{ "cells": [ { "cell_type": "markdown", "id": "985c8633", "metadata": {}, "source": [ "# Estimate the Snow Surface using the NIR\n", "In this notebook we show how the snow surfaces position is detected using the NIR signals. \n", "The Lyte probe has side looking ambient and active NIR sensors. Using these timeseries \n", "we can estimate where the snow surface is reliably. \n", "\n", "The measurement used here is manually driven at about 1 m/s " ] }, { "cell_type": "code", "execution_count": 22, "id": "348dae62", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'RECORDED': '2022-01-14--13:07:11', 'radicl VERSION': '0.5.1', 'FIRMWARE REVISION': '1.46', 'HARDWARE REVISION': '1', 'MODEL NUMBER': '3', 'SAMPLE RATE': '16000'}\n" ] } ], "source": [ "# Import the function to read the data \n", "from study_lyte.io import read_csv \n", "# Impor the function to calculate the surface\n", "from study_lyte.detect import get_nir_surface\n", "\n", "# Import calibration function and the plotting\n", "from numpy import poly1d\n", "import matplotlib.pyplot as plt \n", "\n", "# Open the file \n", "df,meta = read_csv(\"./data/nir_surface_example.csv\")\n", "print(meta)\n", "# Add a time column \n", "df['time'] = np.linspace(0,len(df.index) / int(meta['SAMPLE RATE']), len(df.index))\n", "\n", "# Rolling mean to reduce noise\n", "df = df.rolling(window=100).mean()\n", "\n" ] }, { "cell_type": "markdown", "id": "5e23425a", "metadata": {}, "source": [ "## Raw Data\n", "Below is the raw force profile. In it you can see lag time before the probe enters the snow this can make interpretation difficult.\n", "Thus we need a way to estimate where the location of the snow surface." ] }, { "cell_type": "code", "execution_count": 26, "id": "21adefa7", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot up the raw stuff\n", "fig, ax = plt.subplots(figsize=(6,10))\n", "\n", "ax.plot(df['force'], df['time'], color='k')\n", "ax.set_title(\"Raw Profile After Calibration\")\n", "ax.set_xlabel('Force [mN]')\n", "ax.set_ylabel('Time [s]')\n", "ax.invert_yaxis()\n" ] }, { "cell_type": "markdown", "id": "5d89e7b4", "metadata": {}, "source": [ "## Workflow\n", "\n", "\n", "\n", "**Assumptions**: \n", "\n", "* The two sensors are measuring the same thing above the snow \n", "\n", "**Steps**:\n", "\n", "1. Normalize the two signals using the mean value of the 1st 200 points \n", "2. Calculate the absolute value of the active minus the ambient\n", "3. Search for the first value greater than the threshold" ] }, { "cell_type": "code", "execution_count": 27, "id": "a2174d18", "metadata": {}, "outputs": [], "source": [ "n_points = 1000\n", "surface = get_nir_surface(df['Sensor2'], df['Sensor3'])\n" ] }, { "cell_type": "code", "execution_count": 29, "id": "8066b846", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot it up \n", "fig, ax = plt.subplots(figsize=(6,10), ncols=1)\n", "\n", "ax.axhline(df['time'].iloc[surface], 0, 1,linestyle='dashdot', label='Est. Surface', color='cornflowerblue')\n", "ax.plot(df['Sensor3'], df['time'], color='magenta', label='Active NIR')\n", "ax.legend()\n", "ax.invert_yaxis()" ] }, { "cell_type": "markdown", "id": "914d8d39", "metadata": {}, "source": [ "## Show off a final plot!" ] }, { "cell_type": "code", "execution_count": 30, "id": "81362e71", "metadata": { "tags": [ "nbsphinx-thumbnail", "nbsphinx-gallery" ] }, "outputs": [ { "data": { "image/png": 