From 2aefd0ba7f85c946c27c5c59e3752c808d4ad8c6 Mon Sep 17 00:00:00 2001
From: Eric Kooistra <kooistra@astron.nl>
Date: Tue, 13 Sep 2022 08:19:58 +0200
Subject: [PATCH] Updates.

---
 .../lofar2_station_sdp_firmware_model.ipynb   | 120 +++--
 doc/erko_howto_tools.txt                      |   9 +-
 doc/sdp_useful_commands_erko.txt              | 503 ++++++++++++++++++
 3 files changed, 590 insertions(+), 42 deletions(-)
 create mode 100644 doc/sdp_useful_commands_erko.txt

diff --git a/applications/lofar2/model/lofar2_station_sdp_firmware_model.ipynb b/applications/lofar2/model/lofar2_station_sdp_firmware_model.ipynb
index 4365109bb1..98917694e0 100644
--- a/applications/lofar2/model/lofar2_station_sdp_firmware_model.ipynb
+++ b/applications/lofar2/model/lofar2_station_sdp_firmware_model.ipynb
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 2,
    "id": "2b477516",
    "metadata": {},
    "outputs": [],
@@ -38,7 +38,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 3,
    "id": "e1b6fa12",
    "metadata": {},
    "outputs": [
@@ -71,7 +71,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 4,
    "id": "eb325c9c",
    "metadata": {},
    "outputs": [
@@ -101,7 +101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 5,
    "id": "3e71626f",
    "metadata": {},
    "outputs": [
@@ -138,7 +138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 6,
    "id": "0ec00484",
    "metadata": {},
    "outputs": [
@@ -225,7 +225,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 7,
    "id": "ac73d7e3",
    "metadata": {},
    "outputs": [
@@ -332,7 +332,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 8,
    "id": "98f1917e",
    "metadata": {},
    "outputs": [
@@ -368,7 +368,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 9,
    "id": "f66c5028",
    "metadata": {},
    "outputs": [
@@ -390,7 +390,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 10,
    "id": "a9fca052",
    "metadata": {},
    "outputs": [
@@ -422,7 +422,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 11,
    "id": "d9972b6b",
    "metadata": {},
    "outputs": [
@@ -460,7 +460,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 12,
    "id": "be2d952f",
    "metadata": {},
    "outputs": [
@@ -487,7 +487,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 13,
    "id": "a9e7fabc",
    "metadata": {},
    "outputs": [
@@ -511,7 +511,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 14,
    "id": "92852a53",
    "metadata": {},
    "outputs": [
@@ -572,7 +572,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 15,
    "id": "a04af043",
    "metadata": {},
    "outputs": [
@@ -643,7 +643,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 16,
    "id": "5ba30659",
    "metadata": {},
    "outputs": [
@@ -735,7 +735,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 17,
    "id": "5ec1330a",
    "metadata": {},
    "outputs": [
@@ -809,7 +809,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 18,
    "id": "33f37393",
    "metadata": {},
    "outputs": [
@@ -895,7 +895,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 19,
    "id": "f0b09a83",
    "metadata": {},
    "outputs": [
@@ -1004,7 +1004,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 20,
    "id": "06c7b393",
    "metadata": {},
    "outputs": [
@@ -1097,12 +1097,12 @@
    "id": "d2086ec5",
    "metadata": {},
    "source": [
-    "# Appendix 1: DFT of real input DC, sine and noise"
+    "# Appendix 1: DFT of real input DC, sine, block and noise"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 35,
    "id": "def6eba7",
    "metadata": {},
    "outputs": [
@@ -1112,7 +1112,9 @@
      "text": [
       "The DFT of the sine plot shows:\n",
       ". G_fft_real_input_dc = 1\n",
-      ". G_fft_real_input_sine = 0.5\n"
+      ". G_fft_real_input_sine = 0.5\n",
+      "\n",
+      "The DFT of the block plot shows that the first harmonic has an amplitude of 4/pi * A/2 = 0.6364949321522198, which is larger than A / 2 = 0.5000000000000007 for sine input. Hence the bin samples need 1 bit more than for a full scale sine, because to also fit e.g. this harmonic of a block wave.\n"
      ]
     },
     {
@@ -1129,7 +1131,19 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
+      "text/plain": [
+       "<Figure size 720x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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/xXz2YcXSbtoSbVX2Ypc7X32PDD/17dZ/qUwXHgAwmPs9yDwm4zkA7zcVuCbG2Anz75UAdgAYo1CmRqPRhIHuvzQaTd5QUbKWAxhNRMOJqBzAbQBe4RMQ0Wju540AtpvHe5uOpyCiEQBGA9gZhuAajUajgO6/NBpN3vD0yWKMtRLRfQDeBFAC4GnG2EYiegiGiewVAPcR0QwALQBOAbjTvHwagIeIqAVAEsA9jLGTUdyIRqPRONH9l0ajyScqju9gjL0G4DXHse9yf39Jct1LAF7KRkCNRqPJBt1/aTSafHHWR3zXaDQajUajiQKtZGk0Go1Go9FEgFayNBqNRqPRaCJAK1kajUaj0Wg0EaCVLI1Go9FoNJoI0EqWRqPRaDQaTQRoJUuj0Wg0Gk3eMHamKU60kqXRaDQajUYTAVrJ0mg0Go1GkzeK2JCllSyNRqPRaDT5o4h1LK1kaTQajUajyR/aJ0uj0Wg0Go1G4wutZGk0Go1Go8kbxWvH0kqWRqPRaDSaPFLEs4VaydJoNBqNRqOJAq1kaTQajUajyRusiCcMtZKl0Wg0Go0mb+jpQo1Go9FoNBqNL7SSpdFoNBqNRhMBSkoWEV1PRFuJqIqIZgrO30NE64loDREtIKLx3Ln7zeu2EtF1YQqv0Wg0Xuj+S6MpbM7q6UIiKgHwBIAbAIwHcDvfCZn8gzF2PmPsQgCPAfi5ee14ALcBOBfA9QB+a+an0Wg0kaP7L42m8DnbHd+nAKhijO1kjDUDeA7ALXwCxthp7mdHpGOL3QLgOcZYE2NsF4AqMz+NRqPJBbr/0mg0eaNUIc1AAPu43/sBXOJMRET3AvgqgHIA7+GuXeK4dqDg2rsB3A0Affv2RTweVxArHLbsbwEAHDp0GPH4Kdu52tranMoSFlru3NNWZZfJ3RbvRULk/Zd5faA+rNjaTVugrcpezHI3tqYtWYVyj2HVt4qSpQRj7AkATxDRHQAeBHCnj2ufBPAkAEyePJlVVlaGJZYnR5bvBTasR//+/VBZeYHtXDweRy5lCQstd+5pq7JnyP3GLABok/eSDdn0X+b1gfqwomk3bYi2Knsxy13b1Aq8/SaAwul7wqpvlenCAwAGc78HmcdkPAfg/QGv1Wg0mjDR/ZdGo8kbKkrWcgCjiWg4EZXDcAR9hU9ARKO5nzcC2G7+/QqA24iogoiGAxgNYFn2Yms0Go0Suv/SaAocVsTLCz2nCxljrUR0H4A3AZQAeJoxtpGIHgKwgjH2CoD7iGgGgBYAp2Ca2s10zwPYBKAVwL2MsURE96LRaDQ2dP+l0RQ+xatiKfpkMcZeA/Ca49h3ub+/5HLtjwD8KKiAGo0m9zDGQET5FiMUdP+l0RQ2RWzI0hHfNRqNRqPRaKJAK1kajUaj0Wjyh7ZkacLm/n+vxy1PLMy3GJoCpaE5gWEzZ+G/aw/mpfxiNt9rNG2FyQ/PxhNzq/ItRuSc7RHfNRHwz2V7sXZfdb7F0BQoB2saAAC/mL0tz5JoNJp8cby2GY+/uTXfYmiyQCtZJkfPNOEXs7flbSlp1dEzaGrVC5fOZk7WNeOQqVzlmhO1Tfip7sw1mpyz+dBpJJPFa8mRseFADf62ZA+A4racayXLJL71GH45ZzvW5MG6VF3fjBk/n4eZL63PedmawuGiH87GZT95Jy9lP/jyBvyGm5Yo4j5PoykY1u+vwQ2/nI/fxot/StDJTb9egAdf3gCguPsbrWQ5SORhRFHb1AoAWLbrZMa5F1fux85jtbkWSRMh87cfw+IdJ/Itho36Zm1F1WiypbapFb+L71D+jhyoNizX6/bXRClWwVPMwUi1kiWg6ugZDJs5C7tr8v/h+foLa3HjrxbkWwxNiHz8qWW4/Y9LvBNGzJPzduCuN+qE54q509NoouJnb23Fo29swavr8rNgpRD51rx63P/vs3eWRitZDhiA2ZuOAgCWHs6vkmV96Bpa8q/saYqPH7+2JfW3Vqk0muxpbk0CAE43tORZksLhSD3DP5ftdU1TzP2PVrIEWIGu8z2Ybz0LnSE1BvluexqNxj8VpSUAgMaWpOIV+kUHiru/00qWA8aAfG0m4pyiaU0UccvTFBTOtqdbnkbjn3ZlxifV70rxItnBSiNAK1kO7B+b/H5qWpLGaKg0pt/Asw3d6Wo0bQ/LktXUqmrJknM2+UXqYKRnGYXygbMsWaUlBSKQJmecRf2rRlM0WJasxhD8aM+qPqCI71UrWQ7yYcciiVbXmrAsWYX9mG7+zQJMf3xuvsVAIskwbOYs/PLt7fkWpc3h7NDPqg5eg+/+3wZ844W1+RajzVNRailZapYst/cseRa9hMV8p4X99c4Dhk+WP8vR4ZrGSCJ1t5iO7yUFPl24bn8N9pyoz7cYaDGV0ieKILBfUGvqtiNnUGfGXVOFMVbU5nqNN1VHa1Gl4/FlTbsya7rQp0+W4JuTyzdy38l6HDvTlMMSzx60kuXA9rFRbOWX/mROJJG6E9Z0YYErWZrwCTKIZYzh2l/Mw2eeXZGT8jTFA2OAXsycPRVl/ixZbuTSknXlY3Nx8Y/ezll5Toq5/9FKlhNWOD5ZKcf3EH2yqo7W4lRdc2j5FRKp5xbyC7vnRB2OnmkMN1NVAjz6xTv9RZNnTDBdqC1bZx1tYf+8ptYE1uZh6zO/aJ8sfxRzf6OVLBdy/did5aUc302frNONLfjbkj1ZrTqZ8fN3cd3/zgt8fSFjmdzDHgFOfzyOKT+aE/j6moYW/H1pwOfm45KzqVPWhAsDaxM+QD/47ybc8sRC7Dkh3qkgF+w7WY9Z6w4Jz1lV2Ki4urDwa1yTLaX5FqDQKJwADmkfI8sn64H/bMB/1x7EuH6dMXlYj8D5Hi3SufdCHQ19+9/rMWv9IZzTvwsuGtI93+IIKdS60+QGxvKzb6tfNh4w9vg7WdeMoT075kWG9/5qPs40tuLGCTdmnEspWT4tWaLZk7ag9IZFMd+qtmQJkK32yzXWS2YpWSfrDOUojPn+YqbQ3tfjtcZzawry3Hw0xaD3LbqumDs9TSYMbeOZx2LRWKv9cKZRvrDEkioMhbUtPI+wKOZbVVKyiOh6ItpKRFVENFNw/qtEtImI1hHRHCIayp1LENEa8/9XwhQ+CgqpYVuyWN9ZazpMWx3EWPVVaEH8LGkC6e45upUCq7JQOZv6r8AwINEGGkGJ+RIlCnScafU9YSiBZ5clq3jv1VPJIqISAE8AuAHAeAC3E9F4R7LVACYzxiYAeBHAY9y5BsbYheb/N4ckd2QwsLxtq+PE2ewKZU9FL3782mapz0LY/OytrXhp5X7bsYKrHoeyrEIQhSxoR2WEcChOzrb+S8a2I2fw2b+sSG1g7ISBtQnHd8uSVahTm5ZUYYhXmHeo8YuKJWsKgCrG2E7GWDOA5wDcwidgjM1ljFmBkpYAGBSumLmD/07lW5lJOk1ZJoX+8j05byfu/ceqnJT163eq8LUCD6IYRIWJ2Ec+5IsLmrOq/5LxzRfXYfamI9hwsEZ43gjhUPiNwLJkFazlwxIrBPkK9RajoJjvVcXxfSCAfdzv/QAucUn/aQCvc7/bEdEKAK0AHmGMvey8gIjuBnA3APTt2xfxeFxBrHDYsr/F9nvt2rU4XGc88ZaWFl+yfOmPb+H9o8p8+XTF43GcaDBGl01NTbbyqk4ZzpMN9fWIx+M4dbIxJSM7KH90tbW1nnJHUcfOPP2WoSK3W9nN5mpMxnJzfzxusldXG4Fq16xZg4a9JZ75xeNxHKw12kS9+ey9ON3M8MaudFv2c//xd99FdY09RMW8efNQXhzbOUXefwHB+7Bs2rwfak4bbXD1qlU4vbMk43x1TQPqmljBye2kxgz6vGrNWjTvz7wPFcKSfe7cuRl9/Wbze1Jz+oxSGRsPG/5dx44dy0hf25zWPOLxeOh1vvd0AttOJTFjaJmtnKhw6++O1Sc90+WasOo71NWFRPQxAJMBTOcOD2WMHSCiEQDeIaL1jLEd/HWMsScBPAkAkydPZpWVlWGK5cqR5XuBDetTvydccAE6HasFtmxCaVkZlGR5YxYA4P92tOAT116MSUMVVpCZ11RWVuJAdQPw7juoqKiwldd5z0lg6WJ07NgRlZXT8fTOZcCJYzh/wgRUju0jzToej8vl5soV8X9rDqBLuzJcNU6ev2eeHmXIcJVboezGlgQw+w3fZT+zcBcuGtIdFwzu5lmGDDfZf715EVB9ChddNBEXD+shz487vuNYLbDgXXTo0EHpXj731xV4c9eR1G8/7Xb69On4w7YlwKlTqVNXXjkN7cuDfcTaKkH7LyB4HxaozQfg5xsWADU1mDRpEi4UtPNfbVqIhtNNyrIElXvZrpOoOlqLOy4Z4vtaAHhqx1LgxHGcd/75rn2gG1nXeeq9qUxNX1ocXmZ8Tzp26oTKyis9s6pffwhYswq9e/dGZeUk27lTdc3AO7MBGO9z2G1l2EzjPh6+85rAfbYSHv3d9OnTsf9UAzBvbnQyBCCs+lZRsg4AGMz9HmQes0FEMwA8AGA6YywVI4AxdsD8dycRxQFMBJDRSRUKWw6dTu0/pYLTbB2mGTvT8d06EVoRGXzpuTUAgN2PZC5PLnSCVv0P/rsJQHT3bLUJP3Yhv/eS7YrT5btPeSdqm5xV/ZcMieeBjVxMF37kD4sBILCSVVIAqwsthKtyzX/D8MnKxz0eOd2Ivl3ahZaf7Hu472Q9l6a4pwtVtInlAEYT0XAiKgdwGwDbKhsimgjgDwBuZowd5Y53J6IK8+9eAKYC2BSW8FHwk9e3+Jrue2fLUe9EHqQClTsamvWiWuIUSGQJTUCcz09JIc/BMy/mDg5nWf8lw/ILlPUhDIWhuHhRSKsLRfXld4WzW7J8PI0ZP3831PxkyubHn1qa+puhuFfMeypZjLFWAPcBeBPAZgDPM8Y2EtFDRGSttnkcQCcALziWOp8DYAURrQUwF4ZPQ+id1OnGFvxo1ibpypmgqDx256aaYSpCshe1mBtkNhRqvcikUuqHFW8pbAU8SF3uPl6HX83ZXlBOyW2h/yoEjGCk+ZbCm0JaXShq5tZ741dhFb2/+XiN3GKABUFWD85y8tllzFp3CHM2H/FOGBAlnyzG2GsAXnMc+y739wzJdYsAnJ+NgCr8/K1t+POi3RjRuxNunxLMDB2UKN/1VHwl05whs3gp51dAH7+ziXS123vSQnkaYSmndz2zDLtP1OMjkwejX9fwphyypdD7r1yQni4Ua+NGMNJCaZFyLEtWIVjdRO+NJZbqd8Ht3WsLz8ML2XPiZ4vyfZ/WSvio3EWKIuJ7szkEC310o5BdZiMSd2Iff2opbvnNAl/FW3mnpwut5cu+sklRBO+sK4V6f7JgpEofCvOaRJJh2MxZ+NP8nW7J8orlF1aoFsWzmZSSJWsojLWNYKQFb8kyCEMJVMnhysfewZeeW51dORE+d1nWfDtkKJwBZxQUhZIVdhvxM/WS2UDFwszffhxr94tj1Mgzd8jl7+oMCmH0dzbjfH5+pgutfSwff3OrWzIub/VnLfxYZNFUdDMrPLweCQPaVDDSQujLhCIwJj/ngsjCqHKP+0424P/WHPRXWEY5WV3ukbc4c35RpuH4nv/nGRVFoWRZ5MMxPKqmcaC6QR7xPWCebaAPzYqCvb2URdLeQJtaExk+fWGRj2etF2YULsxhFc883zb6hxLOsptvhNOF5r+8clFT34LaJv++TrnSO6JUWGWPKcZPFxZuzx0KRaVkefGL2dvw3LK9tmOi9uXnW5H5smf/pZm//RimPvIOXl1njFDSH2dxtON9J+txzc/f9fxgF8Lor5iZt+0Ybv3doow2kfats/OFv6/CxT96W5JbyolGmJcTZ97ZfoScV3//lY14Y0NutkrSRIfcJ4u1if4hCsf338V34Dsvb/B9nZsFmK/LCx56C5f+eI7//H1fEYxolSyZJSs7/9S/LdmDX8/ZHlCq3FIkSpY5SvNQcH45Zztm/nu9a5rMXN2JYkC16eBpAMDqvdUAMj+ga/dXY8vh06nfTy3Yhe1Ha/Hfte5m4zbQh7ZpvvyvNVix5xSq65tTx+ZvP4aDZsR3J/O3H5fmle2zCnuk/+dFu3HP39y3SkotzAi1ZE0YWO0pJunxjdWFhf/kYhE4vj/6xhb8dcke1zTbj5zByj2nbMdEMiQl04VBLFm5mr6N8rvAJCtWKWO60F++D768AT+bvS24YDmkKJQsT6dOF4TXWA7mSmXbU0U5ZWLl/cTcHbj+f+f7vj4XI9XGloTva5btOolhM2fh16sbMWzmrEChOFoTyYKc1//4U8twvNZQuqJtG/bM8+HEnF6YUXjPoZC59x+rUtG3o8J69xNJhoZm8TvaFh5bvuJkXfOLefjQ7xbZjrm4ZCnXZSHUeT4sWSWxs8e3oKiUrLDw8/ijXEWSsbowYD7O/FQYNnMWfhFgpDDuO2/4vuZtM0bJyiNG59/Y6l9R+/zf/W9I/b5fL8ANv/SvrMrwUi68LK22vLKUxY9VQjztEVyCQvhwtCVmrYt+GtZ6JJ98ZjnO+W7mO8pYNIr5tiNnMGzmLCzbdTKU/Ard8Z2lzvn2fM8gV/eYH8d3PoQDUMz276JQsixC140VnnukcbIcFjqZJUTVQuJX1l/mac47SN8ye9MR36/p+gM12HzotHdCn8h2DPC3ajU7GdrCSjFN7rA++kclfpsM0XzUF5hT4q+tD0eRLDG/WAVhLXUZnITx+hWz4zvfFTKwoh6YFZWSFRZ+PoahWLKkkd1NeVLBSLNTIwuiY9Jkh+Ij9GOVKPbVPRqFEA6MRbKUXpZb0HJiqenC/LdZt74/V3GywkDmNxVK3tJgpHyaYrZjFYmSFdVHggHYcKDGtUOIQm+xGmBmMNLs8i2AfilSCl2H9GXJcrRpv/fmZsmqbWrFruN1HuVrigpV5TxHnUTQclJKVgE0UDefrLa0QXTY5ew9UY+ahhYzb3Ea5+rCYqY4lKwsHN9FWBajjScSuOnXC/A3l5Unzs4iGxGsD2uqzVv3ZeXtkbnX6LAQ/BiKGa/alS6fFzwX5yGvgURGCAeXZ/0/f1yCq34al5YVlFQcN93MCg5vS5bxb64GYkHLsRymC2E6XPjepvrwMGY4ss5CibC/C9Men5va3UTJJwvF3WcUhZJlke10mpPjDcaT33z4jDRNONOFkuOO37L7U73vfCpZb2w4jJ3HarPKY1HVcazdVy1PUCAvaiGM0Vpdhvq+dx5Q5CwanBYMjS0JPLNwl6fS4fzoO9MH3dhYlUBbSglIbatTAF9lUZWL4mQFJVfuHVHoq7tP1Jt5q0wXsqJ2WVDaILrQCfvx+PPJCrlwZE4Xegmk2kBr6luyESsr7vnbSgDZbcJ5x5+WZp1HNiSSTGnpsdQPRXKGscxHnGHJ8jtdmGUHndW2OkXcYRYav35nO56YuwNd25fhgxcNkqZzPpHWJEM515bDVA5UCFqOZQHZduQMGlsSaFdWEqZYvnCP+B5G/rkhH3sXOi1ZxUxRWbLCMiH4ySYzTlZ4w/mUT1Yqc5kMavnd+Cv5BtU1DS2ob/YfMC8S8vTWnaprdo3z9cTcKqV8QpkqkClkkuPOZucrhINySnfCtiRrvKk2B051kthXFs4m2Zq0eztbp8P2yZK9C1YxjS0JnKxrFqYRYa0u/PeqA/jGi+uyFc8uU5L5e3cjt2RlnYUSeQnhwGkeQYKRtiWKS8nKA5FGy3UYskSfsKqjtfjzot2p382tSbRK3ppmQQS/FbtP4sZfzccFP3gLVzw6N0uJ80u2FpSJP5yNiQ/NxjU/fzfluMmzTjLN1tiSsHUmkx4Wb5WjOi0sSuv3zrT/3VmGx/N2vhstkunk3PlkGQXd9cwyXPTD2QCA5WZf5DbQ4S0gi3fId0yQ0ZpIokVykyO+/Ro+9eflynkJ31unX22APCwK3aroxe/iO6TtqcQ2X6iVrIInpYyElF/uQzioHRdZyTYetH/4xzz4OmbOT2/j4uWz8d3/24iN5jY+fkaUUZLP6aaGlgS2H63FoipRB85w9EwjXly533Z03HfewBNrmiJWuC2rplrj9BMRWzh6D3Av2vG9cHE+k4z9Na34TjnSsqxyluxMByn9zssbsPHgaew8Jl/5yt+H8542HTyN+NajruV+6PeL8dm36qXn52495no9j6jv92vJSr/XonPKomSFU9bWkMLpP/rGFnk9cN+y2ZuP4J0tR0IpsxApEp8ss6HmwfM2yvfA+QKK7q5UsBmZ5bAPAL+asx2/jRs+G22FoJ1L1J0SY8Cn/7wC6w/UYPqY3ujduSJ1buWRBLq2DzZmMZ6zWttVVUDzYcnSexcWLhnThQnxdGFkju+O9h1Ul+PbvzOL9/7K2L3BzWfTdeGMX1lc7sF3wHfBtytX77CzmDD1bNn0K+/a+vUX1oZXYAFSFJYsv7263+1PZMmX7z6J38V3+CvcweGaRttmz7ZyLXnMF1CkQ5aWuH+crWkvmc9Grj+Izk1WRYhkmr0ptyMdWb0cPdMIIJjvimqfuWTniYwpE7+l3fDL+co+dm7THgDQ5HObIx30NnoW7ziBk3XNylZ35zPJmDIzf+Zq1V5QBcJuycpvOxNP85sWwYCyrdp7SuiqECVOWb/03Grc/Bu5/66/vMXHs4mTVSgzLqoUh5JlovrYeB+mbPjnsr2BZbC49CdzcPdfVwrPqbynZR5KVhS8tfEwhs2chb0n5GZ3GdYmq8+v2IdhM2ehTrA7PWMMi3ecwLCZs7DvZD2W7TqJz/5lhWfezur6x9K9GDZzVmirKqPq0p353vbkEjz48gZ7Gs/CM9vBkdPiLVT8ctuTS5TS5cOSXMxYG6fvFgSOvf2PS3Dbk4uV83I2H5klS9TOfjRrU+gbWAedluSvyneoLNE9WPUXRMdKJhk++NtFuPPpZYHzCILzNl7fcFjqf+o/b29Llh/qmlpTPnxtBSUli4iuJ6KtRFRFRDMF579KRJuIaB0RzSGiody5O4lou/n/nWEKHxRrPy0pBfKtcK4uVJ0ujJqX1xwAAKw7UB04j9+aK/WOnmnKuC8G4IWV+wAAS3edxKn6YCOXvyzeDQA4UN3gnjBH+PE12+ISmy1svDrz1XurcyJHVLTV/uvfqwzfv0U7TtiOW9aSbUfU485lri4U+2SJLLR/nL9LuRxVAk8XFpAlS0Q2066WFXGNOaXZFh3fM+OxidMFHZCJBuWFjucXmohKADwB4AYA4wHcTkTjHclWA5jMGJsA4EUAj5nX9gDwPQCXAJgC4HtE1D088Q3S02ph5+yfMGSwpiutjii9rU5m5l7ThVEQ9lL9BsfUWFOr/c3MtrRCaBeA+gIHcSL302HfY5B+txB9stpC/2XR3JpEC2dhkj3TIAqKU8F3BquN2ifLidu0pFtbdvPJyjXC9SI+Hd95nNfk6v7CVFadbTNsS1ZbRMUMMgVAFWNsJ2OsGcBzAG7hEzDG5jLGrLmjJQCsqHjXAZjNGDvJGDsFYDaA68MR3VY+APUPjVe6wnn+liXL2iA6Ey9LVr47IhmWXNuPnMFfFtu3LZr6yDvpdIwpj3qiHtny+edrBWQBDt7TpFYXFpSQBd9/WYx58HW852dxz3RB6td5SYtzutBSDiLaLDgj4ntwz3fx3yFR6uPrLw5GavlkKebBpXPWfe4sWWHm5bSQitMF9skqnI+zMiqrCwcC2Mf93g9jZCfj0wBed7l2oPMCIrobwN0A0LdvX8TjcQWx0hw5Yjgjb968Bd1rvANGHj9+IlXGlv2Z/jpbt26x/T508CDi8RMZ6Y4czvR5WblyJU5WyaMQu91bU1Mz4vE4qnYZMjU1G9NkNTXViMfjOHLEXl48HkdVddoKtL1qh+0cABw8aFyzfds227WvvjUXP13RiCP17r2qSN6jx4z63rRxEzqd3CZNK7vXeDyOhgZjCu/1xeKAgkcOG47uW7ZsQady+5sly3fhokW2NLW1xndzxYoVONLZ37TqX95Zi6feXosvTmyXOnbixEk0Nxn1tXjxYvRoF7N1Ki0t6bYkknHlypU4tSOzbbw7712USTr3+vp6416ajXIYY8K8jx9vzDi2dOlS7Onofd8LFmQ6uS5cuDCj3gH39ltfb9T30mXLsb9Twbh7Rt5/AcH7sNraWvBfjn0nGzLe3a1btyLesDOVhp/qO3jgoJFm23bEm3ZLy7H6Eotly1fg+PZ0W7Tex8VLlmCnpM3w91RbW6t0j1Zftn/fPsTj6fAKi5cswY4O6XLmzp2L2lpDBut9/dfWZnQpJ9wwPL0yes/edB/Y0toqlEFFLlmaspj4/RJdt3jJUuxy1NWuXel6njt3rm2AKMp300Fj+uvokSOIz5tnS1t1KmH77Vbnfr+ZgNHqGIz3Naw8mx0W0uUr0760fH41Li4cbuVWN9m/V0FkVC1XtY17EWoIByL6GIDJAKb7uY4x9iSAJwFg8uTJrLKy0le5/zm8Gjh0EOecMw6VE+VbS+ANw3mzV6+eqKy8GABwZPleYMN6W7Jx484B1qeXlfYf0B+VlRMysnvl6Brg4AHbsUmTJmHCoG7SsisrK03n70xn1oqKclRWVmJ7bCewdTNKS8uA5hZ069YNlZWXZZRXWVmJrntPAUsM5WLUyJHA1s2pcwDw1qn1wL69GDNmDLAp7Ux9svNw7D69MVNOB6Jn8cLBVcDhQzhn/HhUXjDAdm/W3xnXOo63Xz4XqK/H0GHDgKrtGWX069cPOLgf48aNQ4+O5cCqFbbrRflefvnlwDtvp9J0XD0PqD2Diy+ejHH9umTe3BtyZ94lhxIZ99SjZw8cbT4DNDXisssuQ/+u7Q0H4jeNb3JpaSnQ2mqXkStj4kUXYeKQ7hnlXnnlNGN7EIE8h+sYVrcMwF1ThwHvzEYsRsJn8ve9K4Cj9hWYl1xyCYb36uh531dMvQKY85bt2OVTpxr17kjv9m52WBkH6upw8cUXY0zfztJ0hUrQ/gsI3ocZnbi9L7CuffPkemD/XowZOwaVl6TcxIzVnm+9AQAYMHCA+X6PRuVlw6TllC14G2hKKygXTJyISUN7pH63W/YO0NCAyVOmYGTvTvaLzec/ffr0lNIQj8dd24JFVYnRlw0aPBiVleNTeV08xWybqbwr0XHNfKD2DCZPnoxz+nfBXea5Rz95TSq/BbWbgN2Gj1ispETYF7jKJUrDte9O7SvE1wv6uClTpmCEo65WtWwDdhj92ZXTpqO0JOYq18lV+4F1a9Gnb19MveI84O23Umk77zkJLDUWN7xwsAs272nAO/c78lC5Zwn05iwwBkyaNBlYOD/jfJA8G5oTwOw3Ur8nTrwIWLwoI7+ndiwFToh9o93KPXqmEZg7JysZM5DUoWob90JluHkAwGDu9yDzmA0imgHgAQA3M8aa/FwbFmH5CqnmErQ8WRgDryjfovKCWnqzqakwfG/8REQOallmjunWKHDzLQlr2uyXc9JKqJ8sVe86rKnPVLsoqNnCttN/ycjoFwLVr/0iZ8T39Ko4t/YcpFwxzmmlJGNK77lttjCCdtauTN0CKyyeE0olHEbK7xaZU6j8z1nrDmFnTbhzuZbCHOa0ZOZzFacLOl3YFrfuUrFkLQcwmoiGw+hgbgNwB5+AiCYC+AOA6xljfMjdNwH8mHMWvRbA/VlL7cC5/Uw+CbMRZGyrI8iab9M/em2zeuYhVNb/++dq9LKsHSHDi6dap3xdPPzqJl+rr/zmLzp2urFVeg7IUin1OF8ATT/VaRfYBtEF33/JCLM/ywxGKlayeFetq34atwUxTjKGmM+WJl/skfkxVgl/YFtdmGU7e3LeDvz4NbtriJ+Pv0gh5ZUKFf82Po/MKPzKomRF1MqziKCO7wXWtyjhqbYzxloB3Aejw9kM4HnG2EYieoiIbjaTPQ6gE4AXiGgNEb1iXnsSwA9hdHTLATxkHguVsKvd7T07eqYRt/xmAY6czvSBCZt0CAe543vQuw+r/351/aHU3199fo3v65VW3EmEdXZyl/0kbUb+04JdvmUJQtgb6nrht7T41qP49J+X+7ZQBLHCWY8pKufpILSF/svzHpy/AzQ55yUt5kP6XXwHzvvem6njfHvedbwuFU5AlIcfnK+wc+cW/mPsZlmxrS7M8tV7/M2tWV0vfG+gdh/p9GlUV+aFjWo5P3x1E5726Fed775TmbcIGsKhwKzkSij5ZDHGXgPwmuPYd7m/Z7hc+zSAp4MKGITGlgRakwydKvy7nPFbpYj4x9K9WLu/Bn9fmhmIFAjZmubTkuWHbOTkXxC+/H+vimYmRSaqV+yf1PURmnncPwiO3yoKpSwvr10KJPf4qT8vR5IZHXgU0T5O1TWjS/sylHBD00Ibbba1/stC9rj4Nqdu5bU/k4T58Xv0DcOS06VdaUbebuX6xXml2yo0dUtWfhGVz8unFD2ft3wprswLi9SgSLGgp0wF61NXDJemceYlG4QGnS4MWifV9c3oWFGKspLcL8gpmCVA2eDsQKY/Ptc2OvPDeQO6ZPVRXrLzRGi+OCo+WZ55SEQJb1ozu3uVXW2bLpQ8kFx3siJ/EDerTWY7yP4jJaqJ2qZWbDgg3popSs40tmDiD2fjJ45p6rY42mxLBKle57eu1dFwWSpdbnyynB9fvlzVrX3yHSpEbMlKoxKmws3yFdVgZc+JOhyobkj1sVH6ZMmfZbAyA8UfSzJc+NBsfOtF8Ur2qCkKJcvC+hhnu52IaO/CP87biWEzZ+FvS/amDwp4eNZmvLL2YHblOxq/qyVLkkdDcwLPLtqdShGVNSfr91MhA7klK8dTdZxz7sm6Zjy/fF8oHZSfzlSU8rPPrpBGtbfSP7XAaL8bD9Yo5akikbWH2JubDgMoDJ/IoiTDwuG/zWXsXSjxyYpq9tvZNJy3oDxdyMeq85C16ugZ6Z6nYfQdIjltliwVJcslfVTd2/TH45j6yDup75xsSk/GE+ZuHUt2Gluf8X2PU6lKSEahYYRJU+WMGSX+1XWHXNNF9T0pKiUrDGTVbDmVH6/1VuD2nfS/p5+IDJ8sH9OFj76xBd97ZSPe2ijuZLKaLgx+aQplxcIlmeqLGsW3/wt/X4VvvrQOu05khuKwUJ0uVMHt2lV7T3lebzn4zvLoaPzQbEbmL3eY4LUlKxysdzRzqi0zrVedO09nWrKMFG6KQZABhew9F61Cs9K6+g9K/hYx4+fzpHueJpLMcyGLsHwPJc9umfIQEE7Ll7csoSoCZvvyuyn4429uRV1TK54z9+5dujMdQ9JrgYVF0MFpkCC2p80NtztWyONXAtH1W0WhZLlNpQQhXyNy2UfZdZsJScuwGlatZK+nrEI4cBfn4oMqm79XVdSieJ7WwoeWVvl8oerqwlwqJUKrlceIXIa1/VF5qdF5WYOBQvPJKjqCVK/TciT5oEcVwsH5Djo/7Hy5TS1J6R51dt+t4AIFtaTw1wnbOePTqluyiNRW5jktkGEQZKFKjCjVLydd7lmmtIexd6Uq1fXGt7BTO3cfbb/KpiqhBiPNGwrKiB15Qj/1HLUyxotS29Qq3DRYJm5qOb0kQViyR/VBtTn3SlcXRlK0owz3kZhccco8s2L3KUwe2l2QWkGOAPUc5kIJEdbWLOWlMVt5OV5wWbSklNYMZT2IRcn9t0XYlqxUec57EFiyLO7401J5PiHtXRj0Xvj6YQxYsfskJg9LB3W1WaYUykjPVggUT0H65kQy9b5li9U9BFEuGNLbndn86RztR5Z3UAU5SNuvbjDcGjqWu6s7Ua3mLBJLVrgPslBI78lI+Myzy7F6b7UgjfjaWOqDF20dBM3eywckNbLOs1VEdn9e/haMZcr+6Btb8J/VmSswle4wpGoQ77cWjPR0odOHsW2/d4UOX72qgyXnM8n4bf7r1qzDfKpBwxWorkL0IpEM1rPwcj63fC9u/f1ivM6FseHr1f90ofszAtLvnOy8H1KDogCjoobmBCwvAbcpVFk/GVT0IAO4lCXLI9pAVKFnikLJsnA6rHuZVhljGdYh8XSK4Jhf4Uz+sni3clq+3KW7xOF5ZF2FZcqVjSTCWl2oUg9unYGsc+WvUQ3hEAV8EXx5Xi87g1i+3cflPlxurNtfY8qgftOMiSwIatdWHfUO5NrssGSlylArQhOQYL5RHr/NPF2dzrP4CGVMF7qsLnQjm7ZlV4Ak/Y5HCfx1O48Z7/LuE2kfXFsfoaRlyZUykYhf/tcavLruoPS8H6xvQJBYf5MefltpujCIT9aJ2iZpHMogbb865ZOlLVmh49V4nlm4G88s3G07Zqwgy04B+cXb26WBOb/7f+77BZ6sa8bDswwne96ULH3+MktWzD7VkKFUheT4rtIuD9XIA7fKLrc9OoespxtbzDSqL0Xwm7WN0sDUt6qRduKZTHtsrmd+nxE48LYkkrjm5++mfKNUyhKmEyS8/Y9LcOyM+yIP6/1y+sxpQ1Y4pBzfFaaRGloS2OWiwKtO16kGAlVFll3QmFAq6WTvHj/oDmq1EH1TkrY+QnwcAEbcP8tc8Q1h+ozVhYL6nrftGO77x2qcrGv29TQ+/tRS/MMR29FqX0F9kZSmC6U+WfIyJz38Ni758RzhuSCiNrUY+9B6xcjSSpYLMgdxr05h0yFxbKFsbTyJJAscmHPDgfQSe0t6V8d3yXGvbQuyc3z3d/Xlj7yTcYx5dOopnyeWqSC+95fGZqbKKlYWN+tVhuy9lOrEghNWKAS/HK9twnYXi5PYoV29I6lvFjsfp/Iy/02HGiHHGU02+LHgPvL6Flz107jUeuLsC515WM/Q3SdLeso3ovKzUaDseYmP8ysqg69uS/8tUoLdrN1JBnz/v/ZBtj29Q/F1UQRbEklf7/L87cfx7f+sF54LMl0I8C4pXF6OrL75khGbytkHB3d8D67oy8JJWOjpQhfS1hrxcSfWAy+RfH2DRqMNg4TkhfWL2z0QBd/WwEm2PlNSJYU77hR1/6kGM030H3OvInxvVxOiAlIac399RSX5qTLP94DZ80xHkFYvQ+ONszpVQxzYr3FPZylXbu0j0FZLkiaUOV2olp+KCDIFirdkJRgLdD98/2wN/mQrDkUKq5tFMTMYqTvZvmbZOL4D6f7BrmSK8xrSo4Ptd3DH9yDXmAMIj4u1JSsCSiT7jOTT2Vo0qnD71MnahdOSpRJLSYVfvr3d7rwdoKqOnG70dLrkA7Eul/qjqXH1z961WQj9wLeFhVUn0KK8ekaSX4hNq9TDXKnqSyhr76WK+/DkejuQswXZQOgvi/ek0zjOyX2NHL8zPvbGv26WrIU7jOCT6/ZXS9NklCvJLtNqo9ZonG11qsBKLquDVm7DRKnFz0MMPg9RxHT+ehVFgk+RGYzUfQAX5D372vNrU39b7Svo/qtW98NfL8vK2VcFD6Hh/8LUVLhHoVGFcCgKJUu6utDjOq+PlBdR2LtE7cDN6iR1fHfc2xsbDtvz9C8aAOBZh+O+LA6XG9/ktjeQtXv+ZfrZ7G3CNH7eiWUSRc0LZxkNzQnbb+nqSPO/zOO5xHsk7YbM0pvO3cgs02FXa1lR8hsz4rYIuXLvtJI4LUnM9m+NuSKLZ85mI7Dx0p3+3yVnHxaWT5ZopwNZXvxHNOhHforAV0g+UPTOz2t6UXodWCCF46VV+1N/py3P2b2vvJzS1YQZ1wQrM8iUnsoAAojOklUccbJMMnyyPCqtRKBkiUYIMkUmqC+NDMbEDSGYJct+Fe+PwJi4c1LBWadvSbatcIOPtiz3ybLKU5fFjaCvj9MC5oyULV8dKZY9zBfZK6czjZkKsFcART/5p27F/FcWoVyTHX6ajDycjftvXsnafbwOlT+NS/PIdhsoIPNj6fVeHKppwJnGVqWS3d5JiwQLb77CNl3o4gQulMmWT4a9MTvBvEhZosSnj55uRJ8u7aSXp/toXnnNlLlrReYXLPi2OkEsWcY1XpaqqMaGxWHJSlWOY5WTx3VeI3Uv5mw5mtX1Ivx+hGWpnfqj84X/ucQ6FCanslBCVarB8s2Kklt/v9j2249JX0iWLzJ/uVfZH/jtoszr/XywPdI6P7ppR2D1MjTh4qz7040tOHq60dOvK726EFgnmVoP87GqRDfnuewn7+DaX8xT9MkSH7dNa4XgOJgK+MzVjFNp8npH+ViIfvzUgk4XipDVxZQfz3F1s0hbsrn7F2RVQvCer1Ykm3v2nC6MyJm0OJQs89+m1oRrOgtL/xD5ZIkmeURTYlE9kFaRJSvAtjpOS1ZY8vpxmJ/4w9meaeSdq7e8N/16gbIsYU3tZnSEklEgYzL/J1n63