{ "cells": [ { "cell_type": "markdown", "id": "24a97ea5-19a3-4385-be87-8c44bed7e4b3", "metadata": {}, "source": [ "# Modeling Saturated Absorption Spectroscopy #\n", "\n", "In this notebook we use the steady-state solver in rydiqule to model saturated absorption spectroscopy (SAS).\n", "In SAS, a weak probing field and a strong counter-propagating pumping field probe transitions withing a Doppler-broadened atomic ensemble.\n", "The probe field absorption is monitored as a function of frequency, while the pumping field produces sub-Doppler features due to the Lamb dip effect.\n", "\n", "Modeling this experiment in rydiqule is not a trivial task and involves some key assumptions.\n", "The difficulty lies in the fact that rydiqule does not allow multiple distinct fields in a single coupling.\n", "In other words, every coupling in rydiqule must be expressed as a single field.\n", "Normally, if you want to have two fields in a single coupling, you must define a single time-dependent field that then uses the time-solver.\n", "\n", "In SAS, the probe and coupling fields only differ in their propagation, and therefore the sign of the Doppler shift the moving atoms see.\n", "A full model of the system would require defining a time-dependent coupling where the frequencies of the two fields would depend on the velocity class being solved.\n", "This is difficult to do in rydiqule and would be much slower than a steady-state model.\n", "\n", "Here we make an approximate model of SAS that assumes a weak probe that does not influence the optical pumping of the system.\n", "Under this assumption, we can solve the system assuming only the pump field is present, then look at the relevant ground state population as a proxy for the observed probe absorption.\n", "\n", "This notebook can be downloaded [here](https://github.com/QTC-UMD/rydiqule/blob/main/docs/source/examples/Saturated_Absorption_Spectroscopy.ipynb)." ] }, { "cell_type": "code", "execution_count": 1, "id": "79df8d78-990b-4d98-9568-dc60b373dc92", "metadata": {}, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count": 2, "id": "54ffa371-a422-44fc-bb52-a19504196180", "metadata": {}, "outputs": [], "source": [ "%load_ext line_profiler" ] }, { "cell_type": "code", "execution_count": 3, "id": "8b997f25-fd9c-403d-b802-ee50dfbbe9af", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import rydiqule as rq\n", "import matplotlib.pyplot as plt\n", "from arc import Wigner6j" ] }, { "cell_type": "markdown", "id": "ed066c0c-ec2a-4fdd-a51a-3a50be05b4de", "metadata": {}, "source": [ "Start my defining the states of our manifolds. We use the string labeling of states so that we can use regex matching patterns when defining couplings later on.\n", "\n", "Note that we do not consider the magnetic-sublevel structure here beyond accounting for appropriate level degeneracies." ] }, { "cell_type": "code", "execution_count": 4, "id": "d0df04ff-46bf-41f5-9b90-e7fc0d0a1cbb", "metadata": {}, "outputs": [], "source": [ "g_list = [1,2]\n", "e_list = [0,1,2,3]\n", "g = ('g', g_list)\n", "e = ('e', e_list)" ] }, { "cell_type": "markdown", "id": "f46df263", "metadata": {}, "source": [ "This defines width of the Doppler distribution." ] }, { "cell_type": "code", "execution_count": 5, "id": "1c2be10c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Most probable speed: 242.33 m/s\n" ] } ], "source": [ "k = 1/780.24e-3\n", "kb = 1.38e-23\n", "mass = 1.41e-25\n", "temp = 300\n", "vP = np.sqrt(2*kb*temp/mass) # most probable speed, 3D\n", "print(f'Most probable speed: {vP:.2f} m/s')" ] }, { "cell_type": "code", "execution_count": 6, "id": "b796456e-fcb9-4ed8-83dd-f78490749dd1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[('g', 1), ('g', 2), ('e', 0), ('e', 1), ('e', 2), ('e', 3)]\n", "6\n" ] } ], "source": [ "s = rq.Sensor([g,e], vP=vP)\n", "print(s.states)\n", "print(len(s.states))" ] }, { "cell_type": "markdown", "id": "f66a43b2-cce8-4fb3-9d15-47627701e55f", "metadata": {}, "source": [ "We need to define the relative strength of the different hyperfine states. We use ARC's `Wigner6J` with Eq. 41 of Steck's Rubidium 87 D line Data. Note that these prefactors normalize as $\\sum_{F'} S_{FF'} = 1$." ] }, { "cell_type": "code", "execution_count": 7, "id": "9f60021f-16d1-4b73-a0f9-d529ca7dd4c4", "metadata": {}, "outputs": [], "source": [ "def Sff(J, F, Jp, Fp, I=3/2):\n", " \n", " return (2*Fp+1)*(2*J+1)*Wigner6j(J, Jp, 1, Fp, F, I)**2" ] }, { "cell_type": "markdown", "id": "26c4aa7e-67ee-4c7c-bc07-815cf20056fb", "metadata": {}, "source": [ "Here we calculate the effective Clebsch-Gordon coefficients for the Pump couplings between the ground and excited states." ] }, { "cell_type": "code", "execution_count": 8, "id": "7a13e10a-b7c8-40c4-b83f-3c3e14cf1937", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "There are 6 total couplings\n" ] } ], "source": [ "cg = {(('g',Fg), ('e', Fe)): Sff(1/2, Fg, 3/2, Fe)\n", " for Fg in g_list\n", " for Fe in e_list\n", " if Sff(1/2, Fg, 3/2, Fe) != 0.0\n", " }\n", "print(f'There are {len(cg):d} total couplings')" ] }, { "cell_type": "markdown", "id": "1aa30b3b-dfec-4f29-b298-6b07476992af", "metadata": {}, "source": [ "We define a coupling group that maps our single coupling onto all the relevant couplings between the manifolds." ] }, { "cell_type": "code", "execution_count": 9, "id": "bef8d15b-d5e1-4ca3-8988-bf9b0b0e72f7", "metadata": {}, "outputs": [], "source": [ "dets = np.linspace(-310, 250, 561)\n", "rabi_freq = 6\n", "\n", "k = 1/780.241e-3 #wavelength in um to get units right\n", "kp = 2*np.pi*k*np.array([1,0,0])\n", "\n", "s.add_coupling((g, e), rabi_frequency=2*np.pi*rabi_freq, detuning=2*np.pi*dets, coupling_coefficients=cg, kvec=kp, label='pump')" ] }, { "cell_type": "markdown", "id": "35969974-c1e3-4df9-aff3-3c5f4c98ef62", "metadata": {}, "source": [ "Here we define the \"Clebsch-Gordon\"-like coefficients, but for the dephasings between states." ] }, { "cell_type": "code", "execution_count": 10, "id": "5e100a21-b166-41ac-b01f-4121e04cbf67", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "There are 8 total couplings\n" ] } ], "source": [ "g_manifold_degen = np.sum([2*F+1 for F in g_list])\n", "cgg = {(('e',Fe), ('g', Fg)) : (2*Fg+1)/g_manifold_degen\n", " for Fe in e_list\n", " for Fg in g_list\n", " }\n", "print(f'There are {len(cgg):d} total couplings')" ] }, { "cell_type": "code", "execution_count": 11, "id": "b722cc90-a576-4137-a001-ec26f6be921e", "metadata": {}, "outputs": [], "source": [ "gamma = 2*np.pi*6.0666\n", "s.add_decoherence((e, g), gamma, coupling_coefficients=cgg, label='HFS_gamma')" ] }, { "cell_type": "markdown", "id": "4bab238b-7943-4eac-a0bc-99b2e64713ee", "metadata": {}, "source": [ "Next, we define the hyperfine level shifts. The excited states are all defined relative to the center of mass frequency (ie the fine structure frequency).\n", "The $F_g=1$ ground state is defined relative to the $F_g=2$ ground state." ] }, { "cell_type": "code", "execution_count": 12, "id": "04bd0120-4a68-479e-8c71-1e3c305c53b2", "metadata": {}, "outputs": [], "source": [ "hfs_shifts = {('e', 3): 2*np.pi*193.7407,\n", " ('e', 2): -2*np.pi*72.9112,\n", " ('e', 1): -2*np.pi*229.8518,\n", " ('e', 0): -2*np.pi*302.0738,\n", " ('g', 1): -2*np.pi*6834.682610904290}" ] }, { "cell_type": "code", "execution_count": 13, "id": "22699753-3ece-4048-887f-239022a5ac03", "metadata": {}, "outputs": [], "source": [ "s.add_energy_shifts(hfs_shifts)" ] }, { "cell_type": "markdown", "id": "30caf356-ab72-4a9f-a3b0-642cedfcbe4c", "metadata": {}, "source": [ "Since this measurement is typically done in a vapor cell, we add transit broadening. \n", "In the doppler-free solve shown below, this prevents all population from being pumped into the stretch state." ] }, { "cell_type": "code", "execution_count": 14, "id": "a2f676d8-2338-4c2e-ad15-6236e2ee7a96", "metadata": {}, "outputs": [], "source": [ "s.add_transit_broadening(2*np.pi*0.1, repop={('g',Fg): (2*Fg+1)/g_manifold_degen for Fg in g_list})" ] }, { "cell_type": "code", "execution_count": 15, "id": "39f6569a-c444-448c-8f7d-5cf71ea33f07", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: total: 375 ms\n", "Wall time: 134 ms\n" ] } ], "source": [ "%%time\n", "solRb87D2 = rq.solve_steady_state(s)" ] }, { "cell_type": "code", "execution_count": 16, "id": "508d493c-b2f4-4e68-97f6-7f28800344a9", "metadata": {}, "outputs": [], "source": [ "pops = rq.get_rho_populations(solRb87D2)" ] }, { "cell_type": "code", "execution_count": 17, "id": "12c10111-858d-4951-95e0-3502ed6fc07d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(561, 6)" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pops.shape" ] }, { "cell_type": "code", "execution_count": 18, "id": "6a606b35-9e8d-4b63-ba65-6cef8beba0d0", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['pump_detuning']" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.axis_labels()" ] }, { "cell_type": "code", "execution_count": 19, "id": "30f42a68-0ea3-4cab-a3c8-bb537e453bf2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Population $F_g=2$')" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1)\n", "\n", "ax.plot(dets, pops[:,1])\n", "ax.set_xlabel('Pump Detuning (MHZ)')\n", "ax.set_ylabel('Population $F_g=2$')" ] }, { "cell_type": "markdown", "id": "caacb260-9c21-491c-a4e2-b2f7ceb6c17f", "metadata": {}, "source": [ "We now solve the system with doppler shifts.