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Py5whIitEZLmI/LO76473zQRaeeSfP2X0iA5XbYwxphvCFgQi4gX+BBwLjAXOFpGxuy0zArgJOEhVxwFXd3f9Xs8gkuK/BcCX6z/pnaKNMSYGhbNFMB1Yo6rrVLUZeAw4abdlvgX8SVUrAFR1R09ewCuD8Ug+za0L+L/f3dcrRRtjTKwJZxAMADa1u18Seqy9kcBIEXlXRN4XkWM6WpGIXCIiS0RkiWpdu8e9JMVfhEfyue76S0hLOaq3t8EYY6Ke053FPmAEMBs4G7hPRDJ3X0hV71XVqao6VeSrZxJ7JJPk+EsAqK1/jZHDz2bt2rXhrtsYY6JGOINgM1DU7v7A0GPtlQDzVLVFVb8EVhEMhh4R8ZEUdz4e6cfqtY8xfPhwvN4cjjrqa7z44ouo6l5vhDHGRLtwBsFiYISIDBWReOAsYN5uyzxDsDWAiOQS3FW0bm9ezOcdQ0rCVaTEX0uC70Q89OO1157luOOOw+cdyN///hiBQGCvN8YYY6JV2IJAVVuB7wAvA58DT6jqchH5mYjMDS32MlAuIiuAN4DrVbV8X17X48kl3jeTpPjzSE24mUTf6ShNnHfe2cT5+vPww3/H77eT0Iwxpo24bbeJ1zNQUxK+06PnqPppDSylufUNAlqKzzOBFr8NTWGMiR0i8qGqTu1ontOdxX1CxIvPM4k47yEA+LWE005+zOGqjDEmMvicLqAvqDbR0PIo/sBKvDKYhLhTue03hzldljHGRISoDwJ/YBuNLU8Q0O0k+E5k1Kij+XTZFU6XZYwxESOqg6CpdQHNra8iJJEUdz6TJ8/knfe/5XRZxhgTUaI2CPyBrTS3vozPM47EuFNYtvIqBg/OdbosY4yJOFEbBK3+ZYCHxLjTqGm8xelyjDEmYkXtUUMB3YpIFpMm9Xe6FGOMiWhRGwStgfX4PEO55/65XS9sjDExLGqDAPxAAuPGFTpdiDHGRLQoDgIPwTAwxhjTmSgOAnG6AGOMcYUoDgJjjDHdEbVBIJKIaoPTZRhjTMSL2iBAWxCJc7oKY4yJeFEZBKrNKLWI5DhdijHGRLyoDIKAbgVAtdrhSowxJvJFZRB4pCB0yw4fNcaYrkRlEIgk4vPsR4v/Q2664W9Ol2OMMREtKoMAIDHuJIRUfv2b7/LYY/9xuhxjjIlYURsEIikkxV+EUsfZZ5/Cicdf63RJxhgTkaI2CAC8ngJSEm7EIwU898LtfO3025wuyRhjIk5UBwGAR9JJjr8Uj+Ty1L9+zttvf+Z0ScYYE1GiPggg2HmcGHcu0MCRh1/ndDnGGBNRYiIIILibyCP98AfWOV2KMcZElJgJAgCvZwj+wAZefnGp06UYY0zEiLEgGA60cMrJf3K6FGOMiRgxFQQ+TzEg+P1rnS7FGGMiRkwFgUgSHhlAa2A5ra2tTpdjjDERIaaCACDOO5mAbiMn4wanSzHGmIgQc0Hg8QQvZt/SstPhSowxJjLEXBBIaJMV2zVkjDEQk0GQFLxhl7E0xhggBoMgoBUAiGQ5XIkxxkSGmAsCkXQAlFqHKzHGmMgQc0HgkRxACAR2OF2KMcZEhJgLApE4RLIJ6HanSzHGmIgQc0EA4JVBtAbW8czTHztdijHGOC4mg8DnHQ40cO5ZDzpdijHGOC4mg8DrGQYIrf5lTpdijDGO8zldwN4Q3bfne8nAK0X4A2tpbGwhMTGudwozxhgXcl0QeBEyNX6f16MymNrAB/zjocV889KZvVCZMca4k+t2DQngxbPPU6IMQmnm1uvmOb1JxhjjKNe1CATBq/ueX0kyCIC6hk37vC5jjHEz1wUBBFsF+yqefAAaA2t6YW3GGONerguCAEqdtOz7igQSPZOoCXzEibPu5Nk3r9z3dRpjjAu5ro8AgruHeuO/dO8xQCtvvbvA6U0yxhjHuK5FIEAC3l5ZV0tgCwAe6nplfcYY40YuDALplT4CgMrA6wAk0L+X1miMMe7juiAASOiFo4YA8jyHs9n/CKo1vbI+Y4xxI9cFgUeFRO2dM4EbNAGAdBnRK+szxhg3cl8Q0Ht9BL5QX7nXLltpjIlhrgsCQUjR3gmCAIUArAz8Abi9V9ZpjDFu47og8CAka++UvSbwDgCT5Xu9sj5jjHEjFwYBJPZSiwCaETwMZXIvrc8YY9zHhUEgxPXSAaSp5AAw5WupvbI+Y4xxI9edWSxAvHp6ZSpkFEqAN554z+nNMsYYx7iuRQC9l17p5AHQRG0vrdEYY9zHdUGgQBP7eImykHhy8ZHAFr7olfUZY4wbuTIIGsXfS2vzkqb9qaEUVUWktwavMMYY9whrEIjIMcAdgBf4q6r+arf5twOHhe4mA/mqmtnZOgMoDRLotRobtQYf6RYCxpiYFbYgEBEv8CfgKKAEWCwi81R1Rdsyqvq9dstfCezf1XoVCOzr1evbSaaAJqpoamoiISGh19ZrjDFuEc6jhqYDa1R1nao2A48BJ3Wy/NnAo12tVACfSq9NCaTSQg1/uW5h72y1Mca4TDh3DQ0A2l8QuAQ4oKMFRWQwMBSYv4f5lwCXACSRs2uMoN5QzSYyGMTwifm9tk5jjHGTSDmP4CzgKVXtsBdYVe9V1amqOjWBdDzQa1MTlSSTy3EXjwvn9hljTMQKZ4tgM1DU7v7A0GMdOQu4ojsrFSBOe6dj16/NtFBPMhm9sj5jjHGjcAbBYmCEiAwlGABnAefsvpCIjAaygG6d3ivQi9coE7zEUbPHfDLGmOgXtl1DqtoKfAd4GfgceEJVl4vIz0RkbrtFzwIeU9XeOxSom3wSz2iOYwPvMbf4ur5+eWOMiQjiwOfvPsmRYj1Wbu219fm1hde5jTJW88tvPcyN957ba+s2xphIISIfqurUjuZFSmdxtyngR3ttQnwczNUkksnP7ruSpZ+s6LIGY4yJJq4LgnBIkDRmcT3N1HL0/mc5XY4xxvQp1wWBB0hST69PhQxmNHPYxmf88MB/O72ZxhjTZ1w36JwAiWHKrxz6AbDh/W1hWb8xxkQi1wWBByFcIwLFE7wEpo/eG9TOGGMineuCQICEMA0UmqyJACj14XkBY4yJQK4MgnANGF1IMQAb+SRMr2CMMZHHdZ3FEDyENBxTpvRjMON4n3m8+pf3+3KTjDHGMa4MAq+Eb5ojF9JEA1dfdq3Tm2mMMX3ClUEQTnlSxFSOZgUL+dWEp5wuxxhjws51fQQAgTCPipFB8NoE2z6rDO8LGWNMBHBlEIT76sLNNALgCR1Oaowx0cydQRDmJNioy8mhP5mFyeF9IWOMiQDWR7Ab1QAbWM4gxnLNirldP8EYY1zOhS0CxSfh6yRQIEGTWc67JKS48O0xxpgecl2LwAPEecI3xXuFE7wX0UwDhyWe4vTmGmNM2LkuCBDwioZ1muydyQzPHN4NPM8pORc6vcXGGBNWrtv3IYDPG/6rqs31nEtl8w7m7XyEmw8bx8/fuD7sr2mMMU5wX4sA8AfCP4n6ONN3BYpy64Lvs/njHU5vtjHGhIXrgkABf0D6ZIrTFOZ6vwHAbdMfcHbDjTEmTFwXBACK9Nk0WoLXev408K7DW22MMeHhyiDoS6mSyVAZy4eBBSy8d6nT5RhjTK9zZRB40D6dDvIcSwO13Pbt253edGOM6XWuC4K2o4b6chrv258RnvG8Gnic1/7vHaffAmOM6VXuCwLp+yCI88GhcUfTRAN/++GjTr8FxhjTq9x3HoEoCfGtff66Rd5+0ATqaejz1zbGmHByZxDEBfr8dfN8aQBUYOcTGGOii/uCgODho30tThIo9PZnk39Dn7+2McaEk+v6CAA8oo5MxXHFbGhZzxcvfuH0W2CMMb3GlUEQUGemkXGjqdVaHjn3n06/BcYY02tct2tIgUCg73cNAQz3jQLg7Ya3HHl9Y4wJB1e2CLwedWTKj8vhgIQZvNv4Nn//hrUKjDHRwXVBIIDHo45NX0s/nTiJ47Z//IKWlhan3w5jjNlnrgsCp2V5szgz7UxWNK/gkPRDCAT6/lBWY4zpTa7rI4DgUUNOOizlUOq0hn/VPM3JGXOZV/Oco/UYY8y+cF+LQIIdxk5Px6UcxyFJB/Ns7fOcOeAMVJ0NJ2OM2VuuaxEI4HHmoKGvEuGCzK/TSgtPbHmS8sxyXq18DZFIKM4YY7rPdUEAIBIZ++XjxMMlWReR4U3npepXOSrzcF4qfxWfz5VvqzEmRrnuE0tQfL5I2g0jfD37NLwe4fnqV9gvdSwLt35AVlaW04UZY0y3uLOPQCNrAuHszNO4NPtCVjetY1z+GDZv3Oz0O2WMMd3iuhYBBM8jiESz0meQH5/Nr7bfyZyRR7C80cYkMsZEPve