mixed_gadgets_gpu.ipynb

{"nbformat": 4, "metadata": {"orig_nbformat": 3}, "cells": [{"cell_type": "code", "execution_count": 17, "outputs": [], "source": "#%% imports\nimport os\nimport sys\n\nsys.path.append(os.environ['GADGETRON_HOME'] + '/share/gadgetron/python')\n\nimport ismrmrd\nimport ismrmrd.xsd\nimport numpy as np\nfrom ismrmrdtools import show\nfrom gadgetron import WrapperGadget\nimport GadgetronPythonMRI as g", "metadata": {"collapsed": false, "trusted": false}}, {"cell_type": "code", "execution_count": 24, "outputs": [], "source": "  # <gadget>\n  #   <name>NoiseAdjust</name>\n  #   <dll>gadgetron_mricore</dll>\n  #   <classname>NoiseAdjustGadget</classname>\n  # </gadget>\n\ng1 = WrapperGadget(\"gadgetron_mricore\",\"NoiseAdjustGadget\")\n\n  \n  # <gadget>\n  #   <name>PCA</name>\n  #   <dll>gadgetron_mricore</dll>\n  #   <classname>PCACoilGadget</classname>\n  # </gadget>\n  \ng2 = WrapperGadget(\"gadgetron_mricore\",\"PCACoilGadget\", next_gadget=None)\ng1.next_gadget = g2\n\n  # <gadget>\n  #   <name>CoilReduction</name>\n  #   <dll>gadgetron_mricore</dll>\n  #   <classname>CoilReductionGadget</classname>\n  #   <property><name>coils_out</name><value>16</value></property>\n  # </gadget>\n\ng3 = WrapperGadget(\"gadgetron_mricore\",\"CoilReductionGadget\", next_gadget=None)\ng3.set_parameter(\"CoilReductionGadget\",\"coils_out\",\"16\");\ng2.next_gadget = g3\n\n  # <gadget>\n  #   <name>gpuSpiralSensePrepGadget</name>\n  #   <dll>gadgetron_spiral</dll>\n  #   <classname>gpuSpiralSensePrepGadget</classname>\n  #   <property><name>deviceno</name><value>0</value></property>\n  #   <property><name>use_multiframe_grouping</name><value>true</value></property>\n  #   <property><name>propagate_csm_from_set</name><value>0</value></property>\n  #   <property><name>buffer_convolution_kernel_width</name><value>5.5</value></property>\n  #   <property><name>buffer_convolution_oversampling_factor</name><value>1.25</value></property>\n  #   <property><name>reconstruction_os_factor_x</name><value>1.5</value></property>\n  #   <property><name>reconstruction_os_factor_y</name><value>1.5</value></property>\n  # </gadget>\n  \n  # <gadget>\n  #   <name>gpuCgSenseGadget</name>\n  #   <dll>gadgetron_gpuparallelmri</dll>\n  #   <classname>gpuCgSenseGadget</classname>\n  #   <property><name>pass_on_undesired_data</name>  <value>true</value></property>\n  #   <property><name>deviceno</name>                <value>0</value></property>\n  #   <property><name>setno</name>                   <value>0</value></property>\n  #   <property><name>number_of_iterations</name>    <value>10</value></property>\n  #   <property><name>cg_limit</name>                <value>1e-6</value></property>\n  #   <property><name>oversampling_factor</name>     <value>1.25</value></property>\n  #   <property><name>kernel_width</name>            <value>5.5</value></property>\n  #   <property><name>kappa</name>                   <value>0.3</value></property>\n  # </gadget>\n\n  # <gadget>\n  #   <name>gpuCgSenseGadget</name>\n  #   <dll>gadgetron_gpuparallelmri</dll>\n  #   <classname>gpuCgSenseGadget</classname>\n  #   <property><name>pass_on_undesired_data</name>  <value>true</value></property>\n  #   <property><name>deviceno</name>                <value>0</value></property>\n  #   <property><name>setno</name>                   <value>1</value></property>\n  #   <property><name>number_of_iterations</name>    <value>10</value></property>\n  #   <property><name>cg_limit</name>                <value>1e-6</value></property>\n  #   <property><name>oversampling_factor</name>     <value>1.25</value></property>\n  #   <property><name>kernel_width</name>            <value>5.5</value></property>\n  #   <property><name>kappa</name>                   <value>0.3</value></property>\n  # </gadget>\n\ng4 = WrapperGadget(\"gadgetron_gpuparallelmri\",\"gpuCgSenseGadget\",gadgetname=\"gpuCgSenseGadget1\", next_gadget=None)\ng4.prepend_gadget(\"gadgetron_gpuparallelmri\",\"gpuCgSenseGadget\", gadgetname=\"gpuCgSenseGadget2\")\ng4.prepend_gadget(\"gadgetron_spiral\",\"gpuSpiralSensePrepGadget\",gadgetname=\"gpuSpiralSensePrepGadget\")\n\ng4.set_parameter(\"gpuSpiralSensePrepGadget\",\"deviceno\",\"0\")\ng4.set_parameter(\"gpuSpiralSensePrepGadget\",\"use_multiframe_grouping\",\"true\")\ng4.set_parameter(\"gpuSpiralSensePrepGadget\",\"propagate_csm_from_set\",\"0\")\ng4.set_parameter(\"gpuSpiralSensePrepGadget\",\"buffer_convolution_kernel_width\",\"5.5\")\ng4.set_parameter(\"gpuSpiralSensePrepGadget\",\"buffer_convolution_oversampling_factor\",\"1.25\")\ng4.set_parameter(\"gpuSpiralSensePrepGadget\",\"reconstruction_os_factor_x\",\"1.5\")\ng4.set_parameter(\"gpuSpiralSensePrepGadget\",\"reconstruction_os_factor_y\",\"1.5\")\n\ng4.set_parameter(\"gpuCgSenseGadget1\",\"pass_on_undesired_data\",\"true\")\ng4.set_parameter(\"gpuCgSenseGadget1\",\"deviceno\",\"0\")\ng4.set_parameter(\"gpuCgSenseGadget1\",\"setno\",\"0\")\ng4.set_parameter(\"gpuCgSenseGadget1\",\"number_of_iterations\",\"10\")\ng4.set_parameter(\"gpuCgSenseGadget1\",\"cg_limit\",\"1e-6\")\ng4.set_parameter(\"gpuCgSenseGadget1\",\"oversampling_factor\",\"1.25\")\ng4.set_parameter(\"gpuCgSenseGadget1\",\"kernel_width\",\"5.5\")\ng4.set_parameter(\"gpuCgSenseGadget1\",\"kappa\",\"0.3\")\n\ng4.set_parameter(\"gpuCgSenseGadget2\",\"pass_on_undesired_data\",\"true\")\ng4.set_parameter(\"gpuCgSenseGadget2\",\"deviceno\",\"1\") #Think this should be \"1\"\ng4.set_parameter(\"gpuCgSenseGadget2\",\"setno\",\"1\")\ng4.set_parameter(\"gpuCgSenseGadget2\",\"number_of_iterations\",\"10\")\ng4.set_parameter(\"gpuCgSenseGadget2\",\"cg_limit\",\"1e-6\")\ng4.set_parameter(\"gpuCgSenseGadget2\",\"oversampling_factor\",\"1.25\")\ng4.set_parameter(\"gpuCgSenseGadget2\",\"kernel_width\",\"5.5\")\ng4.set_parameter(\"gpuCgSenseGadget2\",\"kappa\",\"0.3\")\n\ng3.next_gadget = g4\n\n  # <gadget>\n  #   <name>PhaseSubtraction</name>\n  #   <dll>gadgetron_mricore</dll>\n  #   <classname>FlowPhaseSubtractionGadget</classname>\n  # </gadget>\n  \ng5 = WrapperGadget(\"gadgetron_mricore\",\"FlowPhaseSubtractionGadget\", next_gadget=None)\n\ng4.next_gadget = g5\n  # <gadget>\n  #   <name>MaxwellCorrection</name>\n  #   <dll>gadgetron_mricore</dll>\n  #   <classname>MaxwellCorrectionGadget</classname>\n  # </gadget>\n\ng6 = WrapperGadget(\"gadgetron_mricore\",\"MaxwellCorrectionGadget\", next_gadget=None)\n\ng5.next_gadget = g6;\n  \n  # <gadget>\n  #   <name>Extract</name>\n  #   <dll>gadgetron_mricore</dll>\n  #   <classname>ExtractGadget</classname>\n  #   <property><name>extract_mask</name><value>9</value></property>\n  # </gadget>\n\ng7 = WrapperGadget(\"gadgetron_mricore\",\"ExtractGadget\", next_gadget=None)\ng7.set_parameter(\"ExtractGadget\",\"extract_mask\",\"9\")\n\ng6.next_gadget = g7", "metadata": {"collapsed": false, "trusted": false}}, {"cell_type": "code", "execution_count": 