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May 21, 2015 13:51
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QED Analysis
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{"nbformat_minor": 0, "cells": [{"source": "Data Conversion\n\n siemens_to_ismrmrd -f meas_MID00218_FID33531_OLD_SNR_COR_RL_R1.dat -z 1 -o meas_MID00218_FID33531_OLD_SNR_COR_RL_R1_noise.h5\n siemens_to_ismrmrd -f meas_MID00218_FID33531_OLD_SNR_COR_RL_R1.dat -z 2 -o meas_MID00218_FID33531_OLD_SNR_COR_RL_R1_data.h5\n siemens_to_ismrmrd -f meas_MID00229_FID33542_NEW_SNR_COR_RL_R1.dat -z 1 -o meas_MID00229_FID33542_NEW_SNR_COR_RL_R1_noise.h5\n siemens_to_ismrmrd -f meas_MID00229_FID33542_NEW_SNR_COR_RL_R1.dat -z 2 -o meas_MID00229_FID33542_NEW_SNR_COR_RL_R1_data.h5\n siemens_to_ismrmrd -f meas_MID00241_FID33554_SIE_SNR_COR_RL_R1.dat -z 1 -o meas_MID00241_FID33554_SIE_SNR_COR_RL_R1_noise.h5\n siemens_to_ismrmrd -f meas_MID00241_FID33554_SIE_SNR_COR_RL_R1.dat -z 2 -o meas_MID00241_FID33554_SIE_SNR_COR_RL_R1_data.h5\n", "cell_type": "markdown", "metadata": {}}, {"execution_count": 68, "cell_type": "code", "source": "\nimport os\nimport sys\nimport ismrmrd\nimport ismrmrd.xsd\nimport numpy as np\nimport scipy as sp\nimport matplotlib.pyplot as pp\n\nsys.path.append(os.environ['GADGETRON_HOME'] + '/share/gadgetron/python')\n\nimport gadgetron_python_to_xml as p2x\nimport gadgetron_xml_to_python as x2p\nfrom gadgetron import gadget_chain_wait\nfrom gadgetron import gadget_chain_config\nfrom gadgetron import get_last_gadget\n\n\nfrom ismrmrdtools import show, transform, coils, grappa, sense\n%matplotlib inline", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 7, "cell_type": "code", "source": "def collect_data(filename_noise, filename_data):\n\n # Read the noise data\n if not os.path.isfile(filename_noise):\n print(\"%s is not a valid file\" % filename_noise)\n raise Exception('Invalid filename')\n\n noise_dset = ismrmrd.Dataset(filename_noise, 'dataset', create_if_needed=False)\n\n\n # Process the noise data\n noise_reps = noise_dset.number_of_acquisitions()\n a = noise_dset.read_acquisition(0)\n noise_samples = a.number_of_samples\n num_coils = a.active_channels\n noise_dwell_time = a.sample_time_us\n\n noise = np.zeros((num_coils,noise_reps*noise_samples),dtype=np.complex64)\n for acqnum in range(noise_reps):\n acq = noise_dset.read_acquisition(acqnum)\n \n if not acq.isFlagSet(ismrmrd.ACQ_IS_NOISE_MEASUREMENT):\n raise Exception(\"Errror: non noise scan found in noise calibration\")\n\n noise[:,acqnum*noise_samples:acqnum*noise_samples+noise_samples] = acq.data\n \n noise = noise.astype('complex64')\n \n # Read the data\n if not os.path.isfile(filename_data):\n print(\"%s is not a valid file\" % filename_data)\n raise Exception('Invalid filename')\n\n dset = ismrmrd.Dataset(filename_data, 'dataset', create_if_needed=False)\n\n header = ismrmrd.xsd.CreateFromDocument(dset.read_xml_header())\n enc = header.encoding[0]\n\n # Matrix size\n eNx = enc.encodedSpace.matrixSize.x\n eNy = enc.encodedSpace.matrixSize.y\n eNz = enc.encodedSpace.matrixSize.z\n rNx = enc.reconSpace.matrixSize.x\n rNy = enc.reconSpace.matrixSize.y\n rNz = enc.reconSpace.matrixSize.z\n\n # Field of View\n eFOVx = enc.encodedSpace.fieldOfView_mm.x\n eFOVy = enc.encodedSpace.fieldOfView_mm.y\n eFOVz = enc.encodedSpace.fieldOfView_mm.z\n rFOVx = enc.reconSpace.fieldOfView_mm.x\n rFOVy = enc.reconSpace.fieldOfView_mm.y\n rFOVz = enc.reconSpace.fieldOfView_mm.z\n\n #Parallel imaging factor\n acc_factor = enc.parallelImaging.accelerationFactor.kspace_encoding_step_1\n\n # Number of Slices, Reps, Contrasts, etc.\n ncoils = header.acquisitionSystemInformation.receiverChannels\n if enc.encodingLimits.slice != None:\n nslices = enc.encodingLimits.slice.maximum + 1\n else:\n nslices = 1\n\n if enc.encodingLimits.repetition != None:\n nreps = enc.encodingLimits.repetition.maximum + 1\n else:\n nreps = 1\n\n if enc.encodingLimits.contrast != None:\n ncontrasts = enc.encodingLimits.contrast.maximum + 1\n else:\n ncontrasts = 1\n \n # In case there are noise scans in the actual dataset, we will skip them. \n firstacq=0\n for acqnum in range(dset.number_of_acquisitions()):\n acq = dset.read_acquisition(acqnum)\n\n if acq.isFlagSet(ismrmrd.ACQ_IS_NOISE_MEASUREMENT):\n print \"Found noise scan at acq \", acqnum\n continue\n else:\n firstacq = acqnum\n print \"Imaging acquisition starts acq \", acqnum\n break\n\n #Calculate prewhiterner taking BWs into consideration\n a = dset.read_acquisition(firstacq)\n data_dwell_time = a.sample_time_us\n noise_receiver_bw_ratio = 0.79\n dmtx = coils.calculate_prewhitening(noise,scale_factor=(data_dwell_time/noise_dwell_time)*noise_receiver_bw_ratio)\n\n \n #%%\n # Process the actual data\n all_data = np.zeros((nreps, ncontrasts, nslices, ncoils, eNz, eNy, rNx), dtype=np.complex64)\n\n # Loop through the rest of the acquisitions and stuff\n for acqnum in range(firstacq,dset.number_of_acquisitions()):\n acq = dset.read_acquisition(acqnum)\n\n acq_data_prw = coils.apply_prewhitening(acq.data,dmtx)\n\n # Remove oversampling if needed\n if eNx != rNx:\n xline = transform.transform_kspace_to_image(acq_data_prw, [1])\n x0 = (eNx - rNx) / 2\n x1 = (eNx - rNx) / 2 + rNx\n xline = xline[:,x0:x1]\n acq.resize(rNx,acq.active_channels,acq.trajectory_dimensions)\n acq.center_sample = rNx/2\n # need to use the [:] notation here to fill the data\n acq.data[:] = transform.transform_image_to_kspace(xline, [1])\n\n # Stuff into the buffer\n rep = acq.idx.repetition\n contrast = acq.idx.contrast\n slice = acq.idx.slice\n y = acq.idx.kspace_encode_step_1\n z = acq.idx.kspace_encode_step_2\n all_data[rep, contrast, slice, :, z, y, :] = acq.data\n\n all_data = all_data.astype('complex64')\n\n return all_data", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 13, "cell_type": "code", "source": "def combine(all_data):\n all_data = transform.transform_image_to_kspace(np.squeeze(all_data),(2,3))\n \n nslices = all_data.shape[0]\n ncoils = all_data.shape[1]\n \n recon_b1w = np.zeros((nslices, all_data.shape[2], all_data.shape[3]), dtype=np.complex64)\n recon_rss = np.zeros((nslices, all_data.shape[2], all_data.shape[3]), dtype=np.complex64)\n \n for s in range(nslices):\n (csm,rho) = coils.calculate_csm_walsh(all_data[s,:,:,:])\n recon_b1w[s,:,:] = np.sum(all_data[s,:,:,:] * np.conj(csm),0)\n recon_rss[s,:,:] = np.sqrt(np.sum(all_data[s,:,:,:]**2,0))\n \n return (recon_b1w, recon_rss)", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 3, "cell_type": "code", "source": "data_folder = '/home/hansenms/data/QED_COIL/large/QEDTest20150415/'", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 21, "cell_type": "code", "source": "all_data_old = recon_data(data_folder + 'meas_MID00218_FID33531_OLD_SNR_COR_RL_R1_noise.h5' , data_folder + 'meas_MID00218_FID33531_OLD_SNR_COR_RL_R1_data.h5')\nall_data_new = recon_data(data_folder + 'meas_MID00229_FID33542_NEW_SNR_COR_RL_R1_noise.h5' , data_folder + 'meas_MID00229_FID33542_NEW_SNR_COR_RL_R1_data.h5')\nall_data_sie = recon_data(data_folder + 'meas_MID00241_FID33554_SIE_SNR_COR_RL_R1_noise.h5' , data_folder + 'meas_MID00241_FID33554_SIE_SNR_COR_RL_R1_data.h5')", "outputs": [{"output_type": "stream", "name": "stdout", "text": "Imaging acquisition starts acq 0\nImaging acquisition starts acq 0\nImaging acquisition starts acq 0\n"}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 22, "cell_type": "code", "source": "(recon_b1w_old, recon_rss_old) = combine(all_data_old)\nrecon_b1w_old = recon_b1w_old[(5,0,6,1,7,2,8,3,9,4),:,:]\nrecon_rss_old = recon_rss_old[(5,0,6,1,7,2,8,3,9,4),:,:]\n(recon_b1w_new, recon_rss_new) = combine(all_data_new)\nrecon_b1w_new = recon_b1w_new[(5,0,6,1,7,2,8,3,9,4),:,:]\nrecon_rss_new = recon_rss_new[(5,0,6,1,7,2,8,3,9,4),:,:]\n(recon_b1w_sie, recon_rss_sie) = combine(all_data_sie)\nrecon_b1w_sie = recon_b1w_sie[(5,0,6,1,7,2,8,3,9,4),:,:]\nrecon_rss_sie = recon_rss_sie[(5,0,6,1,7,2,8,3,9,4),:,:]", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 41, "cell_type": "code", "source": "mask = np.ones((256,256))\nmask[64:192,20+64:20+192] = 0\n\nnslices = recon_b1w_old.shape[0]\ntmp = np.zeros((nslices,))\nsnr_b1w_old = np.sum(abs(recon_b1w_old)*mask,axis=(1,2)) / np.sum(mask)\nsnr_rss_old = np.sum(abs(recon_rss_old)*mask,axis=(1,2)) / np.sum(mask)\nsnr_b1w_new = np.sum(abs(recon_b1w_new)*mask,axis=(1,2)) / np.sum(mask)\nsnr_rss_new = np.sum(abs(recon_rss_new)*mask,axis=(1,2)) / np.sum(mask)\nsnr_b1w_sie = np.sum(abs(recon_b1w_sie)*mask,axis=(1,2)) / np.sum(mask)\nsnr_rss_sie = np.sum(abs(recon_rss_sie)*mask,axis=(1,2)) / np.sum(mask)\n\nshow.imshow(abs(recon_b1w_old[1,:,:])*mask)", "outputs": [{"output_type": "display_data", "data": {"image/png": 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7BgIvhRK3cQq3sBGBiXfxQdIiAmf5FfYa2fVk9q0IA7aFsyIF4I1NFqIRf4Oj7thnn9n/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Q2F44GdJ7zuWW/pa+Dv8JKdWkl9gmCxNNG0R16y1qHP3T5bCNqK7HwjvJFRyZwj+mzY0Kcl5COe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\nLvka8RiM24c5LUPvZHLQP7fIMfFmnx7o6xnTf0/KwFu3w03Ev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"text/plain": "<matplotlib.figure.Figure at 0x7f7ac8ae11d0>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 48, "cell_type": "code", "source": "pp.plot(snr_b1w_new,'r')\npp.plot(snr_b1w_old,'b')\npp.plot(snr_b1w_sie,'g')", "outputs": [{"execution_count": 48, "output_type": "execute_result", "data": {"text/plain": "[<matplotlib.lines.Line2D at 0x7f7ac9436550>]"}, "metadata": {}}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7ac940f090>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 49, "cell_type": "code", "source": "pp.plot(snr_rss_new,'r')\npp.plot(snr_rss_old,'b')\npp.plot(snr_rss_sie,'g')", "outputs": [{"execution_count": 49, "output_type": "execute_result", "data": {"text/plain": "[<matplotlib.lines.Line2D at 0x7f7ac963ff90>]"}, "metadata": {}}, {"output_type": "display_data", "data": {"image/png": 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YG/gLsI8Z/2zm5J8AZxHmsb9chvDaLNvY+nxgaeB4S+z+yCE512ae1F2TJLYF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\n05oL6bLEQrr0+JrOnbszb+E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"text/plain": "<matplotlib.figure.Figure at 0x7f7ac96768d0>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 50, "cell_type": "code", "source": "pp.plot(snr_b1w_new/snr_b1w_old,'r')\npp.plot(snr_rss_new/snr_rss_old,'g')", "outputs": [{"execution_count": 50, "output_type": "execute_result", "data": {"text/plain": "[<matplotlib.lines.Line2D at 0x7f7ac8afb190>]"}, "metadata": {}}, {"output_type": "display_data", "data": {"image/png": 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4EWADYGVSWYOFm9OwHwO2Jv1fvhDpCKQFm9J2nbVlggf2pTV779uSamDEpGoI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\nRz5bf67vJwgAAAAASUVORK5CYII=\n", "text/plain": "<matplotlib.figure.Figure at 0x7f7ac8afb0d0>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 60, "cell_type": "code", "source": "\n# Automatically generated Python representation of /home/hansenms/local/share/gadgetron/config/default_measurement_dependencies.xml\n\nfrom gadgetron import WrapperGadget\n\ndef define_gadget_chain_dependencies():\n g2 = WrapperGadget(\"gadgetron_mricore\", \"NoiseAdjustGadget\", gadgetname=\"NoiseAdjust\", next_gadget=None)\n g2.set_parameter(\"NoiseAdjust\", \"noise_dependency_prefix\", \"GadgetronNoiseCovarianceMatrix\")\n g2.set_parameter(\"NoiseAdjust\", \"pass_nonconformant_data\", \"true\")\n return g2", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 58, "cell_type": "code", "source": "# Automatically generated Python representation of /home/hansenms/local/share/gadgetron/config/GT_2DT_Cartesian_GFactor.xml\n\nfrom gadgetron import WrapperGadget\n\ndef define_gadget_chain_gfactor():\n g2 = WrapperGadget(\"gadgetronPlus\", \"GtPlusRecon2DTGadget\", gadgetname=\"Recon\", next_gadget=None)\n g2.set_parameter(\"Recon\", \"dim_4th\", \"DIM_NONE\")\n