From bdbda2685dd6ed41316dee74743adde7b8150465 Mon Sep 17 00:00:00 2001 From: Markus Date: Tue, 11 Dec 2018 15:49:13 -0200 Subject: [PATCH] time workaround --- .../.ipynb_checkpoints/test-checkpoint.ipynb | 173 ++++++++++++++++-- notebooks/test.ipynb | 52 +++--- simulations/demo/simple_tracker.py | 3 +- 3 files changed, 189 insertions(+), 39 deletions(-) diff --git a/notebooks/.ipynb_checkpoints/test-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/test-checkpoint.ipynb index a79181a..c8862f2 100644 --- a/notebooks/.ipynb_checkpoints/test-checkpoint.ipynb +++ b/notebooks/.ipynb_checkpoints/test-checkpoint.ipynb @@ -3,24 +3,171 @@ { "cell_type": "code", "execution_count": 1, - "metadata": {}, + "metadata": { + "scrolled": false + }, "outputs": [ { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'ui'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mrun\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mrun\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\engine\\run.py\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mui\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfig\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mstate_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmechanisms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexogenous_states\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0menv_processes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msim_config\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfigProcessor\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mgenerate_config\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmechanismExecutor\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0msimulation\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mutils\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mflatten\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'ui'" + "name": "stdout", + "output_type": "stream", + "text": [ + "single_proc: []\n" ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " elapsed_time follow mech_step run signal time_step \\\n", + "0 0.0 0.000000 0 1 0.000000 0 \n", + "1 1.0 0.000000 1 1 0.125333 1 \n", + "2 2.0 0.125333 1 1 0.248690 2 \n", + "3 3.0 0.248690 1 1 0.368125 3 \n", + "4 4.0 0.368125 1 1 0.481754 4 \n", + "\n", + " timestamp \n", + "0 2018-01-01 00:00:00 \n", + "1 2018-01-01 00:00:01 \n", + "2 2018-01-01 00:00:02 \n", + "3 2018-01-01 00:00:03 \n", + "4 2018-01-01 00:00:04 " + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "from engine import run\n", - "run.main()" + "%matplotlib inline\n", + "import pandas as pd\n", + "from tabulate import tabulate\n", + "\n", + "from SimCAD.engine import ExecutionMode, ExecutionContext, Executor\n", + "from simulations.demo import simple_tracker\n", + "from SimCAD import configs\n", + "\n", + "exec_mode = ExecutionMode()\n", + "\n", + "single_config = [configs[0]]\n", + "single_proc_ctx = ExecutionContext(exec_mode.single_proc)\n", + "run1 = Executor(single_proc_ctx, single_config)\n", + "run1_raw_result = run1.main()[0]\n", + "result = pd.DataFrame(run1_raw_result)\n", + "result.