\n", "In order to correctly simulate SAS, we will not allow rydiqule to average the velocity classes together.\n", "We have to do that ourselves.\n", "We also provide a fixed, fine grid of velocity classes to solve for as a simplification of the analysis later on." ] }, { "cell_type": "code", "execution_count": 20, "id": "17fe1fdf-cb81-4a94-b921-3724ae22b555", "metadata": {}, "outputs": [], "source": [ "dops = np.linspace(-2.5,2.5,1201)\n", "dop_meth = {'method':'direct', 'doppler_velocities':dops}" ] }, { "cell_type": "code", "execution_count": 21, "id": "258a7445-2595-49d1-b461-77424c8c1cac", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: total: 11 s\n", "Wall time: 9.88 s\n" ] } ], "source": [ "%%time\n", "solRb87D2dop = rq.solve_steady_state(s, doppler=True, doppler_mesh_method=dop_meth, sum_doppler=False, weight_doppler=True)" ] }, { "cell_type": "code", "execution_count": 22, "id": "2a119422-7f6a-46cb-b603-ff34dfe6fa54", "metadata": {}, "outputs": [], "source": [ "dop_popFg2 = rq.get_rho_populations(solRb87D2dop)[:,:,1]" ] }, { "cell_type": "code", "execution_count": 23, "id": "a89bedc4-c72a-45c9-bb6b-df4bc242e164", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1201, 561)" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dop_popFg2.shape" ] }, { "cell_type": "markdown", "id": "a26e6291-3619-4666-8749-9a17f1c497e8", "metadata": {}, "source": [ "Here we define the expected detunings of the observable hyperfine transitions and the cross-over peaks." ] }, { "cell_type": "code", "execution_count": 24, "id": "8f2c100a-48e5-4860-a6f8-d8851593a322", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 1217.30871964 -458.11458057 -1444.20145259 -1897.98566184]\n", "[ 379.59706954 -951.15801658 -113.44636647]\n" ] } ], "source": [ "hfs_resonances = np.fromiter(hfs_shifts.values(), dtype=float)[:-1]\n", "print(hfs_resonances)\n", "co_pairs = [(0,1),(1,2),(0,2)]\n", "co_resonances = np.array([(hfs_resonances[i]+hfs_resonances[j])/2 for (i,j) in co_pairs])\n", "print(co_resonances)" ] }, { "cell_type": "code", "execution_count": 25, "id": "d28b70fb-a8f2-4c6f-b08f-eb8bc6958824", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(-310.0, 250.0)" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, figsize=(8,4))\n", "\n", "D, V = np.meshgrid(dets, dops*vP)\n", "\n", "CS = ax.contourf(V, D, dop_popFg2, levels=20)\n", "CB = fig.colorbar(CS, ax=ax, orientation='vertical')\n", "\n", "def VtoMHz(v):\n", " return v*k\n", "\n", "def MHztoV(MHz):\n", " return MHz/k\n", "\n", "secax = ax.secondary_xaxis('top', functions=(VtoMHz, MHztoV))\n", "secax.set_xlabel('Doppler Shift (MHz)')\n", "\n", "for res in hfs_resonances:\n", " ax.axhline(res/2/np.pi, c='k', ls='--', alpha=0.5)\n", "\n", "for co in co_resonances:\n", " ax.axhline(co/2/np.pi, c='k', ls=':', alpha=0.5)\n", " \n", "ax.axvline(0, c='k', ls='--', alpha=0.5)\n", " \n", "probe_res = -k*dops*vP\n", "for res in hfs_resonances[:-1]:\n", " ax.plot(dops*vP, probe_res + res/2/np.pi, 'r:')\n", "\n", "ax.set_xlabel('Velocity Class (m/s)')\n", "ax.set_ylabel('Pump detuning (MHz)')\n", "ax.set_ylim((dets.min(), dets.max()))" ] }, { "cell_type": "markdown", "id": "0ba34df0-d5bc-473e-bf83-ce0628dc9cbd", "metadata": {}, "source": [ "In the above plot, we show the population of the $F_g=2$ ground state versus velocity class and pump detuning. \n", "The depleted $F_g=2$ populations corresponding to resonances with the $F_e=(1,2,3)$ excited states can be seen as lines with positive slope.\n", "The horizontal gray dashed lines are the 0 velocity class resonances for the $F_g=2, F_e=(1,2,3)$ transitions.\n", "The horizontal gray dotted lines are the cross-over peak locations.\n", "\n", "The red dotted lines show the same transition resonances visible in the population depletion, but for the counter-propagating probe (which experiences the opposite doppler shift)." ] }, { "cell_type": "markdown", "id": "2bcbefe1-cc77-485c-a5d8-a8738df9e865", "metadata": {}, "source": [ "Our counter-propagating probe, if sufficiently weak, does not influence the $F_g=2$ ground state population itself.\n", "Therfore, its absorption is proportional to the $F_g=2$ population in the velocity classes that have resonant couplings to excited states.\n", "In other words, it can only probe ground state population using a dipole-allowed transition, and which velocity classes satisfy that condition depends on overall detuning of the probe/pump.\n", "\n", "Therefore, we need to sample our doppler solves above for the resonant velocity classes as a function of detuning (ie the red dotted lines).\n", "\n", "$$ \\delta = f_i - kv $$" ] }, { "cell_type": "markdown", "id": "d59537d1-f9a1-41e5-9b09-bfdd55ace70d", "metadata": {}, "source": [ "We also want to account for the fact the probing transition is finite width, and weight nearby velocity classes by the lorentzian response." ] }, { "cell_type": "code", "execution_count": 26, "id": "b7396fdf-d4c8-45bc-8acf-6eb2a2bb417b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Velocity class step size 1.294 MHz\n" ] } ], "source": [ "dop_shift_step = np.diff(dops*vP*k)[0]\n", "print(f'Velocity class step size {dop_shift_step:.3f} MHz')" ] }, { "cell_type": "code", "execution_count": 27, "id": "941d5737-3c6c-4188-b0cb-d3b9b9f2e36e", "metadata": {}, "outputs": [], "source": [ "def lorentzian(f, f0, Γ):\n", " return 1/np.pi*(Γ/2)/((f-f0)**2 + (Γ/2)**2)" ] }, { "cell_type": "code", "execution_count": 28, "id": "a6121478-4b19-4580-975c-3a3c9f31707b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-10. -9.16666667 -8.33333333 -7.5 -6.66666667\n", " -5.83333333 -5. -4.16666667 -3.33333333 -2.5\n", " -1.66666667 -0.83333333 0. 0.83333333 1.66666667\n", " 2.5 3.33333333 4.16666667 5. 5.83333333\n", " 6.66666667 7.5 8.33333333 9.16666667 10. ]\n" ] } ], "source": [ "lor_window = np.linspace(-10, 10, 25)\n", "print(lor_window)" ] }, { "cell_type": "markdown", "id": "067822c8-d1ef-4ccc-af8a-4c927889eeb1", "metadata": {}, "source": [ "We calculate the weighting factors, and confirm they sum to approximately 1.\n", "Note that the doppler sampling is fairly course relative to the transition's linewidth, so our sampling will always be rough." ] }, { "cell_type": "code", "execution_count": 29, "id": "7517ec0c-ec61-420e-b950-4478b82b4dfc", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.9836287684107903\n" ] } ], "source": [ "lor_weights = lorentzian(lor_window, 0, gamma/2/np.pi)\n", "print(np.sum(lor_weights))" ] }, { "cell_type": "code", "execution_count": 30, "id": "a06b0fce-fff1-4d37-a35c-673c1c2c2114", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 193.7407 -72.9112 -229.8518]\n", "(3, 25, 561)\n" ] } ], "source": [ "resonances = hfs_resonances[:-1]/2/np.pi\n", "print(resonances)\n", "probe_classes_norm = (resonances[:,None,None] - dets[None,None,:] + lor_window[None,:,None])/k/vP\n", "print(probe_classes_norm.shape)" ] }, { "cell_type": "markdown", "id": "9337f43a-e19f-4128-bb0d-866f33ef5e6e", "metadata": {}, "source": [ "Need to find the closest solved velocity class for each resonant point." ] }, { "cell_type": "code", "execution_count": 31, "id": "d8d4005f-66b6-4a39-878c-b4e40fb285cd", "metadata": {}, "outputs": [], "source": [ "def find_closest_ind(x):\n", " \n", " min_ind = np.argmin(np.abs(dops-x))\n", " \n", " return min_ind\n", " \n", "np_find_closest_ind = np.vectorize(find_closest_ind)" ] }, { "cell_type": "code", "execution_count": 32, "id": "43b18af5-ed8d-4580-80ba-db0f9bd54394", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(3, 25, 561)\n" ] } ], "source": [ "res_inds = np_find_closest_ind(probe_classes_norm)\n", "print(res_inds.shape)" ] }, { "cell_type": "markdown", "id": "603d9c5e-d042-406a-902e-1b8f39fb21ff", "metadata": {}, "source": [ "Now we slice out the relevant velocity classes for each detuning, weight them, and sum." ] }, { "cell_type": "code", "execution_count": 33, "id": "dde9df06-5026-4076-9de1-2ac73f2b247f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(3, 25, 561)\n", "(561,)\n" ] } ], "source": [ "SAS_pops = dop_popFg2[res_inds,np.arange(len(dets))] # selects indeces along doppler axis for each detuning\n", "print(SAS_pops.shape)\n", "SAS_weighted_pops = SAS_pops*lor_weights[None,:,None] # weight the classes near each resonance by the lorentzian response of the probe\n", "SAS_probe = np.sum(SAS_weighted_pops, axis=(0,1))\n", "print(SAS_probe.shape)" ] }, { "cell_type": "code", "execution_count": 34, "id": "873cc0cf-6c1a-4bee-a92c-d59fc7203d1a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Rb$^{87}$ $F_g=2\\\\rightarrow