25, "outputs": [], "source": "def gadget_wait_function(first_gadget):\n    g = first_gadget;\n    while (g):\n        g.wait()\n        g = g.next_gadget\n\ndef gadget_config(first_gadget, conf):\n    g = first_gadget;\n    while (g):\n        g.process_config(conf)\n        g = g.next_gadget\n    ", "metadata": {"collapsed": false, "trusted": false}}, {"cell_type": "code", "execution_count": 26, "outputs": [{"output_type": "stream", "name": "stdout", "text": "Sending in acquisition 0 of 160\nSending in acquisition 1 of 160\nSending in acquisition 2 of 160\nSending in acquisition 3 of 160\nSending in acquisition 4 of 160\nSending in acquisition 5 of 160\nSending in acquisition 6 of 160\nSending in acquisition 7 of 160\nSending in acquisition 8 of 160\nSending in acquisition 9 of 160\nSending in acquisition 10 of 160\nSending in acquisition 11 of 160"}, {"output_type": "stream", "name": "stdout", "text": "\nSending in acquisition 12 of 160\nSending in acquisition 13 of 160\nSending in acquisition 14 of 160\nSending in acquisition 15 of 160\nSending in acquisition 16 of 160\nSending in acquisition 17 of 160\nSending in acquisition 18 of 160\nSending in acquisition 19 of 160\nSending in acquisition 20 of 160\nSending in acquisition 21 of 160"}, {"output_type": "stream", "name": "stdout", "text": "\nSending in acquisition 22 of 160\nSending in acquisition 23 of 160\nSending in acquisition 24 of 160\nSending in acquisition 25 of 160\nSending in acquisition 26 of 160\nSending in acquisition 27 of 160\nSending in acquisition 28 of 160\nSending in acquisition 29 of 160\nSending in acquisition 30 of 160"}, {"output_type": "stream", "name": "stdout", "text": "\nSending in acquisition 31 of 160\nSending in acquisition 32 of 160\nSending in acquisition 33 of 160\nSending in acquisition 34 of 160\nSending in acquisition 35 of 160\nSending in acquisition 36 of 160\nSending in acquisition 37 of 160\nSending in acquisition 38 of 160\nSending in acquisition 39 of 160\nSending in acquisition 40 of 160"}, {"output_type": "stream", "name": "stdout", "text": "\nSending in acquisition 41 of 160\nSending in acquisition 42 of 160\nSending in acquisition 43 of 160\nSending in acquisition 44 of 160\nSending in acquisition 45 of 160\nSending in acquisition 46 of 160\nSending in acquisition 47 of 160\nSending in acquisition 48 of 160\nSending in acquisition 49 of 160\nSending in acquisition 50 of 160"}, {"output_type": "stream", "name": "stdout", "text": "\nSending in acquisition 51 of 160\nSending in acquisition 52 of 160\nSending in acquisition 53 of 160\nSending in acquisition 54 of 160\nSending in acquisition 55 of 160\nSending in acquisition 56 of 160\nSending in acquisition 57 of 160\nSending in acquisition 58 of 160\nSending in acquisition 59 of 160\nSending in acquisition 60 of 160"}, {"output_type": "stream", "name": "stdout", "text": "\nSending in acquisition 61 of 160\nSending in acquisition 62 of 160\nSending in acquisition 63 of 160\nSending in acquisition 64 of 160\nSending in acquisition 65 of 160\nSending in acquisition 66 of 160\nSending in acquisition 67 of 160\nSending in acquisition 68 of 160\nSending in acquisition 69 of 160"}, {"output_type": "stream", "name": "stdout", "text": "\nSending in acquisition 70 of 160\nSending in acquisition 71 of 160\nSending in acquisition 72 of 160\nSending in acquisition 73 of 160\nSending in acquisition 74 of 160\nSending in acquisition 75 of 160\nSending in acquisition 76 of 160\nSending in acquisition 77 of 160"}, {"output_type": "stream", "name": "stdout", "text": "\nSending in acquisition 78 of 160\nSending in acquisition 79 of 160\nSending in acquisition 80 of 160\nSending in acquisition 81 of 160\nSending in acquisition 82 of 160\nSending in acquisition 83 of 160\nSending in acquisition 84 of 160\nSending in acquisition 85 of 160"}, {"output_type": "stream", "name": "stdout", "text": "\nSending in acquisition 86 of 160\nSending in acquisition 87 of 160\nSending in acquisition 88 of 160\nSending in acquisition 89 of 160\nSending in acquisition 90 of 160\nSending in acquisition 91 of 160\nSending in acquisition 92 of 160\nSending in acquisition 93 of 160\nSending in acquisition 94 of 160\nSending in acquisition 95 of 160"}, {"output_type": "stream", "name": "stdout", "text": "\nSending in acquisition 96 of 160\nSending in acquisition 97 of 160\nSending in acquisition 98 of 160\nSending in acquisition 99 of 160\nSending in acquisition 100 of 160\nSending in acquisition 101 of 160\nSending in acquisition 102 of 160\nSending in acquisition 103 of 160\nSending in acquisition 104 of 160\nSending in acquisition 105 of 160"}, {"output_type": "stream", "name": "stdout", "text": "\nSending in acquisition 106 of 160\nSending in acquisition 107 of 160\nSending in acquisition 108 of 160\nSending in acquisition 109 of 160\nSending in acquisition 110 of 160\nSending in acquisition 111 of 160\nSending in acquisition 112 of 160\nSending in acquisition 113 of 160\nSending in acquisition 114 of 160"}, {"output_type": "stream", "name": "stdout", "text": "\nSending in acquisition 115 of 160\nSending in acquisition 116 of 160\nSending in acquisition 117 of 160\nSending in acquisition 118 of 160\nSending in acquisition 119 of 160\nSending in acquisition 120 of 160\nSending in acquisition 121 of 160\nSending in acquisition 122 of 160\nSending in acquisition 123 of 160\nSending in acquisition 124 of 160"}, {"output_type": "stream", "name": "stdout", "text": "\nSending in acquisition 125 of 160\nSending in acquisition 126 of 160\nSending in acquisition 127 of 160\nSending in acquisition 128 of 160\nSending in acquisition 129 of 160\nSending in acquisition 130 of 160\nSending in acquisition 131 of 160\nSending in acquisition 132 of 160\nSending in acquisition 133 of 160\nSending in acquisition 134 of 160"}, {"output_type": "stream", "name": "stdout", "text": "\nSending in acquisition 135 of 160\nSending in acquisition 136 of 160\nSending in acquisition 137 of 160\nSending in acquisition 138 of 160\nSending in acquisition 139 of 160\nSending in acquisition 140 of 160\nSending in acquisition 141 of 160\nSending in acquisition 142 of 160\nSending in acquisition 143 of 160\nSending in acquisition 144 of 160"}, {"output_type": "stream", "name": "stdout", "text": "\nSending in acquisition 145 of 160\nSending in acquisition 146 of 160\nSending in acquisition 147 of 160\nSending in acquisition 148 of 160\nSending in acquisition 149 of 160\nSending in acquisition 150 of 160\nSending in acquisition 151 of 160\nSending in acquisition 152 of 160\nSending in acquisition 153 of 160"}, {"output_type": "stream", "name": "stdout", "text": "\nSending in acquisition 154 of 160\nSending in acquisition 155 of 160\nSending in acquisition 156 of 160\nSending in acquisition 157 of 160\nSending in acquisition 158 of 160\nSending in acquisition 159 of 160\n"}], "source": "#%% Load file\nfilename = '/home/hansenms/temp/simple_spiral.h5'\nif not os.path.isfile(filename):\n    print(\"%s is not a valid file\" % filename)\n    raise SystemExit\ndset = ismrmrd.Dataset(filename, 'dataset', create_if_needed=False)\n\n#%% Send in data\n#First ISMRMRD XML header\ngadget_config(g1,dset.read_xml_header())\n\n# Loop through the rest of the acquisitions and stuff\nfor acqnum in range(0,dset.number_of_acquisitions()):\n    print \"Sending in acquisition \" + str(acqnum) + \" of \" + str(dset.number_of_acquisitions())\n    acq = dset.read_acquisition(acqnum)\n    g1.process(acq.getHead(),acq.data.astype('complex64'))\n\n# #%%\ngadget_wait_function(g1)", "metadata": {"collapsed": false, "trusted": false}}, {"cell_type": "code", "execution_count": 27, "outputs": [], "source": "res = g7.get_results()", "metadata": {"collapsed": false, "trusted": false}}, {"cell_type": "code", "execution_count": 28, "outputs": [{"output_type": "display_data", "metadata": {}, "data": {"image/png": 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JEytfSwM4e/Ysb7zxBtPT01y4cIEnnniC8+fPp+b9m7/5m5X8v/3bv83Y2Fh6GzpvQjfw\n0fxW6bMGkG5VpAkQLeh81+yg19e0mmVhPYjdPHtbD3sty/cgkswgSapraH7bcqyJoBNhkuW92Gdn\nQ2z0qpIkj3avtgnrHoVS6zRJuHjxIpOTkxw8eBBwTO3MmTNNgu/ll1/mscceA+DYsWPMzc1x7do1\nLl261DJvHMf87d/+Lf/4j/+YWo91UHVteEFSjJkNRbDG+05DXtYqVMbnbOg0v2Y6Vj2zabVwsIzH\nCkCf1zDLc291XpDGunwGfVu3JMSew+ZLel5JE2iv3pXvfKu2+Mw+7Zazhsi3cRjMzs5y4MCBld8T\nExPMzs5mSnP16tWWef/pn/6Jffv28cADD7RswhrDZ5CVl2qXNNmZWA+STtbxdpqvXei6d6JaSH7r\nidVsTQs3uzrAJxyzwDpIfM/JvhfNVgR6hYF1puh8SUIv9KTR12y7pJ5J9j6b35bbrm1ZI4kJpzlC\nbH9uVYdu6tcHdCE1giBbn+z0W7wvvvgiH/vYx1qmWwfBp1+6ryNbRqFd+JB9XaUNB/AxhH5C193W\nyaaz15LYlP6dRNazsgM7kejztjx9PgvTs23X9wtMGis4WtU97brPeeOrp08FTXtHrZCW3gruVv0g\nqbxO+mw3bUpBitT48qI7kjA+Ps7MzMzK75mZGSYmJlLTXLlyhYmJCSqVSmrearXKF77wBf7t3/6t\nZRPWQdXVhmg9u9vP/Nm1pVZQtgpLsOqhdID1CH1JYiqtrrUqM5dwZHmtlpHp1TM2mDypzKCNa5rB\nJb0XOTp9P9pJpstMso3ZEJ+szy4LZH2tnRCSbJlp1zpFp32rBZK6XQ4e2QZP3904LB566CGmp6e5\nfPky5XKZl156iRMnTjSlOXHiBC+88AIA58+fZ2xsjH379rXM+8orr3D48GH279/fsgnr6Nyw6q2d\n8XzszKo/SbO/9o5pxtinGbAJSYy0FYvR+XW79GDQIR/WJqevZYG+h6iJeiJqxbx8KyV8ZSfdT09M\nSYIp6Zpte6v7aAHrY7m2TmlOhqzMXd9H99skwe5T7btFP8qkK6mRz+c5ffo0jz76KFEUcerUKQ4f\nPsyzzz4LwOOPP87x48c5e/Ysk5OTjI6O8vzzz6fmFbz00kt89KMfzVSPIO5Ume4QQfA0jdle20Gk\nw6XZRix8M7QwFh9kFu7Xzi9JjFTv1GHvaz+UkzTra7VMPzvN1ORa1hleD4xWbEPb0Wxb5Llmga+d\n+p1Z5mOvaU1A0tq62OejJyO92D9rnQRpNmLfNVvfjWGji+OnO84bBAHxj7SR/uud2+v6iXVifElG\n8W46R9Ls5vPo+a61U4c04aJZg7Vz2TAGW44emL77ySCybNB3LQta5bPPIqtQTQvJ8N3PCqI0G15N\n/W+fq+TVNkZbB5+ATnsGNuwnqX/4rvnqu3HCUjrGFlisu46CD5pnZ+v00OlIOKfLs84B+avVHMx5\ne82qlL77aUFj8/nS2XP6r2Yi+rxPtbPl+X53grR8VmWz7LqVgPLVz7ZRmz0w19Lu0+p52Hyt3pW9\nry99konE129sObqOSYI5K9LGwhpgIPg6hZ15reHb2lg0M/F9e9THHvW99EC0h++a78Mu+n548mWF\nbgusViGT8qwH0uyFrWyJetKxSHtnPjtuKzVaBJIN+dH3bmUKSGJzmrH5kNY3LXoVlrLO6nP3y33X\nHesg+LTx3gq8JKan07diDpjzSUjryL7fSQxA/5/FKZB0D8sE5P80ptJvpDGLtPpYtTMtTatr7U4s\nsFq4ZH2GPo0hi6BqR/j0QlCts5o8YHydwMf0bPiKDW71qYTaVuJjS9Zo77Ml+q5paPapv/Tmg25L\nOwPVxtClOTDWA0kOgTRkYYNpArHdttpnaD2n1r7Yqm7aBNKKVfWCwbWLdV62ORB83cKnDmn7WZYB\n4mN6Wmj6oO1zWespzgp9PxvC0YuYKctqrdCzzNIOvH6EZrZbpm8JVjuMOCuSytQhJJ2UaScwXf46\nC52NgIHg6xZJBumsA8M3IDVLarW6ISuS0nerknV6Xx3KocMn9ITR7zqkQQRSErvvpeCzjFjqm+ag\nalWmb+Jsd7Lcwuhik4KNgnUQfPqjLRppTE2uaXtgEnwsabNAq5Np9U8y4vdD+HaCpJCVXq8isO/a\nqqXaSZbkLEuCziMCtlVdkphm2rVNiAHj6wSx+Ws9b0kDI8mJYbFRBn+nyOIY6TfT7BZJ7LjfjFiH\nNVm7sVzX9fM5QGxZSdeT6pN2jy2CgVe3E+jVGVkZQJYZeoABfKwqzSlj03YjpCwr1Cr3FmF6ggHj\n6wSB+WuvdRL+MEAzLAt6v8Cn8mdh0JAeFuULt7LPVTub0spvFxuQOQ4EX6fwzcLthEsMkA4b7vN+\nQqdhNzpsx4azQLMt0bfRgH3m2gGVJSzGB52/X5+17ABbYJhuAdk9wGp0OtAGWO199jE964SzMYO2\nLJumHegdZtYjZtCDLSA1tkATBliNDTJANiWsautbXmjVYrsDNiZPt3XRuwptgPc6tN4V6B4DwdcW\nkoKGe4GkffzS6tKNOtvPtmxG+OIioXl1kA1L0WxMp233vq1CXXyrUdYRA1X3/YYkA3Yvy+9V2VnD\nYnxhRVmwQQZhT6GFUNJ18dTakCJoXrGS9FytWpz0Huxv66m2Za0htoDU2AJNWEtIh+9Hh7P2oyzp\nkxhGuwG6nYQLbVUbonWOWDtfUhrwrwfXIVtZdhyyK098tr113p1lC0iNLdCEtUarjqZn4yxhJUlp\nfLO6PZe0isXHRpLqoBmHLStrjGWremYtQ7DeAjXp+aWda5VPCzrL5izja3V/bYO0jpc1YIMDVXeA\n1cgyY9v0spW7PafzpTEE3840NkDcMkS9Xby2Wdly04RfkrfRBu+2ghUKW4FN6t119LO0TgptW8wa\nsqJDZWyIjdynjzbBLSA1Bssheg6rstrZW+/8LOlrnmtaQFnh48uvj6R8SffXbMFeh2ZBZEM0fELd\nd16XnfQMrOrtO5IYWCfrgDvN10n5+h5Ju1nrtKH5nSP9GeA536f2dfFBcYBz585x6NAhpqameOaZ\nZ7xpnnrqKaampjhy5Aivvvpqpryf+9znOHz4MD/0Qz/Epz71qZZNGKCnyMLuJJ3+GpzddzBS53wh\nFHoBvb1f0sDyDYBWi/etELfex6Tt1m2dksq2uxKnDdakZ+tjzVkg+frJMH3cwvcurN3QbnlmJwlf\nmUnPvMft62J3liiKePLJJ3nllVcYHx/n4Ycf5sSJE01fSzt79ixvvPEG09PTXLhwgSeeeILz58+n\n5v3Hf/xHXn75Zf793/+dQqHAO++8k1qPdd6dxfcxG1gdJuBjUb0mq9Kh+skANLvzXUsbuLbD24GS\nBs0CLZK29rJ76ml7VNJ9fQZ9Xab+WppmNhYyiLUti5T0At+1tDrp+/lMCwHJwna9oJ+BT5vQEyk0\nq8U9QhdS4+LFi0xOTnLw4EEATp48yZkzZ5oE38svv8xjjz0GwLFjx5ibm+PatWtcunQpMe/nP/95\nPv3pT1MoFAC4+27PR30V1kHV1SqNVXH0udhzzpe2V0e/yve1NanN9lra71b1tGmTDl++yFwj5b1Y\ntFuntHT6g+C+9BqdvrekfFnas16Hr26wWlDr9tkJrAt0oerOzs5y4MCBld8TExPMzs5mSnP16tXE\nvNPT03z1q1/lJ37iJ3jkkUf4l3/5l5ZNWAfEpH80yJfGvvB+rV3sddndej19KowtpxX0wMiKmMY6\n0SQhZ9VSLTTT6pG1Lq3qrU0BaSskbN9qdT9U+izvaCNB6ps2lrpEF+QxCLL1wXa/xVutVrlx4wbn\nz5/na1/7Gr/2a7/Gd7/73cT062jjS2ILadezXOsWvS67VTs7yd/qWpKa1uq+Vq3Ug8jm1cJO36+V\neugLwfClketpAyUL89S/fWUn1XezCTyLdt57m0iRGl/+Nnz5v5Kvj4+PMzMzs/J7ZmaGiYmJ1DRX\nrlxhYmKCSqWSmHdiYoJf+ZVfAeDhhx8mDEOuX7/Onj17vPUYeHW3JGyoQzv5JMxFh2P4Bo6PbbXy\nOkqanLpHK89zoNLbQ7pvmHLNlq2v9duru0WRoto+8mF4+n9oHBYPPfQQ09PTXL58mXK5zEsvvcSJ\nEyea0pw4cYIXXngBgPPnzzM2Nsa+fftS8/7yL/8yX/rSlwD4r//6L8rlcqLQkyYMsKFgdw3uJJ9W\nbZKM9j74GFArr2SS99gu7vcJGW2QTyq7U6EkQk7bu+w1y3Cz3KuTTQfSmKW9bxa2nJZmDdCFqpvP\n5zl9+jSPPvooURRx6tQpDh8+zLPPPgvA448/zvHjxzl79iyTk5OMjo7y/PPPp+YF+PjHP87HP/5x\nHnzwQYrF4orgTEIQt6tMd4kgeHotb7fJoAdrOyEI2munGZoMbsvKWvVcSe8Li2kXvSyrG8hz8THh\ntGsa2sHSDvR7tbY2y87TbKS+99k+4vjpjvMGQUD8YhvpP9q+vW4tMGB86wYfK9IDI4s9TlRNDbvT\nh2VaWVhNL1XAjaJO+kJAslzTyBJGJc9e2izvQf7qDyD58vlCT7I6Z5Lq22n+BGwBqbEFmrAZ4BtQ\nVsXx2bog2cBvA551mTpWUBvx26lvrwRVL8vqBml1yFq/LG2pmXRaNdXvzAo/SeMTrkmxn1nrrJ1O\nPcBgre4A2eDrzFbt8amCdglXUtl2QGq2EbMxGNf7BcLYLOO2aSxb9y1t811rNyJArwDq0frdLSA1\ntkATNiqyGM6todqm0V7LNIdE2v03nn1la8MXDqTPJyGtH/jCdlq9W8345XeP+sMWkBpboAkbEVnC\nSSRN2izss/e0g0G00vpBhwNZO6d2TmgBFeHvN1o70GUlOVrSnCk9wEDVfb+gn2EEaSrOAJsDdg21\nZn06nMgyN/vu03af0eoznnSW0dmNJHrYdwff3Hi/IAs766bsgdDbvLChR5ax6SBvq6pab3sSlfJN\nvLYfahaY5DjpEQaMb7PDtyecRiehGFlWL2xmbIQg2o1QB10XHSit1VlxLFlkYflaeGkHie6n2vFl\nvcJ9fDZbQGpsgSZ0isAcPqFnPaZZDMRbncHJwEqyHXXieZR81iCfVH7WtP2CFXKt3rm9nuall/K1\n4LOrW2zZ2mPb6v30AFtAamyBJnQKHTCadVbup8q7FdDK6G6h34GdhFot2+phQG7bkHqnObCsgLYb\ni7YDa6PrdFljj7AFpMYWaEISLCtIG0i+fL5ZXHdiu8/ZRlC7+gX7HH1G9rSwiXbYWSuBp+uxntB9\nwxeGktR3OoWPZcp5rfquwbMZ2Pg2MmyUfJJx2a5+yLKmVJhN2m7SWwVJnknftSz57TX7Sca0erTD\nJvsJ3T+04FkPiWBDVtaABW4BqbEFmmDhi5nyBX/61AW7XXerWdq3vdFGsu/1go36hF2AXxBa25Qt\nY6tCLwnrBbPTMX4+b659F0l9uE/o4psbGwVbWPBB8kC0rn85p+13rWZvn/1vIwk9yOaMyQJfnJov\nXswn4La60NPoxfv37W/oY3XWm6zr0GfTyxaQGlugCZ3CF0cF6R60jQ6fx7NXqqEuN80DbmEdGJ0O\nyDWyX6XeG5rNGtJ/OlVxtYCzsGFRSbu6JJXZx+e0BaTGJm+Cb6AlGZV9HSHJgbHRmFs7sEZ3rX5p\nFSmLs8fHJpKEj7WV6rLkmXZjC13vycjHZAVJAj/pmlzX6qt+J1botZo0dN41eE6bXGrApm9Cq3go\nbVjfCEbxfsN6XJMGX5IAss9Tp8nyDO11GbybPaDbPhdheWltEiGVtgxN24j1hq3tTL4+s01/EQ+8\numuNJGOvvubLY69vlEHYTphHqzIElolYBqeFkc/TrZ+vrZ/PeG/zJ7HK9VRVu0GWUJVO4bMRd6Jx\nrG1/jjaZ1PBhkzVBMzi7RXfaUrFu7DD9QpZQkHbKgdWsLC0EJE31tGwly/IpuZYz19Yz0LgXkLa3\n+/GmdnfG2Yh91I+tIPjWad+iLOqPpNGHz5OadG0zoBcMSAsYYWSaqck53+6/9j1owZkkKH1la1Ur\nqS79UHnXQo1uZU6B/gv4LGaGtVsjvlwqZj58OHfuHIcOHWJqaopnnnnGm+app55iamqKI0eO8Oqr\nr7bM+/RX2m/rAAAgAElEQVTTTzMxMcHRo0c5evQo586dS23DOsluy9x8yBpIvFn3nOuV2mfjukJz\n3rIVny1JM79Wg0vK1h5Jy/x8dZHfvf5Ye7+Dx1v1L+217hfSxolAGGP/l1NGuc6ZaRRFPPnkk7zy\nyiuMj4/z8MMPc+LEiZWvpQGcPXuWN954g+npaS5cuMATTzzB+fPnU/MGQcAnP/lJPvnJT2aqxzpJ\nDT2DWRXCGo5rrH6ZkkY6RNqXqTYq2mErWWZzy3x9z1A/R/vssg4Wy/R8glfOW+dIrxfOS5nWG9ov\n+Ews8qw7va9+D7otrb7oZvOtnfMuIpf5sLh48SKTk5McPHiQQqHAyZMnOXPmTFOal19+mcceewyA\nY8eOMTc3x7Vr11rmbedrbuss+KQKaSEo9pDreoBtNqO5ZlpZmUKS7dIeac4Nfd6qtT7Dus9xYdmd\n3U3Y5o/p33tKKrPf7MuiF2YWX1+3fd73fNfejloll/mwmJ2d5cCBAyu/JyYmmJ2dzZTm6tWrqXk/\n97nPceTIEU6dOsXc3FxqGzagnpg0k/lesMy23cy4a41OPHc+G4+1gWph5ov+t4cu01eWvq9d86yv\naQaUZU10P9HvSdAy6V6VKauGpB/7JnZ7ff0QkU88/unLMX/8dHnlsAiCbHVv91u8TzzxBJcuXeLr\nX/869957L7/1W7+Vmn4D+Gds9HoaA0lihhsJNgQkDb5QCV+arGVpdtVunWJW10OfEweGvgbNsWhJ\n91grRt7Pe2R5V92U7ZsM9XOzY8K2NTZ/+wefCis49kiOY4809qb/s88sNl0fHx9nZmZm5ffMzAwT\nExOpaa5cucLExASVSiUx7969e1fOf+ITn+AXf/EXU9uwzjRJGIKPuelzm4nVtVJhNVMSm5dtpxyt\nBGLEavuQfm7QrM7q+2r2Yq9lHTwBbu7Mp9Q3i0dyo8Nn2+tl2Vab0efk3bV6Lz5beH/QjY3voYce\nYnp6msuXL1Mul3nppZc4ceJEU5oTJ07wwgsvAHD+/HnGxsbYt29fat7vf//7K/m/8IUv8OCDD6a2\nYR0Yn909wsdOeq1OrCU6nXmlg9stoCx8TgU538ruqVlZknPCnkuriy/dVkU/J107UWobMDRPXKHK\nkySINXvsvbBexh+mkgX5fJ7Tp0/z6KOPEkURp06d4vDhwzz77LMAPP744xw/fpyzZ88yOTnJ6Ogo\nzz//fGpegE996lN8/etfJwgC7r///pXykhDE7SrTXSIIPktjdoK1tQFtREjb9cJ3SN/a3YaQWJZp\nHQ6+a3JfzRJk0tkKgcdbCb7xEpDM8HTUw2q2HcdPd1yTIAj4ZvxA5vQ/FHynbXvdWmCdbXybNfC4\nU9gZXP6331613us0VdHH9KB5BYX1xKahj99q2JSwO7EknesWmtFZ51In2oPuU+28/9ZIs/FtFqyD\n4LMvUat2rWxatEizkaGdBNoJYdXMtLxJs7tckw6uy/IZwlF50n4PkK7m9+M+dhzY+1tnR1JdtLe+\nt5PZQPB1BN9L0PatNAM5bJb1jM2w4SZZ1+hqQaaZnw0M9gkwG2TrsxelCcUBktEPO3Q72o+d3Nb2\nHfri8zYb1onxafUsyZ5kZ7/NyvQEVshoNUSghZllwr509n8p155P8zCnwceydZ02+zvJAvs8O4nB\n1OWkXctaruTLupyut4jW20LWA2ygtbq+GCZthN8Kg8wKI90mnwFb8mTtvPq56nt12vl9ywp9tqit\nDN1PhellhX539nmlXesV9PjqHQaqbkfQlD425yw2sxpmw1J0+E4aC/A5Oex1XziLoNX5LIzF1lH/\ntnGBgn555W171pppJi3HywJraxX4bLv2/fju26sJrTuUuwhn2ShYB8FnZwsbs2SvbUb4OqvtsDbM\nxJoAWpUveTRa2Q11sHgaxPwgTM8XNrEWQg9WawVryTT1u+vUpud7NklMOqI58ByTxm4ysT7EYGDj\n6xg+mq9fpF2vuNmQpJ76HAztwBcOkwYbdqFjBGUljNTDtxxNI0toRD/CPDSsY0fgCwFJmkw10uqb\nZZuudpFUf3vfLG1Jal//V8kMbHwdw9rvfKxosxvPk4RHUhqrCunzsbmeJjCTHB/WM+xjDrFJK/e0\nqwWyhB21glX3k/IlsVrLlNt1DGTxiFp1XyOLs8J3vVUaW45eraHvbVVo3U/s8+ktBja+nuH95ClM\nE3DatiPn2tlo0zIKEV66o2qGXaH5ucsAy9OI/xKVV7+jpI6fNcxDs8ZOVookOXH0tbT7BupIqq9u\np2XoPoZoy/bBtzTNd99Wji3fdV122vPpHgPB1zE0o9ADdbMJvqyzalo6nyDMwgSz3M8+Ty24RKhZ\noedjifK/ZZ4WWuBYRmmN+NpZgrnWLrQASOpDelLwORbw5MvC8tqtp2Xkvvq20nzS2Hr/7X4DG1/H\nkMFn141uJrQzq+oBZsMLNKuzs7svfav7+DqldUQUaAg/nVcEgwhF3+TU6v52pYA2a0hZuj4Rjf3o\nsu4uYp+LbV+Szc7eV9hemrNB2tWKyWZxGvnep+37WdtiYc/1b+lhmVLfyl4rrIPg8+2/txnhs0um\nwaqxuhzLeuW8j7Gl3c+yKt+g0gNdM29rN9L10A4Qnz3QVw+tbvtCmHSdRCD0oi9olVOfs8/TCkKf\n40gHmGd1mKQhyUmhbamWxVlnx/pjoOp2BJnhBZtJtbXoRRyVVb2059WqoFk6vva85mi8Yss2rI1L\nBJTeD1DfrwZU1W8RVNoOpvcGlHNWeFsPrE9ts/AJzCS0KkcLM6mz3utRAsn1rjna+9tK3U9DFvvo\nxghZScNA1e0amnFsZWRVWa0x2+bz2co0tACz+QMam4YWaFY9K8CySlMARoBRYBgYqp+TDUet8KjW\n8y/Wj9vAPLBQP1816QNVjmWkSW2q0uzh7GTSacd0YJm4PudzOmVhg1btb3W/LNDCfG0CmgfhLB3B\n0vasu7NsZqQxkLS0rfJpQagPzVakDGF/cq1UP/L1NEt1opeHcBTYDuyAYDsN4VdkxT4YAEH9vQVl\nCOpCL5gHbrmjdgeiRaiWIapBrYYThBVVN/3erXNEq+O6Hfb5tCMsktLa83Zi0SqyDcGybFC3SQtG\nLZzSlrC1A59dtb8YqLodQX8W0gYvvx/YnyCJIbSa9S0zsNvya1WzRIPdybNdAG4Au4FdwIH6/3ko\nhbAjgHzOCUDyEOQgzDWXEQRqd/sYwgLkhiEfQa6uEteqUKnCnRrM1WChAktLwDXgLeDtel00g5K6\nynPQqrX+0I4VeO2Gw1i0ymtNELb/6vzaYaevWYG9edc7DwRfR7AhDf2PNF9bZDFEa3aWpe2+waGF\nkRZsIpG0wItxbG03hDGEi1Acg9xuiPZDPAZBAYpKEGvSYs101g8S5hpasJDKgAapHMUJwUoZaiMQ\n7YSlPbC4APM1qN0G5tSz0PGEWpDryslf66SxNtMsdrJ27Ic2vc9eac9pFqjZos9x0cqUYa+tnYor\nGAi+rqFn+80WzpIE6ZxJAt3aNbUjIq1M+3xEuMl9avXfopaWcHa2RZxqOQLcB+HdUBqFHSGMhLCU\ng2oIYeAI1oK5hY2osEQmpPl7QzmcjJVjBzABFPNQyEF1HBbvhXdq8P0aLEdQnga+CdwByvUbiE2x\nXG+Dhm6zaApSmbjeXp8drlNksQ1qFmjDk/QsoWMoffWSB+y7n+9aO3bL3mB5C4SzrIO08RmMtxI0\nm027bsNDauacL6REBo7+qlkeJ+x2Azvr55ZwDoY9ED4I2x6C3Q/C7n2wbQfkRqA8BHeKTvBVgob/\nQXwbRVaTE+vgtB+Ds9Ei1XpV7tSrcyuASh6KRdg3BD84DD89Cj81AT92BPY+DOFRCPbX2yQCcBQn\nuLWdUR/2C3w+9tePyVWbaJIiFXQoT7foVqXvDbr5yhrAuXPnOHToEFNTUzzzzDPeNE899RRTU1Mc\nOXKEV199NXPeP/3TPyUMQ957773UNmyAHZg3n40jHVlUq6R4Pus1tCqN0Cs9mAo4djdWTzcHwTKE\nZYj3Qf7DsK0EI/VXvUzD2SrsTrM27fCtsjq8zpoWUVUJ1bkajqyJw1eu1X0m7Aa2BzAawPJeuF3/\nLuriHai+DtWrUL0DsdjCROWtqkMEgf34vATHi43QmhZ6NdnKu0oLUemlHa+Xde8c3ai6URTx5JNP\n8sorrzA+Ps7DDz/MiRMnVr6WBnD27FneeOMNpqenuXDhAk888QTnz59vmXdmZoYvfvGL3HfffS3r\nsUE+NmSNxZsBlt5kSdvKbgPulfjO5dW1GCfshmm8wuv13/fD6A4YG4LyXVAtOVX2Jo3QQK1JShMi\nnIYoy3dhtdNYqlvFCTURZlIlrfLKNW1qFM18gYZaPYojcqPAjwMfHoKb98PMXvjPCiy8DVwxFY9o\nhMnolT9VklVcya/jE3thH7Oqpvy2dkcbppQEG3ztQ9bYTj2B9o4pdhPHd/HiRSYnJzl48CAAJ0+e\n5MyZM02C7+WXX+axxx4D4NixY8zNzXHt2jUuXbqUmveTn/wkf/zHf8wv/dIvtazHOgo+TRPW5kPI\nnSHNWZFVSCc5MqxKC6tVJlFtRYLo3/W/QVj3qO4APgClu6C0vVG0CLRqPYveR1ITS3kNdiVhmhZn\n46N1PLAIVmGHEjIocqtc/72zXqd7gaE83LkLxu6CKIa3S3B7CZYXoLJM8yQpjRImqJ+pz6Si7YGa\nXfsmIzznk5B0Hz3Z2TXJaWW1uq+vfkl1zupAy45u4vhmZ2c5cODAyu+JiQkuXLjQMs3s7CxXr15N\nzHvmzBkmJib44R/+4Uz1WOe1uhsdPpWzE/gGmJY0+l6SVux3dt+8EeBunMH/OjAKuTHYeRSG9gLb\noFxw0SJSrES1aMEmMkM31fpQfGRFp5FrPgEp14VBCqMUmV2ioY3O01CJt+FU4R8EpgL49t3w6ih8\n/xpcfxtHXcHpzOL4EKanVz5YFiaNtXGjdrWEnOvWKeJzOvgcH51A8lrBZ0lEfxwfaaru5S+/yZtf\nfjPxehBkIwvtfIt3cXGRP/iDP+CLX/xi5vzryPj0y9mIbC+JpWm0U2/f7Bx4Dq0Xal0yh9MHSziP\nQQ64F0a3w8huyI1DrW7n0+RCa31iDrN+FVtFuyBB91V5JNb3YmWFj3hY/4PV2ORcEccCdwPBEBSH\n4HIMbxbg2jDcEe+v0MoyDfqo1V8tufWkIhXQN7YN7tWk52OD3fb3NMdZ1rSdI03wHXjkgxx45IMr\nv7/6mf+n6fr4+DgzMzMrv2dmZpiYmEhNc+XKFSYmJqhUKt683/nOd7h8+TJHjhxZSf9jP/ZjXLx4\nkb1793rruY4bkW50J4cOpE3qUJ10Kj2g5D6huiaGMQkZkIE5ggs2XgJeAw4CPwp7tsPYCFzLuwUT\n1LNuV9VfxLEp7TcRQolKJz4DW11RWX17G9hYXfkrY0M3TZot7E8cIOCEnRCz6/X6RsBe4EPAd3fB\na9vgK2Nw5x2c3Q8cRVyqH4uqQLmBnQXkf90gn7dXs+xO3rMOPekvA3NYO2/vchff3HjooYeYnp7m\n8uXL7N+/n5deeokXX3yxKc2JEyc4ffo0J0+e5Pz584yNjbFv3z727NnjzXv48GHeeuutlfz3338/\n//qv/8ru3bsT67FBFt35pv6tAJ9TQ/9vnTkBTgLIaxHj2DacNLsOQQHCIzC83zG9qAjvFdx4kkiP\nGCcHtHlJNGZhfT6IUNO/ta1en7dN9F2T33JPCdHT+yKAk1Hz9f9r9aaWcc4PiVa5K4QfLUBhO3wz\nhG+W4M4cTvUVXV4KraiCKqqimnFpHV0bNfX78en/WsDofuu7R6/ZVpqTbO3QjY0vn89z+vRpHn30\nUaIo4tSpUxw+fJhnn30WgMcff5zjx49z9uxZJicnGR0d5fnnn0/Na5FFnQ7idpTpHiAInvac1R+1\n2Sj2Px2X0ekjSvOq+bwGORyzE72xiPPU7gLyEF6G8C7I/zcY2+lUwXdwMXJD6ljEsT+pelE1p4IT\nKj4yZAWYVXntRs528UhgronQ0unkd6ler5LKO1Rv/nYaoXvD9eM+XHvngH8D/g9gdhaW31A3uIOT\noPM0pPsSDRaogxF1FIEIylYs39oIfP02bWbpFvp+nQ/bOH6647xBEPAb8R9nTv+54HfbstetFTYI\n49uI8BmQuylHYONDBLLUQdv4tuGkwC3I12D7D0JhHOJhV+z1etId9SIinBAUsiPjQ5wMOtwNVgco\nW9imJ30CWaB3s7IkSuRCqM4JC5SAaWhePaKF6rv19LuAo7ie+//tgq/+ANTewknEURrMT3Rl6onl\nASQ5HaRhonFYg6W1B0oDfbMG6px92K1UUp3PvoAsoS79x2DJWlfQlvHYc07/Xg/0YpZKKkMLPm3X\nk0FbwtGduuGrGMDQCIzcD/l7G+EpZRqLGeS3BAuLyiuCzsb4att/luZaNVjgW5WVpu3pVyx2xZz5\nLfeSWO0cjsEGOMF3b/0vI/DWCFwrw41lGi7jWv3/RZrjavQ3RjSltfY/2zj9v3WSSJ52fneKjeEI\nHOzH1zFsyIAVdJstmLkbSGCdXoK2DdiHs+m9CWMPwo4pKO9saG6STNhcmebPskqQsV68ILcQx2cr\np7WGdjhbud0qrdYw7XXpBhKiB41Y5DvqERXq7bmGi+bZH8PPBO4xfWEvfHkEp/fP46SixO9Uaaad\n2ntTVtetXdlnuEzS+62Qs327nX7cL/tg7zDYj68rWDc/+Gfd9YZlod1ASw2t0grTy+MG7RBwG4o5\nKB2A0gSEextjScL7YLVA0ec0ofExu3abpIWd/t9n49OROHr8Wxlh/Q32qOBUX5kfdLjLnhrBnhrx\nu0VYGoM3KvBujoZkj3Csb0lVVPe1JMGkdXPtCElKb/uIz8Pjc5r4BFwvBV5/nCEDVbdjpLn1N4pz\nA5oHS7cdRxu5ZCANqXPgRvR+3Ej/Dxg+BLt/GmoFd6qisknAryBPIzzE2u61Pb/bZliHqHZk+ISh\n5AlMHj0erd1PBLnEFs/TvHjlFnAlIDgQEXywDD9XIL47D//7Xnh3GLffn+yJ9R4NfVpsf7pyNuhX\n2wKksSJAfHY320fsA/LZFXWoSz8hdes2GLsZ5S7CWTYKNhhn1cbijYJezZa6HG3TkyVoYzjj/E0I\nAggehPwBKA070hLRGPjSjzUh0Z+7EMGhWV+v+r7IgCSPrk4jTdUM1VeWZooiDyoqT4gT6PM0tr1a\ngvhWCG8XKOwuk/vxJcqzQ0SFEnxrBG7LgxrFNXyx/nuIhhdXbH/6ofrYn66wZX4+RpXlQad5/HuF\nXr74BgY2vp7Ces02grrba1uLlCdCL6/+34eL25iGcAzyD0FhtHmcFXGPRbank306dfCxNi359vDs\nFNYXo3dnSbNQaN+Cz6HtE5rSHlmcIc6bxfr/IrtuhcSEFA7PUzywSO1OjpiQ2lujsBhAdREn+IQO\nV+qZQxrfGNGxfHaZm+2Hlvm1Cn9pBe0o6YdNrz+2woGNr2OkzXZavdhIzK8b6LbISBa3awlnsJKB\neS+M7oL9eXfqNu4tjdIYm3osyneCdP+WdL0gEprdQTN7k7EvrE6gd4kX6Bg+a+qUOmsbpnQPUell\nbW8ZZ7ITz/YyLF8foZbPMXxwnqGfgjtvjhDlq/CdW1Ar4YSdDl6UysoGB9oeIBWt0rAtFGhe6+sz\ncuoHpv+mwcYF9hr9YZUDG19XSJrlNPOz5yXfZoK1/stAko/8jAJ3AfOQq8LQPti5B7blG9s3ycIE\nUXl1XxZWpyM0ehVDK9XVgk9+a63dCjr9W/+1qrGWH7oraBOcsD+ZJ0TQL7ES+li9WSQuhYwcuEP4\n4YjCR0ZgKU90pQTLRajpmUMHM4uRVPRrEW7ildUV1OEsvgcljdCCMEl11i/KqtI6XS/QezY5EHwd\nI825kZReW7w3C2zchgg9sfHlccsR7gG+DaV5uO9+KOyAd0KXfAfN2pUUqdfVigzV6m4vq693WbYC\nUG9cqvMJC9TXajQLPV1WQPN8EHvySruqOAdHDmcdmIf4ZsDSXUMU7lpi6PhtwvkiixfuJZ6bh8VF\nXFxkgPvQkiwd0Y4IqbSuvLaR6Yr5HHC+Pm3ZoLZDWDXUeoN6wQTbHWfZMLDxdYRWXlKfIQjP77VE\npzOxpTqi4gY49UvizW5BcRSGt0FxOwTFhkYmWpo4JqHZiaFVwm4Enu9xW6Kq/xfZoDcztfY/7QCx\n1y0D9DFB6zjRDFdU4AWgCPFyQPnWELlSxLZ7bxI+CIs/vQu+UYHpMo1YyaV6YdozJO9UYv/kOjRW\nfFjPrUCHsPhorNX35Xxs0lmHSRrsPdL65MDG58M6fV4yTRfT+pE+t94qbid7tGkWAY3FqTGOqkwA\nNyCYhpEfhJEPwEK+sWRXGJDsrCJjcYnVwcBa5W1Xu/E9clgd9aGFnN6qnnp99NIzLQz153ztut3Q\npPWRZJFRIvCknVWcDXQE4iik+t4QtVyZ0dEFgskcN/7HGMp5mJYPohdwD0+cGyL4ZEVHkQZt1uti\n9Vo9/ZB8fVjeeZZr1jkiEehpfV0/IMm7th8bGoSzdASf8LDGYR0isBGEHnSmesjAsjsoF2iswF+G\nYBvsHIaxomMwEW5VhsTfihqoY/GkKiIMZEy1y/p8BFt+i+DRX1DT16wtT0iRT8BpoaavJTFLLXD1\ndZFD8mjF+7sE8VBAtZpnPtpGbleF/Ue+x82vbePOv43Ae9X6GuAdNGwFMoAXVMWh2a6gVVzrJEhz\nHCQxRA1tP0jrX9aZInUkJU//MFB1O4LPDmJtIbDa/rKe6CYsQNoggk/Wmo0CAQQjkL8Ltg87564s\n0B+u/79Y/79Iw7mhqyUERUdltAMfudbCRsfh+VibVUetpzepfGs3zJvr1hYov63w157eCKq1Arcq\nO9i97V3Gd71J7dD93PnQbnjtdv3Zyjo/WQ+nV3oI7IeLrDPCOtrSTDOt+o3OG5nz+j7ala4NnmuP\ngarbNaQ3C823es9GEHqdwNIb2Vg0rP8dr5+bgW27YGwCGHUDU8LMhMUN01BlZXzKWJQ4XD0W2iWm\nOnLICjk5LzIbmtmf9gnkTZ4kZmdtfK0Yn89pIs9D5pQqKyvTosU8Sze2UeMmxbEy+R+O4L8P4GYJ\nrom6K98Zllg+bduDZp3aR1mt+ivsziLJuSHpk/q27fvtjgFdp94LyIFXtyeQadx3fjMKPQ0tTcRg\nNwLswQ2sK07w7dvd2GhAPicrHtsSzZ951ERC2/k0A4rVuTQE5hDmpQVRkpDT9r4kddiywiSnhk5v\nzaLW4SH30O2U7a3KEC+GVBdKVEeLRISU7l9iR+Umi18dovJ6CZYCqBVx230JRS7SPGtI7IxIVv3A\nfCqvfpitHrh2zdsX1Au7dv/HzUDwdQ1hemmz32aE7tAi8ETF3UGDQu2C0WG3vfq7OHVtuJ5NtmGS\nXUkkli2gsbuyVm91wHKgzicJPy2MciqfHs96WyibXqJydLibFp66TGgIzCQHhi1DCz6f2i0yRMct\nymc3YlhiiBvsZsf2G+zcP8el+x7gvf13wWwMi0X37InrD1O2+RfHRwG/Oik3ksqgKpFmrvHZtANz\nTT+EbiB16MY8k46B4OsavhdjX9hmZX4imWQr+RyOZdRXaRRyMHQ3DO1ojDUhGzbeTRcnDAcaS9Z8\nZqckaMGSpGJap0Zo/urv52qbXo5m4ST3C1QeHQaXxPx8TNTaBUXwyYQAjcUWNShHReYqY+zM32TX\njht8/wcn4HIAN2JYykGYd8wv1pu/6klYBzT7jIs+m16O7AKnnX7tu5cVsjZt/5weyysTxebFBlB1\nLay+tlntfNAIlBWqs6N+VKA0BHfvh2J+9e4jElmhhZmc18wuz2o1WDNAH0Sg6fEheSUcRQs5H5uz\ndj6t+ibZ+CSNrruMXZ9g03Y+eZSazMj/euNSEXyxE3y3FnfAcMy2oXnyP1KFdwL499Bt1pwPICpA\ndZiGd6hMw6FRNTe1y2MspPHtGFl99j8fLIVPEnoarcLGOsdWYHxZnuAaQDs5oNGJdEdYX09WZxAq\nJ4cE570NI++4Hai2hW68FeuXZVyVaPRd2YtOHouQSCsYfSwpSU0UgiKhhdqBoR+7NEFir8VPIzvl\nayFYUOflmt6HQV/X5yyz1AJT7qctBtYOCc3OnQrElZBqlKNSKxLlA/Z84C3umZqlOFaGUlB/DkUa\nn+yUiurVG/JbP2RLee3E7Jus7QvR15LU4XbRq3JaIyKX+fDh3LlzHDp0iKmpKZ555hlvmqeeeoqp\nqSmOHDnCq6++2jLv7//+73PkyBF+5Ed+hJ/92Z9t+gylDxtE8NkZTc+00PxS1z5uqXOIjicjfqT+\n+wYMz8G+mjP7SUjZMI1mylgUwVdSReprvjHlE3zacQGNMawFn9bU5DFrgeYTXlrwST3l0Gm0ENTn\nbRn2kDJtOVo4QmNOrIe3xBWIayHluEglX+Due95i/APfo3TXsnsNIRCUcIJPaK7cRDdcCyot/LSQ\nDDxp9G+dz6fB9KJfa1W8v6iSy3xYRFHEk08+yblz53jttdd48cUXef3115vSnD17ljfeeIPp6Wme\ne+45nnjiiZZ5f/d3f5dvfOMbfP3rX+eXf/mX+cxnPpPahg2o6m5kaCaa1sF0Z4fGh24LuIH2YYhz\nUCs0dl6RsSfCqUBjxYbPBgYN7UtUXj1n+ByG8rYlnQ5B0XY4K8zsig1tF7Rqrz6n62s9vtaREZq0\n8pi1bTA2Zek2QJOTI4hiwrDGUjDEPKPs5W2K26pc/oEpeBv4NhAHztYa1d/Fyswjn6Rs5XCQmUdm\nIR1bJA89VP+3G/Iiv9uBZn39cm50LjYuXrzI5OQkBw8eBODkyZOcOXOm6TORL7/8Mo899hgAx44d\nY25ujmvXrnHp0qXEvNu3b1/Jf+fOHe66667UemxAwZcU7R6Yv+sBnwpur1t1HZzg2wHkISxAaX9j\nm1XUciYAACAASURBVCURfEIuivhDGmNWCw1t+6ua3zYW1joitcoowkRUan3NCiy9U74mSZZZ+uyD\nlqVZZoq6BzSEvsgVLdh1na1sqUIQQRjWWA6KLDDKKPMUt0fkf6gCM8DrQBBAIYTlHG4HF9n4L69u\noL240i/1zGJnIyuorP3Aqrn2nK9/JY0JXx3SJuXejJ1ubHyzs7McOHBg5ffExAQXLlxomWZ2dpar\nV6+m5v293/s9/vqv/5qRkRHOnz+fWo8NKvha7XKxXpAZ3texZDT6Zuhh3A7LOShV4Z68+0C2DGRx\nLEpSCcuA1WxHqiHX5JbSF2WsauhxKdVMM2fpEDZr2tJbCaLKlTJ0uTpSwwpaOW81P0kLzf4FraJb\ndV4L5RVPt2N81aDAEkPu8liV4MdiuAR8sZ53JIAoD1Vxp0tDAvUgrecoK/SD1255+Z1mk4vN/zat\nDqPRVDlN3e2NozBN8JW//M+Uv5wsdLJ87Bvo6Fu8n/3sZ/nsZz/LH/3RH/Gbv/mbKx8i92GdBJ+d\nnZJmMpunn9DSJe1evlAGXwiOGunBEE7VvQOF23D33bBrqGHjEseibC8f09h9uETz7ix5nFCUfu5z\ncFgPqc/EZB0fWl3VjhD5X++SL73Gqrq+VR2YMn2Mz5rIrD9B91I9znV5+p4xUAuo1UJqhCuG9sJw\nhbvue4ul8SFujuwiJqibGESiR6oR9oZyTu8Eoe3S0lDrdbX93Gouvr5v+5el/j5otaCVGaY7LJdT\nNin4qY9Q+KmPNH5/5n9rujw+Pt7keJiZmWFiYiI1zZUrV5iYmKBSqbTMC/Cxj32M48ePp7ZhnWiU\n7vGwlobZZAjTzBqDpXVAya/tM+IByDnBF9Y/f1i4BLsWXPysbKMuu6PLYM6p/2XJmqinQka04NOE\nBBrCQjyiUmbelK/VRS1kSjTWB2svrmZzhXrdR+qH3AdPOTq/dlJoZ4l2iMh16x3WITM+O6TqzbWa\n27CgFoXEBJQpkitUuX/PG+zfM0O4I3J1ywcQSGN0I9NmBXtYfV5XylSsCUnl2dlDXpRVia0TcG3M\nQFE1n/mweOihh5ienuby5cuUy2VeeuklTpw40ZTmxIkTvPDCCwCcP3+esbEx9u3bl5p3enp6Jf+Z\nM2c4evRoahvWifFpmm7tJRqt2Fc/ISqNr9PaDmiv6Q5dgDAPuQCqgRtou3Ga7xwNAbFMM6vTTMen\nIlonhqi8sTmn2Z0IDu3cyNEsKAs0CxjfOM6ZckOT1jI8zdJ8Kz6syuqruyVNWi5ooVh3DMVhSFTN\nQQ1CapQpEgQx9wZXuTm6i+m9NaIb1L85Hrj3U8s5p9OKkTGm+fu8tr+K/Q9Wh1vZPqLZoU+7kHO+\nclppIFk0FUnbfUhYVO3cxpfP5zl9+jSPPvooURRx6tQpDh8+zLPPPgvA448/zvHjxzl79iyTk5OM\njo6uqKxJeQE+/elP8+1vf5tcLscDDzzA5z//+dR6BHEnynQXCIKn1S/p1dpyvZ6CTu+HllYny/Ts\ngnWJ2RsD9kBxEoofgOVp2DsPvzgFYzthFqcB78IJwUXcAF4GbtIQDjdxQc4hTs2dV7es0Ni8wB5a\nWAqDkqZZB4YOU7FsUMftiWDUDk0RQja/Jk3yeIoqv9YQfU4OnV+TcaswSP1K9ce+HdgVwe4Ke3e+\nxf7tVznIZfZzld28x7e+eISzn/8lli+X3Jco54GlCCpLUJvHbfJXX/zLEo297mVTUr1GTn/D164d\nFNQ8aWzf0tetVzgw5diypd+GCfdvRhw/nXo9DUEQULx+M3P68p6dHdnr+o11dm6kGXi12rhewjAr\n7AyovQPDkC9AMYTKvbiBMtwQFDqQVz6pKAJH70AiTg7N3Cxr0sTC2r0tU9MsKWeu23g7a7ND5S+q\nfElaoe5lck2TKp/gs44RaZd1bmihrUg2YQC1kFxcI0eVKjlqhGzjDiPb5uG+2H1y9xqNVTJRWA9r\nGWIlIHDlRbTqg9rWIA3wRSZohrhikFQPxzI3m6YVfA6U3qJa6ZzxbRRsAMGX5ua3BuG1gs+Y7Ktf\nmnohI3rIqbqFEApjkI8hCBr2O1mh4QsTkWJEEIm2pd+aFRY22kLSWNueZmraq6vtc8LOfI4QK/h0\nGl84jHW26JAVbcKSQ8as3Ms3D0q9tQqu7x0H5KiRp0pcf0gjLDC0bYngvhi+S4P9EsCibrx4l3xe\nGd/7t2prTf2vJbXOG5s0VnDKXztOfM40/b9miCSk6xy1aAMGg7SJDdgCUR19Bt21hDYsgV/w2fO+\nMuojMsAJutGgMTi30RAAQjTki4biOBMzk16ZIOxPNBzUNe10lOppm6AVYDoERausck/5ypuokpZx\nUc+nr1lGKcJbrz+2rDU016BZKMqz8LFOzSJX1PEYcjXCICKvFlEVKVPYXoWDuP0ipG5BAKGeBbSB\nUhtFQ5zaa3d/9bE7aSAmnb6WxAq1zQJVBqzeiicrA+uR9tSFjW+jYIMJPp/h12et7wesPUXP3r56\n6jRJ5YlEqAu+bTiznwxYWTqlBZ2wHNkirkqD8ehx4PMP+WziVg3W41ePcS0YbchKweTRQjLPataF\nKlteoQhYn+CTcq1Z1UeE9CO3Thd1BPmYIBcRhDVcUItjfkXK5EcrBOMR7Kiz75WVadqOII2JaXZs\n6MZbCmqZXZKTQrM93SidXo8Dm1YzTzsmfH3TstEusbTBxEYH2EAt0E4CDT3atfuv1/DNnO3Mpjaf\nVpvy7tQOnEdXGFbBk1V/1CuisZBAr4jSt7GflLRqsB0XWu2141gzQ2FoWpiJ19mGyWj1WcxjAQ01\nGFW2wGdvtLZJLfBQ56Q9UmdZyyvlFmKCQo2wEBGGtXrSiDwVCpTJjZThngpsjyCXb9gEQylcpLkw\nLwtrq/NVLqttTfqY0HWB/SCRFmj6YVno2cPWNylPm6i2TrLRsYEEnyCp1/tGcT+RFs6ikTTjwoqU\niHINlVHUW6sKQkN1FXu6FghawORYPXELc9LCMTCHZmjaoaH/12zPkhwRhjp+Vc6LoBNWpwWflGM9\ntlaA+eYYn9quu0JAs7peiqEQERaqFItl8rkqOZy6W6BCiWWGCosM71ygMjxMNcybV6jfpVZNrQdG\nU1ut81szjSzBsYzLZ7fTDfapwZrm+9LpMi17TFsl0iYGgq8f0KMiKX6h30iKtfLB1k3P2nU9Mco1\ntp6SDQlEiGitSNiUnrS1rU1rUdZZoCMhrC3MMjmtnlqVVwdI6/Euu8eIgNFb1+l1xsK8ApVW31/g\nM99a/0Fkzkk+S/zFtpcHhuqCr1ihUKqQz1XJ444CVYZYYqSwwEhugcXSdqrhUAKxs4JJN0JmAkmn\n3dq24rJ7hJ1AbRiUngW0mcdXp5w559OUYvN/D0NKBoKvH7C2kR6+sMzQjNMn9GznrHnyCBUagmre\nCYocja9KDrE6jEv+ylFQ/0voV0jzJ2FFmGmPro8ladueJTBWOGkhqNXdqrrfsCpT0toNC/SuTto7\nC83yw84xeg7RmiCqLlr4qXoGRafi5vNVCvkyxbBMgUpd+DnGNxwsMhre4VZuN4tS5xhn71uBlugh\nLsjSSkcrBFtBC0V9zoaztIL1GMtDTFJj5UH1iPVVWifZ6Niggk9TFmvgXSvWZ1WTNGOxjiGxqm4R\narmGGquXZmkjvkDW7GoGpz/qFdMc3ydV8QX46v+1ai1jxTJB7em1S8xk3BjTpW7mqr31RG5ImlaC\nTz82uZ9mueKBtVpnASjEzrZXisgVqxQKZYq5MkXKlFhe+TvEkjuCZfK5arM5oalr2Qr6TC3WlmBh\nNQafqirn5Z42fMUHa+jVD8sH+xC7xNp+v7wv2ICCTyAvS389WkbsWsF21FZp7QdfTce3trRC49JK\n35XzNVWkFhryvzbliD3NLjX2sTr9O8mRoYOqdT1jGja+IVVPyxK1h1XuoVVn3Wbf/CLCDZUH9Vva\nrdtSiAmKVXKlMqXhJYYKTsiVWKoLvEWG60eRMiERQRA3P5OW0DNGK2QxlSRFDHSybn0Nx8ZA1e0W\nWl1MCxuR/wP1dz1h6+pjpMrdGsSQC5qFjbA/rYHI7ixV9Vt2ZxZZKuxPCz5tUrKr57TQk7FhD71Z\ngBZgmu3JOWGsWs3UAjPPauanHTNW0GjZIGxQM0UrlJoEXwy5Wl3o1cgPlSkOLVMqLFPKOZY3xDLD\nLDLCAqPxAqO1eYpBhSjMExM2P5NV3cralbXdQOvf+rrVBOS8ZnVa8lvYfmT/JglE6/21ZfbQubHU\nm2LWExtA8FlXpKD3S23aR9IsaoNKrSchwBlClt3fXAT5sFlNE5ali4vrWcS2JeEsWsjZiATtzU1y\naGjGpdmmtc/JbiryWkQdF9veCA1mKHJdzoWecsEvgLUnWKuz0lbN+LTgC3DCTtqZiyAfEeRqhPmI\nwtAypZITeqVgmaLY9FhklAVG43m2V+YphBXKhSJVqaRX8ElFNbTgkzWG9lqSMNN2PDmXJoistBdB\nm5RHzx4+p0gP9dMB4+sWWWaitJms13XJ4sXFpEnr6MvALcgVoFhQA5bmJWraRCibgYhDsEzDqyv5\nNOkVxufzr1iGpYWOFVLa7KTHiY7bsx8XyplzogZboetTs6XteqWJ3DsH5GLHlFfOuwYHhQiCGnEc\nEORqBIUquVxEvlClVFhmKLfEULC0Ys8Ttred22yP5iktVKnFBW4VdrJcLjU/oyaIsBFhoh98En3V\n561gs/9rE4q16+nK2CBoPR6Syrfo8dgZCL5ukcXYKp2j3xZVbVDLIvha2XlqOJ3gJuRGobRttR1P\nBwfLWJEVG8L2RPXV3lut9ehgZxEqWluSw9rctFpqx63MRSKE9N58Nr8cwhCHVHt8Kquyya2cF6zE\n/MX1o24mAKjlXPUKFafe1kLCsEauUHUe3EJlRcUtKfVWju3cZlt0h+JiRFQpcjvcwfJSafVzWoGu\nsDTSt7W1doDIOaHg8lv+Wuqu08pL1zZEKxCtKp0Uw9dnDATfWkDrgfI7i+Dp1X11Z/MtnbBC0uqt\n16G4E0bucpdkJYb9sJfI9pH6/8s0YufkVnZMBDRUTkljPaA5lVauWzueZlqy+kIzNWi2SVq7Ycjq\njUi1I0cLFmFz+cgJtxjnZAhicrmIIIyJaq7gQAu+uEoQQK5QcefrjC/M1SjkKxTyZQphmaK26THP\nNu6wg1vs4gY7uEWeqtuM4HbefVhcJhr9faEmK4s8zNBz0dr7tANOHrplb2mqatKkm2YSsqxR6tzH\ncTIIZ1kLWDovnbCXZfsMyL57xeYI1F9fmWXgPSjc29hsVD4erndlGYqdKqfXngub0+xLV0ELMusc\nkHzWQ6xtfHbZGqxWWaFZyGkV16qtNlhZe69zyi4Xxu53ztnmCGKCMHZrauuCL6i5Zx6EjXcf4NKF\nYc0dQc39DmoU6/F6RZYpUV5RcUeZZzu32clNdsfvsZOb5HIRLAXw/RwsBM1ror2sTx6eflDW1CEP\nQws7XxowUtXcC5IFm0991mla2f4Ar1DtAINwls0MbXC2HgA7c+JJZ+00GtpaPwfhghszN4F3cf8P\n4QZboQbDNaiGUKl3fmEgvg1G9diyQkk6pHiDtQorAksHMQu0rU6PkZJKLzKgRDMj1AJUBKpeoxvU\n2d2KfbOhwgZhTL5QgTB2l4O6YMtHhDkn4OI4IK4FK79rtdAJu1yZMHDvZihwsXkuQHlJOTTmHduL\nb7A3epvd8XXCoQq1CnAFWKDxYTWxNYrZYKVxskTFBiGKEbZmM5o+4LPRBZ5zAs3qsjA2n51c30fX\nIcn50iYGqu56QQskn+DJgiSBpa/5DMmB+R/Pb0EFuA21ZffvHdzuwLASdxfkagSlKnG5QCzCqkbD\nqSEqsHZg+I6Q1U4Oq2LqJWqa9WmV2xdrGHrS63yiodn4PZzQcw6J+hOqs7o4DgjzEblC1W0kUH+s\nQQC5fEQuF60IvlocUgzL5IKIai1PQEwxXCYM3HtxMXpL5IgYqsfsjTLPTm4yxhxj8Rw77tyhUI24\nMzTKfHWY+FrgGPgQzRsiixc7ltlALzmzrA6a6Tc0P3DwT442rZQj97Gs0E7SaY4Nex87U/ZA8G2B\ncJZ+G8r6BNEDbVBbu0jrDFq3s49J23Pw1EHqVwYWoVJxKu679SOOIe/SB7kaYalCUKw1r+oQZiWb\nlcp+fXIIQ7MxeDLm9O4pett4EXDDNK8bRp2XvDJe9MeA7FTp8xQHOKZXiAiKTvAF+arzwBYr5Ivl\nFadEvhBRKFYplioUilV3vlAll6+SC6vkclXyuSrF0C01G8otuRi9oEwBtxxNYvVkVcYwS2znNrt5\nj928x1h0k5HrZWrXC7wT3MWNaIzaO4F7PaPq9RZ0+8QeoZmTtiek2eOS4ohCk87XxwJzToSXpqRp\n0HXVAtPaNbpAtY3Dg3PnznHo0CGmpqZ45plnvGmeeuoppqamOHLkCK+++mrLvL/zO7/D4cOHOXLk\nCL/yK7/CzZvp2+NvMsHnUxV6BekkviNJsFomaOsWA1VYqsFcBIuzUPsOhLch71hOLh9RLFUIR8oE\noxXYVoXRqCFsZF2v/l+Ylt40QH+RTAca66BkOwaFyck4g8bqEC0AfSarnCq/SajGrm0FEXg1grBG\nrhBRKFbI5yPyhYjS8DKloWWKhTKFXMXZ6HJlSrkyhaBCgWp9N5UyQ8ESBaqEaoeVIhWGWGKUBUos\nU6DcZNfbxQ328RZ38w67ajco3imzcGuU/6p9iJnyAWpzoZMlQ/XXZDdRWeU0sLS5FcRwqNceai3F\nCqcklqcNv750ms1Zmh+aA0/+DtCF4IuiiCeffJJz587x2muv8eKLL/L66683pTl79ixvvPEG09PT\nPPfcczzxxBMt8/78z/883/rWt/jGN77Bhz70If7wD/8wtQmbTNXVRt5eW1izlJnkOdN1syEGNfch\nm6UyzrB0C8JhCNx3I8NcRKFUJopCagEQh8RxDu6ErCyar8dBr/TZZXVbIb3W6SgmKe2fkWBnu5JC\nkwu9ptiOJdT/wjK1Pa9QZ7Jhzam0hYgw5+x1ubzbOAAgCGMKBbdzSoDbJDRnBqQ7FyFfxhUUKSO7\nKReouB2VqVCgwjALbONOne3d4B6ucXf8DruqNyjNl3m7vI/Xo8NcLt9H7VbOCfdhGoJvlTdXv2dt\nJPVRGR/j10ba5tY1p7WTq52E00JWklig75yvLh2gCxvfxYsXmZyc5ODBgwCcPHmSM2fOrHwtDeDl\nl1/mscceA+DYsWPMzc1x7do1Ll26lJj3537u51byHzt2jL/7u79LrccmE3y+TmKvZVEH0qBVGnuf\nLPfXNhVJdwN40/2u7IT3huBWDsKAXL5GIV+mHBYIwjxhWCEeiqkN5YkLgStqkYawi2neNKSibimC\nTYShXh0BzTY4u4pkiGb2V8Vv3/OtwQ2BXExYqhIUq3XHRY0gVyOXr1IoVCnknKCLCSCIyYfVurCL\nyRO5MBMgIG46QmoE9QaExCs7rQT1/0ssE1KjQIXt3GGMuTrje497uMbuhZuMzpXJRxG3cjv4j/hB\nvlt7gKiWayyu0Ru5rnqfekLTX0nTklLH4WkNQHuBfWX6NAbr3CjQLAi7iWrQdeoCXYSzzM7OcuDA\ngZXfExMTXLhwoWWa2dlZrl692jIvwF/+5V/y0Y9+NLUem0jwSefxBYZCsqu/3XvoQ8pNur++poWe\nvhbivh35JoS7INoD7wzBjRB2QZBznsxcPiJfqxLmImpVKA/TIKB6fSw0GGCME1jC1Gq4cVKupxHh\nJvZ5vQGpdWzYAGVpVsjq9bgFoBi7Q5UTFCJypbLLGrq4vHy+SqFYoRBWKAQVavXnmK+zuYCYfF2t\nFch5LfzkfInlFSHp1uIuOmcHFXZwizHm2MlN9tSus7f6Ljtu3aE0VyXOw0I4wuW5+7l2+15qYdhg\nevKcm+Y0rQHoB+xjYWkqqFaVdXmSV//2OUD0BCp5LGOE1X3f3qOH6ELZCpq2/kpGp5+k/OxnP0ux\nWORjH/tYarpNIvg0i/PtTqkFTafQTK9mfqeosU06IjQe6bL6fROCCPL7oHYvXC3BODAGtSAkivMr\nxv2YgEotpDJcIy6HjU0KJJBZqrFQP8QWd7teBRtIrB+V/t86QISwaEGoTVoSprLy/doIhiKCnHse\ncRwQ5J2HNghr5HI18oUqhXyFfFAlF0R1IecYXK6uwroPAbmNQuVLaKLKitDLERETEBA3CT5n07tD\njogSZXZwk128x17e4Z7lt7n79g1K1ytwC2q7oRLlWPzWNpYvDROPBA1tVKC3/2pi8LH6LQ9Ph7dA\ns6DT0N5YzRC1QNTmG+0484XD6EnZhsFYG6LP+9sDQZjm1b38ZXjzy4mXx8fHmZmZWfk9MzPDxMRE\naporV64wMTFBpVJJzftXf/VXnD17ln/4h39o2YQNJvj0LGWZk3bNWxWh1/f31SdJvbX2F/ktQrFu\nE4pvQ1yG2xH8xwLhXQH5oyHkYpYXhhgqLZHLV6jW8tSKFUo7FqjOl6jMl2BbzY21hfozGatXbZmG\nF1Xi/uSD3vKooHndrXVw2Fg8GTPaS6uFoXaWhAErMXm48JRCsUwujMiHkQtLCZ0aK8LMPdV4RejJ\ntXDFawl5qsgHgnL1nZNrhHVmV165LttMDbHECIuMcZM9XGecK+xdfofhuWXCmzGV+TzXxu/mzcUP\nsPCdbdTeyrvvn9yuH9CQMStyRoeX6Iua2WvoCVALrlD9Tuqrvj5tvbK+/L7y0hyAPRJ8aTa+iUfc\nIfjqZ5ouP/TQQ0xPT3P58mX279/PSy+9xIsvvtiU5sSJE5w+fZqTJ09y/vx5xsbG2LdvH3