g2.set_parameter(\"Recon\", \"dim_5th\", \"DIM_NONE\")\n g2.set_parameter(\"Recon\", \"workOrder_ShareDim\", \"DIM_NONE\")\n g2.set_parameter(\"Recon\", \"no_acceleration_averageall_ref\", \"true\")\n g2.set_parameter(\"Recon\", \"no_acceleration_ref_numOfModes\", \"0\")\n g2.set_parameter(\"Recon\", \"no_acceleration_same_combinationcoeff_allS\", \"true\")\n g2.set_parameter(\"Recon\", \"no_acceleration_whichS_combinationcoeff\", \"0\")\n g2.set_parameter(\"Recon\", \"interleaved_same_combinationcoeff_allS\", \"false\")\n g2.set_parameter(\"Recon\", \"interleaved_whichS_combinationcoeff\", \"0\")\n g2.set_parameter(\"Recon\", \"interleaved_ref_numOfModes\", \"0\")\n g2.set_parameter(\"Recon\", \"embedded_averageall_ref\", \"true\")\n g2.set_parameter(\"Recon\", \"embedded_ref_numOfModes\", \"0\")\n g2.set_parameter(\"Recon\", \"embedded_fullres_coilmap\", \"true\")\n g2.set_parameter(\"Recon\", \"embedded_fullres_coilmap_useHighestSignal\", \"false\")\n g2.set_parameter(\"Recon\", \"embedded_same_combinationcoeff_allS\", \"true\")\n g2.set_parameter(\"Recon\", \"embedded_whichS_combinationcoeff\", \"0\")\n g2.set_parameter(\"Recon\", \"embedded_ref_fillback\", \"false\")\n g2.set_parameter(\"Recon\", \"separate_averageall_ref\", \"true\")\n g2.set_parameter(\"Recon\", \"separate_ref_numOfModes\", \"0\")\n g2.set_parameter(\"Recon\", \"separate_fullres_coilmap\", \"true\")\n g2.set_parameter(\"Recon\", \"separate_same_combinationcoeff_allS\", \"true\")\n g2.set_parameter(\"Recon\", \"separate_whichS_combinationcoeff\", \"0\")\n g2.set_parameter(\"Recon\", \"same_coil_compression_coeff_allS\", \"true\")\n g2.set_parameter(\"Recon\", \"upstream_coil_compression\", \"false\")\n g2.set_parameter(\"Recon\", \"upstream_coil_compression_thres\", \"-1\")\n g2.set_parameter(\"Recon\", \"upstream_coil_compression_num_modesKept\", \"-1\")\n g2.set_parameter(\"Recon\", \"downstream_coil_compression\", \"true\")\n g2.set_parameter(\"Recon\", \"coil_compression_thres\", \"-1\")\n g2.set_parameter(\"Recon\", \"coil_compression_num_modesKept\", \"-1\")\n g2.set_parameter(\"Recon\", \"coil_map_algorithm\", \"ISMRMRD_SOUHEIL\")\n g2.set_parameter(\"Recon\", \"csm_kSize\", \"7\")\n g2.set_parameter(\"Recon\", \"csm_powermethod_num\", \"3\")\n g2.set_parameter(\"Recon\", \"csm_true_3D\", \"false\")\n g2.set_parameter(\"Recon\", \"csm_iter_num\", \"5\")\n g2.set_parameter(\"Recon\", \"csm_iter_thres\", \"0.001\")\n g2.set_parameter(\"Recon\", \"recon_algorithm\", \"ISMRMRD_GRAPPA\")\n g2.set_parameter(\"Recon\", \"recon_kspace_needed\", \"false\")\n g2.set_parameter(\"Recon\", \"recon_auto_parameters\", \"true\")\n g2.set_parameter(\"Recon\", \"gfactor_needed\", \"true\")\n g2.set_parameter(\"Recon\", \"wrap_around_map_needed\", \"false\")\n g2.set_parameter(\"Recon\", \"grappa_kSize_RO\", \"5\")\n g2.set_parameter(\"Recon\", \"grappa_kSize_E1\", \"4\")\n g2.set_parameter(\"Recon\", \"grappa_kSize_E2\", \"4\")\n g2.set_parameter(\"Recon\", \"grappa_reg_lamda\", \"0.0005\")\n g2.set_parameter(\"Recon\", \"grappa_calib_over_determine_ratio\", \"0\")\n g2.set_parameter(\"Recon\", \"spirit_kSize_RO\", \"7\")\n g2.set_parameter(\"Recon\", \"spirit_kSize_E1\", \"7\")\n g2.set_parameter(\"Recon\", \"spirit_kSize_E2\", \"5\")\n g2.set_parameter(\"Recon\", \"spirit_reg_lamda\", \"0.005\")\n g2.set_parameter(\"Recon\", \"spirit_calib_over_determine_ratio\", \"0\")\n g2.set_parameter(\"Recon\", \"spirit_solve_symmetric\", \"false\")\n g2.set_parameter(\"Recon\", \"spirit_iter_max\", \"90\")\n g2.set_parameter(\"Recon\", \"spirit_iter_thres\", \"0.0015\")\n g2.set_parameter(\"Recon\", \"spirit_print_iter\", \"false\")\n g2.set_parameter(\"Recon\", \"spirit_perform_linear\", \"true\")\n g2.set_parameter(\"Recon\", \"spirit_perform_nonlinear\", \"true\")\n g2.set_parameter(\"Recon\", \"spirit_parallel_imaging_lamda\", \"1.0\")\n g2.set_parameter(\"Recon\", \"spirit_image_reg_lamda\", \"0.0025\")\n g2.set_parameter(\"Recon\", \"spirit_data_fidelity_lamda\", \"0\")\n g2.set_parameter(\"Recon\", \"spirit_ncg_iter_max\", \"10\")\n g2.set_parameter(\"Recon\", \"spirit_ncg_iter_thres\", \"0.0001\")\n g2.set_parameter(\"Recon\", \"spirit_ncg_print_iter\", \"true\")\n g2.set_parameter(\"Recon\", \"spirit_use_coil_sen_map\", \"true\")\n g2.set_parameter(\"Recon\", \"spirit_use_moco_enhancement\", \"false\")\n g2.set_parameter(\"Recon\", \"spirit_recon_moco_images\", \"false\")\n g2.set_parameter(\"Recon\", \"spirit_RO_enhancement_ratio\", \"1.0\")\n g2.set_parameter(\"Recon\", \"spirit_E1_enhancement_ratio\", \"1.0\")\n g2.set_parameter(\"Recon\", \"spirit_E2_enhancement_ratio\", \"1.0\")\n g2.set_parameter(\"Recon\", \"spirit_temporal_enhancement_ratio\", \"1.0\")\n g2.set_parameter(\"Recon\", \"spirit_2D_scale_per_chunk\", \"false\")\n g2.set_parameter(\"Recon\", \"min_intensity_value\", \"64\")\n g2.set_parameter(\"Recon\", \"max_intensity_value\", \"4095\")\n g2.set_parameter(\"Recon\", \"scalingFactor\", \"10\")\n g2.set_parameter(\"Recon\", \"scalingFactor_gfactor\", \"100\")\n g2.set_parameter(\"Recon\", \"scalingFactor_snr_image\", \"10\")\n g2.set_parameter(\"Recon\", \"scalingFactor_std_map\", \"1000\")\n g2.set_parameter(\"Recon\", \"start_frame_for_std_map\", \"5\")\n g2.set_parameter(\"Recon\", \"use_constant_scalingFactor\", \"false\")\n g2.set_parameter(\"Recon\", \"filterRO\", \"ISMRMRD_FILTER_NONE\")\n g2.set_parameter(\"Recon\", \"filterRO_sigma\", \"1.0\")\n g2.set_parameter(\"Recon\", \"filterRO_width\", \"0.15\")\n g2.set_parameter(\"Recon\", \"filterE1\", \"ISMRMRD_FILTER_NONE\")\n g2.set_parameter(\"Recon\", \"filterE1_sigma\", \"1.0\")\n g2.set_parameter(\"Recon\", \"filterE1_width\", \"0.15\")\n g2.set_parameter(\"Recon\", \"filterE2\", \"ISMRMRD_FILTER_NONE\")\n g2.set_parameter(\"Recon\", \"filterE2_sigma\", \"1.0\")\n g2.set_parameter(\"Recon\", \"filterE2_width\", \"0.15\")\n g2.set_parameter(\"Recon\", \"filterRefRO\", \"ISMRMRD_FILTER_HANNING\")\n g2.set_parameter(\"Recon\", \"filterRefRO_sigma\", \"1.5\")\n g2.set_parameter(\"Recon\", \"filterRefRO_width\", \"0.15\")\n g2.set_parameter(\"Recon\", \"filterRefE1\", \"ISMRMRD_FILTER_HANNING\")\n g2.set_parameter(\"Recon\", \"filterRefE1_sigma\", \"1.5\")\n g2.set_parameter(\"Recon\", \"filterRefE1_width\", \"0.15\")\n g2.set_parameter(\"Recon\", \"filterRefE2\", \"ISMRMRD_FILTER_HANNING\")\n g2.set_parameter(\"Recon\", \"filterRefE2_sigma\", \"1.5\")\n g2.set_parameter(\"Recon\", \"filterRefE2_width\", \"0.15\")\n g2.set_parameter(\"Recon\", \"filterPartialFourierRO\", \"ISMRMRD_FILTER_TAPERED_HANNING\")\n g2.set_parameter(\"Recon\", \"filterPartialFourierRO_sigma\", \"1.5\")\n g2.set_parameter(\"Recon\", \"filterPartialFourierRO_width\", \"0.15\")\n g2.set_parameter(\"Recon\", \"filterPartialFourierRO_densityComp\", \"false\")\n g2.set_parameter(\"Recon\", \"filterPartialFourierE1\", \"ISMRMRD_FILTER_TAPERED_HANNING\")\n g2.set_parameter(\"Recon\", \"filterPartialFourierE1_sigma\", \"1.5\")\n g2.set_parameter(\"Recon\", \"filterPartialFourierE1_width\", \"0.15\")\n g2.set_parameter(\"Recon\", \"filterPartialFourierE1_densityComp\", \"false\")\n g2.set_parameter(\"Recon\", \"filterPartialFourierE2\", \"ISMRMRD_FILTER_TAPERED_HANNING\")\n g2.set_parameter(\"Recon\", \"filterPartialFourierE2_sigma\", \"1.5\")\n g2.set_parameter(\"Recon\", \"filterPartialFourierE2_width\", \"0.15\")\n g2.set_parameter(\"Recon\", \"filterPartialFourierE2_densityComp\", \"false\")\n g2.set_parameter(\"Recon\", \"partialFourier_algo\", \"ISMRMRD_PF_ZEROFILLING_FILTER\")\n g2.set_parameter(\"Recon\", \"partialFourier_homodyne_iters\", \"6\")\n g2.set_parameter(\"Recon\", \"partialFourier_homodyne_thres\", \"0.01\")\n g2.set_parameter(\"Recon\", \"partialFourier_homodyne_densityComp\", \"false\")\n g2.set_parameter(\"Recon\", \"partialFourier_POCS_iters\", \"6\")\n g2.set_parameter(\"Recon\", \"partialFourier_POCS_thres\", \"0.01\")\n g2.set_parameter(\"Recon\", \"partialFourier_POCS_transitBand\", \"24\")\n g2.set_parameter(\"Recon\", \"partialFourier_FengHuang_kSize_RO\", \"5\")\n g2.set_parameter(\"Recon\", \"partialFourier_FengHuang_kSize_E1\", \"5\")\n g2.set_parameter(\"Recon\", \"partialFourier_FengHuang_kSize_E2\", \"5\")\n g2.set_parameter(\"Recon\", \"partialFourier_FengHuang_thresReg\", \"0.01\")\n g2.set_parameter(\"Recon\", \"partialFourier_FengHuang_sameKernel_allN\", \"false\")\n g2.set_parameter(\"Recon\", \"partialFourier_FengHuang_transitBand\", \"24\")\n g2.set_parameter(\"Recon\", \"debugFolder\", \"\")\n g2.set_parameter(\"Recon\", \"debugFolder2\", \"\")\n g2.set_parameter(\"Recon\", \"performTiming\", \"true\")\n g2.set_parameter(\"Recon\", \"verboseMode\", \"true\")\n g2.set_parameter(\"Recon\", \"timeStampResolution\", \"0.0025\")\n g2.set_parameter(\"Recon\", \"job_split_by_S\", \"false\")\n g2.set_parameter(\"Recon\", \"job_num_of_N\", \"32\")\n g2.set_parameter(\"Recon\", \"job_max_Megabytes\", \"10240\")\n g2.set_parameter(\"Recon\", \"job_overlap\", \"2\")\n g2.set_parameter(\"Recon\", \"job_perform_on_control_node\", \"true\")\n g2.set_parameter(\"Recon\", \"CloudComputing\", \"false\")\n g2.set_parameter(\"Recon\", \"cloudNodeFile\", \"myCloud_2DT.txt\")\n g2.set_parameter(\"Recon\", \"CloudNodeXMLConfiguration\", \"GT_2DT_Cartesian_CloudNode.xml\")\n g2.prepend_gadget(\"gadgetronPlus\", \"GtPlusAccumulatorWorkOrderTriggerGadget\", gadgetname=\"Acc\")\n g2.set_parameter(\"Acc\", \"verboseMode\", \"false\")\n g2.set_parameter(\"Acc\", \"noacceleration_triggerDim1\", \"DIM_NONE\")\n g2.set_parameter(\"Acc\", \"noacceleration_triggerDim2\", \"DIM_NONE\")\n g2.set_parameter(\"Acc\", \"noacceleration_numOfKSpace_triggerDim1\", \"1\")\n g2.set_parameter(\"Acc\", \"interleaved_triggerDim1\", \"DIM_NONE\")\n g2.set_parameter(\"Acc\", \"interleaved_triggerDim2\", \"DIM_NONE\")\n g2.set_parameter(\"Acc\", \"interleaved_numOfKSpace_triggerDim1\", \"4\")\n g2.set_parameter(\"Acc\", \"embedded_triggerDim1\", \"DIM_NONE\")\n g2.set_parameter(\"Acc\", \"embedded_triggerDim2\", \"DIM_NONE\")\n g2.set_parameter(\"Acc\", \"embedded_numOfKSpace_triggerDim1\", \"1\")\n g2.set_parameter(\"Acc\", \"separate_triggerDim1\", \"DIM_NONE\")\n g2.set_parameter(\"Acc\", \"separate_triggerDim2\", \"DIM_NONE\")\n g2.set_parameter(\"Acc\", \"separate_numOfKSpace_triggerDim1\", \"1\")\n g2.set_parameter(\"Acc\", \"other_kspace_matching_Dim\", \"DIM_Repetition\")\n g2.prepend_gadget(\"gadgetron_mricore\", \"RemoveROOversamplingGadget\", gadgetname=\"RemoveROOversampling\")\n g2.prepend_gadget(\"gadgetron_mricore\", \"AsymmetricEchoAdjustROGadget\", gadgetname=\"AsymmetricEcho\")\n g2.prepend_gadget(\"gadgetron_mricore\", \"NoiseAdjustGadget\", gadgetname=\"NoiseAdjust\")\n g2.set_parameter(\"NoiseAdjust\", \"noise_dependency_prefix\", \"GadgetronNoiseCovarianceMatrix\")\n return g2", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 69, "cell_type": "code", "source": "def run_gadget_chain(gadget_chain_function, ismrmrd_file):\n g = globals()[gadget_chain_function]()\n dset = ismrmrd.Dataset(ismrmrd_file, 'dataset', create_if_needed=False) \n\n gadget_chain_config(g,dset.read_xml_header())\n\n # Loop through the rest of the acquisitions and stuff\n for acqnum in range(0,dset.number_of_acquisitions()):\n acq = dset.read_acquisition(acqnum)\n g.process(acq.getHead(),acq.data.astype('complex64'))\n \n gadget_chain_wait(g)\n return get_last_gadget(g).get_results()", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 93, "cell_type": "code", "source": "def run_snr_scaled_recon(filename_noise, filename_data):\n junk = run_gadget_chain(\"define_gadget_chain_dependencies\", filename_noise)\n snr_scale = run_gadget_chain(\"define_gadget_chain_gfactor\", filename_data)\n nslices = len(snr_scale)\n Nx = snr_scale[0][1].shape[0]\n Ny = snr_scale[0][1].shape[1]\n \n out = np.zeros((nslices, Ny, Nx), dtype=np.complex64)\n for s in range(nslices):\n out[s,:,:] = np.flipud(np.fliplr(snr_scale[s][1]))\n \n return out[(5,0,6,1,7,2,8,3,9,4),:,:]", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 