSy50wFSN77mQfX3Cq+Qzmbgxksnc07776m69C0m/PkiFk74KauN2qF7J2jQWXSkjE3lX3dGEcpeobLw+0hSlN5ON64YnUIw7CbU58PXOADEEfUhR4ELL5rAlLFpHG3oZ9sHxwg5A+xcfUymaJiRhU4nIAgCgUAYg0FLKCSqFOKFM3P45qVT+KtWQzgYQavTcnh1NaIvjM6iI60kmWuunEqe3cSqXU3sfPWInI2ehGNQHN6ZV09h8YxcXl1TRfWaYzJgICzSuLeNNSvu4KJHpuDVj2VB4yWYJtzJlJsncNlz+4iKUZoPd9Gwu1VtGEdjOhFHDrTzB0MBiybfyvzptYg//gmu36zjp2/9nvX/GTmcGRWjfPz8Qbo6p+DzhqhZd0CmShfj1PNMsgZ1sMvLwf8cVj8f6PDw+p4mfnrOCojAgzecRql1C4SOssl4Fts2HYcIL5Zx6ewGzr1yCj9Keh7N+Gk8cXQ+bzy1F9EXpqvfB3oj7tjc29oXurnuT+v4W14yzljZUgU1iDJFerDbm8AO+0nWebSH11wB+g92fuLfglzqXbigiJQkI5Uf1Y1gE4iKUQIdHjpiWhNfRmD8pToFJfVLmprJuKlZpKdbOHKgna79HUQ80c9MNW1It2CfmIYUiOAq7/nENFQxwajDG05HY5xDQ1Xl6Oc8Jpkrr5zK+Cw7Gf0rIS2f1MxsepJcculGjCJJEoRJaEwrMpLv/WUn7z9rJNjtVek3nAc7VZUqc6EDQ4qJ05aM4drT9DzeZ1CnRkGOEguWFDNmYgYbN9TxxrE+gl3eIa4araxpseyqKVQf7aHdN4uKHj+u5iOcV/8LVi5bC8CiC2yc+e54XpeiHPi4Hn+zHO/YJ2fw9HdLsOp6ubM9naagSGqhgzPzmgm9XI++JJm+Ac/nQvQXj1xRLD7aG45E0TuMTE43In7UyIALsFlpcwdVygX1+/Raps3JpXXZjezY6Ofa9U5S5v+cifkOngyJdO1NpCZQaJtBXjtX3DyLb2ifwv/PvVx95Uzeuf/7bDnQhs1qYOHUbKZnglXXSyRqpD+QRaHtCJGogfufTaVqYyONe9v4TreH4GCQl393Bpn+9TyYMoMNT+4l4gkRDopcOLYb84d/Y8Hl13FFZRade9uJeKLHJWpT1sa135jPuBw7OkFgSUE5zXMLqdvcSCgWxUc8IY7U9zE3dx7Huo38641+sh95nfamAYKtvax9sgRjzz5e6b+YP3/3A6SwnBnmzcrmsW/M4umtHbz4/lEqZ+Ry1zevIPro69DbSP6sInrSXPjb3CqlNcib+4FX5GwkgfdKkIOstPFppGZaqd/bJku2SvKGHPGEOLSvjdaFyxnn2If49N/p39KCY3kxGSvOxe8NDRHliYlEjUq555tLQqzUv8np89/hKx9v4U2dFikcpL6qh/bzlgE/Vj8Tef09xkz4DgfLutR+gXqPwyIRZZ2N0jMbblExiudYP94650lv3t46J6//56DsIHtHR8wNz6T/2/blOgW9FltpKj/7ziIWpG4Fo52fCXl8XNFN5DNyS2m0GhyT01m6vJSGpgH2VyYKyChRhhgQE9AjShno9l9uVukNlJ8rZSO9w8iE6dlcPElHp1fHWvdVdFX5EYQuLMlmNUoiLKHRSuhTzBjTzQR7/XIkEiuDKHV/S3EyF1w4kdcbXXJ5xGZg8TVTcdiMfG2BnhTnWgYHZ42ocztSzaTYjfTX9o9AJWkEDZZMC2dMzGLzulpuum81of6APGQWkl/akmLI+9uN/G1PClXb98mKXsEIhnQLsxYW4TjwFJqCAsaPP4dFZxQyLtPO9jaJvK+/wkeVnRx9actnyhKUzVsZZhJMuhH1dkXwXQyKslbvxDSSsmzsafdxxe1fY/7ycnbov8pbr5apwjOKSYEIu9fX8bP1T3Lt0Q28Y7iDbe83MntyFqmZVnotegxpFjKKkykcl0Zni4uWw53qYNP+g+0M3nErjsJiIlnT0DUIXLN4DPOMq/H/4T/sfb6ezi7IzoI51xahu3EB+smnM3niRJqO9RHo9NAXa+6/tNfD9XOWIwhy3TnsCtJf1cOv1uRw77JHqG8Hk/mQqiksgVy2Goao0tkMTFk6hntOryHaUs59mxexSrBQU56orR12BVm38jAV5Vlyeayim5rYmjPn2zH27CP4zGqu+NEcnrDo5elkvUB6jp2cY49zwxk/5Ht7tvFBWzV9Zy7mx4/kEn72BR5+6TDjf76Y5/Ie4pXnD8j9HgX9FB6dDVhnM5CebUOnG+qvDCEBo7j7/fgjWkKP/IG3HpS1iS/NtNCxOEiob0jmdTSLhkXW1GVyzSvzES68mF9s6Ub0yzMefXX9vLirm/t/WMKqR2X4rugKUDjLQVmMOjv+PORFk9hYPhk7lXcg7AoyEMsqvuze6fHsS3UKgl6gaEomC0yriTz/HtqF0zAbrxohovJpzdfmZuf2Jlzt7hEbqjHLSsmCQtqqe3FX9qi1XmX4RylhaS16iI98YpGKs9dHRa+JgiQtBq2A3WIgJ9NGf7cXb78PKSggxq4xfVI6OYXJHN3erKJ40MqbnxSMEOz2snFDnQr5BPB4QlxzRjHJh35D1Galt9M9okZbu6OF5qM96ncO10Fwlvfwp6d2q8NQSt30vdKfc53rbHr0i/jDLg/rVh2WoxZpKAsRNKCZOB2PbSbXJhVRIrwP1jT+ta+EVz6oonZHC2FF2EYYYjcdzY6rKxvLDhS6Ap3NgM5hIiXTisGoI+AP42wdVOGYxkwr1371NFq7POwq7wSmkWw6jVffP0pbZc8I4REpLOGp6eeC7wk40ufTU7URjVZDxYF2/LEegi3DyvjJmSyflUd5nos1vV5cQZFwv5/abc183RuicNx80hwmzpyoY17WPnzf+Tfv/LlZPU5bO7Q91sQV6WaMuQUUZZ9OUqpZbu7Hmp+rnt7H3r1Z9LQNcSqFXUF2vltFa+MAWp1An/IcBblB7picTqDbqxIEarQaufxwej4NvumEks+kYssHMgpIksteCkspooS/zU1jbI5EHdaU5PLE810r+MoPz+Slg3Y1O5ICESq3N9N+xXfYVtOHN9brCEckOq7+OZvek0t5By7ZzDL3X3gzNhWtnLMmBieNp+RQnnFahkxhEj/spqxZQS+Qb6tm8x+GxOq1KSaq2lwq9fuoEXVYJOKL8Pbqo4SW/5Yj63sp29OgHiPsCnJwfxvm715HzvO/obcP9CXJGPTauP5l3Pf9lzbp/1VnoNiX5hQUZMjUyVlEG9YiugLocoqp2tQzYuF8GouKstCJUiseXq83ZlqZP7eAXUBVdZ/cGI7pDZfMz+fsRcUAbNjcQNOeNkRPMCGq6O10U9U+yIxMFwW2XsSojkxrMX0Dfvq6ZcphuVYrYDTp8LgC8pRxzNnpLDqZErdtkGCXl9aOevX7RV+YQ+tqmXZlF/63D2D+9lV0VPaMqD3K1xe7ptgLKDc/o2pqPlyQPuIO8ci31/BYugWNsA0xKKo9DeV7omKUYxXd/Cv/TCbmJLGo5gECG45hvG0ZrV3p1O9tU8tMeocMI5Rr4SOdgjHLyr+euoJD7TK+XpmiVvozWpMWncOEOc9OcWkaUyZmsmRiJgAdniBrdzezf80xomERQ7qFwjQrx5oG2L++jrLdLTJzqEI7rQgAxaRfDekWgt1eBo9044obHvM1DKgoKm+/j7ZWF8dykwAZ+VRvNdC2p42w00/b/nb62gY587xSZmc1Ev3wzQSHEG+dHzVReHYt2UVGTGb90MYjSvgaB6iN3TNlbUuiTLFe2+1VUWyK7nXx4kJevrmJaPoYln5NLjsocwgTM+0EIhK/fn5fYkAgCGi0ktz8FqUE6dD4dSP6wvztu2v5p82gIsuUkoWnqpdrv7aG5HEp2JJN+PUCP1weYtWFQ70djQZe2d2Mq7ov4T2VJ86T8bfIWat9Yhq549II+MOMK0xmRl4yez6sVSGWGq0GvcPImAkZmPa+Sl9/3Lq5dQVbXmxQg7P481d6AFJYg+gJ0rKvg6djQYFyzVJYIuwK0n64i6ebl3D76joiO48inL0E997QSfGX/V+1L88pxMoxXX1euufch2naAH/fJVC3c2/CdO1nseN9R1SM4q7qY+XfdsklhxhaRIlaktMtnFGchs0APU4/nfX9+Hxh2XHEzlsQBPzBCEn6TqJP/RUCERbecweH8sZTX91LyBhEo5XT9bb97YgBuYmnRE76FDOZY1Jo7fWBO7HZpEiMrms/h3N+8xD/2JuPr3n9qNehONeSc0q47SvTWeZYh982kWf2ONi2rZGWXa2J+tBaGbGSXpoGwECHjIpREDBKyabvWB9brI2cefNspKZuPHUDGAdcdHW61azClGNj3lem4POGOfTm6Dw45jw7kzsfZ8rEOfzNohviJIonNMu2MWlmDlcsGsOivCOw7UnIzmSawcCCK5ZzzeEk+rq9eKp6+dVPPlJ55VW2yrjMUpkt+fsP5pMXfI9bXirh2Ac1svOLDzQkkYhbziSq2tw0VXQzf/k47lhaSsfcQn7vjyTMjQiCBgFJBhloQRylopF9bhGaSTOoqh/E1e8bEdzEQ3AVptloMKZdPWwosqvOSb99CWG/GYRu9XPeeie/f34fF54/nqVnldDV4iLU61P7VfLdjajT2KO9BwpKLDwKfYQUlgh2ezFOy+SMBUW0dbmpHRjHBbdmse6FLk5fYOTovzfwzjdl3qWEwTngrMsn4QtEOLK3le/eNZ+le76DsHgRW8Kn4QpGMKaYEtCGpmwbs2dkE9lZq57D+TdnsjrwFTrL1x8f2ixFQYrK5d+wn4grTt9Zmf0QZUrx1187QvrX7mX+bVra3AbaWsuHoOKfsm85YiBRK3xu4Jgv2740pxCNPdCyPa082OXB7w3RVtZ1XEGNz9ukYESNdlW66xjXSMX2Zh7zhjBbDbQ2OFWpSRiqhWv1AqlJRoStb7HyroMAXGV6jsJz/orBoE2YXQjHIheNoGH6VZO5avl4Nhxso6/nxEMpq7c3sqMyhc1v7xt1alYw6TCkmLj+uwu5/eP7eau4mldiv/tG/V3kX34Df67ulaPoWPlBKU1kFzgQY7wxof6ADH0Th6iiw64grn4fPb4QE7UaDDY9msmnU//vLiKeEFqLnl//8UIWWz9gi/cCDr5WPuo1DFb2csv6xfS97BnB4aLMeaRmWpk8Lp2p6YNoOo8SHXSjmXUGvbq5rDos4W0fwsUrA1OjRXoKIqd0aiZ5oTVEq8uBogSKieHZVlQKq4i3ra4gfT0+NIIGd5dHvRcRT4iGun52TpnM2cuv4rLfd7DnsXJaWuXvKSmG0x+eQ+TG7/J+cyEbNlcy2ORS9aSVDdOUYyNvdi5NmxrVe6E4ByQRlHUYBndlDxdds0mFm8YPanVV95Jy5TQujDxD4Tfv4B8vHUSMSISCETwtg0Q8sVr5cWr88dc/6s/DIn0NA9TlJhEMhPnOLz/GMe5h+AX8vaaPwD3vDukeKHMSsQxkZmk6l1newn3REjp9Zhof34f+6cMsfsPBTzZNJOQKqmI4USlKSo6d4nQbujw7C5dbyfvadLyXPsBTvz4wgoVURUnFN4NFhmDBo9CtS4EI7qo+nvr3fj4oSUFv1NFcJ08kf5oStaAXEvpgOrsBc4GD1EwrbQc7ZYDE/+OO4UvtKUjBCP5ODy0xDv7wKENDX7QNR75EwyKBTg/HtjWr6bxCs5uAUBI06LQCYtUQgmVwWyuW87UIcXV2SJx8vGnFJBaLz1N09h38/Nk9KrIi4ZxiGYvfG6LDH07oNcT/jc6iwzo2hStnWKm+vTrx98Uz2bWqUx1EU85DmZxtqe1HkuSMJOz0J5yH0nsIDAbp8gQR5s/APnkM77fNw9+2FkQJ2+R0FjmfQNpWQ8qyi4/ryKNhkap3qxOai8NN0GoQBPBFUknJnoRmSTb73UtYc7CVbevrCPUHhmrlxNWvSdwA9A4j5kIHdUd7eHbsQpLTzqFx78YTBhnx1Cf+Zhdlb1eCIKiT0UIsUGhvHmD9oTYCkamcf99dnLlwM7R3gsGAZupkejMu4439IgeP1NIec8RiQEzIYFInZnDjldP4o+KoiSuHDLsWIOG5JzgzKYrNoCW6t5Lpl4bJzE1CjIiEgiLBwaAqcBSNJW6nStoWFaME2gap2tWC1qjFW+9kMAbUUKPwUZxNNCzS3OUh6q/ANsbLEfEmLn9wIZGGPjDZSXPIesyGdAvjFhTgHghgthqQolGYOY38H9jxLb6Tp3fqcCobd8wUqnZln4i/XwpSa7R7qKx3d5OLxoiEyaQ/7tDYJ5kCE9c7jPKwnE5LWkkyl140kSVjHDz0fBkVbx09qVmd/2X78jIFUZ6+DLuCTFpUzOwZ2dQ2D7Dl34c+s7qQEhGPNjE7Gg+NOqGsNEwDEVVfOH46Nv7fkhTFH4qgnTMekDOFpCWFBCKJi234y/jW1nrSln+N1Qda6azpl2vIgmbE3xrSLdx16VT8EZEHPj6+aHnYFeSF3QN8/9DjTNu6FgJBDs/9A+d84yN8jY0jcdBhkWCXl17l+mLNuvjUVyPEMNt9fvaWdzJu8V2QBKs/kBuBGr2W+cvHEdn8Erpz5/DK9oZRMxljlpXx54xBjEiyYxj+Ikry5uIeCFBZ20coJGEyFtPjzOLg/n30NA4Q6PSosyMg90QMqSZSSlLwDgbx1jtlh63XMmHZGM5eUMQL/z7Avx/briKXjreJKc9VARhExaja2FTvl6RVG9Y7XEEO7mrhnbFp5OTegDlJhz8YoXuTl/amKgZaZYUzBS2VmJ3IUpovvC5LtCoMsfEwY82wYEKhSImPyEGGf/7p6b2k3Ps31u3qYKDPS35JKoJGlkONeGIiPIoi3qfoz0U8Ibx1TnkDdJiAgLpORruHSsDxzstl3PL4/Th0zZSv62Mg58dMOS2JonCEb5+2mZ07UnC7Atxy0WQEDTy/5ij1XR5ap3ydXqvIMyurObrz2IgswZhu5syrp9DaOMCxd6rUxrv8jEaWxxJNRGvUcv6FE0lNMvLMH7adEmW2co2CUYc5387UpWOYOTmTTIcZKRrlsuKDRNe9zTUrfs0v1tYkTNX/v2hfbqYQw4Wfu7CYy7t+T+OfPuSRt7/P4m8mj0r8diKL3+x1NgNJ0zJxV/WNgCmOMCUV1CeWfOLrk6NJbophCedgkMCiK7jmjRai7hDaG26m9YCXUHBo8EWj1aCRhmrF5Tua+dauFoJ98gSqId2CpTAJd2VPAo1GenEys1O24dEUJ7BRqucQi2Aj7l7efHQrqyx6NMLpcmT0+Ksj+FniP6fRajCkWRD9YYJdQ2WjhJcsLBJxh6gp7yZ5+QRyrdUsW1DEsf1tQ+iZu37KQ+9F2fLs/hHH0Vr0FJ1ZwB2XT6Wqc5Dq1ceGnpWgQRsbNBN9YTydXo5GOmio7iUSFvEPBAh2x2YuYiIx084uZvaULAC8/gj5GVZ2HOlgT4yfyOAwYrYaONrQryKaIseZElfuga00lfOvn0Fhtp0jtb3sfLdqFMy5XNYJu4KIPllL2VXdR7nSRI4RtikbfHx/w5hjp2R+PrWxbMff6qahw4PeYWTiJRMY6PXRsbkxYfOzjk3BlGnF3+lBo9PKGVucVrJSpunY1sy9VT0yPYlZjyRGMVsNhEKJ6J8on16cXqPVkD83j8nTs9m5uQFXZe+ookXx5qnp57dr/CyYNp2O9gY2v1uFYNJRPDGd5WedibNjF+HBIM+vrqSoOIW+bi8Hj3TQ2O6itXGAloMdo+qai74w9dW9OLuHSq6nAhmNeEJ09XnR6wT5Xn6K0pHWpCV3aiY/uyqbtCP/QNrWgPaWO+n0z8F6bjHr3m7+r5PXfRH2pU80i0ERbyBM4INydm0Nsiv9V/yieRcP3vveSWcMSlqnmCHdwrxFxWztj5F2SaNEN3FlI4XhU+F4lwDNcQZIFGchSRKRiERvYDwF518KUYnm4Fl0OxsIB4ecikavxZxtVwd9UvOSMFsNNA22EwmLpM/MZv6ZRaztl2v7ysbscvrZ27+Iqm73ce+DUstV6uJqxnEcTiHFLMXJzFxSQvXhTkK9PqJSjMoBhoZ2JJn6WW/UMjb6DvQMkm65Gr3NgEarobG2n0fWprF95aFR+d6TpmVy3jnjONDYz8fr5SaiMnugTzUz9qxiWg7L5a3IYAB/RCQgDOk/i76wPKiVZ5epPCZlcsuMGqK71sH8FbxxbDyDriDWDAu5RclkZNmwWwz09PuwJhljnx86r/jNS9GEWPvPsWifexitLpe0GQ+y/c3K0csiUoyVU5SQwgKaUXDzWpNWdd5iDBpqzrOzeGERTfvbVYcfFWUEUU5+EhargU69dkhnQtCQMzmDBWcUsWdvK0azjowsG7tjwjzxBHTRsEioP4A2IKK1iPTpB9HFoNRK9qEEO0iSSpdw0pP9gowMuviCCVhNOo4d7SEQc9QKfbuSYSnaDco09dFDnZiMOno63QQ65cnhijonx7Y1k1TkICXHTv3eNpqPyjNAQX+Y9qYBPJ1eNbMdfp4Rd4jWna0nPWk83IJdXra9dBid3aACKk4p6IyVLJPTrKRH9uB/cx8dW1sZs7SMNuNpvL0nzKGNDZ8JJPO/wv32pTsFKRDh5VfKuPHGhfDbegDG3LaU63/+Fs//aN0n3iDBJBPXmfPshJwBQr0+wq4guzY1EOx0H5czRfl/FSOfZFI1hpVGnWbYKL+66UZEIhFZWavXJzIgLMMdEqns6Kat1YXfG1LT7MyZ2fzk6/PZ3+zkP3/ewenzClg8MZMXk00ceKMSz0CAphZXoji5VsDf5ubB321WI+YT1YVHDOCc6H7pBUpmZnP61GzcrgADR3sRRJne2JhpxZpkjE2Uy8ipMxcWE/zXX9GXJLN4mZ0Ni2awvqafpj1t1G9qHFXHQDDp+M+v5mP+1lUk/fIm3v5PKqddN42ag+14avqZdfkk/nS9xLdfLuHAG5WqE1CuQSkNWIqTOeuSiezd3kQoLBHd9C4rl74PvMW15TfjnHITp03L4pppfuzU42Ic6xpyZKTSQCDhfHQ2A4JJJ2dnaRY2/CuTN43XEQ7DV/4a4e26upEDgHEw3YT7rfRoYlmkzmFiwvKxlIxNxeMLc2BjPYOVvXiqe1n57wMEOhInMcNOPzvfPprwXcr3ddb0U5ubhM8bIrvAwbJZ+QwOBqkIRuRJYMSE8xB98v/7YyU0tSQlRTEXOHjgx2dTkGTml8/toaOqD9EfPqUs3OkOotMJXHHhRJpm5/HBu0cZLO9WBy/HnDuGx++ZxFNbnax9Zj+BDg89B9rZPuBX9bqVpnTEFaBgVg75xcl0V/fiqelHa9ETiJ23Rqth4vKxdLcNjoBSS2HpM5WVpbCccTFS3uOkTHnWBXlJoDNgWlRMns0AuZMoOzzAzg9qVFGqUzWVXFMvyA7xUzq+z8u+dKcA0LezlVuyF/O9SS9QcRS6G3zcerqPlx3GE8rdabQaLIVJnHZ+KSvmF/Hqh9VUvFdNxBNiYF/7qA8onr5Y0AvyhpFkwpZtRW/UEQ5G8MRS9OEMiWqWEJaI+MI0tbjYbtYTDIkEQhGcrgBpGVY6WwblFDgsMdA0wKvbG2RRjkCE08elMy/yItOvW8iKTWYGj3RzIDZcppjWKIu+R8OJOryn2jA8nrkHAuh0AhPHZ1C7u1Wux8/N555LJpNhCfLAc1U0Huok2OkmM8WMLseGZnIpeJ1IMciqGAyMCsFTmuQ2fTfdVf3Ya48xZuZl3HT+BP7S68VT00/1vjYey51E+YZDic5QMVFCazJgSTMz4ArgahhgzeqjfP0XVwCylsDKqS9wx/puNHMX0X/743R6QmQ/fgN2w42EQ6IasWkteuwT05gxrwBJlDh6qINpc/LQ9B7kkt9MwXDXjfzoo3Hs//OuhBcx/kXVCLJ63RmXT2LulGwOVHez+eUjBDtkUh1jppXbL5vCwvTtRFur8F18Ppc/oMNV1sXAgUROHGXNjTY/ExWjuCt72Fknk8D1VPXSXNtHMBCRZwBiU/HxTomwiCgNlTnj1/ykMws5t+uPRHe18dUrH6O5z4vLHeLNP+0AUKG9IGfXgl4g5AzIa04rIIUlduxs4sdfPZ3JjY+CFU7/9nf4+e82o7PoueqqadyW+yHR9S9z9aI/s2ltDcEuL2GXrBJnLnRgKU6WuaAkeaOr+6iOxlhGo9FqYrBdOQAxppvJyLLhHQyOyk76ZZsx08qPTE9TOfddJv1yEfX3rOStDc3s31Mt61J8yixBMOqwFCejNcnUF6Fe30nJ835R9j/hFKSwxNE3K/nj3c/w/A1N4Hdz0FmI1nYYTuAUBKOOkjl53HfBJEp069iWP44KSKD0HdWUspFeFhi3ZFrIL0nFaNbhdgUI+iNyGhsWVVHyoZOV68fhwSAN1T0M9HnR6bRIUpTcomR+c04NB884lx/9cQthp59Ah4etT+8nKsnpvEErEHlvG1rfRsaf8X0O1h1GCg4tAI1eiz7VzMS5+bgHAjR5ZCWw0RqGeocRwaSTS0BxC0hnN8g1dV9kBN+TMcfOtVdM5YqUD3AWn8UHb+qJilGKi5KZFn0TegZ44s5lNA7O5LHXyzja2M/g1b9iXb2J9e83ULn2AGJQVMkLpWBE5aCJuALqAODP3xO59/09/PVQO7V7D/Jom5ueA+2IvjADBzt5M6aRMJopG4KrYYB9DQNEXAEGap3c9nc7f7uvgHf+KjOndj2xk6wH7ejMOsx5NsL5Z7Drwy66GmUYsd5hJG9GFrmFyRRm26lvGSDkj1BxoJ1bWsfS0Xkbg1/pRvTtHJEhKH0PY5YVfZKRFVdO5Vvu39N63x4uvWUKf7n7Z7zx+A5Vke6NLfUUXnwOBb6DWFrXMX3eEna3uUdAFI05duZdMYndbx2VdSyGmdIzADmLbur1kTormyuun8lNs4M8v8/Ay7/dnFiyEyUS4srY3E3Fhnr+OenrZJ5mZuXKMsIhkYA/zNxrp/L45e38dH0J657YjegLk39GPkuXjOWD96tp39iAzmZg8vnjuOeyqSSbRMSyWkKHe1j4y/VMmlvCmaflcXX9z3hl9kYK8qHwqAZbsolBvZZoMILoC+PItTP/zCLee3KPOmQXdgWJeEJo9FrS5+bi7/PLfZwYAGLri2VDswb/YyboBcL76ik7AuM7Pfz2xf007W9Xr+lEYIbjIu/0AqYcG6dfUIpnMEjN4U55UFaSwQlfhmM4jvL8f9+iYpSjq6q4+l85/KVhOS9urAVppPBLvGm0GgSthsFghAHtVJoanEMN3riMQP2f8m+GIlqtUYsj1UxOrp2CnCTSMm2YrYa4CdFhpYNYpiAGRILOAH3dXpy9Xlz9Plz9PoiESLdoMRiGIJOqgxIE6no9aHOTENs9FBeljLwevYAp28aiOfkUlaSgjdEaDyeME/QCeQsLmbqiVBVhB7lUkrOggIzTchN+rnz/1HPHcnlpNdEDe0kxNCEGZJ2CphYXJKUTrapBePh7lP7nFh6+8TQ8nhCvHIB33q/i6Lo6Iu4QxnQzhWcWYMqxobMZGLdiHNf/cBGm/CQM6RYypmSg02r49cpDvPvqYXyNA3TGaDGUenE86mbEWogNpCmMstYxKVgKk2it6+fQj97k3OvSmTIJkqdnoikoJumnV2P+2b28V1dMVXkXwT4ZYmtIMVE8Lo1QSGTv/jYOb2vCW+ek71AnVe9W49zTljCAlWBaOaI3pFlwZNlw+0Jopk4iY34uwrQJdPV5Vax7yBlg/7vVPPZeJa7x1+MvWEZ3u3tUqnONVkNhThLGdPMJp2qVdSf6wgQHg+RnWLF9/GfunbybtJnZsipZ7Hkb0i1y416UhkASYRF/m5uXHtvGk//YjdlqYKBlEFd5N0tPLyDwu39y15I8jJkyqkunFyjOtJFdkKT2Qx66fhYzK35KurEW3cXnYb58Otgy0OkE9pZ3ojn7fLIyYeysJLwhCWOMu0q5rsFODz39PlmnYxhEW9ALTJqZQ3Zp6lDZRIqisxmwjk1Rr+9/xaJiFF/LIJ67/8S1689n4JY/0HxIboorAeSIwEI7hGoU9ELCz5S/UQge0xwmDEYtJqsencOk3scT7X/Hs0/zmXj7n7rzUiBCw/s1tGxpQqPXfqKiVVSM0lTRzeNiGZIUpW5z41BkrJU9cMrYVDw9Xhm6GEuvFYoFwSSLxeQWJjNnfAZJRh1VnYO0Nw3g0o/uEDSCRi7rAGFXbDgt1uSu7fNze6QQV/9OBmqHUCxKw09nM/Dhx3Us//qvycz/G1ZvTMA8DmmhdxhZuGws15TsY71lAbu3NqoLRPTFbTJaAbvDJDuwOLEVU46NX31jAQ0Dfp7iQAK6RSNoyMi0IkZFdPMWsqFlOqG+1bJD3trIS7PP5Kojz7Aq1tu5tO4ixl37FJ19XmbMzMXV78dZ3oMpL4lxkzLorndSck4JL989SHT7Y1QsvYm0DAuXnl6IMxDmT//cja9x4ITP8Xg1fK1JiynHxvSzillxRhEbDraxf1MDP/nFx5x989Pc/Uwyoq6fisFidrb1Ubt3gIoDBxiodaoIGX+bmz3r62SVMFfghDw6yvETYMqx/w4HRbauq+WCzaU4UqbT95wHT9X+OD4pkUDbIHtePsxtdfkYDFqat7eMWhoLtA7y1jP7Yg3+kaU3RZhHn2rGnGfHW+fEXdXHX/68nZm/fQybrpvMvFYGUvsI9vpJmprJ1NPzqD7cKetcS/HMnyJhV5CsGRZ+ctNsttb38tLT+3ju5UMsfeB3PP2x3DvSCBoatzTz5GCIwXaZY0sw6bDrOwkfasY4pYwnas/G7jgH984wjTV19B3rY/luK7dv2kxOVhLbG3oxGHVy3ybGQRXo9GAx6xkzL59qV1B9LgocveJAOyFXUP17jaAha3IGE6dmsbFtEP8XJHf5aS3U6+Pi6zdjSFtM+MlNCc9wtEwzfshNeT+Vfo9GGHonpbBET7+PZIeJ3KJkQiFRDYhO1cyFDlLHp+Gs65fpXD6F/U85BRiCWsIn19CjYRFvnZPq5sERmql6h5G3nl5I0hP3Y7j/cpb8wI6/za1+r0avRWvWY081M3FMKvNzfRi1biJSLrut+oRNIeGY6ossO4aIJA0NUolRqtfXx6QD48o9whClQ1ejk5+tbOCui79H69Z6OUoKDx3HNiaFe862E/jtP5j5wEwMBq0aacVfnxSIULmqCq1JS/6ZhfQ1u3BX9xENi3x8tItgWCSvJIWeA0PIFykssfm1ch41zKAwu5ANWytJn5bFBSsmsGlzPWvWHeP6eWNBLsLRfbiHmT9MYeHpx4ge28eEr97B73/4Id6aPja3DRLs9dNrM7DfvYTChaeT8XE/vzjrKNHuD9lj+Rq+NrdKZ32yz16JnhxTM7nvjrlMy7SwpqKHxpo+0vKS6Ov0sPHNSg7uSSIpxQw46W4fJNDrJ9TnSyB+U3S1le8+kcVvyIo+sEarIdTnUyeT/b4wrmBEFmeJmxZWvjvUH6B1ff0JKQ+MOXZyp2bStL1lxPG1Fj0Zp+dy6WWTyU+1Mjs7xCv7/bz62HZ8zYNc/9U31GBJgVVGPCF6Oz2EPKERA4LKJuTq9ZFpaeW23M1Y77uIP967mmU7WlSAhtaiJ9jlpXNHi3re7soebv5VFd++42lCXol339opD40JggxCiLHpGnUCYx2VZE3JRJKKaCnvVhvLUTHKscpuvG75fRZMOjSCRgUx9O1sVdelOd+OKdtGZ3k3/QqX1X/BRouqTwToCPX61Ib3aH+nfl+cQqHOZlCzdqXUJHqCKvurFIxw/wUTseh6+cv6KPVHPlnB7XjXkjElgysvncz6LQ1UxMgkT9W+dKdwMqia45kyrRjfYFM8NIAU1SPYDWCyE5VC6vFUzp10M9n5Dmblp5Dqeg1CfoocX5OVqITRK2vDMewSJAxlKeiB+Ig0GhZlzIjTT1gQOLq1kb+EIqSkWZh8yQTq93fgrekDILcomXTfZrqP9hEWkwj65TLIcAZQ5XvzzynhtZuOsUm6hoe+9z7BLi+vPrYdvcOIIc2C1mZE4xoi8wt2e2luHuD2hRkY9GPZeaid2UUp7E82Ub2lia3X/JjLf1JJ59ZWin65HK9JT3TXeujtwzBDUJ224mgG9rVz351vYy128NHjSfi++Q/MSwrZktxNsNv7qSbUNYIGg0FHICKxo2WQ158/wKQzCjhjZi5r1h2jZU8bvRUBehna+KTgkKTlyOd0gmMp5Hwx0XZjtp20kmSmzszBbNRRWdlNf7eHwGCQoFIKDEvqtPBwk8LScfl0BL3AQ79dztKP7mXbn//OQz9Yq1KpK+vXajeyZFwGKcYeIlEjKUlGzrplJoun57LlcDubnt6fMA3tqeqlOkaWN9oGEA2LuCt7+OO6MXx/+SJcLfJ7EHGHMOfZGT87j95ON85M+Vn5mgdVdldPp5c/PbUbX59fzviCQ4OWWpMWR7qFy8YdJbppFSkmE1fM+gZrcu3qxLFGq6HtYKf8bIIRTDk2sidlqDQfyvkKeoGbvreIRWPTufPutxk80v2F9xSGs7jC0LS2cm9Hs5NZVyqq0WbEXuQgtyiZaZOzyEgx09nno6FlgKqDHWoWLQZEml0BBoMmDu4ux9cyeNK638PPrXN3K891eVSgy6exL5UlFYbS9E/C1o9mSmQ5YgHFGCLv/XMFF57/B3a92k6oVxbLUfQLjElGsgscjC1OoTDJQ3RfFUgSxjmJje3joSDiMwbVgUjSiCaZ8rJqYnVeGXEh0FLbz1XnTWB6ppa/5yXx0V92y2m7oIGkTNIuHstbrbK8p85hQhoNjqcVGDcpA2nHCxQsv0WmZwhLhPv9GDOtTJ6dS2uqk+YY6R3EuHwiEjrBD+io2NXCX7u9NO1vJ+IK8P7eZhZ//UIKp+9BM/dsdD4NDLohyU59p3vEUJEUloi4AliSs6GzBsFhRDPnNHa/2PyZMNvuLg8r3yrH7Qrgb3PT3jTAXp1AT7MrJu6OOlA2Qh7yFI6paHHrHUbEoIgh1URxaRq3Ly6mwLKTg9PmsvVYD/UtA1TsaiGgOHitMOr8y4lMo9eyPG8fe357gLO/dghLYZJMpxEWY8RuEm0HO/nj2+Wkp1vw+ftxOf386dZkrGsf4PwLLmfJarvamwH5/guxunRolLo2yH2Jza+V0+8cS9OxBtVxhl1BOpoHALj0K1MZk23n9Q+qqVxVhSnHxpTFRaSnWdj8ZoXKphslVvLQavB7QxAFqakb0Rkgffo+ktPTGYg9H2VqG5sByRbBmGll8aISXq/uHXpuMWvv8VCdbEqQyPyicPvxyDIlIxyCgmpOSRL1RCboBRypZsaUpHLB1BzGOfbhF5OpcU7gRYOWQzHafNEX5vHn9+N2BfDU9Ktlyfj7cLLnEnYFP7NT/VKcwvCHokyFyovk1Dru8RQBMIQnlsISTR838OSmRvXvtCYt9skZvPizOUQkIwDJxhYsoUqikgQWM/6IkfBx9JDjj5ngjMRErLmaIcRxCQ1/SMFuL3l2I7nGnYzJm6h+n9cdpMx5BmOvzeedJ2R4rd5hTBjLV0oNUTFK2Z5WGn7wJA89vXtIqwGYf0Ep3zw3m9cPpfDCxw1q9BoNixzb2MBd/X766vrx1PSroh9ai56eLg/kTkCYL3HQtZhjfYNMWHEN/cGxfPy76hEbvUYr92bGTs6AbCPCz37OX3am0r5zyylHKsozNKSamHveOJx9Plq3NxPxRWjf1kzHjhYM6RZuuO8MJucmsXpPM5uf2n/K2YFigklH3llFfPXaGUxIs/LrF/bT2eIiFBJJNx0j+s5LzDqtnPHzllE/cTy/7/bSPBgcsaGdig1IpUy/YRz7u+cwdlIVmWeV0O/001LXT2+FnF3t+/dB+Y+1ArbSVGwXH6HjqYNk63TYsi9J0MXWaDXYJ6Rx/lVTefcfe/C3DpVIlTUiBSP4GgfY8+9DCfcp2O2lY0cL9glp3Fv4Ab0/eIkLfv0VlteN4+avnsZN2euI9rRz39nnseeZAyNKU4P1A7xUcRbXnzkTdpVBOIAj1SxPWVv0ZOTZMZr03H/VdHSChof/sYuUJCNjTs+jJcWMq6xLvZdr/7KLdXpBpQ5HlNDZDSrrweeF21foKhT+ImOmFZNJTyQ2eySFJZWqJOwKqhxYJ2vxM0NRKYpOpyUt2UypZROhPz5FuKqP6ReO5TsrHuORkEj14U48Nf20bmkausbRZqu0mhHHON7xP6sz+687hXiHYMy0YitIQqfT4hvw42tRaolDjSdI3GhPxpQJ1CiCupEqDigrN4lMaSfi66/j2daKdnwKXLsUzaSp4Mikpt2HV2lIjkKupZxX/MMfHtHEszgez4LdXh598QBjSnPZueGQrDOg1yKJUfY291Pe6aCrpVadXI2fWLZPzuCO+87g2X/toW9/B1+9861ErhitgLPPz/5ODXv3tyWcR1SURVaaOmpHbY71d3tZVTcDk24mr685gnsgwM5DybQ31dK1t23Ey6lMvhoMWt5vmMJ7m+qp3LT3M9WEBaOO/Cy7+pJGw6I6JZw1OYPbjvyIt87Yz49uziJw0+Nsf3LvqU2nxjYGrUXPxGlZnFU4gEN3mNzCDLraB6k62MGvk4q569y/oBf8NPcZ2Vnbjavfj0anxZBugf7ACMqHTzIpGOGanx5l3qLfYSxr4/tXTKO44ueQAtJ1F/HQe9PZ9noFAaUWHJZ1GH5ftYRbX1vBhx1mBv69dsS1Bjo97NzelHDPNXotjpiG9MDRXoJdXoYTtUVFeXo82OcnGg5SvrGX+c+uZ9Lsxdw08TANy/5EV0uQq7d+Q3Yow5696Avz9ntHWXT/fRSe9wp+20QkKYKgF7CnmllwRhEOm4EOT5BARMLZ4OSD9bX87e5M7JFerv5LCfXvyfQnEXdIVkN0mGI3S2LM0jF43UG69neMOiR5qqY8d0O6hfRJ6RSVpjF7ShZz8lOYFHqZaEMdmonTebVzCe+8X0XjpsZROb1OxpSeVk+nm9qWAQamT8Bq0tJ3tA+9RU/usvXMnjIf90CA+g7PUFM57l0dzlAQfx3wxU0+f+5OYTTEzmgm6OWUt2RiBulpFtpaXdTGqCCU26IIuETcoYRBm5O2GF5bMZ3dgNmqB5+L/b/YR10DLLrAQ/5NJsT08bhCBbQ7fQT84ZEbfPw1xfcvhl3j8TaJ4T+XwhL5xclMK01n99ZGuXSk1ZBfkoLdYiDFrMeRbiHQJktk6h1GuTYbiDBxfgHXjT/MpmlZHKrsTXAIGq0sQFJzsJ3/OH20Huw8CcKwGOLHoicUjLDjSCeewSANh+R6cE+dk8hgQKVKSPhcTMXK2edj7c4mqrY3yb2EU6jpD7dgt5e3/3NIbUjGn3N+SQqvX7sfgA+e6+JPz3iZ/6wuQZt6tOuMRxZpLXpsE9MJO/0cq+jmzYLxpNqnkZI0wIx5BRza0cyudbV0tA2SkWUjFBJx9voQtBpM6WasdiPO1kE8cZQPJ2NRMYqrrIttriDX3j6HEuF9yu94i9Y2WHzbfu79xWscq+imtddHVAqrMwvvPbOfLRuSZU6oXv+I7wz1+mjb3DRCG7m4NI2c3CS2e0IjZlniLdTro9x+B2c+tBPdWTNwbwoQ1qbJTXcNMovpMFN6c4P9frY29HPD1DG4AzkE/TJyzWY3csbYdHJsIs/v6GLv7hZZkN7Wj10fgc5GHKmnj/hedcbGHSK/WEbi9B3rG5Up+FRMnT0xaTHnyfMTiyZlsSBjF6E//5NXvi+DK1bcvoPpv7uUNTrtCWHTJzIZpSiXsAPdXhqP9bJleg6XXHMRRakmhBQ72NIQBA0Go3YIrhrjqhoeTCbAeaXENf1FOIbPzSnEs00KeuETy0FKmmOxGsjPsiFJURodJoReP1ExrBKqnX56AQ0tAxx4++gpbTYJ5ybIEYKjMJkFc/KRtvyTugaw2yFzaRHO1OU096XT5Q1S29KO1x0csSBO5BzijwMkRuYniCQdNiMpJr3cR4h93mLW4/aFaO50M9DhRgyIWIqTOf/Cibz/Zjnu6j76uz0ccZ1Jc92WhGPEc0B5amRBcRXu9wkDNNbSNG66ey59rgD5mTY2bG/E3+waKluNJqcZ9731R7pUmhGl5j+aE1E+k3C/hpkUiOCtd6roGJUcD5mdNt5alt+PMeuH+JtdaPRarMUOdDYDgU6P6iwVzvuMsSn0xJg/v3LNdDZtqadxUyPPHuggbUom7/wkSr84iW+3DVKzvp6jzYMcs+hl2djZuXzz1jkEIhIT0qyUdQ3y+G824W91o7MbEqg6TmSiL0yo10ffgB80AuWVEI3C6j83cdXC73DXDX/gF5U9BDo8aAT5nvubXar2R/wzVFXvYprS8RaVojh7faRlWEdITw5fCxF3iHu/s4Zzrv85kQaJpiPVPLXnNO556X4K+jq556OaERuVUoKJhkW27W1lavZs2twBVVI0KdXM7JRtRPdu4pLTfkpFeRc6h4nwYJCb/6IjNXMhFWtGanAY08yYkowMHOuj4kA7eqNuCAr+GTZABUxgzLaz5LxSfrikheiaP1D10GYOlg39nW1hPs9vrqVpf/ungoQqFpVk2G2gw0OHL8K/IxI1C89k4ZLLsOi1tDQEqKhrx9Ufc3Yx1Tz5v7UJ36OaKCX0X+GLcQyfi1NQ6pd6hxHbGHkgy9Moi4yIw9AnCn5aRE57D25ror6qh3BQVKMBc74dR2Ey2fkO7FY9yQ6TCsv8tDdAa9JiTzZhtxgQ5s7mqn8F0F2wiNbkm1lX4SQU7sM5GKCt2UU4pnkb31eIH7AZzXQWHdYxKXjrnUPkYydwCNGwyJp/7uVDu0HlxhGDIlvfrkQw6gh0eoi4Q2i0GmbMK+DWeRrKKzNQhCB/+vedeOqd6vcpDkFnM4AkD9dFRVnXOHlcCn2HOmXs83EkMx//zXlMfPoGND94iIq+bLYadEPNxRPg+kHe6LyNLkRfmKTJ6Zx5XimHD7TTsq5u1Ghd7SfFIIoqhjuGINIIGubdNIMHLi1AL/i59bdV6rzFka1N3D1Lw4GD8vcabLK2s0bQYB2TwsM/XQrAs2+VU/VuNQJgHZPC+VdN5aIZefz13Qpqy7vYsLGOzqM98rNyh5AkCTxOnEIG3e3lai1ZCsqKaB53kIlpEtnaXbi+8TtWTMvg6PW/ZuOHNTzzs6W8vLuVN3+56YSOU7GwK8iWDXXkZszh5o9X0PWX7RiTjAT2drDs3E38dWyqPCcjaFR95tHMPjmDr1wznS3bGql++2jCMaRAhNbtzXRV9siUEnH3XZ9iBuQMQblOX+MAa/+2G32KGb3DyDurKmicNxuLSUf5R2UjmFF1dgMpJSnY7EYaa3r5y5uHCQdFWQ9dkoO9aEslrmd2MuWvHzFpykRyi5IxGbTqFPVo6yriixCxyPM7roYBmWQwBvv+JOqH45VV1HfDbqBwehZfX2Rm4Gs/54PnhqCfdjss/+kUPj77L+z9ncxG8Fmatco5KD2TrgMdrG5ysSXXjtEsb7tuV4BQDKKq7DXDA6YE8ISgHSqNf4HorM/kFJQL0NkM5C0sZNk541g0Lp2N1d1y+n8c2ur4wRpPVS/eONrqhbfM5NGslwjvq0N71be57W9emg93jTrsM+o5xU8yDztP90CAHWXt6GZdA0uuYd+hbnq6K+VFHhO16e/2JJQtAGylqVx6/Qw6+7xsfv5Qgh6wcixjppVll0ziw9fL1bH945nSh9Ba9BjSLCqKRoEEarSaOApmLc21fbxzNJVIWOK++xYwN1fgX1u62dgyKG/0QbmMY0gxYSlORvSF8TUOoNFq+OXvzues2l/wh6X38PbjOxNqs8ombSlOZkb6fho+aKDkrhqe+RAqNtSf0CGYcmzYS1Px1DvlhlwMmy4GRbq7vbjjVOWU+y8Y5SzSkG7BVpDE2MmZ5GTayEqz0OcKUFnZTXe7TP3wp9yXWGldTUkxfOWdDfx1WzOEJdzVfbS8vYN51y+muz1M9is/JfI1j4wLtxsIRCSae710trjUZxTxhKis7MZs1BEOydFf6542Qs6hnoDzaC8/2TaTPZs/xl3Zo0aJiqpX5+5Wnvg4m18W7uf9ZzqBTh7o20dFeQGdnghe/+hZgs4ua0WH+/2IQVHOogMRBsq6+HfvbmouuZ3L//ELxiT3YSp/FiIhsguSyCtJISpFOdjmRgqPnnEFu71EIhLJ6ZYYw29iZCtTq8u6CvpUM5biZLIKHFy4vJTOPh/vPLcfX/MgUVF2DGJAxJpu4Q8PLKHHF+LZNw7TsqdN5lsatg4MKSZmzslj6pg0/vPaYer3tqn3WmczYDJo0diSMWZbIRwgxW7C5QkxECsDHw/Rp9BmR0V5ulln0aHRadX343jrUWvRYylOBhgxMKmUjQxpFubNySdl/99Y+VziLIDJCPoZeTj94VPuYarHGVa6VEvQQVn6VJmbUUpvSpCpXJc6wSwMsS5ExaiMahSGAlKFDv3ToJNOxj5zpqDRazHlJfHA7XOZHX2VaHcPzqK7eZsTR8oQw++LEmjlJqKlMInfzd3GKwWrALh28krcrksJdrpPHrc7HHfMEJbd0+Olprybvm4vwUAYd4xJ02o3Img1hIIigcGh0pHyoOadN477i9fC6UVcsCuNvj3tIw4b8YRobBpQeyUqlfBx6vkaQYMx00peSQoNfXEDMWFRxcDrYhlSV3Uvr8UcrNsfJsnQx5zxOWy1DD0+rVGLMdPK9Hn5dLYO0hCjLE4x6ZGausmcaVapnRPq+FqBjGw7eHuJihJEQtRVdp+wVCeYdExaPpZLzynlyWf30h/rW2j0WgJtgxz5sHZEY1DBbuvsBmwFScxbVMx184spdewiWrkDzeRsqmdex8dV3bR2u/GsPABAQyPotEN11mhY5NHHtvLiR5uZbzjAvw5OIOLaAKLMgvnsq2U4O9zyBHsMiRZ2+mWCv0AYSYUeDukeKLMXW14tl6Pn+LKB4lh8EXq6PEgDQ7oQwX++RnL+Qzzw6w2j4sI1Wg32yRlcc8MM3v/gGINdHrLHpFL3UR2iL4y/xcX2VUepPdrDmAnpfPPc+whJVrzufZitevzeE6Oc/G1u3n79yHH1AZT7rzUJFJ1ZwJ1XzyDHZiTP1sSa2pRRy3dZBQ6m1/8GIiKBy77Prz9uSFAdVL5TbzMwc1w6ZxUOsGNCOrsretT7KhglfP4w4fRpmC6uJWQfh9sXomx3ywlnV5QeinzOWnQWHfYMK153kFCfT72no61LrUVP4XRZb6Mmrtwo/1KeSzKkmhifm0To5aoRnw8EASlKls14vNv9iaYMP4pBUXXQw7M3JROO73Gpn485BKXHAIAeGSbLSITd8FLS52WfLVMQZL3fO7+1gPx7lvHKWwOcdaEd49PfIDw4si6vNlOG48klEfRaZiwbS/tdjw79/YJz6P5nZwIu+4TnE5PKM6SY5KgjrgErBSIEOj2E+nw46/qBIURSwBtW6SvCg0GVF145zwFnAByZhHUZiOHukQcWJYK9fqp2trDkysmYjTq2bWqQJ0TjGpHDexRhp59uozah9q4cU2vRc/G985g2Jo0/PrIZZ3kP5jw7ZTW93DDVSUny2LiReZnF88wV4/nFik42tJ7GLw53Eujw8K0HP2TG0u9y8LHtiegUYSilXjC/kGjFWsL+CKQX4m3pGpWdVSn5ALj6/ZTV9eJtd8v4/nQL0y+aQPWeVjw1/SPrsVpBJSDMzE1i6bQcxvU8ge+nH/LOX1uw22HFv7Yw4Ypreb/1dGyTV3CuaxVp31jIJSvLEtaMq6yLq+/8CGOamWDfDjUazpiSgdcdxFXek8AkGvFFiLYN0uIJqcycCoGZiveP0+wedW0hyc3mKRb1Z10bmil60MHup0aKDIG8Hs8+v5Sv5X9E5rWX8MaaKgrHpNC4vVnV1/DWOWlsc9O2v53meid6g5a28i7EoCiDK05Q15YCEQb2tY9YWwnnIGgw5dj4w10zyVn3HSjMRzNjEdsP+PC3uUdQd3vdQTlS7e4kr9Q4hN6LZXmKopwjxczSol7ML/2K+7/yDDe+U0XYGRuM82g4vLuV54qns2Laj6lsCdLYVIenpl/Wmz4RpDJGBy7oBWxpFopL03C7AtR0y+yrx5sNkYIROltdaslPvX6tLFtryrZRODaVCWkmvJV9Iz4/ZaYRzcRSpCifirpaYWxOHZtK37E+mUZ7GIRXPlFZtEnJQBUHoVDhKHuSwv0UjYgQiA3JIqKRvhhHEG+f2iko+HTr2BSuK9nLK28NAJAyPYO/7Gwk3O9PiF6UDUWfYlbH9JW6ouIsvO4guS9+l2s3fYRmxZWc+4B0akgWQVbTmjGvgEhYZN971aoeq7yBhpFiKBuFWz9hCjYGI1M2WyW9O/haOUu2JxGNNMo48GGNZMXjWzIt/PS8buiuZVrxZfzySBehEyig+ZoHVVWt4SWdtNk5pCSZaO72IPrDFJ5ZQEq6lYA/zKbW6Ww60pgQnWZMTOfH5wfpuOxHLH3hu/w2xYq/1Y2nupcdsWnpeNSUgiW3FCezZEImlPkp/fEZ/GPPGCKuJvVcQEEYQdrcXGbNL2D/jmYaP6qn8aOhEpNhTArjxqRybF8iBFYpkynzKBF3iN5ON4ebB1g4YwLa/O1kZUJ+gYAmTeaqd/nDPN53BdZvX8OGjXX07G1IuHdK8zXQOqj+W6vXkpJuwdXvJ2VODmMmZFC5qUF+OWMZaYJE5nFKBPEQ44S1pdfS2zhA37d+CpwDQN6tU+k5ASomGhbZtKaanPTlvL/2MO2726jd0KDWuIe0D8LobAYKx6RwtKwTf5v7pLH5n/huaAW0Zj06IYjmrHNo0F3Jtso+6ioPj1rX9w4GOVT0QypNbl7+8zYZKh3L4vOmZuHs9dJ/sBODUYdtYA8bfriPJSU/xpS9RG22R8Nyf3DDxjoa2114PCGa6/pGZM7GLCuW4mS8dc6hTFmKqsNjdoeJGRMy6B8M0nSsj0CHB+k4Wt8RdwjnbrmElaB6qJcz6BkLi5g1MRObvhtzsQPoSfh88cNLaEu/lTder1bLiqOhC09kglGHzW7EadSNIACMN6UvEF/ihqFATWuR+2RKszoqRomGNUQFAYg1m79AKfvPlCnobAZyipIBJ5feV4A2x8qxO15i1/ffHyILI+at7QYcUzO5/roZ1DQPG4Hv6AABAABJREFUsPOjWlxlXUOQw7DI0XV1nFvlIOK+kOCzPSedISim0WpwpJgZX5SMJMEhm0F2TgwNkGmNWuZeNYVxhSm89q89KlcODJWZhtcuRV9YpaFQbPjUsgaJ4GCQ7sBpZCZ7CLkltTl+vJdbHv9P/FlUlMswZy8bhyRFeeP5A2gEDY/fO52WQT1/f/MI/3qtjK465xDhmlYgu8CBsXMXLeWDZHc0kFVwJt6aPrk+Oxwlpdeic5gw59mZs6iISab1DL5yEOvZBew92KaeA2FRpgPJsSGFJR74xgIW29ayavJSfnvgvYQU3Vvv5M0/7UiADmu0cgbz8GMr8IREHntwHRFPCOfRXlb5y5mYt4gl5x1iyewinGd+lw87rFRscbJ/fzVddU7Cg0HC/f5Ro+XhQzqiL0z5m0fRWvT85omLWZhXxd2RQg7FdAsM6RZMOTYGj3SPeB6CSce0qyZjMOg48MoRlQ5cqfsa0i0Uzc2js76fh15u5++rl9DzfBmRq79N4x96OZ4pWc2/HvxILVdlLSpk1rwCNq08kpCZRKUokhQl4A9/rsNaiBKBTg+PvNtOVvrpVJTvZUxp2ujnG2NWfXFjLeV723BX9qibY0ZpGl/7yjQEjYaf/2Yjzl4v2NPo6gbX83s57eJb0J1VTNmeVvoOdaKzGXCkmjlW0Y0rxhc0/JqSJqRx9rJxrF9TncAnpFQTtDqBojQrWQ4zBwsc+FtchMTocRvOo90zQS+QMzmD71+YR5a4GaImopfMYt6GJsrLwtissOjhGXTM/znPbG3j6PZmNXPPWlQoqytub/nEfUhp1tf3+tT3UkF9aYTjN4aj0uhByPDBV5l4UUr43Rdlnz5TEIbgj+3RxeT+TqBLmsdvn9wnD8oMO3Gdw8TZy8ZxU8rbMG0KvzZM4L3qPqTwEHVxqNdHyBkYkoQ8xeZJVIzi7PZSWduHIGiGHJMSuWrlWl1aihm7Va86AUVDOSpGE8pGJztzoFigw8MTH/cyqeQ0dh9uwZhpJdjlPS4XTkJ2EF+zFSV2bG1EI2gItA3KcEpTFV3eGbicfgZaBwn1+rCVpqK16OWNToxCaj6lizJBp2PWrFyZ0ru6b0gBK4akUfoemXlJzJ+SDdpmbOeVoJk5Ff8qmeteq9eCJEdZ4+bl09niQtAAehPemOgLMKSYFhYTOG9AXiOpUzJYrnuVSOEs/qTVIAUihMUo3nonL75Xie7S+5FSo7z0QpWqmBaMlQqiYVGuz57kWlDOZfvRLiLSBBorthIVZaGdzJnZnD6vgPcaBxKEmzRaefju0a8WkmJoYtn2JLx1TnlTm5xOxBdm9tkl3L6klIf+tYvasg5qf/4nxo1/gkHJjsHoOd7pAKiZAMiR5Kx5BSydkcvWd6pUNJ3S09j9fg2hvkRAhUIV8WmdRFSSB9TK97aRtnwceqOWg7tbGawfSFh/ynkEO9zseb0iJqQ0FI76vSEiUpQJaUZyStPoanTSJi6hIB8MNgNpyWaMei1mq0EuNZp0LJxbwMGj3Ryo6RtRrgMZobhre9OIsp3yd8FAmIgUxaLXsmB+IT5viM4DHQS7Tx4qqhE0FI5JJbPt3wTf3o7h3JloJs6k5Efd5Ff3osu3I1x5Pav2DVB+qEO+/2ERtAJF49MxGLRs29M+ook/msWrzCnvmWDUDa3j4b0UcWS2oOxBYozVRmm6fxHzCMezz5QpRMMinS0ufvNWLd7BFFrrduGp6k2QVlTMmGnlq2eksG3CXymeZuP77/2TD/5lSNCOjYrR426giikpoUbQjBhBV1hT9/f6ZIbL4dGJKBHxRVj7zH6Z7THWvI0nN4svGymfSbjmEziKUK+Pj589wNYUE0n5DibNzuVAs+uETfJ4Yi4YiiraNjSo36tPCfGfw0Vs2l5GT0UPxnQz9tJUPnjQR7S7jmten0V3+yA7Bs5iwe96QNAx25TG6ePS2VTeQb/Tz+HNjSoqSum95Bcns7jADQOdCBdfzVuNs+nr3itndSXJlEzMYFxRCl+b72VN3Rje3d3MZttE9mwvZ9rF4wkFRSreOpqw2OPRXxpBQyQi0Z9+EW/sh1C/rIEcDUaImrRYk4w8v7pSZpZVFr8UTWwCn2JwIPrCvPPoNt6NrQ/5+Yr01/WzFxJYWxXnJQUibGsxMC3zNMSgzF5qzLRyx61zMGgFzilqxdz5HDr9VLz1Tr71+03cfssteKvCdNb3n1QUqVhPt5cNZe0EO90Ja1MKRHBXJpY0zIUOzrx6CrtXV58QmvpJx1a0tn98ZhXBpYWc+9VmVeBHmSJWMjxFtlIBBURFGfkzcKyP1TsaOf1KOzdfPoVHf7eFf25s4edPLIL0NJrqXRz5qI5AhwxwCLuCXD5pkJvGtXB9ayaNmxpHlJOD3V5ah4kQQWwNSRLugQC1nW6KM21cPDUF52A+H9b0JdC5fJJptBoKs5MIPPMx259pYFFIovfb30FasgLDOV46vWbWbmlny4ajuJtcKr06ksihd6qG+JtO8l7HA0m0Fj2WgiQEk45Ah0cufylrclh5KioMcaRFJCmBT02tXgx7F4bPK4yYE/uUGedncgqiL4y33klZm1ulgpWOU0OPuEM0ugwUTrSQlG8nJFk/lRiEYNSRfFo2YydlcujNikRBdGSJwfjoVf2cSaY1EH1hdSr0RNTYykO1FjvkGnaLi4gvggZ5GErnMCEFInL9Ma4nEe6XeVMivogcXY0mNxlnyoPUp5hl9bLY5HB8CiroBf7zwkF8jQNknZbDCz/M4997TWBrRgO4nH7CriArN9bSP/9SdIKGF94qQ6fTEomIZOc7Rj22FIWA6ICMYjoDMzlQ3YHoC6M160lJtzJxTBr3nF5D4OePc9W3rsVx2gq2lXcSiYjYHf8/6t47PI7q+v9/7cz2olXvtmXLvRtjTDMd01uoIYWEQDrpCaSSRiohPSEFAqGFXo2pNhgb9yJblmXLKlbXaqXV9jLt98fsjHYl2diY5PP9nefhwbJXszN3Zu6595x3cZLKbUbnKE7ai50UTC9mZF+QkYYBrvhk2MTEG2EvdfP1K+bTF0vzrTfasJe6kaOZvJ1i7n09lhhbblIlldgBncyXtzvLwnitPjt3f/s1swENkOqJsGrNQWbOKMVpnUwkXU37O68ihdMMbe/jtwdfR0kph4VdT3heaZndL+03dwbGeRzuOgWbQColH9HV62hCUzTC+4fYGr0cKayhJPbqZbFiJ65JfhadWcf2VQeIt47yXkS3jaKFFUyZUcquZ/ehJCTCw0n8wztYWVLIH0u8bH6ng4E7f4nP1s/wpkEywYT57qV6IqQUH7bf38cvb3uMT3aMEG7QYaA2v4OZF81g2dIaHrt7fR7b2rwvpW5UVaPxwCCd/RFeiqRp2tA5zu/6aK69OxDFeckClvbFsZ87jzWtUbbvHUCWVUaCCfrbhkn2RM3dtHEux/JdY1FRxv2aPKeMRfMrSUsKr73YrO/q0/noNg19EhcAVAtKFrIP5IlsmotVZXw5yniWT7hqDqcsrqZ/KMH27T10bOg8ZtLv+04KBiZdlVTI0bCHiR9wKZTkyfXt3H3fJ8Hl5/GDRaae+OFiIviZRbTg8TmYMsnP7gncmXIbVbkTvLPKS+3CSvpahojuH994HTvI9lI3006dxOevWcCe3jCP3bedZFcYJaVQd1YdN1wxj+7BOI/+Zn2+2UZ2JZwJJlBi6fdmRWbhcsUzS5gys4Rgf5T+LBtZkxSsbis18ysYCugP6LLlk/B0vIDTcT2f+psNq7WWyJ59WP1ODh0YYlVaYSgQo78xgL3IieCwMtAa0m0hpdEHTUlIhIJxeqJ2oplT2TUQoSuL7za03w90DMP0XkYaB6lobmTlslKc1lPo6Q4T6I3qbMwsy9L0vHZYcdYUsOTkSWzoj5HojJDui44b38xwiud29RCKpBCcVi676QS6+iK0NA4wsi94TCbtR6sFM/bfrV47V3xhOctnlPHb+7fS8/Yh83NyLEPza610Hhhie3m3rvGfRc6oKZl0jjLr2N3e4cqfmqIvGox/M1bjRnlv7OelUJKdq1uyukXHVz7IBBN851dv65aSWX9k39wylp0+hYuXTqKzdThvJSs4rZx+1jS+dJbMTb019KzvZGQoAeVTQUphtYlEDoV5bk+MmVXVREfy/SHkaIbn9qrcPLOYWs92qqeXEG4Y0N/f+iI+ddV8zqjZyYszignlIJIMLa3q+eXY7FZGggkiw0kGenVQxrGufFVJpePgEMkvf5yiybUka85k59MB9u/uR07rZjaGAN77kXk3wjXZj6Zo+X0iRcNf5OK0GWXMKu7G713Ew4F3zd3U2DB6BxZhfHLJTQgThegQKZpTyi+vF3Gu/RnUVjP0uY/xRUmh7dXWoyp/GXHc5aPcR/VwD66maGRCKTY/tZdbQ7reSfueTYddRQs2AddkP2WzS+lZr8P3jGMrCYn+zd2sbh3WV5VjvmciKW3j5/JqH/GobiqeB8Vj9GUG/YXw1vk57eTJLK1oIpqZZZY1RLeNb990IvM3fAVqq1l/yjm0vd42Tr7Z0JB/r5fZIliwFzm54oo53Dy3AQSRd4Pn8pM/bmCkYQDPtCJ+fuuJrDk4zD9//jb7mgKsrv8kj/5jXV65wRASa20KkOwKc+GnlvId/79RW7u45uDH85qGSkohHUzS3xXGKlhoH0nwxjvtdO0NIMcyeKt9FJa66e8K0+u+gopr30De2cbwPW9xxp9vpXnpStq6R7AIFvqzfSXRqWsgGTXUtgPBI+6S5HCKx3/2NqCXam462UnnwkvY3wI3vHQ25764klhzfhN3wtV0VunUZhc59Gb7hKU6wSbgqPJhL3LmjYNvdgnf8D5I+m87ue3Wh/nOu10mA19T9HKgFE4ThjzwQa5ngpEQrD477rpCfGUeXB4bgz1RUn0xE5ef97vGOVV4OPOji+jpDHNwXYepcGoct2RxJTPmV7Dp4Ya8PsjRhAEdNhYlqqQS2txjLrQsogWf30l1qZeEpHDZxbM48TMn86tHd7Lv6SZsfgefWeGiddH5PLXjN5y6WSS4L8gPXl5IdZmX2JCuTbXqpX3sri8h1p/fR1QllQfv3sCCe35HVdxBaeU+U4zux98+m2UV++mKLyczvDrvvI3S5ux5FYyEU+zb1YecVTsw7E+PNkEa9fmehgF+8Gwh0ydfSeeuCLvXt5pwXBOZeBxy2VafnU9943QCoSRP/vRtEzGkJCRSSZnpRf1Yf/pNbp5SQO2P/8A9v12fJ289VrxzQq2jw0jnGPdSdNuYs6Qa699/yrNfa2D+fIEZj9spLj+DtmO9nmMegZyTOabPZ425G5/TiSO5N2LcSfnszD1rKpedNY1ftQyNe9GlcHrc9vu9ItE+wqaHGyb+3jHZV5VUwk1BHu3fxNPlHhKdr5pmKBbBwmMb2vnWhd/BYlEo3DOCs8pLZjiVpasf+8NlsYlcMMtP52Xf5903E5x7XQn/vO8VPv7NNymb7GdS8knOm3EVD5V76Gsb5qdffTmPHCbYBGqWVHLJhTN54+12WvYHmVbjJ/XQDgY29VH/hbK8HgWSXvZIDCUpclkJp2X6uyKmyXxiKEl3e4joYJx71/bwo6Xzabjsfva3wIeWvMCiT13L6tX7ifTHsPrszF5ZT1VNARtXt+iTkaLS2xjQZZBFC5o08W7MmLCkcJoSRzuvt+h///RVa5n0m4/S3BpCS8u6jIVNNAmBuWF1W/nCTUuZWeLm5p4IIzv6zX+zFzvxziolM5Tg/A8v5PITarntmy+bZZJUX4y2md9lyo928eCf9064sj/SCssgRBnPVOW0Ym64fC6XOp9C27IFqitpmv5dvvzD18ejnkSBgunFfPtCCx2RxXxvKEF3XyzvHARBYNqkQrYXuY4JiSe6bdz+58uIpyT++NXVeWNmHENTNHrWd/J4cxBnlZfv3nYa85QnOGfF+ex7Tm+Cqwjs3QdzH30Ue/GVpPpivPWfPZx1wwJe+10xzx88hXt+tIbEUNJ0p8uNVE+EO37wBgvOqiPYH6V6xWSmzChhcXknkubi9r9t0SHDuZOcpC9YNqxt03dkQ3qp91jr4wbu32BJb31xP7v8DpNVLCfk9923GhtKQuLhf+80G81GqGmZ1n0Bgsn59N3fQlc3nHr2eTz7yuNcdGNs1BXQgKiC7tp3FP3MseerJCT2N/Rj+96nuDT9d2xnzONvwWvY/dI7x7wD+p9JZxtNGC1n9Xi4GyFHM+x+aT/NGw7pNPycpkzeMY+hzqqp2tHLOav6pCmHU/k+p1kCVsfBIXYPTmJFwx385cqz+JJtBo3vHCLRMaLjqHNgZu9Z0sieVyxjZf/GBJoGbzw+xHWXf4HaOZ8h0Buh1X4d61uGuPz6hdSWe/ntnW/ml1dEAYfLhs9lw+2xYyty8Y8/bmTjiruoOtfL5n9uG/edSloh1R/j8a1dhGNpwp0jZhM22RMlM6Q36Zt298McKx1Z0SU1liGtaAT3BpDCadx1hVx/4SxmFHs4sDeA3WEllZQYSsijCTuHkTzuPooCSkLi6rvSPN18M/Kb29l99X0c+ORTJlqq5vTJLDyhmndWH2BkZ3/emMrRDP98vIHJ00vyGpCCTWDl507i+xeG+OpjVezfN8gbTlueLlWyJ8rHP/kUgk0gM5w6Ju18i2ih5swpfPS6hTS2DbH6D5vo3R+kOxgn9eoqnr3TYD4/ze/mwat/WsVzf92slw6yCLN4b5Q3D5XTPTiYJwsC+nPTt76Txw8MHVHhdKIQ3TYuq+9A0lz81W0lc5gSpmENmeyJ8t2vr8JdV06sZS2gl08HE8Vc/et5WK+6DGGLPlVkggnOWFCF+vDPufQjX+BX0UxWakWfZHOBIKBLTux86QD2Iiff//ZZCBYLN9+zk0xGn6hEp87XMBYNmqohh1OM7Ogf1coy0FGqdlTvvOi2cesvzmdmRQG/e2Ab3W+0mbs2GL8gPd7SnKZoJkdi7N9Hm4e4762D3FRmoatb4921SXBczlVrV/HEXW/nNZ5tfgeOco/+bvbFDg9qmWDxoiQkAlt7uOKnNcw74YcMbI3S/Nq6Y3524P/AT+FoTtBA8hgcg4n0jAAzu/5XcbvZ5qmRmESnyORpxZxRuIbHLl3Ludc2sPD2x2ne2Td6Xtlygq3IZSqNHi40SUGJpXn7YJBLz/Lx9iq9hKDFZZwuK6lgkvveOkh3xwinnjwZr13M2x0ZbMjhQIxtzQFcHhszT59MsD/Gvi3d7Awk8pKhWWLLarFs3HAIKS2TGR5tmBsNN9ChiJaSGubN1UXonGdPpz0Y00EFKd056pEX91FVq1uHOl22vO/KjYkSAuj3r3d9J+ccmo7VN5f4p58dlcK2iXz02gVcOWU7fypYyL8bBvKOq0oqHWva6drUnX+dqkZwKMFAch79XTvo2dJDx9aeMS+JksdjOdZwe+xUeh30+116kukMs/rl/XzmupPhzlE5jD174Ysbv07l9/7KH7/5iolwSQ/Eue+hnSTjmawG0fidVGoCdM57hRzLcPcaP5KiHlap1ggt29Q00DGg18d//JXTmb33TtIJiYRvPjZ/J4LDihzL8MAzjcy89Z/c/0obcixjol0g++y7R4ETmSG9t5ZRVTYfGERWVLq39OAo97Ds/HoG9w6ipHRGvFnbz3IRDNi7RbWYifRowuZ3cPV8FZ9tH/+p9dOV3R0bKMPj3RkcS8ixDNveOcSPP7OYHZ/daf791884xLN/cpAJKubcUrGshisvn0NL1whv/nUr8kR9h8OctyqpaLEMga09DDcPmiCI91UOO+bf+C/HuHJQdlLOXTWA0XjRH0aLyIQd+eOKbOPUVuTCUeoya+VGyO4q5s2BorMm0Ts4qrWiqRrOKi+f/8E5XDung4u/NURsfxAlpUxY79YUHUf+5GMNXPf0E1x55+eRw2msH76W5H06GmZ9FjfevW9Ql4XOvrzGFtlV42PG/ApOn1/FhdWbIBllc/Iint10iM0v7UcKTbz1VBIS/Vt1mYTcvo2RECw2kaJSD/32k5j55wambjrA4Bnf5637D5rXIoXTHFjdQpuRBON6w05JSiZyYqKxhTG10yx3IXdLD/rzcPaUMNI//8XHbrmLh2ziONiynF2tjh3XLQ/s4pqnmg7rxXE8+G9N0Wh7q4M7u7JqwNlGcd/bHXyi9Ez++rW3ef6eQ+bnn/lOM5e03EDLl+7llT9swjujmJlLa9j3TgfxjvAR9YAOF4dtcEsKz96l92uOhRUt2ASctQWs+fcUnhRPMmvRF3d+kZUfvY9V4d3EWoY58Hwz17x0wPw+NeeeGVpos0+exIWn19HRH+X5RxtI9cdY9a8do2qtTiuqpl+Do8LDrd9awerXW2h5fn/+fZEUNFU4pvdbCqf5yt+7kTIyHetaJhzDCQljh/mcoWkkum26/Wwsc0RxvtxQUzLhhgFe+/yfgNPMv4/ddhf1Z/6YA6tbkGMZRLeNh79djeXrn8B19SI21a8gPGYB9F5hoNpy3+X3E/+nSeFIJZY8RIvhpZqFZ4L+sKtpGUuWLKLCBy4pa6CWVl42m48tL6bStg1CvaSLJ7FveAHBVzbxq+YB1r3YTKp/FBmhKRqyoiJaZC7/0DxWzCjjyU0dvPa7TROzc1WNaNMgN/3sIJ/81L+ZWuhm494h+g7tHoVJKmoe6xMwlUFnnjKJb19eSWn/vxn53LPYCxws+c10tlaUsFkQ8urIed+raKNcjQmShuCwUDe9mB39GlNmfo+ihRK/W91Jx65+E21mQUWVLOakKMcypo5UrhfDRGObKwpmlgZySk2g3+efr0py+6d+xf3vjmdnw3h1SvN3U/JhSyfvFUdjf5gJJsYJ/6mSyr6nm7j6lK/wz9jp7KxaRjTbPw7sHuQbf7OwdUMVy06bwqfOqOOHKYndLe/NdZjo/IzJyoBbA2YpZxR0MbFE9YTHtIk4SlyIe9/M+3vXvFJkRTXfRVUZU37JuSdWt5XyaUVUVnpZXg2XSY/jveWjPPx33R1v3hlTWDi7nOa2IQb7dBioq8bH2dP8tM4p5+CLB8Y/r+rRl3qMBv6B1S1HLBeP3bVOVPI1Grhly6o5/4KZXLOkmLVtCR7413biraGjdoNTEhK//OGbvJ26h8ecXwNgaN8w135tNr/ZPUBsf1BHZSX3cnBrH1NmFumL0ONYsBxPfGB+CkeKw2VgZ5UXZ6U3T8AMxiSErG2ize/AUeTEbtdPOZWS9OZuJJX1DpDGScoeT2hZg3Y5nMLntlMZeZbojx7hpXtHFVJveOV81mtfIB1MmkqVVq8dJSHx1++/yQM1Pj7+6anMLDrI5Mriw5rKGDXBgc3d/K5zBHuJ2yxHvJdksGATqKn1UxZ8mszT7xDuiFA2v5R3e+vZsrXRZGgeadt5pDHYvLadA3sD+AqdWK0CLdt04lUe6zs7VqqkYndaKZlZgpSWGdw6Rk02Z9cH5P3Z+HkcAkNRWffALnZsKCbVnw/ly50YgSwD99g8vicKw7YxV+32cOM00Xepki6N8KErn6Twc/fw9c+dzLmTdYDFht4qfP5+3n56L++uaSO2P3hsoIQcmLZRpnRWell+Xj1z6orZ2zbEwX06Km3RCdW8fO/Wo6orW0Sd7BdrGeavsY/yOfVMLIEDkI6Trj4d3taVUQ3fZ8aQqUydoUofcxdWUl/jZ3MvXBYc5splGq/UFTLcE6Gm0seZ00uZWenjhXd11n6yJ8qdD+/Wnf7GjLOR3A431rlhJIRbv7WCZ1/YZ/p5jH3GcpWUzZhg8WLAY09eUccnT5Lw7vwVly75JC9Weo8onjhRJHuiXPfbMp6I/xr5oUdBsJAqdlMxo1j3YEnI/GDdPL761irW9vmI3fbiMR3/g4zjEsR7P5/NvbHFs8tYesokXmkLIYXHb5+NGr7VbcVV4qK8ugBfoRNV1YiOpAhaBRKqCmRMKvn7vp7DwMCkcJq2njDSiQtxn/oW5CSF/1z4Oj+Ir6R5bxWdG7uRFA3v7FLUlP5yRfcP8Z/Hd9Owv4o9mxuO6Pdq4N8NLLZvip9EthRw2FB0M53gUAK8JYgVboqmF2KfV8obu3roagyMMjTfR2iqRjzLIB0qcGARLboLWkrJm8BHuSE6gWbpSbVE4xnW7eofR/u3Fbmwuq2jTWFDO14A0SbqUN4s/d/md+jM12DSLMGNDdEh4qzyAmTlMY5PKcz0mJhcwGlXzmHL2x06jPUYt+SaojOJQ5t7+FHLy/x+Tikl5V5gD0M9EdLBJInOyLhnwhAqnKjkJdh0XwSD6wD66t5X5uEzZ09jEq8Qmz2HllOWsbF1iPmTCnmjxD3aNznM+RtSH3JCRklIPPXgDjZtKWPBwjP45pmdOML7mTPlPJpr/aSy3gpyTANVyUtSNr+D6cuq6eocITAYR0rLXH79cmRNl6NWEhIHW4fILKlhbmmG7RU+rD47mWCCA2vbD7uyP5pxF2w63wdBYOe+AOHe6LjP5C42jfc9r4lt9A5zFpaqpHJgf5C102ZxxuJbeL7JTqAtdPRS/jnnl4xneKb7ZFbePANvvIGRpITLo9uPKgmJt59opLmxjFBf9D17Qe/1XcaO+7/OaH4/DOQjHWOkPcSunCYV5Gdyw0nMXupmyoxSFs+rYFq5PgG0BWLs2jtAu6qRArNU8X7KSBbBgtXvNG+OoZmkqXo5ZO/2Hu6rmcu0Fb/nhvW/4j+nP2f+rvzQo3z5xr9xr9PGwfWdrFg5nURSYkNIRy8Nb+vjnSMIauUlzOw5L7xgOn/6SJjP3D+DXY/vRT3MA2ic3563OvjT1JM5/9IrKLl2mG0BDxt+9IautjkGUWOU34zf1xQd9eCdXYpgEwht7skrg0lhnakuDSfN1aCr1qdD+3I0rswdQ0ohFE4Rj2ZM/2ZDPlx026hZUsnchZWsfbIxz3CkcGYJM+ZXsHv9ISJNOkzyrBsW4Pc6eOpX75gTRu61GKzzwimFAAyG01iE46un6icqMHlxFbdfYOOX1PF2V/g9mem5kdf3UvSGciCYYDDn2Z5ItsAiWjjl5iVcePIU/v7ITjpXH8w77rSLZ3DXZ07msz96g/COPnMHaLUKVLoaUR5/luF//YQiSeVzf7uRN5Vbdfa9zz7hJJM7mZ91y1IatnQT3NJLvDVEj2Bh3vwKtJZtqNsbOe+6+VgvmsW6ah/b32on1jKMAqbXgsUmMunkWu7+RCnffnSEXc/rO6M/zT2NcCzEcI+OIpw0uZAFifvRDgX52PIv0N41jaYNnTpyLyUf1Rwz9v4avZCpy2tRZJVtL+43FXTznMxyqw9ZXo1xPDUtj/IWGAWxSMNJDqxu4Vdvd3C306qjEqPjdZyOFEbSnXdCNcFwil+8olDiX0Y0oVv/CjYBBR2p1ZHdgbxfrStjLBzlHtKBOKnuyDEf66iTwgeREMZGsidKfzht1kPHavgb2d9f5KK+roizZ5Qxo+BdUGS6S04jIymMDMUZTCv6Nl+YuBTxXmGxiRTNL2PWwkr27+4nuKXXnMjUlMzIviDPq3ux2638NnMmPxv4HksyD4G3mF7bhTQ0hRFEfVfTuKsPKaM7bK24ZSkNm7sY3tg98fdmHxZ7qRvBYdXd0gQL5WUetNa1LJh7DXvcBw67KjGQI/G2EE/8ZTOv1xdTP7eM/bv3mLT9sfXRoiWVeHwOFp5Qze4dvXS+1sqlXzmF70x6FkYiXCysYHBdZ9536C+MLscx7aw6Tj+1jp17+tjzzD5zsjFIg4mOETY93ojVZ6d0QQWR3ijxliEdBpuQCLSFdNG0bFNSdNtwVnpZcGINddV+WhoHSLityNEMOzd3IQhC1rRk/L00jjlyaMT88wfSU1JU2tYf4nOySvfu/qMmjhkvZMnMEga29uRxC8zm6Xu8oOHhJG39UaLD+ZLcFtHCkqU11Ftfpai8QCfUqRpKLM1wT4Q3Oxex0iay4Q2977Rx9v1ce+9OQp/5PY89uYeBHX2ju8YxsGnR6+DDp02lssTDk20jZu/K5bCiNTYz+OR+KhY8z8VTZrL0grP53mCcpi59sskdbVlWScrFxKMDpnvhU//YisUqIkdSOGsK+OHKHtrP+SeaolJ7zV5uv/UhfhjPsK8ngnqYYR5rmpVXQssmpKL6Yq6+aBahWJp/twyRGvv7OQnB6rNjK9D9rW0OUbcCDqdRklL2eZb1lJAdJzmWOXo4+xHOf2gwztBgnI7mIHI0Y5YnjTge4IMZooCz0svU2aV0OsQjCnIeLv7PGs0Gk3fxFbPxeu3s2dbD0Pa+cY1YwyrT57FT5upDO7QbUikqZhZRXVqHx+cg5Ejoq1LDxAdhVH4hJ440YRSVejh9SQ1Wm8g7O/tNSWtDzyjcGDAnsUkFYbT9QZ6Ifog/3/WK/rksxLM72KnXMW0ig33RI+qOWGwiJ924kN99qA/pHw+w+fv/4Fuff4G3X2jmnO/eSjjWrz844dSEKxPRbdNrwSkZ0WXjq7cs48zeX7Nxxe184zPPojD6Alm9dipPruWR71QiIvFOTwXDoSSdosDMKUXIb+5E6YtTVnlxntK8uerNjp0gCMRTEqExYoO5yKVMUEFwWqmfW0anQyTZFTZZnsnOMAcCcdOgyVHu4cyV01k2s4w3dvRQO7WYG29YxL2/XMfAu91H7IcYvZh4R9i8Bx8E5lxTNFJ9MQ5kV7tHuyq0+p3ULqlk2sxSArv63/PzuWH0MZqeax4nMmic16p/7WDP7mo63zo4OvaCwIwl1TitAsKFlwIbzd958rM7ueGtXzH90z/kx/duIrg3oAMXshpexj3NBBP84Zk9hIeTWN1WrHWFeqlQUrBYRaSEDKkUxEeo8G/E46s1x8UMRaV3cw+fu1slkC0bAiS7o4hOEQSBZWfW0fehm9n0jv5yRYYaWXzrK0yaPJtmm6jvohhT17fp5UF7qdsU2zQSmplwVY3oQIzXNh4iHklPKJhnESyj7n9eO/5qH0WlbvxFLqLhFP1dYRJDyazzoZLnWXDcz5SqIwwPrO9ETcumV7ro1HcqH5hMOvo7EG8LcSCUNFFSxxr/d+gjUa8vn3fyZIpddiLhNKHdA+NWC5qqIUsqqbRMSvFT6C0GZ5qM6iEtKagToVGMxGDEBMSpsQ3NUDDOvo5hBvvya5HGAyEnZCyCPuk81aBwWv3XeOA368xt6viQOPBmWx7zOg9pkz3u9ClFKE/cw1O37eaGJT/HWbUcKZLmwEAUSVZHVSxjGXLZtYJNoPCESgTBwvC2PuoWV3Jm6p+8euHTXNg4GdHtQ07IelKJZnBWefnYDYuw//sriLMm8drAR9j12kE0SeHxpxs566u/x2sboPPWUXy9MVEZL6Aq6SvnQE+EZNfEMMpcuGt3e4hkPIOtyIUUSlIwvxyLaCHWMmyKnpVWeTlzXiWnV20nlllAbzDO6VP83Od3HFUzT1O198Uiz73Gseee++djOaaalulrHmKwMzyhw9hEWl5GCA4r7skFpAPxw+5Mkp1hWgbyrSYdpS6uP38GK2qaaBi6kAs//nte+feoO2DkoW0s+f2bLDhhBhv6Y6SVOEJOiURT9Z3gvtdacVZ5mX3aFObNLmPNmlaSaRmqKymZU4JlUh1U1AMC4eGkee9zOURKQmJge1/+6teUhxH52Ip63rpSf1/qp0LdqRXg9k/4DpvXV+Fh6SUzmT+jlH//cSOJzoh5v817l7VibVyjy81I4fRhF4BGBcLpsuLzO6kq9+J0WgkPJ3UHRmNxqVry1JPfb5hKvJJqsr41Re8Vyqp2TBWNowkTlmqUuN7HsY/LT+FYY+xErKZl9nWE8HnsBHqzJJ0cgpohFBYNp+joCbOpwssJVdcjWjK0B1Q6egeIR9LIRoNZEBCdE0Avs9Z3RkMJyFuRWEQL6Uiandt6CE1QgzPKNBp6Jn7gzjd5ACYUMcuN9IC+YjEmdmeVFymraGqgip5/fDdX/ewvXLH1Q1jmLUF0iCQ7kzz6r+06QUxSWXzVHJo3dRHZM/qy++aW8eCdJ9MTtfOlzz2Hr9AJwWHda9amN/b8iyr41R1n8bvHG/AVOrkq9Dv+86ntnHVZC9ErrybZHUVTNA69cpArNnbpcuJZy0oAW5EL3+wS/EUuhnsiZEIpUn0xEu0j77l6loaT9DUMMPfcqUw7p55XHm3ggktnk8rIvBFMYCtyMXNRJVMnFTK3JIK2dyMnTj2Nu7d28dlVzcRahvOelSPtFt5vWETdJ9s12Y8mKUSb8yVVjhUVpCQkYvuD4343N7lOhGQSbAK+2SVccu0CmpoDNDy+97DGQmNLielgkpc2HWJw4XQef3ozd/5mNTd8/me8eenTDAZBSshoTdu4ZOnZHNw/SF8Wyq0pWh5aLxNMUDKvnK9cNZ9ZPM9wZLH+BU4nrnOnEi4+k82HiukYjHNozza96Z+jFWaYSR1ONdbqtTNz1afZlf152dMfRll0Aet659B1aK8+TmMXc9mxcWfLy5qSVTawiflKulkW9ETw6onGUJVUMmmFdFImnpRMdvXh4kjJ/GjCSDTG9WADVbQcUerneOI9RTjfI46t0fw+EsGEv59ttqlpmTWrD2ARLGSGEqPln+yEo0kKckImFUzS0TKELKvsr/RhFQVC0RQ9nWHCWaEs0SEi2JxYrKKJpzY68Epal/RWEpIOX5UUVCkf/qhKKpHhJFJkdJVxuJWjKh1b09Fe6qbutMl86JLZvL6+g4ONA8RbhnSxtYYBrr3xOYrmf53099PEWnRTnETHCAj6ZPHVK+fzZ4fIu81Bc0Kx+x1Iqou2Eb130PDqQf782U/zifDH+fwjMaTwHqaeWsiSgrdZumQuHT1hiOlJKtYfx5aFcRrXlBlOYfXZqT27jmBzkPRAnFkr61l+Yi39Q3EClT5WnjaFfz28i771nWjSxA9ebrlJkxTmzS7nxhPtvLHKyQv3b9fvsVVk+vwKPrlyFvWFg2iopOZcyra2CP1d4eyOUXc/c08u0HsVnZHjftjHhui2cdK18/jqJbOIpDW+9P3XiDQGjjkZjK15j23sW312ihZWYHdYGdjeN14BNrsQKi9yUX7KFBqfaz7qa5VCSdbft4PNRS7sRU6+98d3mbP4Bm7v/jSFbY9BRsJSPQW3RcTndzLotiJl1YWVlKJPwqre9A53jvDcjm4uXXw57a27mDmzFGnNLmwzyzkYqiSWSTIYSuTtBCw2Ec+0It2HoSdiCgrmjZFgoWp+OXt/vhWAS26tYlXBF1n1QBuD/dsJdY96O+QuNjRVL+Ot/ddO3rYJyNGMaWqvpBQTFps79sb45xH7DJKdpGLJks/CAzHSSV0pOJNRiI3oDWTT/nKCd/9Yw1gMiE4RW/b+CE6rKbkvhZLmfPT/Uhx9o3mihDBGdmJcTERLzzZ8DH/gyfXFxKNpBrN1NwN2aGx91LSMIlqIDsbpBqIjKQTRgpRWiIZTKJKq1x3L7fj8TkorfRQWOSnxu3A5rKiaRnAkSSAQp69zhNhQgvRQMo/kA5j1NzmaOWo6/dGG6LZRPaWQOaVeumeWUlLmZmNKRgrrK3/RKZIcSpLsDOc1wwz5gJGURCadv2WOdYT55bNNtDcP6mqMfTGevm8bb75RaNZ0uxoDPNO5grfe2o0iqaS+/0lmzXiFKR+eQyCLBskNW5GL66+ez8tvHOTg2nZOO3kSyyYX84edPURHUlgFAYfLatZmLTYxb+U6FoygJCQOtA/TN3u2CbcVnSLuukKmTPKz1P82WtM2LIvP5t2+xWzZe4gRA1MvCohOkdmnTcHmENn2xF6kD6BnMDYEwYJoyeC0Okyo4rGgSgw/jFzsfu44uCb7qVtWw4pTJ+OwidzfFRlHehJs+g5t9RsHdcfAY4RWa4qGs9zNnJNqGQ7E2bern7tklStP/RyzipOompWW7gTJuH6vjF6Upmh535Xqi/Hqk43s3N5DoC2Ev9iFtcID5aUMxNPU+BzY60tZW+Q0d8Gi28bS8+rpPDhEZyAOZJ+HnLlBcFqZu7CS4ZD+c8FPPsaqVW0cbNSTv5qWR43qJQUV1dwVaIo+L5jAE69+nwinkQ8DPzaSi7GDUXJ6xJqky7NIkbSuMZRdBBpOc6Y/+3GWdIxnQ3Tq1rcFU/xMnV2G12snFstw6MAQIVUDUsg5JdD/F+LYykfvlQSO4vOGFaSzpoDTzp7GqXMq2HpgkJ3ZlUZuGNh9I3PLsQyRgVgebNVT4KC43MPSRdVcuqCSydKzaAf3klnbSOpASJedmFSA44RKLB++ipbkSp7a2snWzV0Mtw6byUGXSuCIXAI4PAs79+/HkmXkWIZ9O3v5F2AVLUyr9bOzyEkEcNYWcNWnl1FfXcCvv/8GFQvKqZzkJ9gXxVfopHVHHz//5xZC7aHRxrKk6sqP/24wm6tKQiKW9WEwdhOx/UF+/ZVVunGO28ZHfiHw8807eezQMJ13rhnfzJQU4slR17zugRiVhW5aN3eT6ovxy4YBvS6alnFN9jP/vHr2rD6QZ0aSK7ugpBV2PNXEl7OQQ/0B0JNeUYETgp1obYewLJJ5Z98Ae7b1jDLDs4nZ5hApLnIhOkXkY5iwjyaUhMTmp/byqR29qKqmS6ofLWLNIGuVe1hwwXRadvfr5b3cMRUFFpxfzw+vraTQ3kVcLuU/5W5i+/OPs+iauZxzWh3//MdWwjv6jrnxKNgESmoKuPCUKRzsjbB+fQfbX2tl/+5+aqcW43DpNfPB/iiIAoVTi0jGM8TbQiaCyHyGmoPEWoYRHSKN73Zy6K6/YRVU1r1+EFlWWTa/kplLa9jRMowmgXdGMbetnM6fLdC1qTvvnTcWks5KL1edOAmtCOacWshzkcuIR/cjRzOITr23ZEeXp8gEEyCoJtTVKPlaBAuOSh9l9UV4fA46dvTqC5Kc3YLVa6fq1ElMn1vOpmeacNX40BSNSGMgO4+gF40TUlYex5KnkJArpf1ByOZYbPoOoWCKn8sun8u1C1WK7O0E0zN4YmcJq19uJtw+gioldJb4B7zgeb9x9ElhjF7N0cgATBRmPyEl0946zHAoSXtzUFfpzIHMAaYjkSVbilDTVoRYBgQBq1vXZp9UX8zSeRV8dOEhtH/cwUvf3GnKCozGANACvMOkWrjj7+cS+NqP+OtbgzTt7iewP4gUTpv13iM1qWxFLn1HkUNmspe6qVxWTehQmHjLUB5jUlP1xk9wex/rdwcQ3TY2eu2m0JkczbB7Tz9iFo2TTkok4/r2vKbWT0dzkP6tvTjKPcy+ZCa+QiftzYMM7da9DxAF08h8om2vie+PZehe28HNO3SE10T6KOlAnH/9ch1KQkIKp3nzwZ28U+I2Sze5WHeLYOGUE6rp7w7Tmx07Q4bEeDaUlD5OsZZhHQ1mE02Dn7buEfZOu4nyi69iY5uVPbv3Ee+N5nNEohm2P92kryIFgYIF5SR7ouYq9XjDQBkZSe1on2PDq0B0iBTPLOGac6ZzfyiZ9WrIf08a17Txu0Inp86bQSieIT6GVGURLNRNKaKu0PW+yEYGn8dT4ECwWIgmMiTjGTJDCYaHEoTbR/QVtsvGtMWVlJR5uOrkKbzVNMDLD+4c10PRFF2MTkGHjH//vi2UV/sIBRN86dqFLN76TXxn/JTtj+w254RNPVH27ug1S165VQXBJjDrpFoWuNcQXFxK6a8/RpXg4NSTJ/Nsb4SiEjf+YheCIGBziOx5q0P3+s4uHo0+lyYpTJpfziUrZ+B32vhzfzTriTA6FoWLKnjmuzakv/6U13/8F+qL3OwfivPr775GyrhOSUFRNSzZpJC7LDJ7jsfp/Je7S7CXupk6u4zrFsp4//1dIlv7KL5gKldc+Hsa9haSHEmNCn8eZ+/ig4pjKh/lNUuyYTZ/OHyDJ2+iyikTtQR08lPeZJxbulFUNEVvFOu9BgnVUE4ULQiChdISN/Or/Ah73mTjzyZKCPnR1Q3/ufhNLv7UXn547/d4Y95S/v3cXjp39SGRhYcZzMYxN8liE5l82iTiUX2SNyZcm9/BtFmldAgWUj0RrH6nLgmsjApU6ZNdxjS3N8tW4RS9nSN86dqFvDujmM4NXQzt6kdwWEmnZORwCv/cUj5z84lcITwEwyF6r/wW973TQ225l6vnp/j5qiRv/nbjER8o/YWXybwHxDPX6CXVFyM9ENeRQlnfAKO5nuwMc/8fN+lbf5uAs6oQW4EDu9fOeRfMRFU1nvvbVt0eNT1aElQlFSmSZt+ufv6WkLDbRQK9UfpahrAVOJi8uIquxgCx/Xr/JBPU4canf3opd1xeze6Aje9/7vn3jRsf17c6xv6Bs8rLTd9awfaGfhqebWL4wBC/+dsmIgf1xrjoEM1dBOhaRBtXt3Bgb4BwKGkmtFxE10t/3cJqt+0ISLb3CFVlOBDnpfXttDQO6Lo82fExSi8Vy2v5yUdnUpl+jbZLb+T2F7/Nq0+6Dn9MRb/XPY0DDPZH+d3tZ2O7ZBn/2SQze8Bhrt5dHjsbdvQQzoIPcheNBrnwpgtmIj9yD6qsonV3MmvxEG3DLpxOG3UzSigpdJGRFGrLvezb0o0c0xcXhVMK8fgcBHoiZIYSTJ5WxCmTCgGwGa6L4mizPNkfY3P/aSy/7Awuqt1H+oe/ZNYtF/LbgiowEr/BWFbQE0qOmKARx5MQ8kIQsDpECgocFNoPEdnaR/+OASZXefFeNoDDZRudV0Xh/5kS0jE3mnP10jVVA0ED9Qj6OWq+dknuakRN5yOCDlvLV0adiQSbANm6oJxWSKVl0ooKqkrqGMypXr6vnxuufZ4V55aweU4Z/VmrTUtCOiwUzSJYKC734vLYGbKNIoHSgTjbX2tFSUo4KjwUTS3SPYyTEoMtQ6Z0w+EIWJnhFDu7RxjO0XtXUmlGuiIIDitXXDmPK/2r2HLy30kk4cxH4/zgpBNBSpH87hP8+Bd3sfYv1qOi3h/Lg27wLWZdOpOzVtTxzsZOmp5rNpN3vC2E6NBrpjNOmcS0qcV4XDa+tGgHJMKsmzmJob2BLAxv9JhKQiI6EKNVVhAEC8mRFJlQiplnTuGmS+fy2rRuXvtdCE0xej4KQ4E4acVHkVNBdNved1Kweu0m2ig2gRDd2L5I3qJAsFA6r5xb5u1mduXpNDzfjBRKEtqtk9SMPovJmM0uYOoWVuh8muwiaCwMcazQYS4L971QXvo7KODxOWhtChBtGsw3kMkuquLDCdpGrFSEO9j8rsTUQy1oyoKJn8mcer4UTiM4rMxJPMBjm/SdwO5eHSps9dm54MKZ+L12dr02yr42EoLVp2slLS/dRMs/GpBSCmUZibhcSktXD3L2/tfX+OkNxmnpGkFJZxnSooVMWka0CuaYpVIy3ZE0CUkhHh1//5PdUb77s7UsPuN07l6+jo7nW5h1Rj1QNX7MjDjMRPxBkMgAFEklkZAYTtdTeMFUJtf6cJw5k65ELfHoTnNnKDpEtKyiwv/1buGYGM1GQrB67SYCQKfrZy3+xrYQVHXCLZGZHA6TDMbuOCyCxfyMmv3ZkvVBbtsfZEOBk+Vnns9J16zjxb+MN7s4XCRX78c9bxMLpl3LnvI+ArGM7qssTZy1NUlh/5ZuszFlhAFFtBW5OO/WpVx10mRK3SLDSZW/rdrHntUHyGQT4URjEW8Z4r6fvoWSkLCXutGcIlI4zczlNVRU+phf6yfz7xdpbdd/J/rYTmyvN+P63udwfetGXmifAjT8V7afziov//58AuWRO7ju09/j4o0erF47Nr+D0OYeNFUv5S1dVM0p00po7I+AtwwSYabOLiUxkszKDWtmcjca48auSZVU1JRMf1eY9mCM1BjkjaZo7HuumY9s7kZ02cbZsB5L2IpdnLhyOhXFbp75w8a8UlQuYsXc/Y6Rowj3Rlk/dA5PrWvLY1DnMmW9ZR48BXasVhFV1Zg9q4yMpLDf0NzJrgrNmvnYxJRdYVv9Tp1sdhjpbyOsPjtnnF7Ha6+3MJQlRhnXAzr8Ot4a4u77t3LnZ27jhnXDvFn8JaTQy4ff3asaZIEemqqhhYapKIfT75jPBX/ebJIPBcHC3rahPD6OYBOwl7q57KYTaGoOoL18P9u2KhQVgmPhj/njC/vobB0mE8vQ1x1BmllGPCmxd2efmVw1RSPRHyPBKLmro2WIp1SNaDiV18cyxkyTFCJ7AmwJpeDcEuqumIHlnA8h/bVLVx1Vj6wl9kG8O3mLClXnTHS3h3ixuYyLL/4ZniuCdKVm8NzWLgI9EZSkzo248AsnIssqax/albdb/7+IY0MfCTky1gYpQ9RvBllvlfxSUU6yUHM010XL6GpE1cbJJY9dSeXxG7IlJQUglKR/3yBvhVNYxZnc9se7uNL3I1b/rp30eywkRREcJ1VCcS3RVglZfu9Gj6ZqRPcP6X8e83AZiJzyYjdLbC+hrn6V2gIv1537E/Zv6c42tCc+riqppAfiZu3XWu5BlUbwF7vxeew8ta6N0z9yIae92o6rxEXBH79Jj7yCn/6nmYMNGlJkrV6uUie2jzyeZCFHMzx2YCHn3/BL/vJmFFuBg6Xn1XPGomp+0foKmWCCdCDO6pf3s3tqEZFQku1NBZxz4hUsmZMik1bYG9JdzQzpABXQlFEjHwN+PLiph7/tHjB/zhujlJzvgvc+I9UdYePje3CUe8bpARlIImOC1yTF7DWBfp+iTYN841PPmM1Io49i8zvwTiqgvLqA2bPLqM1qdPUG4/T0R/VJcDill5XSyrhnPO88bCJWn52KBeVEhpM6TDYrgW1ArQ2Agabo2kpPPtag6weNYZkb8iOqpBJqD/HLf2+jevJH2PXEOlOSfdz3iznsX78Tf7WP8MwbOWf/dL61Zi6Jl7ea3IZH7lmv82myLnkAZOXa+wZjCIIFvB6KCuGUqyv55VsHadrea157d+swD/dHKa30UV7tIzaSGlUGVvMXj6HuCJFhnalrwGJzFQyMUBISTw6uZPZtV/DXB/dhL3XjritECo2KEP4vVuOaoifVkWCC7XsHgArcrnIGhvpoagqQiqSxl7iZemINs6YUMTCUMMuO/5dxzDsFBEE3T0EwHxztMHISmqKZRiuqBBYBLDnGK0dMDLklKmUCmFiWEagNxJHCaVZF0rR3TeKGLz3O5T9rQnzxPlp/sZEdW2WUMYuDMy/xUfX1FSROv4WNvVVs2N7KSF/0qMwpDAq8MSZjr7t/KI7W2UL/g43YCxzMvS9joobeK4yEWTWlkO6ExPoHd5n6SL+pOoeLH/8obfE0f/9TA907X0aTFJw1BdQurKR3f5BUT2QcbFN026g5cwrDrcMk2keOuZEph1P88Y5X+ZNNpHBOKd+94yysgoXmvkheQ3hgczfBPQM4yj0IgoVFFW5qp+2jrGAOBxsHRj18c5zscneKBjM5V+X0g9j5jD2GKqkku6OHXY0ZDUJHuWdUHFEZFfXLJZAZvS3RbcNe6qZ+bjmL55Rz6Rw75dZdyGIBe4LzeTEt04m+ojeUZuVoRkfOTFS+yUqmRLLMYUOiwSJYWHTjAopKPWx+bp/ub5w9n5FtvYcvBWUTQyaUoqdhgN7GgG4/asAvJ/g90W3DUeHh9A/N5cOnTmUoJfBM22lseXFURkOHcKbMJGLMDxZR5yBte/kApTNKsNx8Kef/YQjxyivp/KeOS9VJXAKCYMFqE1l+Yi1FBQ4G+2PEshwmo+eCqp+nkpT0/1KKOXcYHKPcxKAkJP55/zY9IQF33HEmiytkXj+o8PdfvzOKhPsvhzFnOlxWli+s4iNlL4PNwb6yq9nbPEhJTQHf/uQyFu7+Lu/4fsSffvjmBwaiOJ44+p6CkI+osQjG34Fl7FGyN1ETNDTVAigI2Z6Alp2TjshUzU4cRrNWlSx50DPzHFBR0qMkl/bmQV5w29hQWMqyE37NKe+0Mz2yD23fbggOgdsF5WVYZiymU13JyzsGaG7roHVfwPREMLa/Y0k4Y9UWTaq9YMnL7pFIGktlDUXzSxG8djrTBe8JbctDckkKXr+T8mlFtGVRO2pK5o1XD9DaESIeTdPTMEB6II6jwkPFJD8nL5/EekmhJ5weV2t31fq4/VPLePDVA+wcaEQ9xlq81e/EVeNDjmWYtbCScyo3kRHLeOQVOa/+aaymlYRELJomo1iwyAkKnTYcLiuxnOfH6rObCwarX8e8azlQYItNxOZ36Ois9yidHGlMc++NATc8UpjcGEm3sdTk0Qb5uOMLo8+DxSbiLXQyqbqABdV+ysNPoG7YgDiznprp0yj0OnC6rBTNLGHJwio2be7i0KYuXc5iIkc4VUNOyObkZZy34LRyxsmT8blsNGzu0tE3ymiyeq9QUzKScb9y3PXGelAYqsHV88v52vkFFK76IpbF80lN+Sz/KXGTHojrtX+jxzgmIQg2AQQBW4GD6XPKaE/MZOqH4uxLrySV3IjNISIIFgRBQBAtuoVsVQF20YLTZSWdFJGz0GuLqOS9//l9l2xTX1RGiXjZ3l28NYTVZ6d+eS0rhUdQntrCRz7ySf5dpXsh/Dd3CnnoK4eV4nIvH5nVwGOOX1NUCBds6cdfdAGJeIYa3wiZLR1MWuY8Lpn7DzKOvaeQ8xAIOTXSsbpCxvZWTcuo2WxuUdWsuMT4h9DcLWR/tvrs2EvcqGlZR/BMQFQxEoOqqGhuGxddPJsrFxbyUlOEn/1+AzaHiMdXRVFpPS6PHRKQ6VGIvZUiFHyL+GDChF8a29+5l89CVTSanmsex1mwCDo72WITdeMSVcNR7mH++fWEgnE6N3Rx6MAQ7559Daf8fCay6uC5dd2jq+QJwjAQMrRR5FgGu0PknLPr6dneS6ovpjMwm4IEfA7KKn04yj2k+mLYi5ycfuoUbl3WTUXJXB4Ip7ICfKPnrSkanZEUgd7IMZOiBJvAyTcu4K6rLTQP11PhUdBaXkabdRFtTYOjFqTKqF+CmpYJ90Z5aEM7J885kbb+UN7zI7ptXPDpE4klJIp8Dj5+ahXfuX83TU+OSh14phXx+W+cjs9u5Y//3EJwS+8xbfmNZ9RR5WPKSTXY7CLdB4JEmoLv2Yw3eDFmkkuPyjmM3e0Z74Ho0NnCteVeJhek0d7ZRezVdnyCBeuMNFZRwF/kZub0Eq5f4sRuq+OhLT1HROtpijzagM5+TpVUnnhyD+U1Bdjtem9POloDekVfQBlQzOkXTOerNy6haSDCP+9eb+46DAb++Tct5tNnlFPw0Bd5/NM7OHnFduY9WUh5zQzdKSznfR+bEIzycu3MUq49dSqbukLsdZ5OQ2snQhaxZLXqCcFqFXF5bMQyMklJweG04fNryLKKlFbIZGSUrDSHwSUwACeC14qj3EPVlEL6Do0QbRo0F4lSOI3gtDJ7VhnJe1fT/cYhps+fjttzGqEjPgEfTBiLBdFto6KmAPnBPwMQGoHMU+sR5l5I78Ehbn9A4Nob7ufvf1h/zB4N/6046qRgIiuyN11wWrE6RKxWAZvDitU62mXWb6iMLKvICV362JKWUSULFkXTtUvIf5hza6xWnx13XSELltcyFIjTtTdAPCHlrSbN31VHJ6SrF3kob/0TF8/5Co8E4sRCSUKCQM8EYnTjapaKiui2UVVTAMC+Cco9gtPKOZ9YgiBYeOvJRqRwmhU3zOecxTW8u2+AzncOMdIe4t4nd/PytCJkWaVhS4u5VR8bFtGCp76IUy+ZSSyWYeP9O5HCaYYDcZafN5PXFlXSNdxhljFKyj1865qFAHzyyy9g89pZOKkQ4c27ufa8DxG6ch4P7B7IKyElOka456svo2QJO8caZcVunC3PsGTKPEjHSdz/Nu6vlYAyiuiwiLrPbtmCCkKHwkiRNFs3HKKvN0ImrZCKjyYPTVLo6Q6TSsoM2QTW1/rp784XvhNsArNLPMwqaoZbTuI3yXeJ7AmMS9JHnARFAWe5m5uvXsD8sgyr90/h/ns2kOwMv6eLmpaVCods0i726s+/VUSTFbO3YZZTBQFV1YinJILJAipOO4eCOTOhfBrt4SKs1hBl5R7au0a4o2mANkPq4j2Y87kNY4tNN5saOTg6pRkyCvZiJ0pKGSefkbcDNaCYCHhnFPPwR1oJfuXbnPDlM3lx7nl0BROmlaq7rpAfnN1G6mffpH9rHycssVB7w2zixScTHenJO35urzFvfrAJBHoj/P3FvThcNtxuG7FYRjeVEXTlY6tVRLQJOJ1WWnsjJFISngIHTpeNTEYmnZTJpGVUVUORVWIjKTQpO96CXlY96cw6fnrSZrSRIHe2XMrah3aRDiZNqHvz/kFcd3yS6Te101XzZcK9h2+uH29MhFzTJIVQMI76qa9z6mO3UHXxNEa+8FcO/n4vqUCCfR0H+dEL+3V5+PdAmh3tOVhytKHez/GOqXxkvBiCTcDusuLy2LE7RGx2K/bsllBVtazYlC40ZajCa4oudWXWZ4WJrTMNVEPd7FKuPG0qBwYivBbPHLEOqKkaSkph54DAhfNOYUebvuLWJTMOAznLJcllz0FJK2zKQuuMFWPueTnKPXz/ojhxuZTGXSUEmgaZVuunoX2Y7Ru7TFPvnv1BhgIxfaLoyrprHWYCcBQ5OXluJW6byNYn9yINJxnojWAVLEyuL6Z3c4/ZeJNllTJXO15Lt1m7H0lJUOiHeIiZlb6sXMBoiUhTtPdt5K1KKuvWtHLdHV+hL5RmVnESn/th1Hc2ctoFt/Ny1ljcGJvpc8vZOZxEjqRI9MfoVDUEwaJvi7Pfr6QU9r5y0Dz+wXe7xiFJ0oE4T246xCVLZ2AVFHzFLmI2wZyojXt2uJ6Dfk46H6JrOM4UfwmSMpjHrzlcjL3nzpoC5p0xBbfHTpHPQTIt8/pftoyWzlQVTVZIJSUGhhK0FDjxV63EWT5CKFVCcyBENJ4hlVEI9EQYyBIlRaeoS5sfxYtrlNPcdYW6E11SNm1CbX4HFYsqCY/xi87V/srVB7Kg7xQom0bJxxaRWnod03oFnC4bLa8eRElIui5WOorFKVIyr5Sar55K8uxP88hOH5HeqLmjgHxUYq6BjeCwIggWQsEE/mIXqqqhKhpOl9VcRNodVqyiBasoIGffD6/Xjiyr2DLZBaddpKTcQ12NnzVrWomNpMzrs9hESvwuoj98mJfu7eXHHU4+d8Ep7HhsjymR07lvkAfbTqXCfy4NmzuPW7/tmELNOiP2x7h/20yufOlN3h0SeOWVLoZ79Hkhdwd0vGHs8oSsTtSxGEPlxjHtFAy/ZKfHhq/Qib/Ijctjo8DnwJV9CGRZJZ6SiMUyxKNpoiMi0ewuQEmBYNNAFXR9EzW/t2Cs+CvmlvHFqxawsPlHnLF4OQ7bKdy7peeI2ys5luHXv17HA3WFDHas1yeiI8HPJlgtaJJCuGHAPJ+8EAVmnj6Z6Ce+hu+Uas444xf8Z2MXTz/VSCoQJ9UXM1cGSlIiqWp6OSgHIgjjZTJiHWHe2tHNNy+pYfn181n/163EWobZ2DFEVbkXR6nLLFUdfLeLn1X5qKuehduznxnzKzjYG2Fw6W10Rdy8tadLlwwodetyFzkll8Mx0N+rmTu0sZtPfPoZLDaROWfV8edaH7t+sIkfvr2e14tdpvtaOphkf0O/Xu7KkhPlhO6kZWjUG4k4d0VrwFJz70smmGDtQ7voaB3myotmkYzrtVZDIM/qdxJtGjRLPBOdvyqpJDoj/Ov37/JEtY9MLKOX1o5ylZj7PFqtAguml5BMKxzsGhn3OSWlEB6IsW9vgIHBOPvKvQiChXAsRF93hHAoSXQwrr+kgsCJV8+lqtzLzu09dKxpH21cjy3BZq/L6rUz5bRJ/OIzJ1PhbOK+rcU8ef92lISky10vqKDj4DAj23rNe+qsLaD+lEn0d4cJNwVHSZOqRrR5iLNuzmAruoglSYFfuu6FlZO4PLiEwKYeMrEMa0bOp+ozl3IonKShbYhNPzvESNeBPH/u3F2CkQiM+cHmsOJ0WXE4bdjsehKwOgWsVgdOhxWbVcBpt+KwiwgWC1arvtvyue1IskoqI5NMywgWC1edNJkl/nV09k+irXkQq1XAV+gkFEzQeiiEmO0b7Tj/L/yi8WIuedlBJqR7t8dahrn/ng0UTPGz6KRayuoKdeb5f6GnkJssjfsnR1KMHFJ5/rm9vPa6nXg0TWY4RWYoMbp7Nzkl7/+c7MVOpp0zjcsunMmLrxyg/Z1DaO+Xy3O0HxQcVt0UxWWlqNRDcbmHqnIvRQVOqovdFDisCBbIKBpDyQx9g3GGIykGslhtXTM9A6qKKglYBA1NEBgLDbLYRM46cxoLO37Bf854gTMvWcvHXvw19xc587xpx4YmKUT3D5n6NceSeY/lZjjL3VjnTaI7oGv0jBhJRNV060OvnZMum01FsZuXn9gzCnvMMRDJJfSlA3F2b+6m/MS13HPpVFY84yEdTPLGm63MnFuOrchlTtzJzjBr/7YNq9/J1JNrWTqnnKGRFGs7NCq8MmctqGJqdQEnTiriW/esY3BTD8ZOyej9CDYhT77ZqP8ejjSjSrpWvaPcwxnLJyPNuYMlk/8OJZOweiN6UlA00n1R+oOJUZ1923j9mrwxP8LkbCSZvrZhRmJppOxOxze7hNc/3Qid3Xy48nraX2+bsKRonntKJt4aMss9YxuVxafUctWH5tN4YJBtj+weB1E1IKhb+2NUlXtpax+m8eWW0RVYtimtJCTSQ0n6JJVAT4R2lxVV1XRHr2yfyOq1UzilkFkLKvjFyja0TWuJfOWLXLk/SKIzYqqpIug8DjUtm/7WNr+Day6bS9nPrgKfnekfvt/cCaQDcTa9dlAvmRi7BIeVK245ka9Z/orlI8v48aZlrPrNu+YiQUlIxA4M672sUyahJZJYsk110W2jbl45DzzbSM/+IMmeaPadzYdsG3Im5mLRZcPqs+PxOfD6HDiylQSny4bdJmAVBRw2EbtNxJP9O5fDissuYhUEBAuoGsiqiqRqpNIy0bhEWlJoHowRl06jxB8gM7WI4iIX5yyoYnNLkIaGPnbc/hRXOq7HNrOYkOLV/TuyngpKWiHZE0UKp9mUJUn+t5u5+vutgzRqllRid1jp7wozdGBo9N5+gBpLgtPKWbcs5YeXprEL+3n0P+nj8ng+6qRgdes32VNgp7jcw5QaP3MmFTKpwEmNdwCfrQ/RIpNWfATT09nvstMzktDLSRkZKaMb4hiIAlTdyAI1X1LCIlo4f04FrRc9C8Dbq6Jc/9SD2EuvJd565BaRJikcy/AeU2ZWVA5u7SX+hwdZ1Smw8/ktOMo9ZtNLdNlwFjmZs7iKz5w7g2LnAAPD9Wx8ci+asa0XRPOBMR8GRSXVHyNScyl+DmItUEgPxOlrDODy2BFzlDtVSUWLje48OnojtLUPs2heJdfXrkdd9SLJq7/FU3shOZTMMxcyml6+2SW6J0K2sWgvdlI6r5zgvuARTW0Ep5XF1X5eaBF4J3gz8X+kkUID5r8bGlVmGBozY0AJqmQ5uoStqIQbB/nnnW8iJ3QpjeKaAuS3G/SxOIYV/0T32V1XyCsf38XmS7/GLXcs4erlN9PzRtu4zxkckud+s0F/kbMTq0W05JHxDE0nwSaQNGDbWaAFgsCCi+q48PQ6gpEUaCokktgFXULEUeHBN8XPkpMn4XHaaNjZy0DLMMmusLmalBSVeEcET10BCUlhwYopbB9KkOqLEWkK5jXDLaKFyxfXsHbWS9TPe5M7Xv8nbz1XRKw5mDcWSkJi0+ONfOv6LzO0Lk5w+wEcFR4uObuep15qJtExkr87zwJBjNKNWTZyWPGWeaicVKAnAqcVn8eOz23H6bAiZKt2NlHA6bDisVuxixa8disuq4BVsGAVBFRNQ1b1/+KSQqpAwW0TiaRlVA1uX9HKnsgKXmvsR9XAYdeb1Pf8axszT/0102oLaX6hXweNiDk8EEX3eYg0ZT4Qd773CotgwVHq4pbbV3DT5LfBW8wPXpvK2kd2m6Vc4x34ICQ1bH4HkysL6IlX8MTWTgY2H1/T+ujRRzYRm0PE5bFT6HdSW+FlRrGbSncbnsgOtP5ONFnBUVJKTU2YjHIKqqYR8CYJOm2IVh2TbGCLNUEAJmi+ChaqPB28uGl0BZja1IvdffykjuMZeE3VSAfi/Hr1IL2HdJGxKUurEa0CFdU+/F4HQyNJPnXuTOqVZyDj4PrTLmDP1m7C2UnDOAdN1UznK6vfSeX8cp5udFJacCJK8k0QsyieUBI1WzbJE72TFGLRNKqqER5OsnVHD8xKk9rah/v6YR7+10F90lfzV3ZWn51lp0+hq2OEfTlwxqrJhSTiGVOkb6JxSw/E+dWjO4lH0wSyyCM5MXHj13ghjcamkYxAb3yng8lx/aRxtd4sos2UUlc0BpoGafrNvwilJHq/uuq46rA2vwPaDjEyApqikkod+SWaSFYj9xqVlIKgaKgp8nZHmqJh9enPbn8oya69AxT7TufU807jme06Ucs/tZCzzp3OZ09LYxOiPFY2g9W00BmIo6ZklFiatzd3cd23LwKrSKHTxsJZZex0tOjnYezOckqTuwYinHF6Ib5JBdiEJPNXTGFLx4hJgjNCjmXY9HijCWaQoxnWbOmip2nQbH7mIY2yJTXDYtNI+MXlHpYtqcZltxKOZfB77RR57HjtVjKKSkrWn3+nVcBtE3FZhWxSUBAsMqJFH39Fs6FodhKSiKpBtbeHaKaKvpiKZvOSUTTCsTTb24aIJjLUTSmirMLL3p197NnWQyYpT6h0bPBg/ldhK3KxsNqP1teBpUYkFs+MLxd9AAnBGP+NW7rY3tBL89qO40YxHXVS0OGdDnx+J9VlXuaWeql3r0M7uIPkQ+voWat7E9ecXI3rIycxeZkdWEpfOMnAcIJIyKZvp8Wss1fOeecijyyiBZc4nPfdakpGtk+QQHIw6P8LhqIcy7DlxWaK64tZedEsvnh6BEemG6LNUFbHI3vnMHvP93nsrJdwOOCye57ghuvv4V+/f3eURKSMlhzUtIzVZ6duZiltPWEe/rsuIGf4yGrZRq3+Ejqw2ESzhDZ0YIjQ3HJcHjvdB4K8Er6QC3/i4/6ddUT3t4xb4VlsIrYCB9Nq/Pi9Dg6sbkGTFDLDKVr3Bd5zu6kkJPY93TSueXk04azy8qdvn0ON9yB/XT+LZ/6wESmcNstpnvoiHvz9ZXSMpPj5n99leFsfotumSz147ShJCWk4Saovxmc+9sQ49Nj7iVjLMN9f+SUubPwVX3+rldC2nUf8vIm0mSgMln3umIxh2Da+0creNW1kggn2rD7A77125FgGwWHlrHOn8/XT2+m+/A5Eq8BNz/+KQKiWnp39uqNYNMPul/bz3aIrqCzxcHBHG/1dYbNBbzKxbXpy0lSNv/52A0X3rKbMbWfgUJqKMr3RbkyLBmKsYHoxiUCCTNZzOx2Is/mBXaM9oAnCXurGU+0jlWWqi24bJeUelk0upsobpydaQqlbpMLVhK35VSx18+jlLEIpG1bBgtsGdiGO0zqCXYhjtWSwCnrSVTQriuogY/cgaw7cYoiM6CWUsvOfpukMhYOomkbPQJRzltZysfVRtNZ2/njObbzw+G4yIR3pZy4+c/of/0sOQCaY4HePNzB37rV0bB5hz8t7Ta91I443GYB+XVI4zYHVLXk72eOJo04KdocVt8dOgc9BZZGLGu8AWuNGUo9u5Lm7O8zPbdvaxZVeG65psygqqMLv9uG0i/pOIefFOhzDV1M0+hKLWLQAGvbof+e5cRmZR/JXav5FFfzr5yt5dlc/T9275QMjpBxJEA1F1zIpKnVz+eIatJ99ldhAHM/nzyVefCoLqwrYefVLAKTTsPpbDZw/4OWZyYWINgG3x46qqsQjGaIDMTLBBNJwkvUP7gLgqq+cwjdP2c+64On8/cndLFhYyaGuMHvaQninFXHOyhmsea2F4IYu0oE4HQeClNcUUDWtmPsfb+DJwlLatm85LPzV6rPTHYhxcH9wFHGVkBjZ0f+ejGuLaDElo49F+12wCVTOKGZqwS6sXVu4/sRP8cqkAiLRIJqil6XW/qOYx8RFALy87XrOaJuFxSbin1rIySvqcNqtrHm9hcjBYb12nuMPbDBajzXkaIZXf72