1CHC+BdDZNDphJCemz2FF00r+Nvkup98qY4zpkitbBAGN7CNzsrzZAFRtr3W4EmOM6ZorWwTBg0gjd8r0ZgDwZq1d39gYE/lcFwSq4vjun66m/RLGMyN5Os9UP8+FA851+i0zxphOuXLXkDo0DHV3CV4uzfwmzYFmHt7yKFkzs7l94Z1Ol2WMMR1yXRCoQktrZAdBkJeL0y/hz4E/c8d7fyJ7dh43L/ix00UZY8z/cN+uIaDV73XF5AkkcV7qxSjK7W/fwZoX1zn99hljzP9wYYtAaG11T34taFgAwAjvGLa9X8rwY4udLcgYY3bjuiBAwR9wTxBkSA4Ak+MO5OBbDnC4GmOM+V/uCwLAI37cMshngif4FmeFAsEYYyKN64LA41GSk1qdLqPb1jd9gSDkJ2Q4XYoxxnTIdUEgosTH+Z0uo1v86ue9xoV4EA744f5Ol2OMMR1yXRB4RElIcMdF4x8s/RfN2sLcjKMoGGm7howxkcl1QRBsEUR+ELxU8S4vVL3BsZkHc16/kyg+YYTTJRljTIdcGATg9UX2rqEv6tZz37Z/MzV1NBcXnsypK65wuiRjjNkj1wUBovgiPAj+tv1ZsuPSuHbQ2Zy6/FqnyzHGmE65LghEwBuhF6YBWFa7nhV1G7h84FyOffE7TpdjjDFdCmsQiMgxwB2AF/irqv5qt/mDgIeAzNAyN6rqC52uVMEfwYPOPb71TdJ9KczJnUrGoGynyzHGmC6FLQhExAv8CTgKKAEWi8g8VV3RbrEfAU+o6t0iMhZ4ARjS1bo9ErktguV1X3JQ5niOW3Sj06UYY0y3hLNFMB1Yo6rrAETkMeAkoH0QKJAeup0BbOlyrRJ8UiRq9DdT728mNy6DuLg4p8sxxphuCWcQDAA2tbtfAuw+2M5PgVdE5EogBTiyoxWJyCXAJQD5cZkErwQWecpbaggQoCDezhkwxriH053FZwMPqur/iciBwCMiMl5VA+0XUtV7gXsBRqYM1EjtLN7UtA2AQUm5DldijDHdF84g2AwUtbs/MPRYe98EjgFQ1fdEJBHIBXZ0tmJ/oLO5zqloqQMgJy69iyWNMSZyhDMIFgMjRGQowQA4Czhnt2U2AkcAD4rIGCARKO1sparg93vDUO6+a2wNnt/g1QSHKzHGmO4LWxCoaquIfAd4meChoQ+o6nIR+RmwRFXnAdcC94nI9wj2AV+oqp3v91HBH6EXpmnyh4IgYB3Fxhj3CGsfQeicgBd2e+zH7W6vAA7qyToDCs0tTndtdKyyuQEAjz/Z4UqMMab7IvMTtROBgIfa2kSny+hQa0s8glDXEpn1GWNMR1wYBEJDU7zTZXSoodWPDx+N9RYExhj3cF8QqFDfGJllb2zeTJ6nkJTxqU6XYowx3RaZn6idCKhQ3xyZRw1VBHaSITmc8dxJTpdijDHd5rogUIWWQGQeNZROHuW6lbqdDcT3j8zdV8YYs7vI/ETthCL4lYicimQMlZTyyxH/dvptMsaYbnNdiwAid9C5AgYDsKlhpcOVGGNM97kuCATwRuaYcxQzjgRN5hOd73QpxhjTbS7cNeT8LqDOpjgSqKeGhXev6HJbjDEmEriuRQDBDuNItFKXUEsFx3IJE84a6nQ5xhjTLa4LAgESIvPoUTTQCAHI9RSQmpXkdDnGGNMtrgsCCO6CiUQeDR4y2kiNw5UYY0z3ua6PIJLlyyBAeC/wPPX19U6XY4wx3eLKIJAInQplMEfIWXzBIvZLOZCGhoZwvg3GGNMrXLdrqO2ooUg1Q04igTRe0HuZnHwYy/0L8XhcmbfGmBjhyk8ojfBpkhzOEXIeX/ABYxMOYOPGjeF7M4wxZh+5rkUggC9CTyhrbybH4QHeaH2M4sHDOGq/E3lk/r3k5tqF7Y0xkcWFLQLFK5E/+TxwsPc4rvT+nskym1c+m8fwvNE8/vcn6OpqnMYY05fEbR9K/aVYv+W9zekyemyzruE/gT9TxhZyPP048fxjmDVrFocccgjFxcWIuKCZY4xxLRH5UFWndjjPfUEwTC/2/tLpMvaKX1v5VBewWj9mEytpoBaAdF82U2dOYcoBkxg3bhzjxo1jzJgxpKSkOFyxMSZaRFkQFOulPncGQXsBDVDGZjboF2zSVWzXTZSxBT8tAAhCliefMaPHcvAJ0xm/33jGjx/P6NGjSUy0S2EaY3omqoJggBTr5XHuD4KO+NXPTrazQ0vYoSVs103s0BLK2EoAPxAMiGwpYNSw0Rx2xkGM328848aNY9SoUcTH28VwjDEdi64g8BTrlfG3Ol1Gn/JrK2W6je1awvZdIbGZct1GgAAAHjwMTR/BZTdfzFlnncXAgQMdrtoYE0miKgiKPMX6veSfOV1GRGjVFnboVrYFNrMtUMJK/2eUBL5EEI5KmcvLtc84XaIxJkJ0FgTuO49AlIR4v9NlRIQEPAxlAEMZAEwHTmW7fxt/qv0tr9U9y1/+eB+XXHmxHZFkjOmU61oEQ+KG6I9zb3a6jIhW0rKZB6ruZ0PrRobGD+Xa31/LWWedRU5OjtOlGWMcElW7hobGD9FbCn7odBkRL6AB3qp/l1dqXmdz6xa8eNk/ayLX3HUdJ59yMklJdr0EY2JJVAVBccJgvbXwJqfLcA1VZUNLCe/WLeK9usVU+KtIkkSOGDGbvy96jIyMDKdLNMb0gagLgl/0v9HpMlwpoAFWNK7i6coX+bxpNdOSJ7Ko7hOnyzLG9IGo6ixWhZYW15UdMQplMCUtWwGYkDiB1sYWfIlxDldljHGSCz9RJTgEqemxgAb4w867aAw08dPcH3L2n86xEDDGuC8IRJQ4X6vTZbhSeetO1jZ/yXFpR3Lhk99i8OzBTpdkjIkA7gsClDhfwOkyXCnLk0ySJ5HSwA4LAWPMLu4LAlHi4pqcLsOVfKoMTejP+qZNvHrmPzjq8XOdLskYEwFcFwQeb4DUVLsofE9VtNRyf8krrGhYx9f7HcGAWUOcLskYEyHcFwSeAInJFgQ98eK2pdy+5mWaAq2c2X8m5w2cxdjLD3K6LGNMhHBdEIhHSUppdLoM13h43WLuWPkWk7OKuG74cUy56HRGfvtIp8syxkQQ1wWBxxMgIanJjiDthkWlJdzxxVvM6T+cn+13Age8dLvTJRljIpDrgkA8EJ9gh492x19WLiY/KZnbZhzO5H9ZCBhjOua+IEDx2nkEXSpraOD97Zv5zn77M+ovv3C6HGNMBHNdECCKxxfAhtjv3M6aOgBGZmeQkpvrcDXGmEjmuiAQUbxxLU6XEfFWV5UBMCDNhps2xnTOfUHgUeISLQi68mn5dhK9XqYOsmGmjTGdc18QCHjj7FKVXRmRm0qj38/fv/iCm1TtcpXGmD1yXRBAsI/AdO7cSYN5ed1mfjj/E57on8P51/+IuXPnMnz4cKdLM8ZEGNddmGZSYZbO/8ZhTpfhCgFVHl26gXsXr2PZjioARuRlMvuk05k1ezaHHnooRUVFDldpjOkLUXWFskmFWfrGNy0Iemp9RR0vr9nGa2u2s2hzOTVNwUNwh2Slctgxx3Hi187ipJNOwuPxOFypMSYcLAjMV/gDyvIdVSzcWMa7G8p4d2MZlY0tTOyXyZ1PzOOQQw5xukRjTC/b6yAQkaXdWH+pqh6xt8X11P79s/SNi2f31cvFBH9A+dfyEi595kMK0xJZ+cYLpE2xsDUmmuzLNYu9wHGdrRuYt7eF7TV3NWIiXkV9M299WQrAwYNz8e/Y6HBFxpi+1FUQXKqqGzpbQEQu78V6uqagaodC9oayuiZ+Mn8Z/1peQos/wDUzR3LjrNFkHnuB06UZY/pQp0Ggqu/s/piIZAFFqrp0T8uEkwIaALuC/b4776kP+KCknG9OKebiqUMZddBRZJ13g9NlGWP6WLfOIxCRBcDc0PIfAjtE5F1VvSaMtXVMIeC3I1v21aryaj4oKSczMY7f/eqXZB55ptMlGWMc0t0TyjJUtVpELgYeVtWfdLMjOQwE1IJgXyX74gEYkZ2ON2+Qw9UYY5zU3SDwiUghcAbwwzDW0y3WR7D3dtQ1ct9Hq7jvw9UkeD1cPWMM3px+TpdljHFQd4PgZ8DLwDuqulhEioHV4SurEwrqtyDoqdrmFn69cBkPLV1Dsz/A3FFF/PCQ/Rh14VUkDxzqdHnGGAd1KwhU9UngyXb31wGnhauozmuB1mavEy/tWmX1jVz0wpt8smMnZ44ZwpXTxzD8wJnkXeJ4484YEwE6DQIRuURV793XZXqTquBvceFYeQ7684cr+Wh7Ob+bdSDnnnMq/S65zumSjDERpKtP1BtFpKyT+QJcBfRZEARf0jqLe+LIwUXc/9kK7vp4GTN/8gusR8AY015Xn6hvAid2Mp0AvLqnJ4vIMSKyUkTWiMiNHcwfLCKvi8hSEVkgIgP3dkPMnh1Q2I/HTjyGhlY/hx11JDd//WyampqcLssYEyHCNuiciHiBVcBRQAmwGDhbVVe0W+ZJ4DlVfUhEDgcuUtXzOlvvhLxcff7UE8NSc7Qrb2jkxrcX8vL6jeyXm8PT73/AsGHDnC7LGNMHOhtrKJz7WKYDa1R1nao2A48BJ+22zFhgfuj2Gx3MN70oJymR++Yczn1zDmNDdTVjRo7kwhNOYP78+bS02OU/jYlV4QyCAcCmdvdLQo+19ylwauj2KUCaiOTsviIRuURElojIkp2NjWhAbNqHac6gIbxy2imcOXIEj730EkcccQQ5KSmcfuKJPPLII5SVddYtZIyJNuHcNXQ6cIyqXhy6fx5wgKp+p90y/YG7gKHAWwQPSR2vqpV7Wu+EvFx99qSTw1JzLKpraeHdLVt4feNGXt+0ibKGBgTYv6iIEy+6iBNOPJHJkyfbBWuMcbl9vjCNiBQAvwT6q+qxIjIWOFBV7+/kOQcCP1XVo0P3bwJQ1dv2sHwq8IWqdtphPCEvT589yfYghUNAlc/Kypi/aRMLSkpYWlqKAgNTU/n2tddy1fXXk5KS4nSZxpi90BtB8CLwN+CHqjpRRHzAx6q6XyfP8RHsLD4C2Eyws/gcVV3ebplcYKeqBkTkF4BfVX/cWS0TcvN0ngVBnyhvaGBBSQlPrl7FB9u2MSoriw83bCAlLc3p0owxPbQvF6Zpk6uqT7T7Vt8qIv7OnhBa5jsEh6bwAg+o6nIR+RmwRFXnAbOB20RECe4auqLrUhSPx65M0xfyUhI5beQwEnweFm3bxtrKStZdcTn7PfyI06UZY3pRd4OgLtSJqwAiMgOo6upJqvoC8MJuj/243e2ngKe6XS3YZQj60OflO7l54Xss3r6dEZmZ3DR9GnlH9NlVSY0xfaS7QXANwUtSDhORd4E84PSwVdUlaxGEk6ry4PLP+cWixaTFx/PrQw7mjJEjKPrLfXi9Ns6TMdGmu4POfSQis4BRBL+Tr1RVxw48F0/AqZeOeqrKz95fzAPLPufIQQP53aGHMOq3vyexnw1MYUy06u4VytouYj8k9Jw5IoKq/j6Mte2hGMXjtSAIl/s+XcEDyz7nm/uN4eYZ0xh870NOl2SMCbPu7hp6FmgEPgMc/RQWQKyfICweXbGany38kGOHFvGTg6ZQdLeFgDGxoLtBMFBVJ4S1kp6wIOhVZfUN3PTmIl5Yt5FZRYXcedTBFN1tRwYZEyu6GwQvisgcVX0lrNV0k4jtGuotb2/axhWvvEt1UzM3zZjEpZPGMfjuvztdljGmD3U3CN4HnhYRD9BC8Du5qmp62CrbE1E8vtY+f9lo9OaGbZz33NsMzUzl8ZMOZ/+vX0zW8ac4XZYxpo91Nwh+DxwIfKbhGpyom0QUb4IFwb5aX1nLxc+/y4jsNJ48bTYj7nwKn8+u/GZMLOruX/4mYJnTIQCAgDeu05OaTRdKquo556mFeDzCI6cdwOg/P41YD7wxMau7QbAOWBAac2jXpa2cOHxU7PDRfTJ/7Q6u+M8n1DW38uhZB1B80bUWAsbEuO4GwZehKT40OSoC2iWus6W6gZtfXcG/l29mVG4qT557APtd/CPSpxzpdGnGGId198ziW8JdSHepCoFWG+agJx5duoEbXllKqz/ADYeM4qqDh5N7y9PExcU5XZoxJgJ0GgQicpeqfkdEnqWDAX5UdW7YKuuU7crorkv/s5inlm9iSv8s7j1pGiPO/DZps052uixjTATpqkVwPvAd4Hd9UEsP2L6h7njkk/U8tXwTiT4PL5w3i/xfPe10ScaYCNRVEKwFUNU3+6CWbhFRPD47aqgrGyrquPqFj8lKiuODS4+yEDDG7FFXQZAnItfsaaYzg86Bxw4f7dSW6kZO+cd7ZCbF8eKFMxl825NOl2SMiWBdBYEXSCWCdsoL1iLozIuf7+Dyfy2jqdXPMxccwJib/kx8YrLTZRljIlhXQbBVVX/WJ5V0l4DHZ30EHSmpbODsv3/MoMxEXvjWNMZc9jtSi8Y6XZYxJsJ1FQQR0xLYRcA6izv2n8+24w8od5w2lv1/u8CGjDDGdEtXnxQReIFaBbEg6MjofikA1DS1WAgYY7qt008LVd3ZV4X0hHgtCDoye2Qmg7IS+eNbGzhb1YaOMMZ0i8fpAkzv8Xk9XHPEED5YX8WrV05zuhxjjEu4cv+B+u2b7p4cPjwHgNXb65njcC3GGHdwXxCooDbW0B4leYLjB9U32e4zY0z3uC8IAFXbo7Un971XAsD0oiyHKzHGuIUrg8D8rxZ/gF++tprfLVjLmZP6M2VYkdMlGWNcwpVBIGJnFrfxB5T/LNvOba+v5fPttZw3dSB3nDye1Gsed7o0Y4xLuC8IRG2sIaChxc8ji7dyx4INfFnewPDcZJ64YBLH7TeMlGufcro8Y4yLuC8IIKYPeq1saOHutzdxz9ubKK1tYdrgdH5x4ghOGJdP4lXPEheX6HSJxhiXcWcQxOAli5tbA9z51kb+b/56KhtaOXZsLtccPpiZxZnEfed54uISnC7RGONSrgwCDcRWk2BzZSOnPfAxn22t4dgxufz42GFMnDCepAv+6nRpxpgo4L4giLFrFqsqpz3wMet31vP4+ZM44aBxJF/yd6fLMsZEERcGARCInTOLP99ey2dba/jxkSM4/tZHSS4a7XRJxpgo474gAFRjJwgGpCeR4PPw7oZKPLmDnS7HGBOFXLezXRHUHztTWlw8vzpmHPPXlDJ9aD/mzZuH32+Hzxpjeo/7WgQKgVbX5dc+uXD/oRSkJHHjy8s46aSTGJCRzDevuo4zzjiDsWPH2nDTxph9IqruGpxsUr8sfe2CCLxeTh9o9gd4ec1WHv50HW+u34ECecmJzD5qDrOOmsPEiROZMGEC6enpTpdqjIkwIvKhqk7tcJ7rgqAgW1851wZY3lxTz5sbtvHuph28s2kH2+oads0bkp/LpJkHM2nSJCZOnMikSZMYPHiwtRyMiWFRFQQTC7L1lXOOcbqMiKKqbK1tYHlZBStKK1lWWsGKskrWVdTsurpzemI8o0aMoHjseIYOHcqQIUN2/Rw8eDAJCXZCmjHRLMqCIEdfOedop8twhbqWVlaWVbK8LBgOX1bWsLGqjs019bQEvnp6dr+MNPoXFVE4eCj9+/enX79+u6bCwsJdt5OSkhzaGmPMvugsCNzXWWy6LSXOx+TCXCYX5n7lcX8gwLa6BjZV17Gxqo5N1XVsqq5le00F6z/YwqL6RsrqG+noK0KSz0dWeiq5+QXk9CskJzeXnJwccnJyyM7O7vB2VlYWPp/9qhkTqeyvMwZ5PR4GpKUwIC2FGQM6XqY1EKC8oYkddQ3sqGtkR30jpXUN7GxspqKxiYrGWipXLaPk02Z2