GgmomX+UmuDokxWldQ1Ge+fni4MFz8DL2fS9/w0jik/nuMZBsI6n0gynJTsz+rcS1UFWUrx3oXeqFgyvFYsW3+HpqTYLFn2vz3j6XHn/U6hZmuLU3F83yrYZcTCsoAlGbDnRB1aEklhXjESTRf6FNYkyDOzlCuRbGca09Pi2vFdgS89t8b226vOVH2yXlt/OOOYSBkKgy89t1pw1M0nS0ymD0RwmbJhUdVx1/Oe04WS6+Jbw7ciqqAaqZrB3+olrx3ixWX4591tx0LL3zqfZAwHqhtSCpxWscIhiGWQgaG5NZl6P47XNpvHHeky2rH1L8MJmQXaYblUk0dMYD8+hWSyoKn8gCiMSOQinybb6kKJJev5FfuE6TP2k3S5WeZTVhEqju9O9wibDKYAx880YfrjcXzvlY3C/rCERJbTYMJnE4jXacnad7Ie53//rXS6iDquolCyghpDREvgVStaFsk2W0Tl7zjmEn1bIoZH3DVfNLUm8Id3dwidYb0Q7UOmslO9V6d71zPLfcuy5fBpvLhyv3dCH3htC+TkyXk7hcetKM5+iNpgpDrVwZjRYaWPRyrWWYefVU9JBvzi7W2465nlWLrzROp4pk+W+KOXZCylmDmRffRfWXsQi3a4D6a8VkEqh3AQHGtNqPlJ2i1ZauW5YSnBq/acwjdeWAvmsI4lJX5f/12bftfdLFnu04XBHN95RKsjnTRLAh0DaeXMCg69YPtxYZ8RpiUr2OpCsSVrDxepH4hudqqoHN8Bf3Ptcsd37zy8ApuFiWskZ9nqwhBjfT21YBcee2Mr2peXhJanhayqo2jv33hhHdYfqMGVo3uhr4tDpx/cIrv7uYXSAFpxWK67smegbFxgDLWck32+95ssFoJse8IYw17z43FMMu0HyC1biSRQ0yBWsmRd3v/7p2F93/TQdejgsQmvReBtdQTpGluT6MS9P5nTbsYuHs7pwrBYvvsUlu8+hQdvGp/hMykqpluH8nQa7njGBtEuMsrcEcRpxSlT04Uu7avZxZjgvCyRZEInesOSpSaTF0GusopyGkZ2HrcbL/R0oSvpyvn9vB3cUfdKEypZio8xqulCv8jaRVjBRgGktldpSQQL4OeEQJwVxH3UGVa7ZwD2nzI+Pvw8fLa4WeL8yF6mOHVtyzNyS5bHefPfJLNv+6MtWeHi9s4533OGdL/mZm2XD26YyzJ89wd7ukE9nEvmdKWikiU41tjiHlbFytpt+5ew4G8jkRTfV7f2ZRnpCZkGArcq8eNT5pXOrS5aZEsPka53azAgs67FiKQhQ/zitdG52zX8fdY3t2ZscZdXJYuIrieirURURUQzBeenEdEqImololsd5xJEtMb8/5WwBOfh68a2LY1HnYn0kHxPF4ZFliHAbFgvf5h5pvLO4Rc5tWl2iEbIsOQv8YjeHiXyOHNe04XidLl8pioUev/lhZ/2ypJpX1O3gaB0Si0p/3h76SVuH2Qnmat0VT+YmcecShZz+EKlp0KRcSwb4lvt/o0rdp/0DGcAAF15JcvFdcLVJ8uHqVwmh4pPltt04az1h2z5J5m4LJElK/h0of23ymO0kvDvg+i+orKbePbsRFQC4AkANwAYD+B2IhrvSLYXwF0A/iHIooExdqH5/81ZyusLrzoTrZJRredCt2RlG56Cx7rVKLYbUlluHRZWp+LmzOkX1wjbPm4hSGDcqFugdwcmtjYWxpth0Bb7L6ey4ccni4GlLFlBXBrcXru9Dh8WJ6KNyt0sZm6/ZYgtWU6fLPsWOmmLK6fQRODt8elnV2T6ZAkE7tI+PaVqt3z5s2RlU2eA2upCFcXZ+hYmGRPWq+izkWRMOJPkRZC+JjVd6LV3YR4tWVMAVDHGdjLGmgE8B+AWPgFjbDdjbB2A3DkqSRCtBJQh1BmYmnacS58sN2SiOiO+Z4PV+KqO1uJUAOd3lbxVjwflqfk70WBueJyLR2f4TKjfw5CeHbwTCcoIA6lPluL1GQpxIWlZbaz/Arh9BFNKrLxCFzpW7zKW9u9rCTBd6Lqc/8iZ1LXz9rdkBDR2s3rAscIsNU1mdlM3/2ZhhiOyWO5M+TKnC5lwM2j79i+SwVGWL5VztaBs+iyjPPLn+K4a8sJIK7NkWXK6WLL8KlmKlizGgs2MBFHOrTT87JMoTmQ+42QNBLCP+73fPKZKOyJaQURLiOj9foRTRVY1gXbsVmy6zj3s3HB2Am5cf24/5bRA9p2CClYj/euSPR4p1bAF75N94EO+rYM1jagzt8QJc48q6Yvps4ggjvihOb7LjnvUk3XaGUeuwBzfC77/cmK1T6t+3Tr/7UcznXctq+iDL2+QXiePDO/97Jpak3h6QzNuf3KJ7biX1cO2/YpZDv+dVZkdUJkuND72mdcwQflhk2HJEqSRBe8UOezLy7FPia7f7xI0VJqNt/tES6t3PVmrO2XThSJlylqMIDsvQ6SseV5jyck1Cn7/Tz95BSEXqwuHMsYOENEIAO8Q0XrG2A4+ARHdDeBuAOjbty/i8bhy5owxHD8hXkWzcOEidBGE9D9+/Dji8Th27sxcRVNdXYNNmzd7lnv4iHecJus+nlovX+XD09zchOPH1eMYxeNxrD8idjbduVMcKsAv8Xgc+w+oya/KqVOn0NhktOjDh8WWx1PV1QCAbdu2hlo2ACxfsRLVO8JZKSlrK/MXqAdKBYDNCm3O4o//mYPR3UtwoiEcw8vixYuFx5csXYa9nezjMP7d3HDYaHsNDY22NGvXrUfssPr9FDie/RcQvA+rra2FM8BB/N15qCgh7N1vvHe79+xFPK5moV+4cBEOH/LeaaFqxw7Ek/syjm/duh3HTroPCnfs3AUAOFxTb7vPpStW4pTjvbL62H1792Eudw9btmxFvF6tj+LLOHI0s99dumI1aneny128eAk6lqXr9N15Rn3uqknf16Jlq4RlNTY1+fr+ODnE9Wdr1q5F3d7MfqaqKl33O3cZ9XPk8BFsaE1bJefOnYvNB+QLCZYuW44u5el7fN9vFuDp6zoIXTqaJBbN5uZmU4Zd0nJ279uHeNz9W3f4qPHNampuxpq16zITJBNoaGq01WtdfUNKq4lBbEIWPYdNJ+xt891573ouGtqzx7jPhkb3Z7t8xQoc3RYDwXAtqa2tzaotWKgoWQcADOZ+DzKPKcEYO2D+u5OI4gAmAtjhSPMkgCcBYPLkyayyslI1ewybOUt67rLLL0fvzhXpA28YaXv16oXKysnYSjuAbVts13Tp2hXra2MA3BWL7j16Ah6KlnUfj66dD+C0a1oAKC8vR+/ePYAjah1qZWUlXn5uNYDMGEsjRowAtmevoFRWVuLVY2uBA+HFl+revTtOtdYBjY3o3acvcChT/i5dugKnTmH0mDHARvmIPAgXXDgRU4b3SLWHbBgzdhywPrNjqRg0HpOGdgfe8Y56DwBjx40DNgg6KAEDR41H5fn9jemad9/xJa+IdgPHAVidcXzKlIsxqk9nWz3x72bdukPAmlWoqKgAGtOK1i9XNWHGOV3xpzsvzlq2EIi8/zLPB+rDjE7cHqJl6hVXolNFKWafWg/s3YtBgwejsvKcdAKXdnvZ5ZdjfesOYI/8wwkAw4ePRGXlyIy8RowahaM7TwBH5XuSDh4yFNhRhVgsZrQHM49zz78AU0f1sqXdhCpg21YMHjIYV04bA7z1BgCgXa9BeOVoE5JMHp7Ggi+jd+/eGf3j2PHnoXJ831SaKZdcgm7ty4E5RqDJK664Eh0rStF17ylgsbHV1Au7xJM4FRUVqO85FlNH9kLXDmkHddW+om/ffqm+8rzzz8fkYT2At9+ypRk2YgQqK0cBANa2bgeqtqFvv74YN7o3sG4NAGD69EocXbkP2LBeWM6kyZPRq1MFMPft1LHLr5iGdmWZSl1dUysw+03BvZYDzU2p5/npK4bjqQX2dtOnb39UVk5wrYOu3XsAR4+hpKQU5513PrBqhe18WVkpTtYkEBtwLqaN6W2UvfQdlCdb0JxoRVlpCVoFsz2id6h0+3FgeTr24pVXiu+ZZ3H9ZmDXTlCsNJ2n4F4unHgRfvbWNizbfRLbHr4B8XhcKINfVKYLlwMYTUTDiagcwG0AlFbZEFF3Iqow/+4FYCqATUGFDQtL2RevLmRYzAXxk+HH8d3XRsY+TJb7Ttbj5TX+g1j6JYx9vnjcTOSpNKGWaCfMuXfZs/3sX1YIj8szUk8atr/aff/IVLAA9bZ4sKYx49jbm/MTkV9Am+u/EikfF/tvFRhjSitVZdOFbqsLLVokTo0i/x3Zyu8/zNuJf69S1nWF+Vk0OrZTm/543O6SYP7LV6PM/+tQTSO+8PdV+H/CnTcU5HP4ZImiz/P9qS1CvMNnzMvx3dn3iBYeGGWIcTq+ixzRXf3sTCxfJyaZLrR2r/vE08vSMnE+WX4c4J3tVqWPsmRqSSax90Q9dkviTiYZsKDquNI9+8HTksUYayWi+wC8CaAEwNOMsY1E9BCAFYyxV4joYgD/AdAdwPuI6AeMsXMBnAPgD0SUhKHQPcIYy5mSFcQ3RPUKPx1fVKsW3Hy9Hn8zvGm2KJfkh+lPp0qYSta3XhKPNAF/9+CnjlNbDkXsj+eVe4H5Xglpi/2X9RG2nq/dh8fDTw5AmWBP1ox0kmxU2mGC+6jyiD5OvLwf+p14WtoPojbnXF0IZIZreHXdQelgQoTTqV9ZPoffl0he2V6HvJ8Yc6RzsvNYnS2oKSD3ifN0fLem7QRWBzXHd8sniwllFulQ/OpCP/7vfsJcpNKw9LXTHp8rTefcOSAslHyyGGOvAXjNcey73N/LYZjhndctAnB+ljIGJ8A3YPXeaqV0KnGyth4+gxN1TZHF38gVYcvPGO+MKhlRC5xVwyIqp1cnfkrxlZYxvLz6AEb16eRXJF94fXBzVI1Z09b6r4RDubJZPjzqPNtnYpTtnollxXemEn3kw24jovwaBINNp3P5LwSrydwIujabrwNZQOKkQ5my/nDG2HJTIL74z9V43wUDbMd4JffNjYcxYVBX9O/aXrqXY6qspGXJyjynFMIhkba8qji+3/ir+ThU04ieHU0l0ZfjexBLlvGvl4VKtp1UthTdtjp+8LObvBOVEd91/zsPADCid0d1mSII+JktYSslSqsLQy3RTtjTnzL8VJuftDuP1eGXc7b7aldBaCtKVLHhDJ7pJ4jm8domxVV64jQqzzxlCVawZFlJs+lreUTifeflDThwym55clr/OrUrc17mStB+2D7lJ5Y3KTBlMdhX+RlTb+5lLXG4tVj1zxjD5/66EgO6tsOi+6/2tPZY5bjFVnSzoLYkeEuWfLrQYuNBwz85FsCSlbG6UOUaxa9JU6t6FAA/FLWS5VW12Sg0/iwPwctxI1cKWdjTUko+WRF+4QslkCyPn6k3a+S+85i303A2eFpNIi397MVyebI+xglm/3C7cdOvF+DuaSM8y5A9W5WpdGt6yNlmhdOFIbeS2ZvEDvm/f9e+FoE59Jgu7fx96oIqhXzfkkiKlQ6bjmX9y5h9upD5Hww2c6EUAMNX8hsvrMXh05k+kzyJlCVL7svnJoo9TlbmeZnLlVfA7GSSZcR79BPmIp3GMwkAuU9bthS3kiWp3FBGVT7avx9/G3+6RW60rLCDtBkTEuYHRNKuk9wIL2yiCjrnxM8Hxp/VqzDkz5UcZxtu04UqfYmKK4PbTgueju8Sn6xcTBeq8tu5Vam/k4yhs18lKxRLlvd0Id/POacL/fZTlpLLX/fCSvmqcH7PQUA8Xejl1gE4pgsFMsvqMuWTJUmQZAwxxzfOKYZKFan2U00+4ln6oUg2iBZjfSQOVDfYGh6D/wbsxI/iFNVHPVeWrDc3ypdzZ0t1vXge3NqUOgoW7zjunSgMIlKccmWIYyzTGbS2qRUn66LxXdAYJJMMJ7hpP78LTxLJJLq2L8OXZ4yWpskmGKmsP3th5f6MtpHenDe3wfSf5YJNJhnQqcKvkhWsc7X7ZInr2Wmxsv61f6P8P3fLEuO7vViO7wKTk5WTW5/TknSfLnQqShZeCzT4empNSO6N+7n3RL2w/arWRmNElqyiVrIA4yM+9ZF38NB/0ztuv7nxCEZ++zWXq7zx05D3nwq2UqUQ2HTQO75XEKxR1Io9p4Tnj5xuMtOFz7OL9+TMmqWKH2mcsWyiYsexWox64HXbsamPvIOLfqgW+0sTjIPVDZj08Nt4dZ2xAS+v56r0O7VNCZTGyDV+kHS6kHnbX2WO7+v21+CiH862TRsyxzX5gIGhU4VPn6yAZSWS6Yj7MqugbSqTO+70vfPqo5wyNrcmUXX0DN73a3+BkN18slJKoEur4EM4iGTms+WVIGv7J5k+ayXddPA0Rj3wOuZsPiJdXThv2zFMe3xuatNqHtVvdZNglWoYFLWSddlP3sHn/roSAALFZHEjqj7DzwAqF4asD/1uUeh5rttfrTyVVt8sj3qcDc8sjF5R8dNECnHmTaRg1zQYe1eOffB1fOm5NTmW6OzgYI19UGZ9mI6eacxw8BZxprEFJTFCu1J59y5rbrIVYjyWVcGSy9ln8fvCiaY8cw1j/vfJCzpL0Mr5EUmnCwWrRZnzeNJ7wZFTxpZEEo+/uTVjqyUv0qsLJZv5wr1/4q3d1vP+2YcvSMvJpeX9nko9HN8thW31PmMg/vbmI3C2XEuu1zcYAWqtwfnXnl+bClSu7pOlHd8DsXTXSQDAmabMj3UhftgKDdHy6GxpbEmisUVtOvDHr23xThSAoxFOR1oUop+VH9wkispJVJPpU2V9bKf8aI7S9WcaW1EaI1S4RcKWtLdk0tuC4rRkEext5RCnJFrF5CpsigjG/FvSsvHJsixCStOF3CbgzuO+Hd9bkygTOVZJcMbJEilZ/HSmDH4fX0vfKuWmAvlLeWtRWcqSJffJcsrirJID1Q0oLSFsOWwMCAd0NfaAfWlV2hdNtRqj6tOK2pLlxbzt6vsEOjksiHKtaTsUWqiMwlOxspsq9ms50KRxflyO1zZh5Z6TytefaWpBSQmhXZn/1WJekcaBtIWBt8Lw8B9r5rgmHwRxIg+8ujCRTE8XSurSdoy3ZDlWkVoKywcvUtvPvDmRRLkPJcvCKkekZNU1t+JAdYOrdZO3ZFmKIi8Hfyk/aC/18Ml6fb1hneKfhVOMm369APf/ez1qGw0jilhKxenCiCxZZ7WSNX97cAdor2WxQWDMn/UjqHOmRhzdOGyiWl2YKxZUBX8/clG/xYpzkd7CqhO+oqUblqwY2pW6+GRJ2mYiqTBdaJvuylQkSrmPtZWXyopHGftOirfAUeWDv12UM0uWc7pQHMKBt1il/3CuIrUUFlnQYWtqzMK3Jcshj0jJWrLzJKY+8o5rT8Y/24TZeEt5JYtLyytZZWbICFlVf/OldVix+6TtWYjqc9uRMxkrci0++ofFUF1z8bcle9US+uSsVrIKET8O9fozFpxc1J0fxSnKrYvygVay1BBNE2c7tXamsRUxAipcLFlLdp4Uli1TDGzyJe0r6JzYPtYhTBdebwZ1Dsrh042+VzcGHcBuPHg65beYlAyaEw4lFbBWvIM7bsaJIu94UhbNrUlP65AIS55SF/NzwkVJtsUGM/8stVkz0+cbmjMtWW63Z9UlYE0XZsrRrX15SkF16tJLd53M+/ZfWskqMPw0B/0dC45WAqJFV68aove9JUvfkFrTklXqElxy5Z5TeG75vozjsoCSNvm4D+6fFuzMOF8isGRl4/he15z9NI5fS1oYzfd0Q4skTlbm304FgpnKbkmMlPuqpoQ/S5a1sbvb3oUWFzz0lvScLTaYpbCVZCragH0z77S1yy3SvPeziMXSSrwwhEOex69F7/je1sh3gzhbEMWECRs/j7LYnrtWYtUQ6R4qm/K60ZxIoiRGrpYJANh9PHPHAMMny2O6kJNPtDCFV+6srPK9ywJffgmlLS4yZGEH/PD4m1ux9fCZjOPMpkyl/3VOIyYYAxEpD1gamluVNgd34jZdqEJLMtMny9YGuLSNzfx0ocJG5o7fwm17YpTeJUFonfUsJrPcEDtkbckqONQf7ieeXhahHMVNLlSAn765VTltvk3aYaMd34PTGMKK3tISsvnFiBB9kAyfLPe8RU7k/MedV7CtlKrT4eUuYSeygZe5vYJp4WB1A67/33k4dqYpoz6uHN1LudxX1h50lcV67/edqs+IBJ80VyqqDlh+/NoW/N+azPK8SLo4vqtgc2w3lSiZsneCC1arMl1oVzzFCwliRKl0ovFJkL41zEGvVrIKDD8Pd8+J7BxCz2ZyYWn5z2r12GzFZsnSizLUECk0YSwlL4mRp3+OqGxrmsoNkVXqK9eMSf3Nl+vX8b0iwOo4FVo5a0uFgrXnyOkmbDl8Bi+vPpBh1bjr8mFZySLYHxobD57GnM1HbWkSSZjThep5BwlNk4r4HsI7e6zWKL9Usrrwi/9cnfrbaxBgXWt3fM9MwytZorYbpG8N00dWK1kFRpF9awuWQrO0FNtzL7DqLVhEzz0US1aMUqu3ZIgtWd4+WSKFif9A20I4WI7vinM2QRy3VeDL92Msa01m2kGCWnwsZFNRW7ipRUvZjVH0AxaWmuILQckylbxSQRgPJ1aYB/dS7VOrMuXHsmCJfbL8965hzm5rJauAYCi+VWaFSi58svxQbM9dG7IUETz2ZxbuzjrbkhhJl/5biJzRZZv88vA+ONb0Hv862T6w1jSOYvvOVoGRYfPJ8vHVE622zFZGp4O7CMbMwKY+LVlBsBTQMJS5o2eaQCSOleYkFfHdpVjD8V3sRG9hDAzEqwtlx7zQlqwiRSUQYFvi4mHdI80/m5FXoSkBxfTcgcJTYguVqOLml8ZiaF9egj9+YrI0Db+JckoepRAO6fPW9J7UkiW4xo2opvF565tqSARAvP+gn+uFefLThZI0VpyskhhFbsmyHk0YxZyobUKpY0WkrDmlfLLcVhc6fovaZksi6T5d6CGzCK1kFSlJgWm6LRO131M22QeN6KxRQ68uVCOqRXeWkuvXxUklhAOvsHitGvQ7XRidJSutzvopQnR/2chYVkIOS5Y4XTpOlrrje1AsecIopb45gZjiiki3ECMWzKGQippRgtsKSmzJ0tOFGpPTja2Yty34Vj+FRlT+FRbZKEqi5ev55HEfKxHbAtqQpUZzFpHQ3Sg33z2/H2jDcuPl+G4oLDec1y/1N1+ONd24fPdJ/HXJHvMaeZ5Xj+uT+jsqJYtX8vx0S4lkMtTpwpIY4diZJtz/73VobElIV75ZWwHlYrowpWSFoMzVNrVmyCx78um9C+X5MbCU9vfiyv146NWNGWlqGlpwxtpWR9R29XShpljxExgv1/xrRWYgRk14VLhs6aJJ0xTNdmmpzaH9KgQq04XN5urHitJYKjApX0yCMby67iA+/Pv0VkBVR2ul+fEfWZm8Xv5lXjjjZKmSSGZam/ip8MlD/blElMViWLbrJP65bB/mbD4qtZgwGPWYC0tWyicrhLyaW5MoIbUpTpWYXs66b2zJnGDfzrUtkT9hoBAOIc7jK30Fieh6ItpKRFVENFNwfhoRrSKiViK61XHuTiLabv5/Z1iCawqfQlayNNHSp0tFvkVIUcj9V1NElixr30K//kNJQZwsp95jKVZ8TCte8WhNMjzwnw22a05y8ZEy4fy5JPJ2qsgubjZvyfKjdyZZ5iea9wX1a63n03dtXyadLnxz42G8tfGI6ZPlqwjfhOmTBRhtQcUny1Ko3Ypl8Lc6UPQ6+dxRybgml5YsIioB8ASAGwCMB3A7EY13JNsL4C4A/3Bc2wPA9wBcAmAKgO8RUbTe0JqCIciO8L7QU1IFS/uywrBkFXr/FZUlq525b6HfBQgJgSXL6TtjRaTnlSxCOkhnMsl8fbD5tDJ5s1WymrnYY366JX7lmgWvQKj4FfGUcOnbl5dANpf12Btbuam3iH2yUqsLw8mvNGZ35JCpK1Z5blYvxpgwwKhb+oxjPixZ1nuT7f6hPCotZAqAKsbYTsZYM4DnANzCJ2CM7WaMrUPmYpnrAMxmjJ1kjJ0CMBvA9SHInSKqOXxNms4BO7ggWzz4QT/5wqWAVksWdP/V1BqRJSvgdCETrHB26hGWVYi3VBMRnv3kFHQoL0Ei6e/d5NMGtWRdMKir63k+wKsfpUW4upCrU7/1W+YI1Or1nhBFvxI67fieXUFWVfCWrM7t5M9NpbyWBPOl8GS7rc4DN46X5hMUla/nQAC8A8t+GCM7FUTXDnQmIqK7AdwNAH379kU8HlfMPruNRzVqEGsNdN3xY0e9E2UBC2IH1uSEU9WnfL3HERJ5/wUE78Nq6hoRxXDhyMH9iMePouqUP1PZ0eMnUFvneK8c75mlZB06kN7RoGr7Nsxr3AViCezZtw+tLep9xokTx1N/19eLF6ScOXVceNyiXavc5wsAamrTu2OwRCtU63zv/v1YsMDej61auSL1d/Wpk0r5WBwyN2UGgJWrVuPgIfd6amyox5YtmftDhkl1zWkAwPr167LKp6IEaGgFWpubMX/xEgBAx1gCLS1JiOp7z15jUURjY2PGOYuvv7BWaRski6qqHYgn7f62x4+7tx2eHdu3AQAWLlyEstb6UPqwgtggmjH2JIAnAWDy5MmssrJS/eI3ZxVfuOwCo11FBU43+9+uYdCA/sCB6BzMS0pKgGRE8y2arOjatRsqKy/Ltxg5I2gftvDvswHI/ZU6lpegrtl/Gx87cjgqK0ej275qYOlC5eu6d++Bk621QEND6lh5WSkaE5nKwIhhQ4DdOwAA48aOReWUIWg3fzb6D+iH8hOHgRY3P6w0fXr3Bo4cBgB07dIJ+86czkgzetggLDy4W5rHoAH9sfTwful5KimDVc8VZaUA1Oq0X/8BuPzyscCc2aljV1x2CbAgDgDo27sXcOyIUl5OJky4ADuTB4D9crm7dOqE8eeMBNatCVSGCp06dwZqajBhwgRg5fLA+XTt0A4NpxvRoX07XDt9Kh5Y8Dbuufoc/GfJVojqe/jQocCOKrRv3w6xpgapxanBxxh/2IgRuHLaSOCN11LHevToCRxVG/CPGzsW2Lgel152GbauXgpfuogElenCAwAGc78HmcdUyOZaJaKclvjZ9PbRZd6GCOoToKdyC5fbLh6M+64aFVn+BTRdWND9l5dPVoeAU/XWdKHfV1A0hSV6j8lx3OoiSmKERJL5CgdQ25T+isqmkDpWuPv4eW0sbZ8uVBYNiUSmRw+/cjabMDWtSTV/o6inC8OK+N7BfEaxGNC7cwW2Pnw97po6XG4DkQSwzQbGjOCkPIt3nlC+3vrWqcZ1U8pTIc1yAKOJaDgRlQO4DcArivm/CeBaIupuOoxeax4LhSB7EvkhjL2cioGg1VCIqwt1jEyDJGORRmUvoLC6Bdt/Ad5KVlCHb8uBN4wQDiUC5+4Y2Qdf1gc6rWSpl8l/FGXidqooc82jwlPJSle0H72oVRA3jFfo/Dq+84gWGTgJYx9LTzmSmWE4gtCx3GirVp2klFHJLVrFEQUfyDtJJhlON7bYjtX7sARbYoSpWni2EMZYK4D7YHQumwE8zxjbSEQPEdHNhmB0MRHtB/BhAH8goo3mtScB/BBGR7ccwEPmsVAooNFyURP0Yxy543uA7LWOZeAVqTtbCuXdLOT+C8gM4fCDm8+1/e5QHmyVpvWB86sEiFbTicZKTkuW9ZGMEflaDcZfa2Qsc3zPzpLVwm+r46M/e2nVfnzrJbuvUkkWIRx4klykchl+FAQnd1wyRE2OkBzfrbbqrF7P1YUIbwVlgjGPcCHuWHLk2vEdjLHXALzmOPZd7u/lMEzpomufBvB0FjLK5YoiUw79QTYI+gJEbckKIhURFY4GkEeSSRZpPRRSDRdq/wUArQ6FxLJAWVjWAb9UmPn4VQKSLHM1lkhRc24CbP1VWkJIJJO+rCI2HUuSpqOHRa93J/W4bH7HjG9vtvvzyDbD9otIoXVS19QaeBrPy7pnEUacrBjJV7RKlSzzaTvbUjYkGXCyNriSZcmht9UxiXq6UGMQ9AWI2icrSOejZ4ANGKJVhPS7qUamkmW32Hj5Ismw8inzaclKCqbHRFk4pwutNCVESLDgVhHZK+01bTqkZwe8+sUr8P+uHu1dRhDBOOybYavX748+cJ7tt0p0/SZnA/GBqhU0FScrcElGnZQJNgsHIOxourYvw+Rh6ZBzfL+cjbLHGMOJLCxZVtl6Wx2TA9UN3ok06N+1XVbXB1VMClGf0RtDG0Qd+WTV3mpUHT0TbSFFgDMGkHM7omwd3/1bsjI3iBZasmCfRozZfLL8WbL4CBGyy7yUrBgRzhvYNcMSKCLbmSl7MFL1zJx9T6vCdGE20/ofvGgQbjivn2e6lEKRjSUrRigvNTJwtjmRf+acr01PPVPn1HO7LLbkSjKGE7X+V8JbWM/2X8v3haZotWkl69pfzIu2AP09BpCFRYoIl47oEa4wJo99aEKwC/UzBaAWCDFbdh2v9050lpNI2ndGcE7xWME5y0tiuGS42rt01djeOHdAFwD+lawEyxzFi17/xoSX47t6uSqLJNp7WGWsKlRxbcj2o6ey16KwXEfShOLqwqC0KyvJ8PETkQjBJ6uEKKWMq2zlFCP7dkH8c1NRlGUkGVDjJ+aDQC4AeHLeTiw+GDwfW56h5JInsjGlnk2EaR73dx3w3Zu8X/IgfOTiwcF8sjzOj+jdMYg4bY8czOYV4OLSgqOV2RUr56tmfQATjGHamN7SfG6a0B+A4QD+zCenoJfpo+R3utDYxsTp+C5+a0Q+WZaS5YckA5Y9cDWWPzAjtffcgzeeg39/4fJUGi//zpSS59JXXT6yJ/7f1aNDtWSJ8pp5wziJjPbfKtOFQPD+uzRGSouWLEtiNvVSEuOmCxV8smLE+2TZ5XROmaswfUxvVJTG8Lv4Dvzqne2+r7fgm1kWupoN3Q260FaNHsO72h9rtvFPgl5PiHhz0yCrCz2uGd7Tv5LltaVHIcLM/6Ik6j3XioFE0rEHoKPOLH+ZRJK51uenrhguPO7bkiXYRkZWrmh1oVcIh2vH9804xhhDn87t0LtzRep+O1aUoku7dNgG1UU0bk1uyvAe+Oo1Y0JQsvi/MzO7Z/pIsWyODiuRVPP9CSpvjEjJqpReXaiOc19aIqSmC51lim6Rb+fkuCaIksWHgcgmxhUvV1j+u1rJKkKcDzVbJ+RsLBKF9p31MokHkTfKeFNRkYsdibKJIXS2kGDMZslyNiXeJ8d57vUvXcmdM60Cjvz9rvBNCqYLZcQEH6SSmOn4LnklviAIgMsXZ91veUnMNm3kFaLBGi+4KaJWXYRp2ff16jstWQo+WdmiZMmylCwfnZ8zPE9JLD1dqFJmjLg2QnaF3W1V5NeuGSM8TghHKQr8bN3yDCeb4qTtfToNVOOUqOcXrCYYWLSO5gFuLAqlT2W0WGgwRO+TpXUsbxIMqOBG7s73pW+XdGgC53vIrx6TtUC/IQaSCmEFLOwR39NWjKOnG1HbKJ5rEYnDl5cwtf/y0pjNouGpZLnk75Q324+nzZfIl09WpuO72nRhMIETSabkM5YIMF3oVJZLuNWFzjYnni60p+F/ulmyvnj1aOEKUibIMwhhrXK05RlONhoRd142NC/lZihZWX5M1+2vCXQdcxnRhoGf3dktvMXxL3C+LFm9O6vHBnKSZNG7ZbVF5TPXJJKQWrIe+eD5uO+q9AfF2c5EvkHOKve7aCUhWF0oo0RQfixG2HL4DE7VtwivEX0I+eIsy055acxWL87pqcw8WKp8GdbHX7VG2ks+9vYpJfX6daZMCPzfxOUpF5HihvP6oXfnCtd3sFNFKYb27JCa6VAt5o+fmIx7HUpWLEYos6YLnUqW4BZ5x3dyXOPl+C56xE0tycDfmq0PX2+TK/V3sOwy0EpWhFwwuFteys20ZOUnZhFjLFJrYJAltl6dYpAXNR/KxIcnDULngMv7gdzEsdJ7V3qTYHa/Kf4DfsuFA22r6tyqU9au/fpT7jxWi2bFBUW8QmOV72U5ixHhM1cMxxRupSSvZyRs04XqlqxUFAKX+7Xao5XEK46USowyP008FgMev3UCbp9ibIeZTLJIpu17dizH7z42CSUxd59YY4qN0qsLFe9FdM8xSi+yUFE8iewWOr4PdYYxccKntRThhpZE4P6GL49v02H1X1rJKjB6daoIvF+ZDD/LhGeck+mYGhSGaC1ZgdwZopguzPJltDpdP2RbryzagO8A2qavWq5JJO2+a7bo52S34LhNsYSFn3fKHifLOuahZMWAB28aj9/9z0WpY7zCb33wy0tjNn8yL0uWUw4RaUtWemrTDa8o80Z5fixZhA9PHoxvXW+sPtxxrDZjnz3xdf7gFU3X50FG3snUQ1crSXTL/HShQixSuyXLsbrQuw2lz/foWA7A2OMx7OlCbcnKgoHd2ueknCDP/HhtU9Z6gLNR+7H4hNlxR24sCZK/xzVBbj9bJStI50BWDxkQP23iY5eq7YHmRE8XepNgzOZEXOr42JS7OMULpwtz6EkqKt/L0T7loM9dyzfFRCKtZPGoWrLc2lxJytJiyuLx3sqmC3mclrO/fnqKS1p7uX9ZvAdbDrsH7FWRwQl/W271QaZM1r6OfNtzs/KJ2hg/XeicAhVPF9rl4OWUTaHGv15ppOeu5ZUsL6utM+K+CK/wHEEoCiXr2vF9fTl4qsZCyts3IgQrBY+fFSxh3rIxTRldJQaZLvS6ItB0oU8l62JuOwkgmJIVxB+Nh4iUp5G7dyhXzvcTnB+ini70JsHslqxSTkkpIRI6l6d/WxYf4pSXiAXmEMnmtV9eSsHh5OTfY351oayshTPfg99yljB7/vIKsKZlrRRe3wyV8Bd8Fu3LSnDlaHksM9UpVYuPXToEC751lVJaHv6tdlMkiQhEhIYWYxNq3nIX/0alUiBTi5IYpZ6Z8xl4W7KAH74/rQDJ+vVhvTpm5N/dVLIaWhKeU7dubeOFey7DvG9cpbSPpl+KQska268zxpsRjlXINm6Ucjl5Wp+YYckSKFkXDu7mOuoKgyTn+D6wW3tcNqJnVvk9+ym7vNkqGiL4Z/bYrWpR5bM1U8s6h35d5Nshvbhyf1aty8+gxI8yO437yOg4Wd60Ju0fc/658B/I0hhlKK0EwrrvXYv1378uUuVK1l/y1oeYspLlYclK2i1Zf/rEZMz+yjRbHgO7tZe2fbd6SE0XKlqyVPYl9DVdmFIw1a4Z1L0DenaqcL2nj07OdDU4dkZ9Wxm+CngftBiRXMkUHI4RpepXJWK7s+6nDO+RsjR5GQX4S3t0MGKpNTR7Txe6nb14WA8M6dnBlkdY0TWKQslKJP05WOeq6w/a8YUtn0gZOW9gF+GoK+zpQiu78tIYKriX7wuVI3HLhQNcr3/2U1NsW4l0cjiiBtGx/Dh8f0TQgYnINrK57CPmZWnKZrBQWkLK061BdVm/gTDPRhLMPsUmsv79+vaJeOsr0zKUcSJjuXu7spJQBnQyxXtE74747JXDM47bHd+Nf3knYtG9iCxuIkuWVSczxvfF6L6dPWW3cnBrq07Hd+dUmvN1Ko0RnrnrYtdyvcYqt108OOXkzwdsVUFlBuJRxYGgCKfzeYfytCUrRuRrt4AYAWWmYuwMweDVf1gyWJYwr0EdrwhZbaOxxXu/TJXukn82Ya1JKAoly6/GWejTGM6P5yenDvN1vbM+RC+rbK4+TOsbryQ4Vxp+/LKhtojOIs7p1xnvGdfHJl3U5GK68CcfPF/p+ih92lRG6RZBR3TakuVNIslsyo0zyCMAvO+CARjRu1PmdCH3t5VFNjUus+yUxggP3Dg+47gthINZsuWXA4id1a02wbcNvp1bVncvi5isaTW7rPJJBcs0fzvfO9EUpZfzO19nopmBB248B91Na4uVUtVXsdXaY8jjqb791elK+TlJOiLzd3TEXfMzSCqJpaPLO5+drPtIrwg1/rWsl14rLq33oE/nilR0/Zsm9Le9HyJfNpVvG98kwgoUWxRKFmPeG5L+8rYLU38//H5vBzgg/E/6K/dNVSvXUfCN5/f3VY6KT1YupkyNOFlcZ8qXD/IcsRjRp205Zi+Tx/kg1eJXmRjVxz4ylypZjt+LZr7H9jvb6ULV2pxgbht01dje+NBFg1zT8nkW+mCmEMi0ZMm75Ix2xvuPhFDVMkuWTCkoEViyeBlFzurW7fFF8YMxfnWhHywLdYuLkuW0ZDlvy+l76PSJE2Hd79RRPTF5WOYG3iUxyggv4VRmZaF+3Nwh4l+vxH/MvR1H9enkKqOMVm4j74rSmM0f0JguFD8DUY3EiFJ7CXuFYLBw3p6l5Iru+45L0otvrOp77/n9URIjrP7ONXj01gm24Mc9OpbjT5+YrCQHD/+90pYsjiRj+NVtE13T9Omc9m/p31Xu68ITtiFhwqBuga7z24E65U4whhvO62c7Jo+r468sVzlcOgkibwuJ6gaqYeI12vnhLZnOoNkqE7Kr+VsvKyEMCHFVrNH5q9Wt1VbKSmLo1sHd+lg5Nj0FrVcXeuOMk+XmK5fxzbM9PkuBCF7n1vNyLgySWbj441a5fEqhkiWwZPH9gMzxPRPj+rF9O2PamN4p1wc3JcsZjNRZVd06lOGL7xmVsiaWlpBC3C93KWOUHszIHs0gyXttWfVE1w3r1RETh3R3L9yD1gRLye8MG0QxoExyc6I2ZihZhgN9hiVL0s043SGswYbIKPApbjbHuUdh947lKCuJ2eNuCXwYRfzPJfaV0zILazYUiZIFDOnZwTWNbdVArhzfQysnnc9tF3v7CTnbBmPAjz9gn6KStb+wfbJkEICbL3D3yereoTz0oJn1zYmsrv/4ZcMyjqkoE25O9PJ2kr53kfKXzbPy4/hulcPg3fHwVhm9rY43zjhZpSWET0s2e3azmIZhNCwpsfvGeOVtd3w3/rXFaDL/7tzO7uvjxL6tjj9L1pCeHfCXT01JBW11C6SaWl2Ymlq1y9K1fRm+du1YXGQqL24f6vjXK/H85y5L3Y/svbBZsiRyycpIBQmVXJctLcl0lPQODn9XN0uWiJIYoanFtGSVqU0XWlhtxvLpEg2seQuvVV3OdLzSFiO1/tG5PZB91av39SoURTeo8h3Ox5g6aJnO66zGMqh7eyXTsEp95CJQpEjZS0HAZSN7YvcjNwqv3f3IjWhfXmJr6DkxagWZLlSoy8tHyldWyvoyr5c8G/85vvP3wiqFMX+7B+jpQm+M6UL7CPw7N40Xvhdug7YwBnSWUuSMdSV7jrwSLSrfUmr6dK5I7cHYajrc2JQtwepCr3hbcp8seft0+mQ5b6tr+zJb3qUx+Qq7Yb06YsrwHp71XkJpi7F0laakfkWrwrOlZ8dy3HuV4cfEWPo5dCy3W7Ji5M8nKxaTTxd6+WRZOB3f+WoRrbp1Vg+fX0zRkuUWey6nShYRXU9EW4moiohmCs5XENG/zPNLiWiYeXwYETUQ0Rrz/9+HI7YdpU02A3ZCn8jD/oNuDq4qiGrDeft8YxrcI22uJpBr6AA/uD0XVQUh6l3qAWOLGiejffg5qFiy3K0Q4nNRbn1zvWP62MkA4ZS6P3kKZbqwkPuvRJI5pgvlXbKzPTjGLLZ/g2B9vJzO97L2yTsopy1Z6WODu3cwjxHuv+EcAGm/J1mcrBsn9BfKIMP5ijgtWc98Mr06MNMnK9OSZchGqfSq04WyVzXG+T76nT1ojaDvIzIWUqR+m/86g4+6rS4UiVtCwLQxxpTt9DHyWGFu+ZWngpkCyx+YgVXfuSYtj83/z7IeOt4H7mcJkVL/4/wO8e+ii77uC08li4hKADwB4AYA4wHcTkTOpSafBnCKMTYKwC8APMqd28EYu9D8/55wxLYzcUg3zzRB+nvGgIduOQ9P3enfgS5omYC9EV/Nra5Tzc/r2zyid8dUFO+1370Wb32ZW51CwJJvX60oqTu9OqU3MHaKpH4v8puxRsfZ8IGJA/H4hy+wHVvz3Wvw3y9eAQD4/ccuwqUjMh1aeZyWLKuz5nG7X9kAICn6iirm6cb671+LK0f3dlXG+3N+IrKYRjKseGhe1ohcUOj9VyvLnC6UkTE9wo/c03NggfFryTpY05D6O+2TlU5rBY8kAO+fOBC7H7kxtVpPtiDmFx+5ECsenCF8J+6ZPhKfrxyZylOE0ydrCueMnjldaMeKtZgOGhpLTVPJ3rXUB9/xNvFTvsmUJUuch0wZSKR8ssIdrPDTwSR55uRiyRKJEyPClOE9sPuRGzMc+T1DOJAll6HoJZMMvTtXoBu3EMFmyZJMFzrlUZlhcCYZ0Ss9uM6lJWsKgCrG2E7GWDOA5wDc4khzC4Bnzb9fBHA15cDxydpnz8u3B8hyhJfjEblV3PIHZuCpuy72/Mj90bGKQrQ6wzrUtX0Z3vlaJQaZo8yuHcpsm9CG6e1/97QR8qCBinm4NfSX71VbrelGhhwM6NahPBXr5frz+uO5uy9zz8ORidOZ0ijHnmj3IzfiTtNKKutkeQUzzBaoYlUTdWCqhrWn7pqMv3xqSioac54p2P4LMEbtvP+Rm+Wk1WVo7Sbtq1+8Qmlls6VMOf2hZO3lGm6fU5El67pz+0qvl1myyktjtsEZz8wbxqX2/ZPR6lCy+LJTlqzUIoF0ut/cMRF3mv6W1nHekiV7R60PufPd4Kd8vTav5pXYQd3Tg5uoFv1UcOENLJGcSlaMSNmaCLi7THhNF6ZksCxZQp+sdP5WPTrXOPD1FYspyu9IUl4aw6vmADusmQSVnYgHAtjH/d4P4BJZGsZYKxHVALCcUIYT0WoApwE8yBib7yyAiO4GcDcA9O3bF/F4XEn4UycbMagT4d133/VMu3r16tTfqvnX1tUhHo9j/bFWpfRO1qzfaPvtLLdzOXCmOfO65mZj09CFixaiW0UMO2sMZ+3GhkZU7diRkX7X1g223w0tSfCt54FL2mHhwoUAgNbWFtf7P3L0qHL9uHFezxIsnD8PR+qMN6GhoQEdW9KboS5atAidy+UvgSXD7j3pCtq2YU3G+Ww5fOSILS+v+4/H4/jUeeV4ekNart37DtrS7N27N+O6xYsX2fIAgP0HjMjMe/bsEpZ1/RDC89uMv1kymSHXqdO1UjndWDB/PipKCfv2CSJDmx1LTc3p1KENG9YDAI6fOAGqP+maNy9j/KA8XQ6JvP8CgvVhiSRDcxI4djAt3qIF86WK1sZ99s2EVyxdjE7mO3Ss3njPWltbA78bzU2NAIDT1acwoCPhYJ3RFg4d2I94/FhG+rXLF6F/R8KhOobVq1ahZmcJDuw32tRHxpZh+yaj3dTV1brK1NDQ6FvmDUeMPvn48eO2aye3Z6gaUIqFB43z8+fPQ/tSoKEVWLd6FU7vLEFLcxMAQnNj2hJXdnQr5s0zXrZTp4x6OH7sKJYtPWUk4D64fHlVe41nUl1dLb2HEyeM/NavWwc6lPnJPXz4UOrvL5wLfHuB8XeHusOIx4+jrjHTmd+rvmTnm5ubsWLpktTvM6eN9/x09SnbNfPnvYvdp8WLCNauXYfEAfv04pka+f0nEwk4tZl4PI5d5nftzBmjfRysNcqrra3LyGvJokWptr7tgFHnhw4fRjx+KpWmsTHdn9XX1WLdmtW2PLZs3ZIh25JFi9Glwi5bizmyb2hqDuU7o6JkZcMhAEMYYyeIaBKAl4noXMbYaT4RY+xJAE8CwOTJk1llZaVS5v/YuwJ1VI/KSnPbhTdmSdNedNFFwFLjQ1dZWema1qJjx45G2q1HgZXLlWTiGThsFLB+U+o3X+59V43C9LG98eHfL864rry8HGhuwuWXXY4+Xdqhx/5qYPFCtGvfDiNHDgUcjeWiiROBpel8mpL2RvPZD1yNU3XNwDuzUVZWBmH9mnL17t0blZWTlOrHje49uqOy8hLsPl4HzI+jXbv2+NVnpuO1B14HAFx5xdS0OVhQliXjkoYtwM4duHZ8X9xx02R8e8Es2/ls5ezXry8qKy/MvH8nb6TLrQTw9Mx0uZ269QQOHkn9HjxkCLDTrgxPnXo5EJ9jk31O9QZg7x6MHDEC2L41o8jHPnUtnjfLiZXEMtptK5UBEGjpHkybNg3ty0vw7pmNwJ7dtnMlsRgSiSQ6d+kC1FQDACacfz6wagV69OiBgT06AHv3SPNWfXfbCEr9FxCsD6upbwHeegvnjRuN/1QZ/cR7KiulVoF9S/YAGzfggxcNxKemDsd5A7umzh2obgDmvYOy0lL5M/B4Vzp37IBjDXXo16cXXrrjIvz+3R346VvbMHToEFRWnoO+i97GkdPpD1llZSV6rV+AQ3U1mHjRRZg4pDsW1W8Gdu/E8OEjMXlED2DpInTq1CndRwvkqaho57vdtGw6AqxegV69eqKy0h6V/X3XAiO//RoSSYbK6dMxaO18bD9ai0mTJuP8QV3x1u7ZAJrRqVMn4IzxKKdPn5ayXv951zLg+DEM7N8PV0wdC7w7B7GSmLEHEuxt/NCyvcCm9ejarRsqK8UW76d2LAVOHMeFF1yQ8lvin8WggQOAfcbA7NJLpgAL3kXH8hLMvGNGKs37rweGcX1ORn05nq3tPHeurKwcV02/Apj7FgCgW9euQPUp9OndC5WVk1Npr6qsxOZDZ4DFmWOKCy+4AFeM7mXLt0eP7qisvFR4/7TgNTjtWZWVlanvWpfOnVFZeQV2Ha8DFsTRrn37jP592rQrUsGrT63eD6xfiz59+qCycmIqz4pFcwBzoNC1S2dcOmWCTf5xY8cBG9bZ5LjiiqmpTaYtWhNJ4K3XUVpWHkp/pjJdeAAAHzdgkHlMmIaISgF0BXCCMdbEGDsBAIyxlQB2ABiTrdAWxt54aibNbIz/QacLzzS6W8BkUY2tLSy6OHx7ZM7iTvEqQt7OxLky7sLB3Tz3p3KaggFj81srqrCq47vl6xA0JszXrw2tuUlxXUVpIrpfK3bZZS4rD92u92pf0rxcqt46Z5uqzO1sedgUbP91utEYkXfmYhS5TbskzPmRzhWlNgULCGc62XripSVGYMqYw1F84bfeg3e/UWm7xoqsba0sswLXjuvXmXM0d5cum2kZFX+fMebWK7VNraljTkoFjtV8CAfZY1FZQPthc3uuMZLtgfipSMsPq59iLMcg8N+ddAw8+42Q3+lCl2esurrQqgfRdKHo+WSsLrSFcCAln1CR1NYzz6VP1nIAo4loOBGVA7gNwCuONK8AuNP8+1YA7zDGGBH1Nh1PQUQjAIwGsDMc0QGAKTVyIFgnZNVx0I+M18dT1jA/N30kdj9yY2pkxX9gVfqjL06swENm0MyrzOCQqu3Fmf+S+6/G0479uypKY55Kkmypv18HXUueoNEARB386u9cgwdvPMcUw37eT38/ZXgPfOO6sbhpgj0iv+ijIXrUl4/qhd2P3Ihx/bz3ZhPhd7slpyyie013MAKfLIQfoDcHFGz/ZX30+ThSblguWaKo8KnXymdn9ampwzPksT5oVggB6+NXWhLL8Ney/DkbzPhzN00YgLlfr8RV4/qkfZ88ZAjyMfPK83PTRgAwZP/xB8/HN64bm9oH1bqWf09tPj/csdKUkiUb4HrX980XDMDuR26UKk68Ym2tKHRbZeqXzhWlNl8vWxw0zv/MiTTiu+CW3cIlSJUsR37WLYu21eHrn0jcR9kXgqgEtBU/VyJCjHIY8Z0x1grgPgBvAtgM4HnG2EYieoiIbjaTPQWgJxFVAfgqAGuZ9DQA64hoDQyH0nsYY+5OHT5IMnUrk+xluOvyYTjXXFXixIrPFiQe0aUjeuBiwTYLQUg1QqkY9hM928XwicuGYfcjN+KZTxr7aaVitfgsu1/XdhkbfhJ5Kz1Wg3d+q1OPQbFjtTr6oNZE0XXdO5anrITO06pxoHY/ciOe/9xluPeqURkdoigHN/lV4rmILr//ved4XifMy2wFV4zqlXFuuLki7IvvGZ2Rvi1SyP2XZYnspKpkmV8fkYUh6PsxoFv6w19vKlkpi4L5leHbp9MB3Fr6zwf5tdqQhZdofmKvZV4r5pvXj8PuR25ELEbo2r4M9141KqXMiF43EnzES2PpFWqyW7A+5G6R5r0QWbLCjGO47vvX4sV7Lk/95u/VKkZk9fETsNgtXMKl/cXt2/lN6t25AqUxwjeuG+sqiyxsBq+sl8TIto+mFEkSP3EEvVB6uxljrwF4zXHsu9zfjQA+LLjuJQAvZSmjlCRjylYmWbLv32xYfPj5bgtr2i1Ie1fbjNJfxrIoyBnZuE4HBbNAjenbCQO6tUd86zGlfGTEXCwlIqwXJ6g1kb9uxjl90qNJSfFhvFhCS5ZLepXd7sNUc6z2PGN834xznSpKMwNhcp1ajnc5CoVC7b9qm8zpQo+N0i0sK4dIKQ/SPirH9sZV4/rg4VmbAQB1pqJk5W9N2/D9lPPj/7npIzFny1FMGZ45oLT6Eq93N5AlK4sXwutS6xZ5i6Gsv7O2o6lvCr6TBP88EylLVnhvvGWZERHjFEonfkKwuH0P3jeiDPMPx3DsjGChjXExACOIadWP3ytMYt8n0xoEZDhq2ORRs2TJjlPu4mQVMs4NiN3w+1LO/grnqMld29ERtC2b8kb07qi0j6L1YSsriYljG3n8BtJmfT7ulltZTt76ynTcfeWIdBmKlixnOrcQASKSgo7eD/xL9Kc7L8bD7ze2F0p9ABzpZVKpvLCpPMxMLhiU9ptxE180ap1xjl0BumSEt9+WG1aUZ0OWdHkfHO39geeNj9lYHTR2UpasCkVLVkKuZFkPyc9r8udPTskIQgmkp4mSAqXOabG4cHA3bHv4BvTunBl2IT3V7zGwC9CkhvQwQtB4xbAT4VVH1vnSEkLnilLcOmkQnv3UFGFaywppTbUGgX8fR/fthH5d2uH+G+ShKlT33hWV4azq9L0KLFmyOFnmv29/dTpuudAIn+TWPRIR/v35y3GrI+izn8dut755TxeWEKW26XFD1jZLYhRaCI02rWQlGVMewfmd8hjNOSlaD2J4r47KSp3s+dx1+bDU3+3KSrD4fu/An82mKZq3ZH1u2gh0aZcZ2A8QK1kdykux5P6r8eMPni84y8ntKY28DHs+4pzumDIkJY8K002fssnDuitKZsfruWdMF0oqYOV3ZmDNd68Rn3RgZXHTBC6yso+vHxHwJ0cA3J85Aqb6hbeW8N/o83qpDRqAaCPQn41YSpaqT5alaE8VTPMGHYTw74cVbd0aSFqWLJsVwYeFJanoohCkXY3u2xkLZ74Hn+UGfqp4VRXv+E5E+OmHL8CkoeL+J2XJag6uZPEKSofyUiz59tW4XPCMLd74kmClpgfOe25fVoJbJw2SOr4DLhZ2M+moPp1SU8Ne7W9wjw54/4UD3bJTJh2M1H6c/1kSU7NkycQuIdJ7F1qovvOxmN2y4Ac/jeDvn3GG4LHjXC6qgjXfX1YSsykB6SjLavTr2s7TBKy2D6R3NF1nPpbS9bVrx2D7j26wB0A1+cZkY4TGN/yrxvbBtodvwIRB3bwFE8kqEdNvv965XZktArFbGSIrXrYr9JybrjqR7QEpwuZ/4jgn+uAP7Wl0pNPH9M55YN5i5vKRPfHp88rRrYPadOGU4T2w7eEbcKnAqhn0qVivcZd2pRjV24h2bVm3RFZkP7NYqVfMo80EtRgM7NY+kNuC10fPbQrNifW+1PmcLrTtV+nzHroqthc3Nv/wevyUG7iJHd/VXV5UbsFZhKXEqQ4yLEaY7fTK0XZF1B6w1D7deZ9jI+i0TBJLVkl4SlbUcbIiJcmYcqdfURrD8/dcllpqPHVUT0wbnd5j6ZX7pmL3iXr8v3+uzrg2yIsc5rfIUrKcq0JEUZbDLpuHb3PeliwrncPK5rI0eFT3mDBvmS+aCrJnJ5MvjPVzaV8UuTLjl6icz61ce3WqwKevGI6PXpyOdnDN+L44p19nDO/VESsfnIEeHcvx0KubxBlpfDOidydcOagsY0NdN7zeBb+txLJWtS8vQUOLoSh0MK0z6UUn6fR+Nv1WXRmca/uoav+ocq+WJavZp+N7eUkMLQmjvvNpH05bstSnC+3Xq5fl7IvPG9gF998wDh8S7B1r8fD7z8MLK/fbjo3q0wkrHpyBng6DhTM0Bf/8vn7dWDy/wgj6e+ukQfjP6gOu++IalqxwnkzbVrKS6i9MWUkMFaUlqQ7t75+xB06bMKgbJgzqJlSyQvRBDIS16WlZCdmm4aLZ+SM9RSBrhETpsmMkcVzNYll2GNYSS35ZTtZovUt7+yuQ7XtVUZq2NvJlZ3tPURuQiJDaF86C366pp7nVSSHsR6iR47dPaGwx+pZ2ZSWoM/2KrHdDtLrQTzu2BoU9JBZgi2RYJgNFUj6GzFBanZtKWx9XFUuW6spQJ2WlMcBcaFDT0OKROjpSPlkix3fpBtG8xYgyjslwFkFE+Nz0keLEJh+7dCg+dunQjOOirZfaCbYLsuHw2UpAvnAuFtPThQAMi4Fqp5LNx8E27RM4l+C0CHyyiGvWBErtt2T8zp7ZX5mGX90+UXpetPR3ZO/00m2nT5Yf5SWoQvHRyWkrTDq+jTjtTRMG4MEbz8FXr7EvF87mverbpQLxb1SmlyaHOF2YLbLi/crlx5KhyR1B223/ru3Qq1M5vnPj+FSsK8tfUjRd6Of5nzewC35w87m2aSkRubdkpe/h3W9U4qXP2yO1p+5b4V79WCF5+FmJ6vrslSyv/Xul77/5r8jxXW2DZWs6xTNpqGEpRHgFyPaD9skySTL7s+UD6znJbgSeLoX3JbKiHcs2NOV59EPuDuduWE747z3fHvQy1b4JtgjQ2XzQLWVoRO9OHi9u2jkUAOZ8bbpNvm96bOTqhp+psW+/N13O924ejw9MHGiTy21T1s9cOSLDNywb5+6LhnRH/67tuanINATCtYKQCX4Z2K29dyJkrrqSBRb0S5lLR5lNG9eEg9ubc9mInvjxB+zPqF1ZCVY8eA1mjO+Luma7JavVjMvltrrQVRYi3Hn5MM+NwnO9noJ/E/p3bY9JQ+3vivVx9RNG4fpz+/mSgR8wV4dgyXIbELthVb31Xlubelt8ZUbmBgd2y6bxr0pNRT0+4xVerzb1j89egg9PGiR1ji/RliwTRzDS775vvDSpnyX4TvjG8dzd6WnGpQ9cjR0/fi9euEe8ZxWP84H5UYRG9u6ErQ9fj1suHBh5h/TwB85TSpeOJWP80ZpIT819ZcaYjECsvhQ/H2nvnjYy5eNFoJTVz2s7DCefvsJQ0LOp3kxn/zREwO8/Ngnbf3RDFiWoUfWjG/CPz1yKX3w0bUH4n0uGCNOS418vRNHGASPkxEcvFpehKQz+efeluEPSDoB0QNH2julC3gIRpjXiHnOqKCzfF1WsvsgrHImsrTvZ9vAN+O3/XORLBpuSVe9//9GwSMXlMr+Pv/ufSaji+qgvzRiNj3PTdfddNQqTuZWWfvr1aNxb0si2qRMxeVgPPP7hC1wH4cmQbKxtWslKMgbV3QeUor9K4B+EtbIBALq0K7NtvQCkRz/O+DMZH2DJ83vs1gnC406zNBHQYsbNcZpJs2nK/bvKLSX2bQuskY8xguveMb3iRdR5ufWjH7poEK47ty9KY0D3DmV4+P1qip4zb6L0Vkaj+3Qyj6nVhij6uSoJRzwhy2rK+7QRGR8omUX1vIHiXQf464G0pc054uSx9p0b3cewgPbsWJ4RtT+dr7/WouIMq/HPbRcPxoxz3GPYuRGGnmK9AyN7Ge9OMoLAmDz3vcdY8ZVrS5bV5GXvovWuqN53eWnMt/L5yAcnoI8ZW6xfl3D3KRRt02X1186VfM7gp7EYZVi9P3BROvTC168ba1e6U765PqYWI4Lv47JtUiUxEm7vE4S27fjOmPLUUjbThTHHBy7jPNfopgzvga9fOwZ3XGJo/4/dOgHffHEdRvXpJLyW5z9fuNzXRsjpVYcOBUw5h2AYju/G31+6ejRm3jDOmDIVvEQ9OxlTBSLnRYuffcSwuMTjcaz+7rWuZV84uBs2HqyRnr9jyhBcM74vfjF7G1btrfY9dRqkw7dWrFphFixFhF9x5NVOX/3ilXhjw2Hc87eVwvPW9ZXj+uAfS/fisVuNOvu/e6cau9e7EGbfFtUH92znkQ+JB1eqWB/PO7k4fH757JUj8MGLBqUCi1oWJr8hBlSx8s25Jcv8t4sk0n4H82MdpQ/RlOE9sOyBGZi37RguGNwNb/3grazzrCiNoak1iTe+nBlHq3vHcnznpvEZLgtpS5b8Xt2Uo5Tju4pPVsRdB29syDamX5h7F7ZpJWvCoG6e8YMssvk4eH0g+ayJCPdx+759eNIgXDq8J4b07GDPU5ClHwWLYEzRAYIYSjn4DvLxUdx80jqKtmnJgpfvnSo9Z6167NO5nXK06fTFxj9BXs2mVmOaxbI2WlPTrdy+DCrNT8WS8YObz8V9V41CV3PvxQsGd8MFg7sJ06Zv3aWTNP9VvW/t+F6YtCsryfo9i8XIFrk9IVjAESZp62w0+cuwirPeISfWdKlz1WEUTBvT2zuRIssfnJHaFUCE5RLBk1pJ6WKEcHvl/flkRWzJCrgIQcSkod3RXH0klLzatJJl7TuoQjbzwW5bDwDuIz0iylCwsoHX0JtF8bOQ2xWQog4yX50mrwyrRpu2SCkbAYRvMpfBWz4B1obR/KaxKu3PLbgsP8UxQNH53aoPt07SOqW6MmdQd7WyNW0f0bY6YWJ9dHO9VVNDi1GeM3yLhaVkNWQRxT0fyCxzblj7YbotaHFTjiy3mFqFYKxRr7Ae1dd7tkiVx269APF4PJS82rRPlhuzvzIttdop2z7CCtInG/lE7dDnhao1z407LhniuYca3xlavnC2CPRZS5Ed/GPwa8kaP8Dwifq4y7SmjEbLkmU+B8v/r4UbVapIYaUZ1rOjazpVrFt3q4NWs6I6Vah10Ndxq6he+vzlGavVNMWD1XyjUrKs2YWvXTvWI2W41Ju6k9d0obUQoJhJJr0tWW5Yu0HsP1Xvmdbqh1R3OPDL56aNxI3mCndrgPvWV6Yh/vXKSMpTpU1bstwY3bczjpw2dv0WbUPhhxozjolMyQrSB/HKyatfvELZL0Gk0FmWLCswaBCd78cfON/zg2mFD7h8ZC+cO6AFT87bKRwN5jruDRNYrS4b2RMvrNyPsf06Y3SfTth+tNY1jz6d2wWebklbsozOuUxoyfLOJxYjPHXnZJwv2P4pm8+cm6XA3D4PnSpUNz5PSzJpaHecqG3KQjJNITN1ZE/8d+3B1AIKnuFdsx/YxWIUqiuBKvUpS5b7dGEulax/f+Fy4WbdUdOqsLjBbZBm7V2454S3kmW5T4Tt6G9REiPccuEAzFp/KLUv6Ji+mW031xStkgWkfWWy2ZYFAMaaqzVuM7ccaVcWS0VKBrIf6fExrry48/Jh2HOiDvdUjsRv4zsApEchH714CP65bG9kFqURvTth4cz3oH+XdmAAPj99pG0/v7wH3OQE+OBFgzBtTG/06lSBV+67IqsNXL24cEg3AMAlw42wFVZIidZEEq/cNxVvbjysbO28+hzxqsEg1tL+XY3ObOpIY9XYRycPxr/MrSUsLPVrUPfwprQ1xcFHLx6MGeP7ZvhcLn9gBlYtW5QnqbJnRDejv7TeVydWMNYGh5L1+K0Tsv6WyLjIhz9umKg4vg/qYQyuRasWrRWSIn8vJ1YfLMonLKzv0enG/EXRd1LUSpbluOgnfsbsr0zDjmP21VqDe3Swjbjmfr3StloxiKk1qELSqaI0tbLs69eOwa/eqUqde/j95+H+947DqiULg2WuAB8M0yvIYL6xPg7ty0uEG1KHxVVj++A37+mAqeYS+KvG9cH3/7sJ7584MLVdUz7o1qEcc79eiV7mCs9Hb52ARx0hQsZ2j+H77xuPW7lo+RoNYCj2okUtvTtXoKINh/KY2KcUq78zTdp/TRtjvMe3TrbvqffhInxHLItPe0mIF8CYVl367atTVi8eIsKun7xXaRA4ZXgPfO994/GRCOvRmoq07qsQKGolK7W03seqg9F9O6cirMtwxpLqmAczLwDc957RtpWMJTEK5PwYFv9zyVAs2H4cH7s0t0Epn/3UFPx9yd687jHZqTxd+NCeHUOfBgl6a5Y5X5ovEe5y2SlBU3z84OZzcfh0Y77FyCtuA8RB3TvkZRozHxyobgAA6Qpli74uU3yqVnYiwicj7mu6tXdRsvL0fSg6Jatfl3apDiSs6UIviAiDe7THJcOz8/1q6/TuXIEXP395zsu9cnRvXDk6vKXQbZW+XSowQzLdGBX9zCnJ8f3z7/ugUSObWFqa4uLuaSPwn9UHlLaGawtYfnYJgdVtkDkLMzbHflpFp2S98eUrcaLO2KagR0ej4YzsHd7SThnzv/kepXTWNGOpaqh6zVlNh/ISZQfcpd+eEbE0BkN6dMDek4aj64RB3fDyvVNxvg+/Qo1GUxh8+73n4NvvPSffYoSGFfV9SI9MH9PLR/XCf75wOS7IsftG0SlZ3TqUp5zfZpzTB8/cdXGoAd+y5ZNTh6G6vhl3TxuRb1E0bYD/u3cq4luPRb6DvR/+84XLsedkejXRhR5TDRqNJnu+cEEFLpl0gXfCs5yX751q8x3m8RPwOyyUzClEdD0RbSWiKiKaKThfQUT/Ms8vJaJh3Ln7zeNbiei6EGVXkRtXjetTUFGq25WV4P73nhOpI7ameBjdtzM+W2AKec9OFXlbDRWEttp/aTQ8U/qXYnoBGQwKlQsHd7PtXJBvPJUsIioB8ASAGwCMB3A7EY13JPs0gFOMsVEAfgHgUfPa8QBuA3AugOsB/NbMT6PRaCJH918ajSafqFiypgCoYoztZIw1A3gOwC2ONLcAeNb8+0UAV5Ox5OAWAM8xxpoYY7sAVJn5aTQaTS7Q/ZdGo8kbKj5ZAwHwEQz3A7hEloYx1kpENQB6mseXOK4d6CyAiO4GcDcA9O3bN7Q9g7Kltra2YGTxg5Y797RV2duq3D6IvP8CgvdhbbX+26rcQNuVXcudW8KSuyAc3xljTwJ4EgAmT57MKisr8yuQSTweR6HI4gctd+5pq7K3VbkLjaB9WFut/7YqN9B2Zddy55aw5FaZLjwAgA/ROsg8JkxDRKUAugI4oXitRqPRRIXuvzQaTd5QUbKWAxhNRMOJqByGI+grjjSvALjT/PtWAO8wY9feVwDcZq7eGQ5gNIBl4Yiu0Wg0nuj+S6PR5A3P6ULTR+E+AG8CKAHwNGNsIxE9BGAFY+wVAE8B+CsRVQE4CaMjg5nueQCbALQCuJcxlrutzTUazVmN7r80Gk0+UfLJYoy9BuA1x7Hvcn83Aviw5NofAfhRFjJqNBpNYHT/pdFo8gUZVvHCgYiOAdiTbzlMegE4nm8hAqDlzj1tVfZCkXsoY6woIi367MMKpf790lblBtqu7Fru3OJHbmn/VXBKViFBRCsYY5PzLYdftNy5p63K3lblLhbaav23VbmBtiu7lju3hCW33qVYo9FoNBqNJgK0kqXRaDQajUYTAVrJcufJfAsQEC137mmrsrdVuYuFtlr/bVVuoO3KruXOLaHIrX2yNBqNRqPRaCJAW7I0Go1Go9FoIkArWRqNRqPRaDQRoJUsDiLqQUSziWi7+W93l7RdiGg/Ef0mlzJKZPGUm4guJKLFRLSRiNYR0UfzIaspy/VEtJWIqohopuB8BRH9yzy/lIiG5UHMDBTk/ioRbTLrdw4RDc2HnE685ObSfYiIGBG1ueXWhU4Rt/lpRLSKiFqJ6NZ8yCiiWN9VIrqHiNYT0RoiWkBE4/Mhp4i22s8o1PldRHTMrPM1RPQZXwUwxvT/5v8AHgMw0/x7JoBHXdL+EsA/APymLcgNYAyA0ebfAwAcAtAtD7KWANgBYASAcgBrAYx3pPkCgN+bf98G4F8FUMcqcl8FoIP59+fbitxmus4A5gFYAmByvuUupv+LvM0PAzABwF8A3JpvmX3I3SbfVQBduL9vBvBGvuVWld1MV1D9jGKd35XNd15bsuzcAuBZ8+9nAbxflIiIJgHoC+Ct3IjliafcjLFtjLHt5t8HARwFkI8I21MAVDHGdjLGmgE8B0N+Hv5+XgRwNRFRDmUU4Sk3Y2wuY6ze/LkEwKAcyyhCpb4B4IcAHgXQmEvhzhKKuc3vZoytA5DMh4ASivZdZYyd5n52BFAoK9faaj+jKndgtJJlpy9j7JD592EYipQNIooB+BmAr+dSMA885eYhoikwtPYdUQsmYCCAfdzv/eYxYRrGWCuAGgA9cyKdHBW5eT4N4PVIJVLDU24iugjAYMbYrFwKdhZxtrT5QqFo31UAIKJ7iWgHjBmM/5cj2bxoq/2Malv5kDm1/CIRDfZTgNIG0cUEEb0NoJ/g1AP8D8YYIyLRKOELAF5jjO3P5UAzBLmtfPoD+CuAOxljhTT6LBqI6GMAJgOYnm9ZvDAHDT+HYRLXaM4q2tK7asEYewLAE0R0B4AHAdyZZ5E8aeP9zH8B/JMx1kREn4NhcX6P6sVnnZLFGJshO0dER4ioP2PskKmMHBUkuwzAlUT0BQCdAJQTUS1jTOroFwYhyA0i6gJgFoAHGGNLIhLViwMA+JHAIPOYKM1+IioF0BXAidyIJ0VFbhDRDBiK73TGWFOOZHPDS+7OAM4DEDcHDf0AvEJENzPGVuRMyuKmqNt8AVKs76qT5wD8LlKJ1Gmr/YxnnTPG+PfwTzAsiOrk2/GskP4H8DjsDuSPeaS/C4Xh+O4pN4zpwTkAvpxnWUsB7AQwHGlHw3Mdae6F3Qn4+QKoYxW5J8KYgh2db3n9yO1IH0cBOKQW0//F3Oa5tH9G4Ti+F+27yssL4H0AVuRbbr9txUxfEP2MYp335/7+AIAlvsrI900W0v8wfCDmANgO4G0APczjkwH8SZC+UJQsT7kBfAxAC4A13P8X5kne9wLYZnZyD5jHHgJws/l3OwAvAKgCsAzAiHzXsaLcbwM4wtXvK/mWWUVuR9qC6PyK7f8ibvMXw/BjqYNheduYb5kV5W6T7yqMVe0bTZnnwkWRKTTZHWkLpp9RqPOfmHW+1qzzcX7y19vqaDQajUaj0USAXl2o0Wg0Go1GEwFaydJoNBqNRqOJAK1kaTQajUaj0USAVrI0Go1Go9FoIkArWRqNRqPRaDQRoJUsjUaj0Wg0mgjQSpZGo9FoNBpNBPx/QL+etZmLi8MAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 720x288 with 2 Axes>"