BVzn8y5lLgDN4M1afHatDRM72EeRoZlSW4TA7FyUhmaU5Aw1m9Hg8dX7qawrQuhpp3hqjuAiqklEES37TORNM8Nb9OxHdNnNVb3BrrD473lmliDaBWFsIJaWQ6Bjhzq+u0vWJHFbTM8TczQkWppw8idNPncJrr7cQbxkala6ZqE9jcIhEC/7JhVRPKaRt/yCSquHI9lBaQwkqPFYWlu5GDOxDfmINq76ynbM+VkP1XSk8vqVomohdjGOzJLGpIUhGQZZA0lFFVtGKVbDicPnA4SOhFANQ5XWgaqCqGumMQtiSxm0T0YLDJJ/dw22/72HL1mJ6GgdMEqpFze6cxshgfFAaYWOd4oz7C3pS2PdcM/ueax63iDreZGAuTsRRL4gP8vqOOimIVl3v3OOyUei0UWjvQmk8SOvqjnGfDe8bwpUMYy+KYxUKTLr50YQqqbx2IMQn/3gpjee+xMWfqWFo6W1k/vSOObCCw8ob3wnwmG8Jt71yPpuWXMOB49TFyZ0UD7ciNLDPoWCCfcEY08+diyORoGvK1+kLplletpFdOajH2bMFeqICM+aXU1HqYUatH1XT9XB27u6jY/cAyZ4oalrGVuTiqwd/wGPn7ODca4s545uP89ElYYKpBdzSOIAUy1Bd6qFuRilDG7t1uYZ4him1fhacPpU3tnWxZ1sP6f7Di2mpKZnAYJz+rvzegfESHelBsggWSpZWMWVGKS07exnZ2T/h5/OSi6iXjs44fSqWf3+H6OZear/noKJuLrGWYXNlvTd0MgAlxWBZtAJnjW5V6PLYue6kKczgeZZMu4D7n95D25o25Kgul+Gq8eGs8jKyo/99bZnHnr/otuGs8pqS3ZqkmBIWSy+ZyYdOn8ri8j68/W+Ct4R3kxfz0tZONr960Ky/H01JwJyYUZETMjv2D3LquTdy7sOtUF3JKwOnsX37HpRYWt/h+ewAJszQ5ncgum3m+bkm+Tn9vHpiCYkOv5PlJ09m585eDjzfnIf0yjsnUSAUjLN1Zw/B5qCp+WQkGyMMIIJRDrMIFhIjSbqzYm8WwWLyEBRNw2sLIDS8TubN3XQ/e4BoFEIHR/C0NOJf4AWrA+Q0ZBIQH0GLhSGVhlRKL7Farfp/BT4snkLc/hgORwSYhmApwGnVew8APSNJLPOW0/TyY0zq+QbX/OJF/hbQyZ6KojG2PP1B7hZctT6+/8sLee6tNnY+0ZhXXjT7WO9jsXKkyJUjtxW7ELOWuoY0f65F6fEkhqNHHwkW8z+rYAElg5ZSSCTGf1Z0WEG0o6h2M7vnYqXhCC+NqtLYEkS59hou/8EBnJ84n1Xt+bLQotuGtkP3e2VyDcHXYxMf632ExSaag61K6jh5DVVSGemO8NrGQyy+6kfYhTiPbDhENJFBWXYSJ//wZPZ9eBMAM+46l/8MxZk+uYiFkwpZWNaLBZVAZT2SrGvgZ0Ipc5VpvfhM5s3ZQdkXTkWWVVxdbzDZs40pM2ezd1ULz69qZiTnJbfaRGZNKuT8Kc1M8S/hpVIPL3SMHLYUJCdketpD+WJiyntPYsa4LF9Rx6fPKOeJ6SU8chTsYOM7aks9RNd10b9jgFltrbg8i8wHXFN0b+RH4+uJZLw81DTMxVemePHRBqLhFMPJDFhizCy2UVtXyCG3TXfscogUTy9m3pIq1rYMH/e2WXBaOfvzy/jC+dN5qy3E33++jnQgjuC04q7x8dVLZjFp5AHk+9ex5gc78XrglAd3MPOCL/P9oRr2RtLm83IsOxclIdG0u59HCpxcecrd9MXSvPDaAQZ7dD6A6BQpXVCBv9hFx9Ye5GiG+jPriIZT9L7Tae7gRMFCIp7BV+hk0dRiuvoi7D9SeU1RiRwcJtYVQY5mKJhfDuhlNVMCI0sQs9jEPEhrekhHVGmqhtWrs5M9Tht+hxWvpRt55wEGXu9g22b9+bC5rdAfgEn94PSClIZ0HC0cguERPSEkkmiygsUqgt0OsThacQoLIHokChwuJKcLVbMRyziIJ2VkRUWxFjASBuveEeqL3Lg8dmKCAIxqlH3QZSOLaKFgejErS98mdfoK9qw+8L6l3I/5u20itmIXk5dWM31WKdVlXg52jbBz/SFSPZGsdMrhS39HE8fkp2BAS2VVQxHciLVFTFlSSOuYVXrR2ZOhdBIRqYpwIkYqoyDLCnKOsmNuGBcw69KZ/ObT03lgU4wHdpZw9e1/5I1uL8++uCdv9aKmZZ6a/iOuVW7i8jvDjGzrfJ+Xnx8Wm4inzs+v7roAwWLh1w9uGwd71CSFZGeY3bGDfK55EKtVJDqgr062vt3BLbf+kg/Ht4DNwUudS+nsGObiRdUs8L2N+vQTqOE0dSfN5poTvkXPQJRIf8yU8ljxNR/eK35L/B8R5p0TgOIMWv8BFsw9kz3P7KPjdR0do0oqgijg8thYXg3KH3/L7MVTmDN/IUM3LGHNn7bkNdUM5I9FtJBKShOXB3LHwVDJzHoPG2WR+ho/lUOPccPSa3my2El6zAQ4drdlvJQdA1EuuX4h7kX9WJadSXhDMtuk1H833hbiymteYsYF0/n6jUsIFbl5QVKId0X4y1N76LvwcsL9I0TC+soZMetdfWiEJsFy1Cb39mInjkofciRl8kqMc15w7Vxuf+4LvPOxAB99ZAX/rrqCdJ++6yoqdTNp5AHU9ZsZebuLgYDu5Tf5nrepuLuSZYuv42BDH9JwUkdcvUevQ3Dq8G7Dg3ugoZ8X2kd449UDCIKFSfUl1M0uZV9nWEfjXTCDFTPKuK/UzZ41bZRX+8gYktaKDoBY92YrmSwr+efZHaimjFdVNe+TKCCF07hq7Kz8zIlcu3wK/3htP1tahs37Zyt2MWdlPcH+GP2bus3f1zlAOurIIliwO6x4XDaKnDYIHmLwuRbWvzYq7Nb0bhj/9EM4y0uhtERPArE4BIIo/VHUWAZ1JJ2FMAtYHCJCsRNrTQFadRxKS3CUypS4wCrUkFHcJGWFoZEUb3fP5dwXPgSZDP8Z0O+XYBNQxRxkozBeSfV4VtKaohFqHOSO1xez/a13/+cido4SF7dcs4CzbE/TffNfifXGmPXsZ3lW+gh//OXbJDojpi3sf5XRbGiRJNMyIymJ4fQ0yhYvpvwTMufZGhlsGsJV6KDmqhlYLz2LQfUEDoU1AqEksUiaVFImk1FG1TGzkD3jpG1FLv5V/DeecG/lG+su57vdH+fnhzQ6DrQx0DSYN/koCYm7v/gSd8MH0lgxJkF7kZMf/eh8wguW4XTCQx0PceYGW17TTVNHTb6l4WTew+Ws9PL25i5eWaMjbVS1mZmzSqnxDqM1baX5Z5vo7IKlF3Qy6f4TmFa7kD0+O0LICtFMnjVk88Yumq+8jfrpTSTf0mvHeZOfoiJLKh5bkM7/7GNSWsZx6kpdUyZH0M/qtTP13KlER1KkkzIl5V4GElK2sSgcfgITBTzTiphzyiSktExb0yBPPNVI2Sc/TPP2MKXzyplxfQUbHm4YJ69gHsIp4qrx0dETZuNpd1G0zMbr2/oZ6mwwJRpyDZq6dvfzk/hmZs4r59pblrFpSxcen4NdLUEatvUQHYzrpa5ssjGw6N4ZxcTbdLexwz0LgtPKiTcs4IdXl7FjwMsPv/4ycizDnMtmYbUJLFlQhbZB48SzfFgu/wjxfx4yFWB7GwO8cc0NzLnso1Rct5frL78XuTuK/SMX0ltwHU07OnSEWFYW+0jPmXdWKV+9/Qzqi9w0BWO8s62b7U83kegYIdUTwVlTQMEJ1UTCutxL7cJKAsMJNhwMEh5Okg7Eefffu0wvEyVr4SkNJ3W0kKSYCrCi20bB3FIQBWLNOpPaIursc9dkP5lggpr5FVy2dBJWwcL+3f2ju0ibfu9mzyxjfdY32+TXqCqaIpjPiUXQWcl2UUCLRelrzneQGwjAwNZ+Jp/cp/MhYnG0SAz5UAS5O0o6lCIVSiFnDXasDhFXiQvHQAJbJIVQPoKmqjgqBIrdaSTvPOKSg0RSoql7hMGiLwOwbmMn6WSOqKWi6XyoMSz1XFBLbhzLPJIJJnjj7neP+vMfVGiqhhRJo2qgbd/GO6v1+WLntHs5+8rHGP7eUzz4q3fIDKeOyOE5Uhx1UlAkFVlWSKRkQrE0wWQxZZUzsJwIpXY7xYERBK8dFs5DrlpE75CP/liCUCRFMp7RRa0MQasJdgsW0YIl28i0zJrLwfsHdTONgC6NPVYm4gPXRBcFnaBV08B6J9RMdRDKTAG1yfxI3vep+gtpNHtEh8j5l8zinLmV/PgvG0mMJPGVeZCyUDxUlVhMF8RKBBKQDOPMakflCg0aD2y6P8ojG9o4a+E0ZCUwoc56OJRkMDmVgkkFiH4n/dKJNGzZarJxjZr+osXV9AViHGoZwuOz4yxwkLCJWIRRXkcunNGQibCIFq45q56zKrcQkE/is7/Zw5//tZ1MRuaMc+r54pnw0YYK+tZ35jVZc3X1/RVeptUW8sLmQ2QyCr2HRnQLz2xC0JTRZl0mlCLQNMjHr1vIZfXNTKucy+m1AbxiL9fsEkgHkyahy9jJ1EwtYthjIz2UPOwW3iLq0ikLZ5dRLG1hQdmZesPYZ+cnNy3CJiT55Yvd2O//B+HkVG68Z5OZYNS0TKovxm/u3URZpY/CUjdnLfs15SvsNPdGaF7fwWB/jMWnTmZzID46qY5hkBsS6D+681yW/f56rFMKmHfLTTjE5exa3aJP6pKiu/i9cwglIWH12vWSWXuI7Rs7ifdGkRPyeLSdpKBMgMZy1fq48Kp5hKIp1naMYMminWadN435cytY9eQeBjpCPL+1k0g0TbgxkAdvVSWVAweDjBwaOWI5QlM1VE3/z2K14XSO/0wmlkENpxGtQ6ihJEo4jdwXI9oVIRVKkxxOkslWPe12SIcz+NIKbtGCVVIRvR7w+LFa7XisQfyOctwuG/GkzMGuEcKxDNFwSodwW0UQdIkbi2hBFPV3x0ikec+GIdF/HCvr/2koKpnhFG/s7Oacc1cCa81/WvtcmGsfT/N4TcG43tCxxFEnhUxGJh5NY7WK9A7G2ee2I1iWU1w4m6LzF2C1pFA0O6HMFNoDBTQHwvQOxgkGE8SjaZLxzCiTVlXN3YJ5/GCCG9tvYuX2n3D2d7eQ7u/EYhN1nHZ6jGz2B3zjjEkQYDg9lWXtj/GHd3y88vV39ZfwMAgDQHfQEixYRCuLphZT60uRSkl68yfrPTuQKKV41mIWfmInU5uG8C+vgqJqEvvlcSQso6ehpBQatnSzckktnzvDzdoXC/K0bTRVI9kT5cH1bfzw7g+Dw8PuAKZXLqLuiGbzO2huHgSgsMTN5MmFpJIyIwdDCCkZNa1vtx1VPgqnFzHwbrd5n2Itw+w+FOLMbY9TsOMPPHLnXVz21Q4SHSPsLfdycNESHK4eRIeInIMIAkyuxWBzkK1+J4HeCOlI1rbSYcXhtpkQutzFgpqSefbVA1gvmkP3UBxP42/JtA0zuf67DDT0j7JAFd1Detfzzabm1OGeC1uRi3NuOYFbal8nZD+XP78eML2nN/bEKXLa2LW2nZWPxpBjG/ImXU3RdwtDG7sZzhK2tj2x10SeAExdMYWqUg+QLdVlfZo1Rcsq0ypYvXamnTONU567lafu7mDeHFh4C7y45qAJVdQUXRolvCMFom7cs+mFZl2qOtu/GduvOBw5T7AJTF5cxcULq9nYMcSarC+Ds8rLHTcswWdPsOpJiO4f4s2uiIloyj1uqi9G0+utep16ovcmS76UZYV4UiKUkqG4mskramjYk++A6CxyIh+KEHzxII4CB1a3lWhXlOGOKCNhGA5h9iftdij0J6kMJilJSPiGUzgEC4LTgUVTKaz0EXeXEkrZTVkdXXHDgt1uJWMbXWghitSfWUd5tY9tLx8YFTO0idiLnFSeUEUyniG0eyAPnfX/Ypjlu1iG9U/u5TODddzbfDP/mX0/AKed58YthqiY5OdQVm7mv1s+Sisk4xKCkKIv24iNpyT8bjuFzjlYBQsZRWUkJdEbHKY7EGUklGI4ECMaTpmZy1QmzZncjD+3vdrKva+2mt9pEUb1csYOzAcdmqoRaxnmhu/tNv17je34YX8npyZt9TtZUduFgGq+XMXlHipLPBwKJ/GVraT2a+Ac6QeXj57kMroHOnQJB2MSEgXO+9JyvnR+NXev6qKlMcDSih7cTU9TXncG4YYBHbvvsyMNJ5GGk7y7po2XZ99IodXGWw2deolJUfXmo9vG9OW1fOu6xZQ4B3mr08m7Db1EQknsRbpPQmx/EDkh463zc9HFs3nswJBZfpCjGf7zy3VYv/MDbvyeSkbyUL8oxb5Qira3OvhHkYvymgIGq7zEW0OjiTtHSl0KJTm4rgPRbTO1cUoqvaZ2TTSLw9dUDVeJbiR/cF0HP1vbjq3Ixdwf/oqy0+003rXGJLIZ9wtVGSemN1GoaZmhwQR4S9gzWMjGNXvMJHL3F1/C5ncw4+ypBEpcBLf0Yi1yITpF0gNxcxLWm8jZ5zCaydsJtL7eSvs7hwBwTfJTMrWQZDxDIpAg1RNBIVvKKXJhXXEyV9wWwvG7H/Gpv1tofLpp3GRsIFfSKfm46tV9h0b4w3ONHGwcMMEHgk3g3Y4hDvVHTcSUIXGSC3fUVC2LajkyclBTNWRJJZrIMBhPE6+ahedDC7g4rbD6/n40DZafaqN0TglaQiLYEqZsViHpSJrhjigdnRAdA5hLp/WSUyQKdZkR0pEMZU4Rp9+BBlhcBfg95ZS5J6Nq4LKLFBU4iMUyJOIZXXrDpiupgt4LjefalNpE/Isq+Pptp3Kh82karDfylTtW62TMDxgx9F8JRWfZ73nxAJe2L+SKLa9z5vRSworK4+v6CPTsPy5i51EnBTUlk4hnUI1sJamEY2mdtu7SvThVVSOayBCJpAkPJ0nGdVE8KZYZp4c+NnIp6cbP/8ucbZQKDFGxY2EbaqqGNJzka//Wt7BSOI2j3MN5Z0zl4pkZhlNu0gp0iysRimR8tgHUjJXQUMJUczW+b9HMMiqHH+cjZ36Mr758gHs3TOPihd+gr0nHnNefX88F59Zz/+/fJdkTJd4W4v7/NFBS7jFLM8Z5a4pOfqt0d+K1BUhL09m1roN0MEnNkkrOWDGV555uJLSlh+FtfTzRHyMdzBeoywyneODbr/Fw1mzH0JVSEhJNb7djK3CYnsmmARGjTEuj1i44rLhKXPj8TqbNKqWsyE171wgjwQTh4SSyrLB8RR1Tqwt49LEGwo0BUj0Rvv2N1VhEi2m2835QFXI0w/Z/72LF6z6U2HN5KyhdEsHJWadNYW/bMO/s7MdR7mHOaZPZu6btsNIqY1nBVsA7u5RZS6pYtqCS7XsHaE73kepB9zJISOx7p4NbUqcg1Z9E23XtJmEsN5xVXormlBLaFzyqhHe40FSNaNMgu5qDJpPWIlpI9cX41+/f1TkyOT2YXKVTA35q9drxzigm2RMlHYib162bZ2kmEVPKKEQiaQaGEvRWTWPGCSdT4HZzVd1elFAKscKNWKQn/HqbQPTgCIP7h2nrgPgRcl4yCQfboDadwO4dRix1Y7NaoawUr7+fElc5bpsHvz1KT6yYg50j2B2iWRKy2ETUtEzbWx2mYqmxg/7qF0/hvLW3sekn2zl5T6n5nR8Uh+G/Fea5SQpyTGNoex8PNA7yUNbdUVM0lFg6b0451jj6pJCWUW0CKUCIZleBGdmUzYYssSQpkYzr7ltSWiaTlHUPVylfVEsX1BIhIaEYonjvoS//Qd0so8k5ti9h1tOP4cEwPq8kJBqeb9bla0vcFBS7WFztpyj0GEUV9WhYScjF9ManIWtTCKdk4lk1UbOMpqjc/8B2Uh++nPVbGskEE7zy7F62btHlpQWnlQULK1k53c4zUwpNc6Ch1mEiw3pN3Zw4sw3xlm293OlzYLX52bN1p+njGxpK0D8UN5OIkpAOa9eZawCUO2mk+mKmC5c5tsJof8RU0HTohjD+YheFJR5K/C58HhvF2YkCIBZNExiMYxMFs6SmqdooFFLJXzQca6iSOqG2k6ZoZIZTPPqY3jBXEhJSKEk8mjZ9Ad6LOW0S3LLy8dGERDopmbwFA5yQ7ImyL3DA9DSe6Jjliyu59qp5PPF0I93ZifhYmPu5TFej/5L7O0pCItkdHff9Fpt+jyyi/nsWm8iM86ZxxQUz+dt9W82kMNH4yZIulT8SS+scAimFpbYG+7nojoeqqnsgRJNYnFYysQyR6JETghGSBIFBKO2PU9AXQyh2IoYjWEqjeKxBfLZ+PCPbUH3nYreJWK3iqM9FdhzGuu4JDiszij0oA3GKiiwMW09Ejr2D6BCxeO3v2+3vfxnGTlnNAg1Mwyv1+N4TOJakIOlEG0FSiUkqmbRMPGo1NY0A0wwjk5aR0vqDb7wAxkOoqZpOuKnxUVbpo3d/cJxOz5Ey3OEkoI8lLDYRe6mbdF90wmO8n+Ma2217kZOiKX6WLqtlsq8b7VAIWl5G3nMIayzDjI9dTL//at5piermG9lxcVZ5dWLc5h7+vLnHZLtqqkZntvGoqRqbN3ZizZZn6s+so2tvACmUxF7qNtUsAROZEmsZZmNnGE99EcU1BaRrCoi1DDOyo581raE8zfUjvQh6wpRNbR+LYMFV56dwSiGR/pipTlt8YhWzFlay551DZIYSOtGmyEXlJD919cXUVviYVu7FLgr43HaGilL0Fjjo64lw6ECQ/dt6SAUSeTX9iXaRH2QoCYmh9V3mz+lAnAOrW7DYRE761AnUTyniuX9uy/P0yIN3oo93oifKvoTEpOoCzjmtjv6uCNH9QwDZRdF7K1cOtw7zyppWhg7ov2crcpnPQTx7v/Im8+y9cFR4WHL5bObPLKN/KM7Gt9sZaRjIu7+HI1UJTiuuGh/uGh+ZcJpkV5g5F03nvs+kofMxnipfNLFbmar3BzMZvbQciabx2FS0g61Y6mZgWXIahHrRgnotyJLKoKVk5ISc2058z4jHob9bpqArSoHXjjhlECqG8JV268dv2o148pmomoZo1aXO5WxfAdVIEPlfuLqxj5u/8QtqvgGf+HULzpoCrv3wIqaVe7nrx2/q0vNjxslwDsxVGf6/iNxnz0gOY9+I/wmj2ewHyDq2Wk3JZLIY6DwdeClnVyArZskod5dgK3Cw4MQaLj+ljkdeaWZPX+yoXhjjpli9OsMzE0odNUY971pS8mHF344nDETM5PpiVi6sxjf8OJrVSubt/bz92/0MDYPte/u4/NevMfuqf7Aq+zvuukKevGcprSOFfPnWZ0xdoKq5ZdROLWbr6hYc5fpDOLh3kKe39uIodVGyvFZntzpEzlk5g6amAPs69NWwmG3kGsnBIliYu7ASeV45bz24i0xQ17uxl7pxTfKTDsTzGtlHCgPdVDW3jLtuPZnhpMS3fvQGsZZhyip9LJ1XQWfrMMNJHUFTWuVl1pwyzp5fxcziKKW8C6KNQMlieqIlNBc4ECwW3WkrM4p+yZv8D5MQPohFwkTXJ4XTuGp9/OmsTaT+vprr//oIN3zmFdO+Ndfy1OBNGGO4fWs3PZP8RHqjpmfw0UbswDBNrSG99JedrOecVEt0JEVL1prVCMEmULCgnFs/fzJnfudy1n5Uv/c337uEn515J6uzJki5kVsmMq7VVevjnOsXkJEUdm7qItExQlGJm4TiI1Z+A9071+rjL47S9XPLSKqkkojrpdOX9g5x9szvYBcFCi39FBVbIBjI7hYsIKnIaZnUxL3rw0ZvH1R2RXAU2LEPhyE6jMVqh0wSy/zFHBguQlWjWG26eU86B5aaS2QDfRHw3MO7eHW1y+z7fOon5/Ip/gmNAR5ffhO7OyMI6LpccixDOpjEWeXlkXsvpzWU5ic/f8v0Xvm/iv9WmeuYdgoC6NhfUdWTQ3oUeqh/aLQpZzaUDahcTuNDSUoMBeL0hJOkkmOUNo+0S7CJ+OaWMW9ZDcl4hsaXW8i8TzG8/0ZCMP6vE/xUsDuwuAtAtDCU1fiTJHj6K3u44dL7eWH+2ew4pNtD7gr4aOwMjqqCqho9DQOEs1yIEy+axU1n1jOUlPj13zYR3N7Hwc3d2EvdVE8vYSicMtnOziov1952Cm1dI7z7zx2oKZlIU5C1oRTOKi+Oco9ep49mqD2phkVLqtmysZOeNe2HHZexgnCaqtG1pYf7Zh7E57abZaiDa9rp2KojT2wFDlyFTqonF7JibiWnOl9Cfe41+h5tonB+KeWfvYiS2qU4rUvISArlxW4kWeW11fuJ7B/C7ndiL3LqLNvDiPCNJdl90Cu43uKPUnWzxGDCSv0pk+hpD5mTQa5NppGE1ZRMx5p22o/jfIx74Knz89kvn0o0nuGV11rGwV0Fp5XLr1/ItU3f5vHnRktjYl0p77x8YJzzlmATsPqdlC2upHpKIdFwip6mQc6+ei53nB/nsT2VbH6jFVVS2bm2nU93l9C7v4VUdz7vQFeHHT1XTVJ0DlNcoqM3zDtWC9VFbuaUVlLka4fObrRoErKlOCWtkDlGVRJFgciQjCeQwD0QxzoSRpNlyEgo886lr1X3wrZadZ01o69gjJeW832qpBJvDengCEnvMzQdHCJz9ZUIi2Xav9KLJin4F1Xw+m+9xJRqvnDvENFwCt+XLqS+J8Zjz63m0oue/3++zPR+4qiTgqZoqKhYBA1USzY5GBNFDjrIqGEa5LRsQshFG0nhNJ17A6zKKPS3DR8TnlZNyWYT+4OwsPugQ5V0+Yq9fRGWL6wEwL64BmjO+1zyj6+w5BPX07CmncxQgp/8ZA1yJJVX6032RPWauiAQi6SpK+hgXnGQXRfM5MmDIZS0wqyFldx03kwda55dmQoOKydOK6GixM1mR4MO682KsknhNGd8YjEAG5/dR6AtxHZZJdQ+cUM1NwzNFYtgQY5lyAynePvhBgSHddRkXlKQoxmsPru+0vXYKSt2M7skjfzvF3n56zv1WvKLEa5fuB3xXD9lvin43HbKCpxUeR20HgrR2B9j4Xn1fPL8mdzxm3WENvcc8Zw0RReNOxwZz5jARYeIklbec4dpSBk4xQjy5NN4e2MAq02H2WqSkudJbC9146stQJZVUsEkqZ6IWc9/vyHYBJZdMotri16ms+bDvPJaSz57POuPkZYULCcsBHQi1SlnOgmsuJPU/a+NP6bTim92CR+6ai5ul42tewcYCsS4aHEN9pHnCYT8eulOUkh2hWnvj5kEwXF6YKqKlmUMa4qGLCtk0jKplG6OE3XL2AQZAm3g9WCxWsndHhxL+ciIWBwyMQk1Iel6SYV+LE4ncbkUVdMhqaJtVKIfxhPUTNE/w7VP1eXPd752kK8m65AyilkKFZxW6GvB6+xlytQTWP/SfvyXTceyqhXJcnzy1P8vx9EnBUk3xtEEIas+qB5ROM58MXOSA+jJRUlIJDpGaOuJjq6mjiDHPPqS6+5q+9pCJlro/yWkgGE92d82zO5KL2r7o6Te7sL9iy9x6Wf38dK9veZntz3ZyYU/c/BUpYeRSErvq+QQhwzMfialG4Y0vtHKU4tPZ3HNFKKJbqxuK0paobTEzfySFlZZXaYeTbInyo9+vx7I7xMYvIClcyuo8DjYs62HwU09xFqG864Bxu+kjJ/nnFXHzZfMYbn/bbrU87j5u68T3T80Klcsjprq2F1WPD4HddUFlKfW8Nhnd+YdM7y6jcKpNZSeNBnBMo+2/iipUhVF0hPb3OmlLLG+wPIVi3llW984PXyLTcQ12c+JK6eTScvsXN1yWLVci2Bh2gX1/Przy9kTSHDXN1YfEe5pyep8tYeLiEsFvPXWNgb3DprWqPWXzuRLHz2Bl7d3MdAX5ZbL5lJbYGFPQOJvD+2gf1P3+/bIBT3ZnTy/Ei0+QlM8zmDLUI6rnb47cVZ62bO7n68HzmJJw3WUeB187/kmOj/9pumIZ4RgE7AVuZg2q4z6Mi+v7exh5/pDOnRT02i1XsNrz71mWpZOSATMmgoZjVzj/VclFVk2egsZEikJQbBQfu/Hefmne7no2YthZj0caMWSlVwRhGN/b2NxSEfSqCO6gJ7FWwglk4mkahAsMRw2nQxqteWe4+hzCeCa7MeVhR6bSL1sv2ZbR9ichyw2kcieAJ96cSF2u5XmDTqJ9XbtK9R9qoBXf970P5Xi/l/G0ScFs9mnJwcga5QzJnLMZHJ/L4+XYBwrPYo6eu+EkN/s/F/GUaGRsuxhm9+Bpmj0HhrBMs/GwK4AXssyCv74G2a8/mFasjSM0hIodrRTXO4l2h3BEk7nbXFzQ1N0K857v/M6Vr/TlEEGWPtkI5HoDLa/0qi/0FkiXmRPwOQ0wKgcspqS+cuv1mH12ol3hE3TFGPVm0tiGnvNcixDa0M/9svnseuEb7JvP7z0s9m8ets/+fmXXsrTrrcIFqxWEafLRpHLhjbSP+66bF6b/qYno6RllfmTCvHYRHoPjZAOxHn0jxtZv3QSndv3TIimsAgWKmcU86WV01Cx8v1gggPBBJZs+dJYDRpaPzd+aD62j53FGdVe1t/0e974w2bzOGNLPZqqEdjUw5f3PW/KXeS6ri07aRKnxP/O3JVX8/y+Qk5q/iHt33udpZMKePyRP3DZdzJE9gTel1Ca0U95dV0b9VfcyqOP7jS1jAAsNj0xVM0o4bpL5zCn1ENXJMXfn2ig462OPF9wwSYgOHUSo+gUaW0K8JvuMMG9AZSEhKPcw+8f3clAa0j3vT6MllXumBt9PbNPmAU4SGmZTFYKp8bnZNsf9xIOA5NrsXiL0ZzdWNw2bG4bdtuxj0siAZmUipZWMOtP2TnIKugNZqtVQBCyu4UJdgm1c8qYPquUV3YPmBwooy9iNOB9s0v46rfPwi4K/PBzz6OmdM+I8z9/Et+/KM6rHdXvacr0/+c4pvKR+WdjO6lMXL6ZiH08UdNwos8f7jv/r0JwWvHU+bHYxFEVwjFhTKz2IidVc8vwF7lJxDP8q+b7XPdmjLtfD5GRbNzV8k/m/+RXxJqHKVpZR0iqxu7oMoXF8o45JhEZOyxD66dymq4dn+qL8e7Du82yhhFWn53SeeV84aaldARjPPDzdeYEFe8ImwQfR4XHhIxWTy8hGk7Rv7U3u8pVxp1DZE+Ar96+mrde/Tz76v7Cqh81c9X87/L85Z9g36oDOQM36p+RUTQsxZWccqaTjW/r41dSDJ4Lp0N1JUn7ZHoH47R1h4kmMoy0h3RjpWiGAy8dOOyiQVM1UkkZp3WE0tDLPPjZKXxSmElRqVuXN39x/ygxTFF5+Kk9XP67j6AlEkS2pbF67Xjqi/AWOhk6MKSL5OXIsIxl+eaOwzN/2MjBS88kHu3hovOm0/ebd9j8rgQMMWvHR7jlxXX89bcbUJqDx1R3NspcmqKxf0MnjxS76WoM5JW7DJ6ILCmcO6Ubx1O/xXbfbh752SXcPvU6Nj3TRKo7onMN/E6K5pdx6plT2bc3QE/jAH3Z41lsIqm+GO19sVFXsDHXOXZiNVRbVVUlcig8CkCRFGRZRMooZCSVnmiKa9d9nlNFG3L1EqyJPvB6EHx2bF4bXm+aweBRDwug9+MyGfTJW1X1naOqIlhkrIIFwWJBEAVTzdkYz9zr6dzVR39XGMFhZeY50ygu97D5gV1597mg1s8FdV04xDA/yarykpDYubmLzhVn4rQmKSn3ELKJ+YCD/6P4oLkVx6SSOg4KdRSfhfeXEP5fCavbyoqr5nLe4hqe29DO+r9vn7AebREsWP1OZs+rYGF9CcFIiq27+tixV6C9eRBBsHBf7Vxu+f43cPbuA7ef9pECMrkCaqJgupFNhLKx+uwULazgkR/Mwv/yT0nd9iW+/rDEzicaR9maBk7dJrLghGrO7bgLKst567RT6N7djxROoyQkRLcN96QCrv/4EsLRDAfbhjlj+SSSaZmHuyM623WCno2maiTaR/joM0t4+NAXkN/YhGXRfGa6Szngbh9lHasqqqIhZWSiKQmpoJ7Jn12Mt/ogsd4YtR+eg2XhPPBXMJiazcDQAfbt6ic+nDDLYO8ZikpkIEZKLkTbvRsttpGlS35AMi1jtYkccOukSqN82b2hky9NOp3SEjd71+nlAG+hk8ISN2FvVC+lKPk73cNFJphgx2N7sPrsdM6vMNE8APtb4LJpwzw9rUgvzeXcmyPyHUQLzpoCnFVekp26s9q7j466iZn3IFtaHOwYIZhaTlVCYsMbCZYnnuMrr32DO7rCHAgmUFMyjnIPy06bwsdPncy/VI2+lqFRLkvWRxtFPerEJbptFJd7CPZH89QJNEXLqiirqIpKKi3TP+mjSKqLIuUQXtswOB0IXjt2rx23672/61hDECzkrq0m6iukB+JkggmsfidLl9Ywq9bP9jGs8sFd/dz+ZCludylSWN/aq5LK4NZevnbPO4g2geHW4ePiAnxQYfXpJkofJLfimMtHuRr8R/N5MyYYwCOKbL2PzGeoUNbMLaN3f5Bo0+BxZ1BVUplXX8IZ6r9ZdvUZXLK2mMiewISfdfscXHZCLUukR8ANc6s+zC//tplI2wgATz6xhwOHJjGt5iKKChzsbe1iOBAzSzuCTcBa5DT16w35AfP6bCJTZ5fh+cft/Oe23SyYt4G/bLyL83cWE24cNElURukklVFAluFgOx+65JOsKXXTtKGTVE8E74xiHrzzZNZ6TmVaOXy98TtsVWbjtVt5qbaAdCB+2FUywP5n93HK6z68M67H3+8lnWzTER/SaFLLZOQsIiXCjuJZnHDtFyi9tJvS2DAUVhIVZhCXS9nQHqKvO0J0INvYNFbrYyROxp2LqpHoGOEXzzZz+1W/oshxiE1/6qKvZQjBaaV8cSUuj43BnqgJwd38wK7R44oCgV39DHvtTFpSRd0lM3nnyb0ke6KM3SVN+P3Zpvr6t9q4/Xcfgef+Yv6bN7KNmXNP4NBbHQAUzC2lsMrHQEN/HlM5d6ElOKxc+4XlXDCvkl88upO9T+4ldRhvDEOb6pt/2cxdn7mPK/ddSSaUwi+u59wzF3Noey+pvhhWtxW/1wFAJJpGSUr6LtFtnVBcT7AJ2IpdZrksL7KQ1O7WYVLZ3YWxQBRsAkUlbvxFblRVw+eyIVoy9CXsOMVCPM4CLAV+xFIX7jIXBeVOXB0pkvkE+vcMpxMsDt0fGssE5WtG+0F5IQigZKHxaQ0lFeeFh3ZhL3aOK/Flggk2/FX3a8ljrkcz9LzRdti+2/86rD47K7+4nMoSD4/9boMpB3/cxz3WXzjmlf0xJgN4/4Ndetokniv6O6u/u48rHlvBecIlRBoDxzVQcjTDX3+1jt5PXkJqvzyhS5lRk5RlhUkFYaJffZyBXQFO+V0nJ532cV5tHUaOZvRmVjhNg9+Bx+dASsvE+kcnX5vfwfQz6vAUONj7VrspsWCsxDLBBDueauKhO+/hk00PY6lfzP2N80kF9Kay4LTiKPfopYVYhobNXVjuPBd11TOcMTnDjv1Ok/ykpmQyqi7iNhAAbSTA6tYebKJAdDhpchGONHZyLEO4YYCwODjq2WAbNSiSExLRkRSdXWHW20T6YnUUu2ZyYmUXmiawc6CCkZTEzv2DBPujo7aWY5vcOc/LWOMUOZph2yO7+fDzzQhO3YzJXuKmqNbN1VfM5YRqPy/v6WPr1i66tvXpKCmjHKUqSGG9jPSjm5dR4uxj81q3/nLlfH/uJGAi7kTB3B2E9gX55cGL+NaaDvp/ux5PpYeEfzGyouKo8FC/vJazTp2Cx2njr8GEWfPXryF/N/bm6wfZdyBI2+bu93xPlIRE++tt3NwY4Nwbf8clS2rp60nT0tWva03ZBFL9MV5Z1czWrV30NAyQCSaoWF7LbTefyMubDrF91QEzSYlOkbqz6vjyjUv46V82MvBu9ziEnxzNEG8ZyrlHOvTzE7cs4yPzW9g6MJuH3mxhd+sQ8ZSfEm+GEncRCbkYj9sPxUXYqoJ4Q2kqK1K0dxzxEvOipBicPhsWtw3sNhBEEK2omhVVy4wTlzxSaJJCvGWI2PsoXduKXDjKPaZ21P9FGKq7p8+v/MCPffRJIXdyFyfO0If9fE78txKCRbSwYFktb9+6j2QSuv+yA+/J1xE9yp3NkSLeGuKp372L4LAeluClqRqyrCKrLuT+OM17ZWpfamDJJ0t5w2UzV16GEUrGpdsPygk5T6fEU+CgtMSNrcgFYjgPcaMpuuzDP+98k8fnLkSVZFI963S9IkVF9DuomFtGPJpmeGc/yZ4oneqVTJq9G9AZ5wb7NRVIcOeD2/lX/7fAIvDMyCVMrZY4sbaId9e1E1ePQWdIUVHJSpeoOkJFU3Q8ejKeIRSMc8ghEk9JeJw2hpMV2EWBuJTGZRVRFVWXVjb0sXLBCDlhNjl9djLDKbOeraZkZMBm2EbKOm4+HMsQyyjIymj5ZixmHfQE9nZrkIwk5qHAQC+XOCo8yFlNKxiFg4pO0dzRvLn6AMu//APOem4HxIZ4o6eGgb5miqYW8bFL5tAVSrBuezdKQmLKWXUM7Ne1jZScfokmKQxs7iawtWfcLvFwoaZlkp1hXv/XDja/04HH5yAZz5glOCmcJtYyTLw1ZMJLy2t8nFe5kakXnsOPA3HaN3fjrPTicFlZsLCK2oL8VbazpgDRqfcf9IZsPm9FcFi5YIaKtuNNCutPwm7XFwaBUBKXw0o8o+K1+vA4fVDgQyxz4y5NUFRkobNLO1xrclwUFIDNa8fisepSqlY7GlY0BIzHRdWyi7T/UlnaIlqYdlYdZ5xex3/u23bYqsF/OzRFBz88+doBknFJ9y7/gHYux7xTAN5XLe1oJpjjuShN0dj22kF+1fojlOdepO+avxG47skPpM6mKdoR4YuaomGxwdc/dzIVe34DH5/PSbZ9CMVOJhU4sfkdZIYSyAl9+2pJSMi2bFlAHdWEkqMZoiMpvv2hmdxZ8xSrPvd57v7pGp1kkyP7kBlOMbS+y3Tw0lf0uqHOR69ZQKHTxg9/uoZExwi3/uRNPvqRTyK0WIjFMvzlt5cyT3yRD/2pjLbN3Zy7txglKWEr2MjrP0mT+t1D3HjD3/nzviAwHiEyESIldxw0Qcd9G81QgGEgnZIJ9kexWkUa9w5gd4jU1PpZPL2UM5bUALD1xf16gjSeg7HPmSjgqS/i9Itm0rCth4HtfcjhlElqvOXWZTz/cjMd6zoZTgzy1HCCVYVOUpE0mVBqwrqrlvUw+Mf33jAhu8a1ALgnF7Diqrm0tQzpTW/0HV3RnFJqpxaTiGfo2hsg3hbiJz9/i3vrCqmeMoPeQw0Esw3NdXv72fxOB+H9Qyy+Yjbf/tBMvvfAHva/1oqm5shQZDWSjuU9MHaRhg7VUNaTJJc4OlYDaf+6Qzx39jmUe/4/9s47PLK6fPufOdNbJr0n23uvLH3pvUtvKooNREVFUBQLKnYURUGUJrD0zgLLsr33TTa992SSyWT6nDLvH2fOyUw22UIR5P0918XFJpk5/Xyfdj/3HWfmzHw82XZuPW8mXcEYz6yq4/N31+Lf24MSlbBk2/jz/edjEgx8//fr6N/WOSoU/YltMWaU3Ublng6mjs/GNxTFH4wxGLQw5LbitmST7czCkJePsbQbU1gksyPA+MFB2jtVZtTDWU6eEWd+klzP5QSzFVGxE5PUYVFJVvSe0Fh2JP3Qw1k8JhGKiJ/4nJQUjLN3ReVR9YSOxI6qpzDWXMKhvqPZoUSzPyoPN7S/l2MuN2O0nYH06FP/1WlDg2BgcWEI8aUaxFt+Rv757SDGaOpML5rq5zpCFEWb9LRYjeQ9+Hme+V4l8B9euamQc3O/pOLjR1wno9VI8TEllE7IYttT+0goCebku5lgeodJC4uo7BjCt6OLR3pDeMozMZoFSlw94B1k/JRpXHbRTFq7A7z2wFbiA1F2Ri5h4bVtNDb4dY4qbUp3rHNWDySF/kBJHPTiScE4gZhEcFAtNWgDRu1NPqore/jpTcdw4UVd/D5vEW+9UEGgun9UWouEqFJlNzUMYBAMOMd7kGMuoh1DxHpD7K3tIzCoZhByWEQKxAknswet5KfV04Hhmro4LBULKRmFnCDcOsS6FypVBycrqkaux8qCY8q47Jhx9IXjPK4kaI2psEWT2YjPG6a/O0jcF0UciPBmRa/KCGAWqKvo4T7BQP2GVn2O4cM8/6kEeJpT0a9XSiCRanFvmFVbWvnx58ooWFzGP96ro2kwzO6GfqrWN6vDWynfre0PEYqIRDWCvmSWoO1bCsZZ+UY1/pMn8LXleWzuSFDd2I/JbMSYfEZispGAWIjb0welxZgliYy+MEVBEZstRGMzh+wvTBgPGWVu7CUujLl2yHCD1UlMdhOVZGKS6hQkSRkzS/goHEJCTtC8sp6W95oOOXT732Bb1SD6H7UdHfroA1xQg6CiZpwTs0goqmbBaA/uR2VjwQg/blNiEp/71g4Wn/kTuv82QPnEEmaMz2Z3dYdex9ccq2A1Yc21I4UlfRhKWwQbdnVhvqQYqATgzUe6uXHbyfzzeytHrbWXT8rm3ssMfLl7Io1rmnl2eyvXHnMqirIPBFWbN9w6hCIqzFg+ni2dHlbtWIbPG6Igw0ZDh19/uP/9Tg2rSy9k1XP7EMwCp91yDPU1fdS/VjtMtJdSVzdn2zn+unk4bCbee2JYljMhJxCsBqafNI4TlpaxZlMLjds7APW+iCm0KOHeMDX9ISa+9h9uP+8c/MH5vFM3MObMRqw3RM0btTgnZrHglAmcuqiUJ1+qoHVzO+89sA1IajHnO1FERUWauC2Uzy8iHpMY6A0xfUER5x8/ng0V3bz/xB419RbltAVWsJn0SfBg7cBBWgPRuExIlJGUBE63lRMunckvTj6A8vbzGObO4PHjr+X5FyrwDgyrxSmygm9HF1t2dB0R42mq6QuxRm+dfBdNLlVBTpfk1JhZDzHVbS9x86sr3RjvvJ7cJUVk5dzFvT9epbPEpjVXg3H+dPtb+jZtRS711iWHKhEEjDYjd3zjOE4R/4XB52RXzYl4u4PccPEsTve8g+ieSE9kJgGxAIfTi7FgkISiYBmKkhOWsLjMmEyDdHbBoF+FngKYzera73BAdo6Au8SNqdQNuTkYXJlIpkyisQzCokw0JhGLy2oJV5QPFq8yGkgkY0Qts/6glqatMcIEmwlHeQbl84toXN/yoejPPyn7YOWjozCD2UjBkhJ+9vVjkZQEd/1xPf2b28d8cP9XLSEnCFZ7Wd/qx5LrIBoROXdRGa2ZdtVJKcN1bW2RGvJF6NneoYp7JC3S6mf9rF8A7+q/u3yuwj+Nw3rK+qi+qNBU4yUiTSO/OEEjsG61CqHr7RgaHiQUZcwZVr543gyWDv6NZRdeyfW/6OZvT+zCl5wJEGwmyooz8LisSENREASK81xE4zKNNhMJ5eCyRuaMXH5z3DYSNVU4b/4qL9y3Pu3vZquJIo+dklIPLclhKdW5DHNjGYwGugfDCBmqhqOQdJoGwaBScisHv71KVCLui6p6FEoCu1Ol1Ej4opgcJhZcOJ2LT5xIx2CYJ/+zB4vFyPITx+MbirHypUoEwcDsPCNZi8vYv6uTvn09unPWghhroVslTRzBSqoNstUf6GWlxYgkq2RwF50yCWnFvbQ9UUnJJQFKLv0yZqsprT+iNbeP1gSzgMltwV7mIaPQRXa+k0goTl+rn7xyD7mFbno7hujY3T3mNQO19m9yWyiamYfzlV+x4g8tlJW2sGRDHu9YTVhyHSpFesrwG6iOQcuQxi8poa87QLDZj8mhli/zyj2cIj9K9OF3sf/oZhrrVIbX0ws34//Gg2R871xMZZPwx+xkWLJxOTMx5EYRCvsxjYvgBqK+GLnxCA6HOougKCrSyOUEk0lVbjPmOzDmOvQsIa64iCtOIpJMTNTKRxo0NqUvNQpLKkbhY4GUmhwmZp48gVsvnMXdoTgtK+v/59a5j90pAEyYlsucxAuAwpwli1i7o+sDi0p/mk0RFXURURLYFhVzrO8Bpp94PW/+K73mZxAMTJiUTTQm4a3yJvUm1AdHDovccfNLbJb/xtPGrwOQNfgeJocJ0Z8SzSYJv7q3tPP1P5vw+9Rod2B3Nyt7w2r0n6Qi0NBRFqMAkkwmVeQWZlKzsk4XWkkoIu++fIDMIreqD+yx4bSb+P652cBiNj9XqTPLag95uDcMZisJ7wCLluTyklnQxVzMWXZsNhMOs5GfndEMJ0TZHj2XFzY3s3NNk86xkxDhpRcr8dxwL1XbBqjY08kvH7iAYFzm4cd3pek/p75csa4AB16toXG7molJgbjaRM938oUzprIg8SzxWfPYPreQtoYBduzrZsgXIdIRYOfLVXzlQC/uTBvB/rDeOJZjKpumJdfB5CXF1MYkdUE0GnDPzGPSwiKa9vcQrBugd08365t8umbE6pIMTvj8j5nwuT20mS/ib3/aRO+e7mGOneSxGx1mshcXqYJU1f16djWWmdwWZl08ne9eMZ8ZiRcIZUylMzSRPT1DrFrfxJIFxRRnOXhnaysdu7tHn/xOIlVc03LJLXLxuQtmsmLaZgDa2uEq5TFO/7mbJsd1/OKpXVS8UIVhROlXMAvYS9wsXVLKps0thNqGMHlsLDhhHN86qxz5yb9jO7aUP+6aR193NUtOGMfQrT/mzUe6uepGL11ZFvyxGE5zGVZHALNggokxzEoCwWMlKyxi1sp6MTWLMtqMWFwWzHYT9vIMzDNyMOTnYsjJBVcOYSmLYNxEMB4nGlMZdsW4hCQNM9OmElUmBAGD8PEEo4LNhK3IRUJOEArE6AvHEWOfbM/hg9rH7hSUmMS25yq5yTcFk0mgas2BT7xB83GZrnwUFqlf3cRPp1xC7cpGtRmafLFAlajcuKoBk9Wop/uaaXXhG/9q5aGfTyPa7OexrrNIyO+qcE95mL5AE0Vp26JCBzX50GjHEEXHleFyW6lf1YAUloi0+bn9x+9ywvmXcElBOXPn9OLtDtC/r1dHVPn39uhoCqPDzPKJWbjuvZzffOMS7nWdwKu/3ZjGNxWq6+ek2zKwl3yO8PMrkaOyjrcvmZnHry+LIP7oKp7+VWPy7H7CLx5ayKvfuJ9/PLJdp24Itg3x4B83YhAMLDp9EicfuBscdmxfvJMf7+pE9Kc/L9r+pWCcYN2ADhE1e6xc/41lzN97F00/X8v47y1j+sS7kESFUCBGNCJiL/cQ7Qriq/ISKXKRUaCWQ0SbipiJD6jSsS21/cOspGYjJ104nW+e6uKpXYX85xdr1H6BhkYyGtjw712c+6INo92CI38tVrsZW5FLhwdrZvZYuerKuQiCgaee3ot3Y9uYi5TRYab0pHE8cn0v/tvOoXZPL1NX/YTH1icIBuP094ZYvaYRQRDoODDMyzRy7sCS62Du+dM4flEJgbDIpRP38nTKfnw/fgHHeA+mO67Bm5zqhmHxIG07BrMRt8OM3WlRNQWiEifNK6ao9k8wexz+xV9h4++bsFiMzJmci8lh5pSLPbQt+Dmb93eTlWHFaTZiMY7DYzVhzZdIKArGDDdOmxFre4BEIAlLFhUMViOC24LgsWIqcqnl0F4vlBYjGrIIxHMZjMbwh+MEwnGiUZVmYyR6TTALKIqCQTkYefZRmMFoYOnn5/PAJa282HkCf/nteu5a20zcF/2fyxLgv5QpxHpD7Htqv/7zx3WhNI4XJSp9IM6ZD2parVY19YUSfRHe/M0GvQYs2FQVLzmqLtyBA+qwmUYGmGoJJUHNG7WcP/PLkAHBu95Fjsm6joQUVKNie4mbiXMKmDo5l9ef2I3cOqTj6z9/1Txm5bn4assg0Y4hfX5h6/pmvn+WwII5e/j2Qivdwnlc9Y33dISTdm9iPSHufHgHj/70R/TFxhOJ9WO0GdOaawazESkQ15Eqqqmf6e8N0jR0CpNPngS6U4Dw9i5mXOYiK8dBQk5gthqxWE1YbSZiUYnuNj/NX/gdSiLBy69WpvUyRr32KTMDZo+VfI8dw7TTGH+fC8Pi5czvy2JioZtAXOJy+woSosjvWi/klb9uVRvgQggpLOEuzWDRseUIgoE9Ozro3tquZx+CWWDtS1XUVuXQneyJJZR0uVglCZedfupEHry8FQa7aci4ia/++B38FX16QzDui/Lk47sxCIY0IsKRz5PmEL509XxQ3ifjvElk3HYGv9+zmMrdTSw7fhxN1V46NrSOijCyZNtAEPDMzOWqK+dyw7wmeiNmXquSSNRsS9tf1l3n8aR4Aw9/9VW9Bi5YTRhtqrSl1v+Ie8Os29JKa1Ufclgk7osSlxVo76TjwZ14xm3jib/8ke88Hqe1O0DoV49zoM/G629UYbEYkWQXTqsJm8mJaJ9MnkPBUgjYnQiAxd1PIi5CkjYDOaGWFV1OEoNDrLhULU9e/lCU0A2XMBAR6Y+I+INxQhFV7U6MS0j/5aDTIBg4Zl4R8qv3c8plC/m7x0rgQN9hv2f2WHGMzyTWG/pU9R4+EPfR4WCJn4QJZoHJ507m7i8sZUe7jwfvfu+wqflHYdoLbPZYkaOyHnWnXiOtPGHOsKqljmS5Q5/aHcXksDgs4pGCs7dk2XTiOkemnQtOmcz543bT1jWJnS9X6dBZJZFAMBiw2E3IWXaWnj4JQTCwbW0z69ryONfUR/j+1/GYH2LZhb9hzT926CR7oJbCGt5t4JrYONyZTbTW9h+E5nKMz+T0y2dRVdmbxlEkR2WCjT5++Z9dXH/Bz7nq1Tj1P9/AuGtnYrnpGh7e0UksKnHOhTPIzrDS3hvE47LSOxCmpzvA3Y9swz8QZrDel0ZqN2ZEbR2+vm+sbaR5+jymFp/IjJid+fkNyIqFiJxJ7PerSIgKS770RV5BzQo0Liuzx8qUskxOnpDBiy4rT29qI6EkMGfZKT++jEhIpPHdxkPqRFvznSxbWExiw6MgSWSd0XfQVLZBMOiSnyNV1LTztJd7yJqUzTHHlpNhNbHSewqupaeztrKbd57ciSIqbBBlBmv71WFKsxGMAgkxORlvMzHrgmmYTEauPWsqJ5heILGtAWn2PJo7ezCcMDFtn53l3+DBa17WpU8Fa5JAL6n7q8q8qsfcsLVdnRFJCjhVNPZzyeQJrH9rLRDkmH3X8fVXtvD0xib++E6Unq4AipzAmWEl4IzjC8bIsJoQDBasxkJyXUmodywCJiMGSZ3CNwz4UHwRlTkyw420r334GjlNhMRc/DGRkKgS8MXjMvGYjJR8RvNK3EiiwkDHEEoyQPy41qaEkuCV16s569u/42/v9acp9B3KshcUcsN1C9he0c26v2771GgzfKBM4dOYEpncFv59WwZtJy7higsns+acO9j31P6P7Vi1Rd/oMLP42rkcv7CEF145QOt7TWjZgr3cg6ssg2DbEJZcB+VTc/B5w/ha/PpipwnESMF4GiJFo+GG4eutsTVmjsukd0834UBMjdQG2rn9guP5i9XEtteqiXvDPPL4LgrLPEiSwqSFRZwwp4hFhQnWTc3jH4/touSWLzJz0gYG3mth2U2FbPRY05wCgOiP0fRmXZpGcKoVTc/hh6e00HvifC7b0kbMG8HkMKkqZN4IVa/V8KN3GrAVnU3mhVdAACL3xhGDFRROzOYLxxjINNcQlItxhbYiZ5SxzzuXv79aSdvuLuTg6NleWlBiFPQmaOnUXDxZNgSDgQKnlbLIcyTeWw8mE67pUzB892aa4qfz9LN7VC3i4LCsYrBugBdeOUDVvEIqd3fputPO8R6+eMVc+gIx/rypLe0apN4rULOA99Y2MeXyX9EdjPH8Xw/o3Pza57IXF/G9rx3LP5/bS9N7TQfRYU88dSJPXLwT9rxD19I/8N2/76ZtmzrMpkFoAbo7VOEb15RsZi2fQFN1H73rWwGw5tr5+sWzWZC7jeCtl9JSNUD5T04jLJopzHHSaTkHuEe9fEb47av1iL6Ijrqyl7jJm5KDySzg7QrqAYwkSkitQzo0N6EkWPdGDdvu+jpO56OEQrB1k8i4RYv4aeMTXH1fgEgojtNtRVESWCxGVQjHKBCVFCCLRGIqLrsHe4kNMn0gxkhEAsQ31GKwmTCOnwA2J+b5/RxzXA05M3KQrvg2rb0y/ZE4/YPqPEQoEEeMS4hxGafbyn1fWUqurZYH1pfx2iOqIyV1IPMjbDIn5AQtK+u5aEPrEWl1aJaV62RxsQeA9WbjmIim/7b9V8pH/w2TAnF+/paFX7z9EwYsy2j8xtaP3SEYzEbmXDqDPwz8msgf+8j50Yvc/X4zieTfpp5QzqJ5xTz32C5MViNFJRn6xGm8P6wuZHMLEQQDLTs707QARjt2KRgn3DyI2WXB7LES7Rji5bdquPSbEymtvp8fX3Axv7HMorqih45tHQzW+5h4XBm3XDIHSUmwtzfOcaVWqo4t4/afrWLZmT/mzPtLefxRNUsYLRofCwudkBM0r2nmO6XzkKR25KjM9Aun8cVLZ/P02zXsfrpC17INNfiIdgUx2lTOGkuWDafbQpM/j1k5Azh3PE7P71bjLstg/ldO5fQTv0hzXb9KER6WDo3YkdWBM9Efo7vdjzvTxtTiDObl7iT26xfZ+3A1ggCTjq8k6zefZ8egHyU5g6E5QYNgIGt2Hja7iW1rm4m0+fVFY+iAl1/+9L20wbaRzwEkYartQ9T0hLj97Xo9kk5FWQHEfFHe3NpCx96etMa2Z1oOsxaV8KuLAzxn+TsA50U6aNnYpsqzpqrepTgjS66DjAwr8aSCocllYeIJ41iQsZbgrX9gy7MdLDw7j8Flt+KUYkwuyuD9xjAXfrWYvn19THz6Zh78aYNO6Jg1O49zzpuO22Gmod2PtyuoM/Cm8p8Zk2SDoQYfd/9+PW9vvYFnZj8OQEsr5B37eS546B2eWbGPSEi9zpGQBZ8liskoEIlLKIkEcdlFjiMLtzkTh2cAq9GPwd+DZflMDBMmQcEUpIQFTp5MydrrGYyXUd1noTcUo28ggj8YYygQIxyKE41IRCMi0YjIprYh5hdOxTfUMDzIlzIT9FFbQh5Dg+IQVv92PTfXqb2roxEa+7jtM+MUFFHhvQe3s21tHqJ/u84b9FGb1hvQBqF+ft1UXnCq/ZIzHogMLxSyQtP+HgKDUcShGAajgcHBKLGIqNNNWHIcXHTuNErcNh6xGqnsDY0K/dRMm3oONvsxGNUyRHtVH1W+s5kubMEl1lFaMB+vN0ynYECJScSSddewKLNubyf+WQXYrSakYJw1j+1h0xu1KvldbFjgJ3V/hzLRH2PzY3t0Z/KLm5ZQtvcneC78EV9dUaliwWUFBCMmt4XC2fk43VacGVZOXlqGPybhj5eQO+inZfcgrrpBpi2qZOKZDtV5JhFW2lGMVULSmvvR3hCRUJwCpwV66+l4vYGGJvUzvd5ezrlmNxPnX0FWrlOFvSax/0aHmXlLSynOcxGJSWze2EJ7j9oHUWIS4abBI3o2EkqCRExCGbE2pF7TcPMgW1ZU6AuIxlc1bkoupfkuEoQ45iQ72dOyeGSrHSUq4ZqSjdljJdIRSJKeDSOaAtX9bErWpBNyApPbQkm5B3p3MdQyxMKz88j+1TU8sDOBxxWipsVHOCxy7Y+vxhX0s9V8IzHvKxjMRjwzc7n9K8s4tXALW/uPo6FdVepLpWExCAasRW4mLCulvzdI/84uQo0+npQ/z9UPVfDczbsA2LFd5rJZAV5zW5ElhcIyD2XFGfQPqgu5kkioojiCgbisIDsLEc127IoTe4YN0+Is4kIOEdGDrFiJK04CcQdDMYn+SAx/SG0uh6IisYikZgkxCVlUkMMijz+5m5c8Nrxt/k8tsEUKxFXSzk8ZPP8z4xRAXaR82zr0GutHmY5psL5LvnM8hTkOXnq1ir79PRRU3a9/pi+crf87oSTRPAe8KvohKlGzuwuDYFD7DkqCjGw7lxevJ7F6JTO+8CMu2dp+WGemiAqxroA+bRxuHuQnD23hhzd9g9yEQjTmpammT+cR6tjdza97N6p8S94w27PsZJZl6LXtYLX3oMlzndQuJumLAIoyKgujHJOTcMcsMsyd4HLSEYilI0CS8pW337iIY0wvgxQDeRttmZ+n0gvLl53IjLOqkGMyQnEOACaTkBYdp96HkabtSxEVVfHLESGx/UBS30A1vx8CL1QwaXkfpfkudjvMSfy62vjfvroRi8tCTr6LoZ7gQds/GjvU9L4SlYgnp5s1RI84FKN6Sxu1OzvYf6CAOT97kb0V3VTdtw73zDx+8M3jKXBa+MNze6l8uTpNZEpjfwW1r2YryWByWSYkEhR972TaFt3LW00DuB0KTz29l2CzH0uWjX9OPhtJSvDWk1uRw6JKrnbKRE7NXU+iZh/NpgUc2NdNrDuQtqjaSjO44KZFjC/OYP2OdrzbOpECcR761To2nHYbf9+1kp2XP43DAc7WN5m78FzmTsrh4nE7CRls/PQ18HrDhCMikqQQE2XcDguBuIzHaifD6sZuHofRECeREIjJRuJygmBcwhcNExZlBgIx+v1RfENRgsF4Elmm9hWUmIQSkwi2DRFsG9Lh0YCeUSZk8UMNr32U9mnpI6TaZ8opgPpCal39YLU3rZn3YShvNQz8hfNLsBgF1uU76ZUTxOddzLEnv0L5V+dz0R/XjcqtozBc+tEQR0pMwt8TRHYWISgKWUKNyrzYNHjI4xNsJjJm5iJHZUKNqvB427oWfhSKM25qLrKoEEvy7ytRiVivyh+vQUVjvSF6ugPqsSUX0zQQQXJ4y+SyqNGsonDvb89hQX4PX/hdO81v1KUdj3NiFld+aTFfnbOX3f48/r79TA68+3Z67V1R6SL++doBJl5/JnlDb9LuvoaH1zQx6I8SPvYMzv1bMYR9+M2z2LzDSyigRtJGmzGtdHE4EwQBizEEAwc7V2+ll3HmLjyuceqCnHwe5JiMv6IPZIW+lKGmg5xSks5j1EU/+R2jw8yUsybTXtVHqMGXjtZKQUoJVpN+DFIgjjgQQREV9tcNsP/FKh1KmpFtV2vnkTjtTT6dLgPSFxSDUS3/zFhUzOR8NwO5l/CiF9b/ewdnnzoJMa4wVNOvTj0HYzz7tCrMFOkI6Aunzx/lxfZlRFjMU0/uxl/TnzZYCWpTv8cb4p2XDxBq9uvC93FvmP2vVHP1wMn8ad+tFEZXgjOT2XIORS4rEaGInd0l+AeqCQXixKJqxhwTZZx2M4EMKx6XFY/VhM1kxG4SUBIyIVEkLiuERVnNDCIS/mBM/c8XJRoRCYfiREJxfThSo/J2FLqYMD2PC0+cgMtiYl/bIG+9WUN/Za9O0Q6fzh7pJ2mfOaegLXKTZxfQaBbw7+1BCsSxFbmYcsoE2qr7GNx1sDTk4UwRFcKtQ3zjx++giIpa/1cS3PmCgVMfeY+fv1tL3/bq0RtYSRbRRCCd/CzU7Od7z5Vz8fF/oKs+ihTYfMhjMBhVGOr995xBVzDGT779hs662re9k8HafsxZqlazGolakMJq5GQv9zD3rMm4XBbWP7EXcWB0ohltsbPkOrDmOwk1+piQacFV8xzXXX4zv3ynIW0xiveH2b2/i965x7CzzceBdxtGJQ9MiDJ7n9rPxSvryZ5VgqKsJ9yrOqvuNj9bZheQlVGKbyhK7YF24hGJgpl5ZOU6OPBeE9FkY/VwJkkyMdkN2VkH/c3kMJNgDIZf7b6l3D+T24JjfKYOv3SUZeApcKk9oWCcWH9Ep8JQRJULKH9JCY9/rpIG14187efvMbi7W6XQSBmiEsyCzrIKyaHHFPx80XFlXH/VPBYUZvDH1yr5+/2biPuixHpDmFwWpp05iYA/Sut7TcOABasJ9/QccnMc7Gke4Ik3e6h+T0VLPbSnS4WPJvmzpECcYI2XhKxmV0abCptubRigvcmHt81PuHlQdwi20gwSokx8IEqo2c+GR3bpEFhAJUBEhVu3rm/h+o4hJs+bwpL5RfT7/bzd0E9JqYe7Wr7KiVOLuLnuCvwDERQ5QTwuE7SZCEVEAiERn92M3WrEYjaiKAnioowoK4QjKgldJCYRDKnZQSgQJx6TVFbYmDzcx5ETmFwWZswv4tqTJzG/6VcwOMRxy8+hu38Ca9v9SWDHET1S/9/ZZ88pJJE87U0qBlyLquzlHs5cPpGVybLOB4kO5LDI0P5eHbuNUWDP+03s39pOuHlQXSyNQlpjVI82k6LnwxtTkIJxdq9qpLvdjyAIxH2HF59OKAmV6yVlajMhJ/UR5ISufGbOspM3PRe/N4x/VxdGq5Gy4gyK85xs1rKA0bQukttCVsgt8RBp8/Or5yv52vnfYsv6xoM+q0WI19f1E+sNqTxCqcN4qWgqRf18z6Z2jFajPt/RJ8qEAjGsdjOyqBDsC2EwGsjJdzFnRj4Ne7uJdQUOmy0ookIsIuGL2ikoLSYnG/pTRgEyJ3iISNmEItJBxzWS9sBgNuKcmMWJ50ylqzNAfUUPX7xpMSeNdxKVzMTlBH9+uYL6ih4irX5dXCgSikPBJMxRAwuPL2dD8+Do0GhFAYwH/drksrBgaSlnjPfhNleiKG4iHQEdnWZyWxg3IYuOdj+t2rEmG84LlpVhNRvZf6CHmnUtunMWByIHkS+mZQCCgBwRGegYQo6qrLEa0snoMFO6oBCzxUTT1nYirf5RFfm051uOyoSa/VQNxejvDeJ0WxnsDzN+XBYVf9yNM2MfeT/8Ir0dAf2rYkxCURJIScU2i9mINUm/HUtSn0dikj61HAmJelNZjKl8R0pU0nW2tXONx2UCcRny88DhIGYsYigQG9aW/j8b1QyJROKIrs6i4//+cR/Lhzajw6w+EKKsQvxcFp0gz5LrwD09R0fCfJh9eOYVAOhzBNpiZc21Y3SYVd55DZY2UntiBPunBkXUBsFSJ4ZHM8Es4JiQqTJ4NqeXmlIFYDJm5vK56xbgtJt48O73UGISzolZuPKc+qIb80aI9YYOSqONDjO5S4u57LLZvPBCBd5tnbpzHQ1bn7p/7fvWfKd6HUY5n1TiuZHlFO3+wXC2EqwbOOTMiUb5YfZYyZmVz3XXzOOSKY2Id9zDK79vJpGAceVw7ENnUL3kNzy9qYlNb9epRIHJ4xvpcASzgL3Mw/j5hVisRm65eDazO/4Ajc1IXUGk1iH6fvQy9z6zh/pdXVxy3XwGhqKsfbUag9HAzGVlZHpsbHq7jkB1/3A5bWT5KDncqE0+Gx1mcucUMGV2PgF/lIo36/QIP3UmRgrEVVRSsixYfGI5V1w2m9Ubm6lZ10K0feiQ9eqRBHsGs1GncddQY9q9XHj1HMrLPKxZVY9va8dB0q/6vUyen8agqpH1GUxGLNk23r7XAhE/n/tLFn5vGIvdhNVuxmIxYrWbcTgtmK1GTCYBk9bXklXW03hcRoypE8uxqOoUYkmnoLHiKqKiQ08RBKy5dnLLPMyZX4RgMNDYNEBzZS/R7qBOgggfrnwk2Ew4J2bhyHfQt6XjiOGon5Tt3PjVw37mM5MpaA9vX3eAjg2tiP4YYkpEE/eG6d+gLiwfhtbW6DDz1ZuXUpZh4wc/e4+hCpUWQrCaOPGauXz1tCl8/2+baV3fMvzyJGvQKskbaRFpQpRVTqPkZw53XIqoEKwdfRJWm0g2JHl1VvxzB65CpwrtDMQZquglaDVReFwZt35xMXFZ4Y8PbNabzUpUAqOAJdtGuDvI2o0tRH1RNbI8hDNI3b9gFnBOzOKGm5fw1NN78e3oOgjSqlNZJwnilNH44GVFpb5uGhwmlBvRG0r9LIJRhb8OhNlb20eJeybH3XYRl2S/jTIQxTIrF8Pyc9hVO0hX59BBTLoj+wcaICAUiHH6SdOZE3uShCRhOPNCwuZZZJi76e8TMZpUZNUZMwqo8gZZbxYI1PSz2xshb1YeUxeV0JJhxVehTria3BZV1yEmkRANmLNNjFtUjEEw0JCkRuja0ErXhlYVGWUz6seWtbSE629YwGMPb9ezAK0cNdg2xCN/30qowZdCPDhsusNN3iPBZkrjxdLIFrVoWzMlJrHv9RqqcxzE+8MHZYF6n06j1k/eC1AzayUmqRrCQ1GWf9mMYDWREAeS8pjqgi/GBOJxmVhExJIkQxQEA4LRoBPcyUn2U0lSEGMy8bikciSJcpI7TOXccpV4iPmiRLuChFtFWluHaNuiDr6ZXBaOvWQGPZ0Bql6rQT5KptqRZnJbOOGmhVx78iRMgoGv3/oakVb/B97ep8U+M07BYDTgzrQRDsXHbCgbjAZsRS4K5hXSublNn2Y9GpPDItsqu4lMzk1bnASzwPfPdhK//hT+/dSDXOqL4D/g1fHcRpsRT3mm3uQipXmaSi/xYU1nUI1JBGu8qnRiyn6UmMRQu59JWQ4muDbSeuVc1qxt5LjjxrFzdycFJRn85tQD4Mnngh8HCdT0HxWkL6EkiPWG2LKrU4/uR7sfB5VvxphFONLromUYoj9GY62X960mipZ9kQnfLodIADIL2d6ziN3VjfR2BJJsoqNHvFpZS7CZGPSGeWtVPXXTTiIak+jbHsQ/oBVutuFrHyLui3LLPe+m1e0BsnId3Ht1Hpm1b/O85csYDQbkRII/37MaSdMm8EeJxySy812Ys+xqCTE5HDbz4ul864p5/OjPG+jf083Sk8dzfdk6Wi9YwBvNg2qmkIyoNU3tkZmZwagCJOZfNF2VRt3ThRSWsOU7iPaGkfwqP0/pSeMon5TN7nfqCVT369fAIBjSEE6p1OnadU+9B4LVhL3UjcFkJNYdSJZqVAlbRVQwCPEkrDuBIgpq2UhSkKQ4sYgBk8mIkLJ9zWRRQVFUvQQ5CdTQeghahnDpl5by7YlvIxfPY/m1MZ1dQGs8m1wW/L4o7bXew0b0adls6jOWcm2zFhTy26mvkljfxIrpv/yvMCj8N+wz4xTksMj2N2qQwtJBk7mamVwWTr5uHnedk+C6Xydofav+qPejxCQ2v1TF3hJ3UuBdtYSSQEmYGGobIt+/n7zC8YTahrAVujAYDTicFpYeW867ParguZyyEH6UghzaVKr2Moxc0BNygkhHgH+va+CSpUuRZC+nnjKJm2fsoGHOOaxt8JLYvhFycyibdB4961uP6ti0PsO+12vUSdhkk1Xn0BnxYo0Z/R+tyQoJRUCJSfi8YZqbfWwodBMvOxWnWaHfCztb+uluHyIciB10XbTyGIKgl2RMLotKyy0YGArEcDktFJVkYHda8HYFGOgN6YtiuG0orewih0UGekNkyfuIvbWD5bfehMUYpN5XqCKSktmiFJbo3NdDf84QckTEaDUi2KyYXBaOX1bGvNztlE3Kwd80yPYNLTw9cTkb3t+NHJVVfiKHWS2bijIj3ap2Hp5pOdx7pQdPooen60/g0cd3YbEYiftU1TqDkmDi9Dwml2Wy39WqU21MOaEcSVSoeaM27Z2y5DpwTckm2hXUAReamdwWJh1TihiTaegPk4imHpUM2nOQfE49WXaVMdYfRRbVRV+9DWq2AAxTYac80yMdAoqC22km0dGO0eHBYByWSgV0bY39b9bqsqSHK4PqpbWUjAqGs4uoN4KhfArE41Q29n+qBtA+jH1mnII2yHMos5VkcMdZZoRffpezz/krD7/TcNSLsVbL114GLXJSohJ3PtnJP975KTvDZxEMbMWa7+T8i2fisJuoqPUSSeL+BZtKBcGIaOjDOgZNq8E9Mw+n24qvRUWRKDFpeNFDXbDe/ds21r90AEuug4nT8mifcSZ1A2FeeqmS+PnfRYooVG/ZPfZ+XBZ9oR852q+ICrGekB5RWgucTD9tIlm5Tip3duDd1nlQRDuWczgaGHEi2WiP+aJ0CoOsA+paB1Wd4KhIW/MgPW1+ldLBbCRrbjbxiCp05CrLIDYUI+aNYLQZseY7GT8lhxOWlrG0LItpvEKiuxlDfind9nN5uzbCph3tdLcNMSTKeqlSc/BKTKK/tp9/Vh3HKV95mn+vasBkFNi6fn0a8ssgGBAHVA0DS7aNspPGcfppk5ld4iHHbmZDZwZQh7Msg2Czn7/84G0UUcHsseKekk1eoZt4TKK7qk/XrDYIKuDBmmvH5FKdmkeqQn7hWc6/cjL/ikqY3VYu/tJi6hoHaGvoZ/faJjb3hon1hjA6zDz594spefXLGE88htOqSvT+mWAzcf7XlnL9sSU8tKaFlX/cTEKU9PskWE1IooI/RfAn3VSItsGcwJPrYPZ8tflbtaeLaGA4cx+pKKfdX+2dS0iy3jDWgo0nH9jCxsXnEXgvStzbchClhRSUj1jtziAYMFqNWHIdmD1W9T4NxVS0X7IJH6zxcvJtmVhyjyFYvf0z07z+1DoFbVgM1Gjqo2jgxLoD/PYdiW9+/0Fe+MneD3wTRyv3JJQE+1+p5tLuUhLKFoKDUbLyncwfn834TBvxuML2XR16s1CJSsjaZHO2DXOWXa8HfxgzuqxcculsLp7jYn2bxAP3rlHRMWZjGuulFJYINfuJdATYs7+Xq96sVaekozKvKQnmLS3lxq8u5d33G6l/u15X5NIcwvnfOpZ5k3LZVdfH9o0t9GxqP+geadfI5LJw9zULKKv6BYbzjuWaBybRvKZZn+o9qMyXzCxSxYQ4KA5Ov/b64pEsCYkxmdaGAXo7AhjNArKoEB6MEPep9Xyjw8wll86iOMvBys0tlBVnUN/QT92OThVxMyGLE5aWcdWsDkzbHmDVhS/S54VFiwSmPN3N0vJv0NDuJxSIq417s4TBrKhlwSTUM9YT4l/3rOZfgjDc0E4uYFpw4BifiTPPgSAIzFtSwvUnTGRy3wOwuwHDwoUMGa9SSQaTJZOEnMA9LYfjz5rC9cdPoMobYs2udnrqBrDmOzEmF6z8+YWcf+406tsG2butnRsezmLO3J+w6dfNhJr9XH3jQq6aO8i2CZNZcq2Dn74ms+bPWwAQkmy8QlkhsaLjiHZXpl3v7Vvb6OoNsn9N80EZV9wbpu6d+jQZVw11l5ATCAggqNch4I/S0e5XheeHYsiR4WdfL9sIKUANZRhZpGUMqY4i1hOi+o3a9CxgFFnXIzKjKmyUPTmbxcvKKMp14g/G2bqllY6dneqkd1gk1OAbc+D000gceiT2qXQK2jBN8TEl+NqHCDX60qY4P6iJ/hjv/2cfOzdnfqBZhcOZHJXpq+zTGSadbithUUZWoDTPSX2ug+4kIkoyGzEkm2OmDBvTl5VRmXzIPpQpCoXZdgrZyPGlJ/FwvpNYbwhrvhMYVt+K9YZ00XutFq+9OP6afho9Ni45ZhyTrpjLfQNhvDu7hvUFjAYml2Uyv8BGXYcFp9t6SP1u0R+jeTBKWTBEormCU5dfzTPNgwwd8KYtKqkvkdGmMp+aXBZ1GnsgmragaiiXkSykoA5YWe0mokMxwqKiDgwmBXP0WnQgzq79PUgz8hFjMl29QfwDEbX2bDVitZnxOCyYE34SQwGiSeoKk9WIweEmGJeIxmXiyZKBvcRNvD9Ze0+ubTr0MxVYoC1ORhWBNG5OAdOn56EoCc6cW0y+ox2D1UqivJRY3mI2bOqjtaGfeH9Yl0CdOKeAM+eXUOaqZEdnKd3t6gxH5rjMJPZfYvnyiZwxNYee/jDxYJy63fXUr2pQy05mgZ7+MBvai9nb5GVS1gS623bqqLpQo48fPbKdk064ldX3N6ZlNglRpnNDK50bWlUYaAos2mA06DxJIy3NcSdLXbHeEI1J6ngpGNf1OCBFHMc4TK9xKIegqwymfOZDmyCQW+ji9DlFzMlrxWyIMKFoFo8EYgzqrMGjN6sPynhHDDz+NzScP6h9Kp2C0WHGNSWbRUtKWe2t/cguXkJOEGn16wpiH6Xpi6KiYLQacbiteLLV8XqHKcCMXBfNJR46WwYRg3EMRtXLKaJCvD/MQG9Q5+P5MBmM6I/xyjt1FF16sk43UXRcGQ9+dyFbOmT+/PsNTJlbSH6+k6aGAUKBGHmFbuoretT5DVHFqTe928gvoxJXXDabs86ZxpZsBy3bOvSX/h/3b+K5cg+9B/rUadxD1FPlsMiL6xs56dLTwd/Lifm51J86kXWtQzpds3b8qfVca76TmcvKEGMyVeubifWozVSMAo7yDCYsLmHCpGzef66CcOvQMOmZUUj2AgSOO2k8dquJLVtbadvRpc5xKAoJYO+r1VSsatB1s0ETZEkQCcWpa/ezP/NYJp1WyPn7p5CIRjGMn83WgRN4a1c7jdV9+L1hbFk2rrh8Duu2tFK7tgU5mtJwHE0JLcWBmswCHpcVQTAQkWRW7LUSCJ2LPxjjwGvdDHa1Ujgxm3lLS4lGJdqbfAiCwJ4WH039xXR6A6qAjNHAvMUlTCnLpDU5sf7Snm5Wv15NsG4gDT2WMAu8+c8dvJ6M5l/12Dj3iwt5/IZ22s0XcNXnX6bqhQNUv1R18IR+cvhtrOdPBw6knqsWrafO6YgycW9YJ/vTnUbSoWr6zwKq8zQ5VL6u1AG1VIcwqiMYgwn1iN4vWe1TmExGZue2Y3/vH/ie3MNFXz8R8bpv8dxLlbRvbEUKpOt9jAXTVf9GWubyYRgWPk77VDqFhJwg0ubnnWcrCDcPfuiSymjb/6hsZEQgWE2Mn5XPzOn5jCt0YxIMxBQ3SkLBZBR0ZIWGckmIMuJAhKZ31cGw1AVKW2iP5njlsEjNyjp+WK2K+EQ6AkycmUchG5mRezqx3hB1uzspOW8aM2fm09g0QGlpBoqisL+uX1c5U2IS7asaeWBPN/nzCymZkMWyW5dR2zRA1c5OAgf6dCjroY5PK6U0HOhl3Ykqh8+WlgH6+0KgKMMoltT+gqIS3MW8EbzdATJznOqkdiCuE/flT8vl3s/PomjffZz6szv46W/WqmI/okKsN8SAKGN2WfC4rNitKdTfyQVEfyFFGWtJBhNn5lG/q0sve3i7A+yVFNq6hshwW/G4zkNJJPDXxuhoraC3c4hIfyQJ9zXy1jt19NQN6HTfemQ4ciguZZhRDotUv99M0/4efVETfRG9Vm5yW5h16kS+fuFMMm0Cz+/qonJXJ701Xmq2tfPo787F9ZsryLpuPpfHLsHtsHDihGwqnBYefXYfXbu7iPWEDl7YlcRBPFZnzysGcRMH/DFdMeyDvCep11bb12gDnCp02oAh9V6klIoMigGDkEBwmMiZkMnE6XlU7e4k0D6ENEJQaOT+RzqHo80atO1KAXUItjUwm+n5uQzU+7Ct2MHZvxmkflEJvTXeNBbfgxyCNpNiHj7nhKKSaaaiDz9t9ql0CkpMUgfMRjy4qVH0p9XLKjGJrFwnly0soXzwXwTyT2N9ew79wRit3UNEQsP8LKkTpppmtb3cwwU3LSI/y87Dv1ijc7QcDXGWFJYYOuBVf5AV9r9Wy+d6S/C1v404EME/EOHt/gi2QhdGs0DjgT69ZwDpUY/ojzHUHWTanAKOnZSLPxijWjCkR2pHcE28O7v48d3vJHHqarkqdap2pHPVeJua17ViciflH7VsRFborfGytjnCFU3tHJ/9Dz7/pet54I63kWOqk5XDIhHgP3/apAMBtODCYDbinp7DnMUl1FX0UliWwYI5RTRVezHEJCwuNcvweUME/FHMFiNLjinjc4vKqPIG+efebqLJ3oQiKiTCoi6JqkfLcmJMx6CZlpXFelNoQZJwVKPNiNFuxucN8bdXD5CT5+D0+SXsK8+kKtn07QnGyc2xQ2YGQwMRtm9rozDHgT8YZzAZhRsdZoSkkwU1C3dPyyHaG9YzZkVU+MHv15GQ8wgcePVD9+8O6RhG/Jy2MMrD5SPNeWSOy+TzV87jjHGt3GspY2N/OhIqNUs46FkcEaEfzSKckNVrNljbz19ereTGs77N0qc8EA7TI+Visw5itJvVzHLEvdUcgslhwjklhznHlBIJiVRvbFEdroYU08qhn7JS0qfSKYw2X2DOsiOYBR3Hrf8t5eH6b1/YkfXPhKKiX1rr+2leGqXM78OdVUVUOo7W7gB9PUHCodGnlrUXyZpj58zZRZS5B/jP+Ewibf5kvX9sSu3U43HPzKN4Wi5N61t0egPRF6Ht/eY05xJtH0IciGByq01FbQBotHNLKAl6OgOs3NvBto2tRNr8R/2CScG4rh+gXSudeE6TMk15cbXZBSmoEp3pL07yO9GuIE8+tYerfnEzhkAXnj4LRodZRUKJCglFTJYjZJxTVObVUINKIGgwGhg/JYezl5YT8EfpbhtitTeMHBaZvLCIU48fT02Lj93b1KEnQTAwpdTDBPtazPnHY7Ob8CevjSYID+oUrQEl7bzHtJE9Ee1nBB3+GOsN0e6L0O22cMKlM5mVG8VmN+sN6x/9cT133v4s2+u9hJtVttM1m1sQkgy4RoeZqadNJBKK0/RuIwklodJhnDyB/dvbiXUF9Hvt39uTFhh8WBvTMSTPVbORvSg9IEEBo5HcQhcnlA5hqXydSaVfZLN5DO6qlP1oZnKY9JKs6I+l8TUd0Tkoajm2dmcH/5JkxHO+wIR8ge0dUbq8oVG3pZ2PNrU+55hSvnv+BGKyjbv9UToqepJKdiqE2sDROav/hn3iTsHoUAXORV9k1GEyDYV01x/OJcNq4sd3v4Pojw3X37WJxpRhFvjvZRAHIWViEh07O7nPH2XwCzdTFrLR1NlDa+sgnS2DOoHdWMyfoUYf9z2+g+OOHccpZ02hfzDC7vebCNYNHJYCw1aSweqfeInc/xD/+eFD/Oue1fo1HVmCU0QFJYmg0F/SFNlPzRJKgmjHEHXeMLXvNeqzB/r5H4X+QkKUVQGilBoyRkFtDBeoGgeiL5JOQSDKkFJr1o5JiUr0bengtK9lM2lhGYHB/Wl/17afMSWXB+4+jb5wnB/e+bY6XCbK1O3o5NFAnN6OIRJKgux8J85Z+fzjqk6if7gX2523cFOzGzEmYzQJbNrfTad3Gp3eZiRRweKxYrGozfuhzgBS8lgVkTTHoB1ParZgclvwzM5Xv1ujwqhLlpZQPimbrS9WEesKIIty2j2rr+rjtRIPTrcFe6mbaFeQgc3tfH/rc5iz7Zx4/TxaGweo3dCKYBZ0yoxFC4oZGIrSVdGrn3tjdR/h5IyNa0o2f/jV2Tzw4v40HfWPwkY6Bs200qlgFlR0kaKkZc26Cep8QlT2kFlYQue+kD65rG1/LDN7rMw9fxrnnTwRk2DgwUd34t3fowZKRzoQmcwWgnUDVHYF+Vm1l/ySDOIxCb8vkqThHmVbKZrhBblOii1bid//Dx785q/48p9kepLv8qeRNhs+BU7htFuWcu/4l4jPOp9TLtl3kGPQauwmwUBUksmelI3bY2PCpGycdjNdvUF6OwN01fUT7VY5hwSbSUX4JNNMwWZSR+qTN8FW5OLMLy1i7avVBGr69Qh1LBTBIdEFSeiardCFI8eOr8qLFIjjq+jjD/e+j7PYjSAYVCGQ5IOkPxCjpLSKqNDT4GPm5fM41fk6ZBXzA9Mk1ncHdaTLaGYwqvrNiS3v0r2pk8IvO9LhfGOYRnGQer0106JYKRCHEc1FlSsncZCDONQLZzAbMVqNmDw2NYKflEVWjoOO3d1Ychx882vLmJbj5Oktzax8cLuKdhql7CJYTWTMyad8ag7N+3qoeLlan5hNQ/ogIIUl3q/to7nTr6OnQEW+tIVFHCVuTjp1Ej8Q/qYiWMTjsC4qoUdcyORJAypbZ0KduvUHY5QXZnDB4jI21PRRnOdEURI89cxeQpJaEhDgkI5BMAtMPn0St145j+reAP/83QbKFhTxm5vnkmer5fwdGWmMsJoI0Jz5RXwh8CfkgQ42/OxBfnn/RgZ3dGIwGxl3fBm3njmee/6TXooSzAJr1jSSX5yBqyyD/t4QsZ4Qre806M+dJdfB7Jwqpk7JZd+Yd+7QpnEnpaKCxjSjyhBrzbVj8tiIJzO0g+ZWlAQGRaG1tp8/v9vJxNIz2LTugHoPBQHkg4cydT6xpIDVlWdMYblrJZjMrJ43lcEm35jswGOZ5hiUqIpeCzX69KBm9DmM5DskqBPUp84qpP2iq1n/VpB5j1/InN+9wkCyZGgQDGi6Dp+mEtIn7hTGF3sIPbQG57mdWAvPSHMK2sIrBeP85r615I7P5Jhjyzl1ViFLnO+Q8LYhnXQ8u3rn8cSqOqo2t5E3PpPrLpvDur2drPnzFgxmI+fdfhySpCQHbWTu+s3ZLLz9XO564X7Ovs2gE7elzkNoxHMzT57A/rdqibQH0o5bi4IMgoHMGbn8484TKWn6E7cfOI+tKyqAYWH4VP57fap3jJRRMKtOZn5+AOmptzBOKmLGhG+zMckJo+17pCXkBMG6Ae44/VvMeSCHh370bhqy51CmbU8wC0w8dwqXXTST11bWUv1qTVqJRxvkEcwCn7tuAbv3d7FnRWW6VOMYUVxCTmAwqw56wrJScgtdFOW7mDUui3+ERPore7GZBCa3/Ja7I83k/uDHPPvQNr0hmjqIZLQZWXLyeC5aWs4rRW56ugKcevx4dlf3svHfu9Mi7Eibn8d/uVYne9PTe6uqeLbouHJOnlWI+FwH/e+1YFpxF6snnoK/MsKiKbkUOK0IBojLCSRFocRtpDS0ggnHnM+GNgPtvUGsdjPxLDuGJKGhISYhR4cdQ2p/QbCacHlstA1F2VmplhL6ugO8WuFHUfIJ1tXo11uDNrvKMjhmWj4dX1tD7c4gp960g/9MzdEHynoafNz5z700JTl+tODH7FG1kb3dAYbqB0YtD/m2dnDGl1Rn8UEWJcFmwj0th8kLi/B5w3Rs6zgk3YPRamTGOZP52fWzMRlifO0Pe/G1+JEjahCXSHEOCgrRjiG2vFXHdoeZuDesZwoGowmQUEQOdhDJkumKd+voOmY5gbDI7rW71UzpA5RqtKZ7QhEPnsUZZXupJdCOQJR5p46DtyqZcP5E/MH0TDt1H58W+0SdgmAzsfr9Br7w51/TEZlJ7KEXx/ysFuWV5ruYkDlEYutmpP2tmC80MiW/kKICFw0eKxedN53zTf9h2bmXsf6fKu/QOfNLMAkG3rVuQxJlOn1hTpmRw2C8DIPQi73ErZZ0uoJ6pCPYTMw4cTw/+NwcvtHQf5BTSDWL1USOrR5lfw0lxVdhclmw5jsR/TGiHUNIyUnmNAz1GI0vLTJpGcpi/rFzMOQW070vfET8Q3JYZN1DO9hgFsZObQ9hgs3E/bfMJu/V73Lm137E2W/XI4eHa8CO8gwWLCvD74ty2rR88rPs7H+tFsl/ZNxICVFGCsTp7w2SW+iits5LS7ufoaTs4xOvHuD4r12Itf5+Lp6bydaFxbRX9emTpAk5QQIBRVTYsb6FQV+UrtZB/vCtE5ng/ycnnfNFtj5fqSOUTA4TRpc6AKnEJAyjXI9oVKLFF2bBN35E0VU7Wdltob6tjyy3DYtRIMcODtMANpMfJWFCUqxEchbRM+jE6x8gEpOYOiuf/r4QvR0BfUBObeCiN081x4CiUF/RQ3e7n4EGFSoaqO7n2X/vRA6LiAMR1XFMyWbJGZNYMD2faEwiGJco+fZx5O1opWroeLzdG/VrGm4epDUikjsjl8GWQTXISdI/eKtVnp9UTqZUOxTB4pGY0WqkfFY+5y2fRF27n9dqvHovSysP6tmbUc0olh83juLOf2Cw2SketwxBMKiCUyOclvYuqAGFyuJqMKsNeP0zijiqLoIUjFPxbgO1O1RW12jH0IfumRzVdxUVXbZ6WxtLvvYoV035Dj1n3Efzn/eqa5ny6eslaPaJOoVLfnAi32u+h67II9xw2xtjRhgJWa0hB/xRGjr8HMgq4qQlJ2Oe0IxYsoyGvkx6+vqQwiJ2i5FEMIi1SE2j5bDILx/ait1p1h+Kh3/wLq8su43BG9eiiAqZM3LVh9hsTNtnT+cQ79d7CbSPLfCSEGV693Rz7a8NHHPs3bz7QgWWXAcnnzmZQCjOukf3ICcdmmBW8fVGh5lwktBMs9Tp3WhXkD8/t4/lx19PoE5k3erqtOj3UKnmWBjysWxkzddqDCB7wziMvqSsqazXgCMdATpb/QQDMe54YCORUFzXldCj4pSMYbRsQYmpvQB/spauZWjICtWv1nDungwuu+nHFLTHmDotlzOXT6Smxce6FyrT1LKCdQPsqRvA5Lawo8uPtfQ6HlnbjCIqWPOdTFhWyh3XLWSyp4bO8Eye2tLMO0/u1bMnJSYR7w+zf0cHzXX9vOOxYbPPwWpvweWyUJpvosBppdSxGTprSOzcheKLIBRlYVh6PBM8VuaUbUR6ZRWyP4r1ti/zRM18QhGJzdvaaNzUppbllHTcvhyVCRzow59EoCErSP4oQX9UX0jd03O44oaF3LyghsiPvo9lVi6xq+/gHwPfxb80xprfriFQN0BCSajoIpuJ/Gm5nHrKJN55t44BJcHMkyfQ0eSja0PrQSUdwSx8ZPVsRVToaPKxamsrvR1D6pBh8jzs5R4yJ2QxUNuvR+mCWWB3VS/LzroFh1nkC2ca6AhEefzZfaqS4IhSpk6oZ1YwuS1Ysmzkj8/EbDGpsODuIHFR1gcGAdzTclBi6sS+1kz/qIAohyyPau9SslQt+mNUbmzl67X9ZOZ+nsH7duJvHVQp8keUOj9N9onpKRiMBv714rVU5i/iypdO5PrGG6h+/sCon9MoAcweK7aSDHIKXYyfkoPHZaVvIIy3O0hnfT/RriC2IhcLT5lIXUUP7asa0+CVI+GtGn+P9pKMrG1qtdJDNXhH8tKD2rOYd/okZk7OZcW/d+p9C8/CIt79lUCicR9/6ruCp3/6PiaHieJj1QGtrrXN6jZTSjUJOUG8P6w3eLUXzjkpC5PLQrBuIM25HI0JZgHPvAIMZiPBGi+iP4ZrSjbjFxbTWtlLsG5YqEhnDh1RCtP5Z1LQRKkv9qGuGxyMPtGgfLaSDDJLM1hyTBkmk8D2rW0MdAypIIOkcplgNeHMsGK1mVCUBH5fBJNJ4Nl7yglc8QXef9lPSTEc9/NFiDd8nyt+2k3/vl51wDCp+TBlQTFzZuRTmOOgorGfQX+UDLeVWZNyOKlMoKDvKcTXNvDG9/cRSVbjTrs8m+wzxvP6bbv031216VLeKridDKuJN3e0sfGFA8PykCOIAEdOYWvXy+SyUHRcGQXFGeTkOfjCSZOYuO42KC7EO/Nr/GRFO9Vb2oh2DGEwG1lwyQy+f8ls/FGJx96vp6GqD29lL7ZCF3+4+zTW13l5/Ker9UDBYFSfm0nHlHLg1RrkmIxzvAeTx0aorn94sToKS2VT1XoKoJbnpp0/lQvOnspTK/bRs7NLR0Q5J2Wx/Owp3DXpDRj00zX9O9x493qG9vem6U4MTzWrfUXneA+T5xVx2WmTybGbeXNPBzu3tdO7p1ufyHeO9/D+HxPgyuGEK7pUvZDDPIMfVelmpE6I0WrUCQsFq0kvkY3WU/xvlY8+1XoKCTnBLd95gyeHdvObre00rtky5ucgiVsX1aZnqMFHx85OHbmgDRzJMZm4L8rapKjJyFQ01Sy5Do65cjYOh5kNLx7QeftTP6eIChzmJdFf9CQ/PkYVUTF3Wh5fnLYT6foFPPHz95HDCln5ThIbXiG2pp6TbrmFFWYB55QcvnnDIoZiEvdu79CbV6Ivog9C6S9qMiI3uS3c/ZPTybGbuetP6+lZ0zLqsR3uoTeYjcxaVgbAzo4Aoj9GqMFHZbPKCa8N3mhsnBoXlaYxLUdlDLFhxSsFVJx9ykj/4RpompOzFjgxptSN494wfsFAa5ufyROzKZ+UjcVqor83iM1upqg8k7KSDJZMzWN6jgWzEKE3rE6QG26/mPdfVs9h4jwX8g3f5T97swl2N+jnbc6yM31pKZ9bPonpOQqCQWIw6CIYFpFkhXhcRk5YSYTDRCu9+uIP8N5zA5yXaeXYczIJ90UYd9siNpbfwevv1hEKxOhu9+vnblAMeraQdi1GaEEbzEZmXzydh78cZSBWyLtNFnpCcSadfzXdkbm8WRWmoaqXaJc6+V5+bCkPnF1J+K4fUHbOVEoKbmfz0/uJe8PEvBG+9WOVyjstwzQbcRa7KSn1UGU04Jmdx523n8Sy4naeq1jAP36yWqebPhLTs9tknT0NiSbKtFf18bpgYEDrZygJUBQKyjxcsXQcypt7MJ51Jit29hOqS2cZFcwG/Xpp/bviyTlcecYUTvbeD929lC6/m79KCqurvSSSUHXRH8NrWU4skoEivjzmuWga2FkzcvE3JQkuP+TCrM+oKAkMKKp2Cup1z5zgxu4001XRS6xX+kQcwpHaJ1o+CjcPcuW1z+vKSWOZ/mAoCWRZAaOMHDboEWxCTiAnBTcAEhw+zTN7rJy6uIxsu5mdm1pVp/ABb85BD0NEpKc/DHY3JtOwQHzPgT5qv/Irxp9czSNPVqNEJSKtfv6zsppoRNJr4anbhfQ5CIzqROTe5gHMJoFw9+gqcoLNhNFqHEZJjNacFmX2rm4CRTmodKdPXZtVERlLjoOicZlYrEZCgRj9vaFk38Kift8XJRGI62iKtG0dwjFoWeAp18/nzAUlPPJyJTUr69TascNMeZmHk6bn01eeyW6Pl0pFQRAE8vOdTBuXxbLiHpzNr0NrO/mF+RgmzEGan8+sGW0AFL7+AN/4V5zGA3uJ94dVhJFRRUGNK/VQ4rYREqHKG6K2xUdgMIrdaSYQFgnELRS73LomRqpZCpy4bzsDQ9kUKsXz2Lyvk/x8J1GPlSFfhLhZIKEY9aBCbzQnsxwNVivHZNWRGlWq6MH4OHrCuSiKH7fFiDc2hXfro6zd2KJP9xtzHdidFsgsxH7RLAwnn8/6XzfpfaSELOniT2mBkSgzWOVlfXcQKRBHCkt0DIZxZNcxt/iMNGjo4UwwC5izVRZWjTFYCx401Fi4eZCG5BCdngEIAtn5TiQlgfHiy1nRspS3Xt2RNsgoWE1Ych0AupMSzAIl5R6WFHbS88038bcMMe3NhcyYcD5rrcZkqUmlbb/mR3tU3in/2IRpRoeZvLkFfO2Ghby8ppGd/9n3kTInaGuBIqp8ULlFLi45YypbZnSz6Y1agtXej2xfH7V9ok4hISeOSpgi1TkYrCbyl5QwY34hVXu66dnafsRKSgk5QXwgymvv1+N0Wwm1DX3opk/qfqNdQd781y4qK0robtylUu0qKuX2569doUpvBuMkFFWQZveTKWBATc5wxPxD6vyFHJNZ8cu1eo1+pJk9Vs755jIuWFjKC1tbeO/B7aOWmBRRIVgz+sOplQWMDjOe8kxmzC/kplOmUORspi8ygWe2trB/XxdWm5nJU3KIxWXWvVBJpD2gvxCHu6apVADHzSrkBNMLCJd8jjvWNWNymCgs8/ClEzLIefM2DCYj555xIX+0zWLP3i6CoTgDgSiSYoUBH+K+dgxVnZgzPMSu/yHuz03ixV3tfO/OGnUxjcopzXqZaG+YTeub6fGGiMdlejuGiEUljCYBp9tKp9NCTY6D0nGn4PymzMXmt1j/cBPBECw73Y11+WQMM0+gNXQM+7p9lOa7OHtyDAMKf3VaWDMYhYDKNyTLCZBlvQSy5NSJ9PUEadzTndT2Vu/xgfeauLFtiMzcTpxuK3s9Vrrah2jdo5ZBtDKQ6ItQu7aFCzqygHMYemyASKs/rfQzFkItnjLpHOsO8O+/baXu/GlsXb/uqMqQRSeP59G7ppIV28aZPytmqKIX15RsJs0rpGZjK5H2gC6Fq9/vZIAhiQorK7r4t3c829/aqE5Wp0iwliwtYdGSUrZsbKFrk+rcEQTKCzNwet/m1WQWOHXrFsqWXJ7eC1QS+LZ1HNE6YDQJ5DksZOfYP7yeh7b/1HK1koDk9L63K8jy8j7OL6znnTkn8/M7Vh5ENfJpsU8ckvpBzeyxct018zhvUi9vzJzHn6v7jsrTi74Ie1aolMCHGwr7ICb6IjRq1MLyMDRRe/G0OimQ1itIVf7SqK4BlKiBhKyenxZljoqRNhrIXlDI3Ya/0nfLTn6x4tesfco2ppxm6oBRKvuoJvNoclkYNzWHK4+fwLjHrmfHH/ZTMsnOnS/+gL+5ltHYPkhpvou5xR683jB7X1Wb4gnBACkoq9EoSvR9CgK/+8N6VkyfTnf7RhJyApPLQnG5h9yKv/LM5zYBYLev59uhh7ilxUlrwwC+/jC+oTyOn3YHk74Sx2n20h6ZwJZaH1VNDdQe6MVmM+OcU0CgL0So2a9rDhCM0bunG29lL6YMGxdeP59wVGTT2iZkSaHLaqQhw8b8wkk4c2qwnTKZM86ZBS4nhtwcyJuANzaJvpCIoiRYWJJJZvXvYCjAd069FptlBsV5Kt3yc4/tItKmLmRSMM7OTa1qMCQIXPjt49i9s4Ou3WrN3ZNtZ9KUHLZvbGGwVq3za6WX4Z6Euth2HyWxo9FhxjE+kzt/uJy5+fCHlR3seKeet+/fclTvgMFo4KKLZuD8y210rWtj/ucfYf0BlWvLPAZ02mA0YHKYmHJCOV84exoOs5HntrakDXEZHWa+es+pXF++HsxDfKE6j84UOHIgHIfsEqZMAm8/MHcWHYFoio6EIWVa+tABohSM076uhe80Daql2o+TX02R6d3ewa9XFvPN048ly2YmY3I2MW/ksMf5Sdj/rFMwGA1EYxJhKYt4XD6iQa1U0+BuH/lxCQYdCikkoaEY5VHlJq0FTixZNsKtQ6Omutqwl9rAMxzEQDmWFZZ56Lh/M5veCXJFLHBIKN6hIiRN+6Egz8m07GbeuXs/gQC0tEYwX/c7TntiHV5/BIvZSFmGn+MXlahspqIMVkHXBdaaa2PtS2v0N+3oSOcncloQN1Xrn4tEQH7sIcrKfqKyzcZk+gcjPL++EUEw4HJaiIt19PUECQxGUZQEN147nwsnN9A0dDzfvO99/Ae8CGYBa75TRYj4IhiMBo6ZnEtHIMo6qQFJihMYjNLnC2MyREns207dD9+j/MLJ2L5wFmLxUsxCBJsUwCRksrg0k2mxp/D94V2sWTbs+blcufgblJrXQniIvYsnUdEfxlboYvK8Iqo3thDzRhh/Ujm3nRLnUddEnq3xIodFnG4ruR47sYiURhd+qP5Y6n081H22Fjg58aLpnNH3R5RNLVx48l+o2t1JsG7gqBemPVW9fPGq0ylcUEP95l7kmEy4bYgDMZXPaeRQo0Ew4JmdzwNfcmB79wcweQKTSq9hdcrMjr3EzfXjNxL55aPYrj8Rv++YtMn2ju4A7eLJzL9jIbIvQqj0bKreG1CZb1GzjNKlJTgzrBx4teaQmU9CVp9LTa7241qYte1KwThrn9rP0NBUXC4LkpTsvX0se/1w9j/pFLQb+vwLlWwal0lrQ/9RNcg+LjMYDZg8NgqXFDNxWi4Aeze0EKjpRx5lzsBZlsF118xn3dZWdj9dcRBETcW6D2cZR3p+bfUDFJw/iaXhGvYHlyP5nzrq89Coi01WIzkeO47AbgIpoxpr3whw1cCD2K0X0t4bJGdmN1fNEth44ngqVzeixCQyJmfjdFvxNg+qZZKU7GYk6kgwCyRkIwiyDm+VlQSKN30A76Wbd7CoLpdN65oJBmLE4zLNdf0Mtg8dpKrlGu/hjPE+5N/dx+QFZZxz3rd5Q6ni2JMncM+JB8Bi5+tPl1O9pY0/Pb5TXYi9YQSrCR/QXN/PexOyuX7OEkzW14g2+7FKIvX+2bgtCjHZiKRITDO+DhY7mT84F0NBOVLmRKyxAN7EPBI2I93tFZQtLeGqi2ZxTDHc7o/SuLqRvlY/j24fzxuvV6m04DGZva9UU/F2HfGB6BGx5ApmQS/BQcqA5IjvaEpiAIZpczHOWMgzL9UzWHt0Gtza8Wx7Yi9n7i7H7pxM14ZmdRYiyfg7cpE1mI3YS9y8+otcnjddCcDFd0yg+6yL1c8nJVunHltGoupVBJcF78Qv0rNrlZ4ByGGRmn3d/NNj40tXP4xJiPHYun52bm5GDqr027f+/HQ+t/t7BFe38thd/+bJH606LJrqw0Jztd6KYBb0ezZWRh7tGGLro3swOswfWbnq47D/SacAquft3tRG7/YOHQ3zSZtgNVF8TAl//dZcCmKrCLrm871AEXsbfSiC4aBoz1/Ry+qNzfgHIvrIu45IEGXkMCgpPYUjiWgScoLBHZ1cav8iZZ/PYd9XX/7QD34kJoEn/6Dfy6t3YJ91Kdt3dnB7r4uyogyC/k6dwyjQ4ieeY9epGhJyQi0pMUwYpmU/cV/KJHuSMqG7O4j1quPgF8Na2qIIBU4L0ahItClIX6UqQ5mqj6zZoD/KFT+BCy78E/5gjPdW7CPui9LUMADn5ECgj7KSDOocZrp2d6lfEgSE5AxBn6jw0qtVbB1XzhUrNiC7rGwbDLN7dzuBcJxoVMJiMdI1+1QWFxlwTe5FUqxIcQtBMYt/vFfL1tdriPuiWPOdPPn8fh5XEnTvUnUdhg6okNFUzLroi6RC7g8Z+Wu8YfZCFxOm5eHtDtC1u2vUxUkRFULNflY9vJP1L9mRwjFiXbs+8LMhBeL4tnXgSznG0QIXjX7lmItnkPjzTwEoKwX7ty/n7V9U6mVNwarCiptn/JiB8SI/+OEavd+YUNQ+WrDZz05Tq9qQ90ep2N5BpCOgz7m09wQRinIxWjuwWY98abMWOMmYlkOw0XfIIdXRLOfYUn5z+0lk2eDWP2ymY20LCXn0tUirTnwa1qpD2f+sU9Au8NHFOIc3LdU92pTSYDRgyXVw95ePIXHtqaze5OekXy1ixtRf0DAtFykYJ1jtTXsJ4wNR9j93QI2StWgtyTmvcRKNZIE91DS0Zoqo0LW6ma7VzR/oGiTkBAlBhQ9KYZGWDj9bexZzwplONrwzzK1jnJhPKCJiMgssmV3I5j2dNG1qVe9LVEYKB4n1hPQykpaBaOWbWcsnMHNyLtWN/VSsb1FJ3GxGimfnM2duEZGYREXBbSw7cQVb1qulgNMuz2ZfKK4HAhq2PhX7L9hMycwjgW93N49u60w7v5o3ajlpqwNLlg1FrMSeY6f4hHF0NvmI9Sb1B5IltyFRJjgY5be13qRwz7CYvCYyHw6LzDpvJrUDRexpHqA030JFYxO7t7TpVMmRVj+tzYPD91C71ilR+pGUBjVnYC9xM/XYMsrLMzl+RgGLC7v56zoPr+3oHJN/SIlKxKPSR5ZVH8k2tCxhclkm5ikLuPwRK96r/sJ1/6hk6EC6gFbT/h5uaXmfYNtQGkTUYDTo9CZf+fxizvP+gfdnfo/tLx5QS0TJ9+G5X69j5bTlFEy+hMafrT4ihyeYBeaeP43vXDqHR96vY/UD246KOlyKyfhjEjl2sDvNRzSQ9klXNA5n/7NO4aM2rblrL3FTMDMPSVIYaBgg3DR42IdLcyS2IhfzjK/wdBIdEdzQjmu+me/eciySkuBXd759MIeSKKeVljR64VQxEkhZMEYpMX1cpiGdvN1BNtV5+fYjX4Ky+wGwWsGwYCn9a6KEhmL4g3Gk5ACgHJWHnZeMrkWtOQTBqlIaFxW4WDIhm/EFLgZ6Q/Qkm7ETp+ZyxdJyWvxRNjb089Wnvop48V8IBxXyvraMZm9Ql4I8iEfKKGArcuEsdhOPSGTlO5MKWXH975p+suSPYi10k5Pv4vhl5exxW6na2IocGcbUJ2TVoQmCoDsCWVSQJBlFSWAyCQT8Uf69roEBX4TAYBTX8eMozXfhcFoIW00oMHr0OJpM52HMYDZSdFwZZ501ldmlHpr6VSfdMlTO3j3b02RVR7MjGcj8qEx7LzAKtPcEaFv+XXry4vz2zzvo3NF50PlHOgKEmwdVDrJRkFSe8kzOL1hH9WUvcMruc7g7OZ+kBUxSIM7g7m5dbOmIzCjQ3xtkR8cg/X0hVf41hTzzcBY40MfP/7yR4vJM2nd3f2qpK47G/s8pJE2wmnBPz+H5382led45VNXAGVfn8uyv/pM2FTqWGcxGiidkkdj8hv4758IC2nuDXDjTgKTYR8W7j0TkpPLOj4b5P5Jp4aOxVPSRDiVN4fSXgnF62vxsFWUesp/PzcosErVbMGRksTp4Ls21u+lv9fN0xZaDHIJuSSen7UewmRBsJhobB1hlMDC+OINTT55AMCwymBzY29sToNcXYW9lDw8J53Pt5pNx0k5d6CS2P79Xl2aE4SE7o8MMgsCExSVMnpJDvy/CqYvL+H1df7qgT9JBAUhDUTpqvGxMZoe2XDti0IgiqlPP46fnsmRBCVfMlQhJuWztiPLuhmZaG1SajimzC/jOeVP4z9ZONr2tzlfM+twc5uc1UJq9kPaBMM89tx9/RW/6gjGKMMtYaDLtPhsdZpVVeO4qqH+Vobxv8tDz3fR0DhHrDRFuHTpklGvJtuGalovVbqJve+dRU6IcjemT/kYDcW+YLWua2L+rE6NJda6WXIcqxallwGP0I1ItHpfoNR7P1B8ey/O1UxH9NaM24Y8GRWXJsuHJtuOxmzlhaRmtDQMM7urWj+twJgXiDGxux7e142NtWP837f+cQtK0FzJzYBVVKlEl7z7t5WvX/4Z3j7uEjtVNo0YPOg2HWcBkFjDMn8+yEzfhKnYxeNOfWff9rax5WS11hJsG074zct+j/azNKxy00H7Mpr2oclgk7g3TGxZ5I1RNRW0OJYWXIAgGDhw4QH+rX52cDcYOKuOknQcKCcFAQkjoMF1JVIjEJArcNhZO9pFJlcp4aTJDrJLKnHNQlARXz4vgjFYRsU+maTBMX7fKaquJtZtcFuzlHk48czK5Hjtef4TJZZm0tw/xh9+uI9IxnJ3pGUty0MqSZcNT7ObK86Zzju/PKAvO4t+7y1i/qZWEkmDRvGIummXHs+9+MotLOW/iUmJiGc+0DBLoCdJwoI/Hsuzs0EpFcoKfPbCJ5adPZslkAbfTQtQ7LLE5aiSpwShHQajB8PMy5dwp3Fv4NK03vUnJldN5yAR1b9cfBFnVN+swk7mwEDksEqjuZ+EVs/n2hbPY0enn/j3dY9577fpoinVH03dIhTfrFDIxiWh3ENEfY8HpE5lYlsn2nR3UdQylNdMPOXAqGHB7bDyyYZD6/pup+u5bh6fpPsxxmj1W8uYWcMLSMs4aX8NAbCKrSz0M7e9FOQrmmKNxRP8L9pl1CgajAVtJBiaXhWiSM+dQpkRVCcuvvTWfBx5fwrM3bGfGNFDOupboOr+K3x+JqkjhPTKYjfi8YbYbr2Hhmjl0hedx10O7CTX40mYEtO+Y3BY9stVEyaNdB08nmzw2ll41m8rtHQSrvQg2k87t/rFZSl8DJMRk/2YwLBLoCVKVRLFIYQlpKKqrtqU2TEd1dKKMrCSAODFzhF7HEIJgoKM8k5NKe0js3wmd3epin5/LrMU5lC4qxf7YPQxu7sCSYeVEh4nwFx7jD9Wr0VjQ5LCo6iO0DuKcaubry93k7L6fU6/+Jhe/XoNgNWEvc5KQZGI9Ib2MZXJZyCn3MHt+EefEHuaZxSs4+4b3ueqfD1FR66C1YYA1axupqHUzfdJNWDqNmHoM7NjXzlC7qs/Q7w3zVmVv2qI8WOXl1cZB3rAZVebTEYy1msKaJr1pK3KpJa2uwOjZQrKsecf1C2Hj45T/9UrWur7KS7e8Nub8CagN1DPPmUZXX5AtXUHmz8wn29ZDe4+UljkdtD/BgGtKNlMXlwCw96UDowpgHfS9FGeigQtMbgulcwuZNbuAyooeOlsGCfij9HYODXMmHWFvwmQWaG7xUfVW/REdz6HM5LKw8IrZfOWc6azY3Mwzxgl094foqPUexCzw/5t9Zp2COcvOhTcvZsHEHP65Yi+Nb9YdHp4Wldj9bAVfPO+rfKXjr3hFmS/+9QD+A96DopKRIjtmj5VIKM5rO9rYnlNCfWsdrXu6VPqNkQ+9UcDksZEzNYfcQhfHLCqhotbLpn/uOmhRNTrMzJ2WR3ebH2SF8hl5tNUPqHXTj6EunFa+SpaREGWUJNWvEpMQkzMhCVHWj+GIJslTRv8lf5SwYKBDVNhf5Ob6mUES8Tg47GCxYJg4jbjixmiQMJ66GM/EfOI7W/Bt7mReQQbWHJXnSPKrTinuDVP9XiOdLYN8YdksEAxEZY+ud3HBhTPp9YV599+70uZTxLhMb1+I8GkXcu5NK8m480Jeac6hs6WCoSSdc0+Nl8p1LfowoeSPptXu9XNPZi6KqA4XSn51MdNo1JWYpFOzKKKaOZncFsYtKsY/EKHbG9a1ulPN5DCRPTkbm8kIF97Ig1vKeOEvaw7bMI57w7zzVo2+7137e4jH83j79erDwlAdOXbOO3ki8wts3NruTyOXHM20yNs1LVcdBovJWLJsZBS4+MkXljC57wEqFnyN7/z8PQZ6Q0yamc/lXzuWfz63l6b3mg4btCWUBF0H+ug8DH3FkZrJY+P8EyYwx7GaP3XksndbO9GQSLh1KFm6/exE/kdrn1mnAOC0mfFYTSpPjFE4LLkdqDXCymcquO25Sv13h+LtMVqNlCwt4ZrLZhOTFFa8UMHmvhCx/ogqXCKmiJGncBlZc+ycddZUFpRnku+00tMfVqPXLBtGh1kX/on1hnjiwW1IQ1HOvmkRNx5XyuuV/azwqfXXaFfwQ9eGU/sKkO4YlOTxGhQDiArKiLJXWiN8rCxhxOh/IjmBqoghpGCc/WaB4AUTcLZ34l1RTaQ/QtmvzVjm5GJueZ+2rz9FzB+j7KwJFHzrRERnBT/87kl0DEZ4e3UDTZvURnJ8IMpQ/QBvN5g5d8EXeLeiX+cUMpkMmFO0gS25DqYvKyMUiFFX0cMldybImPhjhh6MIPrW6j0LFUab7F1Y1V6IvcRN3pQcLFYTgcEo/tbBpO5uOijAIBiYcvpEvv65OfzrjSpqN7dhyXVQUJxBR42XSEcAk8tC6fhMAEYr6GiMuVabiYdX11G1O0Tf9tVHpA0gBeL0b1BpIgSzwL7Xa9j7avVhJSkVUcHfNMj+xn6OKc5gyuwCure066yvo5kl18HJN87nG6eP497nauhqHWTGvELmTc1jSugx/Pe9zpw/TkARjZx63jTu9P+aFeM2868fTeapX/ydv3/nrUMGbQlRVjW2P6q6vaLQ3BukKedMGra+qtOzfJiS1GfFPrNOIe4N89iP3+MJs3DIh3ksO1xUpP/bbOSSC2ZwwZbv4HuricDtT/PYb9brVNcGsxHP7DxMDjP925JwQVFGHIrR74/QFbSzan8Xq56rxOSy8MW7TqY8x8mvf7dOzQaiEsEaLwazkf17unjVbWP3/i6mHz+Oe66awCuVEf75g3eOCkY31vmO5Rj0jAFG74UcxhmM/FnT1laSKI/BvT1c+wsT3/7S71l+3IvEHn4F4iIYBAxFkzAIBnZsl2moq8fyUD2lZW9ywtcXYJwzkTNv/gY/cpqp3dpBrDuAwWxk1fomtu6zUbGpkvhAlJ5N7TyyU51DUEQV2jhpYRH3XZNBe7CMP79cwb7Xawgc6BsTPaL1Iqz5Tk46bRJnzyvBYTayo93H8y9UMlDdhxJNd5TatZITYLWbmXPqRJbMKcQ7GMU/ECHWG0L0Rdj6dr0OXR1pgllAjsp0VfTSNmJhPhoJR41na7T7MppFWv289sBWtmwoRFEULLkOpGBcJ7cbuQ2Ty4LTbqbFnyAUiFEyIYsbT5rENPNKcEzB8Y/f8rdt+ciRbTQ3+zCcMQ/YjKnUTfwI3s2Pum4f80Z45u9bec5uPiKE4f9P9pl1CsDHMscABy+gcVFG8cdo3uZlQWkWj6Y0FAWzwLwTxpGTaee1A159ICfuDbNtYysdnQG83QGkoSg5s/L5gvHfsKeVdWd9m7eScosJOYFBUOjY2cmLbWpjd/45UygYeoWr553IYx4rsQ/hFMaarkx1DPrvSKETOcrG90GcR9pmYio3zE/b/fxr7lSuuuZflLhtMAghUea4pyNccv/T9G3pZMM7IXp6FfI3tJPhsZI3biWXnX4hjwfitATjmDOsjBufhd1qolKb8RBlpOTULKgDgf6BCL2RcjoCEUKBmE7/Pdoxa0yuBrPaI6iv62dnlgOrWaCycYDw4MGRt4beatrazm99EYKDUSbOzKO1O0Bj4wBDnQGdflwlxTt49kSrt2vlkoSSwDOvAKfbSu+eblXn91AMtMkav8aVdbQmh0WGOgOMn19IYZmH7jY//ft6R5V5FX0R1r5Tz+4dHURCcfIKXeQ7eknUHsAwYSbN0nEcqK/EIBioWdfCt90n83t5KU9VjGfFX7b816GcCVHW4eH/5xDS7RMT2fkgliboMcZL/F89luRikTE7n+/ecixnut/knq2LeOcfO/TJRYNgwD0zD4vHysCOLr2erUEoU2mU//nEFezLXQjA1ZHfs/S87jSu+tSF1Frk5oe/PJO4rPDbu1epTJMfIJISbCYKjivFnWmj6b2mQzawDxLEYWx8/WgNeT3bSOoP20vcqvhI8lppJRtLlo2bv3MCp00U2NIhsGl/FxceM45ji/bBy/8mXt2P5YSpGGbNJ+6Zxt6+qTy3qZntb6gQxeLZ6vR1246uUZu3WhPUVpJBQpJ1jiFrgRODyYjoi6DEJCy5DiYsLmH69DzKC930+6PUt/g4sLNTF/sRbCb9WZSCcaRAHMEspEFm9X3ajLoGSDpr6+jXceT1Nmfb+ftDlzAps4l7Xk2w5q/b0thz075rNJB9bCnnXTCDTZtajqinlmoafcPJN87nl2c0QaCPJvu13HLf+3i3dR7EG6a9myaHCXuZh/JZ+Xzz8rkssL8NCQXsHnql+by4L8pbb9bgbx1UqT3GQE79n3089qkW2fkgJlhN5C4tZsGyMkxGgdVP7dNhnkdrqYM1wDCbZ/J3RxJZadFg4EAfP79jJb8ryyLWu1990LWXVUmMym2viAqJYHz45TIKzMregU6i3dMAOPVj1c2oomYMRgP3/WI15iw7ebPy6DcLRDoCRxwRats0uSzccN0CCpxWflzRe0incCTR3GiLkzXfiXtKNoN7e1Q9AKuRyadM4MuXziYsyuxv9uFxW2jvCVJf46WzxstTz+xl3YRs+roD9DcMsHNNE1lFbqbO+g7jz8xAUcC3O0pb5xAdTVsJ9IV0RE133cCwQFASrZU236EkkIIqGZp2Te0lbs64ei6l+S627Oqks2WQz102i2tmN2NsXwlWJ53jL2NXgZuWun4GekOACZNbwJ3r4JrPzaHQZWVjTS/FuU4e/sMGtU6tL9ogHeX0sv48Jk0Oizz4ehVTJmazd2t1WqZ2UN/KbOSyz83hynkxBoYKaHyz7rD7S7tvZiOWLBtXHjeBwO3fJNwbZvzvhiget4z+ZCku7VjlBCAjRw2IQzEGekO0+CPM9zexfvk/6UgOld/8/HFM/uLveeLVA9S/3/SxkFL+n304+59yCgajgSsun8MNob