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "result.plot('timestamp', ['signal','follow'])\n", + "# result.plot('timestamp', ['signal'])" ] }, { @@ -51,5 +198,5 @@ } }, "nbformat": 4, - "nbformat_minor": 1 + "nbformat_minor": 2 } diff --git a/notebooks/test.ipynb b/notebooks/test.ipynb index f4fd863..c8862f2 100644 --- a/notebooks/test.ipynb +++ b/notebooks/test.ipynb @@ -11,7 +11,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "single_proc: []\n" + "single_proc: []\n" ] }, { @@ -36,8 +36,8 @@ " \n", " \n", " elapsed_time\n", + " follow\n", " mech_step\n", - " mirror\n", " run\n", " signal\n", " time_step\n", @@ -48,8 +48,8 @@ " \n", " 0\n", " 0.0\n", - " 0\n", " 0.000000\n", + " 0\n", " 1\n", " 0.000000\n", " 0\n", @@ -58,40 +58,40 @@ " \n", " 1\n", " 1.0\n", - " 1\n", " 0.000000\n", " 1\n", - " 0.000000\n", + " 1\n", + " 0.125333\n", " 1\n", " 2018-01-01 00:00:01\n", " \n", " \n", " 2\n", " 2.0\n", - " 1\n", - " 0.000000\n", - " 1\n", " 0.125333\n", + " 1\n", + " 1\n", + " 0.248690\n", " 2\n", " 2018-01-01 00:00:02\n", " \n", " \n", " 3\n", " 3.0\n", - " 1\n", - " 0.125333\n", - " 1\n", " 0.248690\n", + " 1\n", + " 1\n", + " 0.368125\n", " 3\n", " 2018-01-01 00:00:03\n", " \n", " \n", " 4\n", " 4.0\n", - " 1\n", - " 0.248690\n", - " 1\n", " 0.368125\n", + " 1\n", + " 1\n", + " 0.481754\n", " 4\n", " 2018-01-01 00:00:04\n", " \n", @@ -100,12 +100,12 @@ "" ], "text/plain": [ - " elapsed_time mech_step mirror run signal time_step \\\n", - "0 0.0 0 0.000000 1 0.000000 0 \n", - "1 1.0 1 0.000000 1 0.000000 1 \n", - "2 2.0 1 0.000000 1 0.125333 2 \n", - "3 3.0 1 0.125333 1 0.248690 3 \n", - "4 4.0 1 0.248690 1 0.368125 4 \n", + " elapsed_time follow mech_step run signal time_step \\\n", + "0 0.0 0.000000 0 1 0.000000 0 \n", + "1 1.0 0.000000 1 1 0.125333 1 \n", + "2 2.0 0.125333 1 1 0.248690 2 \n", + "3 3.0 0.248690 1 1 0.368125 3 \n", + "4 4.0 0.368125 1 1 0.481754 4 \n", "\n", " timestamp \n", "0 2018-01-01 00:00:00 \n", @@ -121,6 +121,7 @@ } ], "source": [ + "%matplotlib inline\n", "import pandas as pd\n", "from tabulate import tabulate\n", "\n", @@ -140,22 +141,22 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 3, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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e1GsHXcabm6gQRZgyZQoPPvggbdu2xdXVlYULF17x+IULF/Loo4+SlpZG06ZNWbBgAQB+fn5orenduzcAPXv2JDo6Gk/P0hmFWOxlt5VS3YApWuubLK8nAWit/1fE8ZuByVrrVZbXKVrrf7WnZGVedjs7N4+7PttCZGwKyyf2opGXpWNJa1g0Eo6Fwri1ULe1uYmWEdu/jz+e7GV0RoPR+b5opLEu1PhQY5VWISj/y26XFbOX3W4AnLZ5HW157x+UUr5AE2CtzdsuSqkwpdRWpVT5W3+2jM1YE8HuU0m8NaKNtSgA7JgH4StgwOtVpiiAMVJp+t0d0Bqe+W4vuXmWLzJ2dsYtJZca8ONDkJ1ubqJCVCJlPSppJPCj1tp2wX1fSwUbBUxTSvkXdqJSarylgITFxcWVRa5lbvvxBGaGRnLbDQ0Y2q6+NRB7GP58BQIGGBO+qpjGtVx5fVgrtp9IYFZopDXg7gPDZ0PcYWOOgxCiRJREYYgBGtm8bmh5rzAjgUW2b2itYyx/RgHryN//YHvcXK11sNY62Men/K0YWlzJ6dk8/d0eGnq68vowmxZBdobxjdi5urFHQSXtbL6aER2MYjltTQS7TiVaA4H9octjsH0uhK80L0FRrlTEnSlLUnF//5IoDDuAQKVUE6WUE8aH/z9GFymlmgOewBab9zyVUs6W595AD+BQwXMrO601r/x6gHMXM5g+sr11tVSA1VMg9iAMmwXutU3L0WxKKd4c0Zq6Hi48tXgPlzKyrcH+U6BOa2O01qXzZqUoygkXFxfi4+OrbHHQWhMfH4+Li8t1X6PYo5K01jlKqQnASsAe+FxrfVAp9ToQprW+XCRGAot1/v9aLYDPlFJ5GEXqHdvRTFXFz7ti+G3vGZ4bGESHxjajDCJWwbbZ0PmRSjWJ7Xp5