F_e=(1,2,3)$')" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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hysrKNOYJDg5GTEyMxsfGjRub8GoQQgghxGIxJnb27FnG2dmZuX79OsMwDJOYmMj4+voyJSUlOsffu3ePcXV1ZY4ePcowDMMcO3aMcXV1Ze7du2fQnMHBwczx48fVn69bt46xt7dn8vPzGYZhmKSkJKZNmzZMZmYmwzAMU1RUxPTs2ZOZOnWqxnqio6M5f63FxcUMAKa4uJjzY4zFlM9tDej1azp6DZuGXr+mo9ew6Wz1NTTk6zZ5xiohIQEjR45EUFAQAGDKlCmQy+VITEzUOX7dunXqLBEAREdHIygoCOvXrzdozs2bNyMqKkr9eUxMDKqqqnD//n0Aqmae8+bNU3dYdXNzQ1xcHH7++WeDrg4ghBBCiO0weWB15MgRhIeHqz/n8/kIDQ3F4cOHdY4/fPiwxnhA1b6g9nguc/br10/9/+Xl5fjss88wcOBAhISEAAB69+6Nt956S+N57O3toVAooFQqG/GV1igpKeH0IZFImvQ8tYnFYrz33nsQi8VGm9OW0OvXdPQaNg29fk1Hr2HTWdtrKJFIOL8nc2XSwKqgoADFxcXw8fHRuN3Hxwfp6ek6H5Oenq53vKFzPvfcc2jTpg1yc3Oxa9curf5StZ0+fRpjxoyBUChU31ZeXo4ZM2aoa7FWrVoFqVSq9+v29/eHm5tbgx+rVq3SO48hxGIxli9fbjX/GFoavX5NR69h09Dr13T0Gjadtb2Gq1at4vR+bMg5vybtY1VRUQEAWt8gsVisvk/XY/SNN3TO3377DVVVVXj11VcRHR2NU6dOwdHRUWvc9evXcfDgQaSkpGjcHhQUhFdffRVhYWHIyclBbGwszp8/j507d9b7dXM9Y8lafnAJIYQQc7Rs2TIsXLiwwXHsWYFcmDSwYgOYulteEolEZ3DDPkbf+MbMaW9vj3Xr1sHLywvfffedVo+L0tJSTJo0CVu2bEGHDh007vvhhx/U/+/t7Y0VK1Zg1KhRuHXrFrp06aLz+Zp61hchhBBCmk4sFhs9iWHSwMrT0xNubm7Izs7WuD07OxuBgYE6HxMYGKh3PJc5GYaBXC7X2NJzdnaGr68vrl27pvG4qqoqPPfcc3j99dcxcuTIBr+mTp06AQDu3LlTb2BFiC2RK5S4V1CO+4UVyCysREG5FMUVUhRXyiAU8NHWzR7t3B0Q5OOCbm1dYS+sfzueEELMncmPtBk0aJDG9hrDMEhNTdUqHGcNHjwYp06d0rgtJSUFQ4YM4TxnRkYGXnvtNezdu1c9RqFQIC8vD+3atVPfJpfLMX78eDz//POIi4sDAPz8888YMmQIWrVqhcuXL+Ps2bN4+eWX1Y95+PAhABi0H0uItWAYBlnFVbh4vwgXMx/jYmYRLj8sRpWM2wUfIjs+Rvb0waJnguDvoTvDTAgh5szkgdXSpUsxZMgQ3Lx5E127dsXWrVshEAjUgcz06dMhl8vx/fffAwDmz5+Pb775BidOnEBUVBSSkpKQlpaGHTt2cJ4TUF05mJqair59+wIAPv74YygUCkyYMAEAoFQqERcXBycnJ/Tr108dqG3ZsgXh4eFo1aoVCgoKsHr1ajz//PPw8PBAZWUlEhISEBUVhe7du7fI60eIqRVXyPDXjRwcvpaL5HuFyCvVvprVSSRAgKcT/D0c0NpFDDcHIdwchJApGDwqrkRGQQXSHpUiv0yC3y5mYf/lbLw/pgcmRrQ36loZhsG5e4+x68JDXMsqRqVMAZEdH55OYrR1s8fA4DaICWoNsR1lzQghjWPywCoiIgKJiYmYPHkyHBwcwOfzcfDgQbi4uABQbcXJZDL1+ICAAOzduxdLliyBSCSCRCLBvn37EBAQwHlOHx8fvPPOO5gzZw4cHBwgkUjg4uKCQ4cOoXPnzgCAAwcOYNu2bQCAH3/8UWPNbM+skJAQvPDCCxgxYgQcHBxQWlqKsLAwrFy5Ejwer/leNEJMrLRKhgNXsrHnYhbOpBdArqw5GUvA5yHYxwV9/N3xRPtW6OPvjkAvJ/D5+v9NMAyDSw+Ksfrgdfx9uwBLf72MrOIqLBza1Shrvpdfjvk/XsA/D4rrHfPjuUy42Nvh2d7tMOPpjujU2tkoz00IsR10VmALstUzloh1UCgZnLiZh19SH+DQtRxI5DXbe0HeLnimhzcGdGmNXr5ucBA1PuPDMAz+e/gW1h25BQBY/a8QjA9v2tb637fz8coP51FaJYeDUIBne7dDTFBruDoIIZErkFcqwY3sMuy//AjZJVXqxw3t7o33RneHXyvaliTElhny/k2BVQuiwIpYoiqZAr+kPsCmpLu4m1+uvr1Tayc839cPsb3aooOXk9Gfd92RW1h76CacRAIcXRyDNq72jZonu7gKwz87gaIKGUIDWmHD5L7wcdM9l1LJ4Ex6Ab79+y4Op+UCAFzEdlj5fC8827udzscQQqwfBVZmiv3GdO3aVasRaXx8vFabB0JMqahCiu9PZyDx9D3kl6ma3rra2+H5vn54vq8vevm6NeuWt1LJ4PmNp3AxswiT+7XHR2N7NWqe17alYu+lR+jp64pf/t2fc/3U7dxS/N8vl3E+4zEAYPEzXRE/sDNt8xNi5TZs2IANGzZo3KZQKHDz5k0KrMwNZayIJXhUXIn/HU/HjpRMVEhV52L6ujtgxtMdMSHcH87ilivNPH2nAJO+PgMnkQDn3h4CR5Fhz33lYTFGrT8JHg/YO/dp9GjnZtDjFUoGqw9ex/+Oq05tmNyvPT4Y0xOCBurFCCHWxZD3b5MXrxNCzINUrsS3f9/FZ4dvoVKmCqi6t3XFnOhAjOzVFkJBy5+AFRnogfYejrhfWIE/rmTj+b5+Bj1+W7LqUPVRIe0MDqoAVSH+shHd0M7NAct/v4ptZ+9DrlAi4V8hlLkihOhk8kOYCSGmd+lBEUatT8LHB66jUqZAeIdW+H5mBPbNexpj+viaJKgCAB6Ph+f6qGqbDqflGPTYKpkCv/+TBQCY2MTi97j+HbBu4hPg84AdKQ/w07nMJs1HCLFeFFgRYsNkCiU+PXwTY784hZs5ZfB0EuGTcb2xY86TGNCltVlkZWKC2wAAkm7lQ67g1mgUAFLvP0ZplRytXcR4MtCzyesY3bsd/m94MABgxe/XNAr5CSGERYEVITaqpEqGl75LxqeHb0GhZBAb0haHF0bjX6F+ZhFQsXr7ucPdUYjSKjn+eVDE+XGn7xQAAJ7q5NlgDy2uZg0IxJOBnqiUKbDs10ugElVCSF0UWBFig/LLJBj/5Wn8fbsATiIBPpvYBxsm90UrJ5Gpl6ZFwOchLMADAHBJT3PPuv6+nQ8A6N/Jy2hr4fN5WDMuBCI7Ps6kF+JIdUsGQghhUWBFiI0pqZIh7ttkXM8uRWsXMX6a8yTG9PE19bL06t5OdRVO2qMSTuPlCiWuPFSNjejoYdS1+LVyxIynOgIAVh1IM2h7khBi/eiqQBMIDw+nPlbEJOQKJV75/jyuZpXAy1mEn2ZHItACjm3p3lZ1HNU1joHV3fxySBVKOIoEaN8Mhzm/OrATfjp3H3fyyvHrhYcYH0aHrhNiLerrY8UVBVYmcO7cOepjRUxi3V+3ceqOavsvcUaERQRVANCtrerfy82cMsgVStg1cJViWnYpACDIx8Vo9VW1udoL8e+YTvho/3VsPHYH/+rrR72tCLESuhIdbB8rLmgrkBAbcTa9AOv/Up2/99HzvRrV18lU/Fs5wkEogFSuRObjygbH38hWZbaCfZrvD5gX+wXA3VGIu/nlOHEzr9mehxBiWSiwIsQGKJUMlv9+DQwDTAjzN/uaqrr4fB78WjkAAB48rmhw/I3sMgBAsI9Ls63JSWyHf1U3LN169n6zPQ8hxLLQViAhNuDAlWykPSqBi70dlo4INvVyGsWvlQNu5ZbhAYeMFRt8BXgav76qtonh/vjm5F0cu5GLkioZXO2Fzfp8rIuZRdh98SFO3ylAaZUcdgIefN0d4OvugI6tnTA6pB38m6G2jBDSMAqsCLFyDMPgqyTVWXfTn+poli0VuPBrpQoUuGSssopUwZevu0OzrqmLtwsCWzshPa8cSTfzERvStlmfL7OwAh/uu4aDV7W70GcU1Lwuaw7ewIAurTE5oj2GdGvTYE0aIcR4KLAixMpde1SCfzKLILLjY9qTAaZeTqPVbAXqz1iVSeQoqZIDANo2c2AFAIOD2yA97y7+up7brIFVyr1CzExMQXGlDHyeqhP88B4+aOvuAIlMgYdFlXj4uBLJ9wqRdCsfJ27m4cTNPLRxEWNyv/aYHRVo8CHWhBDD0b8yQqzc7ouq8/KGdGsDL2exiVfTeDUZK/2B1aPqbJWbgxDO4ub/FTcwuA2+TlJtByqVTLNchXgnrwzTN59DaZUcvf3dseaFEHT1rr9+LKOgHNuTM/FzSiZySyX49PAt/JzyAO+N7o5nevgYfX2EkBoUWJkA9bEiLYVhGOy79AgALK5gvS6uxesPqwOrdi2QrQKA8A4ecBHboaBcin8eFOGJ9q2MOr9CyWDJz/+gtEqOsIBW+OHlfrAXCvQ+JsDTCUtHBGPh0K44cOURVv9xAw+LKjH7+/MY0q0Nlj/bQx2oEkI0UR8rC0R9rEhLySiowMOiSogEfER1aW3q5TRJG1dVti2/TKo3M5RVVAUA8HW3b5F1CQV8RHVtjX2XH+Ho9VyjB1bfn76H1PtFcBIJsG7SEw0GVbWJ7PgY08cXz3T3wfq/buGrE+k4nJaLk7fzsWhoEF4e0NGszoUkxBxQHytCSL1OVR9E/ER7dziIuL8hmyNPJ1VgpVAyKK6U1TvuUbEqY9XWrWUyVgDwdBfVeYTJ9wqNOm9xpQz/+fMmAGDpiOBGZ+EcRAK8MTwYB+YPQERHD1TJlFi5Pw1zt19ApZT7X+KEkIZRYEWIFTudrgqsjHkQsamI7Phwc1C1M8gvk9Q7jr2vtUvL1ZOFBaiyVBcziyAz4tmBW89moEwiR1dvZ7zYr+kXHnTxdsFPsyPx4XM9IRTwsPfSI4z/32l1MEoIaToKrAixYpcfFAEAQgOMuz1lKp7OqlYR+WXSesew97FjW0Kn1s5wcxCiSqbEtSxu5xk2RKlksPWMqvHo7KhORiuK5/F4mBIZgB9m9oOHkwiXHxbj2c//Rur9x0aZnxBbR4EVIVaqpEqGe9W9jXq0s46aPvaqRn0Zq4Lq+9itw5bA5/PQt707AOCCkQKU5HuFeFhUCRexHUY1QxuHfoGe2B3/FIJ9XJBXKsHkr8/gTHWGkxDSeBRYEWImJHIF9l7KwrJfL+H5L/7Gcxv+xlu7LuN6duMyIGnVmZN2bvYW2xS0Lq/qLFSBvsCqXKoxtqWE+LkDAC4/NE7Gas8/qjYZI3u1Nahg3RD+Ho7Y+e/+iO7aGlUyJWZsPofzGZS5IqQp6KpAQkysoEyCDUfv4NcLD1BUoVmUfTGzCNuT7+OD53oaXGNztTqw6m5Bhy03pCZjVf9WYIF6K7Ble3aF+Kle58sPi5o8F8MwOJKm6q4+olfz9p1yFtvhf1ND8XJiCk7ezseMzefw8ytP6u2TRQipHwVWJkB9rAgAyBVKfH8mA2sP3UQp2ynczR6je7dDL1838Hk8/HbxIQ5dy8Fbu66grZs9BgV7c54/PV91EHGQj3OzrN8U2MCqoFx3xqpKpkCZRPVatmSNFQD08lUFVrdzy1AukcOpCc1JrzwsQU6JBI4iASIDPY21xHrZCwX4aloopmw6i9T7RZj2TTJ+ebV/sx8JRIg5oj5WFoj6WJHTdwqwfM9V3MgpBQB0b+uKJcODENWlNQS1ipRH9vLB279dwdaz97Hs18s4+X+tIeR47ltmoepKrwAPJ+N/ASbCBkt5pbozVmztlciOD5cW6LpeWxtXe7RxESO3VIIbOaXo24R+Vkm38wAAT3X2arZtwLocRXb49qVwjPvyNG7llmHaN2ex57WnmxQgEmKJqI8VIRakSqbA8j1XMenrM7iRUwp3RyFWju2J3+c+jYFBbTSCKkB1Bde7o7vDy1mEnBIJDl/TPny3PpmFqsJ1Pw/ryTp4OKoCq+JK3YEVuw3o5SQySePLzm1U2cH0vPImzZNyT1Xn1K+jR5PXZAh3RxG2zIyAj6s97uSVI+GP6y36/IRYAwqsCGkht3JK8dyGv7H51D0AwIv92uPY4hi82C9AK6CqTWwnwIRwfwDAz+cfcHouhZJRn6nX3sN6ji5xc1T1sapbi8Zitwg9WngbkNWptSqwupNX1ug5lEpGXUAe3qFlAytA1Vj1P+N6AwC2nM7A37fzW3wNhFgyswisdu3ahbCwMAwYMADR0dG4evWq3vEnT55EZGQkoqOjERkZiaSkJIPn/O677zB06FAMGzYMkZGR6N+/P44cOaIxhmEYvP/+++jbty8iIiIwZcoUFBcXa4yRSqWYP38+QkNDERoainnz5kEqrb+wltgehmGw9WwGRn9+EtezS+HpJMJ3L4Vj5dhecHfkFgDE9moHQLWFKJE3vNefU1IFqUIJOz6vRTuQNzd3B9XrVVRP53W2Izs7rqV1aq3adr2T2/jA6k5eGYorZXAQCtDdRG0ynu7ihSmR7QEAb+y8hNKq+jvdE0I0mTywSk5OxrRp07B161YkJSVh5syZGDZsGEpLS3WOz8jIQGxsLD7++GMcP34cCQkJGDVqFDIyMgyac/Xq1XjnnXdw8OBBnDlzBpMmTcKoUaNQUFDTx+W///0vduzYgZMnTyI5ORkikQjTpk3TWM/ixYtx9epVJCcnIzk5GWlpaViyZImRXyViqUqqZHjlh/N4a9cVVMmUGNDFCwdeH4CBwW0MmqdbWxe0dhGjUqZQbxPpw24D+rZy0JsNszRsxqq4QgaGYbTuZy8CcLE3TV1QpzZNz1hdfqj6462nryvnerrmsGxEN/h7OOBhUSU+2p9msnUQYmlMHlglJCRg5MiRCAoKAgBMmTIFcrkciYmJOsevW7cOwcHBiImJAQBER0cjKCgI69evN2jOzZs3IyoqSv15TEwMqqqqcP++qtOxQqHAxx9/jPj4eDg6qrZSFi9ejD179uDKlSsAgIKCAnz55ZdYtGgRBAIBBAIBFixYgI0bN6Kw0LhnhhHLczOnFGM+/xsHr+ZAKODh7dhuSJwegTYuhh8OzOPxMKD6PDouTRwfFbMHEVtPtgoA3KuPtJEqlKiUaWfuTB1YBVZvBWYUVEDeyKNt2DYZPUzcJsNJbIc1L6i2BLcnZ+L0HWoeSggXJg+sjhw5gvDwcPXnfD4foaGhOHz4sM7xhw8f1hgPqNoX1B7PZc5+/fqp/7+8vByfffYZBg4ciJCQEADApUuXkJeXpzFPt27d4OTkpJ7nxIkTkMlkGmPCw8Mhk8lw4sQJg14HYl3+vJqN5zb8jbv55WjnZo+dr/THywMCm3QsCXs5/41s3dnc2vJKVbVGbVrwvLyW4CgSQChQvYa66qzYwMpZLGzRdbF8XO0hEvAhVzLq4NZQV7NUGStTbQPWFhnoiRf7qbYEVx1Ig1KpnSUkhGgyaWBVUFCA4uJi+PhoNsDz8fFBenq6zsekp6frHW/onM899xzatGmD3Nxc7Nq1S91fih1bex4ejwdvb2/1fenp6bCzs4OXV80Bt61bt4ZAIKh3/YDqsk0uHxJJ/d2lifna/PddzPnhPCqkCvTv5Inf5z6N3v7uTZ43qLph480cDoGVCQ4ibgk8Hg9uDuyVgboCK9VtpspYCfg8+LVSZQnZ7VhDMAyjPmvQXI4hWjC0K5xEAlx6UIx9lx+ZejmEGJVEIuH8nsyVSQOrigrVLx6xWPOXv1gsVt+n6zH6xhs652+//YaCggJ4eXkhOjraoHkqKiogEmkXyYpEonrXDwD+/v5wc3Nr8GPVqlX1zkHMj1LJ4MO917D892tgGGBSRHtsmRFhtA7gXX1UgVVGYQUqpfoL2NmMlbUFVgDg5qAKmvRlrEwVWAGqY2IA4H4jAqu8UglKquTg82paN5ial7MYs6M6AQA++fMGFJS1IlZk1apVnN6P/f39Oc9p0s5vbO1S3cyMRCJR36frMfrGN2ZOe3t7rFu3Dl5eXvjuu+806qoaei5dVwBKpdJ6nwsAMjMzOTUIrRvUEfNVJVNgwU8XceBKNgDgjeFB+Hd0J6P2UvJyFsPTSYSCcilu5Zaqz6bTxZoDK9WVlOU6e1mxXddNGVi1b0Jgdae6/5W/hyPEdi3TGJSLlwd0xLd/38W9ggr8dT0XQ7tzPwGAEHO2bNkyLFy4sMFxJSUlnIMrkwZWnp6ecHNzQ3Z2tsbt2dnZCAwM1PmYwMBAveO5zMkwDORyOYTCmjoMZ2dn+Pr64tq1a+rnYR/n5+enflxOTo76vsDAQMjlcuTn56u3A/Py8qBQKOpdPwC4urpS53UrUlAmwawtKUi9XwSRgI8140Iwpo9vszxXpzbOKLhbiHsFFdwCK2fDC+XNHVvArjtjxW4FmqbGCmhaYHU3XxVYBXqZV7d8J7EdJkb443/H0/Hd33cpsCJWQywWGz2JYfLi9UGDBiElJUX9OcMwSE1NxZAhQ3SOHzx4sMZ4AEhJSdEY39CcGRkZGDt2rMYcCoUCeXl5aNdO1S8oJCQErVu31pjn+vXrKC8vV88TFRUFoVCoMSYlJQVCoVDjikNive7ll+NfG08h9X4RXO3tsGVmRLMFVQDQzk0VKD0qqtQ7zlprrIBaTUJ11liZPmPlX93pPvOx/u+RLunVbRo6epnHNmBt057sAAGfh1N3CnA9m3u9CSG2xuSB1dKlS7F//37cvHkTALB161YIBALExcUBAKZPn46pU6eqx8+fPx9paWnqq+6SkpKQlpaGuXPncp4TUF05mJqaqv78448/hkKhwIQJEwAAAoEAS5cuxYYNG9T1Up988glGjx6Nnj17AlBlx1555RWsXbsWCoUCSqUSn376KV555RV4eLR8x2TSss5nPMbYL/7GvYIK+LVywK+v9m/2A3N9qpt96rviTKZQorBctU1mlYFVdcZKd/E6e1Wg6QIr9nuU04irAtmMVcfW5pWxAlStO4b3UF3Ms/XMfROvhhDzZfLTNSMiIpCYmIjJkyfDwcEBfD4fBw8ehIuLqlC3qqoKMlnNL9CAgADs3bsXS5YsgUgkgkQiwb59+xAQEMB5Th8fH7zzzjuYM2cOHBwcIJFI4OLigkOHDqFz587qeRYsWICysjI89dRTEAqF6NKlC7Zs2aKx/jVr1mDJkiWIiIgAAPTv3x9r1qxptteLmIcDlx/h9Z8uQiJXopevG755KaxR/akM1c69OmNVXH82hD0vT8DnqbfNrAkbNJVX11PVZg5bgT6uqu9RXpkECiVjUIPWewXVgZWn+QVWADAh3B/7Lj/C/suP8O7o7iZtYEqIuTJ5YAUAY8eO1dqaY23fvl3rtgEDBuDMmTONntPe3h5vvvkm3nzzTb1z8Hg8vPvuu3j33XfrHSMWi7Fu3Tq98xDr8s3Ju/hwn+rKv8HBbbB+8hNwFLXMPyX2TTtbTzakqLqo291B2KS+WeaKfa3LJZpXRjIMoy5edzXhVqCXswh8nuq8xvwyCbxduQXcDMPgYfUWL9uywdz07+QJL2cR8sukOHk7HwODDDtBgBBbQH9uEMKRUsng/d+v4YO9qqBqSmR7/G9qaIsFVQDQrrqTepaewKq4uqjbzQqzVQDgLFZdLVc3Y1UuVYDtBGDKjJWdgK/egtUXANdVUC5FlUwJHg9o626eFx3YCfgYFaKqQ91zMcvEqyHEPJlFxsrWhIeHqxuRsuLj4xEfH2+iFZGGKJUM3tx1GT+eywQALBsRjNlRgUZtp8CFT3Xxen6ZBFK5EiI77b+N2NojVysNrJzYrUCpZmBVVl1fJeDzYC807d+MPq72yCmRIKeEe2D1sLrYvY2L2KxaLdT1bJ922HzqHg5ezUalVAEHkfmulZDG2LBhAzZs2KBxm0Khv3dgbRRYmcC5c+eo3YIFUSgZvLHzEn5JfQA+D1jzQm/8K9TPJGvxdBJBJOBDqlAir0yi8yxANrCy1oxVzVagZmBVu+t6Swe8dam2/4oNC6yqtwHN/XzHJ/zd4euuOpz55O18ar1ArI6uREdJSQnc3Lid30lbgYToIVcoseCni/gl9QEEfB4+nfiEyYIqQFX318pJFTAVlmk3yASsP7CqKV7X/AuyxAxaLbDYzGJ2IzJWvq3qby5sDng8HoZ0U9VWHUnLMfFqCDE/FFgRUg+ZQol5P17Ann+yYMfn4fNJT+DZ3u1MvSx4OKnqdwrKdZ8lWWLlgZVjdY1VWT0ZK1MdwFwbW7CeW8L9vE9LyVgBwOBuqizVkeu5YBg64oaQ2iiwIkQHiVyBV7emYv/lbIgEfHw5JRQjerU19bIAqLYDAeBxhf6MlauD6TM3zYHNWFXUrbEyg+NsWB7V3yO2nxgXbKF7WzfzLFyvrV+gB8R2fOSVSnA7t8zUyyHErFBgRUgdVTIFXvn+PA5dy4HYjo+vpoViiBnVkbSqftMusNGtQKd6tgLZ5qCmbLXAYoPfAgMCq9xSVWDVxgKauortBAjvoGqCfDq9wMSrIcS8UGBFSC2VUgVeTkzB0Rt5sBfy8e1L4Ygxs149ng1kQ6w+sKq+Ck2qUEIqV6pvN4fmoCxPZzaw4r4VyB5D1MbV/AMrAHiyk+qUgVO3KbAipDYKrAipVilVYPrmZJy8nQ9HkQCbp0fgqc5epl6WFg+OW4FWG1jVOq6m9nZgmRkcZ8PyrK6Dq+8Cg7oYhlHXY1nKwdlsxuqfB0WmXQghZsb0v4FsEPWxMj9VMgVmbUnBmfRCOIvtkDgjHKEB5nneI9etQGvtYyUU8CGy40MqV6JMIoe7o+r1MKerAj2qM1blUgWqZArYC/X3eiqVyCGpzr5ZyvmOPdq5gsdTnVuZVyqxmHUT0hDqY2WBqI+VeZHIFfj3D+fVmarEGREIDWhl6mXVq6GtQDbAsNaMFaDaDpTKlaiQ1vyyK1UHVqb/ul3Edup+YwXl0gav9GOzVS5iO4tpuOkktkOglxPu5JXjysNiDAw2ry1zQhqL+lgR0gQyhRJzt13QqKky56AKqHXFWT1bgWXqIm7TBxjNhd0OrN1yQd1uwQwyVjweT/19KihruM6KLVxvbSH1Vaxevqo3mssPi028EkLMBwVWxGYxDIOlv1zGn9dyILLjY9O0cEQGepp6WQ1S11jpyFgplAwqZaosjqOFZD4ao6ZJaK0aKzM4gLm2mgL2huus8krZ+irLCqx6UmBFiBYKrIjNSvjjhrqj+obJffF0F/MrVNeF3eIrqZJrNWesXcztZAZF3M2FDRprt1woNaMaK6BWZpFDAXt+9RhLq1MK8XMHAFyhwIoQNQqsiE369uRdfHn8DgBg1dheFnXeGbvFp1AyKJdqFlSygYaAz4NYxwHN1sJJR8bKnNotALV7WTW8FchmH9lgzFLULWAnhFBgRWzQnn+y8P7eawCAJcOCMD7c38QrMoy9kA+RQPVPl70CkFVenbFyFAlMfhBxc1JvBUq1twLNod0CAHg6s0cPNZyxYuvlWjlaVmDFFrADlLUihGUev4EIaSEnb+Vj0Y6LAICX+nfAqzGdTLugRuDxeHB1sEN+mRTFFTKNK87KzSy4aC6OIu3u6+YWWHk00BajNkvNWAFAt7auuJNXjps5pS12ZeD9ggocu5mLtEclKK2SQ2THR7CPCyI6eqK3n5tV/1FBzJ95/AYipAVceViMOd+nQKZgEBvSFu+O6m6xv4BdHYTIL5OipKpOxkpi/YXrAOAsZmusVMGUUsmgSqbqA2Uu7Qq8nLmfF8g2e21lgYFV5zbOANAiZwaWSeRYvucqdp5/UO+YTq2dMD7MH2P7+qKNi2U0WyXWhQIrE6AGoS0vo6AcL32XjHKpAv07eWLt+N7g8y0zqAJqCtjrbgWyxevmkrVpLnXbLbBXQgLmE1R6OHHfCnxcrvo+tnI0j/owQ6gDq7zmDaxKq2R46btzOJ/xGADQv5Mn+rZvBU9nEcqq5Lj8sBhJt/JxJ68cqw5cx5qDNzD9qQ6YP6Sr1f97IMZFDUItEDUIbVmlVTLM2HwO+WVS9Gjniv9NDYXYzjzefBuLLWCvG1ixgQa7VWat2MCKDSTZWiseD7A3k++tut0Chz5WllpjBWhmrBiGaZYssFyhxMzNKTif8Riu9nbYFBeOiI7aJyOUVsmw79Ij/JSSiQv3i/B10l3svpiFt2K74dne7Sw2Q01aFjUIJUQPpZLBwh3/4E5eOXxc7fHd9HCzuWqsKdQtF7QyVqq/qpzE5hFcNBenOu0WKqu/bgehwGwykQ11yGcxDIOiCsutserg6QQ+T9XuormuDPzi2B0k3yuEi9gO22ZF6gyqANUVoRMj2mPXq0/hu+nh6ODpiNxSCeb/eBETvjqD69klzbI+QmqjwIpYte/PZOBQdQPQ/00NtZqaC1cHVcambmDF1hxZcw8roFa7hepMFRtQmss2IFBzVWCFVKEO/HQpk8ghU6j6kVlixspeKIBvK9UFFHfzy40+//2CCnx+9DYA4MOxPdVNSRsyMKgNDi6IwpJhQbAX8pF8txCj1p3EVyfuaPV/I8SYKLAiViuzsAIJf1wHALw1sht6+7ubdkFGVLtJaG01xes2ElhJNAMrcylcB1RZNVF1LzF9vazY+ip7Id+s1m+IAA9Vy4X7hRVGn3v9X7cglSvxVGdPPNu7nUGPFdsJED+wM44sisEz3b0hVzL4aP91zNqSos4SEmJsFFgRq/XWb1dQIVUgoqMHpkYGmHo5RlVfjVVN8bplvkFzVVO8rgqo2K/byYwCSh6Px2k7kK2v8rDAbBWrvacjAOMHVo/LpdjzTxYAYOHQro2ukfJ1d8D/pobiw+d6QmTHx+G0XMSuO4nU+4+NuVxCAFBgRazU2fQCnLiZB6GAh4R/hZhN3Y2x1HdVoM0Ur1dndiqk5puxAur/PtVmya0WWO09VIFVRoFxA6tdFx5CIleiRztX9G3ftMPReTwepkQG4Nd/90cHT0c8LKrE+C9P4+sT6bQ1SIyKAitilTZWH1czLswfHas7Q1sTZ3vNdgMsNsCw9svL624FVpphjRVQE1gVVegJrCy4OSgrgA2sjJyx2n3xIQBgfJi/0a7o6+nrht/nPo1RIW0hVzJYuT8NC3f8gyoZ98vpCdHHun/7minqY9W87uWX49iNPPB4wJyoQFMvp1noOisPqJWxsvKtQOc6fazUGSuhef1K45KxYrcJ3a1hK7DAeMXr9wsq8M+DYgj4PMSGtDXavIDq6sH1k55AeAcPvL/3GnZdeIj0/HJ8PTUUbVyt4wIX0njUx8oCUR+r5rX1bAYAILprawR4Wl+2CqipJaqoc7WZrTQIZTNTVTIlFEqmpsbKzAJKLoEVm83ysMDmoCy/VqrA6nGFDFUyBeyFTf8+HL+ZCwAIC2gFr+orLI2Jx+Mhrn8HdGnjjH9vTcU/mUUY+8UpfDc9HF29XYz+fMRyUB8rQmqpkimwI0V13MW0J62rYL02NoCouxVYZmNXBQKqlgvm2G4BANwdOWSsrKDGytXeDg7VwVR2cZVR5jxxKx8AENW1tVHmq0//zl7YHf8UAr2c8LCoEv/aeAqnbuc363MS60aBFbEqB69mo7hSBr9WDoju2jIHwpqCk0j3VmAF28fKzAIMYxPb8WFXfUFCuURu/luBVl5jxePx4OOm2kLLLml6YKVQMjh9pwAAMKCLV5Pna0gHLyf88u/+CO/QCqVVckz7NlnveYSE6GMWgdWuXbsQFhaGAQMGIDo6GlevXtU7/uTJk4iMjER0dDQiIyORlJRk0JxyuRybNm3CwIEDMWjQIISGhmLGjBnIzc1Vj7l37x58fHwQExOj8eHl5YV3331XPS44OFhrzMaNG43wqpDGOJKm+h6O7t0OAiu7ErC2miNdFFAqa65oqum8bl4BhrHxeDx1dqpcokCllL0a0rwCSlupsQIAb1fVdl2OEQKrW7mlKJPI4SQSoEc7btsvTdXKSYTvZ/bD6N7tIFcyWPzzP/j08E26YpAYzOS/fZOTkzFt2jSkpKQgKCgIW7ZswbBhw5CWlgYXF+197oyMDMTGxmL37t2IiYnB8ePHMWrUKFy6dAkBAQGc5szOzsbcuXNx9uxZhISEQCKRYNSoUXjhhRdw4sQJ9XMNHz4cmzdvVn+uUCjg5+eHCRMmqG/z8fHBsWPHmu31IdzJFUocv5kHABgUbL3ZKkCzhqpCptAq5ja3WqPm4Cy2Q0mVHOUSOcrZrUAz+7pd2asCK+vvY1VTY2XZgZVPddG3MbYCL94vAgD08nNr0T+Q7IUCfDahD9p7OGDD0Tv49PAtZBZWIuFfvWAnMIs8BLEAJv9JSUhIwMiRIxEUFAQAmDJlCuRyORITE3WOX7dunTpLBADR0dEICgrC+vXrOc8pEokwY8YMhISEAADEYjHmzJmDpKQkZGWpmtH5+vpi5cqVGs994MAB+Pv7o0ePHsZ7AYjRnEkvRHGlDO6OQjxhRV3WdbEX8sG+39TeDqwp4jb530zNrvaxNup2C0YomjYmNgtVXCmvd0xNjZXlFq8DgLcRtwL/eVAEAOjj37TeVY3B5/OwZFgwPn6+FwR8Hn5JfYDXtl2AVK5s8bUQy2TywOrIkSMIDw9Xf87n8xEaGorDhw/rHH/48GGN8YCqfUHt8Q3N2aZNG61LKe3tVb8UpFLVLzmhUAhfX1+NMYmJiZgxY4ahX6KWkpISTh8SSfMcaGoNiitk+PNqNvZeylLXqPySqqqJGB3Szur/uuTxeFp1VhK5Qn3mnLUXrwOAo7rlhEIdUJrb111TY6U7Y8UwjPrn1xLPCayNzVgZYyvw0oNiAEAf/5bZBtRlYkR7fDU1FCI7Pv64mo0536dQrysrJJFIOL8nc2XSd5+CggIUFxfDx8dH43YfHx+kp6frfEx6erre8Y2ZEwBOnz6NsLAwdOjQQef9jx8/xqFDhzBx4kSN28vLyzFjxgxERUVh4MCBWLVqlTo4q4+/vz/c3Nwa/Fi1apXeeWyRUslg/ZFbCP/oMGZ/fx6vbbuAwWuP45fzD7D/8iMAwNi+vg3MYh2cagUWAFAhqfmlb+3F60DNsT3lEnnNGYlmthXYUI1VuVQBudJyD2CuzVhbgXKFErdyywAA3dqati3N4G7e+DYuHPZCPo7eyMOMzee0Lhghlm3VqlWc3o/9/f05z2nSP+8qKlRdesVizR4lYrFYfZ+ux+gb35g58/PzsWnTJuzZs6fetW7fvh0jR46Eu7u7xu1BQUF49dVXERYWhpycHMTGxuL8+fPYuXNnvXNlZmZy6mNV92uwdQolgyU7/8GvqapuzJ1aO6FSqkBWcRUW/fwPAFXPG2vfBmSxQUR5dbaG/a/Yjm/1GTug1pWRUjlKqlSBCxvImAt2PeVSBWQKJYR1vi8l1QGXUMCDvdCyv2etXVS/r/LLmna48b2CCkjlSjgIBfCv7o9lSk938ULi9AjM2HwOp+4UYNLXZ/DtS+HN0luLtLxly5Zh4cKFDY4rKSnhHFyZNLBydFT9o6m75SWRSNT36XqMvvGGzimXyzFx4kS8//776NevX71r3bx5s1bNFQD88MMP6v/39vbGihUrMGrUKNy6dQtdunTROZerqys1CDWQUslgwU8XseefLAj4PKwa2wvjwvxQKVPgw31p+OlcJgQ8Ht6M7Wa0oy/MnXOd7uts1sYW6qsAze7zpVWq14A9nNpcuNjXfC/KquRavarYgNDVXmjxP7dsoJFf1rQShps5pQCArt7OZnPGZ79AT2ydFYkZm8/h0oNijP/yNH6cHUld2q2AWCw2ehLDpH8ieXp6ws3NDdnZ2Rq3Z2dnIzBQ91EkgYGBescbMqdSqURcXByio6MxZ86ceteZlpaGnJwcDB48uMGvqVOnTgCAO3fuNDiWcPfR/jTs+ScLQgEPGyb3xfhw/+pL7u3w0dheuLpiGFLfHdrkg1otCZuxYa8ELDfT7uPNpaZJqkKd+akdyJgDoYCvbgHBBlG1lVQXtbuaWaatMbyqM1YV0pqat8a4nq0KrIJ8zKv7eR9/d+x85Un4ujsgPb8ck74+g7xSqoMl2kyeex40aBBSUlLUnzMMg9TUVAwZMkTn+MGDB2uMB4CUlBSN8VznjI+Ph6+vL9555x0AqsJ4XXVYmzdvRlxcHPh8zZfr8uXL2LRpk8ZtDx+qtqkM2Y8l+iWeuodNJ+8CAP4zrjeG9/TRGmMvFFj9MS51sYEF27uqXN0c1DZeBzZjVVolQ6nEfAMUNotWouPKwFJ1xsryv2dOIoF6OzO/tPHbgel5qvqqzm2cjbIuYwps7YztsyLR1s0ed/LK8eKmM03O0BHrY/LAaunSpdi/fz9u3rwJANi6dSsEAgHi4uIAANOnT8fUqVPV4+fPn4+0tDR1v6mkpCSkpaVh7ty5nOdkx6SlpWH8+PFISUlBSkoKduzYgfv372usT6FQYOvWrXjppZe01l5QUIDVq1ejsLAQAFBZWYmEhARERUWhe/fuRnh1yKFrOVjxu6q565JhQRjTxzYK07moexCzzW0FVgeQuSU1b2zmlrECAFcH1Zp0ZqzYwMoMA0JD8Xg89XZgXhOCjfuFqlpYcz3ns72nI7bPioS3qxg3c8ow6aszyC01zjE+xDqY/LdQREQEEhMTMXnyZDg4OIDP5+PgwYPq5qBVVVWQyWp+IQUEBGDv3r1YsmQJRCIRJBIJ9u3bp24OymXOq1evIiEhAQC0WjdMnjxZ4/NDhw6hc+fOOrcmQ0JC8MILL2DEiBFwcHBAaWkpwsLCsHLlSouvlzAHVx4WY972C1AywMRwf7wa08nUSzIrTnWagrIBlrl1H28u7NefVVwJQNXbS2xnfl97TcZKz1agmdWGNZaXsxgPHlc2KYvDBlbtPUxfuF6fDl5O2D4rEpO/PotbuWWY+NWZ6mCLaq6IGQRWADB27FiMHTtW533bt2/Xum3AgAE4c+ZMo+fs0aMH52MKhg8fjuHDh+u8z8PDAx999BGneYhhCsulmPP9eVTKFBjQxQsfPNeTgtU62JYK7FYgW9diK1uibLsF9vJ+FzMNTthslO4aKzZjZR3fs6YWsBdXytSd6M05sAJU24I/zVEFV+l55Zj01Rn8NOdJ9dWRxHaZfCuQkLrkCiXit6biYVElAjwd8fmkvlqXqRMdGSu2+7iN1FixX+ej6sDKXOuU2O1J9srF2mpfFWgNWruornpsbI3V/QJVtsrLWWwRW9oBnk74cXakuqB9yqaz6rMfie0y+Cc3OzsbZ86cQU5ODoqKitCqVSt4e3ujf//+aN26dXOs0eqEh4dDINDcsoiPj0d8fLyJVmRePtp/HafTC+AoEuCrqWFwc7SONx1jq9t5vdyGzgkEtDNz5lqnxGUr0BxrwxqjqRmrjMJyAEB7Dwejram5+Xs4Ytusfhj/v9O4kVOKad+exbZZkVYTLNuiDRs2aJ3OolBw77rP+V/zmTNnsGTJEpw6dQpisRju7u4QCoWQyWR4/Pgx5HI5BgwYgLVr16JPnz6cF2CLzp07R32s6vFr6gN8+7fqCsC143ub3SXX5qRu53WbK16vG1iZ6RtZTfG6noyVmQaFhmLPRiyqp9N8Q7KKVPVyfmbQGNQQAZ5O2PpyJCZ+dRpXHpbg5c0pSJwRAQcbqXe0NroSHSUlJXBz43bEEqf9lS+//BJz587FK6+8ggcPHqCiogJZWVnIyMhAVlYWKisrcffuXUydOhXTp0/Htm3bDP9KiM27/KAYy369DAB4bWBnDO/Z1sQrMm9OtY50qf1fWzjOBtAu0jfXrI/ejJWVbQWyneaL6jkbsSHstm5bd8srAu/cxhmJMyLgYm+H5HuFeHXrecgUdHCzLWowsHrw4AGuX7+Os2fP4sUXX0Tbtrrf7Pz8/DB9+nScPXsWf//9t7oFASFc5JdJMOf7FEjkSgwKboMFQ7uaeklmj90KrJDWbRBqngGGsdU9vsZcsz76i9fZ/lvW8T1zd6g/iOSCPcDZx0KvruvRzg3fvlRztuCiHf9AqeR2oRSxHg0GVn5+fvj000+1mmPWRyQSYcOGDfDw8Gjy4ohtkFUXq2cVV6GjlxP+O6EPBGZylIU5q6/dgq00CG3n7gChoObnxMNMDzHm1iDUPINCQ7lX10M2ditQnbFys8zACgDCO3hg45RQ2PF52PNPFt7bc5XzVejEOtClVsTkVu5Lw9m7hXAW2+HraaFmd5CuuXKuW2Mlta0aKwGfp9FE0lzr8fQ3CDXfjvGNoQ6sKhoXWLGtM3zcLKd4XZeBQW2wdkIf8HjA92cysPbQTVMvibQgowVW0dHRiIiIMNZ0xEb8nJKJzafuAVAVq3duY55vjubIka2xqt4CZLcEHW3kqkAA6OBZU+Tcra15/uyw2ai67RYYhqnpY2UlGSs3B1XWsKRKZvAWmELJILf67D1Lzlixnu3dDh+M6QkAWP/XbWxK0j4ujVgno/1pO3DgQEgkdGYS4e6fzCK89dsVAMD8wV3wTA/tMwBJ/ZxrHWnDMIw6c2UrDUIBaGwZdzDTI1DYovq6dUeVMgXk1cGHtdRYsdlmhlEFkoa0Sskvk0ChZCDg1xyNY+mmRAaguFKGNQdv4MN9aXB1EGJ8GJ0ja+2M9q95+fLlxprK6lEfK6CgTIJXfjgPqVyJId28MX9wF1MvyeKwW35KBqiSKW3uSBsA6NTaGUAOAMDOTJvIstt8pRK5OnAAamqu7Pg8OAit43smsuPDUSRAhVSBokqpQYEVuw3Y2llsVTWWr8Z0QnGlDF+dSMfSXy6hlaMIQ7t7m3pZRI8W62NVV1ZWFm7cuAEej4euXbuiXbt2jZ3K5th6HyuFksHrP13Eo+IqBLZ2wtoJvcG3ol+kLcWx1ptxuVRuc8XrAPBKTCdkl1ThOTM+nLt2G4iyWlkctubKxd7Oqo5rcncQqgKrChkCPLk/rqBctePh5WKeFyE0Fo/Hw7IRwSiqkGJHygPM234BO+Y8iV5+3HoikZbXIn2saissLMS4cePQvn17DB48GIMGDUL79u0xYcIEarFAOPn8r9tIupUPeyEfG18MtZr6kpbG5/PU2amyKjkqZLZVvA6oapPWju+DqK7me+qD2E4Ae6HqV23tAvaacwKt6+ffrfrqzGIDrwwsKFP1vvJwso5twNp4PB5Wju2FAV28UClTYEbiOWRWHzZNrI/BgdX06dNRUlKCXbt24cqVK7h8+TJ+/fVXFBUVYcaMGc2xRmJF/r6dj0+PqK6Q+fC5XmZ7JZelYM/LKyiXgL2i21aOtLEk6pYLtQMrK2u1wHKrrhcztOUCe8aep5N1ZaxYQgEfG17si2AfF+SVSjDlm7PIK6W6ZGtk8J+2aWlpuH79ukZfqx49emDkyJHo3r27URdHrEtOSRXm/3gBDANMCPPHC6F+pl6SxXMWC5BfBuSWqH5B83iwmnoda+LqIERuqUSjl1VplXU1B2WxBeyGZqzYwMrDSgMrQBVEJ86IwL82nkJGQQVe3pKCn2ZHwp7+zVoVgzNWXbp00dks1M7ODh07djTKooj1kSuUmLvtAvLLpAj2ccGKMT1MvSSrwG77sR2rnUTWVa9jLVzttXtZWVurBZZL9ddTpuNsRH0K2IyVs/UGVgDg7WqP72f2g7ujEP9kFmHpL5eogaiVMTiwio+Px9KlS3Hv3j0olUooFArcvXsXixcvxvTp05tjjcQKrPnzBpLvqZqAbpwSSn+hGYk6sKreUqBtQPPkquOoF3VzUCsLrJzVJwLQVmB9Ono54YsX+8KOz8NvF7PwxbE7pl4SMSJOOWg+n6/xVzDDMFizZo3GGIZhwOfzMXHiROOukFi8w9dy8L/jquZ4q18IQUcv8+w3ZIlcdGSsiPlxUddY1WRxaorXret7xl4FWbchakMKyq23eF2X/p28sPzZHnj7tyv4z5830Km1M4b3pF5+1oDTv+jevXvj008/1TuGYRgsWLDAGGuyerbUxyqzsAKLfv4HAPBS/w4Y2Uv3Id6kcZztNQMrW+q6bklcdTQJtdbidTawMngrsEyVdbXmGqu6pkQG4GZOKbaczsCCny7Cr9WT6OlLbRhMrUX6WC1btgzR0dEAVL0ceDweXFy0r+ZatmwZ5ye2ZbbSx0oiVyB+WyqKK2Xo7e+ON0d2M/WSrA677cI2V6SMlXlSbwXWqrEqttJ2C85i7ewcF+xWoJeV11jV9e6o7ribX46kW/mYmXgOe157Gt6uln+kjyVrkT5W48ePV/+/u7u7xuf1jSPko31puPSgGG4OQmyY/AREdubZGduSsVtMmY8rAdQcgkvMi7rdQq2rAh+XqwIra/ueqTNWBtRYSeQKVFQfIu7uaFuBlV11G4YubZyRUyLBq1tTIZUrTb0s0gQGv9OFh4fjwIEDzbEWYkV+/ycLiaczAAD/ndAbfq0cG3gEaQz2TYz9RWwr9SmWRlcLArbPk7UFEs6NqLFiXxcer6Zu0Ja42guxKS4MLvZ2OJ/xGB/tTzP1kkgTGBxYBQUFobS0VOd9s2fPbvKCiOW7k1eGpb9cAqA6J2tQMJ2L1VzqHrhsC1dUWaKawEqqvq2oQvX/rawsY+WqzlhxD6zYTJ6rvdBmj7cK8HTCf8f3AQBsPnUPuy8+NO2CSKMZ/KdBSEgIYmJi8Nxzz8HPz0+jCPvkyZNGXRyxPJVSBeK3pqJcqkC/jh5YOLSrqZdk1eoGVrZU+GtJ2O2+2hmrx+rAyrq+Z2yNVWMyVm5WVm9mqCHdvfHawM74/OhtLP3lMoJ9XOl0CgtkcGD1zjvvwMfHB99++63WfTk5OUZZFLFc7+6+guvZpfByFmP9pCdgJ6C6qubkbF8nY2Vjhb+Wou5WYJVMgSqZavvWzcoyVs6NuCrQWltPNMaCoV3xz4MiJN3Kxys/nMfu156yuitHrZ3B73qRkZG4e/euzo9+/fo1xxqJhdh5/gF+Pv8AfB6wblIftKErW5qdiz1lrCxB3cCqqEL1Xzs+z+pqitR1fwolqmTcLlFnr5a09YwVAAj4PHw28Qn4ujvgbn45lv1ymTqzWxiDA6u9e/fWe9/Ro0ebtBhiuW7nluGd364AAF4f0hX9O3mZeEW2wUWs+UZEgZV5YlsqVMlUwQa7DejuKLS6I4hqt/zgWmdVbKXH+zSWh5MIn09+AnZ8HvZdfoQdKZmmXhIxgMGBlZNT/V2zhw4d2qTF2Irw8HB0795d46NuMzJLUiVT4LVtqaiUKdC/kyfiB3Y29ZJshtZWIF0VaJZcxHZg46eSSpk6Y2WNGRoBn6eu/eNaZ1Vsxa9HYz3RvhUWDwsCACzfcw23c8tMvCLbsWHDBq336PDwcM6PNzgHLZPJkJCQgAMHDiA7O1sjRZmdnW3odDbJ2hqEfrQ/DdezS+HpJMKnE/pAYKNX9ZhC3eL1Vk70xmSO+Hwe3ByEKKqQobhSVuuKQOvMMDqL7VAmkXOus1J3oafASsPsAYE4eSsfJ2/nY972C9gV3x9iOzpdobm1SIPQ2pYuXYpTp04hLi4OIpEI7733HpYtW4bu3btj8uTJhk5HLNxf13Owpbpf1Sfje1NdVQurXWMlEvDpl64Zq11n9bjCOntYsdijlcqlhm0FUsZKE5/Pw9oJveHhJMK1RyX49PAtUy+JcGBwYPX333/j999/x+zZs9G2bVvExcVh1qxZ2L17Nx4/ftyoRezatQthYWEYMGAAoqOjcfXqVb3jT548icjISERHRyMyMhJJSUkGzSmXy7Fp0yYMHDgQgwYNQmhoKGbMmIHc3FyNOWJiYrQ+3nvvPY0xUqkU8+fPR2hoKEJDQzFv3jxIpVLYgvwyCd7YqepXNeOpjogJamPiFdkeca1u9mOf8DXhSkhD2KChqEJWq9WCdQYSbJ1VhYGBlau9dRXyG0MbF3user4XAODL43dw7l6hiVdEGmLwT7GTk5O6d1XtAEIgECArK8vgBSQnJ2PatGlISUlBUFAQtmzZgmHDhiEtLU3neYQZGRmIjY3F7t27ERMTg+PHj2PUqFG4dOkSAgICOM2ZnZ2NuXPn4uzZswgJCYFEIsGoUaPwwgsv4MSJExrPd+zYMb3rX7x4Ma5du4bk5GQAwPDhw7FkyRJ89tlnBr8WloRhGLyx8xLyy6QI9nHBG8ODTL0km8Tj8TA1MgC3ckvxzujupl4O0aN2xqqgTPW709PZOmviHEXVGSsJt6sCrfXcRGMZ1sMH40L98PP5B1jw00X88XqUVhkAMR8GZ6yqqqqwb98+MAyD9u3bY8GCBfj777+xYsUKFBUVGbyAhIQEjBw5EkFBqjfmKVOmQC6XIzExUef4devWITg4GDExMQCA6OhoBAUFYf369ZznFIlEmDFjBkJCQgAAYrEYc+bMQVJSkkHBYUFBAb788kssWrQIAoEAAoEACxYswMaNG1FYaN1/Vfxw9j7+up4LkR0fn07sA3shbUGZygfP9cSPs5+kX7Rmjq2nKiyXIr9MAsB6Dxx2qv5ZrJRybLfAdl6nwKpe747uDr9WDnjwuBIJB66bejlED4MDq9dffx2bN29GZmYm3n77bWzfvh0DBgzAmjVrkJCQYPACjhw5olFtz+fzERoaisOHD+scf/jwYa3q/PDwcI3xDc3Zpk0bravw7O1VtUGGbOOdOHECMplM47nCw8Mhk8m0Ml/W5HZuGVbuuwYA+L/hwQj2sZ5CfEKai7erKjuVU1KFgnI2sLLyjBXHrUB2nLX19DImF3shVv9LlQz4/kwGbQmaMYN/iseNG4dx48apP79z5w6uX7+OwMBAtGrVyqC5CgoKUFxcDB8fH43bfXx8cO7cOZ2PSU9P13h+dnx6enqj5wSA06dPIywsDB06dNC4ff78+bh48SIYhkH//v3x1ltvqbco09PTYWdnBy+vmp5NrVu3hkAgUK9Hl5KSknrvq00sFkMsNq9fvFK5Eq//dAFVMiUGdPHC9P4dTL0kQiyCd/WFHdklVbW2Aq00Y6WuseKWsWK3DB1FFFjp07+zF8aH+WFHygMs/eUSDsyPgsiOTrdoColEAolE0uA4ru/bAIeMlVwuh0JR/z8OJycnhIaGagRVDT2GVVFRAQBawYNYLFbfp+sx+sY3Zs78/Hxs2rQJn3/+ucbtffr0QWxsLI4fP459+/bh8uXLGDJkiPprq6iogEik/YtRJBLV+1wA4O/vDzc3twY/Vq1aVe8cprL20E1ceViCVo5C/Gdcb5s9MJUQQ/m4qQKrnJIq5LOBlZX2HVNfFcixQSg7jrazG/bWyO7wchbhTl45vvv7rqmXY/FWrVrF6f3Y39+f85wNBlaFhYWIjY3l3KPq3r17GDVqFMrLyxsc6+joCABa0aJEIlHfp+sx+sYbOqdcLsfEiRPx/vvvax3J8+mnn+KZZ54BALi4uGD16tVITk7GX3/9pX4uXVuHUqm03vUDQGZmJoqLixv8WLZsWb1zmMLpOwX434k7AIBVz4eo/wInhDTMp/rfS1ZRFQrLrbzGyoCMlULJoLL66BsnMdVqNsTNUYj/Gx4MAFh35BZySqpMvCLLtmzZMk7vx5mZ3LvfN/jnQZs2bfD222/jqaeewuDBgzF48GAEBgbCw8MDdnZ2kMlkKCwsxO3bt3Ho0CGcOnUK27dv59QA09PTE25ublpBW3Z2NgIDA3U+JjAwUO94Q+ZUKpWIi4tDdHQ05syZ0+B6O3XqBEC1/Tl06FAEBgZCLpcjPz9fvR2Yl5cHhUJR7/oBwNXV1eIahBZXyLBox0UwDDAhzB/De/o0/CBCiBr7h8jDokr1bdZ6BJEhGavaLRmcKGPFyb/6+mFb8n1cuF+Ejw9cx38n9DH1kixWc5TccNqcffrpp3Hu3Dl4enrinXfeQb9+/dC1a1cEBgYiKCgIkZGR+PDDD+Hn54dz586hb9++nBcwaNAgpKSkqD9nGAapqakYMmSIzvGDBw/WGA8AKSkpGuO5zhkfHw9fX1+88847AFSF8WxtVG5uLlauXKkx/uHDhwCgTglGRUVBKBRqPFdKSgqEQiGioqK4vQAWgGEYvL37CrKKq9DB0xHv0mX9hBisjavmL+9WjkLYCayzPsaQjBVbXyXg8zT6spH68fk8rHi2B3g8YNeFh1TIbmY4/xR7eHhg1apVuHnzJvLy8nD58mWcPHkSV65cQUFBAa5du4YPPvjA4EzM0qVLsX//fty8eRMAsHXrVggEAsTFxQEApk+fjqlTp6rHz58/H2lpaeqr7pKSkpCWloa5c+dynpMdk5aWhvHjxyMlJQUpKSnYsWMH7t+/D0BVP7V27Vrcu3cPAKBQKPDBBx+gS5cuGDx4MABVduyVV17B2rVroVAooFQq8emnn+KVV16Bh4eHQa+DOfvt4kP8/k8WBHwe/juhD/1VSUgjiO0EGhmq1i7WWV8FGHZVIDvGUSSwugOpm1OInzsmhqv+yH9v91UolEwDjyAtpVHvkJ6envD09DTKAiIiIpCYmIjJkyfDwcEBfD4fBw8eVF95V1VVBZlMph4fEBCAvXv3YsmSJRCJRJBIJNi3b5+6OSiXOa9evapuDVG3dQN7LI+Pjw8WLVqESZMmwd7eHmVlZejUqRMOHTqkbs0AAGvWrMGSJUsQEREBAOjfvz/WrFljlNfGHGQWVuDd31Rd6+cP7oIn2ht25SchpEaPdq5IupUPAAjrYD1/fNXF/vFVwaFBKBWuN96SYcHYd+kRrj0qwW8XHuJfoX6mXhIBwGNqn6JMmhV7iGNxcbFF1FgplAwmfnUa5+49RmhAK/w0O9Jqty4IaQnbzt7Hm7suAwC+nhaGod29Tbyi5nHsRi5e+u4cerRzxb55A/SOPXUnH5O/PovObZxxeGF0C63Qevzv+B2sOnAdfq0c8NeiGGq/0EwMef+m7wCp18Zjt3Hu3mM4i+3w6YQ+FFQR0kTDe/pAKODBxd4O/TsZJ+tvjtQZKw41VmxWy0lEVwQ2xrQnO6C1ixgPHlfix3P3Tb0cgkZuBZKmCQ8PV5+3yIqPj0d8fLyJVqTtn8wi9UnqK57tAX+P+ttHEEK48XAS4cD8KPB51n0FXM1ZgdxrrKz59WhODiIB5g3qjHd2X8WGo7cxMbw9Za2aaMOGDVqns3Dpzcmin2QTOHfunFlvBZZL5Hj9p4uQKxnEhrTF8319Tb0kQqxG5zbOpl5Cs2vMVYEUWDXe+HB/rP/rNnJKJPjt4kOMD+PezJJo05XoYLcCuTBqWFv76j1iuT7cdw1388vR1s0eHz3Xi67UIYQYpPZVgQ2V8bJZLdoKbDyxnQAzn+4IQFVzpaQrBE3K4D8RioqK8Nlnn+HixYsoKSnR+Edz8eJFY66NmMDBq9nYnpwJHg/4ZHxvuDnSafOEEMPYVwdJDANI5ErYC+sPmsoktBVoDJP7tcfnR2/jTl45Dqfl4Jke1MTZVAz+SZ40aRIKCgowYMAAuLi4aGQz2J5PxDLlllRh6S+XAACzBwSifyevBh5BCCHaHGoFUhKZ/sCqgmqsjMLFXoipkQH44tgdfHn8DoZ296bdBhMx+Cf50aNHSE1NBZ+vvYtY+yBmYlmUSgaLd17C4woZurd1xcJnupp6SYQQCyUU8GHH50FefQ6gG+rPfJeprwqkwKqppj/VEZtO3kXq/SJczCyivoMmYnCNVdeuXeuNgkNCQpq8IGIaiafv4cTNPIjt+Fg3qQ/EdlTvQAhpPDZLVSXTX8DO3u9INVZN1tpFjFG92gIAdp5/YOLV2C6D/0R45plnMHbsWEyYMAE+Pj4abQMWLlyI1NRUoy6QNL8b2aVYdeA6AOCt2G7o3MbFxCsihFg6e6EAZRI5KhsIrCqrrxy0p8DKKP4V6odfL6iOIXtnVHe927CkeRgcWM2ePRsAsGfPHq37aD/X8kjkCsz/8QKkciUGBrXG1MiAhh9ECCENsBeqNkQaylixgZc99V4yiicDPdHOzR5ZxVU4kpaL2JC2pl6SzTH4Jzk6OhpKpVLnR1RUVHOs0eqEh4eje/fuGh91m5G1lE/+vInr2aXwdBJh9Qu9KTgmhBgFW8DeUMaKDbwcKGNlFHw+D2Orew/uPJ9p4tVYpg0bNmi9R9c9V1gfgzNWmzZtqve+H3/80dDpbJK5NAg9d68QXyelAwA+/lcIWruITbwiQoi1MLTGyoG2rIzm+b5+2HD0Dk7cykduaRXauNibekkWpakNQg0OrDp16gSlUoldu3bhypUr4PF46NWrF8aMGQNvb+s8UNQaVUjlWPzzP2AYYFyon9UeBksIMQ0HdWCl1DtOvRVIgZXRdGrtjCfau+PC/SLsvpCFWVGBpl6STTE4sMrIyEBsbCzS0tLg4eEBACgsLET37t2xb98+tG/f3uiLJMb38YHryCioQDs3e7wzurupl0MIsTLi6hqrygaOtWEDLwqsjOv5J3xx4X4RDlx5RIFVCzO4xmrevHkYM2YM8vPzkZeXp/4YPXo0XnvtteZYIzGyv2/nY8vpDABAwgshcLWn7uqEEONSZ6zk3IrXaSvQuIZU70JcyCxCfpnExKuxLQYHVg8fPsTKlSs1moF6eHjgo48+QlZWllEXR4yvtEqGN3aququ/2K89BnRpbeIVEUKsEVuM3mDGim23IKSrAo2prZsDerRzBcMAx27kmXo5NsXgn2SpVKrzUE2FQgGpVGqURZHm8+HeNDwsqoS/hwPeHNnN1MshhFgp++omwxK5/horNqNFVwUa3+BuqqzVkbQcE6/EthgcWD355JMYPXo0jh49iszMTGRmZuKvv/7CmDFj0L9//+ZYIzGSo9dz8VOK6oDl/7zQm87mIoQ0Gy4ZK5lCCZlC9Yc6bQUa35BubQAAJ27mQdLAliwxHoPfWdeuXYtZs2Zh8ODB6p5HPB4PEydOxCeffGL0BVqj8PBwjY71gO7LO42puEKGpb+qtgCn9++IfoGezfZchBAi5tAgtPZ9VLxufD3buaGNixi5pRKcu/sYT3fxMvWSLMKGDRu0eksqFNwDU4MDKycnJ2zbtg0rV67E1atXwTAMevbsiY4dOxo6lc0yRR+r5b9fRU6JBIFeTnhjeFCLPjchxPZwaRBauxWDmDqvGx2fz8PTXbzwa+pD/H0nnwIrjprax6rRP8kdO3bEqFGjMHr0aHVQ9cUXXzR2OtKM/riSjV0XHoLPA/4zvjf9ZUgIaXb2HPpYVclqCtfp1Ifm8XRnVTD19+18E6/EdnDKWD169Aj29vZo1aoVtmzZUu+4zz//HK+++qrRFkearqBMgrd2XQYAzInuhL7tWzXwCEIIaToHDp3XqdVC83uqOrC6/LAYJVUyaq/TAjgFVk888QSCg4Nx7NgxzJkzBz4+PjrH5eTQlQfmhGEYvLP7CgrKpQjydsHrQ7qYekmEEBvBtk/QvxVIgVVz83a1h18rBzx4XInLD4rVgRZpPpwCq9TUVNjbq84aioyMxNGjR3WOGzhwoPFWRprs90uPsP9yNuz4PHwyvjfEdvTLixDSMricFVgppeNsWkIff3c8eFyJi5lFFFi1AE41Vu3atVMfX/Prr79q3c8wDNLS0rBr1y7jro40Wm5JFd757QoA4LVBndHTl1vRHSGEGIM9l+J1OR1n0xL6+LsDAC5mFpl0HbbC4OL1GTNmaN1WVVWFxYsX4/XXXzfGmkgTMQyDZb9eRnGlDD19XRE/sLOpl0QIsTFcDmFmM1bUHLR51Q6sdDX4JsZlcLuF4uJirdscHBywb98+REdHG2VR1q65+1jtPP8AR67nQiTg45NxfSAU0GXMhJCWJapunyDV05iy9lWBpPn0aOcGAZ+HvFIJHhVXoZ27g6mXZNZapI/V7t27sXv3bgDA9evXdWatHj9+jKKiIs5PbMuas49VVlEl3v/9GgDg9aFdEOTj0izPQwgh+rB9qfQdaUPF6y3DQSRAsI8LrmaV4GJmEQVWDWixPlYMw9T7wePxEBQUhB07dhi2emJUDMPg/365hFKJHE+0d8fsAYGmXhIhxEbVZKz0bAVWB1ZiCqyaXW+qs2oxnDJWY8aMwZgxYwAA//d//4eEhIRmXRRpnK1n7yPpVj7Ednz8Z1xv2NEWICHERMQcDmGmPlYtp7efG7adBa5maZfzEOMy+J2XDaoUCgXu3r2Le/fuQanUf3p5Q3bt2oWwsDAMGDAA0dHRuHr1qt7xJ0+eRGRkJKKjoxEZGYmkpCSD5pTL5di0aRMGDhyIQYMGITQ0FDNmzEBubq56TEVFBdauXYuoqCgMHDgQffv2xcKFC1FWVqbxPMHBwYiJidH42LhxY5Nej8a4X1CBj/anAQDeGB6MTq2dW3wNhBDCEnPIWLGF7RRYNb9gH1X5SdqjUipgb2YGF69LJBK888472LhxIyoqKgAAjo6OePXVV/H+++9DLBYbNF9ycjKmTZuGlJQUBAUFYcuWLRg2bBjS0tLg4qJdH5SRkYHY2Fjs3r0bMTExOH78OEaNGoVLly4hICCA05zZ2dmYO3cuzp49i5CQEEgkEowaNQovvPACTpw4AUDVuyshIQHnz5+Hn58fiouL8fTTTyM/P1+j+7yPjw+OHTtm6MtoVEolg8U7/0GFVIGIjh6Y3r+DSddDCCE1NVZUvG4Ounq7gM8DCsulyCuToI2LvamXZLUM/mmeM2cO/vjjD3z00Uf4/fffsWfPHqxcuRJ//PEH/v3vfxu8gISEBIwcORJBQaqDgadMmQK5XI7ExESd49etW6fOEgFAdHQ0goKCsH79es5zikQizJgxAyEhIQAAsViMOXPmICkpCVlZWQAAFxcXzJs3D35+fgAANzc3xMXF4eeffzbo6oCW8PulLCTfLYSjSID/vNAbfD6duUUIMS22xkrJAHKF7qyVut0CZayanYNIgA5eTgCAG9mlJl6NdTM4Y3X8+HFcvXoVjo6OGrfXDlQMceTIEbz99tvqz/l8PkJDQ3H48GG89tprWuMPHz6MAQMGaNwWHh6Ow4cPc56zTZs2WpdSsp3lpVIpAKB3797o3bu31hiFQgGlUqnVLsEQJSUlnMaJxWJOGcBRIe2QVyqBi70d2ns6NjieEEKaW+2THiRypc6aT3XGivpYtYhgHxek55Xj+qNSDOjS2tTLMQsSiQQSiaTBcVzft4FGBFadO3fWCqoAwNnZGV27dlV/LpFIGgwKCgoKUFxcrHX2oI+PD86dO6fzMenp6Rg3bpzW+PT09EbPCQCnT59GWFgYOnTooHfMmDFjIBTWHGJZXl6OGTNm4Pbt2xAIBHjmmWewaNEiiESieufx9/ev977a3nvvPSxfvrzBcQI+Dy/TFYCEEDPCZqwAVWDlpOPtgC1et6fjtlpE5+ra2/T8sgZG2o5Vq1ZhxYoVRp3T4MBq5MiRWLt2LV577TV18CCVSvHll19i/Pjx6nEjRozAX3/9pXcutkarbgAmFovV9+l6jL7xjZkzPz8fmzZtwp49e+pd6/Xr13Hw4EGkpKRo3B4UFIRXX30VYWFhyMnJQWxsLM6fP4+dO3fWO1dmZianPlaG1qsRQoi5EPB5sOPzIFcy9Rawq/tYUcaqRQSygVVeuYlXYj6WLVuGhQsXNjiupKSEc1LE4MBq3bp1ePDgAZYtWwZvb28wDIPc3FzY2dnB29sbH374IQAgOzu7wbnYzFfdNJxEItGZFWMfo2+8oXPK5XJMnDgR77//Pvr166fzOUtLSzFp0iRs2bJFK6P1ww8/qP/f29sbK1aswKhRo3Dr1i106dJF53yurq7N1iCUEELMhciOD7lUUW8BO10V2LI6VtdYpedTYMXiWnJjCIMDK3t7e2zatEnvGIZhOPW68vT0hJubm1YQlp2djcBA3VtbgYGBescbMqdSqURcXByio6MxZ84cnc9XVVWF5557Dq+//jpGjhzZ4NfUqVMnAMCdO3fqDawIIcQWiO34qJAq6s1YVdJVgS0qsLUqsMorlaC0SgYXe2EDjyCNYXBg9eqrryIuLq7BcVwLvQYNGqSxvcYwDFJTU/HWW2/pHD948GCcOnVK47aUlBQMGTLE4Dnj4+Ph6+uLd955B4CqMD4wMFAdgMnlcowfPx7PP/+8+mv++eefMWTIELRq1QqXL1/G2bNn8fLLL6vnfPjwIQDudVSEEGKtVAXssnqbhLJXBdpTxqpFuNgL0dpFjLxSCe7mlyPEz93US7JKBv+Z8O6772pdlafLvHnzOM23dOlS7N+/Hzdv3gQAbN26FQKBQB3ITJ8+HVOnTlWPnz9/PtLS0tT9ppKSkpCWloa5c+dynpMdk5aWhvHjxyMlJQUpKSnYsWMH7t+/D6Amm+Xk5IR+/fqpx2zZskV9EHVBQQFWr16NwsJCAEBlZSUSEhIQFRWF7t27c/r6CSHEWokaOC+wSk6BVUsL8FCVxGQWVpp4JdbL4IyVv7+/OqgxhoiICCQmJmLy5MlwcHAAn8/HwYMH1c1Bq6qqIJPJ1OMDAgKwd+9eLFmyBCKRCBKJBPv27VM3B+Uy59WrV9VbleHh4RrrmTx5MgDgwIED2LZtGwDgxx9/1BjD9swKCQnBCy+8gBEjRsDBwQGlpaUICwvDypUrweNRLylCiG1rqEloFfWxanG+rRyQkvEYD4t0X8xFms7gwCooKAgKhQJ2dtoPfffdd/H+++8bvIixY8di7NixOu/bvn271m0DBgzAmTNnGj1njx49GmzpHxsb2+AYDw8PfPTRR3rHEEKIrWroIOZKuiqwxbVzdwAAPHxMGavmYnBgNXz4cDz77LOYPHkyfH19NRpl7t27t1GBFSGEEOsjbmgrsPqqQOpj1XJ82cCqiAKr5mJwYDVr1iwAwB9//KF1H21/cRMeHq7VuT0+Ph7x8fEmWhEhhBifvowVwzA1VwWK6KrAluLbig2sqky8EvO1YcMGrdNZDDnKzuDAKjo6GkePHtV538CBAw2dziadO3eO+lgRQqwee6yNroxV7duoxqrlqDNWj6nGqj66Eh0lJSVwc3Pj9HiD/0zQ18OqbpE3IYQQ2yXSU7zOdl0H6KrAlsQGViVVcpRWyRoYTRrD4IxVp06doFQqsWvXLly5cgU8Hg+9evXCmDFj4O3t3RxrJIQQYoHEerYC2W1AOz4PQh0HNJPm4SS2g7ujEEUVMjwsqkSwDzUJNTaDA6uMjAzExsYiLS0NHh4eAIDCwkJ0794d+/btQ/v27Y2+SEIIIZZHXx8rdeE6ZataXDs3BxRVyJBVVIlgHypLMTaD/0yYN28exowZg/z8fOTl5ak/Ro8ejddee6051kgIIcQCsTVWOjNW1HXdZNQF7NRyoVkYnLF6+PAhdu/erXEb288pLCzMaAsjhBBi2fQ1CKVzAk2HrbN60IwtFxiGwcnb+Th4NRvZxVVQKBl0b+eKAV1ao19HD6vuImBwYCWVSsEwjNaLolAoIJVKjbYwQgghlk1fjZWEjrMxGd9mbhJaJVNg0Y5/sO/yI43bj97Iw4ajd9DRywn/NzwIw3u2bZbnNzWDA6snn3wSo0ePxqJFi9C5c2cAwK1bt7B27Vr079/f6Au0RtTHihBiC/TVWLHBlogK11scuxWY1QwZK4WSwWvbUnE4LRd2fB4mhPujRzs3KJRKXMgswsEr2bibX45XfkjFM929sWJMD7R1czD6OpqixftYrV27FrNmzcLgwYPVWSsej4eJEydi7dq1hk5nk6iPFSHEFujLWKkDKzsKrFoae6xNVjM0CV3/1y0cTsuF2I6PzdMj8GQnT/V9U58EysfIsfHYHXx5/A7+vJaDU3cK8MFzPTD2CT+jr6WxmtrHyuDAysnJCdu2bcPKlStx9epVMAyDnj17omPHjoZORQghxIrpy1ixt4kpsGpxrV3EAICCconO0p7GyiyswBfH7gAAEv4VohFUsZzEdlg8LAije7fD0l8v4cL9Iiz46R/cyS3HwqFdwedbfu2VwYEVq2PHjupg6vHjx0ZbECGEEOug76pAyliZjqeTCAAgUzAorpTB3VFklHk/O3ILUrkST3X2xJg+7fSODfJxwc5X+uOTP2/gi2N38PnR27hXUI7/jOtt8XV3Bv9Eb926FYMGDcLZs2cBAJMnT4anpyd8fHyQnJxs9AUSQgixTPquCpQqKGNlKvZCAVztVXmV/DKJUeZ8WFSJ3y48BAAsfiaIUxZMwOfhjeHBWP1CCOz4POy99AiTvj5jtDWZisE/0f/73//w1ltvISIiAvv27cOvv/6KvXv34quvvsIbb7zRHGskhBBigfRuBVa3W2CzWqRleVVvB+aWGieI+e3CQ8iVDPp19MAT7VsZ9NjxYf7YMjMCbg5CXLhfhPH/O43cEss9JNrgwMrOzk5duL5lyxZMmDABI0eOxLPPPmvVfSkIIYQYRt8hzGzGirYCTaO1syqwyi8zTpukvZdUrRXGPuHbqMf37+SFX1/tj3Zu9kjPK8fEr89YbHBl8E90WVkZlEolcnJysHfvXrz00kvq++RyuTHXRgghxIJRuwXzxWas8o2QscosrEDaoxII+DwM6+HT6Hk6tXbGj7OftPjgyuDi9cGDByM4OBjl5eXo1q0bBg4ciMzMTGzcuJHzpYi2jvpYEUJsAZd2C2LqvG4SNRmrpgdWSbfyAQB927ujlVPTCuHbezrix9lPYuJXp5GeV45JX5/BjjlPwrN6vS2hxftYrVq1Ck888QSysrIwZcoUAEB2djbEYjHee+89Q6ezSdTHihBiC0R6itcllLEyKbblQp4RMlYnb+cBAJ7u3LrJcwE1wdWEr07jTl45XvruHLbN6gcXe6FR5m9Ii/exAoDx48cDUEVwCoUC4eHhCA8Pb8xUhBBCrJT+I22oxsqU2JYLheVNq7FiGAZn0gsBAE930e5b1VjtPR3x/cx+GP+/07j8sBgvJ6YgcUaERbRiaNRP9BdffIHg4GDY29vD3t4ewcHB2Lhxo7HXRgghxILprbFSt1sw/zdKa8T2rnpc0bTA6sHjShSWSyEU8NDT17jlQJ3bOGPLjAg4i+1w9m4hXtuWCplC+2fJ3BicsVq8eDG2bduGSZMmoVOnTmAYBnfu3MGHH36Iu3fvYvXq1c2xTkIIIRZGX4NQiYwyVqbUylG1rVZUIWvSPJceFAMAgn1cmyVI7unrhm/iwjDt22QcTsvFGzsv4ZNxvc26Q7vBgdXOnTtx4cIFeHt7a9z+xhtvIDIykgIrQgghALg1CKXAyjTYIvOmZqwuPSgCAIT4Nd/Fa/0CPbFxSl/M3nIeuy48hJuDEMuf7dFsz9dUBv9Ed+3aVSuoAgAfHx8EBgYaZVGEEEIsX+0aK4ZhNO6TyhUaY0jLcq/OWBVXyqBUMg2Mrt+VLFXGqpeRtwHrGhTsjU/G9wYAbD51D9/9fbdZn68pDP6JHjp0KH755Ret23ft2oX+/fsbZVGEEEIsH5uNUjKAvM6bNxWvm5a7gypjpWSAkqrGbwfezCkDAHT1cTHKuvQZ08cXy0YEAwDe33sNh67lNPtzNganrcBBgwap/59hGKSkpGDhwoUICAgAwzDIzMzEo0ePKLAihBCiVrvmRiJXQlirtYK6jxUFViYhsuPDWWyHMokcjysadxBzUYVU3a6hSxtnYy9Rp9lRgbhXUIHtyfcxb/sF7JjzJHo14zZkY3AKrO7evavRYT0mJkZrDMMw2LJli7HWZdWoQSghxBbUzkZJ5UqgVo9HCqxMz91RWB1YSdERTgY//lauKlvVzs2+xXpM8Xg8fDCmBx4WVeLEzTzMTDyH3+KfQjt3B6M9R4s0CJ00aRKn5p8lJSWcn9iWUYNQQogtEPB5sOPzIFcyWgXstBVoeu6OQjx4XImiRhaw38wpBQB08W7+bcDa7AR8bJj8BMZ9eRrXs0sxY/M5/PzKk0YL7praIJTTT/RHH33EabKLFy9yGkcIIcQ2iOppElpzViD1sTKVVmwvq/LG1Vil55UDUPWbamku9kJ881I4WruIcT27FK9tuwC5mfS4avKfCnl5efj888/Rv39/HD9+vFFz7Nq1C2FhYRgwYACio6Nx9epVveNPnjyJyMhIREdHIzIyEklJSQbNKZfLsWnTJgwcOBCDBg1CaGgoZsyYgdzcXI05GIbB+++/j759+yIiIgJTpkxBcXGxxhipVIr58+cjNDQUoaGhmDdvHqRS45wWTgghlk5cT5NQdYNQOivQZJraJDSjoAIAEODpaLQ1GcLX3QHfxoXDQSjA8Zt5eG/PVa2rT02hUT/RZWVl+P777zFixAj4+vpi3rx5YBgGfn5+Bs+VnJyMadOmYevWrUhKSsLMmTMxbNgwlJaW6hyfkZGB2NhYfPzxxzh+/DgSEhIwatQoZGRkcJ4zOzsbc+fOxWeffYa//voLp06dQmZmJl544QWN5/rvf/+LHTt24OTJk0hOToZIJMK0adM0xixevBhXr15FcnIykpOTkZaWhiVLlhj8OhBCiDWqr0molM4KNLlWtVouNEZmoSqwau9hmsAKAHr5ueGziX3A4wFbz97HNydN34aB80+0TCbD7t27MWHCBHh7e+Oll17CvXv34O/vj9u3b+P06dOYM2eOwQtISEjAyJEjERQUBACYMmUK5HI5EhMTdY5ft24dgoOD1QX00dHRCAoKwvr16znPKRKJMGPGDISEhAAAxGIx5syZg6SkJGRlZQFQFap9/PHHiI+Ph6Oj6odm8eLF2LNnD65cuQIAKCgowJdffolFixZBIBBAIBBgwYIF2LhxIwoLCw1+LQghxNrUdxAz+zllrEzHtbomqaQRgRXDMLhvBoEVADzTwwdvx3YHAKzcn4Y/rmSbdD2cfqJnz54NHx8fPP/88zh9+jReffVVpKSkIC0tDe3bt1c3Bn3zzTcNXsCRI0c0DnDm8/kIDQ3F4cOHdY4/fPiw1oHP4eHhGuMbmrNNmzZaFf/29vYAoN7Gu3TpEvLy8jTm6datG5ycnNTznDhxAjKZTGNMeHg4ZDIZTpw4wf1FIIQQK1XfVqCEMlYm5+qgun6tpEpu8GPzyiSolCnA4wF+rUwbWAHAjKc6YNqTAWAYYMnOfxqdhTMGTlcF/vnnnygpKcGbb76J5cuXa7QK4PEaf15PQUEBiouL4ePjo3G7j48Pzp07p/Mx6enpGDdunNb49PT0Rs8JAKdPn0ZYWBg6dOigfh72cSwejwdvb2/1fenp6bCzs4OXl5d6TOvWrSEQCNRjdOF69aRYLIZYLG54ICGEmKn6DmKW0lWBJteUjNX96vqqdm4OZvE95PF4eHdUdxRXyjAhzB9uDtyuEJRIJJBIJA2OM6TrAadX4969ezh27BgKCwvxxBNP4PXXX8fZs2c5P0l9KipU35i6wYNYLFbfp+sx+sY3Zs78/Hxs2rQJn3/+uUFrq6iogEik3VRNJBLV+1wA4O/vDzc3twY/Vq1aVe8chBBiCcQ6rgpkGEYdaDXHwb2EGzb4aEx252FRJQDAt5Xx+kc1lZ2Aj88mPoH+nb0aHlxt1apVnN6P/f39ua+D68CnnnoKTz31FBQKBf788098/vnnmD17NkpLS3H79m107twZq1atwrJlyzg/OVu7VDdalEgk6vt0PUbfeEPnlMvlmDhxIt5//33069fPoLU5OjrqvAJQKpXWu34AyMzM5NTHirJVhBBLpytjJVMwWveTludaHVg15kib7OIqAKrmoJZs2bJlWLhwYYPjSkpKOAdXnAMrlkAgwIgRIzBixAhUVlZi9+7dWLRoEQoKCnD//n2DAitPT0+4ubkhO1uz0Cw7O7veA50DAwP1jjdkTqVSibi4OERHR2sV3rNjs7Oz1Vc7MgyDnJwc9X2BgYGQy+XIz89Xbwfm5eVBoVDoPZDa1dWVGoQSQmwCm5GSyGqK16W1+g1R53XTqdkKNLzGKrtEFVh5W3hg1RwlN036iXZwcMDEiROxe/du7NmzBx4eHgbPMWjQIKSkpKg/ZxgGqampGDJkiM7xgwcP1hgPACkpKRrjuc4ZHx8PX19fvPPOOwBUhfFsbVRISAhat26tMc/169dRXl6unicqKgpCoVBjTEpKCoRCIaKiogx6HQghxBrpyljVDrKoeN10mrIVyGas2rpadmDVHIz2E+3h4YEdO3YY/LilS5di//79uHnzJgBg69atEAgEiIuLAwBMnz4dU6dOVY+fP38+0tLS1FfdJSUlIS0tDXPnzuU8JzsmLS0N48ePR0pKClJSUrBjxw7cv38fgCozt3TpUmzYsEFdL/XJJ59g9OjR6NmzJwBVduyVV17B2rVroVAooFQq8emnn+KVV15pVJBJCCHWRleNVe0rAvn8xl8ARZqGvSqwUqbQ6jPWEDZj5WPhGavmYPBWoD5du3Y1+DERERFITEzE5MmT4eDgAD6fj4MHD8LFRXX2UFVVFWSymmg6ICAAe/fuxZIlSyASiSCRSLBv3z4EBARwnvPq1atISEgAAK3WDZMnT1b//4IFC1BWVoannnoKQqEQXbp00Tpoes2aNViyZAkiIiIAAP3798eaNWsMfh0IIcQaqY+0qbX9R1cEmgdncU0IUFolg6cz9y2xnOqMlTdlrLTwGHPo/24j2EMci4uLqcaKEGITlv16CduTM7FwaFfMG9wFAHAjuxTDPj0BTycRzr8z1MQrtG093zuIMokcfy2KRmBrbmf+KZQMur59AAolgzPLBttE1sqQ92/6c4EQQkizURev1+q8Thkr8+GmvjKQewF7QZkECiUDPg/wctZuOWTrjLoVSLgJDw/XaLIKqArp4+PjTbQiQghpHiKdNVbVx9lQYGVyLvaqMMCQAvb8MlWbIQ8nEeys8OKDDRs2aJ3OolAo6hmtrVGB1dmzZ/HFF1+gqqoKP/30E7788kt069YN0dHRjZnO5pw7d462AgkhNoG96k9X8To1BzU9ts6qXMI9Y/W4QhVYtXK0zmyVrkQHuxXIhcGh5m+//YbBgwejsLAQaWlpAIDg4GAsW7YMP/74o6HTEUIIsWK6zgqkrUDz4dSIwKqgvCZjRbQZ/FP9n//8BxcvXsTvv/8OT09PAEBMTAwOHTqEL774wugLJIQQYrloK9C8NSZjVVimOpHEk+qrdDL4p1ogEKBz584ANA9gdnJyglJpWB8MQggh1k3dIFSho48VBVYm5yRWbceWS7nXEBVWqOqxKGOlm8E/1aWlpXj06JHW7ZcvX0ZpaalRFkUIIcQ61Bxpo6vGigIrU2O3AssMyViVqzJWHlZaY9VUBhevz5s3D71798bEiRORmZmJFStW4MaNG9izZw+++uqr5lgjIYQQC6WvQSgVr5uek0gVBlQYFFhRjZU+BgdWL730Ery9vfHxxx+jsLAQ69evR8+ePbFr1y4MHUqN3gghhNSoqbGq2WqirUDzUZOxMmArkA2sDOjUbksa1W5hxIgRGDFihLHXQgghxMrouiqQitfNhzNbY9WYjBVtBerUqMBKJpPhl19+QVpaGng8Hrp164bnn38eQqHQ2OuzStQglBBiK3RdFUjtFsyHut2C1JDASlW83srJOt/zW7xBaGpqKsaMGYOsrCx4enqCYRgUFhbC19cXu3fvxhNPPGHolDaHGoQSQmyFmBqEmjVHkWHtFhiGQXElNQjVx+A/F2bNmoVZs2ahsLAQubm5yMvLQ2FhIWbOnImXX37Z0OkIIYRYMbGw/gah7H3EdGr6WHHLyFRIFZApGACAu6N1ZqyaqlE/1e+++65G5Obm5ob33nvPaIsihBBiHUTVZQ+6GoSKrPCcOUvD9rHi2m6BPVNQKODBQUgZR10M/qkOCAiAVCrVur2qqgo+Pj5GWRQhhBDroKvdAtvTijJWpsdmrCo41lgVVTcHdXMQaTQJJzU41VidOHFC/f+xsbEYPnw4pk2bhvbt24NhGNy/fx/ffvstxo0b12wLJYQQYnnUVwXKaraa2CCLMlam52jgViCbsXJzaNS1bzaB0ysTExOjdduxY8e0bjt16hTmzZvX1DURQgixEvozVrSVZGrO1cXrUoUSUrmywSs12cJ1dystXDcGTn8uREdHQ6lUNvgRFRXV3OslhBBiQdg3apmCgVKpKnpmgywxZaxMjq2xArhtB9ZkrKhwvT6cMlb/+9//OE3GdZytoz5WhBBbUbsJqFShhD1fUNMglGqsTM5OwIfYjg+JXIkyibzBTBRbY+VuxYFVi/Sx6tq1q8bnpaWl+Oqrr3DlyhXweDz06tULL7/8stY4ohv1sSKE2IraW0sSuRL2QkHNViA1CDULTmI7SORSTnVWbMbK1YoDq6b2sTK4+uzSpUsYOnQolEolOnToAAD4/fffkZCQgEOHDqFXr16GTkkIIcRK1S5QZ1suqIvXKbAyC05iAQrLubVcKKoOrKiHVf0MDqwWLFiADz/8EDNnzgSfr/pHoVQqsWnTJsyfPx9//fWX0RdJCCHEMvF4PIjs+JDKleotwJqMFRWvmwMnEfeWC1Rj1TCD/1woKyvDrFmz1EEVAPD5fMyePRvl5eVGXRwhhBDLV/dYG8pYmZea7usNB1YlFFg1yOCf6oqKClRWVuq8vaKiwiiLIoQQYj3qtlxge1pRjZV5YHtZlXGosSqtUgVfLvYUWNXH4K3A2NhYPP3003jttdfQuXNnAMCtW7fwxRdfYPTo0UZfICGEEMtW0yS0OrCiQ5jNinN1ywUuW4FsHRab5SLaDH5lVq5cCT6fj/j4eEgkEjAMA3t7eyxYsADvv/9+c6yREEKIBaubsWK3BGkr0DywNVZcitfL1BkrCqzqY/ArIxAI8NFHH+Hdd9/F7du3wTAMunTpAnt7++ZYn1WiPlaEEFvCZqbYgKomY0WBlTlwMqDGqrRKVWNlzYFVi/Sxqq1Vq1bo2bMnkpKS0LNnT0MfTkB9rAghtoXNTEnkCjAMQ8XrZobtvt5QHyuFkkG5VDXGmrcCm9rHyuCfan9/f41DmQkhhBB91FuBcqU6WwVQxspccM1YldeqwXK24oxVUxn8Ux0UFFRvSuzdd99t8oIIIYRYF3XxulZgRcXr5kDdbqGB4nW2vkok4NP3Tg+DA6vhw4fj2WefxQ8//ICjR4/ixIkT6o+9e/c2ahG7du1CWFgYBgwYgOjoaFy9elXv+JMnTyIyMhLR0dGIjIxEUlJSo+YsKSnBzJkzwePxtO67d+8efHx8EBMTo/Hh5eWlEUAGBwdrjdm4cWMjXgVCCLFOolqBlbRWYCUUaP/uJS2vpnhd/1ag+opAylbpZfCrM2vWLADAH3/8oXWfrgClIcnJyZg2bRpSUlIQFBSELVu2YNiwYUhLS4OLi4vW+IyMDMTGxmL37t2IiYnB8ePHMWrUKFy6dAkBAQGc57xw4QJefvlldOrUqd61DR8+HJs3b1Z/rlAo4OfnhwkTJqhv8/HxwbFjxwz+ugkhxFaIBLW3Amt6WDXmPYMYH1tjVdHAViDbw8qa66uMweCMVXR0NJRKpc6PqKgogxeQkJCAkSNHIigoCAAwZcoUyOVyJCYm6hy/bt06dZaIXU9QUBDWr19v0JwSiQT79u3DyJEjdT6Pr68vVq5cqXHbgQMH4O/vjx49ehj8dRJCiK0SC2uuCqRWC+bHScyt3QJ7RSAFVvoZ/JO9adOmeu/78ccfDV7AkSNHEB4eXrMgPh+hoaE4fPiwzvGHDx/WGA+o2hfUHs9lzsjISPj4+NS7LqFQCF9fX43bEhMTMWPGDG5fGCGEEAA1GavaNVZUo2M+nLjWWNFWICecX50dO3bgl19+gUgkwvTp0zFo0CCtMd7e3gY9eUFBAYqLi7UCHB8fH5w7d07nY9LT0zFu3Dit8enp6Y2ek4vHjx/j0KFD+PrrrzVuLy8vx4wZM3D79m0IBAI888wzWLRoEUQiUb1zlZSUcHpOsVgMsVjc6DUTQog5qH1VoJR6WJkd9SHMDdVYsc1BrShjJZFIIJFIGhzH9X0b4BhYffXVV4iPj0fPnj0hk8mwfft2HDhwAEOHDuX8RLqwZwvWDR7EYnG95w5WVFToHd+YObnYvn07Ro4cCXd3d43bg4KC8OqrryIsLAw5OTmIjY3F+fPnsXPnznrn8vf35/Sc7733HpYvX97oNRNCiDkQqzuvK6g5qBlyFKmyh5UybsXr1tQcdNWqVVixYoVR5+T06mzYsAHHjx9H//79AaiyV//973+bHFg5OjoCgFa0KJFI1Pfpeoy+8Y2Zk4vNmzdr1VwBwA8//KD+f29vb6xYsQKjRo3CrVu30KVLF51zZWZmcmoQStkqQog1qH1WIFu8TjVW5sNeWBNYMQxT70UF6uJ1Kwqsli1bhoULFzY4rqSkhHNShNOr4+joqA6qAGD8+PH473//y+kJ9PH09ISbmxuys7M1bs/OzkZgYKDOxwQGBuod35g5G5KWloacnBwMHjy4wbHsVYZ37typN7BydXWlzuuEEJtR+6xA2go0P2zGimGAKpkSDiLd9W9sA1F269AaNEfJDaefbAcHB063xcbGGryAQYMGISUlRf05wzBITU3FkCFDdI4fPHiwxngASElJ0Rhv6JwN2bx5M+Li4sDna75cly9f1irmf/jwIQDu232EEGLtxDo6r1PxuvlwENZ8Lyr0FLCzx9k4WlFg1Rw4vTqPHj3C999/D4Zh1LdlZ2dr3Xb37l2DF7B06VIMGTIEN2/eRNeuXbF161YIBALExcUBAKZPnw65XI7vv/8eADB//nx88803OHHiBKKiopCUlIS0tDTs2LGD85yGUCgU2Lp1q85jfAoKCrB69Wo8//zz8PDwQGVlJRISEhAVFYXu3bsb/FyEEGKNdDUIpa1A88Hn8yC240MiV+qts6qsDrrYvldEN06B1Y0bN3QGJXVva0yzt4iICCQmJmLy5MlwcHAAn8/HwYMH1Y08q6qqIJPJ1OMDAgKwd+9eLFmyBCKRSN2Pim0OymVOALh//z6mTZum3jKMiYlBr169NPphAcChQ4fQuXNnnduIISEheOGFFzBixAg4ODigtLQUYWFhWLlyJTW+I4SQavU1CCXmw1EkUAVW0voDK8pYccPp1YmOjsbRo0cbHDdw4MBGLWLs2LEYO3aszvu2b9+udduAAQNw5syZRs8JAO3bt+fUMX348OEYPny4zvs8PDzw0UcfNTgHIYTYMrZBqEYfKyEFVubEUWSHxxUyVOgJrNhtQsd6arCICqef7NWrV3OajOs4QgghtkOdsapVvM7eRsyDA4eWCxXqjBUFVvpwyljV7XTe1HG2Ljw8HAKB5g9mfHw84uPjTbQiQghpPuoaK5mCitfNFFvArm8rkG0g6mRFDUJ12bBhAzZs2KBxm0Khv8dXbdb96pipc+fOUbsFQojNENdqtyCh4nWzxGas9G0Fskfe1NeOwVroSnSUlJTAzc2N0+PpJ5sQQkizEtlR8bq5c1QHVvW3W2CzWdbUx6o50E82IYSQZlW73YJERsXr5ojdCqzSU2NVTsXrnNBPNiGEkGbF1lNJ5UpIFWzxOr05m5OGtgIVSgZV1UExBVb6UWBFCCGkWWl0XqeMlVlybCCwqn21oLUXrzcV/WQTQghpVjVbgQpUUY2VWWKbftbXbqGi+pxAHo++dw2hV4cQQkizqp2xogJo82TfQLuF8lrfNzpZRD/6yTYB6mNFCLElolrtFsqrMx+OdN6cWWloK9CWuq5THysLRH2sCCG2hO2yLlMwKJPYzhu0JXFUd17X3W7BlrquUx8rQgghZq12Q8nCcikAOsjX3DS0FVhBBzBzRoEVIYSQZuUgFEDAV9XlZJdUAbCNzIclaXArsDrT6ERbuA2iwIoQQkiz4vF4cLVXZToYRnUbZT7Mi2MDhzCzxesO9H1rEAVWhBBCmp2rg1Djc8p8mBcHoSpgqrePVXXxuhNlGhtEgRUhhJBm52KvmelwFFLmw5ywdXANtVugTGPDKLAihBDS7FztNTNWDpT5MCsNbQVW0NWcnFFgRQghpNnVzlgJBTx1bytiHthDmNl+VXWprwqkLdwGUU7PBKhBKCHE1tTOWNF2kvlhM4hVMiWUSgZ8vmZ39XIb6phPDUItEDUIJYTYmtrF61QAbX5qb/FVyRVawa8tdV6nBqGEEELMXu2tQKqvMj/2djXfE11XBlKDUO4osCKEENLsam8FOonpzdnc8Pk82AtVIYGuKwPZjBW1yWgYBVaEEEKanUbGSkhvzuaIzUbpujKwXFLdIJS+dw2iwIoQQkizq11jZQt1Opao5spA7cCKzWJRtrFhFFgRQghpdq1dxOr/t6esh1mqOS9Qu+VCuQ0VrzcVBVaEEEKa3RP+7lg6IhjermIM7+lj6uUQHfR1X6fide7oFTIB6mNFCLE1PB4Pr0R3wivRnUy9FFIPditQV42VLbVboD5WFoj6WBFCCDE3NVuBmkGEQsmgSqYEYBs1VtTHihBCCCFNVt9WYO0Mli1krJqKAitCCCGEwEGoykbVzVixBzDzeYCYznhskFm8Qrt27UJYWBgGDBiA6OhoXL16Ve/4kydPIjIyEtHR0YiMjERSUlKj5iwpKcHMmTPB4/G07gOAmJgYrY/33ntPY4xUKsX8+fMRGhqK0NBQzJs3D1Kp1ICvnhBCCDE9NhtVt8aq9jmB9b1fkhom3yxNTk7GtGnTkJKSgqCgIGzZsgXDhg1DWloaXFxctMZnZGQgNjYWu3fvRkxMDI4fP45Ro0bh0qVLCAgI4DznhQsX8PLLL6NTJ/2FlMeOHdN7/+LFi3Ht2jUkJycDAIYPH44lS5bgs88+a8SrQQghhJiGOrCq026BLVyno4i4MXnGKiEhASNHjkRQUBAAYMqUKZDL5UhMTNQ5ft26dQgODkZMTAwAIDo6GkFBQVi/fr1Bc0okEuzbtw8jR45s9NoLCgrw5ZdfYtGiRRAIBBAIBFiwYAE2btyIwsLCRs9LCCGEtDT7ehqEVlBzUIOYPLA6cuQIwsPD1Z/z+XyEhobi8OHDOscfPnxYYzygal9QezyXOSMjI+Hj07ReKidOnIBMJtN4rvDwcMhkMpw4caLex5WUlHD6kEgkTVofIYQQwlW9W4ES6221IJFIOL8nc2XSwKqgoADFxcVaAY6Pjw/S09N1PiY9PV3v+MbMqc/8+fMRHR2NqKgoLF26FKWlpRprsbOzg5eXl/q21q1bQyAQ6H0uf39/uLm5NfixatUqg9dLCCGENIZjfVcFqpuDWl9gtWrVKk7vx/7+/pznNGler6KiAgAgFos1bheLxer7dD1G3/jGzFmfPn36YOTIkfjss89QWlqKiRMnYsiQITh16hQEAgEqKiogEom0HicSifQ+V2ZmJqc+VnW/BkIIIaS51LcVWG7FXdeXLVuGhQsXNjiupKSEc3Bl0lfJ0dERALS2vCQSifo+XY/RN74xc9bn008/Vf+/i4sLVq9ejZ49e+Kvv/7C0KFD4ejoqPMKQKlUqve5XF1dqUEoIYQQs8IGTnUzVmzxupPY+jJWYrHY6EkMk24Fenp6ws3NDdnZ2Rq3Z2dnIzAwUOdjAgMD9Y5vzJxcsVcQ3rlzR70WuVyO/Px89Zi8vDwoFIomPxchhBDSkuqrsWIzWGyfK6KfyYvXBw0ahJSUFPXnDMMgNTUVQ4YM0Tl+8ODBGuMBICUlRWO8oXPqkpubi5UrV2rc9vDhQwBQpwOjoqIgFAo1nislJQVCoRBRUVGcn4sQQggxNQf1kTZ12i1IrDdj1RxMHlgtXboU+/fvx82bNwEAW7duhUAgQFxcHABg+vTpmDp1qnr8/PnzkZaWpr7qLikpCWlpaZg7dy7nObmoqKjA2rVrce/ePQCqAxg/+OADdOnSBYMHDwagyo698sorWLt2LRQKBZRKJT799FO88sor8PDwaPyLQgghhLQw9SHM9dRYUR8rbkye14uIiEBiYiImT54MBwcH8Pl8HDx4UN3Is6qqCjKZTD0+ICAAe/fuxZIlSyASidT9qNjmoFzmBID79+9j2rRp6i3DmJgY9OrVS90Py8fHB4sWLcKkSZNgb2+PsrIydOrUCYcOHYK9vb16njVr1mDJkiWIiIgAAPTv3x9r1qxpvheMEEIIaQbqQ5jr2Qp0ssLi9ebAYxiGMfUibAV7OnZxcTEVrxNCCDErDx5X4OmEoxDb8XHjwxHq2+f/eAG7L2bh7dhueHmAbdYPG/L+TeGnCYSHh0Mg0EypxsfHIz4+3kQrIoQQYuvYrUCJXAmFkoGArzoXsMKK2y3osmHDBmzYsEHjNoVCUc9obbbxKpmZc+fOUcaKEEKIWakdOFXKFHCuPsLGmtst6KIr0cFmrLgwefE6IYQQQkzPXlgTEtQuYC+X2FbGqqkosCKEEEIIeDyezisDrflIm+ZAgRUhhBBCANS+MrCml1W51HoPYW4OFFgRQgghBEDtJqE1GSt1uwUxbQVyQYEVIYQQQgDUZKWqNAIrVcaK3SYk+lFgRQghhBAANcETm6VSKBlUyZQAKGPFFQVWhBBCCAFQayuwuvt67XMDqcaKGwo/TYAahBJCCDFHbEuFyuqAir0ikM8DxHa2kYuhBqEWiBqEEkIIMUd12y2U1zonkMfjmWxdLYkahBJCCCHEKOpuBZZLqlst2EjXdWOgwIoQQgghAGrqqNiMVaWMuq4bigIrQgghhACoyViptwIl1BzUUBRYEUIIIQRArXYL6qsCa2qsCDcUWBFCCCEEgPZWIBtYOVDGijMKrAghhBACAHCozkyx/avY/zpR8TpnlNszAepjRQghxByp2y1Ud1tnM1a2VLxOfawsEPWxIoQQYo5qtgKrM1Y2WLxOfawIIYQQYhTqPlZ1GoTaUsaqqSiwIoQQQggAwFGou3jdyYYyVk1FgRUhhBBCANTqY1XnEGa6KpA7CqwIIYQQAqCmlkq9FSipzliJaSuQKwqsCCGEEAKgpt1CzVag7RWvNxUFVoQQQggBUNNuQapQQq5QoqRKBgBwdRCaclkWhXJ7JkB9rAghhJij2pmpSpkCRRWqwMrdhgIr6mNlgaiPFSGEEHMktuODxwMYRrUdWMwGVo4iE6+s5VAfK0IIIYQYBY/Hg0t1oXphhRSl1Q1CbSlj1VQUWBFCCCFEzcNJlZ26l1+hvo1qrLgzi8Bq165dCAsLw4ABAxAdHY2rV6/qHX/y5ElERkYiOjoakZGRSEpKatScJSUlmDlzJng8ntZ9FRUVWLt2LaKiojBw4ED07dsXCxcuRFlZmca44OBgxMTEaHxs3LjRwFeAEEIIMQ+tqgOr9HzV+52rvR0EfO33SaKbyWuskpOTMW3aNKSkpCAoKAhbtmzBsGHDkJaWBhcXF63xGRkZiI2Nxe7duxETE4Pjx49j1KhRuHTpEgICAjjPeeHCBbz88svo1KmTznWlpqYiISEB58+fh5+fH4qLi/H0008jPz8fW7ZsUY/z8fHBsWPHjP/CEEIIISbgUV1PlZ5XDsC26quMweQZq4SEBIwcORJBQUEAgClTpkAulyMxMVHn+HXr1qmzRAAQHR2NoKAgrF+/3qA5JRIJ9u3bh5EjR+p8HhcXF8ybNw9+fn4AADc3N8TFxeHnn3826OoAQgghxJKwW4F389nAirYBDWHywOrIkSMIDw9Xf87n8xEaGorDhw/rHH/48GGN8YCqfUHt8VzmjIyMhI+PT73r6t27N9566y2N2+zt7aFQKKBUKrl9cYQQQoiFqRtYuVF9lUFMuhVYUFCA4uJirQDHx8cH586d0/mY9PR0jBs3Tmt8enp6o+fk6vTp0xgzZgyEwpofsvLycsyYMQO3b9+GQCDAM888g0WLFkEkqj91WlJSwun5xGIxxGJxk9ZMCCGEGIKtsSoslwKw7q1AiUQCiUTS4Diu79uAiQOrigrVFQd1gwexWKy+T9dj9I1vzJxcXL9+HQcPHkRKSorG7UFBQXj11VcRFhaGnJwcxMbG4vz589i5c2e9c/n7+3N6zvfeew/Lly9v9JoJIYQQQ3nUCaSsudXCqlWrsGLFCqPOadLAytHREQC0okWJRKK+T9dj9I1vzJwNKS0txaRJk7BlyxZ06NBB474ffvhB/f/e3t5YsWIFRo0ahVu3bqFLly4658vMzOTUIJSyVYQQQloam7FieThZb8Zq2bJlWLhwYYPjSkpKOCdFTBpYeXp6ws3NDdnZ2Rq3Z2dnIzAwUOdjAgMD9Y5vzJz6VFVV4bnnnsPrr79eb6F7bexVhnfu3Kk3sHJ1daXO64QQQsxS3UDK36NxSQlL0BwlNyYvXh80aJDG9hrDMEhNTcWQIUN0jh88eLDWdlxKSorGeEPnrI9cLsf48ePx/PPPIy4uDgDw888/4/HjxwCAy5cvY9OmTRqPefjwIQDu232EEEKIOWnjohlotLfiwKo5mDywWrp0Kfbv34+bN28CALZu3QqBQKAOZKZPn46pU6eqx8+fPx9paWk4ceIEACApKQlpaWmYO3cu5zm5UCqViIuLg5OTE/r164eUlBSkpKRgy5YtKC4uBqAqlF+9ejUKCwsBAJWVlUhISEBUVBS6d+/ehFeFEEIIMQ2/Vg5wqnUYs7+HgwlXY3lM3iA0IiICiYmJmDx5MhwcHMDn83Hw4EF1I8+qqirIZDL1+ICAAOzduxdLliyBSCRS96Nim4NymRMA7t+/j2nTpqm3DGNiYtCrVy91P6wDBw5g27ZtAIAff/xRY83smJCQELzwwgsYMWIEHBwcUFpairD/b+/eo6oq0z+Afw8XBULRjA5KCVmEBCbncFPQzlFsHE1LRy2vaDFjzbLUsYs4ToKmk1M6mA2aDt5zGp3BBluxVi3L1ipxOYg6pIaXxGBQBON+O8rx+f3hnP1zywE5cOSAfD9rnYX73c+79/s+bfFtv3u/Jzwcq1atsrqaOxERUUen0Whwv2c31JTWAQC0Pdwc3KLORSMi4uhGdBWWb8euqKjgM1ZERNRhjfvwW5wsvLnEwMXVzzi4NY5ny7/fDp8KJCIioo5lRtTNWaDgfrwJYCuHTwV2RREREXB2dlaVzZs3D/PmzXNQi4iIiP7fC+EPo7dHN+j9ejm6Ke0uJSUFKSkpqjJbvsqOU4HtiFOBREREnQ+nAomIiIgcgAMrIiIiIjvhwIqIiIjITjiw6iJMJhOSkpJa9C3e1Bjz13bMYdswf23HHLYdc3hnfHi9HTny4XU+ON82zF/bMYdtw/y1HXPYdl01h3x4nYiIiMgBuI6VA3AdKyIioo6pretYcWDlAFlZWV3qFioREVFnYe1Gh2UqsCU4FdiJ3D6C7mzn7+z17cHRfXB0/bZydPsdXd8eHN0HR9dvK0e339H17cHRfbjrORBqNxUVFQJAKioqWlU/KCjIYedu6/k7e3175K+tbejs9XkNOj5/bW1DZ6/Pa7Dt9btqDm3pN+9YEREREdkJB1ZEREREdsKBFREREZGd8K3AdiT/W4u1srKyVfXNZnOr61rqtbZ+W8/f2evbI39tbUNnr89r0PH5a2sbOnt9XoNtr99Vc2iJlxasqc6V19vRf//7Xzz88MOObgYRERG1QkFBAR566KFmYziwakc3btzApUuX0KNHD2g0Gkc3h4iIiFpARFBVVYV+/frByan5p6g4sCIiIiKyEz68TkRERGQnHFgRERER2QkHVkRERER2woHVPcRkMiExMREGgwGjRo2CTqfDxIkTceHCBVVcRUUFZs2ahcjISOj1eixfvrzRK6SFhYUYN24coqOjodfr8dFHH7VnVxymtLQUSUlJGDZsGIxGI0JDQ7Fy5Uo0NDSo4pjD5p07dw7R0dEwGo1W9zN/tvv0008RHh6O4cOHw2Aw4NSpU45uUodx7do1LFmyBC4uLrh48WKj/Zs2bYJer0dMTAyeeeYZFBYWqvaLCFasWAG9Xo/IyEjMnDkTFRUV7dR6x9u7dy9+8YtfIDY2FhEREZg0aVKjfzeYQxvY/IU51GFdvnxZ+vbtK0VFRSIiYjabZcqUKRIeHq6KGz9+vMyZM0dERGpqaiQ4OFj+/Oc/K/vNZrOEhoZKUlKSiIgUFxeLVquVtLS0duqJ4+zatUueeOIJKS8vFxGRwsJC0Wq18vbbb6vimMOm7dy5U4YMGSIxMTFiMBisxjB/tjly5Ih4enpKbm6uiIjs2LFDfH19pbKy0sEtc7y8vDwZMmSIxMXFCQDJy8tT7U9LSxOtVitXrlwREZHly5dLaGiomM1mJWbt2rUSHBwsNTU1IiLy4osvyrPPPttufXA0V1dX+eKLL0Tk5t+92bNnS0BAgNTV1YkIc2grDqzuISaTSY4dO6YqW79+vfTs2VPZzsnJEQBy+vRppSwlJUUefPBB5S/J/v37xcXFRfVlk2+++aaEhYXd5R44XkZGhmzZskVVNm/ePAkICFC2mcPmff7552IymWT27NlWB1bMn+1+9atfyfPPP69sm81m0Wq18uGHHzqwVR3D999/L+fOnZODBw9aHVjp9Xp56623lO3y8nJxcXGRzz77TEREGhoaxNvbWzZs2KDEnDp1SgDI999/3y59cLTJkyertrOysgSAHDp0SESYQ1txKvAe0q1bN+h0OmW7sLAQO3bswIIFC5SyAwcOwNPTE0FBQUpZREQEiouLkZOTo8QEBgaiZ8+eqpjs7GyUlZW1Q08cZ8yYMXjppZdUZW5ubrh27ZqyzRw2b+zYsejWrVuT+5k/23311VeIiIhQtp2cnBAWFoYDBw44sFUdQ0hICB577DGr+8rKynDs2DFV7ry8vPD4448rucvJyUFJSYkqJigoCPfdd1+Xye8//vEP1babmxuAm1OszKHtOLC6BxUWFiIsLAyPPvooRo8ejRUrVij7Lly4AK1Wq4r38fFR9ll+Wspuj8nLy7ubTe+QDh8+jClTpijbzGHbMH+2+fnnn1FRUWE1H7c/B0Nqlvw0lztrMRqNBlqttsvm9/Dhw+jXrx9iYmKYw1bgdwXeg3x9fZGdnY1Lly7hueeeQ3FxMf76178CAGpra9G9e3dVvGW7trZW+Wn5P5amYrqKr7/+Gvn5+cjIyFDKmMO2Yf5sY+mvtZx1tVzYqiW5Y37VTCYT3n//faxfvx6urq7MYSvwjlUnkJSUBI1G0+zn6NGjjer169cP7777LlJTU5U3iDw8PGAymVRxlm0PD48Wx3Q2rclhYWEhXnnlFaSnp8PLy0sp74o5bO01aE1XzF9bWPprLR9dLRe2aknumF+1l19+GZMnT8akSZMAMIetwTtWncAbb7yBV155pdmYBx54AGazGQDg7OyslAcGBgIATp8+jeDgYAwYMABXrlxR1S0qKgIADBgwQPn51VdfWY155JFH2tATx2lpDi1KS0vx7LPPYsOGDdDr9aq4rphDW/PXnK6Yv7bo06cPvLy8lP5bFBUVKfki6yz5sZa7p59+ulGM5ct1RQRXrlzpcvlNSEiAi4sLVq1apZQxh7bjHatOwNPTEz4+Ps1+XFxcsGvXLiQnJ6vqXr58GcDNu1cAEBsbi+rqauTm5ioxR48exYMPPognn3xSiTl79iyqqqpUMWFhYejdu/fd7u5d0dIcAkBVVRXGjx+PZcuWYdSoUQCAzZs3K8fqijm0JX930hXz11YjR45U3REUERw7dky5Psm63r17Q6fTqXJXWVmJs2fPKrl78skn4e3trYrJzc1FTU1Nl8rvn/70J1y8eBGbN2+GRqNBdnY2srOzmcPWcPBbiWRH27Ztk6CgICkpKRERkbq6Ohk3bpyEhISIyWRS4saPHy8vvfSSiIjU1tbKoEGDZO3atcp+yxpCK1asEBGRkpIS8fHx6RJrCNXV1YnRaJRFixZJVlaW8tHr9ao45vDOmlpuQYT5s9WRI0ekR48ecubMGRG5ud4a17FSa2q5hbS0NPHx8ZHi4mIREXnnnXesrsEUEhKirMEUHx8v48ePb7e2O9rGjRslODhYMjMzld95iYmJsm3bNhFhDm3FgdU9JD8/X1599VXR6XQyfPhw0el0MnPmTMnPz1fFlZWVyYwZMyQiIkJZhPHGjRuqmIKCAhk7dqwMHTpUdDqdan2Se9lf/vIXAWD1cyvmsGnp6eliMBhEq9WKl5eXGAwGSU1NVcUwf7bbt2+fhIWFybBhw+Spp56SkydPOrpJHYLJZBKDwSCDBw8WABIVFdVoXaaNGzeKTqeToUOHytixY6WgoEC1/8aNG8qilxERETJ9+nQpKytrx144TmVlpTg5OVn9nWcZWIkwh7bQiNz2PRJERERE1Cp8xoqIiIjITjiwIiIiIrITDqyIiIiI7IQDKyIiIiI74cCKiIiIyE44sCIiIiKyEw6siIiIiOyEAysioi6ioKDA0U1Q6WjtIbIHDqyISOXf//43jEYjNBoNBg4cCKPRiCFDhmDQoEHYtGlTm49/5swZ5fjffPNN2xv8P/v378eQIUOg0WgQGhoKg8GAyMhIxMbGYvfu3TYf78SJE1i3bp3d2nc7vV6Pffv23bXj327fvn144403AAC7du1ScjV69Ogm60yfPh0ajQZGoxFff/11o2sjMTFRiV2xYgUGDhyoxB8+fPiObVqzZg02btzY9s4RdSSOXvqdiDom3PaVFt999504OzvLJ598YrfjHzx40C7HssjLy2t03OPHj0tQUJBMnTq10dfmNGfbtm3i5+dn1/bd6oUXXrB7/5ty9uxZefTRR6W6ulopy8vLE1dXVwEgOTk5jerk5+eLh4dHo69zEml8bVhs27bNanxTGhoaJCwsTA4dOtTiOkQdHe9YEVGLxMTEICQkBGlpaY5uik1CQ0ORkZGBf/3rXx3q7sjf//53GI3GdjnX22+/jTlz5uC+++5TlUdFRcHf3x9r1qxpVOeDDz7ApEmT7mq7nJ2dsXDhQrz11lt39TxE7YkDKyJqsevXr6Nbt244f/68MiWUmpqKyZMnY9CgQejVqxcAoKGhAQkJCQgJCUFERARGjBiB//znP42Od/78eUycOBF6vR46nQ7Z2dmq/du3b4dOp8Pw4cMRHR2NTz/9tFXt9vf3x4QJE5CSkqKUVVVVIT4+HjqdDgaDARMmTEB+fj4A4G9/+xtWr16NoqIiGI1GGI1G5OXlwWg0ws3NDdu3bwcA7NmzBwMHDoS/v79y3F//+tfw8fFBXFwcFi9eDIPBgMDAQHzxxRdKTFxcHHx8fDBnzhwAwHfffadMze3ZswcTJ05EUFAQpk2bBpPJpNRraGjAa6+9Bj8/P4wYMQJLliyBwWCAv78//vCHP1jte319PT7//HPExsY22ufs7IwFCxbgk08+waVLl5TyyspKZGVlYeTIkTbn+lbffPMN/P39lRwOGzYMGo0G8+fPV2JGjhyJzMxM1fmJOjVH3zIjoo4Jt0337NmzRzQajXz55ZeqmNGjR0t9fb2YzWaJjo4WEZElS5ZIaGioVFVViYjIpk2bxNvbW8rLy1V1DQaD1NbWiohIUlKS+Pr6Sn19vYiIZGRkSJ8+faSgoEBEbk5neXh4SGZmZpNttjYVaLF27VoBoEyHPf/88zJt2jQxm80iIrJy5Up54oknpKGhQUSangr08/NT5cVa3OzZs6V3797yww8/iIjIBx98IP37928UM3v27EZt/+1vfysiIrW1teLr6ytbt25VYv74xz+Kv7+/XL16VURE9u7dK87OzpKYmNhkTrKysgSAFBUVqcrz8vLEYDBIVVWV9OrVSxYvXqzsW7NmjezcubPJqb3br41bc3Fr/MGDB1VtS0pKkvvvv18uX76squfm5iZpaWlN9oGoM+EdKyJq0urVq5WH17du3YqMjAw8/fTTqphp06ahe/fucHJywqFDh1BXV4fk5GTMmzcPnp6eAID4+HjcuHEDmzdvVtWdNWsW3N3dAQALFizApUuXlKnGd999F1OnTsVDDz0EAAgICMCIESOwYcOGVvWlZ8+eAICKigpcuHABe/fuxaJFi+DkdPPX4Msvv4zTp0/b7YF6vV6PgQMHAgCMRiPy8/NRVlZ2x3rTp08HALi7uyMyMhInTpxQ9q1fvx5xcXHo06cPAGDKlCno27dvs8e7cuUKAOD++++3ut/T0xNz587Fpk2bUF1djYaGBvzzn//E1KlTmz2u5dq49bN69WpVTFRUlHJ3Kjs7GytXrkRKSgp8fHxUcb169VLaSdTZuTi6AUTUcSUkJCjTVU2xDHwszp8/j/r6egQEBChlzs7O8Pf3x8mTJ1Wxfn5+yp979eqFPn364IcffgAAnDx5EoWFharnkK5evQo3N7dW9aWiokI5z4EDBwDcHMy5urqq2lNSUtKq49/u1gFPjx49ANycYuvdu7dN9SorK5X2FxUVYcCAAar4/v37N3s8S79dXJr+dT9//nwkJycjNTUVWq0Wzz33nCov1li7NrZv344XX3xR2XZ3d4e7uztMJhPi4uIwYcIEqwM2V1dXlJeXN3s+os6CAysiahNnZ2fVtog0GavRaJqNFRElRqPRYObMmVi+fLld2pmVlYWQkBB4eHgoZR9//DEeeeQRm45zex/MZrPVuFvzYqnTXG6aqnd7ndvPfyeW594sz8dZ4+vrixdeeAHr1q1D//79kZ6ebtM57mTp0qW4evVqky8PXL9+/Y4DTqLOglOBRGRXAQEBcHNzw7lz55Qys9mMixcvIiQkRBVreVgcAMrLy1FaWqpMn4WEhODMmTOq+IMHD7bqzb6LFy8iPT0dr776qnJsjUbT6PjLli1Dbm4uAChThABw7do15SHyHj16oLq6WtlXWFhoc3taw8vLC3379sWPP/6oKr81h9ZYpt1KS0ubjXv99dfx008/YfDgwXYd5Hz77bdITk7GRx99hAceeAAAVNObAFBWVgatVmu3cxI5EgdWRGRX7u7u+N3vfocNGzagpqYGALBlyxY4OTnhN7/5jSp269atqKurA3Dz9f5+/fopr/gvXboU+/fvV94mrKmpwe9//3tl4NVSJ06cwNixYzFp0iTMnTsXADBgwABMnToV7733Hurr6wEAmZmZSEtLw2OPPQYA8Pb2RkVFBUQE69atQ2pqKoCbyzccOnQIAFBdXY3PPvvM5hy11vz587Fz5078/PPPAIC0tLQ7DphCQkLQs2dPnD9/vtm40NBQ7N69GwkJCXZrb3V1NebMmYPp06dj4sSJSvnChQuVPxcUFOD69euIjo6223mJHMqRT84TUcdz5MgRMRgMAkACAwPll7/8ZaOYy5cvKzGDBw+WpUuXqvZfv35dFi9eLMHBwRIeHi4Gg0GOHz8uIiK5ublK3eTkZBkzZozodDoJDQ2VrKws1XF27dolgwYNkqFDh0pMTIx8/PHHTbY7PT1doqKilDYZDAaJiIiQkSNHyu7duxvFV1VVydy5cyUwMFCMRqOMGzdOzp07p+yvr6+XUaNGSUREhBgMBikuLhYRkR9//FGioqIkIiJCpk2bJqtXr5bu3bsrb9gtWLBAtFqtaLVaefPNNyU3N1dpV1RUlOTk5MisWbOUmPj4eDl+/Lgq5tSpU5KQkKDELFq0SMnra6+9Jg8//LDExsbKqlWr5KmnnpKVK1c2+990xowZqrfzLLny8vISg8EgJpOpUZ3ly5dLYGCg8vZmZmZmo2tj2bJlzca///77AkCCg4MlKipK+RgMBqXe1q1bVdtEnZ1GpAWT/kRE5HBVVVXo1q0bunfvrpQ9/vjjSExMxIwZM5qs99NPPyE2NhZHjx5VnrnqCK5du4bIyEhs2bIFYWFhjm4OkV1wKpCIqJPYsWOHakmDL7/8EqWlpRgzZkyz9fz8/JCSkoL4+Pi73USbLFy4EK+//joHVXRP4R0rIqJOIisrCwkJCTCZTNBoNHB1dcV7772H8PDwFtUvKSmBt7f3XW5ly129elV5oJ3oXsGBFREREZGdcCqQiIiIyE44sCIiIiKyEw6siIiIiOyEAysiIiIiO+HAioiIiMhOOLAiIiIishMOrIiIiIjshAMrIiIiIjv5Pwz9B2wAsK2CAAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1)\n", "\n", "ax.plot(dets, SAS_probe)\n", "ax.set_xlabel('Probe Detuning (MHz)')\n", "ax.set_ylabel('Probe Absorption (arb.)')\n", "ax.set_title('Rb$^{87}$ $F_g=2\\\\rightarrow F_e=(1,2,3)$')" ] }, { "cell_type": "code", "execution_count": 35, "id": "5c9623ba-685f-4aa6-9f56-489398a3efde", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", " Rydiqule\n", " ================\n", " \n", "Rydiqule Version: 2.1.0.dev11+gf4e837b.d20250222\n", "Installation Path: ~\\src\\Rydiqule\\src\\rydiqule\n", "\n", " Dependencies\n", " ================\n", " \n", "NumPy Version: 1.26.4\n", "SciPy Version: 1.12.0\n", "Matplotlib Version: 3.8.0\n", "ARC Version: 3.6.0\n", "Python Version: 3.11.8\n", "Python Install Path: ~\\Miniconda3\\envs\\rq\n", "Platform Info: Windows (AMD64)\n", "CPU Count and Freq: 12 @ 3.60 GHz\n", "Total System Memory: 128 GB\n" ] } ], "source": [ "rq.about()" ] }, { "cell_type": "code", "execution_count": null, "id": "adefba85-0c99-48fa-ae29-861c119480a2", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "RQ", "language": "python", "name": "rq" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.8" } }, "nbformat": 4, "nbformat_minor": 5 }