97, "cell_type": "code", "source": "snr_scale_old = run_snr_scaled_recon(data_folder + 'meas_MID00218_FID33531_OLD_SNR_COR_RL_R1_noise.h5', data_folder + 'meas_MID00218_FID33531_OLD_SNR_COR_RL_R1_data.h5')\nsnr_scale_new = run_snr_scaled_recon(data_folder + 'meas_MID00229_FID33542_NEW_SNR_COR_RL_R1_noise.h5', data_folder + 'meas_MID00229_FID33542_NEW_SNR_COR_RL_R1_data.h5')\nsnr_scale_sie = run_snr_scaled_recon(data_folder + 'meas_MID00241_FID33554_SIE_SNR_COR_RL_R1_noise.h5', data_folder + 'meas_MID00241_FID33554_SIE_SNR_COR_RL_R1_data.h5')", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 98, "cell_type": "code", "source": "nslices = snr_scale_old.shape[0]\n\nsnr_scale_roi_old = np.sum(abs(snr_scale_old)*mask,axis=(1,2)) / np.sum(mask)\nsnr_scale_roi_new = np.sum(abs(snr_scale_new)*mask,axis=(1,2)) / np.sum(mask)\nsnr_scale_roi_sie = np.sum(abs(snr_scale_sie)*mask,axis=(1,2)) / np.sum(mask)\n\nshow.imshow(abs(snr_scale_old[1,:,:])*mask)", "outputs": [{"output_type": "display_data", "data": {"image/png": 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bnBuwsj6PCQv4Axyoxe6thbxtn/2IML/iIwj/mFrbJWFeNR9N10wW1kOC6/ahXbew83cI\n60A8ike44OoT1pRwjg37bg+fW7lBUjRyF+SKTgiuzKnNgULAuwSBX+Ogp5iH8vWXuHaWVRv8/HD/\nld1PYVfwMLRo2S2cSDXCXTNFOxRi/8SuSewa4VoH9tyb+HzK2onxNfXdxx0JhwWhkwKCOgRNcYRr\noQke2hLN9lM85vs9gsaUf7yND6SISmJfVoQBVUhLGkT5B9L8nxI21FOcfp0RBIUAUjEtz+2+QuEj\nwgS+xpF+Mdq27VqZ//KHFVKsCYu8Y9dpY2pBybKS2fsIt5IeEDarNKvci1c4/0Cg3Zb1RWCafOZm\nvoMwH7k58vW1mE9xy6bAY/MfEha5EHaNq7T3o0YfxD6VRhSqLwbpFh612MItDkVGFA4WAU55Hcd4\naPoIByl/3ZjPtwRBemV9XhMEpvItlrxrlkf2LAMCYPyR9V3Esk37/gd4yFA05m2cqShFOLB5ErB+\nam08tbGa4pyZb3D3SdEkbCwVRhUAKgLVsbXxCnf9mi6mOCvffdwRIPk/12FBX+IabYcQ0voQ90d3\nG981wZdjPLSoOLUsBS04mXdi9hWERXRubT8jLKJLnFYtjvwF7t+L+ShNKTN7TVgorwgmu7SJSFdC\nqxOCZVMRADflG0hDSNNjfWsuOE2+FlHHnusxnv+xbeMharNIL7IE+gSsZMfaH9qzKyoijTvBactL\nwiaTOyMmorAYYSOljUFu7f/UxlY5AjpfwOoKJ/scEhasCF4yhbWpxKqscUErIpxwqd8D/tdGv3t2\njRKOFBKVNlVESm6i5lzWyIiwIUViOsKtwLmN3/fsnKd4MluBg5fCWgRMvrT7/z7B4vyCMN+7No5f\n4S6roisa/7fW1kd4dE4uzmuCa7lnz3uJA65jXCA8sbGdI5r3b3ko8z9v3FQmvcgfMU7plWku7rr8\n2RIHmQQgCh8QlvDI/j/iXbNQFORLHDgU1jEnLOanhE2vTSyXpE1YBFpYImcp1LWBL2pZBuI8TAhC\n5JmdP8VNyUvCAvqaIBwVCutafz62+4jbIOpwSSDSyFIQtiKqtISvEoZEAjvBhani/Ar/Sug8I1gC\nCgMrRIidJzBXSVRKPFOOiyIYD/CQbd/OUfQIXJDLgixwVqHakd8vsE2bSYDekZ0f41iDwpqHNtZq\nX0ShCGfoykLqEzadlE1TW6uf4p0IT2iCi3JRBYzn1r85YQ6/xKMiAoCVJSo8YmR/7xPm8DUOwKsv\nKxxQF7Zzan9/ZN+f45bgBCeqfUJd/2u/zVmZoksrbFQTBMEr+y1T6YKwaQRMKdNMWYvKh9Bkim4s\nHrxCe7Ieduz+x7hvOMKptRt4zLxv95dv3KQiP7VrTgiTqHtKg2rjKJFKiTiH1qaouSLe1NYnsQ3F\nZBvj/AItxKZVsiRoIoV9RSNW1uEljn4LCJXAEL37Ahe6AjzFoRDJR5GhUzvnU3zxaxOKrSrLTYJI\n4y4QUH63kqXGeISnmZOicdEmVJalhOkHNsdSBuq3Qn8bOKgpLs3fzAxthhrP8WiOlI4yUcVsFIAr\n07yPC35ZNyP7/RbPeVnhOQ/C2hQql/LT2tIYaExlKWzh+RZyh0WUU/+E6eTW/hO7vyylCI+yffdx\nR8JhGzfnNWkCgCQhNYmiOu/goUmF1BTF2MIHRQCQUO4SN5mlWQvChnuC8yemdo5YdyM8IiGTTMDk\nkKCtWnZf5eVrUyh3okm6EQAK7o83/ckeQTD+Je7ja7IhLCotSmUPCm9RyrI0vAA+1YUQAUrhQrkf\nopwrZ0Uchwc2BoeNMZSG6hA2tqwZ0XwF/F43+iGQT2NzhWdMyv8VcU1jIPNaWZ+71p9fWd/lCnyB\nRxvEalzhVhL4GpB7cmPP+CVBqIvivMDxnWYkQlEQRVwEsIp5qDydDZw0tSCsDwHcejblk0xw0FQu\nsTI0FSqtCe6IxluWqvJ+TnD8q8ZBXxGylFgnNq7WrvbN+x13BEie4Mi0UHCRhUReks8sYFHhIE2I\nBIjCVFrgCsN1cQRfvHxJalkuZ7gJKjBHlsYJDsKJAakwq+LnNY52S8Pq3tJA0mICzSrCYhdDTvUC\nRMoRwCYilK5Two/wGFGFn+C+ptKJtRm1WLURVEBm3Ghf1oAW4Np+tnnX9L3AQ5O/i5uxER6SlKAS\n+1R9l4ujDaTEMT3fiODLP270UfwFJT1pvSg8+xxPJxeoWeAuzxy3Qh8SBN2GjZfcQ0U3MjwyoSQw\nuSeaJ9HqZUXJHVbUZIS7QOd4hErYi7Jet3CFJ5dYWZ2yxvJG34UBra0tKah549mFC2mNivCkcLru\nq9yg9zvuSDgIhFJ47wI377o4uCLkWTUBZP4riqB48TFh0rfxxbBr7X6KM8s2cd/7AOe8K5QkptoI\njzkrVCkuvDRHs3iMKvlc4zwK0YOlVdJG+wI7BZzJAjqy+yoxSASnD3BKtkhGzQ3RzFBUSvo3BCEk\nXoTAvn3CRhzjVoI2nQSBFnbTenmOuy+vcI3VwnELMQkVpVEUQ9Gmg8b3CtcqMe4FwZoTIalvz5wS\ngNwpbqEkBOEEXgFrjWvaTwmaWoVwRH8XP0IU8rxxTkxYNwKXn+PCI8JD2yUOcM7xCI3Ykdh1shyk\nIGZ4uLFprb2x55aiW1jbW4TksoqA/8gNVYh0gFOmafwvS0HJXis8rCwi1PsddyQcHhAGThpW5v2c\nMFhiuIlqK20GnpeuBxeZRxWiPsKzNz8iZO3t4DUZBNj8irBRlPyUETZhRFio+3jqr6wCpQu3rJ8b\nuFk6xrXJMZ5tObPnFUlnZH3dJEy6zNk+jolI62tjf01YxFf4placXclZipI8sH5nhBDeHp6MI4sh\nw83ca5yRCR7+XPOuJfMWp/VKOInMpuxGFX8Rs0+pz7mN8y/xehXNZCeBqw9xRqgK8sg6U/2KS7u/\nojITG89tmz8RihTJUWk/adCEIBwVtVFIXaUArq2NQ7v3tt1Llo8s1xS3AIV3qLaCFFpMiEa8tXE4\nxBXBubU5JAjbOe+ugTfAP8EL2Ci0e4JbYi28rMEh8E/hyVf7BEunxsl3CuW+33FH0Yr/snZwR6m/\nMuceE7SeMvZU1ky+mSSyzNldwoD8GaHN3yH4o/Iv53gdSghSX8QexZwf4tiD2JZfAz8hTK5yEkQy\n2SFoMy0QmdAdPIynBCGh5apfofCgzErwzakFIsadXCaRr4SpKHJwjAN34DRzJefId/8VroVlHh/j\nAvjavj/HIzriOjR9cNHIR/ZsqqsgbbZPWLxnBKEknESAnchaAmh1zVu79w9xxqoExzOC//0cF8A3\neGhV2MYBHtFRmHXf2hO5aBsPLdYERfG/44VnBADK4jnH65d2CevoGI+0zO1v1dZQun2TOi2X758F\n/gLX7kvC2j3FrRTlXcg1qXE8SeF1CdMKFyTiAl3ioW3w/JkWnhz2mLr+d3+bcyuEoE8ID/UJYcFt\nEwb8X8BTXs8Igy2Wm0KWKt7yOc7rz/DsPzHhdnCgURMpEpV8e1km4gGkBJPuK2unjzPxpDme4fTU\nDK/VJ2BNbEeBk1c4gq4kr0cEIagyXtok0iADPKQry0mbHt41e0d2/rGN0UN8Ez/AfdcHhA0ijSSc\nRCm9wmgUTRIwDGHTbvIuYCqrS+PYI6TDw7tVsz62c+WSiOo7wTfeKW7ip402t/GaHLJclB15hhcG\nkmujPJwUL2wjQauaEDNCVu2nhLV1QLC82jYvEmD7uBYWI1GFWjT2MZ6T0bexFyX9EKfiK9IAjmUo\n+iZrSkpI/BMB7ApdKoyv7yUo5Lqe4SxfzaFSFZ7ztwCQFGFHm+2SMPnXhEn5mrAojvD0WP0tk1na\nskm0EQkmxsuDCcRTHFkmmcJuAsXEZVApsZc4zqA6DwlOphEoeYTnRoj22gTUhrjvKStFiVZiggog\nUyUf/a8Q3Td2vpiVrwiCRICdFr9Clgr3veLdXArRibW5ZTkpt+AYL6p63Rg/cRdkyYkFuom7Tqqe\nJU0pzS9X6gVBIGtTi0PwBhcKES4Uxc9QjsMxXqdClkof96Fl7cj/T/B6lgqB9/Ewt8ZAyUoCUYUb\nyKpTdETWj+b1ABcKSqtXePYYx24EJl7gxX+UVSklpojaa7vnVWPcLnk3H0P4wcrGVFaNhKkqqolj\noeS+Zr2M9zvuSDhs4aFDDb6AmgQXFgJg+niJcSX1SGsKeCoIgz4kbFxdf4aTXiQItHg7vAuiaQIU\nxhSZRGQTsd+0KaZ4TUEtQpl3KuyiCII4Cs3NLz6+qLhyT5TrINKQzEnhKs1sRG1KaUr1T5wAAZ+i\nCR/jdGHdS26I6gJI+0m7qXqWojty8xR+k7++YZ+fWD8i69uRffcSF1AKYY5wPoTqGOw2/pfg1iKX\nJdgsyCJTXZm4yr6Vq7ePC/QhXrlJGbORjYHSzqUQlOujMV3gUZ8znETXrDeh8KiSnGR1yToUOCor\n6rQxjqI6b9i99YyKRGndLBt9VB4FuCstwtfbxnMp8qSiQN993BHPQaiswn45Xhbsa9yEkw8rE134\nhDLz2o3PZC2IDy+0W5lzKa7ZNgmTcowvfHEZBNhpo4ojoX6K+HNIwB20MMHTkg9xYktJWNTgFZ4E\nuElLKoFL1F4VgtH3yk9Q3QLwSEHLxkwTv+Td4jI3OHtRmIE2nYrPbuPl24YEIfGYIASUayAsQ/kZ\nS2tX7lKFg8kCWZWiLXNWmloRG7EJBQiKQwJuVcml0DPIvVLykvACRR8EtOqceeN7gdtylSTkbvA8\njHO8mE4zNCgezVOcTyBBKQt4iAsi5X1IsyvELhdY/Zni1qPOV3vqo7g/Eghaq+DRlSY+UePrTNiO\n3Mb3Fw53ZDmImKIQmsxPLQ5laEJYjGvcj3tEmCAID/oIN1G1GcREU1ixImxYMSmltUUSGfKub3qC\nJ8L8hHdfGKPFLzLLDPfpVH9iRVikQv5z659IRyM8hCvffwL8AQ5MSrvIDdAYqMyaIgpiLKaEhS1f\n+JB3szGf46nsinkLpFX1IAGW2wSBo2xTuRbgSXOqsKTw4Q2OG8ly0Hi+JJi46vccT37TZ5t4RqaS\n2n6EF4iRPy2uyDOcB6B0ZPVlH6epN9OgZWkqgiN3QpbfCo+8LKxvAmMf2TkqESBmpKIpqguhbEpZ\nelO84IqsgyM8cUohSlkJN/ZzbWP5rDFf+zjtXQCrXBIBoQrJiuWJPfeB9UcC+7uPO4pW/Ce1VzFW\nVprCmj/HaxgoTVkstLd4pltJWPAtu2YbT4QRMi7wRhz/V3hpOS2EjwjAY4swqALudA9FBlLChjkg\nTJiy35REJD9RyPAH9v8pAfm+tj6sCRtcKPoPcEtKCUevcTReqcrSkMrYVAKZKLoFAZuQZpo3rhFA\nOcfNeUUbWoSFPyb4sAd4JKMpoDQmyrXYwIWDqNWyoITNSDiI1CY36gmefgxeo1IJZ8J4FGG5wbkh\nmlNxYKRdRWZT4tIhYZOKDyFNPbbvXuDuygIXFMIr9LPGrQglW4mwJ9dOoXMBvhe4uX9FWLcikAn7\nAWfmfkJgbcr0F/iubNUjfH2LL/EZXgj3Z7hrqqiNFFHHPlPRmGvq+t/8bc6t2MM3ieK+l7zrHypx\nZILjAfv2+Q6+iQTGXBEWjVyQT+37C0KI7DVhsYsNOLR+yGSVWS2trUw2Id4KF4m4IqZknyBcZIoK\nRRZx6hm+cT4hTNxPCNp0ZM+hTf3C7iUrQwJCGYLiS6gGwBYhW1U5AgrzKUO0Y208xF0xVcJSEpCy\nUAX8qd+qYSnaLTjuoPF+TNhsb3FfuWX3EP9BeQEqECOM6Ll9JzS9i6PvsoqO8YpLB/Z79Df+jwkb\nR0rjNSHF+0vCWnjGuxbdZ7jr8S8SuBcxYaMpKqbMzD3cwhHpTMzaGueZPMQjJb/Aayno7z0cYAQH\ntzWXv8JrYig355hggSlk/y2e4CelKsEksp0EkLAI7a9jvLbEt7zvcUeWw/9Uh42gTSiikHxPSfBm\nqbdfErS8tJMSdlSZRxpf7X2Ia3bRU5votPjupzgPQnUYxERUevYOnoikBXqOCw2h0PruBE+CUgbe\nOWExfoNToAXEKYlKqbzKT+jjJvvHjb4JF2mi8vKrzbRudaF1CJ05bDyBzg2k1yTZKVnyPSq6tJmT\n8DkJBRX6xAPNAAAgAElEQVRLw7uvqVgQ8YAW3wIbJMQkxLS4oqRNjxwYEFERF5sk5RxqK4qyKKCK\noUwgvYCiBdEBJHMoMkimULUgHUNbHIUxtCZQ71BFa9ZpTcmamCU5PYrbpK0ZuZGLEmJyUkpKEoYU\nrCjpmMcdOABLRkR1n2WVEVFSrDtQXUPeh2UBcQ8mM8gHsH4B+TXUA4hKqBe4OyUSlDR+gnMrZF0o\n7H2MYypKBFMm5lOC0BKALPBwF+dnyE3R/cUqVbhZ5fJyHENY4iDsKY6TiBIvQlZQHO+bsn1HloMy\nzGTSXRE6/xnuF8kcVXgQnO0GHrWI8IxOIbriF6gOwwucPtyMoSvEp82meLL83scEraiwn5iSzcIZ\nCuPJjCtxACjG49sdQlKVnkPgp1BsafUDu78Ssg6BbTjow6MO0V7EcHPGg+QlB1wS8Q2Pbn4Nkwdw\ns4JpH+YPoL0Dj89Cc7/zVZB1m9DbnbCx+QvyTou9+oze5pxOsqIoEnpbU7qsmPT7DBd/zqh9RRXF\ntOMVG/mU/eKMedXjg/wl1DVJUdJalHSu1q7EXuK1Z4c4nhvjuFuCv+/HMLj6afi8WCdcHw6Y06MT\nLTjv7TCON6jSmKiCSTFilbYYFjOui00WRZfeZM60NWARdSmvUtZFSlTWvO08JD1fc1w+IElKpjeb\ncBJ7lbVNQgkKYY15AusF9aii7LxmmUTMuWFBhzFDCt5yyTNSlsyrIZP5iGo2gPEOXO7DUQqrZ1At\nG+t2hafvf4tXnl7jUbImjXuAYyKyUhVtU9m+uPG3SE6FrVUJXLk9omWLCKZMze8+7kg4fEV4GBXT\nEHr7Bf5Q2rASDHM8THnT+LsJDKmsliyIHH8Dkao9KSnpBjcPz/GQqtDdHM/8VGqu0PG3hEkRd0Jk\nLoULFRpTuu0SN7sFGokF+Bg4gIc5/M6QdHeTZx8u+Ixfcsgb9o5aZN/2YTuDbYh2SpLBGQ/nX/K7\n2QvisuTH9TeOaSoFZRyapcTZuzX+Xhfjm1WfRuSbCfmqRTmQWNxkq5iSt2JmdY8yTtiqbtiZTCiz\noFSzeUUsq/US9zaURydFpQDMGC9BeYB7lVaPJ/o1VM8ioqJm6+qGbSbMOj2+X39NmUacDrfpVQta\nZcEybtEqS1pFTp63aE9XpEkFMSyuOnRPctKyDFP1Aq8rK6KrotWbhHoxigwLUyxgfRgxHfQZM2La\nGXKa7rNep7zJHtFOcibrIefnB5TTDC4i8gvIfl5TVlBXBatsScmanAkLIsZ8xpJt3nDAUb3Lxcku\n5YsU3t7AXyW4IhShSW5vU3ns4wLnAA+tnvFuAp8sb7lEqg0ivO39jjtyK/772v1whV2e428VEi0W\nPK6rNOB+4zox1OSCiGegzaeiGCKiKNyktmU5aAy0mXOcJn3SOL/Gi3ComIkAMJl+orU2w6YQTMoP\n4XsV/Cim+8+P+eDwWz5gwscc88HOax5sX5BGBfu85en6JduzK0ZvbkheVqELR8AZzI5hVcCggnoL\n2h38/bGSmYq2isohC/iBffY0DF01ilh+lHKz1aNMYla0qYiZMiBlTUVCTcTe+ox+saBoxwzHK8qk\npnVaEyvk/rXd75e456PwehPSkTcoiovqzGxCPYKqHTHd6lCncJQd8LA8piJm0WqR1cFqbK8KFq02\nSVxQkDC8mdF5U0InYpVktC8KkpvKC4YpUi0ZLt2x2RgfwQnCYY0GUtWwOGhRtFPyImORdimilKqO\nmRUDusmcYXTDV61n9FYrPk5/RVlntK/X1MQUlyn5os3sL/rkBy0uvtzh8nKbycUm5WVqY7eGT67h\n8ZjJJ32+7D7n/+Dv8Yv8B8z/pA9fJ/DzEsYvCZjKAn+B8ytbX0oSFCA6aCwErc0nQE1d/zu/zW6F\nkqYWeMba54QHUDESZR+KDKNsyVP8/ZMC7Dbw5BvwaIeKqwr0VCadOAMCvHYJJp8YiQJ+XuExZ4Un\ntbJk0agGQJugIj8N1yc/gk8q+L0WPIDk92oePbjkg41viUbw/d1fstW5oMeCH/AL9jmjly/5pPiC\n9tWa7mnhuUe/xiN259BfQX8J9RQiFWES8L/k3aziJSHD+gaHWobhd/4ZpJdQDiBPUioSClJKEla0\nuWHIFpd0WVAnEbO0y87llJthl+5qQbSsvQqbKBgq0HWEv/LzBk/SFP9MgkyyM4LoLSRPavrXOXUN\ng4MZL1uPaLEmoqIXBXZkkaW06xXZes3b7EMG2deUO5Asa3qzPLSX4lQN6Z8Md+Ff4GU0lWyraRxC\nvYZqF+Il5J2M686QDgsqavI6Yae4IG7lVEnCmpgtrsh6a2Z1n+HNjHa+Jk4hildwMmNv99KY3V/5\ni78+x4NPZsCuz1Omq3/C2ep/YVJvUPRbAcf+I+D7c84+6PEn3T/kT/mX+bMvfw/+2x/BnxZw/tc4\nQCvrVViGeESv8XyM7z7uSDiAAyZayQpXVYQHKho/QrhVeKPGayQo5VcEkjFevyC2757jju4BwX05\nIUjSE8JKFvOtaZ7t466HqjVFhNkSG+0Dbl/V9uMashT+MIJRGiKEz4ER1PsL3kR9uo/nPOUlaxKW\ndNgzVt4Bxzwu3tIel6Rf1O4F9e12kj1DbvOyIpEge3gtlhuCjMzxJMav8LquihQuIHsBUa9mcLRm\ntVewGtSsaBNTkVIwYEpMRU6LOK55cHPBdLNFFJV0TiuijvVtjFfzF+tbGexrwjgoUvsKj37KEJS8\n34YqgslOh6PWAa14xR7nxFS85SG71QWrus086bImY5W0+ez0G252WrTTBYnSIT7H2ekDm26V7vzK\nls4eXtOn21hWFoiJWgFDve73uWhv0mHJnD5LOlxG20zTAeNoRE3EU76lIqIgZRoNGLZnxG2IlDAs\ni/4lXqVAZSl+ZUvIIu9ZVLDVG7M1GXsQRks7hupnMX/c/Qvy+T9gfdCi+vdg9p+l/GX2I/4Rf8x/\nN/tXOPmv/pj6v47hL5eErGTxTia2IN7vuCO34r+o/Z2Fh4QNrAiBkH1JPqnPz/AUVUUXlniClqi/\nqm8gQspHhNXwOY49gDPXhPKf4fHrIY76KsqxY+1vAx9CPICDJ+EWh/g7ZAVp/JHd7hnwwRqyhA8+\n/jUPkiM+4AUVCc94wSHHxFR8j1/TIudhfswP3n7lhawVpv+KYLpf4HleKgcpHljEu/jWx/hrIuXC\nKtXin4a6gtXziM5Vzc2ow+nmNr1ozjGHdFnQIuctDxgx5uHihOPsgO/dvCCdlUQLqIz+ECtt4Bu7\nxwQPRJkiv62idogTGVd4CQ4TFqu9lGIjpooj5u02i6hDlyURFdf1JiUpT66OyXsJV50RW+U1W5dz\nIgVsmvrl5zhVQniMsqRPbfwO8LKgT7n1IIsHkJ5BOYwodmuKNOamtUEUV5QkzOhzzi4RNSUxKzo8\nq19wsDxlnbRpFyvScU1WVm5k/hpnefcJ1ktFwIS+wA3iJn5Y4KVUP+YWr3zx40NGyTWruM1VtMmK\nDjktLtnmp/yYFiv+L36fn+a/w5c//ZTyf+jAP4L6r6+px1u/zW6FKuvE+OvLFoSZuyGsMCH9MUEY\nvCHM3hp/+7GIOCrhpbJhGZ7DIMrvJzimoBixwoWarZowQ6ILi1kpFE+FaC2Ls8K9G2GjXYImOCW8\nE3gBvEjh44Jl3WHKgF/xfT7jl6xoM2LMBhO2uWSfE1ZVl9owo0h1VL7A3wn7IWFxH+N46wrnJUlT\nicx5ZEO+gUM3B8CfQfQhdP6qph5G9Nsr2qs1SatkLz6lTc4FO+xxTkJJnqV8tHpBHJXM9lP6l0UA\nJKWMJNtFFRGTXOTVNe7pFXiaxRlelb8H7aIguYmIcljvpXSya9ZJypQBW/MJw3xG1YnZOM2JDyq6\ns+JdxvGMIEQLvFSCiJ5icC+tz+c4By8jbNYHoZ/pC6g+gmUnC9ZT3Wb75IbPHzxno5pQxAk9C0mO\n2eCz+vMQco1gnSWs0x4bswVlWpF0CLpJzG8RZ0v8Xc6qEihaT4W/olT5VNh1X8Oz+JhyK6Lazol7\nFW9aDzljl7/gJ5yzyw1DMgr2WmfMf9LnfPcB5X8A+Z/Lj/vu446EwyWOWElcJjjXX3kTQmjPcYah\nSEI9nH/fx51JUWSVQacVIsbbEY46qRjJR7gJoIpSuo/46X28bt8YqgeOR27ybr6NQs8vgIcQfVRQ\nrxNuZkOGoxs+5IgOSxJKTjjgkGM6LIipeVQeUXYhmkByZV3Zs7YUFVNkYkVY3GLLqlbJAE9FWeH5\nUzT6qUzzB1C2I4puxIApOSk10a170a5XdFmQxgWLXotkvKR9U1AQk1WVc5BinBW8anw2w8mc3xAE\nwQBnNIvYGAeqRFRAWtVB9laRBYUzWuWaKovJ44wiTVg8rhmNFxxtbXN4fhX6omJbKo0gPtYKJw22\nw/TxxMasxgNK8v1XYZziY+gNc1Z7Eb3FkslBh349Y295wVlvh5SSnBaHnBDXFVGUUEUR2bqgk+fE\ncU0yw+uxqF6PAluiLUwJ7qcM1WtbU5d48E2Z3mJCl7DaSZgN24zjDQoyNrhhxDW5mT+XbDOny2zR\nY5G2qU8z2H4vowG4M+Eg7a5IgmJNYqKJ/y1ef41nGqqepNiIezioKdhZdFqFJgVFqxxdH0/62sXz\nEA5xhzDBKamW7BIlUGuWCJMpLaS6q9osCbf1QOsog35Ju7WizZI1GSUJfWYklKxoc8kOT4rXRFFN\nlECiFApVR1MZC1WpU/dE+lTQRQJK9A4RHk/wbGqRLQ27SjsVURJRtXKoKlZxh4wV/WrGRj4loqaI\nU8o0CryHvCRvRx51E7yTNn7/An8jHHgpRbka6rush/0ASLIfpq3oRgyXc8gjkvWScqtm2howqzts\nT6YUrYgyq9maTKlWEbVZKNF1434TnFskMFbkW2E14LoowYWbJUDWm5AsYTrKaK/XxK2SMo1oVyvS\nuGBJJwjSPKbopNR1QroqSWYlmYiJTUC0tB9x6vTKCbGi5WnLNVN5kGZdmyh81jmuOBv1iam4YciY\nETE1ETVjRlyyTUJF1s5JyzXrKvMk0/c47ijxSklVzVoBYgr2cc2uVS3UaIzn6yuyMcF3ywWeSq1E\nmj5uY7cJIvozQkGZP7K/VRKsarQLvpoy3n2Hwsrry8pSUNRAMIbIjxXQqkh6K7baV1QkbHLNkBuG\n3HDACRtMGDHmItohyiuSt3jBaAmBCW6OXoahmB/Dag65cAkZUwKs1ZdT3K/+Cg+LVza8a6jWEVUS\n0ZpXRHVNTovhckY2rSmjmLQs6N4UJLOaqgPti9JfBD3BK/yJpSwGtOrBKOlRXmCzrIYibtoMC4iq\nmtZpRVyW9G5yFmmX1nJNf7qkaENaFIxbG3TzFe1xGZ7xW5zUqipqynLW0fQglfO0xHl4UeO7BOIZ\nZOOauKrp3axJo5JZ0qck4YYBMSVdFqziFls3MybZkDl9khucUqBnVbqPeB/KexPZVcqmuczlhp3g\nAt5ImavdiMiEwT4n5GSsaJNQ3FqlSzq0sjWtpIBVFBTGex53JByUZr3EN95jnNOgzS8+eJcwe08J\nCN8eYfaalZaV8pzg5djlzEUE1fRDiP/Arp/xLlKn8vbKn1dCT8btbqqt7dahp9QrD+gRnicmqoTK\nJM4jqmmbcT6iIOWSbdq2ci7ZJqfFNheMojFVHIdZEUP7Bn8RGNwyqOszaKeQbUPSNQEhbXyKwyeq\n5yJqvxJCISw2Q8OT05pqnZBVJaP1mN2LCet2zGTYpUgTkgJWrYx0UhMrcbC5WLXplJA4wr1CpcGA\nv4lPaRsqiPwWD3Uugkt1fdCnysIGGF3N2D6ZMTxf0rkuiauaLFkTj4ExRKcQVbzrpapMgrxBZTr3\n8HKQr/FkTlFjFA5WzZQSBuOcdSdib3pJP1+SUvBwfsr2ekxCQTudU3ZqtmYTRtczYkXOmxn06ofu\nId311u5/3vhRdvY1nrcl/WQW4noUk1JQEVMTk1ARUbPNFUNu2OKKgpTVeZ/lZBDaeMN7H3ckHHZw\npKWNp+iqYrRmS7uviSmIHv2SsLIHBARKovoNYZYf4EVb/lXgU6hXUP2l3fcD3P4WkQDCrD0mCAQ5\npqouZbTu9dKT5/p2qYRBRDBEnuLs7Rh6e2O225c85SUZa2b0SCj4kG8YckOLNS95wvAqD7dqgnwP\nGt2wNJKoD8keVKeQRBCrVsu5PYao/Wf2mFeEjSDm9v+Jvy92AOzW9C9zFu2UtCi43BzQWy0ZVMGt\noIbRdA67NdGvCZv8Y7wkZ4S/RWCFm9Fv7PN/xqZIRblUc1bhxy28xIaZzaPrOSf9XRhBpygYd4cc\n7++SUFNHUMYxkbLTtbnHdh+F+OVhalzESxN7UuVD5dGqKp0sNYsexFHNuhPzbf8xN50eHZZcdUes\nsoyImlads8oyzjs7tCsL0Wgjy5qSUN7B03z+zMZQZUOv7fMZwRJSKRFlFMwJwuIrGPy0YLSc0Fus\nmNOlz4wBU67Z5IYhCSUJJWQl5UYBy8pfBPcexx0JhyO8nJXy6z8kzObHeOUiid4ZYbY1wqd48dgj\nQtqzstJ+SBAGPyEU9fwEz2ozKJpz4P/GVd1TvPqOSpMVhN2UBawhVpTihU+4/Ft5NYqX/xxnUF8A\ndcX8zYgXF89okfMxX5JQsaTLgi5f8jFT+ny//IIorp0YIzflgpCVK9Mz4TaxMI2hbkMqyrxeuCRh\nsIMXdZrgIe8PCLV4LXwXl5Bc1mwc53RvarrxnGl3QN4Oiz+ua4p2DTlM/zB1nEW1Xjo2dC8Jm105\nStLCY5seGWhP8dKJomErCddy6KpezW5+waoFs2FKnK558PKceg2tV7D7wphfyhwXKPsBXoZBhcBF\nJVdRsSnu3erdMitbIle8+5qMZSCbreOUreiKbr2gqhK2iwuzACPO410GxYynF0fBOX1MEIRHuEt1\nYO3dNOaxh7teUxw/wq77Gi/XoLovb8Mzxgc1nUVJP5mywyURFUva9JmxxRWbXLPJNV2WUKRQx/+v\nUMY7AiRHeKRB0YA3+KvMtMtEjDokVO4tCdDvB3i0Q9bHZwRNr9JYEc7EOSSM+gm+kr+PQ+xK/lLN\ngg8JK3cP+ArqbagnuDtz7ZRkJbw1zdWHOCB2CKwStr5/RNoJEv6EAz7kG1rklCTBZ6XDpN5gODkP\nrEf5v1/hldpUosFi+uUeJG/h5gVsqPhUjicSqpyAfFrhr1i7F4RXbardJdRDqFYRg+mKOlpT1hHt\neXELpi22IgbnRZiKX+I5ampX2eJ9bmUr2DRd4yDlz3FiVmX9VA1fm/50Bul1RZHGrJ4kVFnMejdi\n3YtpVRVpp6bIIJVVJWr2Gxyf2cd9d+HbKmmpZMkOHkJUmo4SJy2cXPZgXbZJKdg+m7MYxCw7LdZk\nZEXBo+kpq37Cqh/TWtTEb61kyWtrZ5PAR1KNoRlBx+0QrLttPB9L6TqKgukalSlRWYkS4j5EuzHj\nHRGyXjFhg7c8pCZiTUZrY0l7tWC12YeN949W3JHlAGHEtvEXmCqK8AbnGqgGQESoyS93o5mm+kOC\nG6AUbZGbdnDUTQVoP8HtbaVCK4lrFydMXeHO+SFEw2A93FKnh9Adu3bab5yuEowv8Jc3bVYUVcIo\nGTNmk5IkMPxok9Nihwsy1hwszim38IWp+L1KN6zwquMLWH0DdQbDg2Ax3pZHlM+tgIxgnJKwQbVp\nVCrByipUQyg6kG8FEtK6jIlXNWUnZt5P4Toim9deNf1xYxrBIZ85XqJBcn5uwyt8WEmyR3jYVbVg\nmj55Dqu9hOE4RE+IA0q/2M6oyyiECgc48UvAoly6lCDTRa1Z4laD4CqFduXXS4DJ1cigImZzNaWO\nIvJ2CnnKPOqTrUt2p9dkq5r2TUE2rlm3Yyf9yiOd4vRtmz8GhLFUrRtZXSqlKWxc86hrLC+wHkGV\nRKw2MnIyamJKy4tpkXPNiISSVpTTbq2I6zpc+57HHQkHOWEzPAtScbjI/j/BcyuOCBtcbJUeXkJL\neexvCDMgcStb33KVbyMi4CjZRzgNTclVGUHdi5C1hKyE9gZkGzZiNcxz10IyD1WiEOvWANipiXpr\nbuZD1nWLh7w1iZ6yok2LnDYrFnTJWymp4u2KSqiq2xAvrm0LurcDhSHgpQFnt/VuFR8Xaq+Aj3xz\n1eLVwk2h2oxIr6F7XNI+q+iO12TTijKNqeKUOovIZNqCp5sIhJWLoA2hnLgMh3REGVFf9vHNOOE2\nrbsYRqx2YxhCf7KmqhPypEUVx+TDiGgWBSAyJwhMWT/SvsJnLvGiUcrUV2Jshuf/ybpQlTgBzlbT\npUoiqkVEZ7Vm2u3QnudsTOd01yvqpA7U5xUk0zpEclRTSLVbVL1QS1qRFLmQKvEoEpmKeQlULvEa\nPOayRi8hWdV0b9ZscUVCyTVbXLJFi5wec5Z0GFcj4nYO68oZq+9x3JFwiPENqZWhkuDNCjmyAvRW\nos8IFsIz3DVRubcTnCcrOFxMG4U7lX2kDE0lBHRx9iOE1Sba9AKqBZQnIVe5tlVVbocJEytRcq7J\nmToMTfcHM7Y3L3kcv+aGIRtMSKhu8xZqInaKC7Lz2jPHpXllZq+ti82NlppZ2YK+yk0IDBRu28aZ\n5grCKJFUeToDKDcgLmqiKHxWdKHYDLVP0qKkN1+RLCufNtGjo0b7SogVyr9B2Pwf4Hiu0qV7OINS\n7ck1eQPRtWV8tqA+j5hsdli3Mshqis2I3nUO/ZpaIWPlyildfIbjIms8+3IH1xHiMyjxqll6QQEz\n45Gky5LuvGAdpQzmS5IlZGUBWUmVGhV9IyLfjryYVYy/TVHLbG6fqWCZ6DcXeN2gG1wIqAC5XM01\n7jKOwtKMWzm9ak7Gmh4zhkyJqNjhkiE3RFFNVcUeWXrP446EwwRfrVc48V1iXIIhI+yIPgHu/hpP\nudYIarOD24EQVux143sV4DzEmSYn+M5WIpWA0tfhvvE+FBWsSyjm0KqDdt6IPUoh01TZ2g/xBNLj\niFaVUxIH7gA3tFlREbHLORF1YEsmBVlUMd7phm6McMT8jAClvLbP5XKsYXWBZ68r/UMLvZnp3sZJ\nSoqdi5Dah7i2RKEWt6UHsxUk85r0pmadJVTtKER0VbBbLoLqlwiklUyHdwt4K/ik6no57wqzVpju\n6iOodyCNqjB1g5pOvqQ/XRDVNd0vKqKshj5EV3hdFW2uNZ6ao2Wh51ddi8d47t/MnlmBMkXIpbE3\nAujLCSRRTtmtGR90GQ/7tIo17aOKKIFsUpNe1w4uqj8TPNO6KaTPcGKaAGPNgTghLxvjquzWt9yS\nz6ItgsKKIlrkbDKmb37pnF6wTKMVnc6SuKydTPcexx0JB+EIKomlXaaCnREeKN8ivAylR0BiZGEI\ndi4Ju/AjnO1SEpy7EWE2Xtt1e3iRmZd4VtKRXaty4/vADyDagGGTRbkLeQsuCuhEAdmWcJBWlza8\n4hYlz6MWaacgi9a84REnHPCE11RmAhSkvI0eEbVqhsulZzZK82rxPMBj5pZj1v2enXuCU4ebKL3K\nY7wkaCu5GVd48etvIfoKotcErGQN2duQrlxVUMbQ/bwgzgKtOXqD19CRFdOx9n5E8KOVTS8CrDTe\nGV6KUvVPC1j9AbcaNp4HMDKyEo0R0DsviYuauo5I2gHsi6Y2bc/w1JcU16z6u8LDl4peNxmnGQ47\nHVp7czwpeAHpG1h8GtNZronKmo3Jkq2bCdm6IrmGOoX4DGIJokNbftJl4nP08QTjXbwc6Qm+fOWu\nSuAZTee24r9A6XEYg7iuqaOIkpgxI9rkvOQJO1xww5CjmwdcXW1TPig8Rf49jjsSDs/x4pwDgrM1\nIoyA8n5V4ntNWMkvgf+NwAia4ND7mOBqzPGSWeA29hPereevfN0TQpB5ThA+sv0EOnagjmBsTieE\n62PL673A/Wtpqsd4WjDcloFYLLvkyxZT+uxzwjNeUBETU7HBmEe8YZ8TKiLi86AR30kiekCQf2c2\nFKpw1ydkRy7hSpmRVzjJVN5ZYUOul28t7DtRTQ7s832C/P1FaKPoRswfpdRFRDSA6AJq1adtMPVu\nU523bDr27F7K+fgW97m/j5cFHXILnLZ/StgoTUwgCs9T92G5FxOlNatWyqqdsupE1C2Yfxo7szLH\nsewL/L01uzieoLo8lzhwKWb+Hq5rnuKM006Yj864CtbUuqKKaqpOTVSH5847UEsIyKWx8pi3eIjm\nNLGxVk0huRgSVKo+qDwKXSc3McJ1XwTxuiKvg1XaZ8aEIbtcUBOxxRXPh18xyJd0uiuix++fhX1H\noUypRtH2znGY+YeEGRQAqajEHuH9lWd4XvIeQT2qJposEAWSC0JBT8WCTEBEOdTPCULlLcFy6OBv\nXxLbpIaoB1EnqFBiKGt4ELkpXDW6eEzYEP86t8h9UhQknYLN1jUVCX3mnLJPlwUZORM2+IYPw8Ru\nL9hZ3hC/wtmVSiJqmr+KdRO6Ge/DlgpMb+LhMw3b5wR5ankLt5pIaSSqpyAh9+NwftGLqVcZWbug\n7kE0Dglht66A8jo2eddVkNB4i/v7AiClCZV2Lsx4A6oBFJsRrSyY5mUHkhKiOhSlWQ0zhkdBGuWE\ne/WuK+gGnz9S3YO31g+9zEpCqIW/muNH+JsAhYOLvyK8+2tuCUtVBrPdFt1ZQVZUVGlEXEZEs/o2\nXDt90mL4Ze4Jx29tXE5w5qYsGd1bNGndX1iDcCEJ39yWp1KH9qDOoXoIi16LKopY0iWiZsCUEWNO\nOLh1Z7PDOfPxiHqpnPDvPu7IcpBtJJhWL/wQKb+Jwqhs0A7u3I9xx06hSpGWmjUgO4Td8QxXkQnE\nhxA9wJklJf4WJ2EZ83BuNIPEckA6FSRr6NeeUac3tL3By19qUcwg66/odBaso5QOS0aMQ2UlIjIK\nWqwpSYio2ZzdUBCFxxL9VglLB3hykBYQjceV6Sxs95H9r8cW3rBBMKbkRX3Iu9mkOSEUN4D2eUl/\nvnzlOdgAACAASURBVIQ55CRUSUSZ4fkIlbWt+rnf2u9d+/yRjY1K02l8VHV/E+dwFBB3oXVWwwLW\nq4RoErGgFejU64pWvWY9iqjaEdEqgnVYBdTBgrrNlRAouoPnWchr1cac4ZmR4HV+VrgVcmD9fAvF\nTsTgNGfRyagqiErI04zlZkLZhvQKhl/nXlhcuMsAf7G4cKBmta4uwSMe4NTzGR6l2m2cq/CmVSoo\nH4aM2mxZGel/TUVMaaX9NpjQJieiZrloE61joo4e+LuPOxIOfRzBusQJ37sEtqMKICoHNyaUzOnj\nTniEr1JFPRRgV8D6kjAzek2ZpRGWXxvj8cra2sVDqEqiN1uSOuQRJy3oJbDfhsJ25pxgyKhYlarR\nKZK6CSs6VHnMus4YMeYLPqEiJrOJHDClx5we85D4lNdhM4nioYVG4xEEyxhmW9/AzTlhQWc4prrG\nyTQ7+ObQ+2OucXReqP4KKstvqLYjqmlk0YSI1TChzvDirKvGj4omiwGoLPuWffcpDhdJIKgtYSuK\nNlRQ9mPqLKI4iFnHEXUWUcURyawmWdS0xjVlCpHupUhIQrDe5JrIWlIZPQGNSgqWlSOgV5HyU9zl\neACtoiZaw/BoFTCGEtJZSeuyoo4DGe2W7CS25RmBoi5BKrdGYKMiFKpFrBCq6NvKKpCSEEnLDNv0\nNbSualrrgq3FDe2yYMqAgpQJQ64NYBhyQzvKyce9kHz1nscdCYcx/iIW5ZD2CGpMKlh5wMIhZFs1\nc4C12pTuJlRKyJzS/1QURlGJxEKSW4SV1G30w5L59c7GuhfO1cLt8S5Pv4/XgknwVGjb4PFwTd2C\nLkvGbNA20rTCmAUpazLWpCR6DNVfEKClQikyoircbSAMY6pUFIF0Z3h9B1k3esyHOJqvLEC5KdfQ\nvq6gB1FRkyVVwARmBWlckTbrlcpCXeOg6YxgMYhH0QwPytLatv4IHFMFv36oZ1nsRXTyNWW3ZvjX\nS+jAfCtj1WpRtuLbZ44UXNJma+GVrUv8/crK6lfilbLwxW1QAu8UL3i7snk24VZuhChNOYR8IyLJ\na1o3BXWrJlbQq2y0JRdLVohyTWQJKNDWxg3j5vyqv1P7TACAiuCahVRsQtmpabGkjELC9oIOKSUt\ncgZMyVjTbc9J+0t4vyJQwJ0JhwFhBOREyyn9FqfKKRtHfIQnePEVrbSmLSnRrySHFsFqEOy7thyJ\nx8BTyFohle/WUhBhoYMPSzvA0GXi8kdpIapMJ3jiBQ60icY8h+qsRTFp0TVf4BFvOGeHiDoUUTG3\nAmDS63mMHXvMV/b3KR7VVU3bTeBRCEF293FWoTSlBMBrHPTaxSMFA2tDZS+UtTmGxW7MdLtNaWh7\nMYA0r8KG0TCpmJY8RBuy29qMIxx5N3CRIZ6gdkMQXiuov4XVfkRcQ7wOplJ6VcMuZHlN67Qgu1kT\nL6pgSM2tGM4Sijhm9qzlaTjy7ROCrpGJnxE4F5/hVQU/IQhyuTwizf4AiucRq0cxdQLxBMoMohSK\nUcRsP6XuQ7qqQ/KXCLmy9iQQhW/P8UTgGLdaxFs5wq2aK1wAS9jIgK4J2NZ5uEc8gTJJmXW6VHFE\nxpqO1QxRFfE3+UOuFlvEvZx68f5EhzsCJOWAKevxFeFtUOKJjgmjpnJCypJ8ir+0VDakHPEvCGpD\n+RRL/A3aRWir7kB9BZSQt0POxC21T6J/aCnI02CztrrOgBS3apMwQWK01QSzWf7gW4IfOYI6ikla\nNYuox5qMj0l5wiu6zNlgYiDllH3OWGUZxSakqo63i79E6ZwAnchfnUL9TQDreMi7JdlkNr/F6dwa\nygkhcgGuObUKptbWFbSnFevtiumwTX+6IjWhsPheTFLWLDodRiy8XF2bYN18g79PKLNx0bsidq2f\n5lKsPozIzmriEeSb0LqsiTKIjM9WHAZ+QWsRsRqkDK5zaAcv77bG4gzSVkVysQ5Tr1oM2wQLZk4Q\njgpkTQkY9Q9tXGWpbTSutahFelWTzELxHU4g7cLL/T0eTs5IlhWRyFRLvKC6+BMShrLyFgTB9MKu\nOcBfF6rCLsoDHOJKaIG/tmJka65l54xC6DS+rMn7LVqs2GDMgi41ET0CMeqD7CVxFmpdvl4pbvvd\nxx1Wnx7iL63ZJ8zUZ4T0aeX3yk67IqxklRGeESD1TdwGP8Ud5xrPZjkmrBJxKwyzqGV7qmLLKbAH\nWQFlCu0BrAbeXDNer1JswlKF1mPd/l1uAyCdT27Y2jkji3N2uOCKLUaMb/3ClDUpBUvatFkH16IF\nf/+/+Zf4j//h332/oTz67lPuj/9/j7//9/6E/+jf+NOwNl4QBMM/JrifP8fD4Eu8CLpo7YLCtEVG\nOOvyFH+lSgzrPShbEZ16CVHNGx4z5IZNrk3WLYmimhY5F+UO5Xnf8zu+47gjt0LhQtHVRITaJ4zU\nhX2X44Fh7UBFEn5GcEN+RRDBimAI5TnEcQjJQFVA/RLf7aqsYYHtsoKo8pBfB2esKRYtEE2x/QQ3\n/ToEU7YDtGtaZc5y3WGdt0goiakIZcwzYmrWtEJGJiOqOqKqIkphoffH39ojkuuphCtV3FbpOGWI\npnh0PsXDvy38jQlK2GrjeIoBqekFpHlJNE+4Zos+U07Z4xFvrLpDeLVgRcxq2SLuN8ocfsdxR8JB\n+bMRDv7NCe+S1EtnjK96i+4oK1JVQ5X7MCTsZKvrdZvlqZjQOSFgfYpTzsSUNCg47QVbNVpZ/D52\n3FMkIkVR5WIIDBPolhK0wzX+Rqd2xTTtsVz2GM9HZHVueXM1NaHC0SbXzOiT06IdrSi6MYlYdvfH\n396jJHDrxNZ8TfCez3HYTLmGUgZ6n5JKovbwDFsZuR08CS+HKIMqTll3wnsz5vRZ0+IrPrrFtUZc\n0yIniqG3f/Xej3BHwkG1srTTpMHBM3mUlq3ROSHY69v4q4kUe9ogbP41QURP7fclzgxSJqZyeY0p\nGV1BnId7RitY1z5htXVNnCuVodT3gk7kDyoN2cJ7CTVJDfm8w37/lJtoSErJObtcsUVKQU6LkoQO\nC5Z0qOrkNm/i/vjbe9RdwppRToTczjZBeYh+rjyPPft+jqcAqfiugEjp0gs8dFtBZ5WT5RUVMS1y\nEkpSSkaM6TGnMJZWO1oRJ4rKffdxR8JBYKLyaoWgSkjEhN12gNdl3+JdFb7E+Qkq0JITEDHlL8+A\npxA/wkvNKxMGQjRiBPkqfJe0IUmd1a0YvWgSshIkMFSow/hStwky9tqMuLUm6y9oD+fEacEGN3RY\n0GFJhyXXbFITMeSGGYPw3oM0hBEZ/X801PfH3RwFYT2IGZkSipyJbg/vVg//f9h7rx5ZsiTP73dc\nhofKyIwUV5auFsPp2cEuuC984GcnQIIYYrlYjuyurq7quirzpggd4doPH+xYnLycATsLO9isBMKA\nRKqQHn7M7Zj9hQKkInff50hS0WvaGl9tOLgOCXQ92PVj2tjQo6AlYMqd62P1mHPMkiMSSpKk+lnJ\n4ZEakopDUJ3yY7xZzV+53xVqpqR/3YQpj1Vxy3pJf4GfXGRIHfeN3N7+hCf76wzt1j3PMUQN9BK3\nz7dQWEiMV3PSRpHqAWgHWtF353gpMAXRhJZmFJCElslgTmg6MnJ2DBix5sQpXC8Z0yE+EUfdil7R\n+P7FIZ5u6ABshedH1PhGtk6dUvwEI8XL12+Q8wrkQqGI1gw51Y/BZobFNKMJQzbBgB39PW27JaQi\nwTgE3cfuGdbA9qcTP636C/FIlcMUnxgyZEuggCgdQ66QxfsaqbFUH1I1G17jj7im3gVyKf8eb0Iz\nkupgPyhWHK2qkgYOFzB3iEMDmZFSb77wCskdn3K+FGLxGk/gmSOFTgEMDfYyJZ8NWewm1DbmB75y\ncKeaD7wAYMyKhogpd9yGp6z6mYf2HuLphjJQnewoE/y4UgdnMVLoKopVcYFKLf8jfjvxt3imp9uV\nmzvL5C4nvobc7YMjGnb0aQldotgxZIPpLLPVFDt++In1SMlBtRZukWpAaWfKl9BVp+yc/xMRLFQq\n9gCpq36NrNQL/Ap9gRzFC7yszxFeR3KOV19dw1kLxoknprXXqf0j0Jv4occWaTh+hnzgE/fSVZYy\nQvRsVcRkA8OLBZP+gu3dEcNmQ0buZOFmBLTUxIS0tER7wErX79h+GXtQ0SGeZuhFReXk/wm/5dSL\njasAOHO3022rFszgYeU/4M2zR+yFYkxk6fULJnaFJWDGlI9cMGZFTewTRVQRRTXNWnlLfzkeKTmo\nCoYiZwbub1/hfcFUpKCHpE2VrVet9SXwz/iujU4mwAvWPkeO4hWCkFRNiHNBSsZjuFvAkdvibGN5\nCoXignwgr9xLG+Ehtda9pClej/GPeCjsuWVjB+Rxwuh8ziKccMotEQ1v+IxYKFbsGLBizIoxA7sl\n3TWkRfuzFHsO8csLo1MFpV6/Rk5zHaqBdyS7T9LShd8h59wbPKhKtSh02BdBtzHYoKO3q7EYUkp6\nlNREJFRENAR0DNhx1r8lDh6uE/dIySFFVlaGB9jHSHWg9kDK8VXVp2d4ZVQlS/0KOaLaqRkiSWOA\nRyc5qeZgIzLFpnQH3/F77Qgap56iWVk1bEuEkXmLx95rtr/GDz8cA5MJXn68sZhFQLXsEbcNA7Nl\nzIoNQwZsGLClw7BlsOda5CZjO8hYjvo0wSN9NIf49wkVJdOeucrDKbxG3Rd27AXN9zxDlY9T0Zh3\neBbrKX7JjEQVu8oi6l5A5+jZMiaX6LPb+7JWbUJ6dt/+6/8/HrHnoMwd7bIoEUoFBSfua4uv2ydI\nRaCMlR2yYVNgRwrmFLGn1lawM76xpWgy2FJo1wYR3LQxbBRcVflG4LF7eSPjcQyKyZrjs3zNp/Jf\niotoLGHREeQB7Tbmxp6x5IgxK/rkrBmRUjJxrkQdASf2joaIqGvo7MMJMof45YUFr9j1CtlKaGNS\n1bxUWcC5ju37WGq1qHKqp3hhtO7e45ZAz5LUNVFXO3k4SKgoSVkzdB0uOb96Ucn2Rg1F/nI8ooak\nGilk+Na/ThL0SCiBXccGBZ63rGKxKtIC3tpI2ZsKjU4gzuHIQDJAMMpK+95CdA+VUt27m05cFfOg\nMmQ7PHJb7TW0caRtjX8OsT2DNQFtHBCahjkTSlJKUnb0aQjZ0eeGs313ubYxIQ2BOYwrnnxo41pJ\nbeC5Jep6rnZ4KkgT4glbQ/61ApTKqjpWaVxA0fX5ED1nwRF3TB1tO3QAuyEFGR0Bs80xtnv4fvUR\nWZkqWKCzQsUeXCOrq4ccgQKBlqlylHJs43v/18dpRS7ZDpEjeIPoMbyF8BlsGmgKR9d2tV2WCjl/\nkMIo8Wo8qhCkorGqRqzza1UCVnbkfcb3BHgB7ccIG3QEWcuREZLVjVM9bYiIaGlwdmpUJKYm7Dqy\nvBUx0EM83biP5bvDI/YnyEK3SDVxjm9kq7akAnzfIdtYleVTBqfK7BeyS466jp4pHQx/REGPG85Z\ncbT3RtkwYLOaQPbwacUj4Rx+xHObXyCJQH3DHNUunMkWoVEPeuUbb5BVqVpeqpulNHAtxzP24grZ\nAPIZ2BCiqYeiYqCOxPRhd6+MV03C3D2MikqpPo16P2hxEyMJRD88Bb0kEA1q8uWIVTDhKJ0zoeIt\nr/kb/p6KmDtOOOWGDUNqYvIwo3dcUI8OPYcnHeqakCKnYYqcpqpIoGoEatUa4unsWzwTU+lCWi3o\n5P+5/F73IrYDcd6qSIhp2DHA0FHRY8odS46oSEknW+rFwxiZ8GiVw3PkSHyLrKY/4WsnN/9pb6FR\nCLWCCH6P9BxUY9LgfeLUD81ZEw/XiEPMAnb/5JRRp9A20LmeA62IXwSJJ7ccy9PvfXSfI4ngBvnA\nVTlILeBe45PGP+DV9WfABExgMTew2Q0ZsSag48x5rw/Y0ifnI88wWEpSMptj6KiN+sgd4kmGqluN\n8RN13Tqo9tAHZGKhmD6VuAMv7X+DB0GpH+lnSFVRQzsPqEzCmCUhLWfccMFHXvOWE+b78XlAx7Q3\nw3z38Bn5I1UOC+SIzPCbedXbVqDAGXLE1M/iDvhP7LcK+44M+E2cgqIy2ChzKUXAU3ciHTToy3TC\nGugyuLFSm1UxXDnxWO13jvAWbQHyATVIsaNKw+/wXIvXeC1Gl7PK9YDs1QqbdLQ2ZGXGTFhwyykR\nDV/wZ875yMYx6E7MDFuEDFcHWuZTDjNHJvGqB3qOnLrqd9khjUqdXhh8q+0lHkujeqFDZLmo9+lX\niMSptWwZcM2589QWq8WWkFtO+Rd+y5Ijwqbh8vJzuvNffM9BJXCUTbnEK6moFXSNF9c7Qi7Tifvf\nC/ykwumAG5XN2bE3WYyU4OVkdUwI+QbK2ueVHhAOIbUytlzg58na8wzxuCsVm+qQfaSKhHTIzkhd\n9wZgTEc83NF0hl5cUhtpehb0GLGmz85JxCUM2HLKLRuGRGHrDWgP8XTDaXzuFabUBk9NbVQq7hI5\n794hi/8WLxGgk3lVjlJjm5/Axobys4gRa46ZkVCK/R1HrBhzwxnPuSQjZ5kfEx/tMMkvXmAWvJbZ\nEhnmpviRpdIhVY1z5/6v+IeVu61qaPUdwEmVWR1GIgB6yoMtZAF3On5wTzM0jjprIAm8OtK1e5j7\nQtgpfpypblQ6+NCfdZCyA3adk4AzTrYrZMyahIo1QxYcMWeyh7xuGFJ3MVgo40fU4TnEf3dYVT5U\nAXWdNCig6Ro5na/wKtyaMFZ4Wb9n7gFV0l8l9hyBqwkCGiISavpO1TylJKbhJe/J6RFTMxqtiGwn\n1goPjEdKDiqNrN0X5Vlod08bi2ocqKKFBtEd0xWst3vhpgUd3kNzB5WFdgXB/YTQ8zLt1nrlZNUd\nVAHUCT5rqz2GPrw6603xWn/3BzBuG2KDgPYuoduERGHjwCgNN5whUqDsP9CYSoByJse2hqA6TCue\ndCijV4WCFNaj4rGqaRngKUEKnFLx4Ld4mpHe7w4P7eksSVfREbBmxJxjNgy5Y4qhE3NmEnb0Wa2O\n2RV97M3DLzqPaIen4IBjvE3yEk9gX7m/neBrrrfItuIKbwfknFKyFmlsKtQsAlqoM5gGYE5EBDCL\nISzFg6IzXjZCj4QeuwyYNX6fpxn71v38O7x8nMFfGQq8vFcSEAQttosJGss1Z3vcO0BFQkXMlgGn\n3NIQk9mchBITPlyx5xC/wLhFTnNVbzpCGpBrPOBJTd90LJ6x98nYGw3db7+pqrjbcpgYKAQfo14o\nFrPfWqwZMWRLQEtjA0xSk327ePBbeKTkcIS84wo//O0jcOj7orCw91A//k/IStZu3zGSRh32tNBk\nEyJJ5Y69MsYdciRN4IqUBPJAbqpotQ0wr8ViSRNBFH3qKaAmIz0+YXzvwZ36IY6R/HUF3SYm6Ncs\nd2Nm26mbdErpF9BRE7NhwEcumHHCLuhjI6cGdYinG9qk1obiJd6upXD/U+Xut/gWm44/j5ERZ45M\nNFRL8nfsxc7sAMpRSIdhzmRP155yR4DljBtqYiJaek1FXaYU//hwoZBHSg5KL9OOjDYi/879X/XZ\nz9irYcy/R47YW/xscYcwNsdg34E5g0ChZGcwjGVCYa3L4BvoFojPPG4vuHF6j8Aglqal5pmX7E2y\ndEK6F/uM3UsA3x7RUdQO6USfAhtDtwx5MfrA2eCGBUd0GDYMGbImpmbIhoKMmBprhXVTJwf49JMP\n1WaYIhcXFTpXz6QWkUFVf80NXiVqjrCAQc4pdRJXd0enUJBWJQN2tETMOOGGc+6YEtFwx5RrzqSC\nmC6JbIcNH77kHyk5/AZZTXuWEv6ovcW7rCjvVbXgFRClzCi1XnKKK9ZAp0SHCjYr4U6YBRQtBEZU\npU0LkXVJoeflt7S5qAhJhVTonFn3eupfmSPZ/RbfQNJe6TVytdhC26UUdUbaFLziPQ0RpZijM2JD\ngGXEmlNuOW4WtJuE5O7Qc3jKYVQS3+KxfeoIpoqGr9gP1vbYPeeejXH3V6CdOqnpCD0QEVtThOzI\nGLJhzIqIhpqYhoiQhhCRj8vbHt0y+VlSAI+UHL5DXqV6e33EWxUpslGhYODl5O6ZCuwRSZ+52zgA\nlTlyfFnHaw2HEB1DPxBbKBtAW8PQ1e2jCNbWkzvVml0xWaF76gQvXqX7SFXV1y3Ad3hOvnpXOgz9\n3d057/PX5GTkZDTElKQ0RDzjkoiGyDbEW8M4z8V27hBPN7TpqKxLPd1vkARwjVc/HN27jSL7lYSl\n5CvVe1Dd5B3YAqpBSGg7t6FuOOWWC64c8WrscA8R282Idht5w/gHxCNStnWmM8X3CV4gqXWJHImX\n7vaqxaZKGCr5pirVG7xSdQxGV3HrRGPXUlXUBqpa+g9l6CCuVp5TIc+KfVfTrCV7dyFA7qPGs2oe\noyNPbaOob2KENBY7S9w19Ho5fXaMHOTphBkdATUJI1YUpse8P/CQj0M82bBqTXiM50IoOU8xewqI\nUnGX+45l953L1I5FjeB28hzGwnBTOue0hobYYWcSSlJ6FPTtjsTWpHFJ0HQ/y/LgEZODusWovLwK\n9p+4n5fI0Xvjbv8GT+HWL4VRX7Afj9oaukv2l3sTSIrdWfnEepFgGlLceMiIMa4KUWmP9AbZ8w2R\n3HXmfm7xJZ7acWr7RJniY/bCVPbUEAxq7LClDUJKUgJaRqypiVk6Jl3t9P7aIBQj28Ow4mmHKpEr\nNkZVqJW3o0WzbkePodlB9cHdNkN20krt1mSi2w9nO9jEhtpE+z8vmOx1I3N6tCaECoqqR/rNBjP+\nxeMclGKm6bVCthJqGPgbZEOmONF3eNFZHQIrVFrbweoRFgHnMHwhCMlWOa4F8BGqEOoA1jk01tls\nGvkgcyQn6Uz6Ej9j1t7pnXsLA/ezyosrCErNu3VLsgmwhaGX5ZyEM+YcE9NQEdMj54jlfgR1xjVd\naKh6odjcHeLpRoicCx/x/YYMOTd+QJKCsjB/gPaPEMYQ6e5a1RKfIb5N6ndxhe/fB5CWLUfLHa2T\npdevlIKUii19+tGO1BaUH/rY4OGN7kc0tdGNueo1zPC2PypPn9/7O0ii0GmECrmkyJGf4xPGFWxn\n0LwVgRcMspLPpXrogKAHkZEPbtfIB5file/Ve/IZXn1njCeCKnBq5273mXuKV8huSBPFEKyNXWa3\nnHNNTM0F14R0BHR7J6xrzglpsAp8OcTTDSXf6QXlBum1a6Wpi/855HMwK+mlB+rarufgP+OtXbQf\npqPPc9h1GW/GL+hREtHwkXNyMj7w0kGnC2aX5yx/eEY7S3Ci5w+KR9xW6CZMESAV4hSrls+XyBHU\njs73SAJQh1Kt1XpIZaFCCinwUqYUunH/JoTjV3AUy7ZjUMPUSK5ZIrRtVZVWzwhFc9+6p/4Bn/FV\nOvzEvZ01kt11y6EOe7fuK+iYf3dB2EgiMFi+5xtaQjJ2e3mvATsGRUF20z6qi+kh/h2iw5+mLXJ+\naDPxjr08vZ1BdgTdAPKF/I5+3bnHUJaAXrgGyNQ/gOHtjm92fyak2Te4twx4zuWeiFWMEuzAYl7+\nPPDMI52CyqrM8d72LdLCVUJWhVeAypDEoSjKV+7+E/xKHPApTvUMSdcNfO+EFhIr4i4qdq3Fi3Ll\nQ/eSFMaqV/8z91QjBISZu6fTCm107+VoI+kWyVt3YE5avvztd9zaU2q3nXjNO1cxSDXRoyCgFZyD\nYiYO8WTDbN0Pqmb+EVi5LlsKdgV2B8VOxMm6xomcL2A4df9PRU2ACV5NUXVDfgvcgJ1CFUaU9Eio\nmLBgzJotA/6F32DoOD26pmsjqiqmqNW75S/HI7IytW6+QFaXkt0LZPUpe8lJuZHiV40K8VV411Hn\nQmpW8kWBnz26jNmYT70BZsj2QMVxVPneGWDt2yJqh7fDIyQVp6WATlWOssjQ5SUyYJmCLRJmy3Oi\nqOEV72gJxbsQS4+CnB5rRnQ2JNuoAO5/90E+xGOGjjFVWNbBc9oNVDeyfQid4tNqCbc7uN5IUmk2\nsA2guEGul+/xStYVHkUJ1G1IbBpSSlpCjllQktAROIRkQkNEL91Sfehj0198z0FrpBP8ytIaXb3m\nVClK3UN1s3+El5Kbub+7RGECCPtgzqWfYJRCWQCdb3FY5AdFO3YdhJ2nc+hIs8FfwQv3UKqQr6PM\nEzyQs4f0UnWYgoULC72Otg0Fy0DDMQuZPTsD3T47QlrG3Zr+sqJTX4NDPNmwOmH4EekRuFMxSgTE\n2xawWYp6QBHCKBbg7mUORQO7OdQKHo6R83KE37ZuHI9rYiitJIOIho6AkI6CnmNq7qjriPVyTDsK\npEp+YDxSclDG0hypEFQuR0EEivxQV1GtMhRVpKKNOX4/UAOB1GedkZpsT3oIXbJwN0+B1Hj2ZY3g\nILT90bqnuUXGmQGSJHJ8C0Rd+ZTn1cdf8ZVxHst9zKhhOF0wMqIE1aNgzJoRayJqGmKGbFgFI5bT\nAUYFQA7xdEMvRHp63gEl2LlDQy9gt4W7DvIC7nJYduKeYEsYhwLJsS2U79xj6jnhNE3NBnqLhtE8\np2kjDJYlR8w44YwbPjoE8jhYEfdLTK+RJffAeEQ9B6Vk58iGTMFP13gDiRbvAPK/IfXVFd6jPHS3\nfwNcOPj0T3Lzdg1G7YY6aI3vf3ZIsrgEbjo5ykMjB32MJIwf8R/uEFG0+wKpNs4RTPydu90aPyhR\n05sMKA3cGYKoZVmMSayQrcRVu6DPjoCOMcu9XfqgKEVF6GCH97RD8QnHyOk9BHLYlnD7EW7XsGqh\nahzYv4N166hABXzYQpLA3TuIB3jXtTu8+piR/nrQrxkZgd+fcU2P3NnjIeS+PKU1EcEm2G9HHvoW\nHilaBNswxrvDPMev0FNk2zBCVukzPCopct/VoOMCbzj4AkkuBXR6Cd/KwdSSaoXfu5UBDEdyM6XS\nqtqTssVn+Olq6F7iF3jWuLopKxrur5CctQJed1LI2ICGmML1F+44ccxMgc1l5KSURGHjVfMONSWQ\nLgAAIABJREFU8XTjCjlvvkPOq++AEgYGRhl8dgRfnsBvn3nBsQkw28F3BWxbGBzByRSCBkkKOd4/\nxfXFzBZmN6fMg2MKehgsfXJmTHnJe2IaNr0B1WZAu4jlfH5gPFJyyJDDoQPbCbKaPiArso/vHL7H\nTzP+d2T1KXTsV0gyuEY6gDovKpFVG4EZQXQKVS6UbEUwdkgFkCGbvf+veW2Nrx4myFZCVel0m6Ho\nthop1zokMbzBK+d/b7CLiKy3Y2ZOaAmZckdCzZxjDJaKmFNuSetSeqkZh3jq0XNfXyOn6DfIuRcL\nB3Czhj/P4YcrOQ1bYBfBVyl8ncDXQ/hwDXeKC1SBogmevn0DTS8g+LpgyIaWgDFrnnHlrA8aImqs\nMWSnS8JpLdffB8YjJYcAL744Qt6pApvUH3OH78ZcI0flhE9Rkm+QGuuv8SxN8PPIDWTWQZ0DSBce\nw64EFFWbVjk4nUX33cMG7PuZ4L7fuJfyDF86VkiC0AzvJCfCX1cEGcRtx2vecM41AR0Ws/cxbIio\nbEyg/Q5FZh7iyYbR3fA1HuOXQzWHJpfegppsB4GcUvMG/msB35Xw/UZQ/lEA7RVSuergboycaxsI\no45RXjBhwYgNHYYPPOcLfiSiISejZwpsbbDGjckfGI84yszx48uf8LK7XyCHTPGnIZJbL5HVOMHz\nYBX5qJdx5GcTgHFY6MJxsU0K/TOPsFabc5Xx0tpOqwdVm1aSlcqHJ8i24s/33kqD75Om7q04FZ92\nl2IaEZutiZlzwpYBAzZE1GTsBLBiI8o4pRkauf/BtuJphyIkK+C/ARbaVHa6RQI/FrDtZLj1f3V+\nzergPQN2nSNg6nBvhyyDK+QcmQtWIti2UIpUXOOUxWonDxdgaeqI1oaQdD+rKn2kU1BNIlROt4+s\nsB2SanWFhYjeu/qQq56DytLfAJ9B5JxFA2VqKh7i1E0uAlg3sLBw20LZycOpo5WOjBS0qQdQIdUK\nbtIiZ+1uO0KSxhCPrPyAx0dYeexonJPFO57xkQlzMnb02TkJL8uOAR+DC7ImF49MdQY8xJMNq6Nv\nJwNXv4VyBh824uHcINemOZ7NrYQApRMWuGtbgB/waRttAai20Vy8WNWQOXPNboCUklG8ZjRcEfaq\np8DKVD2syH0f4IUQlM7W8unlHWTFnLi/K0RxJ+mTyH0iPbCNzIDYQRpAHOKFHo1PAMqNUCrshfuu\noM23eF5YhJRzH5H8deJ+5t7L1pz3Cke+soRnBUHa0mG45LnbBSakVMTUzjmzoEdBtIJk0X26jTnE\n0wylZ88RX6U7KCupBkzrCb7/guchl/gkoT5uKRAGsFTXyAS/e9b+w7hjEszodTkBLSfM+MALcjJC\nWqKgoVr3abcpTB4+I3+k5KC63TG+CZAgnZsADy5XNNECX99ry7ZgT3NrndC/dTL1+5Xfc7iFEvqh\n3D0O5GhvkYJkaT1mXbFX6q3zAileZkhr4yd8IgDZOjih630LxLKHuAZxg+k6ItsRBoJvUFFZgC19\nOtdEmnJHPQ5otQdyaEo+7VBlMYAB9AfQP4cskmuRKr3pGf5XyOmpHvLae6w7uCmhr9Xkwt3oxn3l\nkK5rBvOCwmSsERftnGxP6kspiZOSoF/Bh4eXpI/ErbhBVmOCHMGXyIp7hwejKw5C545rZId2i1el\ndlsHQrxbt0rquFlPV4CJ/M5lhRtxGnkZUyPJwGHfSdxD/jXCiFsjrQ7Hud8rTLstw35rooWQsvCm\n0O0iuiIinqzomYITZrziHUcsaAnYMcA6T4shG7LLRrx5an7WyOkQv8Ao8czeWr5dX8PxBKKl0Hxo\n5NT6vbvLMZIkYK8VxMUAemNnDF8gFx4nkUoO/ATV/xJRnMRUJtkng9e8ZcOQ7/gVHYaqiWnfp0R/\nrZTivxyPqOegss26sVcDiBavvaZWU79GVuEXyOpUTsUJvspQSaYF+95EEkGQQRLDvIXFTj6V2GXP\nyjmV9nbwpXu4gXuYf0S2FVMksbxEUnmLn8Qe4e3VB/jdz1ZuY563HL+8wvQagq7jT3zNijERLZEr\n/wI652XYMn/dF+anakYc4ulGDzmFl7D5QfB4J07ro27gqoGLQCqIBb6vrer1K8R6dbOFjzfQqk+m\nGrqpOFEN6bphUJaklBgsA7ZYDDkZLSE5fU4HN5z+zSXt24eXpI+Mc9C5jFpZP0cO0QZZaXf43dkI\nqafGSL5VoZgTvM637gfcrq2qoW9FCq5rxa+iLqGpnSP2D7A08FXfi8kqhuEEseZUwImCoSb40aUK\nbyT4JqU6brdgVxF2HTNItzRBxJf8uBf/FHptuBcEDVrLZLXzU1odrR7iSYZRwZY5DC9gvYBtDrtW\nTscN8HedJAcVp14ip9Qb5PT6Ghl5jjMBQnVrYWHuIUD/Dd/XKAyZzRmycaY2lpSSE2b0yVmVR8zf\nnBN+8fBZ5iMlB5V7AzkM2hF8j7zzNVIzKclKJeJS5KioQ9Yle0WVvXrGlTxeNoYwgU0nYnu9BLJj\nCHsiudMA6beyuL/HVwxqdTdDEoIWMyonPkJ2NgOkmhi5+53hTbxaMP2O6KLCWghp6bOjIeLEjVxf\n8p4LrmkQU5K4amkUqg0/ax59iF9g6BR+AnYJ4zPoZ174sI9zLkBORdWPVVVVtcdctbDYQr2E5hLa\nd/hp/pfuhn1I6oqjbklNjMFyzJyWkCVHRNQc9ZbEJ1ua8uH46UdKDmpkc99b7hw5NOpE6yDQe4DB\nDjkit0jy0P7CJV6x07I/7FEkf+qFYHNhXjZ43xttKi7dz8a9jAyvmK926WqcqyCUI7ybkSLXdJvh\n0Gtm2hGMK8owpaoTYmpKUm45BaxTBx4yYMuIFdusJ/0GfRsH9eknHXaMnA+hQGwW17AqfFF6fzCv\np2OOfPTqgHCHbDOSQHQdkglEZ0hW0V6+qzSbNGQb9gloGbKhR+EkAXInYixJQwSVHxaPiJDUldpH\nLtMhckhqJCEEyDZDWUwLZGVq/W7wyUKHwfck5dad204gty9z3zRU+W/1LgT53xIPo1aI6gzJSwrJ\nUONuFQmtkE+xcm9FZ1C3hjgtiQcFhe0ROjrtmZOJ049owYSWkLSsCNXDQIcyh3i64eTnVz+IMmHb\nQJZ5pVPVmQ2RAkBb7h1e6khPh9rKTni/k14j52oA3RDqqSEKGjZ2tDdHkpHmHT0KGkLyPCPpl/TG\nDz+xHrEhqdxnlVFq8D73ivhY4L3IFX0EHrX0JVJxqB68akJ0kNRQbYXuljgjXtvJ76X1MIoOL/mg\nivcB3nVIKwiLVBQqR/+37uVOET6YssaPgB9Fpa5uIqo6YrMb8L55JYamjOkI2TLkhnPH8Wpp45BA\nJcXUP/EQTzecLEnWg/UcyhapDPGt9jvkNPsH5NRTF0bVNAuB5wGcnkOsu2nVMUqAd2KeE64tpUmp\nTMIxc2fM3OwRNH1y4qSm3GaY7OESY490Cg6Qrp6uSlWGCvFqmirjfN97/A7PdPoJ6S+cILu4HQQd\nxG7eOAghcpoOPSAJ4UUgLeKhgcDKUx0juekISQwz/DT1DTLSrPFU1+/xqvpagej/S/f1G7AmpPo4\nJusavjr6gS+jHxmzIqVi6HwrLvjICTMSam6CM5oR3o7joOfwtKMGZhDH0EslMfywkw7aW2RT/bfI\n6XKKh7XojlZFpCYdlGvxZtpPzZT/Mwczh2AGcVgz5ZaQFothR5/aQW0zcp6FV2S9HcVPv3ivTKcQ\nvVdEmSIwad1wa22v4i4LvJ3Ua2S7odWFgqFOpJ1bO8+KOTLCDHJYbfwRv6oFSh0ZLwBatXBTeC+d\nHPgvyOT0ezxkTZMJwD/hQS4Ks/gcLz8edaTPVtSp4aY8Z8WYAVsaQm45pSSlJqYmZkfG59s3RFsB\nd/KKA2X7qYduW0shT52MnX10IJe3PyBJQhUJVXDc4FXgVIF+U4sydacYmCWyBH4HXEB9EVClhsad\nTwkVHQEtIa945079jKYLhTP+wHjE5KCQZsUKXyMVxVf43u0ZUhkMke2DIkHUJk8F+hoEC/EcWe1u\nN7exEGdy/85C3kLPysMrXr0GhiEc9TwfTLUktRpQ0JN67SgtZHfv5SvBtEKye25EiW64ZNJbENAx\nYEtKSUVCj4IBG1aMGbMmaSzWQjU1QurSt3iIJxnmmL0kwN1KvJzDQPR/vg78lKLCG1qpN3OFFL46\nGAsbKEpxU6BFsoZFEFMNxJuOeCc8itJpRo5Y84IPVA6qL17cFra/+OTQISvoPq9VHTv+4H7WQusO\nyZ9K49ZL+Ss8uHyE71FcyKgycLZRnXYcLRx1cJZAGHm+RNlKElFcVR8/tVgiClAX+P7pCyQfKZxi\nzafWeR0ykU0M7TZlvR2TNhUhLUuOsASMWLFgwowpGTkFPcokoBtA+t5+WqEc4mlGwR6W3++L/Pz5\nGD5PIQglbwyR00W9ml/hJ+ZfJ6Jk+Hoo0Olecu+GSkq+At5DNzMUUUJBjyFbGY1TU9DbS9W3BHRt\nSHjycObVI1YO13irKLUf1he+w4OknDEgV8jhPEKOuuKLtQLZIduTyLO9CVw/s4NeB7PKQdBcwkjc\n/wLj3YxVJ1LFqWZ4PcAxkjhO3P9fINWF6vJV7uU8A2Joyph806doegzYEtCxZuRUguccsSSkZc2I\neAdWBzEveCjC9RC/0LAlcu5EMBiIgmEDvN3BovZUwzGy5od47NsUKAuoLJxl0LbQlU4XWeedyjCI\nwE4gKWp6bUmCSBHeMeWWU3b0CR00IAgto/OHw28fKTkonkHJCTfIYUqRjXuDnyt2yOEaIatS1amV\nY61dwDfsexn1TD4dE7CfCbYGbCKuIcOdn5yexL7wMHxy0PfzRjX/tvgU/xmSn0q8I/cCKWYuwFQd\nydGOdJCzjgbMOKEhIiejI3SkbSFevV5fssyGhCG0+hYPpjZPO1QEyJkjtWvnp5TCq0w6ZyWSFHRQ\nppMKtYaugflMGprhAI9v0AvVAvgW2iSEwJLZHVNnziwutIaOgNipUPf7W4rl6MFv4RH1HJZ4n4r7\nCHMlwqugo9IiHR2bGM/J0MvsMdJz6KRmC46hC2QiEaai51AGohdu+1D1RI6nsFJF7HIPwFTatT71\nR7xk5ZH7/Tu8UbhS6NSM6wPQQJC2xIMS2xm6IORd84qCHhUxrUNFDtmQk7HNeth+C323G1LFvEM8\n3VCQkutFbXbSbH6WSOtriaxvlQTRzfEQuRQGFr58BpMJrAMwr8DoNXKMnCMO0hPRYpOAPMyYcUKP\nghVHjFjTo2DOMXdMscZQfHx4SfqI3IrP3Hed0ahz1QQvNKvdwBTJtQpJLO79fAX8H+zNJDoDbQ32\no5gDtBv/tCYQOZ7KSdcPjSSRuicFzGd4j8JX/GsW+Rwv7qKfqDY2dSYF8I9gM0O96JH/dEKwFVn8\nIRumzOghsl4BHSUpm2jAiiPx49kiyen83/N4H+J/eGTIKQ0QwdEJ9EaQ72DQSg1cIzXy/zqSs1eV\nUh2+SXoVL2CqPa+P7p8D5ELllMnaiaFN4NaccsuUksQ1J1M2DIhomLCg3sVi7PTAeKTk8AHZBqgJ\noPrOzdz/fkQqic8Q368QoXP/E7JqvsDX/v8BUe8M3e9uw78HVI2cYobzpRgayEIYBrB5Kwt74jaE\nb5C8c8anul23eCt1zTV/h0exzPDFzzHwNXQ3EdXNkN54TTws+Dr8EwBvec0Vz5y4bMeEBR0BKSW7\n8+Bfa1Ye4mmG7g9A/Jam0L6GPIYqEdD/l4mc4cu1b1DWTovoNIBgA8zclKJErpmqNqZi7C2Eq46g\ngoycHiVXPGfMau+8HVGz6CaYvuXF3/7pwW/hkXa2L/DjzC/xq0GxyV+536+QSkI7dUfINGOOXNp3\nSBfx2N1uAeYM7AbfJFhCfSJXddWJjBGpuPSZZ4vH96jcH/DM8c/lYcnw9nlrJGcpzEKV8dWpL3cv\npwA6Q58dJQk5Gc+4JKUkI+eGcypSxqwoSUjUzGaHB4Me4kmGUSr/gr2xTfIBXozhOpc/1