       ]
@@ -1154,6 +1168,7 @@
     "# . Input level\n",
     "ampl = 1.0\n",
     "sigma = ampl / np.sqrt(2)\n",
+    "sigma = 4\n",
     "\n",
     "# . DFT size\n",
     "N_bins = N_fft // 2 + 1  # positive frequency bins including DC and f_s/2\n",
@@ -1180,17 +1195,20 @@
     "dc = ampl * np.cos(2 * np.pi * dc_bin * t_axis)  # equivalent to dc = ampl\n",
     "noise = np.random.randn(N_fft)\n",
     "noise *= sigma / np.std(noise)  # apply requested sigma\n",
+    "b = ampl * np.sign(s)  # block wave, sign: -1 if x < 0, 0 if x==0, 1 if x > 0\n",
     "\n",
     "x = s + dc\n",
     "y = noise\n",
     "\n",
     "# . DFT using complex input fft()\n",
     "S_fft = np.fft.fftshift(np.fft.fft(s) / N_fft)\n",
+    "B_fft = np.fft.fftshift(np.fft.fft(b) / N_fft)\n",
     "X_fft = np.fft.fftshift(np.fft.fft(x) / N_fft)\n",
     "Y_fft = np.fft.fftshift(np.fft.fft(y) / N_fft)\n",
     "\n",
     "# . DFT using real input rfft()\n",
     "S_rfft = np.fft.rfft(s) / N_fft\n",
+    "B_rfft = np.fft.rfft(b) / N_fft\n",
     "X_rfft = np.fft.rfft(x) / N_fft\n",
     "Y_rfft = np.fft.rfft(y) / N_fft\n",
     "\n",
@@ -1207,6 +1225,19 @@
     "plt.plot(f_axis_rfft, abs(X_rfft))\n",
     "plt.grid()\n",
     "\n",
+    "# Plot block spectrum\n",
+    "# . DSB = double sideband\n",
+    "# . SSB = single sideband (= DC + positive frequencies)\n",
+    "plt.figure(figsize=(10, 4))\n",
+    "plt.subplot(1, 2, 1)\n",
+    "plt.title('DFT of real input block using fft (DSB)')\n",
+    "plt.plot(f_axis_fft, abs(B_fft))\n",
+    "plt.grid()\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plt.title('DFT of real input block using rfft (SSB)')\n",
+    "plt.plot(f_axis_rfft, abs(B_rfft))\n",
+    "plt.grid()\n",
+    "\n",
     "# Plot noise spectrum\n",
     "plt.figure(figsize=(10, 4))\n",
     "plt.subplot(1, 2, 1)\n",
@@ -1220,12 +1251,19 @@
     "\n",
     "print(\"The DFT of the sine plot shows:\")\n",
     "print(f\". G_fft_real_input_dc = {G_fft_real_input_dc}\")\n",
-    "print(f\". G_fft_real_input_sine = {G_fft_real_input_sine}\")\n"
+    "print(f\". G_fft_real_input_sine = {G_fft_real_input_sine}\")\n",
+    "print()\n",
+    "\n",
+    "S_max = max(abs(S_rfft)) \n",
+    "B_max = max(abs(B_rfft)) \n",
+    "print(f\"The DFT of the block plot shows that the first harmonic has an amplitude of 4/pi * A/2 = {B_max}, \\\n",
+    "which is larger than A / 2 = {S_max} for sine input. Hence the bin samples need 1 bit more than for a full \\\n",
+    "scale sine, because to also fit e.g. this harmonic of a block wave.\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 36,
    "id": "2e386180",
    "metadata": {},
    "outputs": [
@@ -1234,7 +1272,7 @@
      "output_type": "stream",
      "text": [
       "sine ampl = 1.0000\n",
-      "sine sigma = 0.7071 (= 0.7071)\n",
+      "sine sigma = 0.7071 (= 4.0000)\n",
       "sine power = 0.5000 (= 0.5000)\n",
       "\n",
       "sine bin ampl = 0.5000\n",
@@ -1305,7 +1343,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 37,
    "id": "97e9a32d",
    "metadata": {},
    "outputs": [
@@ -1313,21 +1351,21 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "noise sigma = 0.7071 (= 0.7071)\n",
-      "noise power = 0.5000 (= 0.5001)\n",
+      "noise sigma = 4.0000 (= 4.0000)\n",
+      "noise power = 16.0000 (= 16.0014)\n",
       "\n",
       "N_fft = 1024\n",
       "sqrt(N_fft) = 32.0\n",
-      "sigma / std(Y_fft) = 31.996921\n",
-      "sigma / std(Y_rfft) = 32.018608\n",
+      "sigma / std(Y_fft) = 32.047266\n",
+      "sigma / std(Y_rfft) = 32.082825\n",
       "\n",
-      "noise bin std (fft) = 0.022099\n",
-      "noise bin std (rfft) = 0.022084\n",
-      "noise bin.re std = 0.015340\n",
-      "noise bin.im std = 0.015887\n",
-      "noise bin power = 0.000488\n",
-      "noise bin.re power + bin.im power = 0.000488\n",
-      "noise bins power = 0.499419 (= 0.500120)\n",
+      "noise bin std (fft) = 0.124816\n",
+      "noise bin std (rfft) = 0.124677\n",
+      "noise bin.re std = 0.090000\n",
+      "noise bin.im std = 0.086281\n",
+      "noise bin power = 0.015544\n",
+      "noise bin.re power + bin.im power = 0.015544\n",
+      "noise bins power = 15.917496 (= 16.001391)\n",
       "\n",
       "The ratio of real input noise std and DFT bin noise std shows:\n",
       ". G_fft_real_input_noise = 0.03125 = (1 / sqrt(1024))\n"
@@ -1386,11 +1424,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "id": "17aee663",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "# DFT of square wave\n"
+   ]
   }
  ],
  "metadata": {
diff --git a/doc/erko_howto_tools.txt b/doc/erko_howto_tools.txt
index 3870bca56c..a090fb4232 100755
--- a/doc/erko_howto_tools.txt
+++ b/doc/erko_howto_tools.txt
@@ -417,7 +417,9 @@ then:
 > ping 10.87.0.186 (= dop386)
 
 > ssh -X dop386.astron.nl
-> ssh -X kooistra@10.87.0.186  # dop386 from ASTROM
+> ssh -X kooistra@10.87.0.186  # dop386 from ASTRON
+> sshdop386  # dop386 terminal, alias in .bashrc
+> dop386t # dop386 terminal, alias in .bashrc
 > ssh -J bastion.astron.nl kooistra@10.87.0.186  # dop386 from home
 
 1) gitlab
@@ -481,12 +483,15 @@ then:
    64  sdp_rw.py --host 10.99.0.250 --port 4842 -r xst_integration_interval
    71  sdp_rw.py --host 10.99.0.250 --port 4842 -r signal_input_bsn
 
-
 4) tcpdump
    > ifconfig   # to find ethernet port
    > sudo tcpdump -vvXXSnelfi enp5s0 port 5001 # for UDP only
    > sudo tcpdump -vvXXSnelfi enp5s0 port 5001 > tcpdump.txt  (> is new file, >> is append)
 
+5) opcua-client &  # to verify sdp_rw.py
+   - opc.tcp://10.99.0.250:4842 RPi4 connect
+   - write via RW point attribute window
+   - right mouse subscribe/unsubsribe to data change
 
 *******************************************************************************
 * Polarion:
diff --git a/doc/sdp_useful_commands_erko.txt b/doc/sdp_useful_commands_erko.txt
new file mode 100644
index 0000000000..b86be2a85f
--- /dev/null
+++ b/doc/sdp_useful_commands_erko.txt
@@ -0,0 +1,503 @@
+cd git
+Statistics offload indices bug:
+
+1) Summary
+
+- XST show duplicate subband index for LiLi cell when nof_crosslets > 1. Goes always wrong.
+- SST show duplicate signal input indices that correspond to missing indices that can be
+  somwhat lower or even higher. GFoes wrong for about 1.5 % of the SST packets, more often
+  on higher nodes but seen all all nodes 2 - 15, not yet seen on node 0, 1.
+
+2) DTS-outside
+
+http://dop496.nfra.nl:8888/notebooks/Demo_and_test_scripts/PPK/XSTcaptureRaw.ipynb
+
+xst_rx_align_stream_enable = [1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0] --> should be 2**P_sq - 1 = 3
+
+- step = 0
+- nofXST = 1 --> ok
+- nofXST > 1 --> first cross-correlation on each node has wrong subband index
+- nofXST = 2
+0 subband: 78 baseline (0, 0)
+1 subband: 78 baseline (0, 0)
+2 subband: 71 baseline (0, 36) zero
+3 subband: 78 baseline (0, 36) zero
+4 subband: 71 baseline (0, 24) zero
+5 subband: 78 baseline (0, 24) zero
+6 subband: 78 baseline (12, 12)
+7 subband: 78 baseline (12, 12)
+8 subband: 71 baseline (12, 0) zero
+9 subband: 78 baseline (12, 0) zero
+10 subband: 71 baseline (12, 36) zero
+11 subband: 78 baseline (12, 36) zero
+12 subband: 78 baseline (24, 24)
+13 subband: 78 baseline (24, 24)
+14 subband: 71 baseline (24, 12) zero
+15 subband: 78 baseline (24, 12) zero
+16 subband: 71 baseline (24, 0) zero
+17 subband: 78 baseline (24, 0) zero
+18 subband: 78 baseline (36, 36) zero
+19 subband: 78 baseline (36, 36) zero
+20 subband: 71 baseline (36, 24) zero
+21 subband: 78 baseline (36, 24) zero
+22 subband: 71 baseline (36, 12) zero
+23 subband: 78 baseline (36, 12) zero
+
+24 subband: 78 baseline (0, 0)
+25 subband: 78 baseline (0, 0)
+26 subband: 71 baseline (0, 36) zero
+27 subband: 78 baseline (0, 36) zero
+28 subband: 71 baseline (0, 24) zero
+29 subband: 78 baseline (0, 24) zero
+30 subband: 78 baseline (12, 12)
+31 subband: 78 baseline (12, 12)
+32 subband: 71 baseline (12, 0) zero
+33 subband: 78 baseline (12, 0) zero
+34 subband: 71 baseline (12, 36) zero
+35 subband: 78 baseline (12, 36) zero
+36 subband: 78 baseline (24, 24)
+37 subband: 78 baseline (24, 24)
+38 subband: 71 baseline (24, 12) zero
+39 subband: 78 baseline (24, 12) zero
+40 subband: 71 baseline (24, 0) zero
+41 subband: 78 baseline (24, 0) zero
+42 subband: 78 baseline (36, 36) zero
+43 subband: 78 baseline (36, 36) zero
+44 subband: 71 baseline (36, 24) zero
+45 subband: 78 baseline (36, 24) zero
+46 subband: 71 baseline (36, 12) zero
+47 subband: 78 baseline (36, 12) zero
+
+48 subband: 78 baseline (0, 0)
+49 subband: 78 baseline (0, 0)
+50 subband: 71 baseline (0, 36) zero
+51 subband: 78 baseline (0, 36) zero
+52 subband: 71 baseline (0, 24) zero
+53 subband: 78 baseline (0, 24) zero
+54 subband: 78 baseline (12, 12)
+55 subband: 78 baseline (12, 12)
+56 subband: 71 baseline (12, 0) zero
+57 subband: 78 baseline (12, 0) zero
+58 subband: 71 baseline (12, 36) zero
+59 subband: 78 baseline (12, 36) zero
+60 subband: 78 baseline (24, 24)
+61 subband: 78 baseline (24, 24)
+62 subband: 71 baseline (24, 12) zero
+63 subband: 78 baseline (24, 12) zero
+64 subband: 71 baseline (24, 0) zero
+65 subband: 78 baseline (24, 0) zero
+66 subband: 78 baseline (36, 36) zero
+67 subband: 78 baseline (36, 36) zero
+68 subband: 71 baseline (36, 24) zero
+69 subband: 78 baseline (36, 24) zero
+70 subband: 71 baseline (36, 12) zero
+------------78 baseline (36, 12) zero is missing
+
+71 subband: 78 baseline (0, 0)
+72 subband: 78 baseline (0, 0)
+73 subband: 71 baseline (0, 36) zero
+74 subband: 78 baseline (0, 36) zero
+75 subband: 71 baseline (0, 24) zero
+76 subband: 78 baseline (0, 24) zero
+77 subband: 78 baseline (12, 12)
+78 subband: 78 baseline (12, 12)
+79 subband: 71 baseline (12, 0) zero
+80 subband: 78 baseline (12, 0) zero
+81 subband: 71 baseline (12, 36) zero
+82 subband: 78 baseline (12, 36) zero
+83 subband: 78 baseline (24, 24)
+84 subband: 78 baseline (24, 24)
+85 subband: 71 baseline (24, 12) zero
+86 subband: 78 baseline (24, 12) zero
+87 subband: 71 baseline (24, 0) zero
+88 subband: 78 baseline (24, 0) zero
+89 subband: 78 baseline (36, 36) zero
+90 subband: 78 baseline (36, 36) zero
+91 subband: 71 baseline (36, 24) zero
+92 subband: 78 baseline (36, 24) zero
+93 subband: 71 baseline (36, 12) zero
+94 subband: 78 baseline (36, 12) zero
+
+95 subband: 78 baseline (0, 0)
+96 subband: 78 baseline (0, 0)
+97 subband: 71 baseline (0, 36) zero
+98 subband: 78 baseline (0, 36) zero
+99 subband: 71 baseline (0, 24) zero
+
+
+3) RW
+
+L2SDP-700 : gunzip ./temp/tcpdump.txt.gz
+header:
+    marker, version 5805
+    subband c4 - ca = 196 - 202
+    signal input A = 0
+    signal input B = 0, 54, 48, 3c, 30, 24, 18,  c
+                   = 0, 84, 72, 60, 48, 36, 24, 12
+    block period 1400 = 5120
+    integration interval 2faf0 = 195312
+    nof_signal_inputs, nof_statistics_per_packet 0c08
+
+> cat temp/tcpdump.txt |grep 0x0030 |more --> c4 is correct, not reported as c5
+
+
+4) SDP-ARTS
+
+Signal input indices:
+
+  dec: 0, 12, 24, 36, 48, 60, 72, 84, 96, 108, 120, 132, 144, 156, 168, 180
+  hex: 0,  c, 18, 24, 30, 3c, 48, 54, 60,  6c,  78,  84,  90,  9c,  a8,  b4
+
+
+> stat_stream_sst.py --host 10.99.0.250 --port 4842 --ip dop386 --mac dop386 --setup --mtime 5 --test-header
+> stat_stream_sst.py --host 10.99.0.250 --port 4842 --ip dop386 --mac dop386 --setup --mtime 5 --plot
+> stat_stream_sst.py --host 10.99.0.250 --port 4842 --ip dop386 --mac dop386 --setup --mtime 5 --headers > x.txt
+
+> stat_stream_xst.py --host 10.99.0.250 --port 4842 -h
+> stat_stream_xst.py --host 10.99.0.250 --port 4842 --headers
+> stat_stream_xst.py --host 10.99.0.250 --port 4842 --headers --stream ON
+> stat_stream_xst.py --host 10.99.0.250 --port 4842 --setup --ip dop386 --mac dop386
+
+> vi test/py/base/statistics_stream_packet.py
+
+> sdp_rw.py --host 10.99.0.250 --port 4842 -l
+> sdp_rw.py --host 10.99.0.250 --port 4842 -l pps
+> sdp_rw.py --host 10.99.0.250 --port 4842 -r pps_capture_cnt
+
+> sdp_rw.py --host 10.99.0.250 --port 4842 -r sdp_config_first_fpga_nr
+> sdp_rw.py --host 10.99.0.250 --port 4842 -r sdp_config_nof_fpgas
+
+> sdp_rw.py --host 10.99.0.250 --port 4842 -w fpga_mask [True]*16
+> sdp_rw.py --host 10.99.0.250 --port 4842 -r fpga_mask
+> sdp_rw.py --host 10.99.0.250 --port 4842 -r global_node_index
+
+> sdp_rw.py --host 10.99.0.250 --port 4842 -r scrap
+> sdp_rw.py --host 10.99.0.250 --port 4842 -w scrap [1]*16
+> sdp_rw.py --host 10.99.0.250 --port 4842 -w scrap [1]*8192
+> sdp_rw.py --host 10.99.0.250 --port 4842 -r scrap
+
+> sdp_rw.py --host 10.99.0.250 --port 4842 -w processing_enable [False]*16
+> sdp_rw.py --host 10.99.0.250 --port 4842 -r processing_enable
+
+> sdp_rw.py --host 10.99.0.250 --port 4842 -r xst_offload_hdr_eth_destination_mac
+> sdp_rw.py --host 10.99.0.250 --port 4842 -r xst_offload_hdr_ip_destination_address
+> sdp_rw.py --host 10.99.0.250 --port 4842 -r xst_integration_interval
+> sdp_rw.py --host 10.99.0.250 --port 4842 -r signal_input_bsn
+
+> sudo tcpdump -vvXXSnelfi enp5s0 port 5001 > tcpdump.txt ==> SST has duplicates ~2% : e.g. in 5 sec:
+       130 is duplicate, 128 is missing --> delta = -2
+       130 is duplicate, 122 is missing --> delta = -8
+       125 is duplicate, 126 is missing --> delta = +1
+        93 is duplicate,  92 is missing --> delta = -1
+       166 is duplicate, 164 is missing --> delta = -2
+        79 is duplicate,  75 is missing --> delta = -4
+        83 is duplicate,  80 is missing --> delta = -3
+       165 is duplicate, 163 is missing --> delta = -2
+       175 is duplicate, 172 is missing --> delta = -3
+       179 is duplicate, 176 is missing --> delta = -3
+        95 is duplicate,  94 is missing --> delta = -1
+
+
+5) SDP-ARTS XST indices
+
+sdp_rw.py --host 10.99.0.250 --port 4842 -r firmware_version
+
+    https://git.astron.nl/desp/hdl/-/merge_requests/241 met statistics offload fix was op 15 april 2022
+    sdp-arts: 2022-04-13T08.41.35_209979741_lofar2_unb2b_sdp_station_full_wg
+    dts-outside: 2022-04-12T10.56.45_b8464ee23_lofar2_unb2c_sdp_station_full
+    dts-lcu: 2022-04-29T10.19.39_2c3958e1f_lofar2_unb2c_sdp_station_full
+
+sdp_rw.py --host 10.99.0.250 --port 4842 -l xst
+
+sdp_rw.py --host 10.99.0.250 --port 4840 -w fpga_mask [True]*16
+sdp_rw.py --host 10.99.0.250 --port 4840 -r fpga_mask
+
+sdp_rw.py --host 10.99.0.250 --port 4842 -r firmware_version
+sdp_rw.py --host 10.99.0.250 --port 4842 -r sdp_config_first_fpga_nr  # sdp-arts = 64
+sdp_rw.py --host 10.99.0.250 --port 4842 -r sdp_config_nof_fpgas  # sdp-arts = 16
+
+stat_stream_xst.py --host 10.99.0.250 --port 4842 --ip dop386 --mac dop386 --setup --mtime 1
+
+SDP processing:
+> sdp_rw.py --host 10.99.0.250 --port 4842 -r processing_enable
+
+XST ring GN 0-15:
+sdp_rw.py --host 10.99.0.250 --port 4842 -w ring_node_offset [0]*16
+sdp_rw.py --host 10.99.0.250 --port 4842 -r ring_node_offset
+sdp_rw.py --host 10.99.0.250 --port 4842 -w ring_nof_nodes [16]*16
+sdp_rw.py --host 10.99.0.250 --port 4842 -r ring_nof_nodes
+
+sdp_rw.py --host 10.99.0.250 --port 4842 -w ring_use_cable_to_next_rn [False,False,False,True,False,False,False,True,False,False,False,True,False,False,False,True]
+sdp_rw.py --host 10.99.0.250 --port 4842 -w ring_use_cable_to_previous_rn [True,False,False,False,True,False,False,False,True,False,False,False,True,False,False,False]
+sdp_rw.py --host 10.99.0.250 --port 4842 -r ring_use_cable_to_next_rn
+sdp_rw.py --host 10.99.0.250 --port 4842 -r ring_use_cable_to_previous_rn
+
+sdp_rw.py --host 10.99.0.250 --port 4842 -w xst_ring_nof_transport_hops [8]*16
+sdp_rw.py --host 10.99.0.250 --port 4842 -r xst_ring_nof_transport_hops
+
+XST setup:
+sdp_rw.py --host 10.99.0.250 --port 4842 -r xst_offload_hdr_eth_destination_mac  # dop386 = 00:15:17:98:5f:bf
+sdp_rw.py --host 10.99.0.250 --port 4842 -r xst_offload_hdr_ip_destination_address  # dop386 = 10.99.0.253
+sdp_rw.py --host 10.99.0.250 --port 4842 -r xst_offload_hdr_udp_destination_port  # 5003
+
+sdp_rw.py --host 10.99.0.250 --port 4842 -w xst_subband_select [0,10,11,12,13,14,15,16]*16
+sdp_rw.py --host 10.99.0.250 --port 4842 -r xst_subband_select  # updated after xst_processing_enable
+
+sdp_rw.py --host 10.99.0.250 --port 4842 -w xst_integration_interval [1.0]*16
+sdp_rw.py --host 10.99.0.250 --port 4842 -r xst_integration_interval  # updated after xst_processing_enable
+
+sdp_rw.py --host 10.99.0.250 --port 4842 -w xst_offload_nof_crosslets [2]*16
+sdp_rw.py --host 10.99.0.250 --port 4842 -r xst_offload_nof_crosslets  # updated immediately
+
+sdp_rw.py --host 10.99.0.250 --port 4842 -w xst_processing_enable [False]*16
+sdp_rw.py --host 10.99.0.250 --port 4842 -w xst_processing_enable [True]*16
+sdp_rw.py --host 10.99.0.250 --port 4842 -r xst_processing_enable
+
+sdp_rw.py --host 10.99.0.250 --port 4842 -r xst_subband_select  # updated after xst_processing_enable
+sdp_rw.py --host 10.99.0.250 --port 4842 -r xst_integration_interval  # updated after xst_processing_enable
+sdp_rw.py --host 10.99.0.250 --port 4842 -r xst_offload_nof_crosslets  # updated immediately
+
+XST offload:
+sdp_rw.py --host 10.99.0.250 --port 4842 -r xst_offload_enable
+sdp_rw.py --host 10.99.0.250 --port 4842 -w xst_offload_enable [True]*16
+sdp_rw.py --host 10.99.0.250 --port 4842 -r xst_offload_enable
+
+sdp_rw.py --host 10.99.0.250 --port 4842 -w xst_offload_enable [False]*16
+sdp_rw.py --host 10.99.0.250 --port 4842 -r xst_offload_enable
+
+sdp_rw.py --host 10.99.0.250 --port 4842 -r xst_offload_nof_packets
+
+sudo tcpdump -vvXXSnelfi enp5s0 port 5003 > tcpdump.txt
+cat tcpdump.txt |grep 0x0030 |more   # ==> select T_int block and then find next T_int blocks
+
+
+6) DTS-LAB XST indices
+
+sdp_rw.py --host 10.99.0.250 --port 4840 -l xst
+
+sdp_rw.py --host 10.99.0.250 --port 4840 -w fpga_mask [False,True,True,True,False,False,False,False,False,False,False,False,False,False,False,False]
+sdp_rw.py --host 10.99.0.250 --port 4840 -r fpga_mask
+
+sdp_rw.py --host 10.99.0.250 --port 4840 -r firmware_version
+sdp_rw.py --host 10.99.0.250 --port 4840 -r sdp_config_first_fpga_nr  # dts-lab = 0
+sdp_rw.py --host 10.99.0.250 --port 4840 -r sdp_config_nof_fpgas  # dts-lab = 16
+
+stat_stream_xst.py --host 10.99.0.250 --port 4840 --ip dop421 --mac dop421 --setup --mtime 1
+
+SDP processing GN 1-3:
+sdp_rw.py --host 10.99.0.250 --port 4840 -w processing_enable [False,True,True,True,False,False,False,False,False,False,False,False,False,False,False,False]
+sdp_rw.py --host 10.99.0.250 --port 4840 -r processing_enable
+
+Ring GN 1-3:
+sdp_rw.py --host 10.99.0.250 --port 4842 -w ring_node_offset [1]*16
+sdp_rw.py --host 10.99.0.250 --port 4842 -r ring_node_offset
+sdp_rw.py --host 10.99.0.250 --port 4842 -w ring_nof_nodes [3]*16
+sdp_rw.py --host 10.99.0.250 --port 4842 -r ring_nof_nodes
+
+sdp_rw.py --host 10.99.0.250 --port 4840 -w ring_use_cable_to_next_rn [False,False,False,True,False,False,False,False,False,False,False,False,False,False,False,False]
+sdp_rw.py --host 10.99.0.250 --port 4840 -w ring_use_cable_to_previous_rn [False,True,False,False,False,False,False,False,False,False,False,False,False,False,False,False]
+sdp_rw.py --host 10.99.0.250 --port 4840 -r ring_use_cable_to_next_rn
+sdp_rw.py --host 10.99.0.250 --port 4840 -r ring_use_cable_to_previous_rn
+
+
+XST setup:
+sdp_rw.py --host 10.99.0.250 --port 4840 -w xst_ring_nof_transport_hops [2]*16  # N_rn = 3 --> P_sq = N_rn // 2 + 1 = 2
+sdp_rw.py --host 10.99.0.250 --port 4840 -r xst_ring_nof_transport_hops
+
+sdp_rw.py --host 10.99.0.250 --port 4840 -r xst_rx_align_stream_enable
+
+sdp_rw.py --host 10.99.0.250 --port 4840 -r xst_offload_hdr_eth_destination_mac  # dop421 = 00:15:17:aa:22:9c
+sdp_rw.py --host 10.99.0.250 --port 4840 -r xst_offload_hdr_ip_destination_address  # dop421 = 10.99.0.254
+sdp_rw.py --host 10.99.0.250 --port 4840 -r xst_offload_hdr_udp_destination_port  # 5003
+
+sdp_rw.py --host 10.99.0.250 --port 4840 -w xst_subband_select [0,10,11,12,13,14,15,16]*16
+sdp_rw.py --host 10.99.0.250 --port 4840 -r xst_subband_select  # updated after xst_processing_enable
+
+sdp_rw.py --host 10.99.0.250 --port 4840 -w xst_integration_interval [1.0]*16
+sdp_rw.py --host 10.99.0.250 --port 4840 -r xst_integration_interval  # updated after xst_processing_enable
+
+sdp_rw.py --host 10.99.0.250 --port 4840 -w xst_offload_nof_crosslets [2]*16