+w/oqX+clT53Pmiycd3QQjKVGNy6IPlMlhEed4D8//dibdoRK+9NWXjozvXFaQw4qq+paEyWk8RXDo+m1qndSgJFhRPY2iQpAkeF26loT4wvAxCwZc03P57vdP4vRXvoLgsbL//H+wprqXb5W8jMFs5oH2C3j8rnePaAJb26YUjPO3323AaDMSbh06KML/MKZRMvzyT+dzfHEl1/7OTtuWdgyCgXNPn8z8/C42deRzoLqXvAIXSgLMFpUZdrDex0Bln76gC1YT0e4gPQf6WJdcLDPGeVhwTBmV61r0erk138mUxcVMnZxLXJTZtK6Jgco+nak17fprC7OiMnuueaMGV46DYH8Yk8NMZ1+INxqn4jDPoLk9yJ4D9QQGoxhNAuOXlDDQq0KIFywu4aTyOLnWSgajc2jsDuizK/KIBrwuMTkiIEk7ntEsOYm965n97E5qF4+mja2brPDKy5Vs2OSiaUu7TjOSdv5jPJuaQ1VEhX1dfuZ+9RRc4Qi+osvobKk89DOdbNZKkozFKEBbN91qFVSdgl84l01VPXTWq7TgH3eWMNoz/HHuzzU1G2exm4Hd3R9YJveTtv8pp6CICqKsEH+/io5OeGb569xa9wPu+/5KYj2hw28gxYxWFSroyFcVnqK+KN+45VgGzz6TSVfPwDXxehU9dKQPUMoE8getj77wygGe3XYrCCYueWR3+vE6zNzzo1OZfef5vPBkHwBXd62go+RifPe8RP3GPm7Z5OApj+2IhIv0RUlW9Ih5tEbx0TiG0Xj0zVl2TrK+QuKNjVxwzj28oiToruglGpOoGSjl1fU11K5tocFmxGAyqnxLyQxKR/+QhDxm2fEUu/F3Boj1hjBbTSyclsdGc60eccZ6QzTX9ROPyVisRqIhMe34Up116rnJYRF/RR+BJNuoYDWx0hvh7WRTXApLOnDg2MtnceaiUsKiTF27n9xMG3t6HAzFZrGtspN5U/OYd/ok9q5qSDra5P6Tw1yC1ZTGaqpDi1OCidTrqWtcpJTh9MwA4aCMQRuS69/ZRT+MmT2O5iQMRgN5x5eRX5xBa2Uvq9Y2MeHSbyIYDKxc1U6gxT8mnFXrfclhkXB3kHe2tjL3gp9y+XY3NLdiOPk0/lixjPef26Wj6z6uBfpwlPDa8X6UJpgFll04nVMXlPCb369ncNfYA4KfZvvfcgpRiacf2cnV9/8K7r4UgAsnVfDv+YWHxVGnmtYLyCzLYNKMPASjQF1FDzaTkfylhZguOpPgrb4xI27t5XRNz0UOi+rE7CgkdUfz0CXkBO0bW7nOOpd4TKJrU3MaisU9M48T+//MM0mHANDmuoYnH9/Ked86lbyWl0n0dWPNnzEqfl3Ljqy5dmLeiN7TSB2EG4vyd+R5axFu6uKQtbSE396xnN8/tZvat+qQw+IwnURuOYbjBJwDJiZOy6XnQB//vG89oDbcUyP4g/oWyX0pUQklJlFY6iEzx0HjVlWw/S+dAYJ1A/o2lJiEb2sHvq0do0biIykRUs8tlYdKDouq1oKQXrozWo0013rJOWECiwsHEQyZPPPqAXo7hpDDIhddM4/LVt3KVafO5w9FV/HCHzdhtNkoWlDEzLmFnDCrEJtJoKEvSFXTAHu3tROoG1CH0FIqKakOQYvcNaeiHbvWqE4gpCnd6RK1mjaFMNxwTrPkdTG5LZg8NmI9ISzZNt74Vi+JfSt59tQ7+cvdq7h7T5euixH3RQ+ZiSpJHXUpLLHtqX1c/XIVgnUGCWU60qNxpOD7HzvsM000SjPjoQEShzoea4FTnUNKqskdynz9YVr7Q//TZbH/qUYzDNeof/mn8zmpcDuPVczgP0/uYXBH5xGXkMweK+OWjycel7HZTShygp4alcHUUeJmqKb/kJmHYFYH1q664yS6+0Os+edOnb73SMpGhzo3jWI6NYoSzALOKTnc+cPlnOl+k8SOreyY90u+8+03iHvDZC4s5PM3LKS5c4h3nq0g2jF00KSryW3hhJsW8rsL2rnp0WxqVjViK3IhR2VCjb4jelG1ASVrvhMEgUibX3/4v/anc7nsb9fheeB7nPNTgYEdXXpNf/IpEwj4oww0DKjZ3kDksDDhVAeU+nIbHWaMyUlhOSqnXSfN8QH6i6/RS2vlp8Px64yMMEc6KYPZiLXAyfKr5gCwfWML/gNedTDNaqL8+DKe+bqPkHUav33XwJSyTK6uvJMVV29Rj8cI5982Dsdv7+DF+jn845HtDO3vTdMY1/erOYQkJYc1144z24GiKERDoi7RqauXjRKYGMxGXFOyuekby3jljWo6k+9JKlBjwhkT+c3Xj+Wh9+vZ+HIVf//deZS7a/nWo9E0qdgjLfUINhOWLBsJJaFfb/jvsIOOqiKYnGDXhbgUZZh25DClXpPbwq2/PZvZhRnc/rNVh4z+TW4LG96cgfKX3zPw5X9w4QUvfWio+Edtn7lGM6g3LtoV5Ls3Po8lywb06HrHR2IGo4FzvrmMH3b/At9V93H9T/cRbPSpsocxFR54JDMKiqjw2ouVehRmlBPIMUD54FFQanSXFtEqCUKNPn7y9Vf5qc1EQlyMHH1OX0R8Wzu4f28PRoeZCSeOwzy/kOo3ag9qGmd5bNDXTF5BObVGA/ZMG4C6uB+B0IpBUEWE8qbkqJQDvcMR0RN/3cIZ97/FYzu6iXp34yjPIGtCFm6Pjd7OIYZq+nWaY4PZiGdeAZEOtQx0OM4bbQpYuz6SP3pQGUgDIZx6xhRq673U7egk3DyoCrRPySEn30VjTZ+6AB9i8Gtk9mAtcGJ0mPWmsWAWkAJxVj28U19c9AVZTtC6sY3z/Hlcc1UW9yx7n6dzfsaKlO3LMggeK6vbF/L8a3sJHOjTETip55x2zd0WrPlOlpw8npuWTyYYl1lf18f+Az00VatDcKIvgjJKBGxymLjiC4u4av8dnHzLg3z53nVEutVJba2mP2d+EQV/u4qfX3Qsd5x3Dp+/Vj3iDzLfYzCqFBmzTxxHS20/XWubP7QzSMtQk47viKgxNE6y5PVz5znJyLITCsTobxpEGorq730a7HpENrl1Xxc1LT512vkQJlhNKH/5PS98az+nb74Ae8lthBp8H/zEPyH7n3MKmilRSW8uwtFFIaX5LiKvdZF7YwOyqKiQxKOZZkxGIKIvgjlLZT9VRAU+As6UMRcrUU6budAXxeT/tYUuHpPIL3aruPqUxUaJSqxasR/TNWey5dU9yFGZyGBU5aoZBWo66jEo6qLs6wqojc8UMrNQg49rv/qq/tmcqTmceuok5pZn0TIQ4vkXKvHuV9FH9hI3377teF5aVcf+F6sOOZFtMBrS9BEMMQkZ0uggDGYjzklZrLhnPLZ//QjTTTfxs9IZrPzrVkR/jO6KXrwuber4KAAJZiMli4rxZNvZ/1otYKb8+DIkSaHt/eaDautapjdrUQnXZr9K7Zn3H7RNjweUu37JX365g9493frCPOr+k1Gu0WrEnW3n+hMnkXXLGZSM9zDvZ9/n5bwZvCYpNPpjo84OgHpvWruHEOZNp3FQQkk27RNyQn8+GhsHMJd7YChAab7rAzmDkccdi4jq/MaHnD9IBYUAuqDSETmF5HfNWXYWnzqRa0+cyOy+v0A0xrtZt/LoC/tp29FFwhdRJ/c5ONNSYhI7nlXZDca6xppJwTiWL13HnIfvIPu8iSTe/t+E2f7POgX4YOloQk7w799uQPrOo7xxT7XOIfRBtpeQVWTJf0Naz2A2krO0mCXHj0MQDKx5ruIgOK4Sk2jf2EpvjfegtDWhJAjWDfDCz9eojUqzijYy2oxHtABo5ygORBhKLuKppRtFVIi2D6nMo7kOJs3I48bFcVw1D3DCgTquv+sK7ttwDDmZNo4Zl82+riECg0dAspacKTBnWDEYVVnGuC+KxHBPw5rv5IovLKJ5wZnsr4Qz1t7D1X9bxcoHt6PEYoSbBj9Qc9EgGLBYTZiTKl5Gh5mJ03IJh0Va32tKW0AEqwlbkYvy+UV85+wSYg+spLMjpQFsgMv/tZgHp/6CX127SQUxHMmCKajlo5x8J9P7/pLsKfVxWfafiZz5AL0dQ4eEEcthkTX/3MmlS5Yy2PYeUjCuM5dq6LeqN2p54I6fUJ7pYt1z+4/4+oxmCVl9zva3+T2dqIoAAEBkSURBVFWH9yGyBK1c6ZyYxZzjyvF5w7RV9xGo7j8iyhptG/YcO3eeZ6f/lGU8s0mr9T/N785z85trH2LnqzVqpgXDaC55OBM9EuAGqIHXiV+KsODOpwgFYkTbK472lD8V9j/tFD6oRVr9PHz7Wx/8gZUV5JgaOWhRlRKTPtSEbyoaJrVGrpk138kTP5lL5hPfQ8i2Yb/uTl7506aDIk3RH9Mj79EWHT3DEOUkysegL65jTcemDs7p3EWjNaWTcxtyWCTgj+I2+4m9vp0X76qmqHALd2y7FQqngBjjO/824N/VdUTwWaPViKfYjcttRVES9HYMEWnzq5F/VMJgNOAbijLlhhkEH6wi909f4tfv1qT1VT7IvVZiEvWrGkAQVJlMX4TVD24frkWn9HyMNiPmDCtmq5EtHTInfPMBjru9m7CUzbvNTp58ai9/fauP2KPvplNhJLMLs8eapss8fBBqeSoSEolMOpcJ4/9FyVQnlq/cyO6VfQQ7A4cuhyUbxF2b2vS6upJsqieUhP73Z+5bh9FhPmTmcqQmh8WPpNFqMBvJW1jM1z6/iLM9K2kwXsrvX6pgd4NvTKaCke9RQpSJ9Edo9hfhGPGsrX0jwJ+eque8ikx8FSIg6SW4o6EQSbVIq59Nf9v+gb//abD/L50CfPCml/4yo+iU0RoE8Gj6GlpKnBBl5JiMQVCwFrlVFtKByEE1ZrPHSq7vTWr/sQd3sYvjH8znDY9NbXCP4OE/4pd6DCGfkceqQSgVURnTIcDwSygF47Q2DFDlW8704yYD1XR1g7JtJ8oli3ihfgLRrvePeBjOYDbi9tgoKMnA5TDjdFtoTDqyeJIGYsOaRi78zn+Y+51GvvuSzLZXDs8EeiT71rDm2jmPWuoyqqWtuC9Ka1Ufj3vDPGMxEotK+Dtb1cb/GJh8g2Bg2nlTueisqTz57D7aVzUO718r/cUkfN4QT+4ez00N/yIo5bN9oIDSfC/bQSfsG810XYMUGOzIxqrJbcE5MYtIR+BTRRtttBqZNq+Q08obkB57g7Ibp+HKsAIHB1QjLaEkIBmkiP4Yj7xTw5/fuIMd+b9M/1xPC57sJfjNAgkx2bcYQ9fiaOx/1SHAZ9ApeOYVsOj0STTXeml+t/Fj6f7r0WeSxgIOftHGMs0hZMzMZe4J42iq8eKt7MXoMPPig8toGMzkzntX40/yHmkmh0U6M65gyn2VUJjPO31BzBlWFcnzAWQdx/rsyAhHq9fbc+z4a/qJ9R76emqUHf6KXu7++2YuueB2rmksRX5/O8LSRfRGp7Jmc4O+bRi9j6Nf4+RC6s60MX18FgtKM/HPKuSdPCdVe7vp26cCDQZ2d/OVr7ykl5gSckJHcqUS9x0tEmaszCn17wlRRpYVIh0BIh0B/KiwYV3rewwqCgCMApecPZWLc99DvOwUfv9+c1rGqdFnBFr8PL9iH6vfd2KxdpNfHCIcEofZXMfqSyQpRXJm5WN3mvH7IkR7Q8R9UYiqfFAzzpvK9efN4PcPbcW7s4uS0yaQV+jmwJqmT6xRqr0nNouRuOLEccwstvVMof7AduSYjNljZd4lMzGaBXa/VJWGFtSzhSTLr+SPsm9VA1e0lfGo+Dw9p16LFJWY9J0lrLV9Cf/Alk/kHD+t9plyCu6Zebzu/DsvX9bMPbeWcdG82xjc0fmxeO2x8O5HajFvhKo9XYTahnQRlm1dTvY39xDzHtzQinvDfOXXW5g080bibTItdftRYhImt9qA0xAUI53UhzGtdDNtaSk5eQ42JIfGDmVanVr0x2hf18Lf9/fy9rLFLF1yMaZOgc693bQ3DRxxFK/BcwXBQL7HzoK8XeDvYtaZJ7N6ah4vWIz0t/oRzAJZRW7KJ2VTUuDGbBIYGIrS0e6ns2WQcH+EeH9YhXAmIZmpzK+CzXREdNSg9g8suY40JJQiKhiSvZqp50/lq1fMZTAq8fhz+2hb1zJm3T8hyvzx5+/z7Lwiug6sVx1CCqZeQ1wlRJlwVNJV5JpQMwi9KTwaHFXjeCpysejYMiYUZxAIieRk2njlzWoa31Vne9rqB3jijSoGKvuwlWTw/O1RkEN81z2NtX/Z+l+Peg1Gg8o2YDOyf1cn91FMbubVvP3UJgarVG0TW0k2t1w0C8Fg4JbNbQdByPVjVlSAhkEwMG9hMQ5jFx1Pr+PhFysYqo0Q2LiWmDeSdh3/f7fPlFMom53P/vub9Z/j3vDH/kB/kOZ0QpaIdgwR7RiGuMlhkZ/c/HJaZJlqiqjgq/KytztE3qQsZi1SVbG62/y0VfQS6w2l4dVHIoqOxkmkZgsGo4G+7gA+b+iIG27aecphESUqUftWHY3rW9IoRaRg/LAvoB6Fh0UCg1GURAK6axBfWkVu0TtcefyJFFx7MW2+MJNyXRzneR/pqYfpe6Iek8NM7pXTMdxwOau7l7G1ppdtm1sZqB9A9MeQw6RNEB/x4KPRgCXbxrxzp9Dd5qd7V5fOVmrOsnPCjfP5RdvPqD5jP8u+vZAdc++mdU3zIc8x1OCjrnkQYJgkUZui1tBlslqqTOvtaFnQKA4B1HkB53gPM5aWcvnScZS5W3DRSuy+v3LlD2/nxG1WYj0hBnd04tvWoR/PAzsX0t0fYufKKp2w8L9lWo8lc14BJpNAf20/a+uT8y2+iI48CjX4+Pav1EG4QE3/qNtKzRgQBGZNyCax6q8sOcXDT5t8KvxcTC8B/599xpxC654ulu69l8UrX2fDsX8ict2zn/QhjWmjTRwf1GQcxQxGA7mFbpbPLybTZqaizM/KQIy+YByU5MM9gvoA0rHvR/LwayUSOSrjresnISfUReoonKCWNSQU1Tnog2Wk80MdzhRRIeCP4h2KgjWOf0MH4b4wBZVelt9RTHfOYgrtu1BWvMS+3+6iJsn9djrV5M6uYFz+cuoz7VhtSanIkfQOh2iej2ZyVCYSiiMIw9syCAack7I4e3EZ0ppBsse7MJ25jN3/ak9zsIfqxwhmAbPHiq0kQwUxaMIvytFnpQZBjbbHz8rn4hMnMiPyOIkeH2TmYJ6cxWC8jITcpl9fzURfhKd/twFLjoOCabnYFxZT807DUQUEH9YEs4DdaUFOTkdrwU5qc18Oiwzu6Dzo+Eea9vlYb4g33m9g+Y3fZk2Ti1hvrZ4ZjiUQ9b/cLP4w9plyCsHaAY651IzBeBLyX1b8z9zQQ/K0pCzmGsopEoqjJCDLZqY420FWrgOfw6RSdWv15REUD6Nt84gavUne+bEymCMxzTkgKmnIkCP6rjYb0T5EdbMPphYzUDvAzp0KrKrhEuUBCu/+LmE5F8fJxzP3e3Gm7unBXOrGeu1pNGXezDNbmqmp6qOnbkBH+Ojomw+Q6Un+KG31A5xw+iQioTg9PSEMZiNKVOLl9Y14fvgSSiLBi/VeoB5Ltk2n5z7kuRsFCuYVcvZZU9hX3ceOZyvTBtsSoqxPOVtyHZQvKsY/EMZXpep0pEFkbSbs5R6+dMFMlg48QM31T5A3K5esu87j9cW/48F7tqdJiqaen+iPceE3juH7pS/R84MXMK14kQsvX/WhdZGP1KSwRO+ebrWfkizPjXbNjnSWIiEnEH0R9j13gAvXtaBEJbWnMkaGBcMN+k+aov+TsM+UUwD+5zhHjnTGIaGotfX4QJTmXZ086o+Sk+8kHpPpbB1M04JOXfQ1vpyRU5sjP3eo/SY+4BzHqNv7IOU2UWUxrdnbTfuZZ5I58bewcwCAl35Sy1WXbaCl6Ns8t9dEvPA4lDMTBINxup/y09/6FvH+sE6L8UGdQappiJZoTEKShiGMiqgw0BuiLxxnbr6RhROy2VvkwtcwkLzPRoRk9DkaGZxgFlhyTBmfX9DBzuKZ7H27Pu14E6i06Tlz81lwTBnfOt1N9YCHB57eTcPKBv0+ab2E8dNzOSZrPYGfrWLX7gSnjItjyClk9XttBOoGxh7WlBXsVhPiyu28/7Kfyx68DffML+Lb2vGxL5BaefVwk+4fZLtyWCTS6h/z75oJNhPWfCcTjyujt3MoTQfl/wf7zDmF/yU7nEMY2RdQa58i0S6ZLl+UnuRQlV4XTUaiGi0EQN7CYsZNzWHnCweGqXwPUVoaqS/xaTBN/2CwyceKHd1852vL4Lk39b/3/vhVZjw+i96+Uva9XY84EBlG5HxIPqqxLFjjZVWjL412QRqK0lXXz5MvV2K1m/B2BRmo7Vdr4aKCYBZwTckmuySDrr09ah8oBR2XkBMEwnGiciYhUUGwmjC5LWqGmCy/TT51AvfetIhcWy3d4XzWVrbQ1+pPv1dGdeCtoMgN0RYsEzyceM4QBT+7kL2Gy6nas/aQ1DCKqPDcX7eSc9efuWbD/YSWfp741w98ZNfuSOzjyvKPZLtmj5VTrp3LT2e9g1h+Imd/I0qw2vv/Tcbwf07hU2qHop7QYJ9pyl0pWYJgM2HJcWAwGrj0kpmcMCGHL61pHh5MMh7cc9DKEmaPFQRBbwankbR9QjXWhDyMN9+3vxvDN87H7X6TgArEoWrTIPm7NjJ1wteoSM5TfJy0zNoxJeRhVbyEqOppSIE4jUmEUBo3EsP3KLfQzaBXzV7ElGg9oSSorexl1fTZVDb16M1rbQjP6DBTXO4hy9qCyRDnvbpeVr9eTajZr0f9WjAgmAUUJUHcPRnLeUsoOXkmXRNv4aW1rSptdQot+WgW7Rji7794n83nX87Alh5V2e9TEiR83GZ0mFk6o4DIA29hv7CdzNJzCNYNHPEU9f9r77zj2yrv/f8+S5Ilyzu24yTOJAkhIQkJBAijhQKFQCl0MErHr1AuXbelvbeltLe3u70dFNpCaUtpS1mljLJCgBBoEggBspezE8eOlzxkbZ31++PoHEveTuzEkPN+vXgRD0lHkvV8n+c7Pp93O25QOE70dUroNRhky/6S28Pf/TaCJOAp9XPxx2dTXOCjON9LR1LD1PSufH52zSGDqIj4xuZz6gemUlDgpWZLE4fX1ueIz2Vf97EODs5Q3MYG7tuwiBv/eRFrb3mZ/Qcg1Aqp5Tu46CuVvDy+wJKQUIc/gPVlUmPXTATDxMgcxuyctOSTugb/Ml0z222rTsNwfJjt59iwoYE/x9Kk0zp5pXkUlwVoOmCp2BopjQ1vHuK2aAWphM7uNW9afg29pIFMw6ShrpOl+05icvlXaPCneOKhHezLeDsPVB8ydZNEXYS1f1qX8/3sAbiR/hsQJAH/pCLyJxTQsSNEqinmCO7JhT5LomYE5pD0uMqb2xtZ8qlzEGYuIPJ0/5pH7zVO6KDgKfPjLQ+Qbo0fk/bVgdJFolfGP6kItT3R1e2RtXg7rl3d9OJz7le0dvzVlUGmlQX4x8p9bH273rm/nMGr3oqMKZ1kUkPXBufeZt/umKAbpJpjPPtMDYu/8nMWPfdrym9+hHTM6uOfVLCLCVNL6djV6swNDMe12VPdnjK/I9vtFKy7zavYkiB2QPCWWyqrdsHYU+pH8UpED3VaUs6SmHOd6bYk7TtCKIVeZlwwhZlTSnktpdHYmkBrS9KxvpG31zcOKOCox1Xq9rbx5NKdSLJIW3OM9r1tVmptkIJy9nOysX2bPaV+S/YjnBrRXLsgCnziy2dx87wdPFd7KT/64jP4JxWx4icJzC1v8uX51/HOQ5v7FVQ8EtS2BP9+cBO3XHQx4RcTRGsOHPV0/LuJEzYoSH6FJV88g+vPnMTfVu/jhV+v6fMP3DY4sdM2Q2UwxWRBFCicW8Evvvk+lm0+zJM/W9XjJNBnMBDF3LSBarD67TpqJxSSTGiO1j+ikdMBk+1EZqjWgrtl6S5nKtj2PB7K8xvsYmP7Imhxrd/XNMcjAaszpa2mhTuf3Mw1F3+B9z/oR1/+FlJ5AEOMMa6qlB0FPmcRFsSe095DCRR23/y0S6bxzU+ehiyK3P38djY8tYN0tlJrN/8HySc5MuOTppcRbouTTGjMnl1B0O/hpZd3E6oJWdeZ9drZcxmeMj+nz6nktHFFbNjSQCMMvkiuG05R9UBm2FCLpJ0huCPJjQuS9dz8k4o4/fxJzjT5SKbpDNXg+edqmDRmIbvrO4BMB1awDMrLSOw4OsG9/h432RBl4z+2DU09+T3CCRsUAHTDJKkZRGP9i4ApJXn4JxWRao6RqA3n9J07UgZ9/NFki8kN5MlrpDQaoilawz1b/3oNCFnBwLFrVCRkv4ykiJQW5VFalEeoMUJTOAVxFSNLJ6e3wOBo/fQzFNUfg0kv2QVXT6HXMjTqxy7SLraKioie1B0fgH3bW3gu6GXWFZ+i8vIAaFaB1u9TkP0yasbjAqQcDwxrAGwImvyKhKfMz5WXzWBO4q8QKGZC1els7Jayy/H8zsyLSHkK1VNLuHjBeBrCCbbtbaW1I0FLe4KIvWPvflrLfC0qIsUBL35Fwsi61sFcsz1hrUXTCJmNjiPaeLToBoZhIsnioDY7oiI6NawjCUYNq2v58f52pzU5diDMp+6ppHLCEna8sHF4nlMv2N1KJyInbFAwUhrLfv82yx/cZFlC9rEwiT6ZRdfM5suXnsyT6+v4589WosdV5HwP486pRpZFDr1Z53S8ZOMs2JlUgpTfJf+sRdI9PHk7t4f44deX5iwW2d68zn1mgoFjd5n1tX2qkWWRoF+hME+htDyf1tqwk1KB3AJ1jndx1iBPfwxUCO/v9CD6ZKpPKSeVVOnc2drDRMXIzDMIioR/UhFnXT6d6soCdh1oY93zu0g2RIntbWdjXOUOReLDZ15DdbFEexg0vQFPvgc16HFaCysmFJIXUEjEVBp2t1rpwl7SP70hKiJSnkI6rZOYeg6N8Zm8+foqR5rEfn8LZ4/hYzfMp6zQxwuv7WXP24eRgx4WnTqWDwgPQmWQdRUf5cd/WkuoJmRpVnVzW8t5DXWTw21xDNOkLTOtPmgBwUzhO9t052hrANZtLZe+tdE0WjQ9KGN6OeihdF4lsiLRtK6hV6vYbLpvKoyklqO/ZOoa2x/bxrHthTqxOGGDgqlbOumDmdQsKvThlyN4PVYaQynO44xrZ3PnBRtIlc7nittihCPpXrsTHDtAr4y/ugCfTyHaEiOhR9CipqPfDoBuWVXaJwrHOarQckizFzHbXlBQpK6gIUs5J5f9NSFCjVEUj0SoMdK10IoCpijmPG73wND9+vtyBetuKG/36ttpECewdetaMlSD3a/udzqcBFEgcFIpZ10+HcMwWfPUDhK1YSSvxNwLJvOTha+TfuQllCsW8W3fZbx2/wa0aJpkfSfrlu8l0pFk/PgCCvO9NIWsdInolZHzRU5eUMWNF8/glNItyFqM5Q3n88zq/WxevpdEXWTAwGCoBqnGCH/541s84FdIh3YTr+10dqiCJOAp9nHVtXO5ecJLoHjZP/0s9rxtTduOzfeSuPsFtPYkC34eJFhYSVNczZkbsd37oCtFpCdUNmxpRFJEOg51Dilt2b3OMVzYg23Z0uwD3b+nzM8fbjuTqvaH2fnlz/DZG5/o1erW7rTyjc23BjQH4YfsMjKcsEFh0OgGa1cdIK3qbMn423rLvZw6Ywzmrho850x3crXdyT7y24bmSQavQ2Tns/MnFaKrBolDYUzRdHbRoiJ2SSNn/m0v1Om0RmtzFAAtrjqdLoIkIBiCY/be/Vp7vY5+AkZ2ikcp8GKkNGcXmS3Ulh0YTFV3JnBN3Rqwm7JgLD85fxvmoT089Z2b+eXXl4IoMn1SCcl7n+VfP97D+SsPcfHdN7Pq4c1oYQMtbg057d3eTLg9TlFpgEQsjaZl2i0V8OUpTCxoRtqxAn1TDR/4uEzyzPnsr2kZ3JRxRjfHUA2Se9ut55XtC51pAZZlEUonkBAq2LUnZAU7SeBgW5zFp49Fre0ETx5id89nUcA3voCT3zeJU2aW89TfNlj2qEmdxrowRmb+4UhSLyORB3em0weJIEuMrf0971zxV6YufoS5H7qDt+9f3/P06JWZdF41n7p6DrWhGA/f8yaxve0nVC5/tOAGhQEwVIOWN+t5ZUOjc9zXommWv7qX4OX/xXO/201kZ2v/+iuqjhY1iR/osIqmhuXF0CtZpwR7wamaWEQilqa+Oebs+O20hqiI1lyCR0LOnBxEUcAwTEs7RtOdYSbQsN5yDQPDCQzZ6Z6+5hh6pIsy12kHhPHzK/nK9fOZXVbH241V/OSu14nuDFnPM7P7tQNDj4VFNwgW+oj/8H5W/OkgH/7zHh5cdAPNGxsJhROIZXkAqAmN0jzFKTybqk66PYlptJNqjtFS5kdURGfh1hMqu7c28dqcSpbMvAhZFEHyUOCVKS4L0GF3BfWTl5YLfZx86TSuu3Qm9z60gfrVtejx3IVKi6R5+l/bWLuukLbmFkuiITNH8sLyPcz71G+ZVHCADaHxxCLrcp67oEj81/cu5OKHP8tTn9vFq43f4LyvB9AiaToOWn8vziT2u3CBVNsTcLiR3Xth995m7vlTiLMe8+QYIAF4Snz84pbTKP3VJ/AuGMe+D32BV+95+4hdEV2OHDcoDAIjmdsho7Yl2LtsL7/K+DUMRpALQ8/4HnQ7EmeGxvqS7TWSGge3NTvCXdkBQfIrePNk8gIe8gIePF4JxSMjSgJqRoIhEUuTiKnEY2kMRczqKMoKDKKZU8Poc8AtG90AUXIMXsZPLuGMmu/x1KVLWXB2Hrf/7VW+/4MVpJpjOYqkvQ3AGarBumd2kvzLfSz54C8QFp6NvDfTRbV8L3tvf4BrP/4gVEzltqf3dTOs0R0HNiOlIfkVy3daEtCiaVo2N3F3x1qenFjEybOuJrVfp6lxFw0HOwaVpxckgVRCY19zlHBTtMd7bRqWFlLorcO0rmvoeh8lEdMwObShgV96NjJ5agkd4X20N0S6nO4yXgeRhIqZ+fsy31qLp/gytHCSmYsn8pELp/GzX66kc3vv6cmj4VgMI6ZDcZ6d+B0+/M2dPP3z/ZCK4Snz9xgwNFSDUFykcloRVFWSrNGcGQ/75yM9kOhiIZimOahXecHie0f6Wt7T9NapIYgCFedWc/Z5k1nxXA3hrV3mLNAlV2EvcnYNQfIryPkeAgVe8gIKhcV+ikvzCPo9BAMeZEkkkdKIJ1Xaw0naW+OE2xIkYmnS0TRa3NpFZ8tjOGqRdBuO03sugtndVKKS8RA+tZxnv6+wZ/F/Ujq9hI7fvsyX/u9VOvd1dEke99MSKkgCnjI/+SeV4A94aNnchBpOOZ1KVdNKSSVUGrY2k6iP5ARpu6Yh+RV84wqYsmAsmmpw4O16R0NHVEQkv+IshEZKG5QWkv36Sz7J8WIYzPtrv0aSX8FbESCvMt9KAdZHSIfiSH6FhR8/hd98rJlHds9j064WPnfBSdy9rIY1f9uIHPSw8tFq2PgK17x4HgeW7RlWmQXJr1B1bjXJhErrmroRlXDwlPk57aOz+PE1haw6VMzd971F9EDYavDILPSiT6ZofiXvv+QkWjsSrH9lH5JfYfzUEnTN4NDWZuIHOtzAcJSse/2WAX/HPSkcI7IlCLIpqwwyY2IxbxR46V2qK7ebR1QychReibyAQiDopbg0j/HlQYoLvFQEfXgkgbiqE05p1DVl6gqZD72RaVc0NRFdN61TQqbwbC+Y2UJ5BvQdGDLOVqDRsaed25+dzQ9evw8Bg0fWNpJOaF1WkVk2h73tUO3Cf3s4RdieAFZ1TFUguruNfY1RS1+om/SGfVuwdpOyX2bmjDEA1O8KkWyIWr3/GdvTHPSBW1Pt1sSBip45KSFJcF4jPWUF3vKqAmKRFLF97c77eda8KlI//z7Xfe0mnl1WwKcfesJyUktqeMr8xLUSAvPOp+V3oWGXmJD8CkuWzCCdNnhwa/OIKqBq4SQbntrB12JTGVORYExlEDWatlJLGUxVp3NrM0szEiFKoZcPLJnBRxdOIBRPc6+8lZ2ZSWw3KIwsblA4jpiGyc6X9rL/nXprhL9b73t37I4jya+QF/AQLPRRXOpnyvhC5lQVMqHApMq/CWKt4A0QYzw1RdWsy1MQJZH2UAxZFomIAvYSoBsGIiJg1SbkfI+lf4Q1Xau2JxzLR7q1OILlVW3oBulQnNfu38BFzwWQ/TJ6qilnJziYNldbT8hIdvtepgUy+3u93jZTY9h/sAMxkz6y/QhM3Txm2jVOW6hhdZd5S/P4xvXzaYmn+e72FtQ2axr4L39Zx5U/+zGrmybQUr/KOdV4yvxcfMNcAvoefr3mZGIH9g77QqhF06x4bR+KRx7yKeFI5M/VtgS7Xt1P+5wKfHmyUzNzfkc3rZNY0koBTjunmm+esZnET7/ClLMmUH/Rj9i3ts76OzhBNIiOF25QOM5o4SRaONmVirG1Zbp/4EQRQZaQvBI+n4I/ExTGFPuZMibIKWWHye98B+PZ5URXHLSMSk4fy4KPfQxx4oWkVR2PIhIIegkUeKnTjC4bScUySS+eXMwFF0zlylMr8IgxXq8zefzZ7dRtaLSuM645hWkb59+ZYrq9o3aKysOwwx3K4pNsiLL5uZ0IkmDNEhynnWV2YIjta+c3T23ByNQfwNoZt79VzwWXNwNvO9ITgiRwxrWzuV36PdvPWMZ5L7/DIyNgEWkkNXY/vdO51oHI3qTYdafBTrJnS3i0bGmyiueZtty+UomFJXnQVk/iUARlYieeWeIJo1J6vHGDwijCnkkQFdFxnEIUnXkFySch+xUnbVQQ9FJc4GVCgY/8yHrMla/y1rfeZP8B+x7r+WhcZfZ/VFEfmQJAzO/B55Npa44RiatOEJLzPUw6qZRr5vsoW/E19MNhrv7ohwidt5jHG6IkVN3KwffRNCUoEp4SnyOPkaPg2ovG0khg1wrSId153NGQatDiGpuf3OHMcWSn0rr7QguiwOzpZRjbWymfXcYfNlk2mSMhRNjXfdmWoLYHdPa1ObMpdM0WSD5pwJkP+71JNcWcJobeNgz2RmXv9mbeOP8znH3vRMgLsvJfhy1xxhNIg+h44QaF0YQo4in2kVfkI9GRdCSOoUtKweOxuo28eTJeRSLPK+NXVAiFSG8LZQUEi/Dqeso+WUuRbzp+n0wsoZJO6xi9dAAlYiodqSLKqiqRRBH8BaQH+SH0lPmZc8k09mxtIrK9BS3a96TuSDLUPvqRRhAFJK9E/swyissDhA50WAXTrEUxJ1UoiWzf04p0w42UXaex4j93OPfTV/fWcOIp83PDbedRXZ7P06/sZsuTO9DjqhMQJK9kyY5kmh+q543Fmyez7fndA57Ksjvxcr7uhqEadB4M8+dntrFx7jwM02TD6q09a0IuI4IbFEYThkFwTIC5C8chigIrn99JKiNoJsgS/qCXkvIAFVVBfF4ZjyJZPsEYmOk0envPYqGWUCERIeiRUWSRcDRFeyhOOq11zQwYJqaeZs+mBn5pGCw67UaKpnrZvbqDtWv2kmyM5thCdkcQranes06rIhFLs2tfO0RzlStH00J9LHAaA3wyJ10yjb998A3ST76B8bvvc8WtB4jUtPaQObHz7Btf3c+nGsfQ0pggUR9B8ivODhrdyGkEGM7XNTC1mIfuWcKbJQsYPwX+tPFXnPuK1zotSJbr28KPn8JvLtyAuWMbwlkX8s+D03l1Te2gH2Og67XbjBP1EXa8sIedy/c5Ok4jIZPt0hM3KIwWMpo/8ViaQJ7CeTPLOVTbwZ63D6NF04iKSFGZn4mTi5k6vpBwNI2qGaRVnUjaQ1XZeHxnVgEHc+625H3VUDCGZLtOStWJx1USsTR6Vn7W0skxScRVtjfH2fm69SE3VMPpvMmeTu6OnTe//7dvkm6N99DEGezC5RTSfV0DW+/mPLK9yAeCXmjrQA8lUIR0Tq1F8isoxXlgWBPvgiJRNaOMSy6YyqbdIdZnpsO73geBiRdOZskHp/PiK3vY/fTOYQkMgiQwdnY5481laBpMu3Acb7SdS7rtcSfoz7tyJv+35ds8csN+ABRlKWe2beAP6+r73TQMFbvhQO3FRMpl5BEH/hWXkSSnaKubpJpj7NnbSkM0RV7Ag+yXnYG1QNBL1Zh8JpYECPoVNN0gltBoTajohdUI807hshsrKSyEidVw0XVlKGfPJC5PJJzSSKZ00indkoGwu3KMrtOCoRro0ZSjCaW2Jbp68wcodupxldi+dtS2RI+872DVNL0VAcrOqKL63InkjQ86cwX94ehADUaeXBIG7YktSIKVJvHJg75Nd+zXtOb1gzyQ90V23/YkX/m7QMJ2ZpNEgjNK+dgXF3Hhp+fjG1eAUujl0g9M4xMnrefLF09jwswxeMr8yEGPI2fykStn8emKpfzwpkWWKmsvz/FIrrnlQAeP1C7m2sbbeOubT/A/P1ph1bUkkbxxQT5wZjWHlndtOk47XeGFHSFnE9CfSOKR0Ov0u8uI454URhGmYZJqilGz8iCxSJp0ZuLWzud6vBIBn0yeLJJSDSKxNGlVZ79foTRvPuMnFVH4w0Iu/fRukGWoLIdJC9nfNoW6tjZCHQlSSRU1I8bWQzjNyLWOdAJWH1LfPa5f1btaDTMFSfv7zmP0giAJyIU+Fnz4ZD563hQ0w+QX9745oFihqIgEphaDKJKs7+xXtVP0yRTMKiOvyEfHrlYSdZE+f1eQBPJnlPGbn17CcxvqWXrfO/3+fm/YnURGSiN2IMzvb3vJ+b7jpe2TmTKngk+fodOZnsT2zY10hOIk0hq6p5gJkX9y1SWX8fCTWwmpmcntlMmzy3Yx65PXs35/a841K4Veik+tINWZIlLTOiRBOVM3iWxv4c6vLuUuSUCPP5YzW6N2pnjh3/t4/xvPcO3aPyKccxn3vjWFt9fVUTS5mFDmRDNcxkYuxw83KBwjBrVz0w0MVSAdinPwrXpkv+wY0JuqTrgtwd76MJG4Sm1jJx3tSeRM95AoCjQUVDGt+HqCixoASOkF1Lf4qWnppK45Slt7glhninRac+wge51k7qZLNOBzUyyHMaXQ6wjdKYVe5HzLdD4dipNuTwL9FyIbD4VZtr6OhvpOOnaELDmLPn5f9MlUnD2ev3x7Ns9sN3ngt2sGlHKumlbK/PlVvJ6nUNsU6zc1JftlqgsamDOllJfyPUdU4O36fWt4rjtGUmPbsj3c2BxD1w7QcbADT5mfPbUdrB07m4UVXhoPJEjE1Eznl7Xg7npuFzct3Z1j+iSIAsFZY7jh+nls2RPi1b3tDLUsa6hGjxkAO8cfr+1kU8N2lizzIPoWIP01RMF4lSuvPJmm1jgv1nZY71cv92sLO8r5lubRcDuluQwvrszFMNK9lxuGoIiaJR2R7Ytgp0fkfA/5VUEKS/zkBRRSCY10SkNWJGteoTxAYb6X8pI8Cv0eAFK6QWtHksbWGM3NMaLhJOG2BPFIyml5NVJaD6mL3k4JfT0XpTiPM6+fwx3v30i6dA7/cW8KXTP4+OUnM6HAx9bGTh57fCst6w/3KRPheEH4ZGcKuL9OFkESCEwt5g93XE71T64i8oMHuO7rb9C5tbnfhVsOevCU+dEi6QF1/UVFxDs2aLXYho9MpbQ/csyRMu+z5FfwFPvwV+ZTWJKH16fQfLiT2OEIWiTtDBH2dvISJAFveYDA1GJSzTHi+zuG9Zq7X6/kV/BXF3D2JSfRGUmx7pmducOKWXhKfFSdNYFbPjGf5evrWHn/+n4lQ1xGDlfm4hiR44qWlTYBIEs/aLABws5FiwCGJXBtqAbJzhSGYRKLyE5LqaTozmPHEyqxpErAZ+WZNd0gEk/T0ZEkGk4Sj6WtU0JSw9S6NH+O9LgviAJVi8Zxx5X1rD7pZ5z9izOZPv12ZkwsZkn6T7AvwskLr+b1ycWEtjQ5r1VvEhdWIFKdrwdC9Mr4FVBOLuWdBol068C+GLYl5WAew1ANkvWdIzpTYQ+3oeqAhJHUUMMpYoZJvNl6PrYUuRMk+zm5pUNxZ2BvpBZc+3qNlECqOcaGtYdIp/X+C82iSHGZnwWVncTnjGVtxrc6FUq4HUWjEDcoHCU57mpeawelFHod0xQ9oToqnraxSr/BIaOwCdlBxPrQpbEGoZJKlvOaIpFKqCQTKh6vRHvAgyyLiKKIYRikU7qjlJpOa6jRdO7pwDBz0khD0spXJC54/1TMVb8inQbxnEWMacrj6imb2H323wmU+6m6uwKP99xB3b/9s+6ptt6CiNqe4NfP72TuaT/nz99dTqopNqhrH8rzOxYzFdmBQc8U/rvXAgbruDaSUh7Z742dtkrrJq0bGy19qH5Od1o4Sc2rB/jV2CAdHSEuvvZULp8/ju/es4ZDL+4dket1OXLcoDAM2AHBU+ZnzMwy5i8cx6SxBRQHPOw53Mk76+pp2NdG/FBnrzacOUhdDWHZHzBDNSyP5SwHNVERLQMYn4Sa0pEUkVhnGjHLgU1TrW4jLaU78tI56aLedIR6SR31xcpV+6n88P8wbuOPuP7hDcw8JUZIO5lp3z4bZp/Mtvyb2LHxNYyU1pVS62exzc4/m4aZsb3suZtMtyV565/beDOl5VhjAo7HhKEaw7oTdWYPbGnuYciN5wQGI9dCM9vZruvxpczJouv2xxLJr5A3Lugo1Wpq1/S4fY09gnjmZ5+/oBqPGOPtBoUJwWbyArmdUy6jAzcoDAN2Ptw3Np/F50ziC2eH8ex+Cv2VzVx69mxqPnkr/1p3iBUv7CK2tz3HX6Cv+8s2pAHr9w0A1XBaMA3V+r+p6QhJHV0RUaV0jt+xvTu3lFG7pLJt28wBTwmSiOyXLdkD1bILdQrTqs7B1w7wy9cPYao6ok8m3Jagrb2CWdO+S807rezY+G8iNa2IXhmlOA89rvaZz7dlqksXjGXuGeOprw2ze/k+1F52oEZKI9XcM38tSFbBdcYZ49m7o5m2NXXD1scvKBJyvocLbjqNc2aP5ce3vzQsKaY+aycZvwu7viQqorNLt4rCxzY4iD6Zxx//OCXfuYr9D/2LW770DMn6zhxxu17nWHRL8+mFmg4CPoU/37uaX4sCndtDI37NLkPHDQrDiMcjUVnqx9u2ktSza9lx/w6mrm/k5DvnUT/9LDasq7cWEbX3bpQeonhZ5jd2isEKBiAYAqICpgEG1sCJYVjWkdaOMqsYmWk1tbuYsoNNd9G67h9qQRTwTypi1pkT8Hgk1j6+jVQo0eUlHFexjYNMw6RzXwdb2pNse7setT1Bui2JHlcJTC3mtAun0N4aZ/szO/vsFJJ8EnMWjuOyhRN4Pd/LvtUHu9sSdT2vvt6HQi+Vlfk01oVpF4dPFsJT7KNqYRVXnl7N/PJt/Lq6gFRDZET0iByVValrFsNWr9WiafSkPuDmYriR/TLj1v0vj95Vy7Vfvhd/9UxSDZGc0y2G3utpQYum+etPV1qmRRmPZrfQPDpxg8IwYC+2yc4UBw53Ys6ahueUSkomHLSGi9IJCr2WZpHdaZItimYjKqI1qJTvcfLmzjRxNroOhmD5IUgCgm5i6nankomRyl3kez0R9NGO2vUYXcVxb4GXC86YwOLxOt9ojrHrlX3ODEH2gmQkLc9k52e225ooUFgV5JuXVwDw2fpOGv99sOcuP2NgL4oCzbE09Y2RHoJxA74XuknHpiaWH+pEbUsM28IjiAJnXjObX8x7Gf3pP/LU++4itnfviC3I2TMCYO3S7a6kSEeSeGuCVHOsS9Z8gHZfwDEakjI2pHZxeLCvr6EaJBd/kg//11Zekf8fsb1LrdNT0Op2M5IaOvQqWmfqloKty+jHDQpHiambCKLluZyoj/DGyv3coUzjfQt/wdwXt6Iaeaw4PJE1NYdpPtzZdfTvnq/PaMtUnVrBlBllRKNptq46SKo5hqGmncfKxpKxBhQJAZDyFDwlPqvlsjNXNjrnw9/XfEJvtQTdoG1DI79tf53n51VSu605VwG1+4Ji6E6vevbPWjY3cf8bE6kaEyCdtobcess9a9E0a1/cw9sr9pFsjh9R3l4NpwacWTgSZk4pIfzTZ3nhL01Ma8iz/KFHOm2TNWQYLPQxd14Voihw6HAnNRsaiB/osBZ4O4j3kZYLzhrDtTcu5MLpY5hq/IsW34Vcc+saojWhQT8HO0WU99Wr+L+fr0INp/BPKuIr3ziXbfvbePXZGiI1rZgcG6tPl5HBDQrDgJ3a0aJpwvs7eO2VPdTsamHK5BJEUeDgoT00H+4k0RjtV15Y9MlUTyvlzFMqOdwap2ZDQ86O3F5IncKkc0PTmWitqi4i1BihM5rG6F4rMHpOK/cbEDLf1+MqkZ2tbDsUtoq3au8LUF/Py9QtV7UXHtyIUpxnpdD62GEbSY34gQ7rUlKjR/vINEyWv7KXm35wLZeoD/NkfcegCvFH9Zi62aM4XzUmwIRiP6WFPjpCcWpb4xk5br3HyROsv6nC2WN4+Vd+4t+8iVQowSMPNAPwinEf7/+sRnRX26CuR0/pPLSphDlV15GofxZBErj5q2dTveRclnx1LsFrfsYjP/k3xvDHY5djiBsUhgH7A6vHVZINUbRwkrZtLez0Kwii4JiOD9SWKogChm6QUHViCbWHsXyfqQpVd3rcw+0JkjHV6TKyTwU519tXQOj+vLLrGYaOGtaPWN/GUA1ie9sRxI4+UxbOvEK095PR8cTUTfa/vI8LD4zl9Kvv4c3fvXlMcvmOPpWqk8p0Uk0rVhjjL+HA4U4a9rVlUkFCj2li0SdzwZfO4Kenvswjyh973Pej4k28ZtzL6ee3D+q1NpIaf/z6MgRFwjRM5HwP13I/j+6GnV/cxBf0Jh4Zjic9CEaqw8zFDQrDRrakgb145qQwBjHApkXTbHmnnrr97cQiKSt1ZBve9zq/kHX3cSsQNLVm5fO7FZEFUXBqFrYctrMT7+WE0B3RKzPxwslUTSxi3TM7h9x5M9hBudEUDLIxUhqR7S28urstpwUzOGsM5VOKOfjaAWvQbJiv3+4ei3Wm2FsXZnJpAM0wMUwzt8ibhaiIFMwq41tL8kh+bzkAY8rgwgcv4tEPvtx13yv+hZw/b9DpNkM1EAwTQZHAMHja8zlOP/Nhpv7sg9z6D2+fp8jhRPTJFC8cS7DQR2h367BPb5/ouEFhmMnRD8rMFPRFzq5bt6STIzWtxH0djmx1Tv3B/n8vwcF6PBCyJbG79e57KwLceOtiZpYH+dHv36RlY6NVrxhEQABLJuJ/P7eIUwpW8zVxJqt///aoXcBHAjuoCVmvT2BqMa98ZSfaqxu49bpv8NZfN/Y6V9EdT5kfb3mAxKHwgHUT+6SQbo2zdWMDnZEUoihwuLajz0K8oEiIXplnavycc9tTLL4dfvrYZn7w93386NCPmPm1Syn72Az+Pf17mMZTQ38xdKuOdsf3V1B66Z2EH4mQrN98TJzRvOUBrrnmVBRZ5PEnthHf3zHij3ki4QaFEaavwp9S6MU3rgAtmkZts7qMTNVqM7TrDs4JoTtZpwbIGhwaoP1SkCVK873ke2TEI5BW1uMqz22sJ3zymezf+daQb/9eIbtt1FsewNy+Cz2UICH2DLA22YVXQRIonj2GRedOYtULu2h/p6H/B8wIJWqRNO11naQSKqIoWhpWth5S9okwU3NKHArzwF/X83dRINkYtZz8khrfvPkp5n30LgJ42HzX60NOv3TVqEzUtgTNGxudBoqRtkC1ZcHjSY1wNEWsLX7MWnJPFFxBvGOMqIj4xhfw7P0LCD75Izqv+l+++fdmNmdsD7PpbWagV/pIIXS/reSTCEwpRvTJlmVmXBtQ8K7H/WSGqOy01mAWAHsCe7R4Jg8XgiQg53s496YFJBMqa/+6scd7aA/kZQ+emYaJf1IR4+ZWUL+pidje9gEfxx6QFL0ykk9yfmabEYH13lgbC7XnKbKP++3v5wNdk/2Y1oUYPWpVI4VS6CWvuhA1bHl/DEUi/ERnMIJ4blA4xnjK/Hzq9vMpPPNiAJZ8biyddzzHZ761nM7toUEdv4/YzCQ7eAwxGMCRf9iVQi8l8yvp3NdBqiHiSHUM1/2PVuygkX9SCWOqCwm3JejYEcqdCh9sO6jU06fCPn2IisiE8ybym/+cS0z188sntrDuoc0jXoA9Xu/he3WTcSxwVVJHIYIkoOkGJ8+AHTsheOVMNLmNcdPLiO5u633SuRvZ6aIh0UtqY6QDgk311FKa8hQa4yoYBnKhD1ERrcncaMo6tQxiAOvdhCBa5kHzz5/MOaeOZdv+Nl5qjGaUTI80ZWPNgQiigJnZIBtAaXk+FQf/CG0dfPWq/+WmF3cP2RhoqByv92qwDQsuR4YbFI4xWiTNiy/s5Etbf8fc3e+wvfw/2X4wSiySGvIif6TBYag52KP9AOopnT2bGkAS8ZblMeakUmadWklHOEnd/naat7dgqPFBBcTjRfaueEivh2EQ7UzRHE7QHkkNytq0L7pLYDioOjVrDrF+yZeonhTlubWH0ZOj97V0Gd246aNjjC2sZstrSz4JKU8h3Z50TEqgp8zB8eJoA4L9fD3FPiuPPrmYqy6ZzozSAC9ua+SNNw5S91Y96fbkqO03FxWR/JllBCvyCdd2DLoF0lZ8tV3ptGiaZEN0yNIdg8F+LFERnc41dzft0h03fTQKsY6+GqmklpMnBno4amXvDI/HdR4t9vOTvFYQzAt4SMTSPLtiD4/FVOq3Nln6PaPchUtQJAqrgoytLiLekRj07UzdmgZP1IZJZL3HI7FYm7rp2ly6DAtuUDiOdO97708mAo5NcBiRBSszlZtMqsQ7Ehze3ESqKdav5MfxoreOHCOlcXhVLY1K3ZClN+z3WPbJBGeNwRdQaFpVO6qDYF/kbGL0nu2n9iyMqVs+GG5X0LuTgXsZXUac0TLpOxKTuPbCoUXSqOEUY6oLufTT8/GU+EZdQMhWEe1eQ9DjXQ56R4JvbD7X3TCPr9+8CKUkb7gu+ZiR3RYr+2XLT9tW/KUrIHzwxgVc8fkzKDqtclSkP12GjhsU3mU4+kDDrOE/Ugu0qVvTuLY72rc+vZDbjN/yoVvOGFWLhiAJeMcGOe3aOVz8pUWULZ6A6Bu+g7QaTrFxRzNrd7WM2tpJX2QHBG95gMBJpXjLA8h+2TGEkoMeLr/5dD595yc4/6YrWHbLLnzjCo73pbscAW5QeBeTHSB6nZwWBZTiPOR8z5BuN1QESWDcB6bwnb9eTXBmaY/F3hYETBwKc8c/NlG3+OesXVN71I87WESfjKfER8GcckrPmUDxonH4JxdZxf4sv+uS6aXcc+Uevr3ldpbesIEpl0wdtsCVaoqx5g/v8MQPXxsRWe9jgW0Ba79miF3Lh5Tv5XOLfax6Icr+A/DolHuZdk718btYlyPGDQrvIbIXekEU8FYEuOJLi7jw86c7rl0jcSKQ/Ar/OvsZEtPPZcXttQRnjen12vS4yo5nd/KpW1+gbmVPk53hxt7BjjlzHNd/63xevqeCZT8r4Hf/cyELr5iBb2y+lQLJtPRKiojx4jKevL2GVRc/wC3XzUP0Dt9pwRa2e7dhv0+GapBuT5JssGYtsjvljJRGU7ws53Yer9TjvlxGP25QeI8i+RXOv2EuX/jHzXz98c/zgf843XKBGwGCM0od5c1Hp/2RJ+46rdffs2sLke0tI94pI0jWKWnhJ07l+a82c/VPPsZj0md5uewmpj72Wa4+ZzKeMr/T828aJq27WhEvvIDJk6C5Bc4aux9PiW9UpbmOF6ZhLfxqOEWiPuLUV+x5CbUtwW13rebaZRdxwdVFXFd3K7vW1h/nq3Y5Etyg8B7FUA2iGbG0zkMRxlcEh3XXm01sXzsnTe36+vW6/BF5nMFi6w35xuZz+pxKzMZDyH6F4iI4/9YZyJeezeK237H0p0WULhiL5FcwdZNEbZjrH5hI6YaNfDT0Qx7dWj7qiuHHC/sEaiS1rv9UI+cU0bC6lstXLuGHH76bJfdMJLavf00nl9GJO7z2HsVWYr3yq2dTXRnkD79cNWK684IkUHLWeB76/lx+vrSNVfetG3Le3FthDXglG6JHfYrIHhDMP6mEOQvHMa4iiKYbtHcmufXiMloWvQ9Ng/HrX+aWO/dw8JX9GCktZ9BObU8Qr+0cla2zoxX7tbeUXd99qbL3Ou7w2gmM3Sv+9J1vIHply094BCSGbQesRH2E363oZMO/9w+5P130yVz3X+cwqSLI3fe9RcvKoytCWwu4jtqWILypiTe2twDWblbO99DZOZPf/ukjaOv2UJsaQ16g1kmtmapOuj2JFm12cuVuQBg89nCmy7sXNyi8hzF1k3RbcsTuX5CsYrZ/UhEej0Q0ls4I2w2NvHFBvjR3EwBLZ4w76qAAWekONffUoUXSvPXn9Zw3dg75k85B+/5yTFUnb1zQ8rZoT1gifZnA5gYElxMNNyi4HDGCIuEbV8BJsysINURY88gW1LbEkNMGqeYY33jxZFoaIuxctm2ErrYL0zBJh+JEDYOyU8qZPKOMadXF7DrQxpZVB0k2RlHVoU0uu7i8V3CDgsuRoxukmmPs3NBAOhQ/ooAA1u791TvXACO/M7dnEpRCLxVzK7ny8pm8b0ox4wPreL16DocPdtAUtaavBVev3+UExA0KLkeMvePWIukebl9Dvq9juPiKPpm8cUEueP9UPjKrDX9kJYTSFHrnIytuQ57LiY37CXA5YhxNoPbEu0aqWfTKeMr8XPaR2Xw+/0EOnfEx1i/6HtqzL+JXJNJZgnfvhufj4jLcuCcFl6NiWPwWspQ3gRGzWbQ9BwonF/HFxe08Kv/d+Vnnt9cx/4Y61FTG8/gIjXBcXN7tuEHB5bhh97RLXskaIMsY24/kqUPySRSXBfA0r8/5fqgVdMNLMqlaQ1kj0L5r031C2j2RuIwm3KDgclywp47loIfyeZXMXTiOeFJjX00LjeutwvVAJ4bevA/6fUzREnFTvBJ4cuWrL/tCNctqfSPmjOZcg30y6gU3OLiMBtyg4HLcEBURX2U+V394Fp+sXgWyh3WLL+K7sdW0RtOWtk4fC6U9NCcqInrSSvkM1Plk6/c0HgqzybiKa5euIfZcDb4FlYSu/S0P37nRCkYj4BVtBzBBFHJ8CEzdBFUf0ZOJi8tQcIOCy3HFNEw8igSdzZiqyoSqOMFCH+2KiNHH3J3jfVwRoLC6iM7DEVKNEcezoT/0uErnvg7ueHQjF7/vO4z5by8HWmK8dOdGGt45jB5XR2w+wQ4Ikl9B8knO9WiGiYBb3HYZHbhBweW4YagGajjFmo2HOedDn8Ejxlh9SCDcnhjU4ijIEoECD/EOmXRGAru/29nS1Vo4Se3GBh5vTyDJIpFwkviBDiuojPCOXZAEJJ+EnO/BNEz0pJ657hF9WBeXQeMGBZfjgq0rlA7FWffMTm7e2oQ3TyHSliB+oAM91XdKxdRN9JROqjFCfcoqTA92TsJUdXTdIF7bSbIhCljBaTDpp+Ei26bUxWW04QYFl+OGHRhSDRFCobjlbZCx7xxo0TSSGqqqO4qqg11kHRMiw8RI5X7/WGCoRleKyjCcgOQGCZfRghsUXI4rjiXoEezSj8ZO9HgswGamdqCngFQmX6SPbPuri8tQcYOCi8sxQPTKCJJgnRCyBuPsgOCeElxGC67MhYvLCCJIAlOumM6b363htUVP8tATn0AOehwL0KM57bi4jATuScHlhESQBOR8D4IiOYXqkUD0yjz43wU8Kj8DwILnT2PMkrs4/Op+Nxi4jErck4LLCYnolSmaW8GYeZV4KwI9pCeGC9Mw2dQy3fl68mdmE2mKugHBZdTinhRcTjgEScBfXcDnbzydWErj0ce2kKzrHJGF2khqfPkzj3PGgw8zcVwhlzy6mUjGHtTFZTTiBgWXExLTMNlxsJ2G5ijh2o4R7QDSImneuOdt3hixR3BxGT7coOBywmHqJom6CM/dtw49mkKLpN10jotLBjcouJyQ2ENz7tCYi0sublBwOSFxW0FdXHpHME3T/WS4uLi4uABuS6qLi4uLSxZuUHBxcXFxcXCDgouLi4uLgxsUXFxcXFwc3KDg4uLi4uLgBgUXFxcXFwc3KLi4uLi4OLhBwcXFxcXFwQ0KLi4uLi4O/x/s/mMkYgRJpQAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -104,7 +104,8 @@
    "source": [
     "from astropy.visualization import simple_norm\n",
     "plt.imshow(img,aspect='equal',cmap='RdYlBu_r',origin='lower',interpolation='None',\\\n",
-    "             norm=simple_norm(img,'asinh',asinh_a=0.1,min_cut=-0.01,max_cut=4))"
+    "             norm=simple_norm(img,'asinh',asinh_a=0.1,min_cut=-0.01,max_cut=4))\n",
+    "plt.axis(\"off\")"
    ]
   },
   {