Z1cj0bI\nNasCvk0hdj1yxtKkNhWSDIb46vQaOdf+s9zWVAbTs7SEGCzXnJNSsXN6kqNgxWx9QpPED/aueKTK\nQY10N8hQf4dchpUnsUQSgI4vTxECax8PNAVJIG/Z1/ymEMjYnjvt/OoS4MbK02oPs2kh3zq6RiMj\nTXUwzpDFPkImp0u8uZa999TaIlnhyzwlmXYWM26o2oSmEBZVSskRK4ZsSClJnEzclgHUAdthLI+v\nW5dDPO1QiZFM4PRVA1XouH0hFJ1c4kKEHzgOoR+IuU06RISJtc+gDIMdcjH6DGF8WuhCqEyMxdA4\nwyTjyH0BLTE1lU3pwoDl+9N/+7X+G/FIyUF114d4skKKVAD3LfFi97f7DM0xcoRyvDqLQyXZHDqd\nPd7zlAuByCWHyllThe45He0CE3lvTKV53N17KjXSnePF/nTyqt6/atp1q/9rCY5qtvWQpospSQlp\n2TB0epIBnfvKupzBys2rdHBziCcbKmGq54XppH8QD6WXcDKC89hfg9adICkbR0i2IOeBsgVq5ByM\n8dBJV5Hko4RFf0RLQOsmYreccs05AVJNpJQkUSXyBQ+MRxSYVVE8XWnqlalUSe3dqqOMelQoh1pt\noVT/QSuKYzzh4U6+W6QZOUAEZ0NtykQysRhGvoWxcS9nigc4feH+p+JVFl9BxEiOOscTYpbAj4Zw\nY8mGW4okYcERFQm1m5FWxNxy6hyKtqzTIWHH3o15jw87xNMM/SzVEW0IJBDmUIZSScQJPO9DP4KT\nCI4ySDOYjiBVaSi1hYVPqZyNPF4XByyyMfNwwiUvWDNixYgJc0asqIlELqBJ2W77kD3cZ/GRksMI\nfxlWuecCWfy6IvX3n/jUYEI1IpVboYqvbxF05DHEqhXh0FBK6iyAbgn1lRwkE8PIScFrI1BJWDEC\nT43wZjVr9z8liFrkyqCG33oSBHL/9mPC7u2YqkiY7U7obMDv+Q0j1gRYpsyoiRixIaRlPhl4WPbD\nyXOH+CXGCM/TUTl51+/qBXJ6JQk0lSAgEyse0GEHgzMIM7C37v5KOxogp3uHnIM9aAYheZoS0JFQ\nEdC5iVhJSY8+OxGbDTtMY+gPd//Gi/2345GSwxhZbSHeMaaH9BbO8ThkNcxVsVmlQu6QyUWBv5w/\nAy6hfQt1AXEkeAdy0QO/m7syz0AwgjSWqUSCLGylcV8izZ4ZIjB7X4NGQZv/xd1OKd3a+lghDaiv\nkAbn39TYo5ZqPuA4XFKbiFPupEGEOG4vmZA7qLgJW3lMFbs9xNONBFnQuj28BWZQ3kFdQeOK44uX\n8KaAKJFeQ5KJdH25Rs6jF8iFSCuQMbJcHNkvuat5PftIYmtKEq45J6LhllPuOOGac2accMMZ/emW\n/G7y4LfwSMmhQRqMqh/ZIEfiGNk+aPclRsYFHVK7/xpJHAXSkVFBmNdIY1MrilA+AbuBaABHAbw6\nlp1HOoVgCDMrSeDWPXTfeMm3BkkKL5FE8AbJWyqYPcUTQxUmq3x7FYTpDPYqpX0/ZDBYc5rdMGJD\nQ8Qlz7jhjC0DzrlmS58eBaXp0XwWUH5jDqY2Tz2c6QxnyGn9GfA1pKcQtBCdiLoTG5mq9zOY9mC9\nlmIhfemakQWyrW3dYz1HztcQOIX2G8Ny0iMwHTUJBstHLsjI+Yw3hIglAq1l8ecLuo8PH1A+okyc\nwj4K5JL+FmEsTZGjOUZW3jVedunKff8CqateuPv/37jLNdCDoJQGYzIW2PQWuK29e/YwhFfGC19v\n3UtR2d8+MjFdIHoOCpU2SG66r+3VIB/YAD8NucITuUroTMBNd0ZJ6tw0SiJqGiIKUhZM+CPfktkd\nd9ER6a09+FY88TAZXiGsRc4zK9csBkABmw+Q5/DqmXw3MYxPINNWHPhevHIR18j5egysoA2g7OQc\n6jCsGZKRc8lz/swXAOyaAWXVI5lsMd0vviGpKlBKmvoK34KNkJo9xeulbZEVqAyoAll5KzyuWTEP\npShNW2f9FQK9FrqN14mc4QfMAXKgE2TwXFr5UMfuKW7YC3fQIg0iVbGrwElCeuh1gqfVBhayljYI\n2DV9KpfZA1oaIgzW0TJKzppbaptwcTf3OfMQTze0l97HX0hKiD8XtmUMjJ7DYAj1GpIzCEf4Yd0z\nvP6RwWuKjpBzbgWMEVWxaEBFwh1T+i4xXPIcS8COjDpP6dqI8nqIzX/xCEld4OpLp3V8hKRGVZPW\ndu0EqSpU5lmPlF6m1XJKW/19GVUa5yayaYW00HR+AZtaFnyg34GhhYH1g5AP7L0H9ga7b/Em3gWy\nLdGGpOYz615+DtG0JOpVpHHJhiGWwKWHyPUzM0p6jOyazgZelvjh4+hD/ALDqiTJM3xva4Tn8KTs\ne1l2CHEfb+amzo1jvB1jh5zaev8hNAmY0lCRsqPPkC0TFjSEvOQ9Ia24bKdbmjKmi8KfBXt8ROLV\nfSnND3gBRlWf1vauq81Z46UxFE/6Ix5d+VyMdIMYwlD0wFk5E5tQ/nZh/MEdOYHaJvBjySCUVvIO\nb5Whtp1qYNPJw/IBj7VSueCFe5mpexvGgDV0XcAuz2htgMGSkwnkgpSUgoycWTyhCSLyUSRv+eFN\n5UP8EkOJxddIoh8jvQKLXHxeyfftVtiatCJxSgz5HbRL9ziKa9AqN2TvH91cQJ0ZeuR764Mf+ZKS\nlC0D8i5jUR9zU5yRr4fYqPPn+gPiEYlXmh61btLNGfhN2gl+mqHzIO0KqqFEhhzpW7AR2JWMK7s7\nIVcVa2gayDO4MXvoA7FL0cehn6pu772EO6SlsUMSiuKyjvCGXKpmN8KPQxOEten4t+2HlOJPR7Rl\nzIAtBktHIKKyBMw5YUeflogmCInz9lOA1SGeZug16wQ5tY/x03fD/nxJp048tg/mG+AIkt/gDeMb\n5FqowseVe/wcdlmfRX/MnBOHoYnpCBixEZtmE1GZhLwZQGcI0wqyh2eHR+JWaF2uyeGcT+mNIXIZ\n1i7Mr5DVOkFwD+rmobf9EVF1fYOs3lP249HG5T9rvIJc1UJh4KtA7hogoyJtOB4jQ5J/RCYWqmDX\nIu0RRarpZFVBKir+coyMI7dgfxMQneWcjG5pTEzteg1bBjT3zClSSvo2J+ws7Wsk4Rzi6YY2Ej9H\nBmkq2qBTrfdAJ1OLPd7Pja9D1Z/UrckYGat/6R7vGXAMw01OL6zoBgENkTOwabjiGWtG1CahH23h\nGOw6pnybicPbA+ORksNLpOlY4hlK2n2534NY4MClyKrr3N/VqPxrfIfQUSfDMZiFAM7tDuxA9Lt7\nQyf5bgXgbkr4UyKlf2LhQycK1ROkjREiPVDw2f8Ub4JzjvQZ1BM4QDBbCu5ULphtqLuYZX2MjQK2\nDBmxYcjWWZXteMYV4ojcYCoIbzg0JJ969JGhmvox/eR+dv0CTpDT/wbpSXR4wp76NZVIMkiB/4iv\nINw5GB7Bh/GUG872WBmAARsiGiIawLC5mdCGYC5a7Ltf/CjznXtqHQKr+Iu6x6geL3i8Q4NUDzNk\nG3GEXMq1X9ECL6EdQNN32wq9fA88PyJXWfpUUmOKYBzi0MvCG2SBK5S6xXMr1ANRwZlfI9NXpX4o\n+OUZTjzbYIKOKKiJqRiwYcKCIWu29HnNG9QBqySV59jws/aGh/jlhdGW2hVyun6LnA9qgnSLR+R2\nSJJQCL9qkp6yd3fc83wcD7F8GdNuAwp63HLKLadOQ3KDJRB/TDtm1/QZTJdEWQXYn6VN+kiVwwjv\nln2Nl37TVKo1tbbsd3jVPR0FqJKUGvBa9vTtMAJ7DF1fnsc4UFFonYqGkWKkUWFwvKJvjSQJJYWm\neEk5B4vem3QpruEZ3rti516OwqCTkOR4xzDaEGAJ3R5ky5AxaxYcE9HwjCvispO3qGofh3i6UeFP\nczVrc4DdvYCxTidWeC6GKqO37j5f4P0qlPnbwjIbko531MSEtBT0SCmdiLHhjimxqRkFK67Lc5pl\nhr2JZXk9MB5xWqGg84I9i4SXyMoEOQp3yLtR8xv1itsgR0xl46p7t7sCswTjmEup9apyWq6FuA/O\neAq3kq60DbLFox9VylK9L1TI49g9rcKmf4/0LlTYypFO46QhDBqm3NFnR0dAQMcxcwI6WueANWhz\nSTKqTXmIJxvW4LU5GvwFBT71Q1WMzSl+IqFaReCxNUq6GgIRnP80JymlGk0p916Yok06ICMnpKGu\nE3bXY9p1JMvp/cPfwyNStjWF6svQzVmDc8DFNxxTpBOoR1m3JMqE6pAjfyr3bXBTh1pk4FQD3BjY\nNiLnGyAOp13hBVoCvFisbjmeu6c6xevhXuNdUHXLESLbi+7eyw6BrqOxAY2NqIlZM6IjYMtAthFA\nTM24WZH3Q7m/2u4d4unGBXJqqgpiz/3tOdKv0gGcApzUIlbxD4G7n0qqKjLXndvlc8Om16dHicG6\nPoOAcSJqamLaLuJ2e0676WFn4adiMQ+IR2RlqjOgApe0zqrwM8XIfamL6HM8AEprsRDp9kQ48gTe\nbPKdoCV3v/fCU10IdSB3r4FR7NGQF+6lqPJ9g2TaGZKHRnzql644LoNcARQye+N+7gGhoVpn3K1P\nyW3GihElKS/4sG8cAVRhQmNjyY+37nEO8XTD4NGOGXL113PuDg/83eLPp6373xY5fXt4vXqF97vd\nc1xbsrLaixZXpKw4YsuAFWMSKgrTI8xqgkHt5QxnPDgeKTk4wZU9xaxCkkXu/q6GuGP8zHCFzITU\nPEJxDj8hl/FvkZWrEMW53LYNoP5GVDRsAYNAxpgqW3kcwqyBsobLShLIJVItqJjnEvlwtSLI3FMO\n3PeN+7s2KF8iH8I1BE1FerzlZDhjawZkFAzZcMuUJRMqEp7xkRebj0w/bileG3nuwyjzaUeH9KK0\nGlXakFaXx+6rxrMBnOYDiXuMyN3+0t3nlL2uSHkSUPRFwfyYOa3bqp47F/et25e0bUgTtEUDAAAg\nAElEQVQwLAm3lVc8eGA8UnLQVaeoohi/IlPkSCos7HO8TtbXwB8RQVm9/7eI8IJTn1bvc6bACzEn\nZA0UknvWeK2E5+5pswhexXCeyEJ3wp17Udnf4gGdZ+6+3yKdZNWRvOTTK8EpcAxdktAPC8bhilNu\nOOGOhogxa0ZOKPKGMz6Mzplf9On9YGWYc/CteNqhVeQFfvp0Bfw/yKlrkOSh/ii6XT1yf3+O7z+o\ntOote4ML03X0qpyClA0jAqcb+Q/8Ne95yZgVA7uVfpc1tEUir+lnIG8fcVuRIEdP7URfI4t9jBxR\nkJX8J7z0kuOp7lWrVW9yiPcnV6s9x166aSGYCEk+7Ml3Oln4H/EjwxzZSrxAPoAayUVqWHiJfEg/\n4ukcU+SDHCEtkZfIB/89fp9YWPrsGLNywDjRjdwwZMERLaEIxVlL0lXYIXJiHJLDkw7TIufUn/GK\nBIr8T/CeFjne1f0LZEUu8T0tLYp/hUfmZlAnEelWoPgtIX12HDPnjFsiGnb0WTCh3KWUV856QXET\nD4xHSg6KbtTeg24lvkWOzHv86tD6/xmSSBJkFat4v2M48Ru8m22Kl6GrINyKZVACJAWElXiN9ZEF\nrvvDCyQBGGQ2rcKyIBWDzp8/xw9OVKnunfuuW5BTeUwTdMSBN8/VuOAjPQpyegR0ZG0OBlqF2R64\nFU87aiQh6LVujZzCp/i2WIOcwgrhKZGLihrmqtF8gZzmOkGroTExm4F4Yq5cHWoxGOweSp3akjBu\nSMelpzP9jCnYI2pIKgpygmy+fkL6BDWSGBQQNcJDolXFZo5HkJwgl3YFKWgzMoAkFu3IuoCmdlSM\nAcQ9QUXm+CaRwRttzfBgFVXy0T6oQqw1Z23xe0VtIE0QTMV5h41CZuUpu66/98qsSFgyJqBjzJqY\nmnU0wrQdkaLGDziHJx1WCcb3WZUZcjHSha/ixp8jS6Bx/1fDJGUCqxJiK9/bcxhtc7bpgGvOaQiJ\nqbEYQloGbKUyNUfQGdo2FCmCBU+hIanzlBKvBqXdPUdXw+LdbL9HJJmUFaVW2MrFUHcsZW66GaOd\nC4SaY4gbb0wYAVngZ80xkn+2SML4iGwdXKtiP+bUp2+QclFNuo74FPbaA3aiTRn0a4K4JjI1BuvE\nXoSJGdFQEXPNGeNuRRWkXlj2kByedmi/QK9pCt/JkOuZqyzJkevifWKVkq1GeAyOMgh6AuMRVm/C\nygkXlyQ0RE4rJGTIhp4pCMLOTenweIqf8RYeIa7xY0utAi7xcxtt2WoSUCLVS6RGO0NW4iWyMnUk\nqgimd8AbqLUkMNBupUpQaMV2BbWVD+AzvHNpg++TqviGjprURDdAmpQ58oFpF/gZ8iF38jeTdvSO\nN/T7O0ZGrLJGzjJryowh232mr4wg3VjgrTwO8XRjjrTLtkgyUOvEOZ4a1EfIVAqK0uHdBR4HoU3E\nC3efBQRz6CJYMOFr/sTAnSwZOUuOOOOWIRvaLqJpQ8JRQRA2vpn+wHik5KAMEiUv5HjPih+45zyD\n3yYM8XBEbU6eu8e6w9O+lVP9Gk7OIT6CcQCcyodxjuM/jOC1g1H/Eb/QtXGj+UZ1bRXsdIW0PiIk\nUej0dYkw51SC4jnYJiKtG9KgoCHCYgjoqIm4Y0pDxIlDSY7qDbfhqUdwHv27HvBD/I+OKb4JeYkw\nfO8XyDlywfkOf0Hpu9v8iJxPFV5NSpfMC/laDUacc82OjIaIlpAVI0LnhJKT8Sp8xzDZ0NpQmp16\nDX5gPCJ8eo4/AkpYWCOr86P7+Rw5Gq+RGv4bvJzcFA8h+wpZzUOEiLUCrmGWi/lgaAUpucUTXpS6\nutrIgVMru7V7GiVWPXdPo2Y3R8D/7N6Gtjwm7iX+CsnwbpBisJRdysaOqBE9h1NEaHbAhpCGHgW/\nLf5AryoZdyt5zFcciFdPPbQHpVLyv+VTRq9OJb52t1eNI/VqKpDzQIdysIf12xhWdsyg3WIJ6FFw\nxylLJuzou+rhhpvqnNubC+pFn+4m/tleKI+UHHRzfoysJiUqvERqn9/hOdFaOfwWSbl/wJMetCv4\nAXErTSE4gfAcgs8gcpXH3OlL2k78w3a1/HwLfD6UK/03yLZAC5EeHuaq1YTSbS/x2Vyh0xs8zMJx\n9NMwZ3w848gsBLFGSkXKiDUhHSU9LnnO+95zujLl7O1aBjUbDl6ZTzyMOlV9iVxgtKegBOMcP6hT\nURfdFU+Qc3GNpxupkc0AuhheX30ksJaGiJKU51xyzkfXnKzI6VHFMecXl/R7W4Kyhan9We7tj5Qc\ntngrnxBZfVeI6ut3yKZbW7a6Av+MrMi/cn9T40AlPBgIU3EGaRvoSkiGMq0gkMTRa0Qjsh+J81WJ\nLMY7C1edH/P08ZSNH5EP7wJZ9AFStGjXN8Oz6cCb2lRQVwltnji6RsEzPrJlwJiV88qsmLAA4Ho6\noTyO5P46fDnE0w31PAU5l1bsqT/77YHFczBUhHYC/Iu733Okb3GGnJsJtCcGG8HNyzG7tk9NvP8q\nyOgICbCsOCLrcopNRhg1hJ8VmJsnkRxafJ9ghjeXjJGVp45V6qQ9xm/MpE8roR3A58jw9z4byt3V\ngsjVW0m5tYGBkSRy7B6qREY9obu7jjF1z+dkwJm7p1XYqxJlFE2pTDwL9BuGowVptqVtw335lzqi\nTEPE0DUndwyYlGKZToifohziyYYtkfNkg+ySdWKguJkKb+dqkQSgmIdzvCrBCd7MpoWuNlhjiGj4\nMf2CDkNDREVCQsUxc1IKRqypwoQoatnMjqAMMUoFeGA8UnLQFahIR9WR1CmGJgEdHzhBxj3dMgXz\nwtGyF3ilFZVseivfe6FjWRroek7sxd2kM/eUeQxMQ3/gtPRTD1+L5CytHHTMdI43/tbeaQl8WREe\nV9g42A9LAjre8YpnXPGRCwI6Ymr67DjpZlRBTH/nkt59ge1DPM1Q4WFNAup1YpCFr0lhgd9apHhx\ntAEeqat+Kh3EZUfQWiZbUZoW5/YlMTUDthwzJ6JlwUR0SlPIXq4IxzVWlRgfGI+UHC7wShdn+BmO\n1k8O7cFzxFVG1TEK9vhT24D9Efh75B3/Ebm06/hgBnXpqNZW8A6dw0ZsrEjCqTLdzj20cryUth3g\nFYO1f7pCcs8AD7lWeXo1uGkj+ENENC5pgpjM7EionHz4msb1lCsSQloys8PGeDWgQ+Xw9EPFho/x\nSmFK45/i8X8XeKjPCq9CdoZXQFdWgevB1z1Dk8jkSwySGlLKvVScuLk3JG3Fdj2k3PWo5j3sLsAV\nqw9+C48QCzw/+g7PiSiQo6AAgwavfnKBHOk7ZOWoT9h/RpKH4h/OkS7QSBiZdxuhbZOJFFxqRYG3\nwvdF1URm7X5+hnwQI/fUPyE9z+/w6MWPSFLROXbmnnYK1AHtKGJbZFgMI7MhJ+PX/IE7TolpqIno\nkRNTMzcn1MRelkKt2w/xtEONcJUfoe01rUjXyPkV41XOJ3j83yv81laVCxaQXlvm0Xi/negwtIR0\nGCJqehT0ybGhYdjf0m0jujr2S+uB8UjJQamRV/htw09IHaV67zv8RKKHh1xrBzBELteKXkpEuC9w\nut/xMQQLkYozkSAi60D8z/sGgi2cWEecsp5aWyMNIbUd+ww5qK+QiamCVVQ1Stsj53i8w8wSfb4l\nDmtiU7NmRI+CNSNuOOPX/IETZrREFPRYMQILV7+b+MbmwQ7vSUeXQfcFHkC3wjN418h5M8UP6wb4\nHtkQX0T/T3i5QhWPCSHE8rJ6z5xjOkJaQnL6zDlxVoshjY2I0pp+sPPbll9+5aDO2OorrqSrEV4a\nRyV6wcvEvUQSiPpX7PAij4UY59oZkEPdQHIKxoprSJ4jVUcB12soI0kkf29hWXtEorpd6f6sci9j\nigex1HgG5v3b6mO8AMqIwWBLYDs6jMvmO6bcUZIyYIdxG8AzbjjerZjka6zKhh2mFU86wo8QvEcq\nA0VDDviUQPz/svcmP5JlWXrf783PZnM3H8JjyMzIrMysrq5WNyg2BJJoiBIggpA2gnZccqWlAO30\nL3ApaCVoTUDSuiFAAjRAZDfEFskusru6KysrMzIzBh/MbR6evelqce9n16MFqaMkQVEJ5AMcHuFm\n9uy9++4995zvfOc7kkAVr0G1huIFKmOvZs8NlJOAzVWCCeE2PafDngMZGQfOmHLKDAg4kHMazHjU\nXlN1Epulg+8DfXrP235zg0dq5ClI56HC6zyoUYRSBGpf5SqkgpH90XeUBkxjpenZ4+Fgh1tEWLAy\nSu1XSTtSkcwdFl/Yua+Qxp+qwpVIEW6Ke60xtLuEqkgwJqDTFrSE3DMBbExoU085FQk5B2a9Ibtu\nRuM0Ao8JmR+O7+chgTPxE1Swp4Y2MZ6tqxpCtcA7xzMohVG8AUqIN4ZwGWHagB09W+5PS0xNTczC\nxSV91pSkzOITkvwAe2Ov593bVrxPhqSaVqr0THJKrtclBi9Xr47bG+xIiec8wWc3phakbKWsEdmM\nBIEXm2XP0eBExgtYK6QA+9CEj8Z4BqTakG3w2QwJ16p024l4BBtD3tswDNeMwwVh2HIg45QZHXaE\nNGycSKQhoCImMTWdYk+85u0irx+O7+Vh5BUoJFCZj4BKiagr0TbAO84Gy7gV8RfeAszrNCKvDwSO\nJr2lR0FORENL6NotWnfWGFdgGAb2HFKZeofjPXoOkr/J8DyHl/i2xOpFod9n+JSACAX6/wVkrt11\n0EIgyWhHWGiXEPQhDDmyTRI8mChNBmUo1CtTHoP0JFd4ma0pnv8g2foeRJ8UmENIeIC4U3FwTU4b\nIjIO7JyUjyGgx9ZlL2wXrPTeeFv5g9jL9/sQkU69UNRFref+FuBbr0hPJOLYqI0Dfr513OcPUI1D\ngqxhtN2heZRQsqXLlAkJJfecsWJAbRKqNiZMaug1flm94/GepqBaBlfYRZ/iV2rx4O8yFGNs8HXK\nkcNgu+G693csuYlb27L42Fkm5uibmQO+/jWHMrBfcV/bDtyn7qtc6wuGuL4TWAPRxYJH5sGptQMY\nd3mX0H6XQCdgH6Vsij6T/J4LbvmML+iwZ8iKioQutudATE2PHePp3lLfZWh+Da2/H47fvKPuBjRN\nQHQwfrMR4C3wMcCmvld4beQY69E+xWMPHwIJFKMEWsMhSrke2MloZQBCSjIMIRklZ0ytkQjOCEJD\nkpXEcU29j33rxnc43pPn8NT9XmNTA9/iYdwYm6JUUljCLwt8Ubr6lXewK/MLS5fmDN+5+xW2wlO9\nLSowAjODY1k1+9ie/hqbnlw1sDTw58AL4+2MNG6lHnzAlnkoGnJNuKJP94RPdwTdhk3ZxxA4jCFm\nT4dXPKYgJ3ZFV7Frk/5mcsp+EvoatB/Ciu/5YeDMzR9lJq7wG4xCR6mN7bHpcuHu4MVhzqBKQ4Je\nS0DLyXwNoWFPlyVDbrlwwkErWkIqYu4457a9YLfvsf12TF3FFoP7NY73ZBzU0voRvtJS3WDugX+K\nLWoQQNlgayq0pZf4rT0B/i3s6p5jV/I9dsV+gh11oYkrd845pLUDiAwkFaQrhzFEttlNDTwOrDHY\nusuWIOgO+yCvsJWYsvqvoG1C+CbjyeQVj4evqIhJKakd/33MklNmZBxIKFkxYMoZhoD8uvXJFwGc\nPxzfyyPeQPQCu8KeYZ+ndJXVJqGLrSf8HexUfY7d39S/qY+d0jeQxC3ZfcOiM+TLyw/YhxbUHrFi\nwNp1116zo8OaAQk1/XDDefeO/GwHwwby74VxUEL3Drt4z7CegRPcCz7Fjtwcu0WrfG2JhW2lIykZ\nJtdElxBLwZYck4rkhV1M3Ptzy3kwQBzYGti8Z9Ofa/e1GT7CkUK+PIeP8d2Sf4H1ONzltOsO5oOS\n+faEhJpT5mzpuTZlHHUcMgre8JiGmAn3XN3P2F/GdkgeCt/+cHwvj7oPjYSBJFuyxoafot9rOudY\nsbPGva+Dr7F5Ypn/0nM4hDldtnYzoSCmInSxwhd8ZjunsaV0IashoNfZEGeVJf/9GpjDezIOauck\neqK61LoVF6k10FPs4n6N9Qxu3XukmTXGy0MrgyHFDGlGHCC7xD4JuQB7iHb2u/o4ZerSis+K1CSs\nVLv4w946M7x0V/zgdhpgEcAyIkwMK4Zs6B8Bxw4Fj7gmprZVc+wJaSnajOl5n+60tkOifj8/HN/b\nI6zcI5QHqMyDnq9K/Z9hp/5HWMf4HI85ObWC+tK2bTQNpBQIiLTK0zFrBqSUXHBLj+2xfyZAYTJW\n+xFmHcPkQeXxu9zD/9tB+H92KAm8wqN9amozgPpb7F2ooqnEGoAFNqMhL+E11gC4lOWRjzpxn1vY\n18Mdb+crDdSuq9b8Dto5lB3I+76Np8Sr1cRbArRyWgRIdrAPseTYkch8l7Kf29LsE+acMnPdjwMy\nCvZ0SClJKemy49LcMj6sLBQyeHDeH47v7RE2Vs6NADtF3/C2PlEf+4ylGaneTnN8CnQMpJC+tnR/\nk4AJQtYM2NInwHDPxKUwg2OFZktEQk1IS9fs7dRfxTCNcFSbdzreU7aiwbe7k2TzCTYT4RAYMjzF\n2o3S0b8Xd3mP9cN/ivXFcX+f4k12D/YrfCn3Bt/KOLPnqFNYtxA1sAttknqHXfSSBgcvViW2pFJP\nBT6CiSDoGrJeQUnCnDFjFk4KN+OUOT22xK4iM6ZmEY05uX9tG4KrzfqvUVr7w/EbeMh9l6HXhqLK\nzBPscw7xiogxXh7Q6UE0ZxBUUPUDDmlGXpdkSUkWFsyYsKWHIWDNgJqYhIo9HQpydqbLwWSEFTRt\nbB3v3/ywQlWXOdaPmmJXncynKqAOWOi2xgKW4ixLJlr5oFtcp1x3/i6EFxAU2K1f4YYIUi1+pKYc\nY4QmgiL0FXTqWSg7Jaq0IpbU/fscLwa6AHqGvL+jdDTpESv2dNnQ5wUf8TXP2dFlxqnrN2B3GnAK\nQgpnfji+t4fJwAyw03SJj6BPsHvfDo89KH0t7s0DQnBYQz0Gk0KVh8zyESYwvOQZKwZ02LOlx951\n197SI8CQUxAGLYc6Y1+nJFcbgk7za2XB3qNxUMm2wQf6v41duC+xhVg7fDePGvhTbD8x8HDup/gw\nAywgcGfZkUZGSKxKl5MMQ0uK4hob9GFfi4Fx4DUhVe+lqEf9LL7EGoY3eDa2k4/LfmeJOWmYvr5k\nt+rSczjHhHsCDC2hK9/usScnpiIAdieh16lULvyH43t7BDu3VYkq3ccbAvHzAqyz/JRjQRU5doNx\n+pFBbdUOk51hsCjoB2vGwYIJ91xyy5QzOuzpsWXGCY2bXxISitOa8dmctL+33/FrCBe/J+OgFaXV\n12B38J9z5C0wA/4Si0t8jJWJ2+IJB7rLl8Cf4Olf0vSeY8ONMfYJSE4OaF9b037MMd0CAdR7q/mw\ndR+RAvUNdqQG7vQf4WsqlFDBvRYZiFqSxnAynLMnp8+GLjt+yp+5WvuGJ7ziCa85beecmTvSg2Ww\n1Uq+LP4/GOYfjvd3dB2txmDTlJIk+RIfYqhOZ4pduBd4nouK+F5D04QszrrURIRbWHBC5Cj4Q1bc\ncU5NxBXXx3aLb7gCDLtdl+X0hMNXQ8x19H3olekE8Y65QrE9VtgaaZU4ZtiV+qX7m/imAirVEOA5\nvthBZCqtWknM3eKVY5+4897i80wtRF3Ypk6OvvSsNoULBb5o9OC+XlmLGngEh2/GsEtps5Z1PaCg\nwz0T3nDFnBMecc2QFSEtLSGHMIMiJqhCmEK8wkvT/XB8r49gjYXEanyL1x6+KPkp8Hfc/1UN8IS3\ns/yPIIhbOtuKxaTPatBzLZj7zDmhImHIioiWBWOWjIho6LNhR5cgMtS7lDpObbXwzV+9yv/r4z0Z\nh4eGocCO3hgbbGn332IZjmoD1LjXn+PVWWq8jD3YFSx8YYxvQqhMhW53gzcsrqVxsPeBvwGilf3a\nEg9VlFhHZWU/QobPqC71npZga+jkGxbzMwpyWlcEo4aniUOFemwt87ozYj1OfTYWfqjK/J4fJrQC\n51IubGPsgtdepXoKg4fYpLfgFKLq5wF1HBAegmMVL0BMTUp1JNPt6bBidOybaQuvAlt0VYaEWUOU\nu3aQz9/9Ht6TcZCPJR9KKpw1dnWIDRLjO3E0WM+h4z6jjrfyx7SlS0decnNiFalTjdiZUp8SMFlY\nEdoIG+jlZ/bfe6zbt8Ra+E/d5X+DNxxq8ekyGcEXhk5nD1FLbOojQxJgyIoDGXNOnM5fS86e/qqw\nt9K4S/ohW/G9PoLawVrKVqhqV6GoQMgbjkmzo5i605Tcn8dWljQLqTuQtiUtEQvGbLDUfGUowJBQ\nMWRFTE1DxIAVQWAIK6AO7JT/zec5JPg0ploAqTf5a/ce8Za/db/vgf8VO5pSg1IAJaneFu9FrN1P\njt3u91gg4WHeaM7ROJgMDgtoHKlBfSjA2x8RV9TwZorf4ROcpFyIuQqoooT+cEkU2DLaEUs67PmG\nD48YBMCBlBBDLMx1ji8N/+H4/h4bLN7tKgDCNW9rlA6wm41qCvf4UiJXa5jdtySFoRxEGAII7MdP\nmR2NQOsUHRJKIkfSXzAmoaIOEnrpGrLaLq09XkH9HY73CEiCXfBqWb3GLvY791v1ruInfIPnoO6x\nI7zDjvIcH8xt8WHFG/eZJ9jStg+xI6967CfYlb7gmDtsgDiC+hvY1N7dE19rgbdJarCrngOuhNt8\n2LBfdejH2+PDU619RMOYBSOWTLg/ti+bX/S83qC6bP1wfL+PFi+HeoL3DKXZMcPLDp7g9zOnj5yu\nGzhA2BqiqqUKEtYM2dIlwDDjlIyDKwwOXVPdhDUDCjLaNmS5PqW+6dLeRl5q7h2P92QcWrwWQ4sd\ntWf4qssRtiArw3MZPnT/XuNLKL/Ad+Z+hmdMzvCdcM+wBugX2E7dX+ONwxSLY3zm3vPSysxVDZin\nUC+skyI3MHUfT/FVnTXWHp24S5hA/2zJYLQ+go4tIRv6FOScMOdAyoxTpz5dU5AxerXzzVYNP+g5\nfN8PNXQ7xU5FObYfYLMXOT4T9hM878GxKHc/iTF72H8SQmCoo4gVQyoS9nR5yVMyCr7iOXu63HDp\nlMYChqyJaTiEKb3+Ek4azCz0EN87HoEx//9X+ATBf2F8ayeRnMRRVr/yL/G+liR4Jfyyx46iwRoA\nyfTO8aMu4b7HWG7ECPukDljv5AzPNBriW1yVHAnuQWJZKNkBuj1PYJEg6E/d14sJ/jfta+Hzhief\n/ooOO8YsueSGmIpP+Ipzbplwz4d8yxWvGbFiXCzpvawJxM/6Guv0fOkuT+IfSvLc4zliimcjfKXo\nh3hx7i02JHrkPruE+jlEe9h8GBMXAbNBH0PIGx7xQfWSfZLxM36PiIYuOyIa7plwxWsuuWHKOQkV\nL3lKlx1PeUnOnhZL7Y1o6bAnojkawJkTImmJ2NNhQ5+UkoiGr3nOJTeEtGzoE1MfG//s6DJiyUue\n8oRXdNhhCGmIiKn5Lf7C9Q5LSagYsGZLj6dvpuwnIbP0lIL8WCYfmYZ+sGG8W7LsDDgEGTt6nDKj\nW+2YJmfH9xbk9NjQoeAbPqTHhk/Kr1gmI26Dc/pma4ufgprxbknYtlYFbmHHOejZdGawxTvGApwD\nrGFQWPqguOqoRnAB29OQdBswndgMxQ6brfiOZ8TU3DPha55TE3PNI165eu8lQ143j9lMx+y/Gtm5\n8sbOT/MfvVvlznvan9SDQipQXfwoHfANKms8/fA1dmtW9ZNEZ9f4Mu5nWK9hjPUujDuf5J1cazxV\nZh4LI15iV+RHWCamK4lLamiMfa9KPO6wtAtlSyUMesGRfRYdKtblgDi14VNDxIUjrCRUXHJLQc4r\nnrJhSRI2JGdLsjeGqgvJ3g3B38DqSvSw0IuanOzdv4W9GjdUn+C9mgLfMUm4rLOjsYHdk5i4ath1\nuqSUzNyuc51csqXL5/wlG/ocyKmJecIramJe8ZQtXfpsecZ3VCSEpiUIDLGrQh3Ua6bxGbdckHFw\n8ukhEa0rM14SUTtVLMOYxVEpK+NAQc6ANQvGTLinJuaMOw5kRxJZTM2WHrdcUBHTYc+OLgtOaAnJ\nrqzhSdqKbdCl0+45DWf0liVvRme87lxxWd6QJwci03IfTbhOLomp2dNlwNrR3BsaIsbM6bDnJrng\ntJxzmd4yLjYQtuzSnH3c5XS5YTeMYNRSnseky5bsdWPnTeXG32CNQg5tFhA+MfZ53nBUeyKy7y1T\n2Oc5bViSVweqMGEWnTJiySueUJKypUuX3ZFG3WfDLedM63MWyzFxDFxV8DK0DOBfI5X5HpWghLyo\ninKKXeRT9/8n+PLr7/CkAgn9V+5va6zhOMEahBP3noF7z5d4uegD1kioyGsHwRMwP8Zu/WpBZOx1\nlYXtv0kJVWb7bkoa7qEgi0Sy3e7Q/3dm1IeYKklYBqMjABlT2zZlpOzoMmbBB+23nN0vOQQpHA4k\ncj+doKhqx47NVsHaN8EkkjnfY+2lxK+6WLuqWg1pUbjh765qNqOYwbZgNurTEtp2fEBNzIa+S/62\nrvFvQ0h7TJ0NWGEIMQQUQX4sQ09oaNqYmPqoSDTjlAkz51UklKR02ZM72bwJ99xywdrRgXO31w9Z\nsaPLGVOmnFGQHb2KlAMhVpszpKUiPaaJGyI29C1RKOxxWd/SK/c2Th8OWQcDJtxznV2Sc6AlsMg+\nhoqUs3bKy/ApLREd9lTEtoiJHU0Qs8qG9Mo9ZZCwTzOCoOHyfsWm38VEFUlrSOauTkbkOXVae9Ao\nqRqEpNOGYI8n6i7sFDZdSN8Ak4CkagiCiLobk1KxoU+XHX/Bj9nTPXpYfTZs6RFioIFhZ8320LcC\n7En4a+ENmmbv4djiwUbJ7qb44H6KXfAPsYk9XiEzwYv4q+HuHV7w5Q7Po5hin87P8WnMNxz13pIr\nvHittmJVXTmDkABJ6OEOZVzVZzPG27oatncjTB3QCzZ02ZJxoHKLInVO8IgFCVoSyRcAACAASURB\nVCVh21L1A6o88p6B0lylO69ER6WMrQkXYQ3Aw11n4265cteqvLpqzip7W9uepZyXkU2NhW1D1+zf\nAk1jt7svGTk5suDY43NHl4TKOfi245KtCAyYpxYtt+K5NhtTkFGQk3FgwMrVA1hTMGTFh3xzHJOI\nmj0dTpkd3/eEV/TZ0mNLS3A0VnunBmYl2lOWrhQ+owAMj5obOuWBIk8Im5awNZwyIzXWU21cz4eQ\n1jU6O1CZhDOmDFgTYBiwoSGmdyjosyGhJIgamhhKEqJ9yH6YUqQR3UVNWrTky4p01jhCHV4RShKD\nPcjeNAQCCeXxhXYOGZctSw4NTRix6nRdl/aUioSUkoaIPmtit2scyKiJaQiJ25o0qDhM+5h1AqvA\nGh41gH6H4z0ZB/Dbr9rXqSmuBBS0+gQ4dvBbdQ976fqsfsD3sP8Grzf/CC8nvcIblBKav8SupgCP\n1iikqe2vIvKqdarEFC1D/QWEC5xDNe0SJQ22PXruJOJy5w7bONz2yqyJt3DIEobzPYdJ4Pttqn+P\nWqmpV6e4F3JV1f9zi1cvFpMT928p+OccE0Jt2JLUJVUaEdJQBSkDs6ZwdO8NAxoiRizpsaUg545z\ndq6WvCRjxcD5DlCQE9ISUVO1Cd2yOBqUIUsiGlYMaYgo6JBTkFIycG5wQU5KRU1CTcIOuxguuCXA\nsGJARmFl0li4Ri4hPbYMXfpauhmlwyAm9YwiyGnrmP76QJFn7CJbLp/XBb264Hw9JzV2N14ysoSi\naOTuiqMnklNQhdbTCuqAoAjZRj169ZY82LHtppzerYnvjZ1iKslWwZVV+vHP7SFuJBEYOdQlhK70\nqF8UNHFAVSesGDoOg1WYHrGidMGY8J5bLgiAMkooo4TYVHaTmOGlWt/xeE/GQT0qCny30R12Z2/x\nxVbiQVxigUXBrWJyqDRSgrUiTald3mvsLb7BAwQX+PbaETRaMRJwyLGex0ugsZ5DkHqII3en0m6s\nhec67tEDsw7Y343Yln23o+U0xAxYM8O2vtvRs8rUvYyobSjOApJrYyMpMSWVxY2wu4tY5Wr5IXbd\nJcd+PUe84Rt3C+Adtcf2+poTyKqKJgxYJz267Jm006PwLcCA9ZFpt6Xn4u6FfW9xT0TDSbsgaSpK\nUobtigEbKhIGwZo6DunXG4bNmg4FB3I+a77gvL6jJCXAYhQVCYYQ2/ex4FN+yYgFOfvjYrczxhDR\n0hCzcaDcjh4tIbdcUJI6j+FAQmW/M8qIwoaqF1B2YggNp+WM0/mWddwnjGraFHr1jpPWFjP12XDK\nzH23pTAmlPRZM09GZM2BVTSkyiwgOigKOgdrQJoeVCeWMtOmYIT3pHjeirLx0h6VIyudBeHsAewu\nY5iGLOIxRZQfPYQOBQnl0YuS0bUgsCXdJVHN6rszmiTyc/PXLOh7T8ZBDR9U0TTDzugRvlf5KRb5\nu8Cavmvs7JZvreItNZZQM4lLPOAoo/Av8QUSgo1VvPBz935lS2p3Lid1b2ooGx/vS4RKojAq/Lx1\nr03tLTVxQM9sOWHGKbNjOrPDzu2KNk5POdBZNuR3rWWyge+FoWjrU7wUnURmvsU6SZLGnOAbhX3s\nhuECj8XOsbVrO4i2kN5Ak0ac7RZUJNxGl2zDnov2O8w4peuuVYvulBk5exbZiBNmRGFDpz7w8f5r\nemwIaZmU90RBQ9gYoqals6ugDo94xXX8iD5r+myONSYT7vmGD2kJmDJhR5eY2pLDnBBvRkmAcThE\njzOmnDG1PT9c0DZyHPa8LWwMXmzpmB1xUFOlEU0UEsYtq1HOuFiBMRxI2CRdBtUaWsOeDiGtE+LZ\nkx335gOGgOlgRBxUmNjQBiFBBUUeM9qvWI17bEcdgsDu/IEy6npuwsfl1a3dszzHO9KO/V88DsiX\nNYvnHbrBjjQs3ZbaZ83A+WhWtHjIkgDDCz4kwFhiXdgSDw6089Reg7D474dxOAA/xrcPDvD9xx9j\nZ/MfYVeB9LqvsRkFCb2MsDMefBNdSTYdsOHIKTa3d4c1OuqI+xXwL7BGQsUTXwD/DJsikI/XQHsP\nTekt/c/xUYrBYp6KJz83hB/tiKOKKk14zRMKMpaMqEisDL1rnX7FazrNHhLYX4W+IGflvmeDrxfr\n45WEhlhR0r/jhkC7kjomCWY5x2eJRe+QtL6BNggpehZItEBfS58NZ9w5N98i/oaAk3bOS/OEAznL\nYExgDAU5zSEhagzDZUnTxpRpRq/akteFXaS9lHXcY8ycTdjjglsKOkw5O6oYHcj4gG8xhGzps+CE\nlzzjhktWDPk5P2HKxGU5GnIKNvQ5YU5Iy4glAzYkh5bz6p59mHO1e8M279IpSjr7GgM0xFRhyun9\njl3WZbLdUGUJMQ2brE8Txpw1U+45pdvumXPCHed8y4fHbMAyGJFjO5gdSDmEKduwS3YwTH6xpVPt\nqQZWDL0aOVH0mXsep+7Z3GM3gCG21kG9nSQJAOTXhqYD4/meuTmlU5Y8Ka4JaFkxYEuXeyacMaUh\npiWkz5YDOQtOyE1B1SQMnjuQ4dYtiV+Dlv8elaAy7EIVr0F6WSkecSse/P4cuwV+ixfYE/toivUS\nArwy9bn7t8gDPezIaEv+wn3Xn2Cf0E+A38dT1Jbuu2+BM8hSe9oM+JV7ucLu0KKmboE/CuAPUkxW\nst91OOtZPGPM4gju9djSZcctlzRZxFlyz+n1zocLEphRX8OX7vvUKOz6wa08xfLFEnebd9jF/y1W\n2XiFnZQf4BM4roC1JMfQsjN2p64D65qqwEfgaUvIbXjBE17Ta3dUQczpbsE672O6LQcTU2Utg3KF\nCUOiImDZHTIqVnS3ew69lGl4xvPtd3zXv6IlZOiyA2C45sphEysyl4VIqJxGwSmX3LCnyzWPjp8d\nsWTKGQNWXPPIpi2zBQ2xlf/pXNi/hTVVHNOWEYkp6R8O1IeQwbbgZjCmNSG5OdCakHl4QhqVjJsV\ny2jEiIVLF/ZoiIlNQxjYepm9yTFhSJ1DHFY0QUR0UhMXEB2g7QXELQShsc9n5Z6RivUMXtxFPVeF\nN22xNToGmszwdHfNfW/otBuKY53Oc77mT/ndo/KT+mYaAlZmRL3L2SwiO6VP+LVp+e/Jc1Chu2pT\npbfQxcvHX2NH8SG9WuJ6KoTHva5Vq1WlnuaKAUT0qvHo4RALVLbYlfTPgf8RayyEFC05ZjCK9q0s\n57GIZYVdxOcciSxxuqd7uqZxQKQ6XsXUx7hxyhk9tkyYWeAuC96uRnfNuji4S0ywi3uNdazO8HVk\nO3zNh3AKCXbLpVzhq9wPsDnpMFmtLVLfGtrG7trWbHU5d1yMM6acMLfFPQ10qy33wSnbtEteVpRx\nSkGXKkpIDoZsXxOYlrwqyVaGZTSiDUMu21sOuU3FAccKVavKHTBgzZQzlozos2bE8hjSxFScc0dK\nyZAVXbY0hJSkxDQ85jWxqVkyoiHmQMaoWTIPTtjGXbrlgSSsiMKGfZwxe9Jnl6Z0zJ6sroialn5Z\ncNY6TsV+QWhaes2erKpIqUg58LR5SY8tSzOk2+zpmh1haY3Bup9SRSFhAW0TUOYhzdzFD9r39ljn\nVpkmTX+BlRJld1mq+ADtIWDbS4/YzyueHEu0X/GYEEPjYOGEymVpYN/mRGmFKWIfUjxsT/sOx3vy\nHATZKusg7qjKrKXwKuEEVabIK5DvvMaaWRkE6TlIMk4l3TI8yiN1+D8jNEs8AetnHHXBubQvBzGs\n+96xkc3J3aXNOIpNld8NaIOMiw++BexC2NMhoWJHlylnnDJjzpicPY+aG5rEdjc8Kk+32AmjbIUs\nvurTBviiVqHgQzz9WlCO+iDE9pxtZnekKo5Z5ZY0VEcW+EuczHlAS0SLoTliIwPWRKZhnfUZsaIO\nIuKoplMXtG1M2lSYICDdGIIA4oOhjaFX76kPASklm6yPATKHIRycFzfG4h6ZS7MUdGiISKiOGQ/1\nFM3Zu+5OwRHQDDCUQeoWSE1GwSbuMTH3RE1Ld1sRmYYqiWmbgPxwIGhtZqhTbdmmOUUWsw9zW83Y\nsYoJm3ZA0tR04j37oMs2KGyaNexShB36rEkHJf1iT9sYFqddhusd8dSQzFuiwm1KM+zOLf7ewwSb\ndEGm7rWOVZuOAkMTw761LoVNMddMuGdDnxVDJtwfhWZjKhp6dCiIaLmrzglrQ7OIvCap+j694/Ge\nPAfN1ghfSxHiJeYlzNjBrgLVvX6MZ1aeuh812ZUGhFKePbw3IXLAlfuMKqau8bwG8BRseSk/A/4Y\nG4L8HMwLWJS+QYmSLS/dj2J9F7lUpBR0jsi83L6amAlTIpdbx0B4hyeJOnFRTty5hlij0cU+XHkt\nJ3hRUjexjmBX5G5V5SpOrjOIrGBpv1kTtTXj7YbYcRLUWi2mZs4Jcwfa9lljCJjHY6LWEo9yCrL2\nQOdwoA4jdkmHshNS9CNMCW0a0GQR3cOB4X5LdmjZ0qNjCk6aOd1DQVvFDwqHrGaB+BGWdt04ZkPP\nUaM3lGQOdLNt58csaAnI6oqLcnrMagwPay6nM1IOrHsZTRyQ7WuqPKSJQuK2IZ/b1Hi/2lEFCZ16\nz+nBgcdBnzaBNC5IzYGYmk3UpyVyBXMNMQ1x3dBE0MQRcVMT1oYgMdYwDNwzlYJYgI1+K+w+eIaP\nfE/c+4YQjA3ktu3r8qLLgZw9HTL35oqELT1mTNjToUPBlh4rhke2ZFNHtG3gJQdmbt58xzsf78k4\niHikxa08oYqhAux2OMMrRdVY97+P9cWkxDnArg6pPn2G9btdgxwe4+Xg1C03whOdxINQ160Ld31i\nHgX2+8yfg/lTqP57KP4QvvwOvp5a+yESppyVmSEaFC6aMbzmMQ0RtuPV4igtHjkK8CbpExBQP3G3\nJMdH0Y2SJyogVW+emuOEOgphyd6KFCUZToA5tDXUJ3A7OiGpK6aZNQJSFrrhgmuujrUEkTMUO7q0\nRCyDESftnO68pg0te69pYoLAUEQ5VcfSpFNKoCWIWnbdjF034WpxTxsE1GFMmDREUUVCRUTjvJMN\nB7f4S5JjxqTHltc8PqYXd3RcRsPyE0JaptEps9gqIwEWK0kNbRARmIAizgiN9Y6aJCStSuK2ZdkZ\nsMyG5E3B2WbJPuqwCXuWqNauKIOUfWBLZA+OoRk70ldDSLwGY0K685rh9YEiTwkMtBMr+ELfPbtv\n7fjzzE0zGYXHeEVqt19GxjbFCSN4tLA11jURFTEZJTu6POO7I/6ifqshLVOXEz3pzq3I0MMyoxM3\nl97xeE9hxT12dqsWWl2txBiRiN4jLGip6iE1p9Gi/pU7xz1eRu6XvJ2SFDtIhCo1KJxjixFUR/sV\ndtXFeAMlD0OiMi+BCpo+NHdQhrCu4Osz+BcfQ/wZ/N0MPg8ogwHzn6Tkj/ZcZjds6NMQcckNA1b0\n2Rz5Dx9vXhIODOErNxQj95VLfH2aDMQYv+hVeDrHU6aFfCuxM+bYkGd7khEOW6o8II8LfjH4ESaA\nE+ZUpGwY0KGgw9xlBlonRnNwHIgtQWB4wxXj9Ffs0i5NeGBSTNknOb1dQbIzrJ8kJHVNcjDMOmMG\nuy3rUU6SbNhzSh4ULIIRt1ywYsgVrx1P0e7kU85oCblwQGRJesxMiPAzYOXQ+ntaQh6Za3JjWZHd\ndsc+7RAnNdm+Ypt2OVsuiEvDcHlg96gmbAIWk5xus6WMU+Km5n4wtKFSs2cRjVmFwyOfoCFkxgmn\nzIicjsLosKfuBUyzS3rJjtYE9FlTnkK0sJiBOXci6I+xiTXBZRHeSXakt3ofYDYQxwYzhNmpBbBf\n8vRYll2Qk1A5bkfCDZc0xGzpsXNp2JaAN998hPnWNYieY41T5ZbbOx7vqSrzvzQ+PDBYcypk7TFe\nEkf4wxpfWvinHNlG9LDG47fce7/GGhA1kXjlzvcLvGl+5M4nkVp1FgGv7jLAei2PsKtMIdAOa0BE\n7T7H07sFlu6Afw/iJ/BkaCs3/8OQJ3//Bc/7L0jiir+V/DE/Tf8148Dmla54w4R7nv3ZlPoS4i+x\ntmrjLkOQzArrJazxXLAzPBqt9h+KyBq8ZGZum6I0XShHITf5OQPWvOaJ3WGwO+PWcQhUmSiVoZbI\nudI1KQdKkxEHFWfbJeP1FvbQjmA+6pHXe6ogIagDumXBrp+SlBXX3UvHdyhtWtBlbv4Zv88lN6wd\nxbN2tQw1MTHVsWCrIGfEknsmPOM7Oq4SNKHijCn9ZkMbWF3OmJrBbkMZptxlZ4zaJYNiR7ZvWY0z\ngqClCSLKIKNf7DGh4WV2xVV5zSHNjvUlo8OK+3BCnURc8ZoDOVtswyIwFKZDGrj508LV/Zy6CWl6\nAZ3r2nNSpDz9xk1bqU0rmh67Z5VZIlVUwfYsprMyfD25IsICrt/wIUvGhLR8xcfs6PCSZ0w5Y0eX\nF3xERsGvXvwYtgG8iO1S+g67h34B5h//RldlCiNosItNTWjUpEbZDFEST7H+9Ld4DoR1W33ZoZLH\n6n6lYE/A5inWixBP9Rm+QUWLXXXKfLzGrqg11ruQFHXu3iPexS0+NRC570+B/wXqBL5pLVPxDx/z\niktedX4Hfi/kf/qDv0X/793ye4M/53l4yyedF3x8+hU/Hr5gVM4Z9lZ0T3d08gNJr/K8L8WPQp/7\n+CpzMcLVlUtCI875MT3YdVPCtKUKIypSgiYgiSoKcu45paBzXJC2MnLDno7jZew4q+65SS45MXOi\nsqVIM9rYUOUQZLAbJpy+3LK7jOiuStK5wQwgj0s2eY9uu6cI7cK74ZJH5Q11HJKFBxpiuo4VWZFw\nywUT7kmomXGKIeCCW9YM6LNx+IjFQ/buutfR4BhuDKst626fTlXwwfINYQNVF6o44DY456q5oYwS\nTjcrDlmEaWPipmWantFglZcKcvaZvf8bLrnmise8oiBnxdAapWLOKu1jIrhcz7gZjbmYLywPoh+R\nzBvCKzelcmxK+YCnvmuPk4d4AdEuIMgMYduyGaWUJmEfDFlwgnFe03d8QESD2uFZIliFqQO2cQ/W\nid8cxJsJ8B283+F4j5IiksAR8vYVdlartkLwv/jDYEdUsG+GxxBe4hG4W3xznC429BB1MMFXf4Ld\nmj/Fq6tI+v4KXwYphWoF99d48Rl1QVWFVI01dkqBCpp+Za9x/6fwxwn88YjNPyr4J4z5J8m/DU/+\nLvztluh3O/z06Z/w27zkeTjj8f6ecb6C5w2X7Q2Xgxs65zvG2wUn1YpwbnyyRp2b5ThJHyADs4Z2\nDGHREiSGQ5BZgKtoMD1LTe6wxxDQwSoMDVyF3wkzClccdYgy4qolbVpm+ZD+YUfS1LRBSFq0FtSM\noHff2KrDk4h9L8EkLXl1IAsKiixnF3QJMdw7rYUBawo6R08gpGXMgi1dWkI67Nm4squA9lgpWTLk\nlks+4sUxGzRiwYIxvW3BepSDCViOukSmITCGuAj4YPeaZGHYPTWUWUhDzLRzStsEDNliTEAZJC6b\nYo8r3jDjhB09EkoSApYMSbKKKGhYM+AQ7MjCA7OzHsPZHpNC/SQgOhgieQ9yUpVmFq4UAs9ss7Vq\nFFi+RBnQqQ4UwxnT+IwT5kfquVoqgpWNO5BxwyW9eMt9OSEMK9p54msVlTp9yTsf77FkO8SHEn18\nGXUXu7i0C6d4URcF4uIQq6rzG+yqUM2FlKl7WIOh+mcJHEhr0uAzFiITiImizltiSq6xhkJG7WOs\nlyNhW9G/5RWJIt59cF0Sr5FveQ3Va3ixhxeG5h+f8DMifsbnEJ3Y8OB3GvjtkOfjX/ERDYMXDY84\ncBlNibsGPoTkZMuj7IaL4JaTzOoLDgdruu2ObrMju6yoehGHNCFuG0b7LfdxyZveGXMH+k2YEbmU\n2d61cVd5eeNc7DJMeVTdMjMTOuWGgg5tWxFFvhnC5jQn3BiyouSQ2b6N+6hDkpQMVgUTs+A2PSOM\nGg6kJJTsXEZH5KshK1oX40tm/SEvYkuPEUvGLKncFM44ENAeAcr1OLcLNIFd2qVj9rRhyO4k4mx1\ngBZrLKqGOknIKegaa3QG+x333THSZCxJ6bElcanT0okbn7jWhnu6ZBxYDzIGhx3UEQQB2dYyM9sU\neGzDrvAlvtmbMu0TaxTqPKQNA+Kq4RCldKYVh7OQVeyFXrb0aAnosuWaKw5kNERMmVCYnOVywnJ/\nAsvWRuA3PKDO7+HlHq8x8H9/vCfjoMUtkya63w7rxj/FhgnSgGywYYAWnTIPymqI9JRisQmVa0fu\n8xJw6WEXrHgUqo0O8SGDSuakF6ECMGU5VNn0C3wYU7pr6Ljve4xNYYj5+Ve7e6mUUy2uwLNfxsAU\nmq/gegfXE/gftnxNxNec+/sYPLe39TQgvkw4Sb5iTMmAgC4TuvTJOjX5ZwXxSUMzCak/jwlHDY8G\nbxy1KOUZ33LCgg62fV9Jyod8Q07Bmv6xoKl0ZcKbrEttIs63W/ZpQxhCvmocrtBwyEI66xaTW7Xk\n3vbANs05hClRAmlYcl7eExBw07G0+YL8mMmZMXGT/QyD5Ucog7F1nBThEXtyeuxsWrI5sEwGlKRc\n1reEcUtnXjF70mW83BDULUWW0Tdbyk5IUjX0byrCHMLMNqJtwpCShHXcd1WZAR2Ko6QfwBuuOGVG\nQ8SSMbuwy6S+ZxP3yYOCddYlb2pMYthMMqKqobOtKU4isnlzbJvYdm3rw2ANrz8+w4wCesGapK6Z\nh2Nyc2ARj4h7Bd/yAS9cC8VXPOGWC/5i+2NWPzunahKKac7qqyHVlzHFXc82erv9GmbXMA+gNLAr\nrHqMWQP//jut0vcESP4jYxeqStBusIvuI7z22Ra74JWj22DTlA56J8GGHufYzlhyAQX3iyus0kmF\nB0KBSrwmhNiYLXbF3bpzqeZWZCtVW8krEALoOnYfw4qX+DRojS8oUyh0iie5P2zfDb7Rr7qcXOLb\nMov1KcBh5N4jCDrDa8b1IUoc7yKCtIVBDYkhzUJMOMKKIE+IqAgZEOQprcnp/ZtboqCBGIJPDfGn\nNXFS0/l8Sdmm9IdrkqZiks34kfmSi+aOUbKg0+6ZhPeEZUsWHggCQ2siLuJr3gSPbEm2iTk1c/am\nwywa85JnPOM7NvQYOzk4W9494AmvnUZEeNRvCBwlqiZizNKKzJgDT81L6tDiFefmjrwu6C8qNqOE\npGqpTEy4CmiH0PQMw1cVX5x9SBuH9KM12aGiTaCNQvK2YBmO6LFhxYjveEaA4afVn/FN8iE1MY95\nzYIxBTlds+M0mHHv6j+WZsSQJV8FH5Oakl5dUMQp5S7jl+ZTxsmSXrQlaSvWZZ//rv/32dAnNwd6\nZsPL8CnXrz5gf90l+rph/a8GFN/mmG3Afp5y2M7Z1S3tYgNmAYcZbDOndN0DI9GimZtHCnttKYEx\n/+k7AZLvyTj8t8Yuhil2IWpn/xEW/BODUgtMAKX4n1vsAlMjSxkE8SKe4psSfoIlI4jP8BqvAyHP\n4HO8kotSrOoC7pQ5eIXFIt5gazHUgPcWL36rnGOKz6TUeGamECEBsAplEnwDA1V3afFv3flVA7LG\nPniFO2qYIbGHJ+4aVfL3FIvnqNcH7nPybjKOEv6Bq3RNPsdmfqYQ5xCdQRAQRAcMFUFQAyeEQUrE\nM0LOCOkSEBMSQhZAZuylRBCtW9pJCM8N5qOAsGlhGdAuA9pXEdE/rDHzgODU0NYRGIN5HhBet9Zh\n3AVwYuAlBI8qmAQMz2eUZczF4I4Lbl34Y4HEM6YWpzB9LoNrBmbDiiErM6IfrKmDmHG75A+r/4Bk\nZfj983/K0Ky5DS44kLm6l3M67HnZPGNtBnwcfcVNcMmhyUijkpaA3WIEcUvxyz7tKKKtIsKfG9oX\nAUHf0NxHNo05MLAJMP8z1J3YYj8Ygl9Cu7+nZIlhBNwR0KHllzTtHpoVNGMbpjR7m25qSzAC5DvY\njU9lt6LNPsduUHP8BqWN7AZj/rPfZOPwXxmPLYAvHZQK9Uv8Dqj6CKeOyVPsbj7F5vEE5d9gF680\nG4T+/JK3mugilVYBkOJaqD46whqUP8JCy0plqsW2Kj4/xS4ghSrCUUQHf+PuZYTnY0zc3z/Cg5ny\nbsRQUVfwjruWZ3jP5Du8Z6Nq0idY45e790/wnVNG7n2/wOvGSQ5KubUMr5D1Cb42fIINr8RABU8M\nU4ZGedUZnlMu+p+4Ka07nySzxGMR0Jy5MVYfuHs8sqpNIsRzXDbuXPKmpParLrQ59pl/jhcwVj2N\nBIS+deP7t7GK5Eqp37mx/XfxnPjU/U0hpEBmiWlI1nCC59Ko9v7swWdz92zVvGLivkNtry7wpZPn\n+MIaPU9xgFSZpU1RKfS++84ZtojwNb7k8y/xG2iNMf/xb7Jx+M/dtqKaCS0skY2kXpLgywrVBetj\nfDeZHp5yDZ7LoCqTMfYBBtjJo0milKb6W+h9whNEu67xrfOEDSgrocV9hQc3h1jD9lPsglBlp/qA\n3rvfDynjj929/Dm+lZ/402s8MCtvR7iGK5bgR9jFr/FrH3z/C3eeh4Cr6ON37hzq5SeVb3k+ckfF\nrCrd2FxivSgtNoNfmGt8+CNsZ+fGReFS5s6jNLJ+DN5IC5we4DkvD5+v0/VEDSFi/HwRaK16GrGM\n9NyMGwO7i9pzKHwt3fMUFqXnqus5cecO8Wph4DeoR3hq6vWDcZm76xjiGzepvkeVfIU7r/KPDzrp\ncolPtTd49ZaH16CGmwe8N65Qee/uawE0GPMP3sk4vMeOV3qw+vnc/RaEqx1CfIgIa11v8BNr5X5v\nsA9W6q8BPnmstnjKHWnB9cB1IvZMI4GWUmrtYRdv6l6XYpUKHiRrJ9VQydp/ge/CO8OX3KkmRICs\nwWZapKyNu3+BtCokE5Yh3fJT3la+unCflWDvG3xXMBnej/EilefuHNpx9dkn+JriADvBcve6uuxM\n3bXKeIiaqSIOyVipeagKO+QhTPEGRUQNtfsK3Pu0UPfunuX1PGxypIUQEUrpXQAAIABJREFU4Ev8\nR/j4WqHhCb7zeg+7m2/wte243+fu8yqNVGZJz+lhZ+PH7rewpK77zg02hJOHEOKBavXC0wb1kG//\nkC//EKdq8ZIGDsk8Elg0FzQvnezhUQRJenD37t8SGpUx+euP92QcnuDBPf0WfvAIP6mE6G/wQi1i\nJM6wlrCPV4JaYSfFAjtQwgDADtIcH0ZoEWp31K62wacnlZSWIZO7r27dPTzoI06zqNeq+tQ5JJ5b\n4VOjx2IMvJXXA6zddQmwfI0HM7V4RGpI3b8n7rWF+07VeYMP27TDn3Dsu3akq08f3OcZviHQPV70\n8MKN0UOV2yV+Ue7Aibv6BaJ7DbCLcOI+q51TxkiLUOpePXc/J/jiABlk8cLlObZ4oU0xYeUFPizb\nb909ZPhwUQZGhlAGN8fXxmvnFyYkAycvYoKvnho8GGsZ4ud4ro0MqBRfQrwh17Oq8JiRao948P4Z\nHnOq8RumMnpneGKgwPMlXo/urz/eUypzh7eSmmRqX7fBx7na6bSbq+hKsb0mdoa12BJ2lFXVOeRS\n6rvk2u7wzQqFSYAXmRlhdzvwojHSlzjBE6UK971neM6D3ESDdfMe4dWuxMiUcstj7OL/EO8Bic+h\n+9nideq1493gK7DW+Dg0fXDd4mYovhfFUvG5mmTU7nX1aFPJoIyrdtARNqxo8YKIqlf5q2M9dOeT\nOM+pGwe9XylpudvqC/cGjyussDG0al+u8DUymXt+L9z3KkzV+eXC69oLfEOkc2wIKA9sga+pFlVe\nndGMG/9b/GY1wi9YYTA9vHeq61eTTAkIxQ9+1L1GfJkeHjsaYp/v9sH75dEoE6dCmw/wddlKl4so\nqDmtwop3iiiA99oO7ynezRrjC7DkNTzGg5KSVL5xr51iAUHt2ODBIekzaPAmeF1wTRBhFMpcmAe/\ntXDu3I9ShSp5VKs+7UzSidDE03WrIc/HD86n0CZz7/0av0iVmei4e7rAezoq3dPCV+stgWLyYrRQ\nH2Z6BAze4CfhFXaBVygO9e4ueKWZV/g4v4ddpLommwi136NqVvWPVxz/Gh8q6DrEI8G95xyPOzUP\nrkfCAwpH5NFI2lncGIkSP8Knpx+7cfzQvecRFgT+wP2U7trkrst7GWDT5Q/deKWUVSX1Eb5DmjwH\neazyhNQjUd7PGhs+4sZSoZJCxp/iWVECRl+661ngw2Zx5cW9SfDhijJtuL/d4UUcGt42eu92vCfj\nIPf9CnuDX+A5CHKrX+D7ZEqS+WPsZCqwE1fpUIF8apV3i8cJBAoK1dak1a4uqTnF7wN3Ldpdnrr/\nS+Nhha+dLvBAnVhnf4ZXt7519yFP4R7felnXrL/d4ifta7yLX7hzfui+I8E3pxA6fo9fII/xBnYD\n/Bu87W422In6Gd491g74rRufLp6Mn2MXf4JNfUqk13VmIXafW7tr3OBDgws8lR13HnWLVb15hdUS\n1X3cPfh74a5fHYwVXgp/kcckz1Jp4wM2s/PbeEDw1o3zFm+oJTrc4mN8GViR6iLsHCiw4XCJ3Rz0\n/BdYDKYD/G94SqJ27Q0eP1Ont+/c53ru/1M8LiNDcMDLRP0I31ha96rxl7EV8zbGY1JXvB0CVW5M\n3u14T9mK/8bYAZQq6wY7wf4A+3AkbQTWVXyGvcEXWMufYi2rJJcj7E7zr/CgmOjSrjnN0W2TdzAB\n/jW+hFGx2wX2wU3xxsvJ2B+bG/5NrDjtR9hFrgkiL+AUm047YO9TacGP3T1oFxRI9JF7P/gFJC9K\nLrQA1i7Hvu5vXfMeX+eh1O4H+BYAifv/azzOovCh675fRkwZEe1sYzxm8CnWWPXcvcndVe34T9x3\nzt2YKe2qIrULfHdihWfyFAbYtJuAtcxd8yt8qHZwY3Tuzvlb2HSkylKX7rnd4auc+u77XuFDNo2p\nU/o6tkgo3fOI3Tle4+eIFH6u8A2T5JE+NKgKnd649wsEXfC2kplSxg8/KzKd6oFkrJ7iU6vitmzd\n83hIoutiQ7AzfJZGIbiVRDTmP/lNzlbMsDe/xC5yEYlK4C/wllau3T3WeCgNd48H3lLsIN7gMxoC\nAn+EfbDKyYvnIMBM7EZxDbRgDXYh19iHqGIwST+7ZodHPcuBe98rPOPzsfuMQLMz97q8D6UPC/xO\nBr5OW4shw7qdER6AEtI9dGP3BjvJ53jgURwDhR053tBo4gjzeIPfiZb4XfExPkTQIvjOnWPmrumh\nNv8Jtg2A9DKW+AzCb7lxkuv8GV4EWGKcM3cfHXwMv3H3IgMtI6SQ52d43EmpYcX58ibf4KWy7vCA\np0hE1258tMP+c6wBVMOPU3wDJpHjFIYIABXIW7t7+RqPMaifocKM0l3bR7xdRSyG7scP/h/g8R9h\nGCN8u26FPgIqv3TnULZCmZKHqeZ3O94TIKl0iib/JV4dWgDbBK/oqskhd0nZDQ28CD+qj0h5W5dd\nrvjDQZPl1fuU/nkY00m/Qbuo0GeDj88PD3738OGBcvdKPSml+JG7xuDB/U3xhBYt/BN8/l1uuDIb\nYkNqDMZuDM6xk1+EMGEwD2PXS/d6B990WNkXVZK2Dz4jwyKDJYR9xtsgmtKuUvcSb0GkIS2sA75v\nn+JlhX3iFcjDkFCPgENxACJ8avNXeG9nilf4yrFeiLQ47rE7ukBmsUC10UyxG5SySYrXczde8YMx\nUuvEFB+WyPjI7d/j06nwdnpVZDGNzQ3W41LHIhn0K+x8kseg71WaV2Gdyi2FQwgbE7ic4T2qd295\n9Z48hzP8jYjyeYNnM4rdKGRfFY/aDQLspB7gY6xbjr3EjvqRSktG2Affx04QuYgjvDHpYnedAX4x\nfI5v9jDEPkiRh1QopRToCX6nHWHdQBmpIR6YG+AL+iX5pPPjrv2TB/dZYHeDMzwRSumzg7ufyl3v\nxt2jCFHaeV9jJ/EO72pqEYqHoBSbshPiH8jIjdz5xvg+HyU+gyJvrIvPGgjsk2t74r5LqUThRffu\n/QoDlXURqCr2pUhFMmwz91xv3D0qPala6Cve7m2ve3rsPiNinTJMC/d8n+EBc3mV0h5Rzc8Nb2eU\nBChrHIf4DUaYj8ZDi1zENMlDiUkqgyL8QXqpSksmD75z/WBcpDgswybyoLJ78pze7XiPArMDvPjr\nDI/U3+EH6RKf1xdrUqQRIcM7PNFDjLE9PjUlTkWLJ+Bod5CVFZ9CdG6lt+QqCyVWTlwTSmlCxcIr\n991SZVF2QdkRceD1sEXuktqoFuKMt2PyGM9zAL+QpG/xMGff4nXpa3x69QKfZz9z/1YqUQtf6Ttl\nDTTpdA8HbHinsEou9kPtCo210owJvv+bDIQIRrpfGQ+h75oPrfs+YQEPGZXyKr7BGkiNsbJGyiSp\nYldenER5cmyo8zEeB5FBU1XvM3y27EPe9n5yd/4FHujVAhfA+zALosyR0t4PRYhUXKcMkMbv4d/E\n5lUKeYfnU4BXjRHXZILHKyRYqQzNux3vyTis8TK4kpIXPpBiFwbYgRYr7Nb9X0i+iE4CgCZ4MPCA\nr16U1VbOWKSlBs9FUNyv/PxDbsUaS2CRMdNuLAKXyFQySJqMXeykk6soN/Maz/F4iECLXixqtiay\neB/KpYuQI3KTWIdKK4qrf4cPlXpuXD9w93OH1+hU/lv8EQF42r2Vcnvprl/EIIVXMjKS+BMesXDX\n+hif7lPqUkVzqsxV1a0wDC0QeXTg0fyKt1XGx/j4XOlL8Sa+wIeIz913NfjuLkqDCqfR50Qik4GX\noZIQg+ppZCAepglLfPwvHEmZsed4gwDWMKnOR0VSIpHJ4/vU/db8lfF7hif2PaRRC9xc4htQizwm\n0PLdjvdkHM6xEzXHs8oW+NhTRSknWNdQwNrH2Aejh7HC99Jc4HcM7Uaxe+0XeCCnxutDfOa+7zPs\nJJMX8RN8FeV32EkmTYapuwcBZkp1fYovBhJK/grPABXH/ZH7nFxpuflyrzNsWKEaAXHoNQ59vMH8\nXeyEU1ZCdF/tVDKgCiO+5u0UrLyWZ3iClhZohF3YYgvWbixVf6B8vRawcvci38gA/BJvIBusJ3CH\nZxvu8CSen2A3AdWIvHKf+cCN+QyfxRDoLG+t5879Fd4DGOMB6S/d+D7GGpC5u59/ie83KA9FYLCM\ncRe7QD9w59Rr8pgeEo3khQ7wTZPEzRBPR8ZGRWAyUgLXP8Gngb/BG1Z5WirSu8OHtcJ4xngOjjw8\nha/CbN7teE+A5ASLMovu+xCcKfEL4A12cDrYBfgSX4uhgiDxz+UWh1gv5AUewVadwcPCrG+wD/Jv\nYCekQoUIC2Q9wk5GeSQv8MCbBlmI/de8zWv/0t2DMjB7bHjzAg96/gj7AJf4nVwpOI2BPCBlUZ65\nc73CE680ISP3WYG3H+Alh/fu/cJTFK7c4uNXucoKUe7wXoDov1M8eUv0XhWmJW7cxu4Z7/GNPJ+5\n5yUatcIwZY5euff873jvR4tujU9lC3SWZynkf+3GT261wVPrBZ6qNH7lrv2n7n4ezikRmEqsEfkC\nX27/AXbOKOtzj8dDNK9+CztnRTVXOvI5fvNS+LPCNyJ5hG3AqsUuQHLrXpN+hzY2yRb+CDvXuniW\nq1LmMijiw+iZK+T564/3xHP4r41HpsUDn2N332t81aEAvy5ezq2LR6y10/4YW9XYx9NNFdNtsA/6\nV3i3UCWxp9gHLostA3H/4P899/4FHkTTzqlUlwAlaSTk2IU3xPfbrLEGboT1RhS2iLN/4+7vOXZS\niqtxztvVpP9He2fSXFWWXeEPCSQhEEgg0QmKJrOycbkqwgMPPHFE/Y0ae+QID/xTPPfQP8IRHnvi\nCA9cDqcrKiOLTAoECCTUIEA0eh7s89U6qnhhmInB2RM6ce99556zm7XX2m+BfH/oFgFldbKO3zM1\n3yYotQQpp2Q9JTM0TGVvE6DP51Y2b5b2liD7brYXVCdGfokArmsgdqD83PLoZltnyWa7BPSTaXqB\n48xWSyidq7Jsu0AHbZ09lBKRzCgtA7+kMgKvaYbn59sjZDXB2gUqE9WZrBDS0zOijxEYF6A1G1pt\n626puEllTFvE2dsNkwcha9e9rfPRaW2Sbpj3d2S5pa+ZJEwmf/e58xxEo0W9FehY5wlKSQ1dpTaB\nU5x2CfL9W0KpFUewvrpKbf6/oNK1WSr1PKAcht2EZ4QOe5naTCL3Z9p1FCMptxap1kv/jtpkbhw5\nAj2HQIKTve6XVPS6TkbIiUjb2ZghrSn1DmYpl0jNbp/e0mCF2jxKiaE2svXrMqE49/37U5TTdIaF\nXRw7Q0tU9tDTvd2cPofYwF5bZ5mMXsOo+obwANRmiGvYbvbQenBskb5u72+DdImk3MuDOds+twCw\noKnzF64S6rVZntmMjsq58f7Z7oYOVvzJQ235eEA5RQPVBeJIBJyvU/vzNMlw3bvLHG/f+vxiSDI5\nxV/c+5YTj7u/67tFn2YnVFaskrbSWzI/cpuAcqaVvSbiiKSNkH606LzMvvcc70EfEqKPbUfR6fPt\n54xcr8jmEUiyJaqSzs7GdepFQBzcIyqC6r0VFNl/t6UoWccW1xF/Gjr7p3LJ+9vqNUPYJ3yIh4Tu\n7QAXFYum77bG5Gt4eA8JVbnvHMij0GEetGd5TByWaLrzEoxOv6DqeFNh76cYysEovns1FXatVNk+\nI6xHBW6LhEhlNJXiLKh3unvG591aGkg22vPY4XpDZn1CMAXLBQlKzpl4QwbtKOgSSLxI9uRjUurI\ngdlqn12cyc/vzFIIuW2hu+ZTjs8/dUSiQdCpZHbZLKXmydwRgXr368fthDIHW4KQl7hLWnS+ZCOr\nMx6srW2LQVqXRjy9bp89KIfdJFxz1YnWYR6CG+RAC/SYJfTsRA+e91f0IydecFPuey+SEvewry5l\n3F6+7SfJSHZCIEDUQVsHW3J+793b7ud7xNy2paxMU/Wex2GEEkCUjGU2cpsI5Sx7TNNNkZ2/6btz\n7b4hw1jcrAYB10hxmNJ86eB3iWP3XUo93yVzJ4y8ZnwybCWPCaaqYDVzdXjKKcq5C+6Kq9jl6h2o\nAWuB487R0neRcgSCp6/bc8oFOUvEW2IJfSdD5+r7URbv1K51giOtt19XScZkp056tuMJvuBT7YSc\ng62zBeow2jM30orG20a6TQZkGCVkE56nor6EFNPHQ+qFPCHKyFukD+wMAyOYab4zIIwsHmQJQSvt\n36TMSszSWcxRXn6HvJTXxCkZyTYIu3G+fY7HJHK4Fi+J0EaVqesgnVylpZ9JXojiNrOWa+0+l9vz\n3iGb02xsm8y4PEWo7EdU+ms6vkhtbpWoy+15NqiN6igz1+z79l4tF86ScmeJiuq/4HjJpl7E1pwq\nWVuqpuYTyintk0PUz4g4R8BmATo1DHa/XpBAdIsqQ+e7zyjVXLGUvJkNMq1MZ2A3yHGFZmym/JZK\nZlIqdS+353hOOApSyg1YOgDLKzMEuy8OyJFJKaDt+xW3+bidkHNwCEXfqfh5exyzCqXKUpLlDVj/\nrROn4uFTpejmtPX3iuPswEWScj6gWpkSoGbac90mkVuClfiC3ABbmQpjPAiyLy8Rp0L7GaOlNFYZ\nbZvU5n9NouZLEjWdf6AwyKlFH0jd2xOKvietvuuE1vwzIjR73tbxiDBFBYNlFX4gwOAV6uAI8Nlz\nNy1+QOYeWH8vtWufoaJyTwU/IqXcYwroM0V26tMtqnRaJ19duN3uqRMTv5KLYLS0Hhc3EBTsBW3K\nx1fbn6+2n7tPuCS2Ds2qZtqa2oWZIV92BAkAD9rnukzYuHY/XlNR/GF7N28obOYVBZQ6JUuQ27bn\nTvcuza7vt+d7QVrxC6S8dO8uUF2RT7MT6lb866Q+iG1BsQGoD+eLMnJBbYJfUcIsWX1GvWVqYe8R\nz2ka9YhyNEq9rTltbdqyvNh+v0mYlHcJu+5ue6bfEr2F7S6Hy0yoF/4jmZ2oBzdjcRqPfXXprYJ1\n96mNJ+ahuMwe/zdETCTWIgfkDwSYE+jaIEQda1/XVd2KWVk/8MXnMQsTcDMd36Ac6FK7jwIppelG\nUBF7OyLfktbtFeJY3ra/u0NKOaXMPrdpvbX4FumKePAE3AS95aP0DsQhKdfaOloaniJA6r12HbOu\ny209HhOsSnzBkuR6W5c+SFh+2FZ9QKA+S5YZKnDQ/vyC4FLPqPNgZ0i8zNJHjEry4D0CutvpEXOq\ndZ1M/v6TuhUn5Bz+ZZK2m4dUppj9YNM35wNYd/0NhaS7IQTt9qkX6KEUKHIRITWpWIJkFcjUYOu8\nJ9RB/I6gwdZ19s1Fts+Trog1rK06o53g2Y+EYfiQODdTwIuE4yC3YZ/MhbTkkj7rczlgxEi5RDlG\nf0bgU9BzjdqoUtR9DypGIcDkDol8jnfbaNe6T4g4D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"text/plain": "<matplotlib.figure.Figure at 0x7f7ac98a5fd0>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 99, "cell_type": "code", "source": "pp.plot(snr_scale_roi_new,'r')\npp.plot(snr_scale_roi_old,'b')\npp.plot(snr_scale_roi_sie,'g')", "outputs": [{"execution_count": 99, "output_type": "execute_result", "data": {"text/plain": "[<matplotlib.lines.Line2D at 0x7f7abd490850>]"}, "metadata": {}}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7aa4439c10>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 100, "cell_type": "code", "source": "pp.plot(snr_scale_roi_new/snr_scale_roi_old,'r')\n", "outputs": [{"execution_count": 100, "output_type": "execute_result", "data": {"text/plain": "[<matplotlib.lines.Line2D at 0x7f7ac9b6c450>]"}, "metadata": {}}, {"output_type": "display_data", "data": {"image/png": 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