uDgyfWR77vpsC5OXHuSjuyyD2Rxd4PZ5MLcPLHkcRv0g+0ZXYQ0bNiQ6OprKesv5Wri4uBRrnaZij0oyQ2UalXQyPpXB0zfSqkENFo3riv3fQ1NjYXZ3cPOBcaHGh58A4KNV4XyyJoLpI9szrL3NOIft/wfLn4Ob34Guj5mXoBDlVFmOShLXKSc3jycX78HeTvHx3e2tRUFrWPIfyLgIt8+XolDAxL4B3NC4Jq/8coDTCTYLiHV6GIIGGR3R5w6Yl6AQFZwUBhPN23ScPaeNoal/L44HsOdbiPjTGJoqi8X9g4O9HdNHdiBPa176Zb/1XrJSMGwmuNQ0binl5pibqBAVlBQGk5yMT2Xa6nAGtqzDrbZDU1Pi4M+XoVFX6CwzeovSyMuV529uzsaIC/y6x2YQnJs3DH4Pzu6FbXPMS1CICkwKgwm01rz8ywEc7OzyD00FWDkJMlPg1unSgXoV93X1pUPjmrzx+2ESUrOsgZbDjVtKoW8ZS3ULIf4V+eQxwS+7Y9gUeYEXbm5G3Ro2/QcRq2H/D9DrGajd3LwEKwh7O8X/bmvDxfRs3lxmM5hNKbjlA1B2sOxZWWhPiH9JCkMZi0/J5I3fD9HR15N7u/haA1mpsOxp8A6CXs+al2AF07yuB4+G+PPzrhg2RtgMT6zREPq9BpGrjWXKhRDXTApDGXtz2WFSMnP4321trKumAoS+DUmnjFtIssfAvzKhbwBNvN14+ZcDpGfZrLbS6WFoEAwrXoS0hKIvIITIRwpDGdoQHscvu2N4LMSfoDrVrYEzu2HrLOg4Fny7m5ZfReXiaM/bI9pwKiGNaWvCrQE7e6PQZiQZa00JIa6JFIYykpaVw8u/7qepjxuP3xhgDeTmwNKJ4FYb+k81L8EKrpt/Le4ObsS8jcc5EJNsDdRtDd0nwp5vjCW6hRBXJYWhjExfHcHphHTeHtEGF0d7a2DrLDi3zxhiWa2meQlWAi8NboGnqxOTft5vXZ4bIOR58GoKvz0ly3MLcQ2kMJSBQ2cuMm/TcUZ2akTXprWsgcSTRt9Cs8HQYqh5CVYSNVwdmXxrS/bHJPPF5hPWgGM1GDINEo/D+vdMy0+IikIKQynTWjPlt4N4uDgwaVCBXaVWvWr8Ofj9Krucdkkb0rYevYN8mLY6nAspmdZA0xBoOxK2zISEKPMSFKICkMJQypbvP8f24wk8d1Mzarg6WgPHN8KhJcachRrXvwqiyE8pxWtDWpKelcuHf4bnD/afAnaO8OerZqQmRIUhhaEUpWfl8vbyw7So58HITo2tgdwcYwhljcbQ/QnzEqykAmq7M6a7H4t3nMrfEe1RD3o/B0d+N/bOFkIUSgpDKZq7IYqYpHQm39rSunIqwK6FcP4ADHzDuP8tStzEfoF4ujrx+m+H8m/Y0vVx8PQzCrMssidEoaQwlJKYpHRmr4/kljb18nc4pyfC2jfBtye0HGZegpVcjWqOPDewGdtPJPD7vrPWgKMLDHwL4o5A2HzzEhSiHCuRwqCUulkpdVQpFamUerGQ+FilVJxSao/l8bBNbIxSKsLyGFMS+ZQH7/xxBK1h0uACax6te8eYcDXoHelwLmV3d2pEy3oe/G/54fwzopvfAk37GIvspcablZ4Q5VaxC4NSyh74FBgEtATuUUoVtonAd1rr9pbHPMu5XsBkoAvQGZislPIs5NwKZfvxBH7be4ZHQvxp6OlqDcQeNnYZ6zgW6rYxLb+qwt5OMWVoK84kZzBn/TFrQCljl7fMFKM4CCHyKYkWQ2cgUmsdpbXOAhYD13qP5CZgldY6QWudCKwCbi6BnEyTm6eZ+ttB6tVw4bEQf2tAa1gxCZzd4UZZnqGsdG7ixZC29Ziz/hgxSTaT22q3MNZS2rlAdnsTooCSKAwNgNM2r6Mt7xV0u1Jqn1LqR6VUo395boXxQ9hpDp65yKTBLajmZDPD+egfEBUKfV4Ct1pFX0CUuEmDjfkj/1t+OH+gz4vgUsPoiJaluYX4W1l1Pv8G+Gmt22K0Chb+2wsopcYrpcKUUmFxcXFXP8EEyenZvL/yKJ38PLm1bT1rICcTVr4EPs2h00PmJVhFNahZjUdD/Pl931m2Rdn0Kbh6wY0vw4mNcHipeQkKUc6URGGIARrZvG5oee9vWut4rfXlaajzgI7Xeq7NNeZqrYO11sE+Pj4lkHbJm73uGAlpWUy+tRXKtmN5+/8ZyzHc9DbYOxZ9AVFqHg3xp34NF95cdpg823WUOj4AtVvBqtcgJ6voCwhRhZREYdgBBCqlmiilnICRQL6vX0opm6/PDAUut+lXAgOVUp6WTueBlvcqnDNJ6Sz46zgj2jegdYMa1kB6Emz8APz7QUA/8xKs4qo52fPswGbsj0lm2X6b4av2DjDgdWML0J0LTMtPiPKk2IVBa50DTMD4QD8MfK+1PqiUel0pdXlluIlKqYNKqb3ARGCs5dwE4A2M4rIDeN3yXoXz8apwtIanBwTlD/w1zSgO/aeYkZawMbxDA5rXrc4Hfx4lKyfPGgjoB369YP27kHHRvASFKCdKpI9Ba71cax2ktfbXWr9lee81rfVSy/NJWutWWut2WusbtdZHbM79XGsdYHlUyK9sR89d4qdd0Yzu5ksjL5vhqckxsHU2tL0L6rU1L0EBGMNXXxjUnJPxaSzafsoaUAoGTIW0eNg8w7wEhSgnZOZzCXhvxRHcnB34j+0GPADr/gc6z+jgFOVCnyAfujb14pM1EaRk2iyJ0aAjtBphrL566Zx5CQpRDkhhKKZtUfGsORLLY3388XRzsgZijxi7hnUaB56+5iUo8lFKMWlQC+JTs5i7ocDy231fhdws45aSEFWYFIZi0Frzzooj1PVw4YHuTfIH10wFJ3fo9aw5yYkitWtUk1va1GPexihiL2VYA7X8jVFKOxfChQjzEhTCZFIYimHlwXPsPpXE0wMC809mO7kFji6Hnk/JZLZy6rmbmpGVk8eMNZH5AyEvGCvernndnMSEKAekMFyn7Nw83ltxlIDa7tx+g81GO1obY+Kr14Muj5mXoLiiJt5u3NO5MYu2n+L4hVRrwN3H2CPj8FI4vcO8BIUwkRSG6/R92GmiLqTyws3NcbC3+Ws8sgyit0OfSeDkWvQFhOkm9gvEycGOD1YezR/oNgHcfIwCL0tliCpICsN1SMvKYdrqCIJ9PenforY1kJtj9C14B0H7e81LUFwTn+rOjOvVlGX7z7LndJI14Oxu3FI6tRnCK+R8SyGKRQrDdVjw1wniLmUyaXDz/Etf7P0WLoRDv8nGjFpR7o3r3RRvdyfe/eNI/kDHseDlbxT6vLxCzxWispLC8C8lp2fz2fpj9G9Rm46+XtZATiasfw8aBBsbwYgKwd0y/2RLVDybIy9YA/aOcONLEHsIDv5sXoJCmEAKw780f2MUFzNy/rn0xa4vIfk09H1FdmarYO7p3Jh6NVz4cFV4/v2hW90GtVsaExVlf2hRhUhh+BcSUrOYv+k4g9vUpVV9m4XystJgw/vg28PYMlJUKC6O9kzoG8DOk4msC7dZ0t3Ozmg1xEfCvu/MS1CIMiaF4V/4bP0x0rJzebp/gdZC2HxIOW8sfSGthQrpzo6NaORVjY/+LNBqaD4E6rWD9e/IstyiypDCcI1iL2WwcMsJhrdvQGCd6tZAZgps+hj8+4JfD9PyE8Xj5GDHk/2C2B+TzJ+HzlsDShlLZSSdgj1fm5egEGVICsM1mhV6jOxczZP9AvMHts0xVuWUfZwrvOHt69PUx42P/gzPv5lPQH9o1AXWvw/ZGUVfQIhKQgrDNTiTlM63205xxw0N8fN2swbSk2DzJxA0CBp2LPoCokJwsLfjqf5BHD1/id9tN/NRyrhNeOmMbOYjqgQpDNdgxtpINJon+hVYVnvLp5CRbHRQikphSJt6NKtTnWmrwsnJtZm/0DTE2Mxn44eQlVr0BYSoBEqkMCilblZKHVVKRSqlXiwk/oxS6pBSap9Sao1SytcmlquU2mN5lLsd2U/Fp/FD2Gnu6dyYhp42S1ykxhub8LQcLpvwVCJ2dopnBgYRdSGVX/ecyR/s+wqkxhl7eAtRiRW7MCil7IFPgUFAS+AepVTLAoftBoK11m2BH4H3bGLpWuv2lsdQypnpayKwt1P/3IRn83TITpXWQiU0sGUd2jSowfQ14fm3AG3cFQIGGNu1yhagohIriRZDZyBSax2ltc4CFgPDbA/QWodqrdMsL7cCDakAImNT+GW3sWVnHQ8Xa+DSedg2F9rcBT7NzEtQlAqlFM8ODOJ0Qjo/7DydP9j3ZUhPNFqLQlRSJVEYGgC2/3qiLe8V5SHgD5vXLkqpMKXUVqXU8KJOUkqNtxwXFhcXV9RhJeqTNRG4ONrzaIh//sBf04ydvkKeL5M8RNkLCfKho68nM9dGkpmTaw3U72DMbdgy0ygQQlRCZdr5rJS6DwgG3rd521drHQyMAqYppfwLO1drPVdrHay1Dvbx8Sn1XCNjL/HbvjOM7uZHLXdna+DSOQj7HNrdY+z4JSolpRRP9w/ibHIG34dF5w+GvACZF2HrHHOSE6KUlURhiAEa2bxuaHkvH6VUf+BlYKjWOvPy+1rrGMufUcA6oEMJ5FRsM9ZG4uJgz7heBbbs/OsTyM2G3rJlZ2XXI6AWHX09mR1aoNVQr63Ratg62xiyLEQlUxKFYQcQqJRqopRyAkYC+UYXKaU6AJ9hFIVYm/c9lVLOlufeQA/gUAnkVCyRsSks3XuG0d19C7QWzhvLX7QbCV5NzUtQlAmlFE/1D+RMcgY//KPV8DxkJhsTHIWoZIpdGLTWOcAEYCVwGPhea31QKfW6UuryKKP3AXfghwLDUlsAYUqpvUAo8I7W2vTCMHNtBC4O9ozvVeDDf7OltdBLWgtVRc8Ab25oXJNZoZH5RyjVawfNboGts6TVICqdEulj0Fov11oHaa39tdZvWd57TWu91PK8v9a6TsFhqVrrzVrrNlrrdpY/55dEPsVxLM7SWuhWoLWQEgs75kPbu6VvoQoxWg1BRquh4AilPi8YExy3fWZOckKUEpn5XMDMtZE4O9gzrneB1sJf0yE3E3o/Z05iwjS9Ar3p0Lgms0KPFdJqGAxbLTPghagkpDDYiIpLYcmeGO7v5ou3tBaExeVWQ0xSOj/uLGSEkrQaRCUjhcHGzLWRODnYMa7QvoVM6P1fcxITpusd6E3wXoINAAAgAElEQVT7RjX5tGBfQ/32xiKKW6TVICoPKQwWUXEp/Lonhvu7+uJT3ba1EAfb5xmznKW1UGVdHqEUk5TOT7sKtBr6vAAZScZseCEqASkMFpdbC+N7F/jw3zxdWgsCMGZDt2tUk5lrC7YaOkDQzcZsaFlDSVQCUhiA4xdS+XVPDPd1KaS1sGM+tLkTvAOKvoCoEmxbDT8XbDWEWFoN26WvQVR8UhgwWguO9naMDynQt7BlBuRkSGtB/K3P5VZDaCTZtvs1NLjBaDVsllaDqPiqfGE4FZ/Gr3tiuLeLL7Wr26ygmhpv9C20vh28A4u+gKhSlFI82S+A6MR0ftldYOWXkOeNVsOOeeYkJ0QJqfKFYda6SOztFI8UbC1snQXZadBL5i2I/G5sVpvWDTyYFRqZf5e3Bh2N/aG3zJRd3kSFVqULQ3RiGj/ujGZkp0b591tITzTGpbccBrWbm5egKJeUUjzRN5AT8Wn8tq/ALm+9n4e0eGMFXiEqqCpdGGavO4ZS/HO/ha1zIOuS9C2IIg1oUYfmdaszY20kuXnaGmjcBZqEGKvwZqebl6AQxVBlC8PZ5HR+CIvmjo6NqF+zmjWQkQzbZhvLKtdtbV6ColyzszNaDVFxqSzffzZ/MOQFSI2FnQvNSU6IYqqyheGz9VHkac3jfQq0FrbPNYqDtBbEVQxqXZfA2u7MWBtBnm2rwa8H+PYwdvrLzjAvQSGuU5UsDLEXM1i0/RS33dCARl6u1kDmJWNpg8CbjKUOhLgCOzvFhL4BhJ9P4c9D5/IHQ56HS2dhz9fmJCdEMVTJwjB3QxTZuXk83qfApLUd842OZ9nLWVyjIW3r09TbjelrItHaptXQJAQadoZN0yAny7wEhbgOJVIYlFI3K6WOKqUilVIvFhJ3Vkp9Z4lvU0r52cQmWd4/qpS6qSTyuZILKZl8s+0Uw9s3wM/bzRrISoXNM8C/LzQMLu00RCVhb6d4/MYADp+9yOrDsdaAUkZfQ/Jp2LvIvASFuA7FLgxKKXvgU2AQ0BK4RynVssBhDwGJWusA4GPgXcu5LTG2Am0F3AzMslyv1MzbeJyMnFwev7FAa2HnF5B2wfjHLMS/MKx9fRp7uTJjbUT+VkNAP6h/A2z80Nj5T4gKoiRaDJ2BSK11lNY6C1gMDCtwzDDg8hCNH4F+SilleX+x1jpTa30ciLRcr1Qkpmbx1ZYTDGlbn4Da7tZAdrqxEY9fL2jctbR+vKikHO3teLyPP/uik1kXHmcNKGXclkw6Cft/MC9BIf4lhxK4RgPAds/DaKBLUcdorXOUUslALcv7Wwuc26AEcirUge9e4329k17Z3vCdza+eegFSzsPtpu8sKiqo225oyIy1kXyyJoI+QT4Y33sw1k+q2wY2fGBs9GRXqg1iUYlFxqbw1rJDTB3amsa1XK9+QjFUmM5npdR4pVSYUiosLi7u6icUwin1LO2rxVL90nG4EGF9pCdCh/vAr2cJZy2qCicHOx7r48/uU0n8FRlvDVzua0g4Bgd+Ni9BUeF9GhrJ1qgE3JxL/8tFSbQYYoBGNq8bWt4r7JhopZQDUAOIv8ZzAdBazwXmAgQHB+vCjrmaLk8sNGap2qnrOV2IK7ozuCEz10byydoIegZ6WwPNboHaLWHD+8aijHYV5vuYKCeOX0hlyZ4YHurZhFq22w6XkpL4P3QHEKiUaqKUcsLoTF5a4JilwBjL8zuAtdropVsKjLSMWmoCBALbSyCnItlLURClxNnBnkdDmrL9eAJbo2xaDXZ2xoTJC0fh8BLzEhQV1qxQY2uAcb2bXv3gElDswqC1zgEmACuBw8D3WuuDSqnXlVJDLYfNB2oppSKBZ4AXLeceBL4HDgErgP9orXOLm5MQZhnZuTHe7s58siYif6DlMPAOgvXvQ15e4ScLUYjTCWn8vDuGUV0a598aoBSVSJtWa71cax2ktfbXWr9lee81rfVSy/MMrfWdWusArXVnrXWUzblvWc5rprX+oyTyEcIsLo5Gq2HzsXjCTiRYA3b2Rqsh9iAcXW5egqLCmbXuGPZK8UjBbYdLkdzsFKKEjerSGC83Jz5ZG5k/0Oo28GoK698FfV3dZKKKiUlK58edp7m7UyPq1iib1gJIYRCixLk6OTCuV1M2hMex53SSNWDvYGz8dG4fRPxpXoKiwvhs/TEAHi242Gcpk8IgRCm4v5svNV0dmVGwr6HtXVCzsbQaxFWdv5jB4h2nuaNjQxrYbg1QBqQwCFEK3J0deKhHE9YcieVATLI1YO8IvZ6FmJ1wbK15CYpy77P1UeTmaR4LCbj6wSVMCoMQpWRMDz+quzgwY22BVkO7UeDRENa/J60GUai4S5l8s+0kIzo0KPVZzoWRwiBEKfFwceSBHk1YefA8R85dtAYcnKDnU3B6K5zYaF6Cotyat9HYGuA/BRf7LCNSGIQoRQ/28MPd2YEZBUcodbgf3OsarQYhbCSkZvHV1pMMbVefJrZbA2Snw9EVZdLKlMIgRCmq6erEmO6+LN9/lojzl6wBRxej1XBiI5z4y7wERbkzb2MU6dm5TOhboLUQtgAW3W30T5UyKQxClLKHejalmqP9P+c1dBwLbrVh/Tum5CXKn8TULBZuPsEtbeoRULu6NZCdbuwh7terTDYSk8IgRCnzcnNiTHc/ft93hshY21ZDNaPVcHwDnNxsXoKi3Ji3KYq07Fwm9gvMH9j5hbE1QJ9/bJBZKqQwCFEGxvWytBrWFGw1PGC0GtZJq6GqS0zN4ou/jNZCUJ0CrYVNHxuthTLaGkAKgxBlwMvNidHd/Pht3xkiY1OsASdX6PEkHF8PJ7eYl6Aw3fxNx4toLSw0WgtluO2wFAYhysi4Xk2o5mj/z3kNwQ+Cm4/0NVRhialZfLH5BIP/0VrIMFoLvj2hSa8yy0cKgxBlpJa7M/d382Xp3iJaDVHr4NTWIs8Xldf8TcdJycxhYt8CrYVdCyHlXJn1LVwmhUGIMjS+V1NcHOyZWVSrQfoaqpykNKO1cEubejSrW1hroUeZthZACoMQZaqWuzOjLa2GY3G2rQY36D4RokLh1DbzEhRl7nJr4Yl+BeYt7PoSLp0t89YCFLMwKKW8lFKrlFIRlj89CzmmvVJqi1LqoFJqn1LqbpvYF0qp40qpPZZH++LkI0RFMK53U5wd7JlZcF5Dp4fA1Vv6GqqQpDRjJNLgNnVpXtfDGsjOgE0fGa0Fv7JtLUDxWwwvAmu01oHAGsvrgtKA0VrrVsDNwDSlVE2b+H+11u0tjz3FzEeIcs/b0tewZE/MP1sNPSYaq65Kq6FK+HzTcS5l5vxzJNLur4zWQsgLoMp+n/riFoZhwELL84XA8IIHaK3DtdYRludngFjAp5g/V4gKbXzvpjg52BXSangYXGtJq6EKSE7LZsFfJxjUukBrIScTNn4EjbtDk96m5FbcwlBHa33W8vwcUOdKByulOgNOwDGbt9+y3GL6WCnlXMx8hKgQvN2dGd3Nr4hWw5PSaqgC5m+KKry1sOtLuHTG6FswobUA11AYlFKrlVIHCnkMsz1Oa62BIpf9U0rVA74CHtBa51nengQ0BzoBXkCRMziUUuOVUmFKqbC4uLir/2ZClHPjezfFxdGe6asLjFDq9LAxQin0LXMSE6UuITWLzy2thRb1bPsW0mHDB6a2FuAaCoPWur/WunUhjyXAecsH/uUP/tjCrqGU8gCWAS9rrbfaXPusNmQCC4DOV8hjrtY6WGsd7OMjd6JExeft7szY7sZs6Hz7NTi5Qc9njNnQxzeYl6AoNZ9tOEZqVg7PDAjKH9gx35i30PcV01oLUPxbSUuBMZbnY4AlBQ9QSjkBvwBfaq1/LBC7XFQURv/EgWLmI0SFMr53U9ydHPh4VXj+QPCDUL0+rH1LdnmrZGIvZbBw8wmGt29AoO0s58wUYySSf1/w62FeghS/MLwDDFBKRQD9La9RSgUrpeZZjrkL6A2MLWRY6jdKqf3AfsAbeLOY+QhRodR0deKhXsYub/ujbfaGdnSB3s8au7xFrjEvQVHiZoUeIztX82TBvoVtcyAtHm58xZzEbChdAb+NBAcH67CwMLPTEKJEXMzIpvd7oXRoVJMFD9jcTc3Jghkdwa0WjAs19daCKBlnktLp8/46RnRowLt3tLUG0pNgelujb2HU4lL7+UqpnVrrq27oIDOfhTCZh4sjj/T2J/RoHDtPJloDDk7Q5wU4sxuOLjcvQVFiZoZGotH/nOW8dRZkJMONL5mTWAFSGIQoB8Z098Xb3YmPVh3NH2g7Erz8jb6GvLzCTxYVwqn4NL7fcZp7OjemoaerNZAaD1tmQcvhUK9t0RcoQ1IYhCgHXJ0ceKxPAH9FxrP52AVrwN4B+kyC2INw6BfzEhTFNn1NBPZ2iv/cWKC1sHk6ZKUY/53LCSkMQpQT93ZpTF0PFz76M5x8fX+tbwOfFhD6P8jNMS9Bcd2OxaXwy+5o7u/qSx0PF2vg0nnYNhfa3gW1m5uXYAFSGIQoJ1wc7ZnQN4Cwk4lsiLBpNdjZG/ee4yNg/w/mJSiu27TVEbg42vNoH//8gU0fQ25Wme7Odi2kMAhRjtwV3IiGntX48M+j+VsNLW6Fum1h3f8gN9u8BMW/dvjsRX7be4YHevjh7W6z6k9yNITNhw73Qi3/oi9gAikMQpQjTg52TOwXyL7oZFYePGcNKGXMhk06aezqJSqMD/8Mp7qzA+N6Nc0fWP+eMXmx93/NSewKpDAIUc7c1qEBAbXdeW/lUXJybUYiBQ6Ext1g3bvGLFlR7u04kcDqw+d5tI8/NV2drIG4o8bS2p0ehpqNzUuwCFIYhChnHOzteOHm5kTFpfJ9WLQ1oBQMeB1SY41x76Jc01rzv+WHqV3dmQd7NMkfXPM6OLqVy9YCSGEQolzq36I2wb6efLw6nLQsm5FIjTpD8yHw13RIkVWGy7OVB8+z61QSTw8IopqTvTVwaisc+R16PmnMai+HpDAIUQ4ppZg0uDlxlzL5fNPx/MH+UyzLM79vRmriGuTk5vHeyiP4+7hxZ8eG1oDWsGoyuNeFro+bl+BVSGEQopzq6OvFwJZ1mLM+iviUTGvAOxBuuB/CPoeEKPMSFEX6PiyaqLhUnr+5OQ72Nh+zR5cbCyP2edFYXr2cksIgRDn2/M3NSMvKYWZogS1A+0wCe0dYKwsSlzdpWTlMWx1OR19PBra02dQyNwdWT4VagdDhfvMSvAZSGIQoxwJqV+fuTo34eutJTsWnWQPVLbciDvwEMbvMS1D8w+ebjhN7KZNJg5qjbFfE3fMNXDgK/ScbS52UY1IYhCjnnuofhL2d4sOCC+z1eBKqecHqybKZTzmRkJrFnPVRDGhZh2A/L2sgK82YnNjQMnignJPCIEQ5V8fDhYd6NmHJnjMciLHZzMfFA0KeN7b/PCab+ZQHM9ZGkJaVw/M3Ncsf2DYbLp2FAVMrxL4axSoMSikvpdQqpVSE5U/PIo7Ltdm9banN+02UUtuUUpFKqe8s24AKIQp4JMSfmq6OvLviSP5A8INQ0xdWTZFluU12OiGNr7ee5K7gRvm37ExLgE3TIGgQ+HY3L8F/obgthheBNVrrQGCN5XVh0rXW7S2PoTbvvwt8rLUOABKBh4qZjxCVkoeLIxNuDGBjxAXWh9vMX3Bwhr6vwvn9sO878xIUvL/yKPZ2iqf6B+UPbHjfWFa7/2RzErsOxS0Mw4DLC7csBIZf64nK6JXpC/x4PecLUdXc380X31quvPH7IbJtl8pofTvUvwHWTJWlMkwSdiKBpXvPMK5XU+rWsFlWO+4obJ9rjEKq3cK8BP+l4haGOlrrs5bn54A6RRznopQKU0ptVUpd/vCvBSRprS9P64wGGhQzHyEqLWcHe165pSWRsSl8vfWkNWBnB4PeNe5hb/rIvASrqLw8zZTfDlLXw4XHbJfV1hpWTDKWvuj3mnkJXoerFgal1Gql1IFCHsNsj9PGGsFFDY3wtWxAPQqYppT612vMKqXGW4pLWFycLAUgqqb+LWrTK9Cbj1eF55/01qgztL0bNs+EhONFX0CUuB92nuZAzEUmDW6Oq5PNMNTwlcaggD4vgJu3eQleh6sWBq11f61160IeS4DzSql6AJY/Y4u4RozlzyhgHdABiAdqKqUu/002BGKukMdcrXWw1jrYx8fnX/yKQlQeSileG9KS1KxcPloVnj/YfwrYOcCfr5iRWpV0MSOb91cepaOvJ0Pb1bcGcrJg5STwDoLO481L8DoV91bSUmCM5fkYYEnBA5RSnkopZ8tzb6AHcMjSwggF7rjS+UKI/ALrVOf+rr4s2n6KQ2cuWgMe9aHXM8YCbVHrTMuvKpm5NpL41Cym3Noq/2S2bXOM5Upu+p8xQ72CKW5heAcYoJSKAPpbXqOUClZKzbMc0wIIU0rtxSgE72itD1liLwDPKKUiMfoc5hczHyGqhKf7B1GjmiNTfzuYf6e3bhOM9f1XTJL9oUtZVFwKC/46zp0dG9KmYQ1r4NJ5YxOewJsgsL95CRZDsQqD1jpea91Pax1oueWUYHk/TGv9sOX5Zq11G611O8uf823Oj9Jad9ZaB2it79RaZxb1s4QQVjVcHXl2YDO2HU/gjwM2O705usDAtyD2EOxcYF6CVcCbyw7j7GDPcwUns619HXIy4Ka3zUmsBMjMZyEqqHs6N6Z53eq8tewwGdm51kCLW6FJb2OBvbQE8xKsxEKPxrL2SCwT+wVQu7rN8NSYXbD7G+j6KHgHmJdgMUlhEKKCsrdTTL61FTFJ6czdYLP8tlJw8zuQeRFCK+631vIqKyePN34/RBNvN8Z2t9mZTWv4wzICqffz5iVYAqQwCFGBdfOvxeA2dZm1LpIzSenWQJ1WxnIZYfPh/EHzEqyEvtxygqi4VF65pQVODjYfoft/gOjt0G+ysY5VBSaFQYgK7qXBxozaKUsLFIAbXwaXGvD707KOUgk5k5TOx6vCCQnyoW/z2tZAeiKsfBnqd4D295qXYAmRwiBEBdfQ05Wn+gfx56HzrLDtiHb1MjqiT2+TjugSoLXmtSUHyNWaN4a1zj88ddVkSIuHW6cbM9EruIr/GwgheKhnE1rU8+C1JQe4mJFtDbQfZXREr54CF8+Yll9l8MeBc6w+HMvT/YNoXMvVGjixCXYthG6PQ7125iVYgqQwCFEJONrb8c5tbbiQksm7f9gsza0UDJkGuVnwR8XuEDVTcno2k5cepFV9Dx7qadPhnJ0Bvz1pLH3eZ5J5CZYwKQxCVBLtGtVkbPcmfLPtFDtO2AxTreUPIS/A4d/g8O/mJViBvfPHEeJTMnnntrY42Nt8bG78EOIjYcjH4ORmXoIlTAqDEJXIswODaFCzGpN+3k9mjs3chu5PQJ3WsPw5yLhY9AXEP2w/nsCi7ad4sEeT/DOcYw/Dpo+NxQsD+pmXYCmQwiBEJeLm7MCbw1sTGZvC7HXHrAF7R7j1E7h0zti3QVyTzJxcJv28j4ae1XhmoM0GPHl5sHQiOFev0DOciyKFQYhK5sbmtbm1XX1mhR4jMvaSNdCwI3R5FHbMh1PbzEuwApkVeoxjcam8Obx1/iW1w+YbcxZuervCLal9LaQwCFEJvTakJdWc7Jn0837y8mwW2ev7Mng0MDpMc7LMS7ACiDh/iVnrIhnWvj59mtnMWUiOgdVToWkfaDfSrPRKlRQGISohn+rOvDy4BTtOJPLNNpvd3pyrw5CPIO6w0XEqCpWTm8cLP+3DzdmBV4e0tAa0hmXPQl6O0eFsO5ehEpHCIEQldWdwQ3oFevPW8sP5bykF3WR0mG54H05vNy/BcuzT0GPsOpXE1KGt8HZ3tgbCPofwP6Dfq+DV1LwES5kUBiEqKaUUH97ZDlcnB55YtCf/KKXB70ONBvDTwzJKqYCdJxP5ZG0Ew9vXZ1h7m23o444ay17494Uuj5mXYBmQwiBEJVbbw4V3b2/L4bMXeX/FUWvApQbcNg+So40hrAKASxnZPPXdburVcOH14a2tgZxM+PEhcHKF4bMrxbIXV1Ks304p5aWUWqWUirD86VnIMTcqpfbYPDKUUsMtsS+UUsdtYu2Lk48Q4p8GtKzDfV0bM2/TcTaEx1kDjbtAyPOw7zvY94N5CZYjry05yJmkDKaPbI+Hi82WnGteh/P7YehMqF7XvATLSHHL3ovAGq11ILDG8jofrXWo1rq91ro90BdIA/60OeS/l+Na6z3FzEcIUYiXB7cksLY7z/6wl/gUm40Sez0HjbrCsmcg8YRp+ZUHS/bE8MvuGJ7oG0BHXy9rIHINbJkJnR6G5oPNS7AMFbcwDAMWWp4vBIZf5fg7gD+01mnF/LlCiH+hmpM9n9zTgeS0bF74aZ91n2h7B7htrvH85/FVdp/o0wlpvPLLAYJ9PZlwo83Oa6kX4NfHwKc5DHzTvATLWHELQx2t9VnL83NAnascPxJYVOC9t5RS+5RSHyulnAs7CUApNV4pFaaUCouLiyvqMCFEEVrU8+CFQc1ZfTiWr7edsgY8feGWj4zluTd+YF6CJsnJzeOp74ybFR/f3d66FpLWsGSCsdfC7fPBsZqJWZatqxYGpdRqpdSBQh7DbI/TxlcQXcRlUErVA9oAK23engQ0BzoBXsALRZ2vtZ6rtQ7WWgf7+PhcLW0hRCEe6O5H7yAf3vz9EBHnbYawtr3TGMK6/l04tdW8BE0wMzSSnScTeXNEaxp52SynHTbfGJrafyrUbV30BSqhqxYGrXV/rXXrQh5LgPOWD/zLH/yxV7jUXcAvWuu/F4vXWp/VhkxgAdC5eL+OEOJK7OwUH9zZFndnBx75eifJ6TZ7Nwz+AGo2hh/GwsWzRV6jMgk9EssnayIY0aFB/qGpp7fDikng389YRqSKKe6tpKXAGMvzMcCSKxx7DwVuI9kUFYXRP3GgmPkIIa6idnUXZt/XkdMJaUz4dhc5uZZtP1084O5vjHkNi++BrMrdFRh+/hJPLNpNi3oevDXCpkWQdAoWjzKWDrl9XqUfmlqY4v7G7wADlFIRQH/La5RSwUqpeZcPUkr5AY2A9QXO/0YptR/YD3gDVad3RwgTdW7ixVvD27Ax4gJv/H7IGqjb2vgwPLMHlvzHuM9eCSWkZvHQwh1Uc7Jn3phg6wJ5mSmw6B5j3sKo74ztUasgh6sfUjStdTzwj4XItdZhwMM2r08ADQo5rm9xfr4Q4vrd1akR4ecvMW/TcQLrVOe+rr5GoPlg6D/Z2A7Upzn0KbLrr0LKysnj0a93cv5iJt+N70q9GpZO5bw8+OURiD0Eo34An2bmJmqiYhUGIUTFNmlwC47FpTB56UGaervRPcCyhHSPp4wlINa9DT5B0GqEuYmWEK01r/56gO3HE5g+sj0dGtvMyV37Bhz5HW5+FwL7m5dkOVD1bp4JIf5mb6f45J4O+Pu48dg3uzh+IdUIKAW3TodGXeCXx+DMbnMTLSHzNx3nu7DTPNE3IH9n897vYNNH0HEsdHnEtPzKCykMQlRx1V0cmTe6E3YKHlq4wzpSycHZ6Ix284ZFoyr8SKXQI7G8vfwwg1rX5en+Nruxnd4OS58Av17GyKxKupT2vyGFQQhB41quzLGMVBr3ZRipmZYZ0O4+cM9iyEiGb+6EtARzE71OO08m/j0C6cO72mFnZ/nwP3/I6Gz2qA93fWlsgSqkMAghDF2a1uKju9qz82QiYxdsJ+VycajbGu7+Ci6Ew8JbjWUiKpAdJxIYPX8bPtWdmT+mk3UE0rkDsHCIUQzu/bHKjkAqjBQGIcTfbm1Xn09GdmDXqSRGz9/GpQzLbaWAfsbwzfhI+GIIpFxpLmv5sTUqnjGfb6dODRcWj+9K3RouRuDsXqMoOLjA2GXgHXDlC1UxUhiEEPnc0rYen47qwL7oZO6fv93a5+B/I4z6HpJOGsXh0jlzE72KzZEXGLtgO/VrVmPx+K7U8bAUhTO7YeFQcHI3ikItf3MTLYekMAgh/uHm1vWYde8NHDyTzP3zt5GcZikOTUPg3h+MDX6+uAUunjE30SJsCI/jgS924OvlxuLxXald3VIUonfCwmHGLO+xy8CribmJllNSGIQQhRrYqi5z7uvIkbOXGDVvK4mpWUbAryfc95PRYvjiFkiOMTfRAtYdjeXhL8No4u3Gt+O6WPdsPr0DvhoOrp5GUfD0NTfRckwKgxCiSP1a1OGz0R2JiE3h9jmbOXrOsiKrbze4/xdIiYP5A40PXZNprflqywnGf7mTAB93Fo3rSq3LRWH/j/DlMGPo7dhlxmKBokhSGIQQV3Rjs9p8+WBnLqbnMOzTTXwfdtrY6KdRZ3hgGdjZw4KbYfMM09ZWupSRzYRFu3l1yUF6BNTi23Fd8HRzgux0+O0p+OkhqNsGxi6HGg1NybEiUboCLpIVHBysw8LCzE5DiCol9lIGTy3ew+Zj8dx2QwPeHN7aGPqZnmQsuHfkd2g2GIZ9WqZDPw/EJDPh212cTkznuYHNeKR3U2OeQvwx+H6MsVdzj6eg7ytVfp6CUmqn1jr4qsdJYRBCXKvcPM2MtRFMXxOBv487s+69gaA61Y2WwrY58OerUL0e3LkAGl7186dYtNZ8s+0Ur/9+CC9XJ2aM6kAnP0tBOvATLJ1oFIIRcyFoYKnmUlFca2GQW0lCiGtmb6d4qn8QXz/UhaS0bIbO3MTXW0+Sk6eh62Pw4EpQwOc3w6ZpxvLVpSD2UgYTF+/hlV8P0K1pLZZN7GkUhbQE49bRjw9CnVbw6CYpCtdBWgxCiOtie2upibcbT/YL5NZ29bHPTDL2Sj7yO3g0hN7PQvv7wMGp2D/zQkomn60/xldbT5Kdq3lmQBCPhfhjl5kMW2fB1tmQeRG6T4R+r1X5W0cFlcmtJKXUncAUoAXQ2bIPQ2HH3QxMB+yBeVrryxv6NAEWA7WAncD9Wuusq/1cKQxClA9aa1YdOs9Hq8I5clFlKfoAAAXnSURBVO4S/j5uPNU/iFta18XuxHoIfQuid0CNxhDyX2h3z3V9WCekZjF3QxQLN58gMyeX4R0aMLFvIH7uubDtM9gyw1jPqcVQ6POi0VoQ/1BWhaEFkAd8BjxXWGFQStkD4cAAIBrYAdyjtT6klPoe+FlrvVgpNQfYq7WefbWfK4VBiPIlL0+z4uA5Pl4VTkRsCs3qVOeRkKb0DvTG+9wmo0Cc2QWefkZHcNBNxsJ1V7lmeOwlft97lgV/HSctO5eh7eozsW8A/nbn4NAS2DIT0hONTu8+k6Be27L5hSuoMu18Vkqto+jC0A2YorW+yfJ6kiX0DhAH1NVa5xQ87kqkMAhRPuXmaZbtP8u01eFExRl7OzSrU51uTb0Y6rqfdhGfYh+73zi4VgA06W0sd+3XC+3mTdSFVLYcizceUfEkWCbVjW4B//GNoU78Dji+AVIsy3EEDjQKQoMbzPh1K5xrLQxlsYNbA+C0zetooAvG7aMkrXWOzfv/2P5TCFFx2Nsphrarzy1t6rEvOoktUcaH/OKw03yR7YFSL9K/Ziwd8/bTPnkfbcIW4Rb2/+3db2hVdRzH8fdn9+pyK53pQJYuhw0pIifRJAnrgY/sQQmGQUWWJDJ6EBH0IKgHPRAyiKAgFkU9KuwPVBQI1SSwqAybU0pTRqRC2MLbQBu1fXtwzmxH9vfe7c7tfF5w2O+enfP7M9j98Du/c+59E4DTrGBwqEA7sLFQQ92CAouWF6jnIsXes9AL1Df+HyYtm/w5RzNkwmCQ9DmwYpRfPRMRH01/l8bsxy5gF0Bzs59aNLuSFWrE+ualrG9eSsddNzDw7yDdv5X45lQfJ35v4gjrOALUxCDNAydYe+Ewzf+cYlldkWX1tdTVFrj0dTmFWlh5WxIEjWv9RTpVMGEwRESlX356Blg14vXKdF8f0CCpmM4ahveP1Y9OoBOSS0kV9snMqqi2WKC95VraW0Z78K0deLDaXbJxVOM5hu+BVkktkhYC9wMfR7K40QVsS497GKjaDMTMzEZXUTBI2irpNHA78Kmk/en+JkmfAaSzgceB/cBPwL6IOJZW8TTwpKSTJGsOb1TSHzMzq5wfcDMzywl/JIaZmZXFwWBmZhkOBjMzy3AwmJlZhoPBzMwy5uRdSZLOAb+Wefpy4I9p7M5c4DHng8c8/1U63usjonGig+ZkMFRC0qHJ3K41n3jM+eAxz3/VGq8vJZmZWYaDwczMMvIYDJ2z3YFZ4DHng8c8/1VlvLlbYzAzs/HlccZgZmbjcDCYmVmGg8HMzDIcDGZmluFgMDOzDAeD5YKkBkkdablJ0vsz2FabpC0zVb/ZTHMwWF40AB0AEXE2IrZNcHwl2gAHg81Zfo7BckHSu8A9wHHgF+DGiLhZ0g7gXqAeaAVeBBYCDwEDwJaI+FPSGuBVoBG4ADwWET9Lug94DhgESsBm4CSwCDgD7AF6gZeBq4CLwCMRcXwKbR8AuoE7gSLwaER8NzN/KTMgIrx5m/cbsBo4Okp5B8kb+TUkb/olYHf6u5eAJ9LyF0BrWt4AfJmWe4Dr0nLDiDpfGdH2YqCYljcDH0yx7QPA62l503DfvXmbqa04XQFjNod1RUQ/0C+pBHyS7u8BbpF0NbAReE/S8Dm16c+DwFuS9gEfjlH/EuBtSa1AAAsm2/aI494BiIivJC2W1BAR58scr9m4HAxmyWWbYUMjXg+R/I/UAOcjou3yEyNit6QNwN3AD5JuHaX+50kCYKuk1SQzgMm2fampy5seZzxmFfHis+VFP8klmymLiL+A3nQ9ASXWpeU1EfFtRDwLnANWjdLWEpL1BkguH5Vje9reHUApIkpl1mM2IQeD5UJE9AEHJR0F9pZRxQPATkndwDGShWyAvZJ60nq/Jlkk7gJukvSjpO3AC8AeSYcpf5b+d3r+a8DOMuswmxTflWR2hUvvSnoqIg7Ndl8sHzxjMDOzDM8YzMwswzMGMzPLcDCYmVmGg8HMzDIcDGZmluFgMDOzDAeDmZll/AdmfJxEq4D1IQAAAABJRU5ErkJggg==\n", 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" ] @@ -165,7 +166,8 @@ } ], "source": [ - "result.plot('timestamp', ['signal','mirror'])" + "result.plot('timestamp', ['signal','follow'])\n", + "# result.plot('timestamp', ['signal'])" ] }, { diff --git a/simulations/demo/simple_tracker.py b/simulations/demo/simple_tracker.py index 9003500..71fa391 100644 --- a/simulations/demo/simple_tracker.py +++ b/simulations/demo/simple_tracker.py @@ -27,7 +27,8 @@ def add(step, sL, s, _input): period = 50 def sinusoid(step, sL, s, _input): y = 'signal' - x = np.sin(s['elapsed_time'] * 2 * np.pi / period) + x = s['elapsed_time'] + t_delta.seconds + x = np.sin(x * 2 * np.pi / period) return (y, x) def delta_time(step, sL, s, _input):