From b3abb71669bec5f342ee7f30bb97766c91a07993 Mon Sep 17 00:00:00 2001
From: Xiangchong Li <mr.superonion@hotmail.com>
Date: Wed, 26 Jul 2023 14:59:25 -0700
Subject: [PATCH 09/30] update workflow

---
 .github/workflows/shear_meas_tests.yml | 9 +--------
 .github/workflows/test.yml             | 7 +------
 requirements.txt                       | 3 ++-
 3 files changed, 4 insertions(+), 15 deletions(-)

diff --git a/.github/workflows/shear_meas_tests.yml b/.github/workflows/shear_meas_tests.yml
index 71e83da2..c2aa1bf7 100644
--- a/.github/workflows/shear_meas_tests.yml
+++ b/.github/workflows/shear_meas_tests.yml
@@ -32,19 +32,12 @@ jobs:
           conda config --set always_yes yes
           conda install -q mamba
 
-          mamba install -q stackvana=0
-
+          mamba install -q --file requirements.txt
           mamba install -q \
             flake8 \
             pytest \
-            numpy \
-            galsim \
-            "numba!=0.54.0" \
-            ngmix \
-            lsstdesc-weaklensingdeblending \
             fitsio \
             meds \
-            hexalattice \
             ngmix
 
           pip install --no-deps -e .
diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index 82f1e5e6..fa362a5b 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -31,18 +31,13 @@ jobs:
           conda config --set always_yes yes
           conda install -q mamba
 
-          mamba install -q stackvana=0
-
+          mamba install -q --file requirements.txt
           mamba install -q \
             flake8 \
             pytest \
             numpy \
-            galsim \
-            "numba!=0.54.0" \
             ngmix \
-            lsstdesc-weaklensingdeblending \
             fitsio \
-            hexalattice
 
           pip install --no-deps -e .
 
diff --git a/requirements.txt b/requirements.txt
index 765a2df4..c67f8fca 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,8 +1,9 @@
-stackvana
+stackvana<=0.2023.28
 pip
 lsstdesc.weaklensingdeblending
 numba
 galsim
+pydantic<=1.10.11
 
 # optional, but needed for tests to pass
 hexalattice

From fece4b39cb4496cd06322a4bf27ce83b253608b2 Mon Sep 17 00:00:00 2001
From: Xiangchong Li <mr.superonion@hotmail.com>
Date: Fri, 15 Sep 2023 19:32:44 -0700
Subject: [PATCH 10/30] commit

---
 descwl_shear_sims/shear.py      |  22 +-
 examples/example_NFWShear.ipynb | 802 +++++++++++---------------------
 2 files changed, 277 insertions(+), 547 deletions(-)

diff --git a/descwl_shear_sims/shear.py b/descwl_shear_sims/shear.py
index dcb1f53f..74776aa0 100644
--- a/descwl_shear_sims/shear.py
+++ b/descwl_shear_sims/shear.py
@@ -2,7 +2,7 @@
 import numpy as np
 # maximum kappa allowed
 # values greater than it will be clipped
-kappa_max = 0.6
+g_max = 0.6
 """
 shear_obj = ShearConstant(cluster_obj, z_cl, ra_cl, dec_cl)
 shear_obj = ShearRedshift(g1=0.02, g2=0.00)
@@ -58,15 +58,17 @@ def get_shear(self, redshift, shift):
 
             # TODO: confirm whether the units is Mpc/h or Mpc?
             gammat = self.cobj.eval_tangential_shear(r3d, z_cl, redshift)
-            kappa0 = self.cobj.eval_convergence(r3d, z_cl, redshift)
-            # we are forcing kappa to be less than kappa_max
-            # and scale gamma by the same ratio
-            kappa = min(kappa0, kappa_max)
-            ratio = kappa / kappa0
-            gamma1 = gammat * np.cos(2. * phi) * ratio
-            gamma2 = -gammat * np.sin(2. * phi) * ratio
-            g1 = gamma1 / (1-kappa)
-            g2 = gamma2 / (1-kappa)
+            kappa = self.cobj.eval_convergence(r3d, z_cl, redshift)
+            gamma1 = gammat * np.cos(2. * phi)
+            gamma2 = -gammat * np.sin(2. * phi)
+            g1 = gamma1 #/ (1-kappa)
+            g2 = gamma2 #/ (1-kappa)
+            # we are forcing g to be less than g_max
+            g = np.sqrt(g1 ** 2. + g2 ** 2.)
+            ratio = min(g_max / g, 1.0)
+            # and rescale g1 and g2 if g > g_max
+            g1 = g1 * ratio
+            g2 = g2 * ratio
         else:
             g1 = 0.
             g2 = 0.
diff --git a/examples/example_NFWShear.ipynb b/examples/example_NFWShear.ipynb
index c1ba54d0..af942d14 100644
--- a/examples/example_NFWShear.ipynb
+++ b/examples/example_NFWShear.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 19,
    "id": "d12a992b-6669-4675-b68a-b705ff09f693",
    "metadata": {},
    "outputs": [],
@@ -24,38 +24,111 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "707e17ec-2aa0-4470-b986-fefe11e4be47",
+   "execution_count": 58,
+   "id": "14f7db13-1476-4197-b54f-219b30bdb59a",
    "metadata": {},
    "outputs": [],
    "source": [
-    "cosmo = clmm.Cosmology(H0=70.0, Omega_dm0=0.27 - 0.045, Omega_b0=0.045, Omega_k0=0.0)\n",
-    "halo = clmm.Modeling(massdef=\"mean\", delta_mdef=200, halo_profile_model=\"nfw\")\n",
-    "halo.set_cosmo(cosmo)\n",
-    "halo.set_concentration(4)\n",
-    "halo.set_mass(1.0e15)\n",
-    "z_cl = 1.0\n",
-    "# source properties\n",
-    "z_source = 2.0  # all sources in the same plane\n",
+    "from astropy.cosmology import FlatLambdaCDM\n",
+    "from lenstronomy.Cosmo.lens_cosmo import LensCosmo\n",
+    "from lenstronomy.LensModel.lens_model import LensModel\n",
     "\n",
-    "shear_obj = dss.shear.ShearNFW(halo, z_cl)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3eae4691-6752-4e77-a81d-d913d7bec263",
-   "metadata": {},
-   "source": [
-    "# step1: make sure the direction is correct"
+    "zl_list = [0.1, 0.4]\n",
+    "M200_list = [1e14, 1e15]\n",
+    "\n",
+    "\n",
+    "g_max = 0.6\n",
+    "\n",
+    "class ShearNFW(object):\n",
+    "    def __init__(self, zl_list, M200_list, c_list, cosmo):\n",
+    "        if not isinstance(zl_list, list):\n",
+    "            if isinstance(zl_list, float):\n",
+    "                zl_list = [zl_list]\n",
+    "        if not isinstance(M200_list, list):\n",
+    "            if isinstance(M200_list, float):\n",
+    "                M200_list = [M200_list]\n",
+    "        if not isinstance(c_list, list):\n",
+    "            if isinstance(c_list, float):\n",
+    "                c_list = [c_list]\n",
+    "        assert len(zl_list) == len(M200_list)\n",
+    "        assert len(zl_list) == len(c_list)\n",
+    "        self.cosmo = cosmo\n",
+    "        self.zl_list = zl_list\n",
+    "        self.params = []\n",
+    "        self.n_halos = len(zl_list)\n",
+    "        for _ in range(self.n_halos):\n",
+    "            self.params.append({\"M\": M200_list[_], \"c\": c_list[_]})\n",
+    "        return\n",
+    "\n",
+    "    def get_shear(self, redshift, shift):\n",
+    "        \"\"\"\n",
+    "        A shear wrapper to return g1 and g2 with different shear type\n",
+    "\n",
+    "        Parameters\n",
+    "        ----------\n",
+    "        redshift (float):           redshifts of galaxies\n",
+    "        shift (galsim.positionD):   Galsim positionD shift [arcsec]\n",
+    "\n",
+    "        Returns\n",
+    "        ---------\n",
+    "        shear (galsim.Shear)        shear distortion on the galaxy\n",
+    "        \"\"\"\n",
+    "\n",
+    "        model_list = []\n",
+    "        kwargs_list = []\n",
+    "        for _ in range(self.n_halos):\n",
+    "            model_list.append(\"NFW\")\n",
+    "            lens_cosmo = LensCosmo(z_lens=zl_list[_], z_source=redshift, cosmo=self.cosmo)\n",
+    "            Rs_angle, alpha_Rs = lens_cosmo.nfw_physical2angle(**self.params[_])\n",
+    "            rho0, Rs, c, r200, M200 = lens_cosmo.nfw_angle2physical(Rs_angle, alpha_Rs)\n",
+    "            kwargs = {'Rs': Rs_angle, 'alpha_Rs': alpha_Rs, \"center_x\": 0.0, \"center_y\": 0.0}\n",
+    "            kwargs_list.append(kwargs)\n",
+    "        lens = LensModel(lens_model_list=model_list)\n",
+    "        alpha_x, alpha_y = lens.alpha(x=shift.x, y=shift.y, kwargs=kwargs_list)\n",
+    "        f_xx, f_xy, f_yx, f_yy = lens.hessian(x=shift.x, y=shift.y, kwargs=kwargs_list)\n",
+    "        gamma1 = 0.5 * (f_xx - f_yy)\n",
+    "        gamma2 = f_xy\n",
+    "        kappa = 0.5 * (f_xx + f_yy)\n",
+    "        g1 = gamma1 #/ (1 - kappa)\n",
+    "        g2 = gamma2 #/ (1 - kappa)\n",
+    "        # we are forcing g to be less than g_max\n",
+    "        g = np.sqrt(g1 ** 2. + g2 ** 2.)\n",
+    "        ratio = min(g_max / g, 1.0)\n",
+    "        # and rescale g1 and g2 if g > g_max\n",
+    "        g1 = g1 * ratio\n",
+    "        g2 = g2 * ratio\n",
+    "        shear = galsim.Shear(g1=g1, g2=g2)\n",
+    "        return shear"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 59,
    "id": "913c7ae0-8b39-4a3b-9e09-c40262170e0c",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
+    "cosmo = clmm.Cosmology(H0=70.0, Omega_dm0=0.27 - 0.045, Omega_b0=0.045, Omega_k0=0.0)\n",
+    "halo = clmm.Modeling(massdef=\"critical\", delta_mdef=200, halo_profile_model=\"nfw\")\n",
+    "halo.set_cosmo(cosmo)\n",
+    "halo.set_concentration(4)\n",
+    "halo.set_mass(1.0e15)\n",
+    "z_cl = 1.0\n",
+    "# source properties\n",
+    "z_source = 2.0  # all sources in the same plane\n",
+    "\n",
+    "shear_obj = dss.shear.ShearNFW(halo, z_cl)\n",
     "# generate positions\n",
     "radius = 50 # arcsec\n",
     "n_gal = 20\n",
@@ -73,26 +146,94 @@
     "# get the shear\n",
     "shear_list = []\n",
     "for ss in shift_list:\n",
-    "    shear_list.append(shear_obj.get_shear(z_source, ss))"
+    "    shear_list.append(shear_obj.get_shear(z_source, ss))\n",
+    "\n",
+    "gamma = []\n",
+    "for ss in shear_list:\n",
+    "    gamma.append(ss.g1 + 1j * ss.g2) \n",
+    "gamma = np.array(gamma)\n",
+    "\n",
+    "angles = np.angle(gamma, deg=True) / 2.\n",
+    "lengths = np.abs(gamma) * 100.\n",
+    "\n",
+    "# Create whisker plot\n",
+    "plt.figure(figsize=(6, 6))\n",
+    "plt.quiver(x, y, np.cos(np.deg2rad(angles)), np.sin(np.deg2rad(angles)),\n",
+    "           color=\"black\", headaxislength=0, headlength=0, headwidth=1, pivot = 'middle')\n",
+    "plt.xlabel('x')\n",
+    "plt.ylabel('y')\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "3d6955d3-7c6d-4d68-a81f-fa8f2003c06f",
+   "execution_count": 60,
+   "id": "a7ecb75e-dbff-48b4-a495-bfe7f544eb14",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[     1      3      9     27     81    243    729   2187   6561  19683\n",
+      "  59049 177147]\n",
+      "[2.78361124e-01 2.74640017e-01 2.56551659e-01 1.98359090e-01\n",
+      " 1.02796620e-01 3.27933044e-02 7.20665320e-03 1.26455112e-03\n",
+      " 1.95016137e-04 2.78421302e-05 3.78395338e-06 4.97313342e-07]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, '$|g|$')"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "gamma = []\n",
-    "for ss in shear_list:\n",
-    "    gamma.append(ss.g1 + 1j * ss.g2) \n",
-    "gamma = np.array(gamma)"
+    "n_gal = 1\n",
+    "d_array = []\n",
+    "g_array = []\n",
+    "for i in range(12):\n",
+    "    position = galsim.PositionD(3.0 ** i, 0.)\n",
+    "    # get the shear\n",
+    "    shear = shear_obj.get_shear(z_source, position)\n",
+    "    g1 = shear.g1\n",
+    "    g2 = shear.g2\n",
+    "    gabs = np.abs(g1 + 1j * g2)\n",
+    "    d_array.append(3 ** i)\n",
+    "    g_array.append(gabs)\n",
+    "d_array = np.array(d_array)\n",
+    "g_array = np.array(g_array)\n",
+    "\n",
+    "print(d_array)\n",
+    "print(g_array)\n",
+    "plt.close()\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(d_array / 3600., g_array)\n",
+    "ax.set_xscale(\"log\")\n",
+    "ax.set_yscale(\"log\")\n",
+    "ax.set_xlabel(\"separation [degree]\")\n",
+    "ax.set_ylabel(r\"$|g|$\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "8c203fae-f689-4405-91be-056aa84a3593",
+   "execution_count": 61,
+   "id": "45d40de9-7fe9-413e-a481-347276e28c82",
    "metadata": {},
    "outputs": [
     {
@@ -107,6 +248,33 @@
     }
    ],
    "source": [
+    "cosmo = FlatLambdaCDM(H0=70.0, Om0=0.27, Ob0=0.045)\n",
+    "\n",
+    "shear_obj = ShearNFW(zl_list=[1.], M200_list=[1e15], c_list=[4.0], cosmo=cosmo)\n",
+    "# generate positions\n",
+    "radius = 50 # arcsec\n",
+    "n_gal = 20\n",
+    "theta = np.linspace(0, 360, n_gal) / n_gal\n",
+    "x = np.cos(theta) * radius\n",
+    "y = np.sin(theta) * radius\n",
+    "shifts = np.zeros(n_gal, dtype=[('dx', 'f8'), ('dy', 'f8')])\n",
+    "shifts['dx'] = x\n",
+    "shifts['dy'] = y\n",
+    "\n",
+    "shift_list = []\n",
+    "for ss in shifts:\n",
+    "    shift_list.append(galsim.PositionD(ss['dx'], ss['dy']))\n",
+    "\n",
+    "# get the shear\n",
+    "shear_list = []\n",
+    "for ss in shift_list:\n",
+    "    shear_list.append(shear_obj.get_shear(z_source, ss))\n",
+    "    \n",
+    "gamma = []\n",
+    "for ss in shear_list:\n",
+    "    gamma.append(ss.g1 + 1j * ss.g2) \n",
+    "gamma = np.array(gamma)\n",
+    "\n",
     "angles = np.angle(gamma, deg=True) / 2.\n",
     "lengths = np.abs(gamma) * 100.\n",
     "\n",
@@ -119,25 +287,39 @@
     "plt.show()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "b70c050a-b590-4192-81da-3b747dd07d27",
-   "metadata": {},
-   "source": [
-    "# step2: make sure the amplitude is correct"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "d95fa97a-1415-44b4-82cf-3cae3106e2a1",
+   "execution_count": 62,
+   "id": "b22ff0a9-6ce6-49e2-bef7-1a91a16ee4dd",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[     1      3      9     27     81    243    729   2187   6561  19683\n",
+      "  59049 177147]\n",
+      "[8.26751816e-02 8.26215008e-02 8.22711368e-02 8.02829024e-02\n",
+      " 7.16882521e-02 4.92520061e-02 2.14542996e-02 5.89296747e-03\n",
+      " 1.17895828e-03 1.96054091e-04 2.92963630e-05 4.10083070e-06]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7faf5d39a2d0>]"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "n_gal = 1\n",
     "d_array = []\n",
     "g_array = []\n",
-    "for i in range(10):\n",
+    "for i in range(12):\n",
     "    position = galsim.PositionD(3.0 ** i, 0.)\n",
     "    # get the shear\n",
     "    shear = shear_obj.get_shear(z_source, position)\n",
@@ -147,538 +329,84 @@
     "    d_array.append(3 ** i)\n",
     "    g_array.append(gabs)\n",
     "d_array = np.array(d_array)\n",
-    "g_array = np.array(g_array)"
+    "g_array = np.array(g_array)\n",
+    "print(d_array)\n",
+    "print(g_array)\n",
+    "ax.plot(d_array / 3600., g_array)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "4f6eb0d7-2809-4b37-8357-1e443e900273",
+   "execution_count": 64,
+   "id": "0d0c3bed-89c3-4c13-aec1-ffedff1a0956",
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, '$|g|$')"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[     1      3      9     27     81    243    729   2187   6561  19683\n",
+      "  59049 177147]\n",
+      "[2.47424027e-01 5.30458122e-01 4.51641183e+00 3.92914418e-01\n",
+      " 1.43436687e-01 4.35660410e-02 9.72263335e-03 1.73440286e-03\n",
+      " 2.70341617e-04 3.88561182e-05 5.30434563e-06 6.99305314e-07]\n"
+     ]
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "[<matplotlib.lines.Line2D at 0x7faf5d491f50>]"
       ]
      },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(d_array / 3600., g_array)\n",
-    "plt.xscale(\"log\")\n",
-    "plt.yscale(\"log\")\n",
-    "plt.xlabel(\"separation [degree]\")\n",
-    "plt.ylabel(r\"$|g|$\")\n",
-    "# We set kappa_max to 0.8"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "0c43422d-d816-4c98-8bb9-8671bbdcd1a1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "band=\"r\"\n",
-    "rng = np.random.RandomState(1)\n",
-    "coadd_dim = 500\n",
-    "buff = 50\n",
-    "# galaxy catalog; you can make your own\n",
-    "galaxy_catalog = WLDeblendGalaxyCatalog(\n",
-    "    rng=rng,\n",
-    "    coadd_dim=coadd_dim,\n",
-    "    buff=buff,\n",
-    "    layout=\"random\",\n",
-    ")\n",
-    "\n",
-    "survey = get_survey(gal_type=galaxy_catalog.gal_type, band=band)\n",
-    "noise_for_gsparams = survey.noise\n",
-    "lists = get_objlist(\n",
-    "    galaxy_catalog=galaxy_catalog,\n",
-    "    survey=survey,\n",
-    "    star_catalog=None,\n",
-    "    noise=noise_for_gsparams,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "717a8a13-7bf7-45ab-a163-a17f783be24a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['objlist', 'shifts', 'redshifts', 'star_objlist', 'star_shifts', 'bright_objlist', 'bright_shifts', 'bright_mags'])"
-      ]
-     },
-     "execution_count": 45,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "lists.keys()"
+    "d_array = []\n",
+    "g_array = []\n",
+    "ghalo = galsim.NFWHalo(mass=1e15, conc=4, redshift=1.0, omega_m=0.27, omega_lam=0.73)\n",
+    "for i in range(12):\n",
+    "    position = galsim.PositionD(3.0 ** i, 0.)\n",
+    "    # get the shear\n",
+    "    g1, g2 = ghalo.getShear(position, z_source)\n",
+    "    gabs = np.abs(g1 + 1j * g2)\n",
+    "    d_array.append(3 ** i)\n",
+    "    g_array.append(gabs)\n",
+    "d_array = np.array(d_array)\n",
+    "g_array = np.array(g_array)\n",
+    "print(d_array)\n",
+    "print(g_array)\n",
+    "ax.plot(d_array / 3600., g_array)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
-   "id": "aaf65a03-5261-4ec1-b8e9-f26d5db85ba0",
+   "execution_count": 65,
+   "id": "d6cb97de-5125-4fdf-927a-8b1dca7c4141",
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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-       " 1.53510355949,\n",
-       " 1.32124757767,\n",
-       " 1.36363995075,\n",
-       " 1.84346938133,\n",
-       " 0.654158771038,\n",
-       " 2.15651893616,\n",
-       " 2.94037532806,\n",
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-       " 0.508974313736,\n",
-       " 2.45654439926,\n",
-       " 1.23254072666,\n",
-       " 1.85351324081]"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "lists[\"redshifts\"]"
+    "fig"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a64792ea-69b8-45e1-9379-334f73cacfbb",
+   "id": "472f1e75-9cbf-4c5d-93ce-4d06c7b16f15",
    "metadata": {},
    "outputs": [],
    "source": []