+sdp_rw.py --host 10.99.0.250 --port 4840 -r xst_offload_nof_crosslets  # updated immediately
+
+sdp_rw.py --host 10.99.0.250 --port 4840 -w xst_processing_enable [False]*16
+sdp_rw.py --host 10.99.0.250 --port 4840 -w xst_processing_enable [True]*16
+sdp_rw.py --host 10.99.0.250 --port 4840 -r xst_processing_enable
+
+sdp_rw.py --host 10.99.0.250 --port 4840 -r xst_subband_select  # updated after xst_processing_enable
+sdp_rw.py --host 10.99.0.250 --port 4840 -r xst_integration_interval  # updated after xst_processing_enable
+sdp_rw.py --host 10.99.0.250 --port 4840 -r xst_offload_nof_crosslets  # updated immediately
+
+XST monitor:
+sdp_rw.py --host 10.99.0.250 --port 4842 -r xst_ring_rx_total_nof_sync_discarded
+sdp_rw.py --host 10.99.0.250 --port 4842 -r xst_ring_rx_total_nof_sync_received
+
+XST offload:
+sdp_rw.py --host 10.99.0.250 --port 4840 -r xst_offload_enable
+sdp_rw.py --host 10.99.0.250 --port 4840 -w xst_offload_enable [True]*16
+sdp_rw.py --host 10.99.0.250 --port 4840 -r xst_offload_enable
+
+sdp_rw.py --host 10.99.0.250 --port 4840 -w xst_offload_enable [False]*16
+sdp_rw.py --host 10.99.0.250 --port 4840 -r xst_offload_enable
+
+for i in {1..5}
+do
+  sdp_rw.py --host 10.99.0.250 --port 4840 -r xst_offload_nof_packets
+  sleep 1
+done
+
+sudo tcpdump -vvXXSnelfi enp67s0f1 port 5003 > tcpdump.txt
+cat tcpdump.txt |grep 0x0030 |more   # ==> select T_int block and then find next T_int blocks
+
+
+6) SST indices
+
+a)
+example on PN1:
+
+=== ERROR ===   bsn 322519378320312,  19 duplicate,  17 missing --> delta = -2
+=== ERROR ===   bsn 322519378320312,  23 duplicate,  20 missing --> delta = -3
+=== ERROR ===   bsn 322519378320312,  95 duplicate,  93 missing --> delta = -3
+=== ERROR ===   bsn 322519378320312, 148 duplicate, 147 missing --> delta = -1
+=== ERROR ===   bsn 322519378320312, 151 duplicate, 149 missing --> delta = -2
+=== ERROR ===   bsn 322519378320312, 155 duplicate, 154 missing --> delta = -1
+
+from:
+
+kooistra@dop386:~/git$ stat_stream_sst.py --host 10.99.0.250 --port 4842 --ip dop386 --mac dop386 --setup --mtime 120 --test-header
+start
+start udp server
+udp server opened
+=== ERROR ===   bsn 322519374804687, index 119 is duplicate
+=== ERROR ===   bsn 322519374804687, index 191 is duplicate
+=== ERROR ===   bsn 322519375000000, index 45 is duplicate
+=== ERROR ===   bsn 322519375000000, index 47 is duplicate
+=== ERROR ===   bsn 322519375000000, index 94 is duplicate
+=== ERROR ===   bsn 322519375000000, index 136 is duplicate
+...
+=== ERROR ===   bsn 322519397070312, index 163 is duplicate
+=== ERROR ===   bsn 322519397070312, index 167 is duplicate
+=== ERROR ===   bsn 322519397265625, index 71 is duplicate
+=== ERROR ===   bsn 322519397265625, index 150 is duplicate
+=== ERROR ===   bsn 322519397265625, index 154 is duplicate
+=== ERROR ===   bsn 322519397460937, index 107 is duplicate
+=== ERROR ===   bsn 322519397656250, index 103 is duplicate
+=== ERROR ===   bsn 322519397656250, index 107 is duplicate
+socket.timeout
+valid packets=22688
+SUCCES
+
+received bsn numbers [322519374609375, 322519374804687, 322519375000000, 322519375195312, 322519375390625, 322519375585937, 322519375781250, 322519375976562, 322519376171875, 322519376367187, 322519376562500, 322519376757812, 322519376953125, 322519377148437, 322519377343750, 322519377539062, 322519377734375, 322519377929687, 322519378125000, 322519378320312, 322519378515625, 322519378710937, 322519378906250, 322519379101562, 322519379296875, 322519379492187, 322519379687500, 322519379882812, 322519380078125, 322519380273437, 322519380468750, 322519380664062, 322519380859375, 322519381054687, 322519381250000, 322519381445312, 322519381640625, 322519381835937, 322519382031250, 322519382226562, 322519382421875, 322519382617187, 322519382812500, 322519383007812, 322519383203125, 322519383398437, 322519383593750, 322519383789062, 322519383984375, 322519384179687, 322519384375000, 322519384570312, 322519384765625, 322519384960937, 322519385156250, 322519385351562, 322519385546875, 322519385742187, 322519385937500, 322519386132812, 322519386328125, 322519386523437, 322519386718750, 322519386914062, 322519387109375, 322519387304687, 322519387500000, 322519387695312, 322519387890625, 322519388085937, 322519388281250, 322519388476562, 322519388671875, 322519388867187, 322519389062500, 322519389257812, 322519389453125, 322519389648437, 322519389843750, 322519390039062, 322519390234375, 322519390429687, 322519390625000, 322519390820312, 322519391015625, 322519391210937, 322519391406250, 322519391601562, 322519391796875, 322519391992187, 322519392187500, 322519392382812, 322519392578125, 322519392773437, 322519392968750, 322519393164062, 322519393359375, 322519393554687, 322519393750000, 322519393945312, 322519394140625, 322519394335937, 322519394531250, 322519394726562, 322519394921875, 322519395117187, 322519395312500, 322519395507812, 322519395703125, 322519395898437, 322519396093750, 322519396289062, 322519396484375, 322519396679687, 322519396875000, 322519397070312, 322519397265625, 322519397460937, 322519397656250, 322519397851562]
+- signal_input_index 117 not in bsn 322519374804687
+- signal_input_index 189 not in bsn 322519374804687
+- signal_input_index 40 not in bsn 322519375000000
+- signal_input_index 46 not in bsn 322519375000000
+- signal_input_index 92 not in bsn 322519375000000
+- signal_input_index 135 not in bsn 322519375000000
+- signal_input_index 137 not in bsn 322519375000000
+- signal_input_index 142 not in bsn 322519375000000
+- signal_input_index 146 not in bsn 322519375000000
+- signal_input_index 151 not in bsn 322519375000000
+- signal_input_index 154 not in bsn 322519375000000
+- signal_input_index 137 not in bsn 322519375195312
+- signal_input_index 142 not in bsn 322519375195312
+- signal_input_index 154 not in bsn 322519375390625
+- signal_input_index 162 not in bsn 322519375390625
+- signal_input_index 165 not in bsn 322519375390625
+- signal_input_index 170 not in bsn 322519375390625
+- signal_input_index 173 not in bsn 322519375390625
+- signal_input_index 178 not in bsn 322519375390625
+- signal_input_index 116 not in bsn 322519375585937
+- signal_input_index 118 not in bsn 322519375781250
+- signal_input_index 126 not in bsn 322519375781250
+- signal_input_index 130 not in bsn 322519375781250
+- signal_input_index 154 not in bsn 322519375781250
+- signal_input_index 102 not in bsn 322519375976562
+...
+- signal_input_index 106 not in bsn 322519397460937
+- signal_input_index 100 not in bsn 322519397656250
+- signal_input_index 104 not in bsn 322519397656250
+
+stream data status:
+- received 23040 packets ==> = 120 * 192 is OK
+- n_valid 22688 packets
+- n_duplicate 352 packets ==> 352 / 23040 = 1.5 %
+
+7) Reboot, flash
+
+# Alle fpgas schrijven duurt 2m28s:
+> sdp_firmware.py --host 10.99.0.250 --write --image USER --file /home/donker/images/lofar2_unb2c_sdp_station_full-r70484fd08.rb
+
+# Alle fpgas schrijven terug lezen en checken duurt 6m15s
+sdp_firmware.py --host 10.99.0.250 --write --read --verify --image USER --file /home/donker/images/lofar2_unb2c_sdp_station_full-r70484fd08.rb
+
+# Wil je ook een reboot doen, dan ook nog --reboot toevoegen.
+
+[0] = factory
+[1] = user image
+sdp_rw.py --host 10.99.0.250 --port 4842 -w boot_image [1]*16
+
+__pycache__ dir met gecompileerde pyc deleten
+
+# flash image
+scp regtest@10.87.6.144:~/bitstream/lofar2*wg* /home/kooistra/Downloads/      # dop349 = 10.87.6.144
+
+sdp_firmware.py --host 10.99.0.250 --port 4842 -h
+sdp_rw.py --host 10.99.0.250 --port 4842 -r firmware_version
+sdp_firmware.py --host 10.99.0.250 --port 4842 --version
+sdp_firmware.py --host 10.99.0.250 --port 4842 --image FACT --reboot
+sdp_firmware.py --host 10.99.0.250 --port 4842 --version
+sdp_firmware.py --host 10.99.0.250 --port 4842 -n 64:79 --image USER --file ~/Downloads/lofar2_unb2b_sdp_station_full_wg-rce96e0f1d.rbf --write
+sdp_firmware.py --host 10.99.0.250 --port 4842 --image USER --reboot
+sdp_firmware.py --host 10.99.0.250 --port 4842 --version
+
+
+
+8) PD 26 aug 2022
+Op een vers syteem gaat het goed,  maar na een aantal keer testen gaan xst_ring_tx_nof_packets en xst_ring_rx_nof_packets  rare waardes geven.
+
+sdp_rw.py --host 10.99.0.250 --port 4842 -w boot_image [1]*16
+stat_stream_xst.py --host 10.99.0.250 --port 4842 --ip dop386 --mac dop386 --test-cm --test-header
+test-cp: PASSED
+test-mp: PASSED
+test-header: PASSED
+
+verder is alles wat --plot en --headers nodig heeft standaard ingebouwd, onderstaande werkt dus zonder dat eerst wat anders moet worden gezet. plot kan worden beeindigd door in de terminal op Enter te drukken.
+
+stat_stream_xst.py --host 10.99.0.250 --port 4842 --ip dop386 --mac dop386 --plots
+
+stat_stream_xst.py --host 10.99.0.250 --port 4842 --ip dop386 --mac dop386 --headers
+
+
+
+
+
+9) BF
+sdp_rw.py --host 10.99.0.250 --port 4842 -w bf_ring_nof_transport_hops [1]*16
+sdp_rw.py --host 10.99.0.250 --port 4842 -r bf_ring_nof_transport_hops
+
+sdp_rw.py --host 10.99.0.250 --port 4842 -r bst_offload_enable
+
+sdp_rw.py --host 10.99.0.250 --port 4842 -r bf_ring_rx_total_nof_sync_discarded
+sdp_rw.py --host 10.99.0.250 --port 4842 -r bf_ring_rx_total_nof_sync_received
+
+SST tests with: stat_stream_sst
+
+# --bsn-monitor of SST offload
+stat_stream_sst.py --host 10.99.0.250 --port 4842 --ip dop386 --mac dop386 --stream ON --bsn-monitor --mtime 3 -vv
+stat_stream_sst.py --host 10.99.0.250 --port 4842 --ip dop386 --mac dop386 --stream OFF --bsn-monitor --mtime 3 -vv
+
+# --headers --> print headers during mtime
+stat_stream_sst.py --host 10.99.0.250 --port 4842 --ip dop386 --mac dop386 --headers --mtime 3
+
+# --test-header --> valid packets = mtime * 192 packets
+stat_stream_sst.py --host 10.99.0.250 --port 4842 --ip dop386 --mac dop386 --test-header --mtime 3
+
+# --test-data --> verify expected SST for ampl in [1.0, 0.1, 0.01, 0.001]
+stat_stream_sst.py --host 10.99.0.250 --port 4842 --ip dop386 --mac dop386 --test-data
+
+# --plots
+# use ctrl-C in terminal to stop plots
+stat_stream_sst.py --host 10.99.0.250 --port 4842 --ip dop386 --mac dop386 --plots
+# use wg.py in other terminal to control WG
+wg.py  --host 10.99.0.250 --port 4842 --setphase 0 --setfreq 19921875 --setampl 0.5 --enable
+wg.py  --host 10.99.0.250 --port 4842 --disable
-- 
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