v27EnM\ne+7cOf7kT/6Er3zlKwwNDdEKG0jwyWi0rvskW1tk8vUTnTpTRE+s66XV23D9HTgP+fEcw2MFysUR\nFue2Udq1TKEu+HKFCqXdiyyywwm+nTUn/GbzEAXuux0RbtDKpgAj9dtowaVNT1IdEXB6DOdwYR0B\njRUM0Mz8xFMswjPKQTUgFnteWKNQrDA0vLxihyMQIVdbcUDU6kxEPvnYEHw1ZdOLV5waBaUGa7U4\nT5WhutDbwe0VNfcurvMBvsddy+8R3IjhFpQrRb4VHuYb0YPceXPUfUh8D852KuxFe7WbIDOD9IGk\nMKpApZNvhFrNRPeLJHuxNbNolpkF+p59UHOhKxtfPp/n9OnTPProo0RRxKlTpzh8+DDPPvssAI8/\n/jjHjx/n7NmzTE5OMjo6yvPPP5+aF+A3fuM3KJfLK06On/zJn+Qv/uIvEusRxJ0q0x0iCJ5OukKz\nHU3PetY1afOJwLQR6p2wQb3Hu75/kjfMqh+63kKZhCFM4D6zFpP773ZT+F/uJ9o1SlTLcc++K4zu\nvM3N6k4AtuVvc+v/b+/cguQ6zvv+O5e5z96wwAIEFiRMAhQpixRJkaLKURw7CsmEVhDHDypW8sBK\nySmWqlQs58nOm/ImP+WFL3SVS5JfVLSTosXEEG1LieNbCDIyylZCUgQlAgR2sdj7zu7czyUPfb45\n3/SemV0AS1Ikzld1ambOdPfpc073v//fpbvXZ1ldvgO33sFxQqJrZeKWZ4psYkyHeoaHXs1FV1v3\nLzvCQfq1Dl0RwCur80OLEsS45QC3FOAW+wPPbanYpVzqIHtcaFtdEbMasqisGvBs4JNzAowpAwzx\nEhDVK69IvN4h1jjevcY9m5eZXN2hsBxBAGv+IV488+/476tf5v99+yEam9NwFLgEvIcBv21gDQOG\nARg63SGNJt9JfneT3z1SABRHh6v+k/agPbtat9ZAOs6zq1/cKFZoaxt2Gfp3TBz/R25WHMeB/3AD\nkPGfnZu2132Q8nPE+PayR+zlhtej5H5XWdmrPDnGXVvqKVRK6JXWQcWLsIaZtwbhtkf40wrMl3En\nQophQIUOO9RxnYgaTXpuFa9ggnrxQpgMiVyXuOsYQJrFgF+LdFvHJsPkVIOf1sAk5k9r6g6pd1ev\nwmzF8TpestpxpUux2MNLppgV3P6QWiuAZZ6GCT0xTzZOVkE2/0la+U8DXlpekDhEQor0BzF7NZrM\nssYcyxyOVplrr1Fb6lDYiKANUd2lOVnj74PP8qPW5+h71dRbHSTPTofbDZqcbQDU6qPtnXWtdJpx\nCWBpG4P8p70qtsRWep3ONvPo8nXcny03SwYsyaesfdSiA810I9Mv3g4P2CsUIMtAfbMio7zUU3YH\nn4Nrh+F/ePBLEH/BYcet4Th9Sp4Jz+g5JYq1Dnd4V9jqTtAOKxRn2sSlHt3lKrHjptOtNBkWrO2p\namSZKuWczArRarC9EIGozl5sZmIUA1w11cxxzGIBRg3tDdRYDYDDwBdRpD8APj9DndXgl5YVJLMz\nzEyNCbaZZpM5lpkLVzi6s8rMeoNCIxgwt+WpWd6dPcXmm4cILpbBc809N0jVXJsVDx5WprvXEt2m\n5AV4Kk9m4VZ+HbKiyxJA0+3I9jDbdf4QJAe+j0KyRjnU94jdI7NWF/ZT/iijscio0IGsOupDGmwR\nViN4fQVOTsEX67R6NbxmwGx5FceNaEY1ioUek4UtulGBTlSmMNGFokPYLBD0Cwb86ph+1iONvesx\njN929fTYoGd8SDpRf4uYkBXZzMcDp2z2tSj4fXyvb5wYTurESI9h0JPpZebpRslMDAG+/gAItZpb\noD8EoAX61NmhSosyHWbY4DCrHAlXOdJdY3Z9k+pqx2ilycKtV+onuDDxIGuLR4jeK6Q2y1UMOJKk\nFW11F3bo+Dx7ANWeWSfjP7sN2KIZo26jdhu37RSRdV6Xpz/JSHMAki9L9WHLKDe9Fm3fO6iXbnvJ\nRgWqSlpIo/1lo1kJvOsA12FnE957G9Y/Tew8SHelRmmjz9R8g7gSs9o/TMHrUy9sUyk1aXkVPD8A\n16F4rAmNCv1GxTglypjOLsG4EqoiKpzGfx2JoTV0Hb83mC4Xm6OQrJ3ngF9MNvPxOxS9Lr4TpN0T\nlgAAIABJREFUJqukiMMiGppVIYxNnBsyU8NR78bDsMUSHQr0iXHwCQfqsKjG5WQObp0dajQ5wgrH\nuMbh7hYzW9sUFvrGmhAnz2QC3ix/mj+Nn2Kxd9y8gmryXNYwam6cPDex7cWwW3Xs6z9JDaGZkc+W\nSHvNSqvjjHSANAx7pOR6OoxFA2tWmTCead6CdPdO8vMuP8fAZxtvx8l+3fRZTopR1xl3fT0i259a\n9YbhRi0NtgX9ZMWWix34awiP+fSOl2hGNQpOl6rbwnf69ChSLbSIvHV6TpHA8ylOdIhjhzDwiSOP\nOHQxiwIAbWe47+iVXUST0qErUkUd1Dyw7zlmrm0xwPXNqiqFci8NSHbTUBQ3YXECcMbz2h84LmSW\nhQG+OHmKUQJ6/cH+GMUEHAX4JL/Y82SpqWk2OdxfY663xsRam/Jy17C4JlCBVq3C1rE6F9tneHP5\nAba60+lssw7pQCG4NmB8muF51kOyWV2W2OqtY6XPouHajqzTafONbodZoJdF6/V/++1L+5Bc1f0g\nJSucRdtdUOey7B1Zzo2s/FrVsENk5JzdiCRPOCKfHXgqdZSguDD5XoMLZWg48G+gd7rEgneCGXeN\nucIyPafIBoeY8LepRztcC+8gwKfg94iqLv3YJ9wuE0eOCXcpJfcTOMOX1rOrxGYvrE6LBEorv46D\nWTPPL/fN7JJCgOeHA8aWrKSHT5jE07UGLM88EQN0sq+tZnuG5XUH4OknMXtm0YG+UpP7TLPJJA2q\ntJhiiyOscKizxfRGE/f9GBYxoJcA+8bUJO+cvJufXbibpb+fJ+p4hh03k0Nmv8jMjcHrCpM/hbEL\nRRb2t5dzQJs07CgDu51m2aQ19Rbgsz0vWe1dm3o0eMt/o0LDbkJyVfeDFNs+B6NHrlt5mVnXkfNZ\njdwefW21OsumM/AOMKxPTsJWAy79A6zNE25N0QymqE63KR9eBJ9BOIjvBEy6DTxCupQoFnoUaz1a\n1OkUqkSxCx64hT5xxyNqq82N9J67cugVWZykesk0NMeLwI1w/chszi0sz+sPbHllOgkjCwaByBqw\n0gUGItLYu/7A9ifnNRgW6CnmZ2yCsoT8JA0maTAVbTHVbTDV2qGy2sW7HhnQW0vucxqYg8szd/En\n/tO807qXcC1p5hHGC75NGqXSIrVCxPIwZNC0HRTyfvV7tQc5UUtDK7+UrcVR/8tL0OBltymdT/Jq\n++KofjCqjd+k5Cswf5AySsW02dkHcZ29xG7odmS/LbarVDx5VWhtQnQVrpeJFqbpFKv0jlVh0sH3\nQopO4p51YNJr4McBq7Gx/5ULbRw3Ii7E9NoV4gL45R5Rq0CMR+w4w07ByBke/COMiuyBU8KsBp0s\nHOr6Zk08vxhQLPYper1BuIpPQIU2JToUlTo77NxIjwL9JAYvVGpw+l2cHOVk9WRRi8t0BouJ1uIm\nE9E2R3orTO40KSzHuNeABVJ73RxEEy79Yz4/nbyHc72nuda60zA8sXluYdRcAb42ihELw9KbG0tb\ns8NEbNBJXtKQK1zPgRtlC9TApwFzFNjq60TsfqGjrnGAtr5c1f2wxR65RjUM+W8vtcS2p8g5PRLv\nx76o7TkCcjDsZXZJ9aoCpqcuAnXoH4EfBrC5AWcnaNw5xU82P82xaJET9QW2maBLiQptJh2zYY7Z\nO6xCvdCk5jTZ8mZoRxViYrxyH+9wSBD6hKGXVNEhDlyIHUVQDbtzCyF+KdkDw43Nysi+WQS04AWD\nBQYEpLwBaxNbncypDQZOCHFMDMfkpfY/cWgUksDmYuIMEZW4SmvA8iZpMNlsUd9uUd7q4q+DswIs\nA0vJo04CrtdqM/x44tP8aOMRrv90ntZK3Tg0mphFcpqkcch9dmPHoA2IjUDASxaY0yCog91FbJY4\nKvAehgFOt8VbZWc6pMZmnQcgOfB92HIjKu5eo9w4FUGrOaPyjruGgKA0OLtRQ0o/yhBW4a02dNbg\nMyXac3XacYVaYYeT9UsU6RHhDFhVxWnjE9CjQMVr4bshkefhhQG9oEDsu1AFt+8TBl6yb4VLnHxP\nbyMeLP/uS1wesVks1AvwnADfCSgl3lk9r9acE9XV2OZksx/DBo3rL2V5Btx8pQpLHpmFkYJem3q8\nw3S8Sb2/Q73Xor7SobLaS1exXsEAWQOYhHjCoTfjszh9B3/lfpELG4+w9ZNZwr5nWnkredwtDPDJ\nxIvMVyzvSTyxmvVltQXtwBjnDLOvkXVo5rbXwG2Xg/rMqs9+ytuH5Da+j7NoELLBdK/l7O1RXI+u\nsnGGDq4W0etCaZrRBLZhow1/dtj8/KcOjekp3udOjrDMUbZY5xB9ChxifcCg2lTpOUWmvC1qbpOm\nXzO+UadIqdCFGILIJ4w8osjbZSryfAE8cJ0I1wkNwyMw35M5sRJ3J6xNVFKZQlakSyGpk158QBig\nxPEZBhjiK3VWH8Z50WAyajARbFNZ71NcCfEWIsPutjBzba+TxoM7EFY91u+a4ifHTvPD7Sf5v5sP\nEvVdA3DJ9sZskTI98V/ILLMgJvXoatCT963BTQY1cVRlhapoO9x+Ql4gbXfaC7xXWNYoLUjqqUOt\nbnU2UyJ5OMvHXUaxtRvNp9XkUenFIyjeQZkXFmCs7TVoefDWKkxGcHqG5mSdpdoJqpU2tZJZUj1O\n/KRFehxhhS2maVLDc0Jix6FElw5lWlSS6jiEsUccOYb5xc7AE+vIXrZObHYycyI8JxiEpchS7+WB\n5zUNPRHngztgbr0Bq9MzNoTFye5n4rktJOfN8vFNqrSoBy3qQZNav0Wl1abS6OAvRXiLGNC7Tsry\nmhgVdgbiOWjdXebC3IP8jf9L/HTpDBtrh8z9dzAODfHkai1V8GJAirRNz2b82pabxei0FqAZm1aN\ns9qNPejadsMsGcXysj51mePCcG5AclX3dhV7BJe5wXqU1mxPh72IjU88vRJTcQz6M7B8FS414N0a\n7XqdXrlM/dgOlVKLO3mfGIcrnKRGkzlWBntPtKkQ4zBJgy4ltqnTpUSAj+eEuGrdvAh3UDXxvDpu\nuiKKMDbD0tJFAgDlnBDVV3t1U/ue7eCoJlZJ8diao0uJ7iAub6a3Q73Zxt2OcVZinKsxzjUM4K2T\nMr0O6UZMVYjugcbDNX5Y/xJ/uvIUG+9NG4AsYJwXK8mn/doyzWlZDComndgcqDT2ElR2eBNWWhv4\nRoWl7BV6Muq/rPCtA/TmiuSq7u0mduiKNLSsHiUjrXSYiOGde2SGvKi+KxD3IayZhUr/4hpxPEN4\naJq1why+H1Kv7DBbWOMk7xPh0aKarEnXokuZfgJToorKLNcYd8AUI8d8OoOz6VJQJL+9xFFRSljd\n8HzZ4alpAo5eEsdXTmZf6Bi8QrKoQIX2oG4V2tSiJhP9JtVem2q3TXmjR2E9NE6LRYzHdhlj0xOm\n1yPdge4YcD/8/YkH+d+lz3Nh6XMsLZ6gH5bM425jyLQsrNJTn6LN7tprQ4s2WehEAk72yiu2N1aD\nTtY0tiwGth+gyipL102zRg2iB+TkyMNZbjexR2NpgFmqiRiQ9GocEsunlzMqY6jJMkYfexCWS7C8\nBPWY+N4JNr0ZgqLPlLfOhN/gF5xLNJjkCieZZY1JGoR4tCmzxRQRLrJ5T4Q7AMQAX/jdAPjs6WPO\nAPxCZceTRQb6Q6xQlocS210pYXAa9MTGJ2qtHxuHxkS8w1Rvm5n2FoVGiLeFUWevAVeABYivQbwO\n8SawCU4bnDo4R4EpCO706T9Q4PzcY/zX6Dd4c+EBthZmU9+UxOy1SNfe044NezGHXe/aZu76PxF7\nakxWDJ6tvurpaPq87dzYq26wGyjdjHQHCHqQq7qfTNFR9lk2GT16yjS0Aumob08yF2eHOEAgjemL\nUFuUJb9/hlkeeBJ+3IPO+/AvZ+lWy1wunsJ1Q6qlNjPuBg/wY1pU6VGkQI9pOsywSY8iHcrJzFmB\nKhNcMryVjzMAPr3MuwY/UXPFcyurpBQsW5+AnNj0tDpbTDy9VVrU+m0qvS6lVp9is0+hEeFKWMpV\nDMu7BtF1CFZhZwe2WlDtQdWFymHwjwG/CD+77xT/59TD/NXOP+Ht5QdotKfNo+9iAG8Vw/bEmSGg\nJ9YFPXbF+t2TvAuZo2uHs0i70OvpCUj6Kn+U8T2rHdkhMLAbPLVktc1RtscPYO5uDnyfVBllXLaN\nxbYak/W/LJmiI4n1VChhkJJmI8lTgasxXGvCkRLBdIHVeBbf6zNd2KTsdjjOIkV6dCgrp0OXgAId\nyoPFnSKcgaIq9xYliq69qY8An7mraHB4pKuuaBVWh6rIb1F5S3QoRT1KUY9K2KEctqm321R2urgb\n4KxjwGkBuArhFQgWobcGvS3obcNOFxp9cHwoTRlHRueeMhufmeLCXZ/lXOWf83drj3BtbT4Ftkby\nGCVuT9YGFbXWDsEb2mdDRMDOnvCcFeNpr66ivae280I7T+ScrSLrtCL79e7q9Pb1D0hyG98nUW40\nnEU3OB3WIhNh9aoemim4SRo9eVY2tAgw+t4MRLPwlw3ilYDoK7NslWZ5q/5pYs+h5HS5i8vcw08H\nQc4xDhPsUKGNLN/ZTZTQHsUB2LlJALJmf8AA9ITxabve8MyL1MFhWF66Eovsg1GhTTXoUm118dsh\nbivE34lwt8C5jrHjXU0+l6BzFRrLsNSHrQD6AZQiM8W2VIf6cfAegmuPH+Z/PfSP+GHhn/E/F55k\nqzNtHuUmxgGygHFo6Jg97YPQr24gEcPBfdoZpQPUfXVOGJ4e5EY5MrQ6rGMC7fZmawx2HeVGxsWS\n6hsNrfN5OAvkwJchtuMii+HBcCPSi41meenEkq7ngOrOpBljkbQT7kDswtIk9H3iw206vRIrzTkm\n79pm5o4NM3+VLabYQnYjEwdCquK6SeRcYaD+7r5rR32mTo5USTY5RbUVBlgYAF+HQhjihyHFqEcp\n7FEMepRafcrbAc5WbIBpAzPFbAGiqxC8D+01aG3B+jqsbRvs6iRP5agHhwpQuxfCR0q894V5fvSp\nh/lB+Ql+1H2Ua60TZkXqdlLu9eRzm1Sl1QsR2FtmRKj3k7XXpB02YsfC2HY0fUTWp60ai8iCGKiy\nHPUbdrc9Ms7Lf/ZKMnabvkXJVd1PqtjUIGs+pD0tSAcsa/ufnrNZxzA6vR6SrO0eky4YJ2pwG4MW\n98DGDPzJNsGlgHBjlrVfneP9O+7EJSTE42H+jnkWKGCWsmpTHtjwqpiZHuLoEPbXpzC0rJQovIb/\nRQMoBAaOEFFtdciLODVKQZ9SJ8DrgteJoRXjbGNAbw3DwsSBsQDhVWhfhaUOLAawEJlkLiZSZQKY\nLMLJOrifh/Uv1/jLX/wl/qz6JH91/VdZ7c0Ru45BSWF6ixjQE5Zna6xa5Q2BSJhaX2WCbNdlhLmY\nHZdng5qktdmfpNOhTzAMXpIHxs8GGSXS5kTEmXaAKu8tqrqvvvoqv/Vbv0UYhvzmb/4mv/3bv70r\nzfPPP8/3v/99qtUq3/72t3n44YfH5v2jP/ojvvGNb/D222/zxhtv8Mgjj4ytQw58mWKHG+iGpwHR\ntdJIo5b/Y4btPWJRl9/iUtSsoc3wZhcAmxCF0JqBSx7xD7ZpXC7x/t/eg/tETOlzPWZZpUSPo1xn\nsr/NXG8dQnDimILfx3VDE8oSeUSBUUoj18NxI3BjIj8mclwixwUnBgfcKMKNYohj3BjcOMaPQvwo\nxA1j3DDCjUP8MMDrhfitEL8V4bTA2cE4FkT9XAWWIbwGvWXY3oCNTVhpwmoI63G6W+Y0MFuBY9Nw\n+HMO4RddXn/oc7x+5jH+OvwiP958kI3oEP1+wTwuUZk3SPcC6mDse63knDg3ZDWWISzQVFDb3uTT\n9tbajE7P55V02hEihzjCRnlks87p6+9lfrHZp5w/YLmFcJYwDPn617/OD37wA06cOMFjjz3G2bNn\nB7ulAZw7d453332Xixcvcv78eb72ta/x2muvjc37wAMP8PLLL/Pcc8/tqx458I0UYXoCZHqk1uqK\nHNJ5yqRTBmC4QQrVKJHG8sl/su+C7BxUVJ9iqS/ASglWAnbOT7FTvhO/1KF4ukW1vINfCKm6TWaD\nTY63V/C6EU4QExch9jGLs/TB6zLA69iHqAC9okvkOUSuS+Q6xI6DH4S4YYgbxWb73Ai8AFzZzU0A\npJtUW/ap3cE4GBoYCrcK8Yo5etdhZ83Y8RZCY8ncToqYdmDWg2M+3HHY5Y57fIJ/UWLp31f4gfMr\nvNz9DS5dPs1mc9Y8mh4GVK9idktrkzoy2qrgDsPhLEPODDsMxV5NxY7R03k1W7QZn23Hk7agB86s\n0JVRHuC90CZr9oitLh+Q3IKq+/rrr3P69GlOnToFwDPPPMP3vve9IeB75ZVXePbZZwF4/PHH2dzc\nZGlpiffee29k3vvuu++G6pED31iR0dwWYXg6Vk8at9jy5FyfdFML6UA9DNCVGbb5ibE8wvRa2eex\nmuRdwqDKEQhd6LisvhTQuzzNtS//MssPzlM+1MEvRsQOHKLBRK+Fswx0DGA5lmnKCc1Vi35M7MXE\nxMQu4Do4kVF3hQQSgSNRHT1SUBFgEdDbwbCtBobxrUF/HTrrsNqC9T5sRebJTGI2iysDR2twaA6q\nD0LzwRl+9NkzXLj7EV7b/jzvtu7jSvtuWnHdPOYVDNO7nDyWzaQ+ct0mw6AnTHBAmmznhT6vbX62\nkVCzvazVWTRT1CCXpQbDsBnlIIxndhyf9jgfkNxCNRcWFjh58uTg9/z8POfPn98zzcLCAouLi3vm\n3a/kwLenZIUjjPotIAbDO21ptdkOaYFhRiGAKWqwgKGL6dGJXTAKIWqzcz5g5/I0i8Xj9FsTHD1x\njc7xClt3THC6f5lCZ5FCEOA1IqN+Sh+WqnYN+Hl+nGhJ8TB5EEemVDNQhyzkKcs8Cei11PcGsAVx\nA8Jt6EUmm+/BpA/TJajXYLIO9WNlvLtrrH/hEBcfuIc37n6Ev3R/mb/Y+VVoeGYuc5yUuQC8nxx6\ndoYwT1mMQIB5SL0V0LPZmlZXRwGe5LfDWUTkvA2GWU4z+W2DU1boyl7hLLaMc4DcotyCjc9x9sc+\nP+i9eHPguyGxGaB0ANk7V29AI2qNqLA90nAVCZvokgKgZgTC9PQafi6G+cUY/W4zyXfCOD7+S4vF\nv63zyol/xU++fD8P/NsLdCp/Q9lrM+ttUS10TFFid9PgJTic5ZSElITay7RrfBD10mZ+iY2t4ECt\nDMdCOBSDVwBnGpxj4N0P3mccrt47x09+4X5+OPUl/iF8iMUrJ1n1DkPVh9Ax9U1i/rhCOo2tpQ4B\nucCq21A/kmevN9sQlLRj98ROm8XK7BFCHoqtGmtGLwOi9r7Kwx8VbKyD6m3V2BZ5edoc82Eyvr9I\njmw5ceIEV65cGfy+cuUK8/PzY9NcvXqV+fl5+v3+nnn3Kznw3ZDYo7I0WGnUcl7O6RkaOvZLmF6g\nytGNOyR9NY5Vpo1Om9B14b0iraUq71+apeeeZsd12JmvceWOu7h76hIn5hY5zCqTtQa1qSbuTozb\nJGVKegqXnqQA6XRjPdLrMDHpx4LxcZKnzACE3Nj8XS9CWHNgzmXnyATLh4+wcvcRVk4f4b2Ju3i7\nci/n4y9wqX03zdYkUeyla+mtYwBvCaPqCrAK4ErMngC6MNGBqS2GWFy74sG1VV7NxDQbywIc7UjQ\nYGezQ3lg2t4Lw2AZW+VlXcsWPULpiAKbXWY5PT4o+ZXkEPlPQ/8++uijXLx4kUuXLnH8+HFeeukl\nvvvd7w6lOXv2LC+88ALPPPMMr732GtPT0xw9epTZ2dk988L+2GIOfDctogPqxq4NytLbNBqIV1cH\nN0t+sQMKXXFJg5rFEdJOvteSzxATuLYCzEPHgaWQlVd8Nv78Pn785MNMPRnw0D9+g8+deoPHZt/g\nnv67nOy3Ka1EuMuxmSK8jrGJ7TAc8iGx10I4IZ1uLCJOSjs8UWv4Yk/0wT8K3OnQfKjA5bnjvOZ+\ngfP+53nDe4zNnxyhcXGGXlQkKPjEddfUawljy7tKusRUj9SeJ6Anmqq27Q36utyIGPv6DLMuGYD0\nIKNNEzYwioiDw57OZtuBRwUPS35bjd5LtJ1Raxja1JKV/oMGvvHi+z4vvPACTz31FGEY8tWvfpX7\n77+fF198EYDnnnuOp59+mnPnznH69GlqtRrf+ta3xuYFePnll3n++edZXV3l137t13j44Yf5/ve/\nP7IeTvxBK9P2BZ1vfJiX+wDF7ii2WhKrNNobDOmCpNreU1TntQok+z46pL1bzstO4hEmRrCCAdUJ\nYAruLVL8lMMdp7c48allTn7mKidPXubkHZc40lpntrnO5M42tVaTUq9LodfH6wUU+n2KQR8vCvDj\nAN8Nk9CWKAlrYbedX38KqS1Au1hiu1hjzZ1lxT3MYv04S7VjbBQOcS08xvs7d/F+fJIrnKS7XqXf\nKA2rqrIU1Sap06LNMMvTYXjidNFjSiTPWKN6Tz1/UXntFZe12qv1fLmglKvTaQeGBkvXOqfDZkY5\nPlBpNKu0maKUP4rVDb+wON4dN7dfMTa6G7M1fsgQsy/JGd9NizR4bduznRaSxscAkuhdQqGKpJ1I\nGEGR4RVcpKH7Kn1XpZVzEj9STvJU4J0mvYsOl/3jXL7vXvjXDoe+uMod9SucKl7irplLHJ9d5Ii7\nyoTboOq0KDpd6o7Zv7ZEl3LcoRR3KQR9/CCk0I/xe7Hx7oZx2pcDxyx2GjlEvkNUdIkqLpvlCRZL\nc7zLPbwTfYoLPMxbO7/I8sXjtK7WTXyfOFvKya13MUB3nXQBUunrOnRmsBcuqbNc1F1IVfSBCMPT\nYSVZTgabTWV5f7PK1awtC+CyPLf7AQVdngyoIprpjarffmyDNyIf/8m6OfDdsohhHFJWFzH8aAWs\ntM0vcakO5uzKiN1T6aRDCRuRzXBlpocOpygl16wkv7eBJsSBSXa1Df+tQvNCgYWTd9G4c5af3fkp\nqsdalI90KEz2KU20KNebTHoNJt2GAT4nWRbe71ByuxT9PqVSn2LUx4/7uHFIHLuEsUc7rtCKq2y5\nU2zG02z0DtFYnaS1VmN7Y5KtjSnWglk2+9P0eqXhkLkeBuzapIHHEpcnO6SJ/6HHcOSP2PVE45OF\nbgYrwo8CFw1wGtxs1VarxPI+dYyeRmA5J/m1ViAioBtY+WzRtjn921ZjR8kHpd5+/Oes5cAH7M/1\nb4chwO4ZGzLyagOXOED08lSQ9kxZjVl7C+QaovoGGWVq9iDnCqTxgrKdWAhRI4mB7tF9a4JudYrN\nMzNwxoF7HLjTgSNQmO1SPNRkorhN3d+h4PYouT1KbtccToeik66xV6CPFwdEkUsQFmhFVbaps84s\nq8FhVnbmaK/WYNGDFceorTJGlDGAJjP4xMYoHmGZCK/tjDpoWptSBRS1tUHwa6jjZ3lb9bms31k2\nN9t5EVvntGgDaGydt7+Py8+IT9uZkaXmZn2/FWkfUDkfneTAt2s0z1ILtDqhe5dQDu2dhbSzyL4a\n0qGEGQjYiVFcOzckv6jRwuak83SS/2VKm5Qdk1IfuW4NgzAxAxDsxRB68HYBFn246MOcB4cgOFQg\nOjRBUKrQKM7ilmOcUoRbinCLEW4hwnWTgwgninHCmLgDcdMlDDzC0DOzeeMivahorhUmz3aaFN8l\n/GWNNMBYr4gsYKenmQkIOsnvNukeudopOphyK4XYTM5meFq91c8ThmP7tB1ORMwQci1UPrtcG3js\ndrdfYLKNqlIPu43qQXIcON+o5KruJ0yyvG72aJwVbGo7NMblE/ueq87Jee0wcTLSS7nSOfXywboD\naTDVKlkM0Q5EAWxVYacGzSoseVCDeMolPOQSVgrpnhYya058LzrqRmuKWX1BPwqtvYcY0GqRemgF\n1CQURfsgtANDB1CL2muTGo1bQ+AXqt9Ze+Tq3/YxjtkJ4GjgscOQBCx1u8hyUsAwcxvHJMfZ9rLa\n4UHF9OXA9wkQDS627SQrVs/u9Zrp6cYtoNYl9cBKI+2ROjy00TtxSgxmaghTc1V6AUGhPMLuZI6w\ndNw+aaxHnTSEZgfjUThknBObBWgUwHdMsmkMUawlVSkm2ezHI0DlkYKkOJXlscpt2UCmV0jR/VLA\nTSJONNGScgQoJY++1gA/bJsYDAOEvAPbnjfOxpelrmaJRt4s0POsczbTtLULG/i0g8Z2etjszwY/\n+/vNSm7j+5iLnkupVRXN6qR32iOnfaD+F7HVX91gYVg10mAn5+2d26SziEdYh7XofDpcRvKJCizl\nJms5RR2IqhBVYLsM/TJsOQbAqqRYqxeM1lWSzbp9dTnNBO3wN/2fxiHbxCYsUq/8LqAZqLJ2HVpV\njcmeehZamWx7ng2E9qG9MiKjmJm+jt1WbMAapfrKdXUb1MA5SmzQzRmfyG0MfLbRWBpg1mKh0vO1\np86eDG6rJ6h8UpZmflq91fNxhd44pFPXbCO7AJvHMLOUuol3V9uBAnUtcZNKcFwZohnoTkM30Wdd\nB6pOuheSrw79Wy6v2ZzcrmaFciuiLodWOk0issrU5lAbDwaiaaZmvhopBY31e9LPVv/WQKfB0E5n\n10F/2udt8Mpip9r8odPamoX93RZ573q0OgjJGd/HWEaNmprhjVrZQo+io+xwtnqs1Rdt8NLXt2PM\nBDkEMPW0J22rkvyCMBHGPSrXFTCEFLVEJxVW2sWAYGjSxEXoFiEoDGtX8l2Dn21GE3DTj05Pmtil\nmjKM7ZrxhRlph+x3dse3AUtXQj8zDY72xUexdjL+tytlM0ZhensBTxb46nYDu+MPBYC0VmE/UM0i\nD0pyxvcxlXHhAzbzs0dwDWq2FxeGGaOt4mTZYRiRVsrV9kebEYinQKvRUl6f4XvQnce3ynRIp8SF\nQBHiMvTjpI1rxEtEQC+LIEuYitymTZJssmMzuKwZYLtUSe3ksYHHdlzYaqymj1mIawPxFvt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"text/plain": "<matplotlib.figure.Figure at 0x7f8a84f75b90>"}}], "source": "show.imshow(abs(np.squeeze(res[10][1])),colorbar=True)", "metadata": {"collapsed": false, "trusted": false}}, {"cell_type": "code", "execution_count": null, "outputs": [], "source": "", "metadata": {"collapsed": false, "trusted": false}}], "nbformat_minor": 0}