From a9ba0719a20c17f65246a63597f6c54d62e1ff76 Mon Sep 17 00:00:00 2001
From: Xiangchong Li <mr.superonion@hotmail.com>
Date: Mon, 16 Oct 2023 10:41:07 -0700
Subject: [PATCH 11/30] add excutable simulation code in bin

---
 bin/simulate_image.py | 217 ++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 217 insertions(+)
 create mode 100755 bin/simulate_image.py

diff --git a/bin/simulate_image.py b/bin/simulate_image.py
new file mode 100755
index 00000000..359726c8
--- /dev/null
+++ b/bin/simulate_image.py
@@ -0,0 +1,217 @@
+#!/usr/bin/env python
+#
+# simple example with ring test (rotating intrinsic galaxies)
+# Copyright 20230916 Xiangchong Li.
+#
+# 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.
+#
+import gc
+import os
+import fpfs
+import fitsio
+import pickle
+import glob
+import schwimmbad
+import numpy as np
+from argparse import ArgumentParser
+from configparser import ConfigParser
+from descwl_shear_sims.sim import make_sim
+from descwl_shear_sims.galaxies import (
+    WLDeblendGalaxyCatalog,
+)  # one of the galaxy catalog classes
+from descwl_shear_sims.psfs import (
+    make_ps_psf,
+    make_fixed_psf,
+)  # for making a power spectrum PSF
+
+# convert coadd dims to SE dims
+from descwl_shear_sims.sim import get_se_dim
+from descwl_shear_sims.shear import ShearConstant
+
+g1_list = [-0.02, 0.02]
+nshear = len(g1_list)
+band_list = ["g", "r", "i", "z"]
+nband = len(band_list)
+
+
+class Worker:
+    def __init__(self, config_name):
+        cparser = ConfigParser()
+        cparser.read(config_name)
+        # layout of the simulation (random, random_disk, or hex)
+        # see details in descwl-shear-sims
+        self.layout = cparser.get("simulation", "layout")
+        # image root directory
+        self.img_root = cparser.get("simulation", "img_root")
+        # Whether do rotation or dithering
+        self.rotate = cparser.getboolean("simulation", "rotate")
+        self.dither = cparser.getboolean("simulation", "dither")
+        # version of the PSF simulation: 0 -- fixed PSF; 1 -- variational PSF
+        self.psf_version = cparser.getint("simulation", "psf_version")
+        # length of the exposure
+        self.coadd_dim = cparser.getint("simulation", "coadd_dim")
+        # buffer length to avoid galaxies hitting the boundary of the exposure
+        self.buff = cparser.getint("simulation", "buff")
+        # number of rotations for ring test
+        self.nrot = cparser.getint("simulation", "nrot")
+        # number of redshiftbins
+        self.nzbin = cparser.getint("simulation", "nzbin")
+        self.rot_list = [np.pi / self.nrot * i for i in range(self.nrot)]
+        self.test_name = cparser.get("simulation", "test_name")
+        return
+
+    def run(self, ifield=0):
+        print("Simulating for field: %d" % ifield)
+        rng = np.random.RandomState(ifield)
+        scale = 0.2
+
+        if self.psf_version == 0:
+            # basic test
+            kargs = {
+                "cosmic_rays": False,
+                "bad_columns": False,
+                "star_bleeds": False,
+                "draw_method": "auto",
+            }
+            star_catalog = None
+            psf = make_fixed_psf(psf_type="moffat")  # .shear(e1=0.02, e2=-0.02)
+            psf_fname = "%s/PSF_%s_32.fits" % (self.img_root, self.test_name)
+            if not os.path.isfile(psf_fname):
+                psf_data = psf.shift(
+                    0.5 * scale,
+                    0.5 * scale
+                ).drawImage(nx=64, ny=64, scale=scale).array
+                fitsio.write(psf_fname, psf_data)
+
+        elif self.psf_version == 1:
+            # spatial varying PSF
+            kargs = {
+                "cosmic_rays": False,
+                "bad_columns": False,
+                "star_bleeds": False,
+                "draw_method": "auto",
+            }
+            star_catalog = None
+            # this is the single epoch image sized used by the sim, we need
+            # it for the power spectrum psf
+            se_dim = get_se_dim(
+                coadd_dim=self.coadd_dim, rotate=self.rotate, dither=self.dither
+            )
+            psf = make_ps_psf(rng=rng, dim=se_dim)
+            psf_fname = "%s/PSF_%s.pkl" % (self.img_root, self.test_name)
+            if not os.path.isfile(psf_fname):
+                with open(psf_fname, "wb") as f:
+                    pickle.dump(
+                        {"psf": psf},
+                        f,
+                    )
+        else:
+            raise ValueError("psf_version must be 0 or 1")
+
+        img_dir = "%s/%s" % (self.img_root, self.test_name)
+        os.makedirs(img_dir, exist_ok=True)
+        nfiles = len(glob.glob(
+            "%s/image-%05d_g1-*" % (img_dir, ifield)
+        ))
+        if nfiles == self.nrot * nshear * nband:
+            print("We aleady have all the images for this subfield.")
+            return
+
+        # galaxy catalog; you can make your own
+        galaxy_catalog = WLDeblendGalaxyCatalog(
+            rng=rng,
+            coadd_dim=self.coadd_dim,
+            buff=self.buff,
+            layout=self.layout,
+        )
+        print("Simulation has galaxies: %d" % len(galaxy_catalog))
+        for ishear in range(nshear):
+            for irot in range(self.nrot):
+
+                shear_obj = ShearConstant(g1_list[ishear], g2=0.)
+                sim_data = make_sim(
+                    rng=rng,
+                    galaxy_catalog=galaxy_catalog,
+                    star_catalog=star_catalog,
+                    coadd_dim=self.coadd_dim,
+                    shear_obj=shear_obj,
+                    psf=psf,
+                    dither=self.dither,
+                    rotate=self.rotate,
+                    bands=band_list,
+                    noise_factor=0.0,
+                    theta0=self.rot_list[irot],
+                    **kargs
+                )
+                # write galaxy images
+                for band_name in band_list:
+                    gal_fname = "%s/image-%05d_g1-%d_rot%d_%s.fits" % (
+                        img_dir,
+                        ifield,
+                        ishear,
+                        irot,
+                        band_name,
+                    )
+                    mi = sim_data["band_data"][band_name][0].getMaskedImage()
+                    gdata = mi.getImage().getArray()
+                    fpfs.io.save_image(gal_fname, gdata)
+                    del mi, gdata, gal_fname
+                del sim_data
+                gc.collect()
+        del galaxy_catalog, psf
+        return
+
+
+if __name__ == "__main__":
+    parser = ArgumentParser(description="simulate blended images")
+    parser.add_argument(
+        "--min_id",
+        default=0,
+        type=int,
+        help="minimum id number, e.g. 0",
+    )
+    parser.add_argument(
+        "--max_id",
+        default=5000,
+        type=int,
+        help="maximum id number, e.g. 4000",
+    )
+    parser.add_argument(
+        "--config",
+        required=True,
+        type=str,
+        help="configure file name",
+    )
+    #
+    group = parser.add_mutually_exclusive_group()
+    group.add_argument(
+        "--ncores",
+        dest="n_cores",
+        default=1,
+        type=int,
+        help="Number of processes (uses multiprocessing).",
+    )
+    group.add_argument(
+        "--mpi",
+        dest="mpi",
+        default=False,
+        action="store_true",
+        help="Run with MPI.",
+    )
+    cmd_args = parser.parse_args()
+    min_id = cmd_args.min_id
+    max_id = cmd_args.max_id
+    pool = schwimmbad.choose_pool(mpi=cmd_args.mpi, processes=cmd_args.n_cores)
+    idlist = list(range(min_id, max_id))
+    worker = Worker(cmd_args.config)
+    for r in pool.map(worker.run, idlist):
+        pass
+    pool.close()

From 9ac1fde947d345bd3e87784e267ba7c914f851d7 Mon Sep 17 00:00:00 2001
From: Xiangchong Li <mr.superonion@hotmail.com>
Date: Mon, 16 Oct 2023 11:52:53 -0700
Subject: [PATCH 12/30] basic structure for zdependent shear

---
 descwl_shear_sims/shear.py | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/descwl_shear_sims/shear.py b/descwl_shear_sims/shear.py
index 74776aa0..8ea49dee 100644
--- a/descwl_shear_sims/shear.py
+++ b/descwl_shear_sims/shear.py
@@ -115,11 +115,21 @@ def __init__(self, mode="0000", g_dist="g1"):
         self.z_bounds = np.linspace(0, 4, nz_bins+1)
         self.dz_bin = self.z_bounds[1]-self.z_bounds[0]
         self.g_dist = g_dist
+        self.shear_list = self.determine_shear_list(mode)
         return
 
+    def determine_shear_list(self, mode):
+        shear_list = []
+        return shear_list
+
+    def get_bin(self, refshift):
+        bin_num = 0
+        return bin_num
+
     def get_shear(self, redshift, shift=None):
         # z_gal_bins = redshift // self.dz_bin
         gamma1, gamma2 = (None, None)
         # TODO: Finish implementing the z-dependent shear
+        bin_number = self.get_bin(redshift)
         shear = galsim.Shear(g1=gamma1, g2=gamma2)
         return shear

From 071f6673f6396e218c406231b7835d845b2ad4cd Mon Sep 17 00:00:00 2001
From: Xiangchong Li <mr.superonion@hotmail.com>
Date: Mon, 16 Oct 2023 12:13:07 -0700
Subject: [PATCH 13/30] add configure file and readme on how to run the
 excutable file for the simulation

---
 bin/README.md             |  5 +++++
 bin/config_sim_zshear.ini | 17 +++++++++++++++++
 2 files changed, 22 insertions(+)
 create mode 100644 bin/README.md
 create mode 100644 bin/config_sim_zshear.ini

diff --git a/bin/README.md b/bin/README.md
new file mode 100644
index 00000000..d24c51a8
--- /dev/null
+++ b/bin/README.md
@@ -0,0 +1,5 @@
+# Code to run the simulation
+
+```shell
+simulate_image.py --config config_sim_zshear.ini --min_id 1 --max_id 2
+````
diff --git a/bin/config_sim_zshear.ini b/bin/config_sim_zshear.ini
new file mode 100644
index 00000000..d6dd0d2c
--- /dev/null
+++ b/bin/config_sim_zshear.ini
@@ -0,0 +1,17 @@
+; run the code in shear_obj_zshear branch
+[simulation]
+
+nrot    =   2
+test_name = zshear
+layout  =   random_disk
+; layout = hex
+img_root     =   ./
+; no rotate no dither.. no
+rotate  =   False
+dither  =   False
+; psf_version = 0 for fixed PSF
+psf_version = 0
+coadd_dim = 5000
+; buffer length to avoid galaxies hit the boundary
+buff    =   80
+nzbin   =   1

From d96fd6ec89d3018cafa358cbef1aff968882c1aa Mon Sep 17 00:00:00 2001
From: ismael2395 <imendoza@umich.edu>
Date: Mon, 16 Oct 2023 12:37:15 -0700
Subject: [PATCH 14/30] simple implementation, inefficient

---
 descwl_shear_sims/shear.py | 22 ++++++++++++++++------
 1 file changed, 16 insertions(+), 6 deletions(-)

diff --git a/descwl_shear_sims/shear.py b/descwl_shear_sims/shear.py
index 8ea49dee..262f21d3 100644
--- a/descwl_shear_sims/shear.py
+++ b/descwl_shear_sims/shear.py
@@ -1,5 +1,6 @@
 import galsim
 import numpy as np
+
 # maximum kappa allowed
 # values greater than it will be clipped
 g_max = 0.6
@@ -46,6 +47,7 @@ def get_shear(self, redshift, shift):
         z_cl = self.z_cl
         if redshift > z_cl:
             from astropy.coordinates import SkyCoord
+
             # Create the SkyCoord objects
             coord_cl = SkyCoord(self.ra_cl, self.dec_cl, unit="arcsec")
             coord_gals = SkyCoord(shift.x, shift.y, unit="arcsec")
@@ -107,6 +109,7 @@ def __init__(self, mode="0000", g_dist="g1"):
         # 0: g=0.00; 1: g=-0.02; 2: g=0.02
         # "0000" means that we divide into 4 redshift bins, and every bin
         # is distorted by -0.02
+        assert set(mode).issubset(set("012"))
         nz_bins = len(mode)
         self.nz_bins = nz_bins
         # number of possible modes
@@ -119,17 +122,24 @@ def __init__(self, mode="0000", g_dist="g1"):
         return
 
     def determine_shear_list(self, mode):
-        shear_list = []
+        values = [-0.02, 0.00, 0.02]
+        shear_list = [values[int(i)] for i in mode]
         return shear_list
 
-    def get_bin(self, refshift):
-        bin_num = 0
+    def get_bin(self, redshift):
+        bin_num = redshift // self.dz_bin
         return bin_num
 
     def get_shear(self, redshift, shift=None):
-        # z_gal_bins = redshift // self.dz_bin
-        gamma1, gamma2 = (None, None)
-        # TODO: Finish implementing the z-dependent shear
         bin_number = self.get_bin(redshift)
+        shear = self.shear_list[bin_number]
+
+        if self.g_dist == 'g1':
+            gamma1, gamma2 = (shear, 0.)
+        elif self.g_dist == 'g2':
+            gamma1, gamma2 = (0., shear)
+        else:
+            raise ValueError("g_dist must be either 'g1' or 'g2'")
+
         shear = galsim.Shear(g1=gamma1, g2=gamma2)
         return shear

From b0f60759e40e23411be8084accfbed2a956175e4 Mon Sep 17 00:00:00 2001
From: ismael2395 <imendoza@umich.edu>
Date: Mon, 16 Oct 2023 13:02:11 -0700
Subject: [PATCH 15/30] ternary implementation

---
 descwl_shear_sims/shear.py | 38 +++++++++++++++++++++++++-------------
 1 file changed, 25 insertions(+), 13 deletions(-)

diff --git a/descwl_shear_sims/shear.py b/descwl_shear_sims/shear.py
index 262f21d3..0aaf61ef 100644
--- a/descwl_shear_sims/shear.py
+++ b/descwl_shear_sims/shear.py
@@ -11,6 +11,18 @@
 """
 
 
+def _ternary(n, n_bins):
+    """CREDIT: https://stackoverflow.com/questions/34559663/\
+        convert-decimal-to-ternarybase3-in-python"""
+    if n == 0:
+        return '0'
+    nums = []
+    while n:
+        n, r = divmod(n, 3)
+        nums.append(str(r))
+    return ''.join(reversed(nums)).zfill(n_bins)
+
+
 class ShearNFW(object):
     """
     Shear object from NFW halos
@@ -104,26 +116,26 @@ class ShearRedshift(object):
     """
     Constant shear along every redshift slice
     """
-    def __init__(self, mode="0000", g_dist="g1"):
+    def __init__(self, mode="0-0", g_dist="g1"):
         # note that there are three options in each redshift bin
         # 0: g=0.00; 1: g=-0.02; 2: g=0.02
-        # "0000" means that we divide into 4 redshift bins, and every bin
-        # is distorted by -0.02
-        assert set(mode).issubset(set("012"))
-        nz_bins = len(mode)
-        self.nz_bins = nz_bins
-        # number of possible modes
-        self.n_modes = 3 ** nz_bins
-        self.mode = mode
+        # "4-7" means that we divide into 4 redshift bins, and "7" in ternary
+        # is "0021", which means that the shear is (0.0, 0.0, 0.02, -0.02) in each bin.
+        assert '-' in mode, "mode must be in the form of 'nz_bins-code'"
+        nz_bins, code = mode.split("-")
+        assert nz_bins.isdigit() and code.isdigit()
+        self.nz_bins = int(nz_bins)
+        self.code = _ternary(code, self.nz_bins)
+        assert 0 <= int(code) < 3 ** self.nz_bins, "mode code is too large"
         self.z_bounds = np.linspace(0, 4, nz_bins+1)
         self.dz_bin = self.z_bounds[1]-self.z_bounds[0]
         self.g_dist = g_dist
-        self.shear_list = self.determine_shear_list(mode)
+        self.shear_list = self.determine_shear_list(self.code)
         return
 
-    def determine_shear_list(self, mode):
-        values = [-0.02, 0.00, 0.02]
-        shear_list = [values[int(i)] for i in mode]
+    def determine_shear_list(self, code):
+        values = [0.00, -0.02, 0.02]
+        shear_list = [values[int(i)] for i in code]
         return shear_list
 
     def get_bin(self, redshift):

From 4fe3991d43ba4b89bcaf83805e968e2c47aca2de Mon Sep 17 00:00:00 2001
From: Xiangchong Li <xiangchong.li@ipmu.jp>
Date: Wed, 18 Oct 2023 03:46:24 +0900
Subject: [PATCH 16/30] some corrections and we need to define zbounds from ini
 file, I think

---
 bin/config_sim_zshear.ini  |  4 +++-
 bin/simulate_image.py      | 29 ++++++++++++++++++++---------
 descwl_shear_sims/shear.py | 24 +++++++++++++-----------
 3 files changed, 36 insertions(+), 21 deletions(-)

diff --git a/bin/config_sim_zshear.ini b/bin/config_sim_zshear.ini
index d6dd0d2c..2d9784a3 100644
--- a/bin/config_sim_zshear.ini
+++ b/bin/config_sim_zshear.ini
@@ -3,6 +3,8 @@
 
 nrot    =   2
 test_name = zshear
+shear_mode_list = [0, 1]
+z_bounds = [0.0, 10.0]
 layout  =   random_disk
 ; layout = hex
 img_root     =   ./
@@ -11,7 +13,7 @@ rotate  =   False
 dither  =   False
 ; psf_version = 0 for fixed PSF
 psf_version = 0
-coadd_dim = 5000
+coadd_dim = 500
 ; buffer length to avoid galaxies hit the boundary
 buff    =   80
 nzbin   =   1
diff --git a/bin/simulate_image.py b/bin/simulate_image.py
index 359726c8..80323ef9 100755
--- a/bin/simulate_image.py
+++ b/bin/simulate_image.py
@@ -15,6 +15,7 @@
 #
 import gc
 import os
+import json
 import fpfs
 import fitsio
 import pickle
@@ -22,7 +23,7 @@
 import schwimmbad
 import numpy as np
 from argparse import ArgumentParser
-from configparser import ConfigParser
+from configparser import ConfigParser, ExtendedInterpolation
 from descwl_shear_sims.sim import make_sim
 from descwl_shear_sims.galaxies import (
     WLDeblendGalaxyCatalog,
@@ -34,17 +35,15 @@
 
 # convert coadd dims to SE dims
 from descwl_shear_sims.sim import get_se_dim
-from descwl_shear_sims.shear import ShearConstant
+from descwl_shear_sims.shear import ShearRedshift
 
-g1_list = [-0.02, 0.02]
-nshear = len(g1_list)
 band_list = ["g", "r", "i", "z"]
 nband = len(band_list)
 
 
 class Worker:
     def __init__(self, config_name):
-        cparser = ConfigParser()
+        cparser = ConfigParser(interpolation=ExtendedInterpolation())
         cparser.read(config_name)
         # layout of the simulation (random, random_disk, or hex)
         # see details in descwl-shear-sims
@@ -66,6 +65,14 @@ def __init__(self, config_name):
         self.nzbin = cparser.getint("simulation", "nzbin")
         self.rot_list = [np.pi / self.nrot * i for i in range(self.nrot)]
         self.test_name = cparser.get("simulation", "test_name")
+        self.shear_mode_list = json.loads(
+            cparser.get("simulation", "shear_mode_list")
+        )
+        self.z_bounds = json.loads(
+            cparser.get("simulation", "z_bounds")
+        )
+        print(self.shear_mode_list)
+        self.nshear = len(self.shear_mode_list)
         return
 
     def run(self, ifield=0):
@@ -121,7 +128,7 @@ def run(self, ifield=0):
         nfiles = len(glob.glob(
             "%s/image-%05d_g1-*" % (img_dir, ifield)
         ))
-        if nfiles == self.nrot * nshear * nband:
+        if nfiles == self.nrot * self.nshear * nband:
             print("We aleady have all the images for this subfield.")
             return
 
@@ -133,10 +140,14 @@ def run(self, ifield=0):
             layout=self.layout,
         )
         print("Simulation has galaxies: %d" % len(galaxy_catalog))
-        for ishear in range(nshear):
+        for shear_mode in self.shear_mode_list:
             for irot in range(self.nrot):
 
-                shear_obj = ShearConstant(g1_list[ishear], g2=0.)
+                shear_obj = ShearRedshift(
+                    mode=shear_mode,
+                    z_bounds=self.z_bounds,
+                    g_dist="g1", # need to enable users to set this value
+                )
                 sim_data = make_sim(
                     rng=rng,
                     galaxy_catalog=galaxy_catalog,
@@ -156,7 +167,7 @@ def run(self, ifield=0):
                     gal_fname = "%s/image-%05d_g1-%d_rot%d_%s.fits" % (
                         img_dir,
                         ifield,
-                        ishear,
+                        shear_mode,
                         irot,
                         band_name,
                     )
diff --git a/descwl_shear_sims/shear.py b/descwl_shear_sims/shear.py
index 0aaf61ef..bda19481 100644
--- a/descwl_shear_sims/shear.py
+++ b/descwl_shear_sims/shear.py
@@ -116,19 +116,21 @@ class ShearRedshift(object):
     """
     Constant shear along every redshift slice
     """
-    def __init__(self, mode="0-0", g_dist="g1"):
+    def __init__(self, mode, z_bounds, g_dist="g1"):
+        nz_bins = len(z_bounds) - 1
+        # nz_bins is the number of redshift bins
         # note that there are three options in each redshift bin
         # 0: g=0.00; 1: g=-0.02; 2: g=0.02
-        # "4-7" means that we divide into 4 redshift bins, and "7" in ternary
-        # is "0021", which means that the shear is (0.0, 0.0, 0.02, -0.02) in each bin.
-        assert '-' in mode, "mode must be in the form of 'nz_bins-code'"
-        nz_bins, code = mode.split("-")
-        assert nz_bins.isdigit() and code.isdigit()
+        # for example, if number of redshift bins is 4, if mode = 7 which in
+        # ternary is "0021" --- meaning that the shear is (0.0, 0.0, 0.02,
+        # -0.02) in each bin.
         self.nz_bins = int(nz_bins)
-        self.code = _ternary(code, self.nz_bins)
-        assert 0 <= int(code) < 3 ** self.nz_bins, "mode code is too large"
-        self.z_bounds = np.linspace(0, 4, nz_bins+1)
-        self.dz_bin = self.z_bounds[1]-self.z_bounds[0]
+        self.code = _ternary(int(mode), self.nz_bins)
+        assert 0 <= int(mode) < 3 ** self.nz_bins, "mode code is too large"
+        # maybe we need it to be more flexible in the future
+        # but now we keep the linear spacing
+        self.z_bounds = z_bounds
+        print(self.nz_bins)
         self.g_dist = g_dist
         self.shear_list = self.determine_shear_list(self.code)
         return
@@ -139,7 +141,7 @@ def determine_shear_list(self, code):
         return shear_list
 
     def get_bin(self, redshift):
-        bin_num = redshift // self.dz_bin
+        bin_num = np.searchsorted(self.z_bounds, redshift, side="left") - 1
         return bin_num
 
     def get_shear(self, redshift, shift=None):

From f77941918662b04028846173958a611bbbb8c423 Mon Sep 17 00:00:00 2001
From: ismael2395 <imendoza@umich.edu>
Date: Tue, 17 Oct 2023 12:38:09 -0700
Subject: [PATCH 17/30] remove print

---
 bin/simulate_image.py | 30 +++++++++++++-----------------
 1 file changed, 13 insertions(+), 17 deletions(-)

diff --git a/bin/simulate_image.py b/bin/simulate_image.py
index 80323ef9..29cbc7c6 100755
--- a/bin/simulate_image.py
+++ b/bin/simulate_image.py
@@ -14,28 +14,25 @@
 # GNU General Public License for more details.
 #
 import gc
-import os
+import glob
 import json
-import fpfs
-import fitsio
+import os
 import pickle
-import glob
-import schwimmbad
-import numpy as np
 from argparse import ArgumentParser
 from configparser import ConfigParser, ExtendedInterpolation
-from descwl_shear_sims.sim import make_sim
-from descwl_shear_sims.galaxies import (
-    WLDeblendGalaxyCatalog,
-)  # one of the galaxy catalog classes
-from descwl_shear_sims.psfs import (
-    make_ps_psf,
-    make_fixed_psf,
-)  # for making a power spectrum PSF
 
-# convert coadd dims to SE dims
-from descwl_shear_sims.sim import get_se_dim
+import fitsio
+import fpfs
+import numpy as np
+import schwimmbad
+
+from descwl_shear_sims.galaxies import \
+    WLDeblendGalaxyCatalog  # one of the galaxy catalog classes
+from descwl_shear_sims.psfs import (  # for making a power spectrum PSF
+    make_fixed_psf, make_ps_psf)
 from descwl_shear_sims.shear import ShearRedshift
+# convert coadd dims to SE dims
+from descwl_shear_sims.sim import get_se_dim, make_sim
 
 band_list = ["g", "r", "i", "z"]
 nband = len(band_list)
@@ -71,7 +68,6 @@ def __init__(self, config_name):
         self.z_bounds = json.loads(
             cparser.get("simulation", "z_bounds")
         )
-        print(self.shear_mode_list)
         self.nshear = len(self.shear_mode_list)
         return
 

From 676c45d31122d06931db11b2e802b90e3ccecc6c Mon Sep 17 00:00:00 2001
From: ismael2395 <imendoza@umich.edu>
Date: Tue, 17 Oct 2023 12:38:41 -0700
Subject: [PATCH 18/30] remove another print

---
 descwl_shear_sims/shear.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/descwl_shear_sims/shear.py b/descwl_shear_sims/shear.py
index bda19481..d5541f2c 100644
--- a/descwl_shear_sims/shear.py
+++ b/descwl_shear_sims/shear.py
@@ -130,7 +130,6 @@ def __init__(self, mode, z_bounds, g_dist="g1"):
         # maybe we need it to be more flexible in the future
         # but now we keep the linear spacing
         self.z_bounds = z_bounds
-        print(self.nz_bins)
         self.g_dist = g_dist
         self.shear_list = self.determine_shear_list(self.code)
         return

From e85d122ccadbc43bcdcf185897c8187056318b93 Mon Sep 17 00:00:00 2001
From: ismael2395 <imendoza@umich.edu>
Date: Tue, 17 Oct 2023 12:46:48 -0700
Subject: [PATCH 19/30] type checking

---
 descwl_shear_sims/shear.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/descwl_shear_sims/shear.py b/descwl_shear_sims/shear.py
index d5541f2c..9f38a2d3 100644
--- a/descwl_shear_sims/shear.py
+++ b/descwl_shear_sims/shear.py
@@ -117,6 +117,7 @@ class ShearRedshift(object):
     Constant shear along every redshift slice
     """
     def __init__(self, mode, z_bounds, g_dist="g1"):
+        assert isinstance(mode, int), "mode must be an integer"
         nz_bins = len(z_bounds) - 1
         # nz_bins is the number of redshift bins
         # note that there are three options in each redshift bin

From bd9e198f32f8d32ace3c3f14b43cd03212781a6f Mon Sep 17 00:00:00 2001
From: Xiangchong Li <xiangchong.li@ipmu.jp>
Date: Wed, 18 Oct 2023 13:06:47 +0900
Subject: [PATCH 20/30] enable users to set shear value (e.g. 0.02 or 0.03) in
 the ini file

---
 bin/config_sim_zshear.ini  | 21 +++++++++++++--------
 bin/simulate_image.py      |  5 +++--
 descwl_shear_sims/shear.py | 11 ++++++-----
 3 files changed, 22 insertions(+), 15 deletions(-)

diff --git a/bin/config_sim_zshear.ini b/bin/config_sim_zshear.ini
index 2d9784a3..0ddc864d 100644
--- a/bin/config_sim_zshear.ini
+++ b/bin/config_sim_zshear.ini
@@ -1,19 +1,24 @@
 ; run the code in shear_obj_zshear branch
 [simulation]
 
-nrot    =   2
+img_root     =   ./
 test_name = zshear
-shear_mode_list = [0, 1]
+# boundary of the redshift bins
 z_bounds = [0.0, 10.0]
+# shear distortion setup
+shear_mode_list = [0, 1]
+shear_value = 0.02
+
+# galaxy distribution layout and number of rotation
+nrot    =   2
 layout  =   random_disk
 ; layout = hex
-img_root     =   ./
-; no rotate no dither.. no
+coadd_dim = 500
+; buffer length to avoid galaxies hit the boundary
+buff    =   80
+; no rotate no dither.. no dither
 rotate  =   False
 dither  =   False
+
 ; psf_version = 0 for fixed PSF
 psf_version = 0
-coadd_dim = 500
-; buffer length to avoid galaxies hit the boundary
-buff    =   80
-nzbin   =   1
diff --git a/bin/simulate_image.py b/bin/simulate_image.py
index 29cbc7c6..3f1e8276 100755
--- a/bin/simulate_image.py
+++ b/bin/simulate_image.py
@@ -59,7 +59,6 @@ def __init__(self, config_name):
         # number of rotations for ring test
         self.nrot = cparser.getint("simulation", "nrot")
         # number of redshiftbins
-        self.nzbin = cparser.getint("simulation", "nzbin")
         self.rot_list = [np.pi / self.nrot * i for i in range(self.nrot)]
         self.test_name = cparser.get("simulation", "test_name")
         self.shear_mode_list = json.loads(
@@ -69,6 +68,7 @@ def __init__(self, config_name):
             cparser.get("simulation", "z_bounds")
         )
         self.nshear = len(self.shear_mode_list)
+        self.shear_value = cparser.getfloat("simulation", "shear_value")
         return
 
     def run(self, ifield=0):
@@ -140,9 +140,10 @@ def run(self, ifield=0):
             for irot in range(self.nrot):
 
                 shear_obj = ShearRedshift(
-                    mode=shear_mode,
                     z_bounds=self.z_bounds,
+                    mode=shear_mode,        # mode tells z bin is + / - distorted
                     g_dist="g1", # need to enable users to set this value
+                    shear_value=self.shear_value
                 )
                 sim_data = make_sim(
                     rng=rng,
diff --git a/descwl_shear_sims/shear.py b/descwl_shear_sims/shear.py
index 9f38a2d3..e88b8e0b 100644
--- a/descwl_shear_sims/shear.py
+++ b/descwl_shear_sims/shear.py
@@ -92,7 +92,7 @@ def get_shear(self, redshift, shift):
 
 class ShearConstant(object):
     """
-    Constant shear along every redshift slice
+    Constant shear in the full exposure
     Parameters
     ----------
     g1, g2:    Constant shear distortion
@@ -114,15 +114,15 @@ def get_shear(self, redshift=None, shift=None):
 
 class ShearRedshift(object):
     """
-    Constant shear along every redshift slice
+    Constant shear in each redshift slice
     """
-    def __init__(self, mode, z_bounds, g_dist="g1"):
+    def __init__(self, z_bounds, mode, g_dist="g1", shear_value=0.02):
         assert isinstance(mode, int), "mode must be an integer"
         nz_bins = len(z_bounds) - 1
         # nz_bins is the number of redshift bins
         # note that there are three options in each redshift bin
         # 0: g=0.00; 1: g=-0.02; 2: g=0.02
-        # for example, if number of redshift bins is 4, if mode = 7 which in
+        # for example, number of redshift bins is 4, if mode = 7 which in
         # ternary is "0021" --- meaning that the shear is (0.0, 0.0, 0.02,
         # -0.02) in each bin.
         self.nz_bins = int(nz_bins)
@@ -133,10 +133,11 @@ def __init__(self, mode, z_bounds, g_dist="g1"):
         self.z_bounds = z_bounds
         self.g_dist = g_dist
         self.shear_list = self.determine_shear_list(self.code)
+        self.shear_value = shear_value
         return
 
     def determine_shear_list(self, code):
-        values = [0.00, -0.02, 0.02]
+        values = [0.00, -self.shear_value, self.shear_value]
         shear_list = [values[int(i)] for i in code]
         return shear_list
 

From d8a061d9aef53fea230fa63b6be529305810062b Mon Sep 17 00:00:00 2001
From: Xiangchong Li <xiangchong.li@ipmu.jp>
Date: Thu, 19 Oct 2023 02:33:07 +0900
Subject: [PATCH 21/30] remove the nfw part; add unit tests; change convention:
 0: -; 1:+; 2:0

---
 descwl_shear_sims/__init__.py                 |   1 -
 descwl_shear_sims/shear.py                    |  91 +---
 examples/example_NFWShear.ipynb               | 436 ------------------
 examples/example_NFWShear_imsim.ipynb         | 141 ------
 .../test_shear_meas_zshear_metacal.py         | 286 ++++++++++++
 5 files changed, 293 insertions(+), 662 deletions(-)
 delete mode 100644 examples/example_NFWShear.ipynb
 delete mode 100644 examples/example_NFWShear_imsim.ipynb
 create mode 100644 shear_meas_tests/test_shear_meas_zshear_metacal.py

diff --git a/descwl_shear_sims/__init__.py b/descwl_shear_sims/__init__.py
index fe93f7ce..7c787c39 100644
--- a/descwl_shear_sims/__init__.py
+++ b/descwl_shear_sims/__init__.py
@@ -19,4 +19,3 @@
 from . import artifacts
 from . import masking
 from . import wcs
-from . import shear
diff --git a/descwl_shear_sims/shear.py b/descwl_shear_sims/shear.py
index e88b8e0b..cfc86825 100644
--- a/descwl_shear_sims/shear.py
+++ b/descwl_shear_sims/shear.py
@@ -1,15 +1,6 @@
 import galsim
 import numpy as np
 
-# maximum kappa allowed
-# values greater than it will be clipped
-g_max = 0.6
-"""
-shear_obj = ShearConstant(cluster_obj, z_cl, ra_cl, dec_cl)
-shear_obj = ShearRedshift(g1=0.02, g2=0.00)
-shear_obj = ShearNFW(mode="0000", g_dist="g1")
-"""
-
 
 def _ternary(n, n_bins):
     """CREDIT: https://stackoverflow.com/questions/34559663/\
@@ -23,73 +14,6 @@ def _ternary(n, n_bins):
     return ''.join(reversed(nums)).zfill(n_bins)
 
 
-class ShearNFW(object):
-    """
-    Shear object from NFW halos
-
-    Parameters
-    ----------
-    cluster_obj (object):   cluster object from clmm
-    z_cl (float):           redshift of the cluster
-    x_cl (float):           ra of the cluster [arcsec]
-    y_cl (float):           dec of the cluster [arcsec]
-
-    """
-    def __init__(self, cluster_obj, z_cl, ra_cl=0., dec_cl=0.):
-        self.z_cl = z_cl
-        self.ra_cl = ra_cl
-        self.dec_cl = dec_cl
-        self.cobj = cluster_obj
-        self.cosmo = cluster_obj.cosmo
-        return
-
-    def get_shear(self, redshift, shift):
-        """
-        A shear wrapper to return g1 and g2 with different shear type
-
-        Parameters
-        ----------
-        redshift (float):           redshifts of galaxies
-        shift (galsim.positionD):   Galsim positionD shift [arcsec]
-
-        Returns
-        ---------
-        shear (galsim.Shear)        shear distortion on the galaxy
-        """
-        z_cl = self.z_cl
-        if redshift > z_cl:
-            from astropy.coordinates import SkyCoord
-
-            # Create the SkyCoord objects
-            coord_cl = SkyCoord(self.ra_cl, self.dec_cl, unit="arcsec")
-            coord_gals = SkyCoord(shift.x, shift.y, unit="arcsec")
-            # Calculate the separation
-            sep = coord_cl.separation(coord_gals).radian
-            # What is the unit of r3d?
-            r3d = self.cosmo.rad2mpc(sep, self.z_cl)
-            # position angle
-            phi = coord_cl.position_angle(coord_gals).radian
-
-            # TODO: confirm whether the units is Mpc/h or Mpc?
-            gammat = self.cobj.eval_tangential_shear(r3d, z_cl, redshift)
-            kappa = self.cobj.eval_convergence(r3d, z_cl, redshift)
-            gamma1 = gammat * np.cos(2. * phi)
-            gamma2 = -gammat * np.sin(2. * phi)
-            g1 = gamma1 #/ (1-kappa)
-            g2 = gamma2 #/ (1-kappa)
-            # we are forcing g to be less than g_max
-            g = np.sqrt(g1 ** 2. + g2 ** 2.)
-            ratio = min(g_max / g, 1.0)
-            # and rescale g1 and g2 if g > g_max
-            g1 = g1 * ratio
-            g2 = g2 * ratio
-        else:
-            g1 = 0.
-            g2 = 0.
-        shear = galsim.Shear(g1=g1, g2=g2)
-        return shear
-
-
 class ShearConstant(object):
     """
     Constant shear in the full exposure
@@ -118,26 +42,25 @@ class ShearRedshift(object):
     """
     def __init__(self, z_bounds, mode, g_dist="g1", shear_value=0.02):
         assert isinstance(mode, int), "mode must be an integer"
-        nz_bins = len(z_bounds) - 1
+        self.nz_bins = int(len(z_bounds) - 1)
         # nz_bins is the number of redshift bins
         # note that there are three options in each redshift bin
-        # 0: g=0.00; 1: g=-0.02; 2: g=0.02
-        # for example, number of redshift bins is 4, if mode = 7 which in
-        # ternary is "0021" --- meaning that the shear is (0.0, 0.0, 0.02,
-        # -0.02) in each bin.
-        self.nz_bins = int(nz_bins)
+        # 0: g=-0.02; 1: g=0.02; 2: g=0.00
+        # for example, number of redshift bins is 4, (nz_bins = [0., 0.5, 1.0,
+        # 1.5, 2.0]) if mode = 7 which in ternary is "0021" --- meaning that
+        # the shear is (-0.02, -0.02, 0.00, 0.02) in each bin, respectively.
         self.code = _ternary(int(mode), self.nz_bins)
         assert 0 <= int(mode) < 3 ** self.nz_bins, "mode code is too large"
         # maybe we need it to be more flexible in the future
         # but now we keep the linear spacing
         self.z_bounds = z_bounds
         self.g_dist = g_dist
-        self.shear_list = self.determine_shear_list(self.code)
         self.shear_value = shear_value
+        self.shear_list = self.determine_shear_list(self.code)
         return
 
     def determine_shear_list(self, code):
-        values = [0.00, -self.shear_value, self.shear_value]
+        values = [-self.shear_value, self.shear_value, 0.0]
         shear_list = [values[int(i)] for i in code]
         return shear_list
 
diff --git a/examples/example_NFWShear.ipynb b/examples/example_NFWShear.ipynb
deleted file mode 100644
index af942d14..00000000
--- a/examples/example_NFWShear.ipynb
+++ /dev/null
@@ -1,436 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "d12a992b-6669-4675-b68a-b705ff09f693",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "%reload_ext autoreload\n",
-    "%autoreload 2\n",
-    "\n",
-    "import clmm\n",
-    "import galsim\n",
-    "import numpy as np\n",
-    "import descwl_shear_sims as dss\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "from descwl_shear_sims.galaxies import WLDeblendGalaxyCatalog\n",
-    "from descwl_shear_sims.objlists import get_objlist\n",
-    "from descwl_shear_sims.surveys import get_survey"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "id": "14f7db13-1476-4197-b54f-219b30bdb59a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from astropy.cosmology import FlatLambdaCDM\n",
-    "from lenstronomy.Cosmo.lens_cosmo import LensCosmo\n",
-    "from lenstronomy.LensModel.lens_model import LensModel\n",
-    "\n",
-    "zl_list = [0.1, 0.4]\n",
-    "M200_list = [1e14, 1e15]\n",
-    "\n",
-    "\n",
-    "g_max = 0.6\n",
-    "\n",
-    "class ShearNFW(object):\n",
-    "    def __init__(self, zl_list, M200_list, c_list, cosmo):\n",
-    "        if not isinstance(zl_list, list):\n",
-    "            if isinstance(zl_list, float):\n",
-    "                zl_list = [zl_list]\n",
-    "        if not isinstance(M200_list, list):\n",
-    "            if isinstance(M200_list, float):\n",
-    "                M200_list = [M200_list]\n",
-    "        if not isinstance(c_list, list):\n",
-    "            if isinstance(c_list, float):\n",
-    "                c_list = [c_list]\n",
-    "        assert len(zl_list) == len(M200_list)\n",
-    "        assert len(zl_list) == len(c_list)\n",
-    "        self.cosmo = cosmo\n",
-    "        self.zl_list = zl_list\n",
-    "        self.params = []\n",
-    "        self.n_halos = len(zl_list)\n",
-    "        for _ in range(self.n_halos):\n",
-    "            self.params.append({\"M\": M200_list[_], \"c\": c_list[_]})\n",
-    "        return\n",
-    "\n",
-    "    def get_shear(self, redshift, shift):\n",
-    "        \"\"\"\n",
-    "        A shear wrapper to return g1 and g2 with different shear type\n",
-    "\n",
-    "        Parameters\n",
-    "        ----------\n",
-    "        redshift (float):           redshifts of galaxies\n",
-    "        shift (galsim.positionD):   Galsim positionD shift [arcsec]\n",
-    "\n",
-    "        Returns\n",
-    "        ---------\n",
-    "        shear (galsim.Shear)        shear distortion on the galaxy\n",
-    "        \"\"\"\n",
-    "\n",
-    "        model_list = []\n",
-    "        kwargs_list = []\n",
-    "        for _ in range(self.n_halos):\n",
-    "            model_list.append(\"NFW\")\n",
-    "            lens_cosmo = LensCosmo(z_lens=zl_list[_], z_source=redshift, cosmo=self.cosmo)\n",
-    "            Rs_angle, alpha_Rs = lens_cosmo.nfw_physical2angle(**self.params[_])\n",
-    "            rho0, Rs, c, r200, M200 = lens_cosmo.nfw_angle2physical(Rs_angle, alpha_Rs)\n",
-    "            kwargs = {'Rs': Rs_angle, 'alpha_Rs': alpha_Rs, \"center_x\": 0.0, \"center_y\": 0.0}\n",
-    "            kwargs_list.append(kwargs)\n",
-    "        lens = LensModel(lens_model_list=model_list)\n",
-    "        alpha_x, alpha_y = lens.alpha(x=shift.x, y=shift.y, kwargs=kwargs_list)\n",
-    "        f_xx, f_xy, f_yx, f_yy = lens.hessian(x=shift.x, y=shift.y, kwargs=kwargs_list)\n",
-    "        gamma1 = 0.5 * (f_xx - f_yy)\n",
-    "        gamma2 = f_xy\n",
-    "        kappa = 0.5 * (f_xx + f_yy)\n",
-    "        g1 = gamma1 #/ (1 - kappa)\n",
-    "        g2 = gamma2 #/ (1 - kappa)\n",
-    "        # we are forcing g to be less than g_max\n",
-    "        g = np.sqrt(g1 ** 2. + g2 ** 2.)\n",
-    "        ratio = min(g_max / g, 1.0)\n",
-    "        # and rescale g1 and g2 if g > g_max\n",
-    "        g1 = g1 * ratio\n",
-    "        g2 = g2 * ratio\n",
-    "        shear = galsim.Shear(g1=g1, g2=g2)\n",
-    "        return shear"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "id": "913c7ae0-8b39-4a3b-9e09-c40262170e0c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "cosmo = clmm.Cosmology(H0=70.0, Omega_dm0=0.27 - 0.045, Omega_b0=0.045, Omega_k0=0.0)\n",
-    "halo = clmm.Modeling(massdef=\"critical\", delta_mdef=200, halo_profile_model=\"nfw\")\n",
-    "halo.set_cosmo(cosmo)\n",
-    "halo.set_concentration(4)\n",
-    "halo.set_mass(1.0e15)\n",
-    "z_cl = 1.0\n",
-    "# source properties\n",
-    "z_source = 2.0  # all sources in the same plane\n",
-    "\n",
-    "shear_obj = dss.shear.ShearNFW(halo, z_cl)\n",
-    "# generate positions\n",
-    "radius = 50 # arcsec\n",
-    "n_gal = 20\n",
-    "theta = np.linspace(0, 360, n_gal) / n_gal\n",
-    "x = np.cos(theta) * radius\n",
-    "y = np.sin(theta) * radius\n",
-    "shifts = np.zeros(n_gal, dtype=[('dx', 'f8'), ('dy', 'f8')])\n",
-    "shifts['dx'] = x\n",
-    "shifts['dy'] = y\n",
-    "\n",
-    "shift_list = []\n",
-    "for ss in shifts:\n",
-    "    shift_list.append(galsim.PositionD(ss['dx'], ss['dy']))\n",
-    "\n",
-    "# get the shear\n",
-    "shear_list = []\n",
-    "for ss in shift_list:\n",
-    "    shear_list.append(shear_obj.get_shear(z_source, ss))\n",
-    "\n",
-    "gamma = []\n",
-    "for ss in shear_list:\n",
-    "    gamma.append(ss.g1 + 1j * ss.g2) \n",
-    "gamma = np.array(gamma)\n",
-    "\n",
-    "angles = np.angle(gamma, deg=True) / 2.\n",
-    "lengths = np.abs(gamma) * 100.\n",
-    "\n",
-    "# Create whisker plot\n",
-    "plt.figure(figsize=(6, 6))\n",
-    "plt.quiver(x, y, np.cos(np.deg2rad(angles)), np.sin(np.deg2rad(angles)),\n",
-    "           color=\"black\", headaxislength=0, headlength=0, headwidth=1, pivot = 'middle')\n",
-    "plt.xlabel('x')\n",
-    "plt.ylabel('y')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "id": "a7ecb75e-dbff-48b4-a495-bfe7f544eb14",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[     1      3      9     27     81    243    729   2187   6561  19683\n",
-      "  59049 177147]\n",
-      "[2.78361124e-01 2.74640017e-01 2.56551659e-01 1.98359090e-01\n",
-      " 1.02796620e-01 3.27933044e-02 7.20665320e-03 1.26455112e-03\n",
-      " 1.95016137e-04 2.78421302e-05 3.78395338e-06 4.97313342e-07]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, '$|g|$')"
-      ]
-     },
-     "execution_count": 60,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "n_gal = 1\n",
-    "d_array = []\n",
-    "g_array = []\n",
-    "for i in range(12):\n",
-    "    position = galsim.PositionD(3.0 ** i, 0.)\n",
-    "    # get the shear\n",
-    "    shear = shear_obj.get_shear(z_source, position)\n",
-    "    g1 = shear.g1\n",
-    "    g2 = shear.g2\n",
-    "    gabs = np.abs(g1 + 1j * g2)\n",
-    "    d_array.append(3 ** i)\n",
-    "    g_array.append(gabs)\n",
-    "d_array = np.array(d_array)\n",
-    "g_array = np.array(g_array)\n",
-    "\n",
-    "print(d_array)\n",
-    "print(g_array)\n",
-    "plt.close()\n",
-    "fig, ax = plt.subplots()\n",
-    "ax.plot(d_array / 3600., g_array)\n",
-    "ax.set_xscale(\"log\")\n",
-    "ax.set_yscale(\"log\")\n",
-    "ax.set_xlabel(\"separation [degree]\")\n",
-    "ax.set_ylabel(r\"$|g|$\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "id": "45d40de9-7fe9-413e-a481-347276e28c82",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "cosmo = FlatLambdaCDM(H0=70.0, Om0=0.27, Ob0=0.045)\n",
-    "\n",
-    "shear_obj = ShearNFW(zl_list=[1.], M200_list=[1e15], c_list=[4.0], cosmo=cosmo)\n",
-    "# generate positions\n",
-    "radius = 50 # arcsec\n",
-    "n_gal = 20\n",
-    "theta = np.linspace(0, 360, n_gal) / n_gal\n",
-    "x = np.cos(theta) * radius\n",
-    "y = np.sin(theta) * radius\n",
-    "shifts = np.zeros(n_gal, dtype=[('dx', 'f8'), ('dy', 'f8')])\n",
-    "shifts['dx'] = x\n",
-    "shifts['dy'] = y\n",
-    "\n",
-    "shift_list = []\n",
-    "for ss in shifts:\n",
-    "    shift_list.append(galsim.PositionD(ss['dx'], ss['dy']))\n",
-    "\n",
-    "# get the shear\n",
-    "shear_list = []\n",
-    "for ss in shift_list:\n",
-    "    shear_list.append(shear_obj.get_shear(z_source, ss))\n",
-    "    \n",
-    "gamma = []\n",
-    "for ss in shear_list:\n",
-    "    gamma.append(ss.g1 + 1j * ss.g2) \n",
-    "gamma = np.array(gamma)\n",
-    "\n",
-    "angles = np.angle(gamma, deg=True) / 2.\n",
-    "lengths = np.abs(gamma) * 100.\n",
-    "\n",
-    "# Create whisker plot\n",
-    "plt.figure(figsize=(6, 6))\n",
-    "plt.quiver(x, y, np.cos(np.deg2rad(angles)), np.sin(np.deg2rad(angles)),\n",
-    "           color=\"black\", headaxislength=0, headlength=0, headwidth=1, pivot = 'middle')\n",
-    "plt.xlabel('x')\n",
-    "plt.ylabel('y')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "id": "b22ff0a9-6ce6-49e2-bef7-1a91a16ee4dd",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[     1      3      9     27     81    243    729   2187   6561  19683\n",
-      "  59049 177147]\n",
-      "[8.26751816e-02 8.26215008e-02 8.22711368e-02 8.02829024e-02\n",
-      " 7.16882521e-02 4.92520061e-02 2.14542996e-02 5.89296747e-03\n",
-      " 1.17895828e-03 1.96054091e-04 2.92963630e-05 4.10083070e-06]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7faf5d39a2d0>]"
-      ]
-     },
-     "execution_count": 62,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "n_gal = 1\n",
-    "d_array = []\n",
-    "g_array = []\n",
-    "for i in range(12):\n",
-    "    position = galsim.PositionD(3.0 ** i, 0.)\n",
-    "    # get the shear\n",
-    "    shear = shear_obj.get_shear(z_source, position)\n",
-    "    g1 = shear.g1\n",
-    "    g2 = shear.g2\n",
-    "    gabs = np.abs(g1 + 1j * g2)\n",
-    "    d_array.append(3 ** i)\n",
-    "    g_array.append(gabs)\n",
-    "d_array = np.array(d_array)\n",
-    "g_array = np.array(g_array)\n",
-    "print(d_array)\n",
-    "print(g_array)\n",
-    "ax.plot(d_array / 3600., g_array)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "id": "0d0c3bed-89c3-4c13-aec1-ffedff1a0956",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[     1      3      9     27     81    243    729   2187   6561  19683\n",
-      "  59049 177147]\n",
-      "[2.47424027e-01 5.30458122e-01 4.51641183e+00 3.92914418e-01\n",
-      " 1.43436687e-01 4.35660410e-02 9.72263335e-03 1.73440286e-03\n",
-      " 2.70341617e-04 3.88561182e-05 5.30434563e-06 6.99305314e-07]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7faf5d491f50>]"
-      ]
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "d_array = []\n",
-    "g_array = []\n",
-    "ghalo = galsim.NFWHalo(mass=1e15, conc=4, redshift=1.0, omega_m=0.27, omega_lam=0.73)\n",
-    "for i in range(12):\n",
-    "    position = galsim.PositionD(3.0 ** i, 0.)\n",
-    "    # get the shear\n",
-    "    g1, g2 = ghalo.getShear(position, z_source)\n",
-    "    gabs = np.abs(g1 + 1j * g2)\n",
-    "    d_array.append(3 ** i)\n",
-    "    g_array.append(gabs)\n",
-    "d_array = np.array(d_array)\n",
-    "g_array = np.array(g_array)\n",
-    "print(d_array)\n",
-    "print(g_array)\n",
-    "ax.plot(d_array / 3600., g_array)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "id": "d6cb97de-5125-4fdf-927a-8b1dca7c4141",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fig"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "472f1e75-9cbf-4c5d-93ce-4d06c7b16f15",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "wl_shear_sim",
-   "language": "python",
-   "name": "wl_shear_sim"
-  },
-  "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": 5
-}
diff --git a/examples/example_NFWShear_imsim.ipynb b/examples/example_NFWShear_imsim.ipynb
deleted file mode 100644
index 0d41fec1..00000000
--- a/examples/example_NFWShear_imsim.ipynb
+++ /dev/null
@@ -1,141 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "177f93ce-ec8b-4c36-9ff4-d16951a4941f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "%reload_ext autoreload\n",
-    "%autoreload 2\n",
-    "import clmm\n",
-    "import numpy as np\n",
-    "\n",
-    "from descwl_shear_sims.galaxies import make_galaxy_catalog\n",
-    "from descwl_shear_sims.galaxies import WLDeblendGalaxyCatalog\n",
-    "from descwl_shear_sims.psfs import make_fixed_psf\n",
-    "from descwl_shear_sims.surveys import get_survey\n",
-    "\n",
-    "from descwl_shear_sims.sim import make_sim\n",
-    "from descwl_shear_sims.shear import ShearNFW\n",
-    "\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "\n",
-    "cosmo = clmm.Cosmology(H0=70.0, Omega_dm0=0.27 - 0.045, Omega_b0=0.045, Omega_k0=0.0)\n",
-    "halo = clmm.Modeling(massdef=\"mean\", delta_mdef=200, halo_profile_model=\"nfw\")\n",
-    "halo.set_cosmo(cosmo)\n",
-    "halo.set_concentration(4)\n",
-    "halo.set_mass(1.0e16)\n",
-    "z_cl = 1.0\n",
-    "# source properties\n",
-    "z_source = 2.0  # all sources in the same plane\n",
-    "\n",
-    "shear_obj = ShearNFW(halo, z_cl)\n",
-    "\n",
-    "seed = 74321\n",
-    "rng = np.random.RandomState(seed)\n",
-    "\n",
-    "coadd_dim = 1000\n",
-    "se_dim = 1000\n",
-    "psf_dim = 41\n",
-    "\n",
-    "galaxy_catalog = WLDeblendGalaxyCatalog(\n",
-    "    rng=rng,\n",
-    "    coadd_dim=coadd_dim,\n",
-    "    buff=2,\n",
-    "    layout=\"random_disk\",\n",
-    ")\n",
-    "psf = make_fixed_psf(psf_type=\"gauss\")\n",
-    "band_list = [\"r\"]\n",
-    "\n",
-    "sim_data = make_sim(\n",
-    "    rng=rng,\n",
-    "    galaxy_catalog=galaxy_catalog,\n",
-    "    coadd_dim=coadd_dim,\n",
-    "    shear_obj=shear_obj,\n",
-    "    psf=psf,\n",
-    "    noise_factor=0.0,\n",
-    "    bands=band_list,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "5db4e4bf-7d48-4bab-9692-1d21f6f0952b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "exp = sim_data[\"band_data\"][\"r\"][0]\n",
-    "img = exp.getImage().getArray()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "516ec59d-c0d9-4a63-9d84-1f7a8cdb405d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(-0.5, 1009.5, -0.5, 1009.5)"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from astropy.visualization import simple_norm\n",
-    "plt.imshow(img,aspect='equal',cmap='RdYlBu_r',origin='lower',interpolation='None',\\\n",
-    "             norm=simple_norm(img,'asinh',asinh_a=0.1,min_cut=-0.01,max_cut=4))\n",
-    "plt.axis(\"off\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e1585107-4a8d-4178-80e0-9ba793475f70",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "wl_shear_sim",
-   "language": "python",
-   "name": "wl_shear_sim"
-  },
-  "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": 5
-}
diff --git a/shear_meas_tests/test_shear_meas_zshear_metacal.py b/shear_meas_tests/test_shear_meas_zshear_metacal.py
new file mode 100644
index 00000000..0e5b059a
--- /dev/null
+++ b/shear_meas_tests/test_shear_meas_zshear_metacal.py
@@ -0,0 +1,286 @@
+import time
+import copy
+import numpy as np
+import tqdm
+import joblib
+
+import pytest
+
+from metadetect.lsst.metadetect import run_metadetect
+import descwl_shear_sims as sim
+from descwl_shear_sims.shear import ShearRedshift
+
+
+z_bounds = np.array([0.0, 20.0])
+shear_obj_p = ShearRedshift(
+    z_bounds=z_bounds,
+    mode=1,             # mode tells z bin is + / - distorted
+    g_dist="g1",        # need to enable users to set this value
+    shear_value=0.02,   # amplitude of the shear
+)
+
+shear_obj_m = ShearRedshift(
+    z_bounds=z_bounds,
+    mode=0,             # mode tells z bin is + / - distorted
+    g_dist="g1",        # need to enable users to set this value
+    shear_value=0.02,
+)
+
+CONFIG = {
+    "meas_type": "wmom",
+    "metacal": {
+        "use_noise_image": True,
+        "psf": "fitgauss",
+    },
+    "psf": {
+        "model": "gauss",
+        "lm_pars": {},
+        "ntry": 2,
+    },
+    "weight": {
+        "fwhm": 1.2,
+    },
+    "detect": {
+        "thresh": 10.0,
+    },
+}
+
+
+def _make_lsst_sim(*, rng, shear_obj, layout):
+
+    galaxy_catalog = sim.galaxies.make_galaxy_catalog(
+        rng=rng,
+        coadd_dim=sim.sim.DEFAULT_SIM_CONFIG["coadd_dim"],
+        buff=sim.sim.DEFAULT_SIM_CONFIG["buff"],
+        layout=layout,
+        gal_type='fixed',
+    )
+
+    psf = sim.psfs.make_fixed_psf(psf_type='gauss')
+
+    sim_data = sim.make_sim(
+        rng=rng,
+        galaxy_catalog=galaxy_catalog,
+        coadd_dim=sim.sim.DEFAULT_SIM_CONFIG["coadd_dim"],
+        shear_obj=shear_obj,
+        psf=psf,
+    )
+    return sim_data
+
+
+def _shear_cuts(arr):
+    assert arr is not None
+    msk = (
+        (arr['wmom_flags'] == 0)
+        & (arr['wmom_s2n'] > 10)
+        & (arr['wmom_T_ratio'] > 1.2)
+    )
+    return msk
+
+
+def _meas_shear_data(res):
+    msk = _shear_cuts(res['noshear'])
+    g1 = np.mean(res['noshear']['wmom_g'][msk, 0])
+    g2 = np.mean(res['noshear']['wmom_g'][msk, 1])
+
+    msk = _shear_cuts(res['1p'])
+    g1_1p = np.mean(res['1p']['wmom_g'][msk, 0])
+    msk = _shear_cuts(res['1m'])
+    g1_1m = np.mean(res['1m']['wmom_g'][msk, 0])
+    R11 = (g1_1p - g1_1m) / 0.02
+
+    dt = [
+        ('g1', 'f8'),
+        ('g2', 'f8'),
+        ('R11', 'f8'),
+    ]
+    return np.array([(g1, g2, R11)], dtype=dt)
+
+
+def _bootstrap_stat(d1, d2, func, seed, nboot=500):
+    dim = d1.shape[0]
+    rng = np.random.RandomState(seed=seed)
+    stats = []
+    for _ in tqdm.trange(nboot, leave=False):
+        ind = rng.choice(dim, size=dim, replace=True)
+        stats.append(func(d1[ind], d2[ind]))
+    return stats
+
+
+def _meas_m_c_cancel(pres, mres):
+    x = np.mean(pres['g1'] - mres['g1'])/2
+    y = np.mean(pres['R11'] + mres['R11'])/2
+    m = x/y/0.02 - 1
+
+    x = np.mean(pres['g2'] + mres['g2'])/2
+    y = np.mean(pres['R11'] + mres['R11'])/2
+    c = x/y
+
+    return m, c
+
+
+def _boostrap_m_c(pres, mres):
+    m, c = _meas_m_c_cancel(pres, mres)
+    bdata = _bootstrap_stat(pres, mres, _meas_m_c_cancel, 14324, nboot=500)
+    merr, cerr = np.std(bdata, axis=0)
+    return m, merr, c, cerr
+
+
+def _coadd_sim_data(rng, sim_data, nowarp, remove_poisson):
+    """
+    copied from mdet-lsst-sim
+    """
+    from descwl_coadd.coadd import make_coadd
+    from descwl_coadd.coadd_nowarp import make_coadd_nowarp
+    from metadetect.lsst.util import extract_multiband_coadd_data
+
+    bands = list(sim_data['band_data'].keys())
+
+    if nowarp:
+        exps = sim_data['band_data'][bands[0]]
+
+        if len(exps) > 1:
+            raise ValueError('only one epoch for nowarp')
+
+        coadd_data_list = [
+            make_coadd_nowarp(
+                exp=exps[0],
+                psf_dims=sim_data['psf_dims'],
+                rng=rng,
+                remove_poisson=remove_poisson,
+            )
+            for band in bands
+        ]
+    else:
+        coadd_data_list = [
+            make_coadd(
+                exps=sim_data['band_data'][band],
+                psf_dims=sim_data['psf_dims'],
+                rng=rng,
+                coadd_wcs=sim_data['coadd_wcs'],
+                coadd_bbox=sim_data['coadd_bbox'],
+                remove_poisson=remove_poisson,
+            )
+            for band in bands
+        ]
+    return extract_multiband_coadd_data(coadd_data_list)
+
+
+def _run_sim_one(*, seed, mdet_seed, shear_obj, **kwargs):
+    rng = np.random.RandomState(seed=seed)
+    sim_data = _make_lsst_sim(rng=rng, shear_obj=shear_obj, **kwargs)
+
+    coadd_data = _coadd_sim_data(
+        rng=rng, sim_data=sim_data, nowarp=True, remove_poisson=False,
+    )
+
+    mdet_rng = np.random.RandomState(seed=mdet_seed)
+    results = run_metadetect(
+        rng=mdet_rng,
+        config=copy.deepcopy(CONFIG),
+        **coadd_data,
+    )
+
+    return results
+
+
+def run_sim(seed, mdet_seed, **kwargs):
+    # positive shear
+    _pres = _run_sim_one(
+        seed=seed, mdet_seed=mdet_seed,
+        shear_obj=shear_obj_p, **kwargs,
+    )
+    if _pres is None:
+        return None
+
+    # negative shear
+    _mres = _run_sim_one(
+        seed=seed, mdet_seed=mdet_seed,
+        shear_obj=shear_obj_m, **kwargs,
+    )
+    if _mres is None:
+        return None
+
+    return _meas_shear_data(_pres), _meas_shear_data(_mres)
+
+
+@pytest.mark.parametrize(
+    'layout,ntrial', [('grid', 500)]
+)
+def test_shear_meas(layout, ntrial):
+    nsub = max(ntrial // 100, 10)
+    nitr = ntrial // nsub
+    rng = np.random.RandomState(seed=116)
+    seeds = rng.randint(low=1, high=2**29, size=ntrial)
+    mdet_seeds = rng.randint(low=1, high=2**29, size=ntrial)
+
+    tm0 = time.time()
+
+    print("")
+
+    pres = []
+    mres = []
+    loc = 0
+    for itr in tqdm.trange(nitr):
+        jobs = [
+            joblib.delayed(run_sim)(
+                seeds[loc+i], mdet_seeds[loc+i], layout=layout,
+            )
+            for i in range(nsub)
+        ]
+        outputs = joblib.Parallel(n_jobs=2, verbose=0, backend='loky')(jobs)
+
+        for out in outputs:
+            if out is None:
+                continue
+            pres.append(out[0])
+            mres.append(out[1])
+        loc += nsub
+
+        m, merr, c, cerr = _boostrap_m_c(
+            np.concatenate(pres),
+            np.concatenate(mres),
+        )
+        print(
+            (
+                "\n"
+                "nsims: %d\n"
+                "m [1e-3, 3sigma]: %s +/- %s\n"
+                "c [1e-5, 3sigma]: %s +/- %s\n"
+                "\n"
+            ) % (
+                len(pres),
+                m/1e-3,
+                3*merr/1e-3,
+                c/1e-5,
+                3*cerr/1e-5,
+            ),
+            flush=True,
+        )
+
+    total_time = time.time()-tm0
+    print("time per:", total_time/ntrial, flush=True)
+
+    pres = np.concatenate(pres)
+    mres = np.concatenate(mres)
+    m, merr, c, cerr = _boostrap_m_c(pres, mres)
+
+    print(
+        (
+            "\n\nm [1e-3, 3sigma]: %s +/- %s"
+            "\nc [1e-5, 3sigma]: %s +/- %s"
+        ) % (
+            m/1e-3,
+            3*merr/1e-3,
+            c/1e-5,
+            3*cerr/1e-5,
+        ),
+        flush=True,
+    )
+
+    assert np.abs(m) < max(1e-3, 3*merr)
+    assert np.abs(c) < 3*cerr
+    return
+
+if __name__ == "__main__":
+    test_shear_meas(layout="grid", ntrial=500)

From eee3a363bf40cbee19e1f28bd3db7ba4cabbd245 Mon Sep 17 00:00:00 2001
From: Xiangchong Li <xiangchong.li@ipmu.jp>
Date: Tue, 24 Oct 2023 13:42:41 +0900
Subject: [PATCH 22/30] consistency in star catalog

---
 bin/simulate_image.py      | 68 ++++++++++++++++----------------------
 descwl_shear_sims/shear.py | 15 ++++++---
 descwl_shear_sims/sim.py   |  2 +-
 descwl_shear_sims/stars.py |  5 ++-
 4 files changed, 42 insertions(+), 48 deletions(-)

diff --git a/bin/simulate_image.py b/bin/simulate_image.py
index 3f1e8276..9b3afec4 100755
--- a/bin/simulate_image.py
+++ b/bin/simulate_image.py
@@ -19,6 +19,7 @@
 import os
 import pickle
 from argparse import ArgumentParser
+from descwl_shear_sims.stars import StarCatalog  # star catalog class
 from configparser import ConfigParser, ExtendedInterpolation
 
 import fitsio
@@ -69,6 +70,11 @@ def __init__(self, config_name):
         )
         self.nshear = len(self.shear_mode_list)
         self.shear_value = cparser.getfloat("simulation", "shear_value")
+        self.stellar_density = cparser.getfloat(
+            "simulation",
+            "stellar_densit",
+            fallback=0.0,
+        )
         return
 
     def run(self, ifield=0):
@@ -76,48 +82,30 @@ def run(self, ifield=0):
         rng = np.random.RandomState(ifield)
         scale = 0.2
 
-        if self.psf_version == 0:
-            # basic test
-            kargs = {
-                "cosmic_rays": False,
-                "bad_columns": False,
-                "star_bleeds": False,
-                "draw_method": "auto",
-            }
-            star_catalog = None
-            psf = make_fixed_psf(psf_type="moffat")  # .shear(e1=0.02, e2=-0.02)
-            psf_fname = "%s/PSF_%s_32.fits" % (self.img_root, self.test_name)
-            if not os.path.isfile(psf_fname):
-                psf_data = psf.shift(
-                    0.5 * scale,
-                    0.5 * scale
-                ).drawImage(nx=64, ny=64, scale=scale).array
-                fitsio.write(psf_fname, psf_data)
-
-        elif self.psf_version == 1:
-            # spatial varying PSF
-            kargs = {
-                "cosmic_rays": False,
-                "bad_columns": False,
-                "star_bleeds": False,
-                "draw_method": "auto",
-            }
-            star_catalog = None
-            # this is the single epoch image sized used by the sim, we need
-            # it for the power spectrum psf
-            se_dim = get_se_dim(
-                coadd_dim=self.coadd_dim, rotate=self.rotate, dither=self.dither
+        kargs = {
+            "cosmic_rays": False,
+            "bad_columns": False,
+            "star_bleeds": False,
+            "draw_method": "auto",
+        }
+        psf = make_fixed_psf(psf_type="moffat")  # .shear(e1=0.02, e2=-0.02)
+        psf_fname = "%s/PSF_%s_32.fits" % (self.img_root, self.test_name)
+        if not os.path.isfile(psf_fname):
+            psf_data = psf.shift(
+                0.5 * scale,
+                0.5 * scale
+            ).drawImage(nx=64, ny=64, scale=scale).array
+            fitsio.write(psf_fname, psf_data)
+        if self.stellar_density > 0.0:
+            star_catalog = StarCatalog(
+                rng=rng,
+                coadd_dim=self.coadd_dim,
+                buff=self.buff,
+                density=self.stellar_density,
+                layout=self.layout,
             )
-            psf = make_ps_psf(rng=rng, dim=se_dim)
-            psf_fname = "%s/PSF_%s.pkl" % (self.img_root, self.test_name)
-            if not os.path.isfile(psf_fname):
-                with open(psf_fname, "wb") as f:
-                    pickle.dump(
-                        {"psf": psf},
-                        f,
-                    )
         else:
-            raise ValueError("psf_version must be 0 or 1")
+            star_catalog = None
 
         img_dir = "%s/%s" % (self.img_root, self.test_name)
         os.makedirs(img_dir, exist_ok=True)
diff --git a/descwl_shear_sims/shear.py b/descwl_shear_sims/shear.py
index cfc86825..976995f2 100644
--- a/descwl_shear_sims/shear.py
+++ b/descwl_shear_sims/shear.py
@@ -64,13 +64,20 @@ def determine_shear_list(self, code):
         shear_list = [values[int(i)] for i in code]
         return shear_list
 
-    def get_bin(self, redshift):
+    def _get_zshear(self, redshift):
         bin_num = np.searchsorted(self.z_bounds, redshift, side="left") - 1
-        return bin_num
+        nz = len(self.z_bounds) - 1
+        if bin_num < nz and bin_num >= 0:
+            # if the redshift is within the boundaries of lower and uper limits
+            # we add shear
+            shear = self.shear_list[bin_num]
+        else:
+            # if not, we set shear to 0 and leave the galaxy image undistorted
+            shear = 0
+        return shear
 
     def get_shear(self, redshift, shift=None):
-        bin_number = self.get_bin(redshift)
-        shear = self.shear_list[bin_number]
+        shear =  self._get_zshear(redshift)
 
         if self.g_dist == 'g1':
             gamma1, gamma2 = (shear, 0.)
diff --git a/descwl_shear_sims/sim.py b/descwl_shear_sims/sim.py
index 537abab2..e0a6aa7f 100644
--- a/descwl_shear_sims/sim.py
+++ b/descwl_shear_sims/sim.py
@@ -459,7 +459,7 @@ def _draw_objects(
         kw['rng'] = galsim.BaseDeviate(seed=rng.randint(low=0, high=2**30))
 
     if redshifts is None:
-        redshifts = np.zeros(len(objlist))
+        redshifts = np.ones(len(objlist)) * -1.0
 
     for obj, shift, z in zip(objlist, shifts, redshifts):
 
diff --git a/descwl_shear_sims/stars.py b/descwl_shear_sims/stars.py
index b32f540b..fa8bbe4a 100644
--- a/descwl_shear_sims/stars.py
+++ b/descwl_shear_sims/stars.py
@@ -9,7 +9,7 @@
 from .cache_tools import cached_catalog_read
 from .shifts import get_shifts
 
-DEFAULT_MIN_STAR_DENSITY = 2
+DEFAULT_MIN_STAR_DENSITY = 2    # unit: per square arcmin
 DEFAULT_MAX_STAR_DENSITY = 100
 DEFAULT_DENSITY = None
 
@@ -125,14 +125,13 @@ def __init__(
             area = ((coadd_dim - 2*buff)*SCALE/60)**2
         elif layout == 'random_disk':
             # this layout is random in a circle
-            radius = (coadd_dim/2. - buff)*SCALE/60
+            radius = (coadd_dim/2. - buff)*SCALE/60  # unit: arcmin
             area = np.pi*radius**2
             del radius
         else:
             raise ValueError("layout can only be 'random' or 'random_disk' \
                     for wldeblend")
 
-        area = ((coadd_dim - 2*buff)*SCALE/60)**2
         nobj_mean = area * density_mean
         nobj = rng.poisson(nobj_mean)
         self.density = nobj/area

From d8efc3b037e69eac5cbcda85b94473c9afab7c30 Mon Sep 17 00:00:00 2001
From: Xiangchong Li <xiangchong.li@ipmu.jp>
Date: Wed, 25 Oct 2023 03:48:31 +0900
Subject: [PATCH 23/30] fix a small bug in unittests

---
 bin/simulate_image.py                         |  7 ++++-
 descwl_shear_sims/shear.py                    |  6 ++--
 descwl_shear_sims/sim.py                      | 28 +++++++++++++------
 .../test_shear_meas_zshear_metacal.py         |  3 +-
 4 files changed, 30 insertions(+), 14 deletions(-)

diff --git a/bin/simulate_image.py b/bin/simulate_image.py
index 9b3afec4..db8f76ef 100755
--- a/bin/simulate_image.py
+++ b/bin/simulate_image.py
@@ -72,7 +72,7 @@ def __init__(self, config_name):
         self.shear_value = cparser.getfloat("simulation", "shear_value")
         self.stellar_density = cparser.getfloat(
             "simulation",
-            "stellar_densit",
+            "stellar_density",
             fallback=0.0,
         )
         return
@@ -96,6 +96,7 @@ def run(self, ifield=0):
                 0.5 * scale
             ).drawImage(nx=64, ny=64, scale=scale).array
             fitsio.write(psf_fname, psf_data)
+        print(self.stellar_density)
         if self.stellar_density > 0.0:
             star_catalog = StarCatalog(
                 rng=rng,
@@ -104,6 +105,7 @@ def run(self, ifield=0):
                 density=self.stellar_density,
                 layout=self.layout,
             )
+            print(len(star_catalog))
         else:
             star_catalog = None
 
@@ -140,6 +142,9 @@ def run(self, ifield=0):
                     coadd_dim=self.coadd_dim,
                     shear_obj=shear_obj,
                     psf=psf,
+                    draw_gals=False,
+                    draw_stars=True,
+                    draw_bright=False,
                     dither=self.dither,
                     rotate=self.rotate,
                     bands=band_list,
diff --git a/descwl_shear_sims/shear.py b/descwl_shear_sims/shear.py
index 976995f2..25ea3b1f 100644
--- a/descwl_shear_sims/shear.py
+++ b/descwl_shear_sims/shear.py
@@ -73,7 +73,7 @@ def _get_zshear(self, redshift):
             shear = self.shear_list[bin_num]
         else:
             # if not, we set shear to 0 and leave the galaxy image undistorted
-            shear = 0
+            shear = 0.0
         return shear
 
     def get_shear(self, redshift, shift=None):
@@ -86,5 +86,5 @@ def get_shear(self, redshift, shift=None):
         else:
             raise ValueError("g_dist must be either 'g1' or 'g2'")
 
-        shear = galsim.Shear(g1=gamma1, g2=gamma2)
-        return shear
+        shear_obj = galsim.Shear(g1=gamma1, g2=gamma2)
+        return shear_obj
diff --git a/descwl_shear_sims/sim.py b/descwl_shear_sims/sim.py
index e0a6aa7f..f9297f85 100644
--- a/descwl_shear_sims/sim.py
+++ b/descwl_shear_sims/sim.py
@@ -55,8 +55,10 @@ def make_sim(
     shear_obj,
     psf,
     se_dim=None,
+    draw_gals=True,
     star_catalog=None,
     draw_stars=True,
+    draw_bright=True,
     psf_dim=51,
     dither=False,
     rotate=False,
@@ -183,9 +185,11 @@ def make_sim(
                 psf=psf,
                 psf_dim=psf_dim,
                 shear_obj=shear_obj,
+                draw_gals=draw_gals,
                 star_objlist=lists['star_objlist'],
                 star_shifts=lists['star_shifts'],
                 draw_stars=draw_stars,
+                draw_bright=draw_bright,
                 bright_objlist=lists['bright_objlist'],
                 bright_shifts=lists['bright_shifts'],
                 bright_mags=lists['bright_mags'],
@@ -247,9 +251,11 @@ def make_exp(
     psf,
     psf_dim,
     shear_obj,
+    draw_gals=True,
     star_objlist=None,
     star_shifts=None,
     draw_stars=True,
+    draw_bright=True,
     bright_objlist=None,
     bright_shifts=None,
     bright_mags=None,
@@ -360,14 +366,17 @@ def make_exp(
 
     image = galsim.Image(dim, dim, wcs=se_wcs)
 
-    _draw_objects(
-        image,
-        objlist, shifts, redshifts, psf, draw_method,
-        coadd_bbox_cen_gs_skypos,
-        rng,
-        shear_obj=shear_obj,
-        theta0=theta0,
-    )
+    if objlist is not None and draw_gals:
+        assert shifts is not None
+        _draw_objects(
+            image,
+            objlist, shifts, redshifts, psf, draw_method,
+            coadd_bbox_cen_gs_skypos,
+            rng,
+            shear_obj=shear_obj,
+            theta0=theta0,
+        )
+
     if star_objlist is not None and draw_stars:
         assert star_shifts is not None, 'send star_shifts with star_objlist'
         _draw_objects(
@@ -390,7 +399,7 @@ def make_exp(
         bad_columns=bad_columns,
     )
 
-    if bright_objlist is not None:
+    if bright_objlist is not None and draw_bright:
         bright_info = _draw_bright_objects(
             image=image,
             noise=noise,
@@ -459,6 +468,7 @@ def _draw_objects(
         kw['rng'] = galsim.BaseDeviate(seed=rng.randint(low=0, high=2**30))
 
     if redshifts is None:
+        # set redshifts to -1 if not sepcified
         redshifts = np.ones(len(objlist)) * -1.0
 
     for obj, shift, z in zip(objlist, shifts, redshifts):
diff --git a/shear_meas_tests/test_shear_meas_zshear_metacal.py b/shear_meas_tests/test_shear_meas_zshear_metacal.py
index 0e5b059a..25a02318 100644
--- a/shear_meas_tests/test_shear_meas_zshear_metacal.py
+++ b/shear_meas_tests/test_shear_meas_zshear_metacal.py
@@ -11,7 +11,8 @@
 from descwl_shear_sims.shear import ShearRedshift
 
 
-z_bounds = np.array([0.0, 20.0])
+# Use a large z boundaries so that very galaxyies is in this bin
+z_bounds = np.array([-20.0, 20.0])
 shear_obj_p = ShearRedshift(
     z_bounds=z_bounds,
     mode=1,             # mode tells z bin is + / - distorted

From 18a808ede4bfde900df0d0263929ee484bb01198 Mon Sep 17 00:00:00 2001
From: Xiangchong Li <xiangchong.li@ipmu.jp>
Date: Sun, 5 Nov 2023 04:46:28 +0900
Subject: [PATCH 24/30] delete files in bin and fix flake8 problem

---
 bin/README.md              |   5 -
 bin/config_sim_zshear.ini  |  24 ----
 bin/simulate_image.py      | 218 -------------------------------------
 descwl_shear_sims/shear.py |   3 +-
 4 files changed, 1 insertion(+), 249 deletions(-)
 delete mode 100644 bin/README.md
 delete mode 100644 bin/config_sim_zshear.ini
 delete mode 100755 bin/simulate_image.py

diff --git a/bin/README.md b/bin/README.md
deleted file mode 100644
index d24c51a8..00000000
--- a/bin/README.md
+++ /dev/null
@@ -1,5 +0,0 @@
-# Code to run the simulation
-
-```shell
-simulate_image.py --config config_sim_zshear.ini --min_id 1 --max_id 2
-````
diff --git a/bin/config_sim_zshear.ini b/bin/config_sim_zshear.ini
deleted file mode 100644
index 0ddc864d..00000000
--- a/bin/config_sim_zshear.ini
+++ /dev/null
@@ -1,24 +0,0 @@
-; run the code in shear_obj_zshear branch
-[simulation]
-
-img_root     =   ./
-test_name = zshear
-# boundary of the redshift bins
-z_bounds = [0.0, 10.0]
-# shear distortion setup
-shear_mode_list = [0, 1]
-shear_value = 0.02
-
-# galaxy distribution layout and number of rotation
-nrot    =   2
-layout  =   random_disk
-; layout = hex
-coadd_dim = 500
-; buffer length to avoid galaxies hit the boundary
-buff    =   80
-; no rotate no dither.. no dither
-rotate  =   False
-dither  =   False
-
-; psf_version = 0 for fixed PSF
-psf_version = 0
diff --git a/bin/simulate_image.py b/bin/simulate_image.py
deleted file mode 100755
index db8f76ef..00000000
--- a/bin/simulate_image.py
+++ /dev/null
@@ -1,218 +0,0 @@
-#!/usr/bin/env python
-#
-# simple example with ring test (rotating intrinsic galaxies)
-# Copyright 20230916 Xiangchong Li.
-#
-# 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.
-#
-import gc
-import glob
-import json
-import os
-import pickle
-from argparse import ArgumentParser
-from descwl_shear_sims.stars import StarCatalog  # star catalog class
-from configparser import ConfigParser, ExtendedInterpolation
-
-import fitsio
-import fpfs
-import numpy as np
-import schwimmbad
-
-from descwl_shear_sims.galaxies import \
-    WLDeblendGalaxyCatalog  # one of the galaxy catalog classes
-from descwl_shear_sims.psfs import (  # for making a power spectrum PSF
-    make_fixed_psf, make_ps_psf)
-from descwl_shear_sims.shear import ShearRedshift
-# convert coadd dims to SE dims
-from descwl_shear_sims.sim import get_se_dim, make_sim
-
-band_list = ["g", "r", "i", "z"]
-nband = len(band_list)
-
-
-class Worker:
-    def __init__(self, config_name):
-        cparser = ConfigParser(interpolation=ExtendedInterpolation())
-        cparser.read(config_name)
-        # layout of the simulation (random, random_disk, or hex)
-        # see details in descwl-shear-sims
-        self.layout = cparser.get("simulation", "layout")
-        # image root directory
-        self.img_root = cparser.get("simulation", "img_root")
-        # Whether do rotation or dithering
-        self.rotate = cparser.getboolean("simulation", "rotate")
-        self.dither = cparser.getboolean("simulation", "dither")
-        # version of the PSF simulation: 0 -- fixed PSF; 1 -- variational PSF
-        self.psf_version = cparser.getint("simulation", "psf_version")
-        # length of the exposure
-        self.coadd_dim = cparser.getint("simulation", "coadd_dim")
-        # buffer length to avoid galaxies hitting the boundary of the exposure
-        self.buff = cparser.getint("simulation", "buff")
-        # number of rotations for ring test
-        self.nrot = cparser.getint("simulation", "nrot")
-        # number of redshiftbins
-        self.rot_list = [np.pi / self.nrot * i for i in range(self.nrot)]
-        self.test_name = cparser.get("simulation", "test_name")
-        self.shear_mode_list = json.loads(
-            cparser.get("simulation", "shear_mode_list")
-        )
-        self.z_bounds = json.loads(
-            cparser.get("simulation", "z_bounds")
-        )
-        self.nshear = len(self.shear_mode_list)
-        self.shear_value = cparser.getfloat("simulation", "shear_value")
-        self.stellar_density = cparser.getfloat(
-            "simulation",
-            "stellar_density",
-            fallback=0.0,
-        )
-        return
-
-    def run(self, ifield=0):
-        print("Simulating for field: %d" % ifield)
-        rng = np.random.RandomState(ifield)
-        scale = 0.2
-
-        kargs = {
-            "cosmic_rays": False,
-            "bad_columns": False,
-            "star_bleeds": False,
-            "draw_method": "auto",
-        }
-        psf = make_fixed_psf(psf_type="moffat")  # .shear(e1=0.02, e2=-0.02)
-        psf_fname = "%s/PSF_%s_32.fits" % (self.img_root, self.test_name)
-        if not os.path.isfile(psf_fname):
-            psf_data = psf.shift(
-                0.5 * scale,
-                0.5 * scale
-            ).drawImage(nx=64, ny=64, scale=scale).array
-            fitsio.write(psf_fname, psf_data)
-        print(self.stellar_density)
-        if self.stellar_density > 0.0:
-            star_catalog = StarCatalog(
-                rng=rng,
-                coadd_dim=self.coadd_dim,
-                buff=self.buff,
-                density=self.stellar_density,
-                layout=self.layout,
-            )
-            print(len(star_catalog))
-        else:
-            star_catalog = None
-
-        img_dir = "%s/%s" % (self.img_root, self.test_name)
-        os.makedirs(img_dir, exist_ok=True)
-        nfiles = len(glob.glob(
-            "%s/image-%05d_g1-*" % (img_dir, ifield)
-        ))
-        if nfiles == self.nrot * self.nshear * nband:
-            print("We aleady have all the images for this subfield.")
-            return
-
-        # galaxy catalog; you can make your own
-        galaxy_catalog = WLDeblendGalaxyCatalog(
-            rng=rng,
-            coadd_dim=self.coadd_dim,
-            buff=self.buff,
-            layout=self.layout,
-        )
-        print("Simulation has galaxies: %d" % len(galaxy_catalog))
-        for shear_mode in self.shear_mode_list:
-            for irot in range(self.nrot):
-
-                shear_obj = ShearRedshift(
-                    z_bounds=self.z_bounds,
-                    mode=shear_mode,        # mode tells z bin is + / - distorted
-                    g_dist="g1", # need to enable users to set this value
-                    shear_value=self.shear_value
-                )
-                sim_data = make_sim(
-                    rng=rng,
-                    galaxy_catalog=galaxy_catalog,
-                    star_catalog=star_catalog,
-                    coadd_dim=self.coadd_dim,
-                    shear_obj=shear_obj,
-                    psf=psf,
-                    draw_gals=False,
-                    draw_stars=True,
-                    draw_bright=False,
-                    dither=self.dither,
-                    rotate=self.rotate,
-                    bands=band_list,
-                    noise_factor=0.0,
-                    theta0=self.rot_list[irot],
-                    **kargs
-                )
-                # write galaxy images
-                for band_name in band_list:
-                    gal_fname = "%s/image-%05d_g1-%d_rot%d_%s.fits" % (
-                        img_dir,
-                        ifield,
-                        shear_mode,
-                        irot,
-                        band_name,
-                    )
-                    mi = sim_data["band_data"][band_name][0].getMaskedImage()
-                    gdata = mi.getImage().getArray()
-                    fpfs.io.save_image(gal_fname, gdata)
-                    del mi, gdata, gal_fname
-                del sim_data
-                gc.collect()
-        del galaxy_catalog, psf
-        return
-
-
-if __name__ == "__main__":
-    parser = ArgumentParser(description="simulate blended images")
-    parser.add_argument(
-        "--min_id",
-        default=0,
-        type=int,
-        help="minimum id number, e.g. 0",
-    )
-    parser.add_argument(
-        "--max_id",
-        default=5000,
-        type=int,
-        help="maximum id number, e.g. 4000",
-    )
-    parser.add_argument(
-        "--config",
-        required=True,
-        type=str,
-        help="configure file name",
-    )
-    #
-    group = parser.add_mutually_exclusive_group()
-    group.add_argument(
-        "--ncores",
-        dest="n_cores",
-        default=1,
-        type=int,
-        help="Number of processes (uses multiprocessing).",
-    )
-    group.add_argument(
-        "--mpi",
-        dest="mpi",
-        default=False,
-        action="store_true",
-        help="Run with MPI.",
-    )
-    cmd_args = parser.parse_args()
-    min_id = cmd_args.min_id
-    max_id = cmd_args.max_id
-    pool = schwimmbad.choose_pool(mpi=cmd_args.mpi, processes=cmd_args.n_cores)
-    idlist = list(range(min_id, max_id))
-    worker = Worker(cmd_args.config)
-    for r in pool.map(worker.run, idlist):
-        pass
-    pool.close()
diff --git a/descwl_shear_sims/shear.py b/descwl_shear_sims/shear.py
index 25ea3b1f..23551258 100644
--- a/descwl_shear_sims/shear.py
+++ b/descwl_shear_sims/shear.py
@@ -77,8 +77,7 @@ def _get_zshear(self, redshift):
         return shear
 
     def get_shear(self, redshift, shift=None):
-        shear =  self._get_zshear(redshift)
-
+        shear = self._get_zshear(redshift)
         if self.g_dist == 'g1':
             gamma1, gamma2 = (shear, 0.)
         elif self.g_dist == 'g2':

From e6f6bebb3fa9717e03fd87cf89ca21cd8af90966 Mon Sep 17 00:00:00 2001
From: Xiangchong Li <xiangchong.li@ipmu.jp>
Date: Sun, 5 Nov 2023 06:40:46 +0900
Subject: [PATCH 25/30] ensure that the code is backward compatible

---
 descwl_shear_sims/sim.py                      |  10 +-
 ...st_shear_meas_zshear_metacal_compatible.py | 276 ++++++++++++++++++
 2 files changed, 285 insertions(+), 1 deletion(-)
 create mode 100644 shear_meas_tests/test_shear_meas_zshear_metacal_compatible.py

diff --git a/descwl_shear_sims/sim.py b/descwl_shear_sims/sim.py
index f9297f85..1d9855c0 100644
--- a/descwl_shear_sims/sim.py
+++ b/descwl_shear_sims/sim.py
@@ -18,6 +18,7 @@
 from .objlists import get_objlist
 from .psfs import make_dm_psf
 from .wcs import make_wcs, make_dm_wcs, make_coadd_dm_wcs
+from .shear import ShearConstant
 
 
 DEFAULT_SIM_CONFIG = {
@@ -52,8 +53,8 @@ def make_sim(
     rng,
     galaxy_catalog,
     coadd_dim,
-    shear_obj,
     psf,
+    shear_obj=None,
     se_dim=None,
     draw_gals=True,
     star_catalog=None,
@@ -71,6 +72,8 @@ def make_sim(
     sky_n_sigma=None,
     draw_method='auto',
     theta0=0.,
+    g1=None,
+    g2=None,
 ):
     """
     Make simulation data
@@ -148,6 +151,11 @@ def make_sim(
 
     if se_dim is None:
         se_dim = get_se_dim(coadd_dim=coadd_dim, dither=dither, rotate=rotate)
+    if shear_obj is None:
+        assert isinstance(g1, float) & isinstance(g2, float), \
+            "The input shear_obj is None. Please input g1 and g2"\
+            "for constant shear simulation"
+        shear_obj = ShearConstant(g1=g1, g2=g2)
 
     band_data = {}
     bright_info = []
diff --git a/shear_meas_tests/test_shear_meas_zshear_metacal_compatible.py b/shear_meas_tests/test_shear_meas_zshear_metacal_compatible.py
new file mode 100644
index 00000000..52fb5708
--- /dev/null
+++ b/shear_meas_tests/test_shear_meas_zshear_metacal_compatible.py
@@ -0,0 +1,276 @@
+import time
+import copy
+import numpy as np
+import tqdm
+import joblib
+
+import pytest
+
+from metadetect.lsst.metadetect import run_metadetect
+import descwl_shear_sims as sim
+from descwl_shear_sims.shear import ShearRedshift
+
+
+CONFIG = {
+    "meas_type": "wmom",
+    "metacal": {
+        "use_noise_image": True,
+        "psf": "fitgauss",
+    },
+    "psf": {
+        "model": "gauss",
+        "lm_pars": {},
+        "ntry": 2,
+    },
+    "weight": {
+        "fwhm": 1.2,
+    },
+    "detect": {
+        "thresh": 10.0,
+    },
+}
+
+
+def _make_lsst_sim(*, rng, g1, g2, layout):
+
+    galaxy_catalog = sim.galaxies.make_galaxy_catalog(
+        rng=rng,
+        coadd_dim=sim.sim.DEFAULT_SIM_CONFIG["coadd_dim"],
+        buff=sim.sim.DEFAULT_SIM_CONFIG["buff"],
+        layout=layout,
+        gal_type='fixed',
+    )
+
+    psf = sim.psfs.make_fixed_psf(psf_type='gauss')
+
+    sim_data = sim.make_sim(
+        rng=rng,
+        galaxy_catalog=galaxy_catalog,
+        coadd_dim=sim.sim.DEFAULT_SIM_CONFIG["coadd_dim"],
+        g1=g1,
+        g2=g2,
+        psf=psf,
+    )
+    return sim_data
+
+
+def _shear_cuts(arr):
+    assert arr is not None
+    msk = (
+        (arr['wmom_flags'] == 0)
+        & (arr['wmom_s2n'] > 10)
+        & (arr['wmom_T_ratio'] > 1.2)
+    )
+    return msk
+
+
+def _meas_shear_data(res):
+    msk = _shear_cuts(res['noshear'])
+    g1 = np.mean(res['noshear']['wmom_g'][msk, 0])
+    g2 = np.mean(res['noshear']['wmom_g'][msk, 1])
+
+    msk = _shear_cuts(res['1p'])
+    g1_1p = np.mean(res['1p']['wmom_g'][msk, 0])
+    msk = _shear_cuts(res['1m'])
+    g1_1m = np.mean(res['1m']['wmom_g'][msk, 0])
+    R11 = (g1_1p - g1_1m) / 0.02
+
+    dt = [
+        ('g1', 'f8'),
+        ('g2', 'f8'),
+        ('R11', 'f8'),
+    ]
+    return np.array([(g1, g2, R11)], dtype=dt)
+
+
+def _bootstrap_stat(d1, d2, func, seed, nboot=500):
+    dim = d1.shape[0]
+    rng = np.random.RandomState(seed=seed)
+    stats = []
+    for _ in tqdm.trange(nboot, leave=False):
+        ind = rng.choice(dim, size=dim, replace=True)
+        stats.append(func(d1[ind], d2[ind]))
+    return stats
+
+
+def _meas_m_c_cancel(pres, mres):
+    x = np.mean(pres['g1'] - mres['g1'])/2
+    y = np.mean(pres['R11'] + mres['R11'])/2
+    m = x/y/0.02 - 1
+
+    x = np.mean(pres['g2'] + mres['g2'])/2
+    y = np.mean(pres['R11'] + mres['R11'])/2
+    c = x/y
+
+    return m, c
+
+
+def _boostrap_m_c(pres, mres):
+    m, c = _meas_m_c_cancel(pres, mres)
+    bdata = _bootstrap_stat(pres, mres, _meas_m_c_cancel, 14324, nboot=500)
+    merr, cerr = np.std(bdata, axis=0)
+    return m, merr, c, cerr
+
+
+def _coadd_sim_data(rng, sim_data, nowarp, remove_poisson):
+    """
+    copied from mdet-lsst-sim
+    """
+    from descwl_coadd.coadd import make_coadd
+    from descwl_coadd.coadd_nowarp import make_coadd_nowarp
+    from metadetect.lsst.util import extract_multiband_coadd_data
+
+    bands = list(sim_data['band_data'].keys())
+
+    if nowarp:
+        exps = sim_data['band_data'][bands[0]]
+
+        if len(exps) > 1:
+            raise ValueError('only one epoch for nowarp')
+
+        coadd_data_list = [
+            make_coadd_nowarp(
+                exp=exps[0],
+                psf_dims=sim_data['psf_dims'],
+                rng=rng,
+                remove_poisson=remove_poisson,
+            )
+            for band in bands
+        ]
+    else:
+        coadd_data_list = [
+            make_coadd(
+                exps=sim_data['band_data'][band],
+                psf_dims=sim_data['psf_dims'],
+                rng=rng,
+                coadd_wcs=sim_data['coadd_wcs'],
+                coadd_bbox=sim_data['coadd_bbox'],
+                remove_poisson=remove_poisson,
+            )
+            for band in bands
+        ]
+    return extract_multiband_coadd_data(coadd_data_list)
+
+
+def _run_sim_one(*, seed, mdet_seed, g1, g2, **kwargs):
+    rng = np.random.RandomState(seed=seed)
+    sim_data = _make_lsst_sim(rng=rng, g1=g1, g2=g2, **kwargs)
+
+    coadd_data = _coadd_sim_data(
+        rng=rng, sim_data=sim_data, nowarp=True, remove_poisson=False,
+    )
+
+    mdet_rng = np.random.RandomState(seed=mdet_seed)
+    results = run_metadetect(
+        rng=mdet_rng,
+        config=copy.deepcopy(CONFIG),
+        **coadd_data,
+    )
+
+    return results
+
+
+def run_sim(seed, mdet_seed, **kwargs):
+    # positive shear
+    _pres = _run_sim_one(
+        seed=seed, mdet_seed=mdet_seed,
+        g1=0.02,
+        g2=0.0,
+        **kwargs,
+    )
+    if _pres is None:
+        return None
+
+    # negative shear
+    _mres = _run_sim_one(
+        seed=seed, mdet_seed=mdet_seed,
+        g1=-0.02,
+        g2=0.0,
+        **kwargs,
+    )
+    if _mres is None:
+        return None
+
+    return _meas_shear_data(_pres), _meas_shear_data(_mres)
+
+
+@pytest.mark.parametrize(
+    'layout,ntrial', [('grid', 10)]
+)
+def test_shear_meas(layout, ntrial):
+    nsub = max(ntrial // 100, 10)
+    nitr = ntrial // nsub
+    rng = np.random.RandomState(seed=116)
+    seeds = rng.randint(low=1, high=2**29, size=ntrial)
+    mdet_seeds = rng.randint(low=1, high=2**29, size=ntrial)
+
+    tm0 = time.time()
+
+    print("")
+
+    pres = []
+    mres = []
+    loc = 0
+    for itr in tqdm.trange(nitr):
+        jobs = [
+            joblib.delayed(run_sim)(
+                seeds[loc+i], mdet_seeds[loc+i], layout=layout,
+            )
+            for i in range(nsub)
+        ]
+        outputs = joblib.Parallel(n_jobs=2, verbose=0, backend='loky')(jobs)
+
+        for out in outputs:
+            if out is None:
+                continue
+            pres.append(out[0])
+            mres.append(out[1])
+        loc += nsub
+
+        m, merr, c, cerr = _boostrap_m_c(
+            np.concatenate(pres),
+            np.concatenate(mres),
+        )
+        print(
+            (
+                "\n"
+                "nsims: %d\n"
+                "m [1e-3, 3sigma]: %s +/- %s\n"
+                "c [1e-5, 3sigma]: %s +/- %s\n"
+                "\n"
+            ) % (
+                len(pres),
+                m/1e-3,
+                3*merr/1e-3,
+                c/1e-5,
+                3*cerr/1e-5,
+            ),
+            flush=True,
+        )
+
+    total_time = time.time()-tm0
+    print("time per:", total_time/ntrial, flush=True)
+
+    pres = np.concatenate(pres)
+    mres = np.concatenate(mres)
+    m, merr, c, cerr = _boostrap_m_c(pres, mres)
+
+    print(
+        (
+            "\n\nm [1e-3, 3sigma]: %s +/- %s"
+            "\nc [1e-5, 3sigma]: %s +/- %s"
+        ) % (
+            m/1e-3,
+            3*merr/1e-3,
+            c/1e-5,
+            3*cerr/1e-5,
+        ),
+        flush=True,
+    )
+
+    assert np.abs(m) < max(1e-3, 3*merr)
+    assert np.abs(c) < 3*cerr
+    return
+
+if __name__ == "__main__":
+    test_shear_meas(layout="grid", ntrial=10)

From 409eaf8a659346f93c5a8beedfe841d276b700a3 Mon Sep 17 00:00:00 2001
From: Xiangchong Li <xiangchong.li@ipmu.jp>
Date: Wed, 29 Nov 2023 06:19:11 +0900
Subject: [PATCH 26/30] commit

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index c67f8fca..1ff67c7d 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
-stackvana<=0.2023.28
+stackvana
 pip
 lsstdesc.weaklensingdeblending
 numba

From 77514297a2ecdf38fb85f8df0abff86f6fa488ea Mon Sep 17 00:00:00 2001
From: Xiangchong Li <xiangchong.li@ipmu.jp>
Date: Wed, 29 Nov 2023 06:53:29 +0900
Subject: [PATCH 27/30] fix bug in github workflow

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

diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index fa362a5b..f69a47a5 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -37,7 +37,7 @@ jobs:
             pytest \
             numpy \
             ngmix \
-            fitsio \
+            fitsio
 
           pip install --no-deps -e .
 

From b22a24e65031f186d1f43bb021658058687ebaee Mon Sep 17 00:00:00 2001
From: Xiangchong Li <xiangchong.li@ipmu.jp>
Date: Wed, 29 Nov 2023 07:39:26 +0900
Subject: [PATCH 28/30] conda-libmamba-solver problem?

---
 .github/workflows/shear_meas_tests.yml |  5 ++---
 .github/workflows/test.yml             | 10 ++++------
 requirements.txt                       |  2 +-
 3 files changed, 7 insertions(+), 10 deletions(-)

diff --git a/.github/workflows/shear_meas_tests.yml b/.github/workflows/shear_meas_tests.yml
index c2aa1bf7..b903b2b4 100644
--- a/.github/workflows/shear_meas_tests.yml
+++ b/.github/workflows/shear_meas_tests.yml
@@ -30,10 +30,9 @@ jobs:
         shell: bash -l {0}
         run: |
           conda config --set always_yes yes
-          conda install -q mamba
 
-          mamba install -q --file requirements.txt
-          mamba install -q \
+          conda install -q --file requirements.txt
+          conda install -q \
             flake8 \
             pytest \
             fitsio \
diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index f69a47a5..1b73c680 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -29,15 +29,13 @@ jobs:
         shell: bash -l {0}
         run: |
           conda config --set always_yes yes
-          conda install -q mamba
 
-          mamba install -q --file requirements.txt
-          mamba install -q \
+          conda install -q --file requirements.txt
+          conda install -q \
             flake8 \
             pytest \
-            numpy \
-            ngmix \
-            fitsio
+            fitsio \
+            ngmix
 
           pip install --no-deps -e .
 
diff --git a/requirements.txt b/requirements.txt
index 1ff67c7d..677a2411 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -3,7 +3,7 @@ pip
 lsstdesc.weaklensingdeblending
 numba
 galsim
-pydantic<=1.10.11
+pydantic
 
 # optional, but needed for tests to pass
 hexalattice

From be959c89af7f9e47db49628743ba159843787723 Mon Sep 17 00:00:00 2001
From: Xiangchong Li <xiangchong.li@ipmu.jp>
Date: Wed, 29 Nov 2023 07:51:13 +0900
Subject: [PATCH 29/30] libmamba problem

---
 .github/workflows/shear_meas_tests.yml | 6 ++++--
 .github/workflows/test.yml             | 6 ++++--
 2 files changed, 8 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/shear_meas_tests.yml b/.github/workflows/shear_meas_tests.yml
index b903b2b4..6f4adf3b 100644
--- a/.github/workflows/shear_meas_tests.yml
+++ b/.github/workflows/shear_meas_tests.yml
@@ -31,8 +31,10 @@ jobs:
         run: |
           conda config --set always_yes yes
 
-          conda install -q --file requirements.txt
-          conda install -q \
+          conda install -q mamba==1.5.1
+
+          mamba install -q --file requirements.txt
+          mamba install -q \
             flake8 \
             pytest \
             fitsio \
diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index 1b73c680..550fa20e 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -30,8 +30,10 @@ jobs:
         run: |
           conda config --set always_yes yes
 
-          conda install -q --file requirements.txt
-          conda install -q \
+          conda install -q mamba==1.5.1
+
+          mamba install -q --file requirements.txt
+          mamba install -q \
             flake8 \
             pytest \
             fitsio \

From 4e43c6b94570a99acda896ce4ff6765c752570d7 Mon Sep 17 00:00:00 2001
From: Xiangchong Li <xiangchong.li@ipmu.jp>
Date: Thu, 30 Nov 2023 06:01:17 +0900
Subject: [PATCH 30/30] g1/g2 float

---
 descwl_shear_sims/sim.py | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/descwl_shear_sims/sim.py b/descwl_shear_sims/sim.py
index 1d9855c0..ade8349b 100644
--- a/descwl_shear_sims/sim.py
+++ b/descwl_shear_sims/sim.py
@@ -152,10 +152,7 @@ def make_sim(
     if se_dim is None:
         se_dim = get_se_dim(coadd_dim=coadd_dim, dither=dither, rotate=rotate)
     if shear_obj is None:
-        assert isinstance(g1, float) & isinstance(g2, float), \
-            "The input shear_obj is None. Please input g1 and g2"\
-            "for constant shear simulation"
-        shear_obj = ShearConstant(g1=g1, g2=g2)
+        shear_obj = ShearConstant(g1=float(g1), g2=float(g2))
 
     band_data = {}
     bright_info = []