NjZR2diMv5PWZXpSIjlZWeT060d2bh657QIkKyuL7Ozsr0xZWVkWIMb0EVf+lblsb5YrecVDfnIS+clJkNf18qpKTXMLFY3NVDQ0UdEU+tnYTGVj8OfOxiYqyreyY/MGVoaWq25u6XS9aQnxZKWlkZ2XtytEdg+MjkIkKSnJOseN6Sb3BYGC+mPrPAK3SPN5SUtNZFBq95/TGghQ1dRMVVMzlY3NVDYFWxcVjaHHQvcr66up/LyMDe2Waw3s+RtBgtdDZmIiWWkpZGXnkDNgIDmF/cnJze00QDIyMvB47PfLxBb3BQEQsCCIGh48ZMX7yIpPhrTuP09VqW9tbRcewVbIV8KjqSl4v7KCddu381HTQqqamqlrad1zPSKkJySQlZFO4aDB9B88+Csd5u1/5uXl4fXGzgCIJnq5LghUBX+L/fEZSMRHv8RE+iUCGd1/XrPf/9/AaGqiuvm/tyubmqlqaqKisYnSkk18vGoVpQ0NVDc3/896PCLk5eZSOGBAh0HR/mdKSkrvbbgxvcyFQQB+vwWB2XtevGQnxJOd0P19WI2trZQ1NLCjoZ7ShgZ21Nfvur+jrJxNm0r4qCH4WEed5km+OLLSUsnOziGnsB+5BQW7dkm1HWXV0W07v8P0BdcFASq0NrmvbONuPnz0i0+kX3xWp62PgCoVTY2UNtQHp8bgz4qmRqqam6hsbKRy5Ro2f7acytBju5/T0V6S10dGUhI5Wdnk9OtH7sD+5OZ9tcO8oyCxADE94bpPVFWhtcV1ZZsYkuGNIyM1jeHdaHCoKvX+ll2hUNXcSGVzx7ervtxIyRergrebm2jVTgLEF0dWairZubnkDxxAQWEhBQUFFBQUkJ+fv+t2230Ljtjmuk9UVcHf6rqyjdmjROLol5BMvx58Fgc7y1uoamlsFxiNVLf893ZVcyNVtU2UL13JqsUfs7OpnvrWjg/XTYtLYGBBPybMmM74iROYMmUKhx56qPVtxAjXDTExNrNA/3nI150uwxhXamhtYWdzPTub6ilvqmdnUx3lofvraspZVL5p17IFial8vmwFWcOKHKzY9JboGmJCBb/ffWUb47TWQICdTS1sb2xkW0MtWxuq2dZYxdaGatbXlrOloeoryx+UX8zWPz1F1u+/51DFpq+47hNVVWLuwjTGdEVV2dlcz7bGKrY1VrOtsZrtjdXsaKxhR1MN2xurKW+qI7DbCFIZcYkUJKYzNrOQEwbsR3FaDsPSchiYkkXRN4+n/2mHObRFpi+5LggA/HZCmYlhda1NrKndwera7ayrK2VdbSnr6sqoaW38ynKJnjgKEtMoSExnRk4x+aHbwcfSKExKJ70gh/yTDqJg7ky8ifEObZFxmuuCQFVoaY5zugxjwk5VKWuuYXXdNlbXbmNNXXDa3Fixa5k0XyJDU/I4smAsQ5NzKUzKoF9SOgWJ6WTl5pA7ezwFJx9I0sB8B7fERDpXBkGrHTVkopBf/SysWMnymo2srd/GmrqtVLXW75o/IDGbESmFHFuwPyNT+zE8NZ+CvFzG/epcMsYNca5w43qu+0RVFZqtRWCizLr6Ldz65T/Y0lROnPgYmlzAQVljGZ7Sj+Ep/RgzdBhTf/N1MscMdLpUE4VcGAQQCFgfgYkeW5vKuW7VvSR54/nR0K9zUNYYptx1BnkHDne6NBMjwhYEIvIAcAKwQ1XHdzBfgDuA44B64EJV/ah763bXuQ/GdGZj0zbqA40cmjWBq969ndzdrihnTLiFs0XwIHAX8PAe5h8LjAhNBwB3h352SrAgMNFlSvpwJqeN4KXyRQweOJCzTjmD635+E2PGjHG6NBMjwnpmsYgMAZ7bQ4vgL8ACVX00dH8lMFtVt3a2zpEpA/QvE74djnKNcYyqsrx2I/O2L+bN8mW0qJ9pBaO5/9Un2G+//Zwuz0SBSD2zeACwqd39ktBj/xMEInIJcAlAQUI6SckNfVKgMX1pWko+0wqOZ2fzbJ7d8imPb1rE9ElTuPuWX3Phj+zsXhM+rugsVtV7gXsBxmYUaFJKncMVGRM+A1LgsqyJnD5sON9b/Bzf+vF1DGkSZv/8aqdLM1HKySDYDLQfzWpg6LFOeXwBUjIsCEz086W2Mjgzhc8qA8x/8UkLAhM2TgbBPOA7IvIYwU7iqq76BwA8ngBJaY1dLWaMq83fsJmfvruEdVU1nDN6JF8bMdLpkkwUC+fho48Cs4FcESkBfgLEAajqPcALBA8dXUPw8NGLurVeb4D4VOsjMNFJVblz0SpufWsZw7PS+PuJszh0+H4U/f5Op0szUSxsQaCqZ3cxX4Ererpej0eJS/rfC4kb43ZldU1c/dxSnl+5jZPG9Oeuo6eTf9ujxCcmOl2aiXKu6Cz+ClG8ia1OV2FMr/IHlAsfXsySkip+ftRoLj9wPNm3POl0WSZGuC4IRBRvfMeX2zPGrZasr+Td9RX8+oSRfPtnd5K2/5FOl2RiiOuCAAkeOWRMNElMEAAG5MRbCJg+57ogEFHEgsBEmRGFCcR5hQVryjnX6WJMzHFdECDBI4eMiSbpKR4unNmP+97eyoGXzeAb97zvdEkmhrgwCBTx+Z2uwphed/s5Q1hbVs9lf11E4dRbOfbiHzldkokRYR10LhymDE3VD35qg3CZ6FRZ18rsXy7n8y0NnHfkKH5670sMGTLE6bJMFIjUQef2jigSZy0CE52yMoW3fjqaW/61hb+8vorHRhZzzYUn8MPbHyUlJcXp8kyUcl2LYOqwFF30m9FOl2FM2JWUN3PT3zfzz7crGJwXzx1/+htzTz+b4DWdjOmZzloE7guC4cm66Pc27oqJHW8vr+WSu0pYtaWJU2dmc//za8nMzHS6LOMyUbZrCPC6K7yM2ReHTEhh6Z9Hcvszpdz04DYajxrE84urnS7LRBH3BQEEw8CYGBIXJ3z/a/ksWlXPi0tq8L9yIt45zzpdlokS7gwCaxCYGPXWsjrGFiUiviynSzFRxJ1BELAmgYk9739RR3m1H9Vm5LCHnC7HRBF3BoHHziw2seeWx7YB8Mg1g+zIIdOr3BcEokic7RsysWdrRQtzJqdyzHE2GpHpXe4LAgAbYsLEKI9XkWk/c7oME2XcFwQeReNs15CJLa8tqeOzL5sYNjDOdguZXue+IAC3Vm3MXntyQQ0Af/xevsOVmGjkvo9UO6HMxKC3P63n2JkpDBh/udOlmCjkviAAO6HMxJRtZa2s3NDC+cdl4BlxrdPlmCjkcboAY0zn/vBoBR4PHH+IjT5qwsOCwJgItnBpA3c+Uckps1PZb+whTpdjopQFgTERauWGZk66ZjNF+T7+fGMBnomPOF2SiVKu7CNQsc5iE922lrVy3FUl+HzCC38cQO4Brzldkoli7gsCAeKcLsKY8LrxrjK2lft5496BFB/2Mp70IU6XZKKY64JABdRnLQIT3baWtzJpdAJTD/0jngy7EJMJL9cFAQLqdboIY8IrOdlDaUULmjrG6VJMDHBfEABYEJgol57qoaYuAFXzIPd6p8sxUc6dQWBDDZkoV1sfIDnJA7rF6VJMDLDDR42JQDvK/PTL9UL/W50uxcQAV7YIrI/ARLuq2gC1DdCC2EFyJuzcFwQeCMTZYEMmul19SSaX3bCDww7O4YFHljBu3DinSzJRzHVBoAJqX5FMlDvv3AzSs7184zvbGD9+PBPGpXLK6dfyta99zULB9DpRddcx+ZMnJuo7rw52ugxj+kRpWSuPPVXDvBdqeW9RA6owemQ8p5x6AWefcyXjx4+3C9WYbhGRD1V1aofzXBcEkxL1ndeGOF2GMX1u2/ZW5j1fw9PzanjnvQYCARg5PJ5TTz2K677/EDk5OU6XaCJYZ0FgRw0Z4xL9Cnxc8o0sXnxmEGs+G8Ydvy2gsNDHb373PKNGFnDPPT/DbV/sTGSwIDDGhQryfVx8YSYv/LuI994YQm6Ol29/+yc8/NfhTpdmXMh1ncUAAYsvY3ZpVaWhMXiWZU5us8PVGDdyXxAIqPuqNiYsVqxo4riTNpGS4uH1lwZxwIwbnC7JuJDrPlJVQL02xoQxq1Y2c+LcEpKShJdfKmLY8PdJSsp3uizjQq4LAlDU63e6CGMc9Z9/1/Ltb5WSlCw899xAhg3/iKSkTKfLMi7lviAQwGNHRpjYtWO7n+99t4zcPC8vvVZI0YCXLATMPnFfEBgTwwIB5bJvlFJfpzz3agFjhm90uiQTBdwZBHbYkIlRS95vZP5rDfz8l9mMHXe50+WYKOHCIJDQZEzsycwKDr1b0C+OjPgfOFyNiRb21doYFynZ1ApAUrJ9GTK9x30tAgXx2x+BiT1NTQHuuasSnw8Om53sdDkmirgzCFqtIWNih6ry8it13HprGUs/a+K3v80jLXWW02WZKOK+IBBQO3zUxIDWVuXpf9dwxx8r+GxpE4MG+3jkkf6cNHciaSkPOF2eiSLuCwKwng0T1ZqaAvzj79Xc/vudbNzQyogRcdx1VwFnnpVOetpnxMcnOF2iiTLuDAKsRWCi07Pzarnh+lI2b25l6rREfv3rfI45Jo2U5E+Ji7MAMOHhyiCwXUMmGv3tr1V877ulTJiYwF1353PYYcVkpL7tdFkmBrgyCCRgRw2Z6KKqfO+7pQC8Mn8ABdlrHa7IxBJXBgEWBCbKvPZKPQDXXJtNbsYqh6sxscadQWA5YKJIXV2AK67YxsiR8Vx/XQ5er9fpkkyMcWcQWBeBiRKBgPJ/v9vJ9u1+HvhbIenp/3a6JBOD3BkExkSB6mo/55+3lTfeqOeUU1M56OAUkpPHO12WiUHuCwK1zmLjfhWVfk49bTOffNLIH/5QwIUXZZKR9oXTZZkYFbYgEJEHgBOAHar6P19zRGQ08DdgMvBDVf1dt9arIHaBMuNy9/2lgo8+auSfj/Tn+LmXkZZ2s9MlmRgWzhbBg8BdwMN7mL8T+C5wck9X7Gm1TgLjXnV1Ae7/WyUzZyRxwrHDSbUQMA4LWxCo6lsiMqST+TuAHSJyfM9WbC0C427vvd/A1m1+/nBbFilZC5wuxxh39BGIyCXAJQBFA3xIi7UIjHs11wcA8CmIWH+XcZ4rgkBV7wXuBZiyX6JaEBg3CgSU/7xSxw23lQFQUW5NWxMZXBEEu/MEnK7AmO7bsr2Vf71Uy32PVfHF2hZGDo3jibv7ceJRA5wuzRjAjUGggH2RMhGurMLP06/U8sQLtby1uAFV2H9sAo/8voDTjk0lbvgX+Hzu+/Mz0Smch48+CswGckWkBPgJEAegqveISD9gCZAOBETkamCsqlZ3umIFaQlX1cbsvaoaP/PeqOPxF2t57f16/H4YNTSOm6/I5vRjUxk9YgSe4pdtCAkTccJ51NDZXczfBgzs+YpBWqyDzUSOsgo/191eypOv1tLUrAzp7+PaCzM549g0JozORkYtwueLd7pMY/bIdW1TCYA0WhCYyPDie3VcfNs2yqv8XHpqBmcfn870g07GN+yPTpdmTLe5LghQQRqtaW2cd9/zFVz6+22MGxLP878byMQjb8I78FKnyzKmx9wXBAFQCwLjsE/XNXDVXds5bEIKz91WRPJxnztdkjF7zX1BoAJN7ivbRI+tO1s445ebSE/y8s9rLQSM+7nvE1UFbbEWgXHGp+vrOfP2dWytaOW5Hwwj/+Q3nC7JmH3muiDQAGiD68o2UeD+N3dw5cPryUn18dJNI5h56WN40vs5XZYx+8x9n6gqBJrjnK7CxBhV5eanSmjxK7eeXsT0776KJ6vI6bKM6RWi6q5xe6YMTtOFN0xxugwTg174rJzL/rGSHTUtTBiQwj9eep/x4+2KYsYdRORDVZ3a4TzXBcGgNH33uulOl2FiVHNrgH9/soPr/72ahpYAf7jpEi66+U47W9hEvKgKgslF6frutQc4XYaJcVuqmjj3waV8sL6K4XnJXH7TrVx88cWkpaU5XZoxHeosCDx9XYwx0aB/RgKvXTmVf164H5lJPq655homDi1g9YqlTpdmTI+5r7MYUL8NMWGc50E4aXw/Thrfj7Me/IRnl5Wy5s6LGHH3h06XZkyPuC8IVAi0uK9sE90un1nM88tL+cY/lnNN0S2cdtZ5FBcXO12WMd3ivj6CARn65uWHOF2GMf9jwdoybpu/ivc2VAAwJCeN4884l29c/C0mT57scHUm1kVVZ/H+/TN1waUWBCYyqSqry+p488sy5q8t5Y21pTS2BpheXMjpl32PuXPnMnLkSLtWselzURcEb3zrMKfLMKZbqhpbeOTj9TzxWQmfbasCICc5gf2nTmX0hP0ZOnQoQ4YMoaioiKKiIvLz8/F47BgO0/uiLAiy9I1vWhAY99lYWc9ra7fzydYKlm6rYl1FLTVNrV9ZJs4j9M9MpWjgQKYfeQynnnoaBxxwgF3W0uyz6AqCwiydb0FgooCqUtnYwsbKejZX17O5uoHNNQ1sqW5gY1U9H22poDWgpMb7GDmsmFGTpjBq1ChGjhzJiBEjGDlyJOnp6U5vhnGJqAqCSYVZOv+iw50uw5iwq2ps5s31pSzcWMaanTWs3VnLpqp62v/FZicnMrCoiEEjRjFo0KBdu5jabg8YMIC4OBuby0RhELx+4RFOl2GMIxpb/ayvqGPtzhrW7KxlY1VdqBURbFFUNbV8ZXmvCAOy0ykeNYbhY8dTXFxMcXExEyZMYOTIkTY0RgzpLAjct+NRQQN2xIWJTQkeH6NyMhiVk9Hh/NrmVrbUhHY11TSwqaqODZV1bFi/iv988jGl9U27lk2O8zKmeAgTZh7KmDFjGD58OMOGDWPYsGGkpKT01SaZCOC+IEDQgB1VYUxHUnzxjMiKZ0RWZofza5tbWF9Zx/LSSj7bXsHnZVU8+/g/+Vu7gADIT0lk6IBCRk6fySGHzuKYY46hqMiG3Y5W7ts11C9bXz1vjtNlGBNVqpuaWV9Zy5cVtayvCv2srGX1zhpK6xsBGJ2XxYVXX8u137/BjmJyoejqI+iXra+ca0FgTF9QVVbtrGb+l1v5xTuf0RII8ODcQ7jgP285XZrpoajrI8Bd2WWMawlCZnw8G6rq8HkEr3g5ZFCe02WZXua+IACwvmJj+syfP/qCv326BoC/Hj+TvIuvd7gi09vcFwQC4rEmgTF95aL9h7N6ZzXzv9zGt55fyGOrzuAHf3mI2bNn25hJUcKVfQSvXWDnERjT1zZV1fGPpet56NN1lNU3MT4/k9c+Xk5B//5Ol2a6Ibo6iwuzdP5FFgTGOKWhxc/1L3/Mo59t4Lmvz+L4RxY4XZLphujqLAast9gYZ+yoa+TpFSW8tHorBamJTOmf5XRJphe4LwgUAn47ocyYvlLX3Mq/P9/Io8s2sGRzOQpM65/Nn0+cStYP7ne6PNML3BcEdmaxMX1CVfn1u8u5Z8lK6lv9jMlN5/qDxnLiqAGMKcwl85ZHbKyiKOHCILCxhozpCwGFv368mvpWP786YjLfmDKSrF88YmcVRyHX/YuqDTpnTJ8QhHuPm8nlL77Hja9/xEPLNjJXfsTRRx/NgQceSGJiotMlml7iuqOGJhZk68tnH+N0GcbEjLL6Rp5ZtYHn12xiydYyWgNKnMfD+GFDOfCoo5kxYwb7778/o0aNsmsfRLCoOnx0YkGOBYExDqlpauG9zdtZvKWMj7aX8cn2ndS3BC+3GefxMHroYMZMmcaoUaN2XUVtxIgRZGdnO1y5icIgONrpMowxgD8QYPXOalaUVfJ5WSUryipZW1nDpuo6Au0+W7KTEhg2ZAijp0xj1OjRu0Ji+PDhpKamOrgFsSPqziOws9qNiQw+r4cxeZmMycv8yuPNfj8bqur4srKGtRXVrKuoYV1lBa898y8eqW34yrL9UpIo7pfH6KkzGD1l6q5WxLBhw0hISOjDrYldLmwRZOurNgy1Ma5V19LK+soa1lXUsrayhi8ralhbUcO6ylrKG/57gRyPCAPSkxlRXMzoA2YyaszYXS2JwYMH29FLPRRVu4YmFWTrK+fariFjolFVYzPrKoPB8OWun7Wsraihpvm/12OO83iYVDyIc664issvv5z4+HgHq3aHqAqCiQXZ+so51llsTCxRVcoamkK7mGpYs7OatzdtZ+mOnUzIz+bRN95m7NixTpcZ0aKsj8A6CIyJNSJCXnIiecmJHDDgvxfGeWHNJq57bRGTJ+zHfReeznl/fdzBKt3LxmowxrjWccOLePbMo2jyB/jX2+87XY5rubBFYNeqNMYEqSqXvbAQjwinjBrsdDmu5cIgABELAmMMgLK1rp5En5eh0w50uhjXcl0QiIB4LQiMMcEew58fNolvP/8B9z75JIfe/GunS3Il1wUBgHgCTpdgjIkQ9320CoDx+RkOV+Je7gsCUbt4vTEGgNrmVnY2NAMwsTDT2WJczH1BABYExhhUlQuffo9NVfXcdfwUDjjyWKdLci13BoF1FhsT83bUNbLgyx2cMmYA595+P5kjJjpdkmu57jwCO53MGAOQnxIckO7pzzfz42u/53A17ua+FoEo4rXOYmNinQBvXTKbQ+9dwJ3PvsHr/XOYe8ElXHDBBYwePdrp8lzFdWMN7d8/UxdcMsvpMowxEaK2uZWHPtzAi6u28d6GnfhVOXBYIVfc8lvOPPNMG6U0JKoGndu/f6YuuNSCwBjzv3bUNvKPjzfx8Ecb+LKinh8fMYZbXlvhdFkRIcoGnbOL1xtjOpaXnMTVB41kaFYqFz61mP5piU6X5AquCwJVIdDiuj5uY0yY7ahr5IVVW5n3xWbeXF/KkMwUThw1wOmyXMF1QYBCoNXrdBXGGIepKstLq3h57VZeXbuVj7buRIGhWSl8/+DRfPuA4WTf/A+ny3QF9wUBgqq1CIyJVZ+XVvHgp2t4de1WSmrqAZhcmM0NB43jmBGFjBtUSMZNfyEuMcnhSt0jbEEgIg8AJwA7VHV8B/PPBW4geBRYDfBtVf20e+u2w0eNiTX+gHLbO8u4a9EqEnweZg0u4NqZYzmyuJCiY79G2vHn4vHYl8S9Ec4WwYPAXcDDe5j/JTBLVStE5FjgXuCALtcqisfn760ajTEu8c9P13PHBys5a/wgbpk9kaKb/kRS/yKny4oKYQsCVX1LRIZ0Mn9hu7vvAwO7s14RxRPXuo/VGWPcpqq5CYDLDxzKyDvnOVxNdImUPoJvAi/uaaaIXAJcAlCUkYgv0VoExsSauRPzuGNhPCf/YyHXFNzAFdfeRGZmptNlRYWwnlAWahE811EfQbtlDgP+DBysquVdrXPywAx9+7sze69IY4xrfLG9luvnfc4ba8pJifdyzrnncvV1NzB27FinS4t4jp1Z3FUQiMgE4GngWFVd1Z11Th6Yru98b0bvFWmMcZ2PS6q5551NPPnJNppaAxw2fQJX/eBnnHDCCXi9dnh5RyIyCERkEDAfOH+3/oJOTS5K13ev7bpP2RgT/Uprm3nw/c3c+24JmyubGJyTwpnfvJy5c09ixowZFgrtOBIEIvIoMBvIBbYDPwHiAFT1HhH5K3AasCH0lNY9Fdne5EHputCCwBjTTqs/wLzPSvnrws28vaaC1oCSnRzHUUcdwdwzzuOYY44hOzvb6TIdFVWDzk0ZlK7vXjvd6TKMMRGqsr6F11bu5KUVZbzyeTmltS14BKaP7M+x51zKUUcdxbRp02JuVNLoC4LrpzldhjHGBfwBZcmGal5eUc7Ln5fz0cYaANISvRwyfRJHnnwuhxxyCJMmTYr6YIi+ILjOWgTGmJ4rq23mzdUVvLm6gjdW7WRNaQMAiXEexg4bzIQZs5g4cSITJkxgwoQJ5ObmOlxx74m+IPi+tQiMMftuc2UT762rZPH6apZvrWXZ5jq21zTvml+Yk874/aczcdIkhg0bxpAhQxg8eDCDBg0iJSXFwcp7LsqCIE0X3tBln7IxxuyV7dXNLNsSDIXPQj9XbKujufWrn5W56UkMGlLMkOGjGDBgAPn5+eTn55OXl7frdn5+Punp6Yg4fw2VqLswDeKu8DLGuEdBRhwFGVkcMSZr12P+gLK1qpmNOxtDU1Po5xZWLPyS16uaqGroeMSDeJ+HvKx08voNIL9wAAUFBf8TFm338/LyHGlpuC8IBMQGGDTG9CGfRyjKSaAoJ4GDyOhwmaaWAGV1LZTWNLOjpoXSmhZ21DRTtuv2VkpXbmTlkuD8hpaOR1FOjveQl5lCXn4B+QOLKSgc8JXgaH+7X79+vdLJ7b4gUFC/880sY4xpL97jpX+at9uXx6xtaqW0poXS2hbKaprZUdtCWe1/g6Sstpwty7bx6fstlNY2/8+uKQARKEhPon//fgwoHs3AQUMoKipi+PDhjBw5kuHDh3erheG+IAC7MI0xxvVS4uNJyYlnSE7Xy6oq1Y1+ymrbQqKZ7dXNbK1qYktVE1sqq/jy47d4941X2Vn/1dGZ+2cmMnnSpE7X78IgELAWgTEmhghCRryHjOw4hnVxgnRtUytrSxv4Ynsdzy0r5amPd7BlwftMLkqbsqfnuC4IVIMXsDfGGAMNzX5WldazcnsdX2yvY2VoWlNaT7M/uDtpQGYCnR0g6roggODFaYwxJlaoKmV1LaEP+eCH/qod9azcUceGnY27PuQ9AkNzkhhVkMIxY3MZV5jKjCEZDCnIIvN7L+zxUsDuCwIBPBr8aYwxUUZV+WJbPe+sreSjjdXBD/5tdV/Z958U52FkfjLTB2fw9Wn9GVWQwqj8FIbnJZHYfxS+OTcQlzfkK+tt+a7s8dKOrgsC2fU/Y4xxP39AWbq5lnfWVPL2mgoWrq2ivK4FgNzUOEb3S+HU/fMZWZDCqIJkRuUnU5SXi3f2FcSNOQqPZ98PnnFdEATZriFjjLutK23gtpfWM29pKdWNwZPRinOTOG58DgcPz+SgYZkUF2TjPfk3xPcfF9Za3BkEAWsSGGPcqay2md+/vpG73tyEzyOcMaWAw0ZlcdCwDAYOHIzvzHvwJXd80lq4uC4IVEEDdh6BMcZdapta+fUrX3L3O5uobw5w3vRCbjmhmMJTbyZx3NGO1ua6IACg4zOzjTEmIj396Xa+/59VbK5q4qzJ/bj+qCGMnXogiaf/3unSALcGgTHGuEBlQwt3LNjIr1/7ktEFKcw/bz9mHDyLpDN/53RpX+HOIPAoETCqqzHGdGhTRSN/fmsTf124mdomP3P3y+OR8/Yj/frXnS6tQ+4LAlHE1/Fwr8YY46TaplZ+8eIG7nqjBAVOnZTHd2cNZsroYpIueczp8vbIdUEgAh6fdRIYYyKHqvLMx2Vc99RaSiqauXBmP248egiDDzqHxCOudLq8LrkuCAA7ocwYEzFa/co3HvyCxxeXMmFgCn+/eCwzJk8m4az7nS6t29wZBB47ocwYExmWfFnN44tLOXN6Hg9cMJqES9/A6/U6XVaPuC8IxAadM8ZEjglFyQzLS+TZT8qZ/43DOMFlIQBuDALUrllsjIkYKYkeXr9hPCf/8XPO+O6tPJqcx0nnfcfpsnpEtLNBqiPQlCGp+sFPJzhdhjHGfMX2qmaO/u0Klm9u4Nj9C/n5fc8yZcoerwXT50TkQ1Wd2tE897UI2oahNsaYCFKQFccHt+zHHS9v5f9e2MrUqVM57uDxnP+dH3H88ceTmprqdIl75L4WwdBUXfTz/Zwuwxhj9qi6oZU/vryNv7y+na2VLSTFezju8BmccdFVHH/88d26oHxv66xF4L4gKE7VRT+zIDDGRD5/QHl3VQ3/WlTOvxbtZFtVWyjM5KRzLmHOnDkUFBT0SS3RFQRDU/UDCwJjjMu0hcKTH5Tz9JKdbK8KXnxm4vAC5pxyPgcddBAHHHAA/fr1C8vrR10Q2K4hY4ybBQLKJxvreWVpJS8vreS9NbW0hi40PzgviYMOO4YjjzmRI444gkGDBvXKa0ZVEEwtTtUPbh3vdBnGGNNrGpoDfLy+jkVra3l/dS1vfV7NjurgJYaHF6Zy+PFncOScYzj88MPJycnZq9eIsiBI0Q9+YUFgjIleqsqyTQ3MX17N/GXVvPVFNTUNAbweOPGgkfz+oZcYOnRoj9YZfUFw21inyzDGmD7T0hpg8dp6/rOkgntfK6U4P4EPV1fgjUvo9jrsPAJjjHGxuHhh5pgURg1MYNGaOj5aVw/PnQqnPN8r63dfEIBdlMYYE1NWbWnk3ldLuffVMuqaAtx6dn/of2ivrd+VQWCMMbHgpY+r+OW/tvLuyjq8HjjroGy+f3I/xo8ei+eAG3rtddwZBDbonDEmSgUCypK19dz5wg7++XYFw/ol8KuvD+Drh+ZQOOZYPAf/qtdf031BIGp9BMaYqNLYHGD+ZzXMW1TFc0uq2FrRSlK8cNNpBdx8eiEJp72Ppwcdwz3lviAA8FoQGGPcbevOFl74sJrnF1fz2qc11DUGSE30MGf/dOZOz+D4yelkT7kKz7hvhr0WdwaBtQiMMS6jqny8toHnFlfz/KJqlqxpAGBQXhznH5bFCdMyOGx8Kgkz78AzYHaf1ua+ILDDR40xLtHYHODVj2t5flHwm/+W8lZEYMaoZG49vx8nTEtn/JBsZPYTeFKLHKvTfUEAiAWBMSaCtfqVPz1bzm+eLGXrzlZSkzzMmZzKCdPTOXZqOvkH34Zn0Fyny9zFfUFg5xAYYyLY2i1NnPfbTbz/RQOzJ6Rw71UDOGJSOglHv4UnIdPp8jrkviBAQ5MxxkSWBUtrmfvTjXg98M8bBnLW4UOQOW87XVaXXBgEWKvAGBNxVpU0MfenGxmUF8cLPx/MoHOWIz53fMS6o8rdqSWBMSZyqCrf/fNWvB546dYhDDx3BeLxOF1Wt7kwCCwEjDGR5Y5nynnlo1puv7SQgbPvcFUIALirWmOMiTD1jQGuuXcrAFecmIMUHe1wRT3nvhaBggacLsIYY4Lufq4cgO+fnodv1qsOV7N33BcEgO0eMsZEitc/rSM/08ePzi5AUsNz4flws11Dxhizl1ZvbuKlJTVcemw2KXPecLqcvebOFoGdRmCMiQA/engbAN+ck40nNdfhavaeO4PAksAYEwGeeqcKgIG58Q5Xsm9cGQTidboCY0ysq67zA3DhUVmISzuJ27ivj8D6iY0xEWDxquAw0mfNysCTlu9wNfvGfUFgjDER4I2ltXg8MH1UstOl7DMLAmOM2Qsfrm5g4tBEMnP3d7qUfRa2IBCRB0Rkh4gs28P8k0RkqYh8IiJLROTgcNVijDG9bdn6RsYOSoTp9zpdyj4LZ4vgQeCYTua/DkxU1UnAN4C/hrEWY4zpNXWNATaXtzK6KAFJtl1De6SqbwE7O5lfq6ptx4GmYMeEGmNc4sttzQAMK3T3YaNtHD18VEROAW4D8oHjO1nuEuASCF7o2cYaMsY4aeOOFgAG5sY5XEnvcLSzWFWfVtXRwMnAzztZ7l5VnaqqU/MyXHnqgzEmimwqDQZBUZQEQUR8qqrqWyJSLCK5qlrW9TPsZAJjjHNKylrweKB/TnTsGnKsRSAiw0VEQrcnAwlAuVP1GGNMd23Y0Uz/7Dh8PneONrq7sLUIRORRYDaQKyIlwE+AOABVvQc4DThfRFqABuDMdp3HxhgTsdZtbQ52FBd/2+lSekXYgkBVz+5i/q+BX4fr9Y0xJlzWbWvm2KlpeEad4nQpvcLOLDbGmB6qawyQmhQ9H5/itr0xqUkeHT0w0ekyHFFa1UosHjUVq9sNtu2Rvu0frmn4MAyrzQW6cdBMjw1W1byOZrguCERkiapOdboOJ8TqtsfqdoNteyxuuxPbHT1tG2OMMXvFgsAYY2KcG4PA/UP97b1Y3fZY3W6wbY9Ffb7drusjMMYY07vc2CIwxhjTiywIjDEmxrkqCETkGBFZKSJrRORGp+vZVx1dxU1EskXkVRFZHfqZFXpcROSPoW1fGhqfqe05F4SWXy0iFzixLT0lIkUi8oaIrBCR5SJyVejxqN5+EUkUkUUi8mlou28JPT5URD4Ibd/jIhIfejwhdH9NaP6Qduu6KfT4ShE52qFN6jER8YrIxyLyXOh+TGy7iKwXkc/arsoYeiwyft9V1RUT4AXWAsVAPPApMNbpuvZxmw4FJgPL2j32G+DG0O0bgV+Hbh8HvEhw6NUZwAehx7OBdaGfWaHbWU5vWze2vRCYHLqdBqwCxkb79ofqTw3djgM+CG3PE8BZocfvAb4dun05cE/o9lnA46HbY0N/AwnA0NDfhtfp7evme3AN8E/gudD9mNh2YD2Qu9tjEfH77qYWwXRgjaquU9Vm4DHgJIdr2ifa8VXcTgIeCt1+iOC1Gtoef1iD3gcyRaQQOBp4VVV3qmoF8CqdXyI0IqjqVlX9KHS7BvgcGECUb3+o/trQ3bjQpMDhwFOhx3ff7rb34yngiNCovScBj6lqk6p+Cawh+DcS0URkIMGLUP01dF+IkW3fg4j4fXdTEAwANrW7XxJ6LNoUqOrW0O1tQEHo9p623/XvS6jJvz/Bb8dRv/2hXSOfADsI/iGvBSpVtTW0SPtt2LV9oflVQA4u3O6QPwDfB9quM5hD7Gy7Aq+IyIcSvOoiRMjve2QP5BHjVFVFJKqP7xWRVOBfwNWqWh38whcUrduvqn5gkohkAk8Do52tqG+IyAnADlX9UERmO1yOEw5W1c0ikg+8KiJftJ/p5O+7m1oEm4GidvcHhh6LNttDTUBCP3eEHt/T9rv2fRGROIIh8A9V/Xfo4ZjZflWtBN4ADiTY9G/7YtZ+G3ZtX2h+BsELOLlxuw8C5orIeoK7dg8H7iA2th1V3Rz6uYPgF4DpRMjvu5uCYDEwInSEQTzBzqN5DtcUDvOAtiMBLgD+0+7x80NHE8wAqkJNypeBOSKSFTriYE7osYgW2td7P/C5qv6+3ayo3n4RyQu1BBCRJOAogv0jbwCnhxbbfbvb3o/Tgfka7DWcB5wVOrJmKDACWNQnG7GXVPUmVR2oqkMI/v3OV9VziYFtF5EUEUlru03w93QZkfL77nRPek8mgj3pqwjuU/2h0/X0wvY8CmwFWgju6/smwX2grwOrgdeA7NCyAvwptO2fAVPbrecbBDvM1gAXOb1d3dz2gwnuM10KfBKajov27QcmAB+HtnsZ8OPQ48UEP8zWAE8CCaHHE0P314TmF7db1w9D78dK4Fint62H78Ns/nvUUNRve2gbPw1Ny9s+vyLl992GmDDGmBjnpl1DxhhjwsCCwBhjYpwFgTHGxDgLAmOMiXEWBMYYE+MsCIwxJsZZEJioJyL+0NC/bdOQPnjNISLSEBpTqCfPu1BEAiIyod1jy9pqluDQ3bUiMrV3KzaxzMYaMrGgQVUn9fRJIuLT/w6GtjfW7s3rEjy58IfAmbvPUNXDRGTBPtRkzP+wFoGJSSIySUTeD1304+l2FwRZICJ/CF045CoRmSYiCyV4IZlFIpIWGj30tyKyOPT8S7vxekNE5AsReVBEVonIP0TkSBF5N3SBkfbDKD8HjBORUWHafGO+woLAxIKkdruFng499jBwg6pOIHgK/0/aLR+vqlOBO4HHgatUdSJwJNBAcCiQKlWdBkwDvhUa86Yrw4H/Izja6GjgHIJDbVwH/KDdcgGCFyz5we4rMCYcbNeQiQVf2TUkIhlApqq+GXroIYJj2rR5PPRzFLBVVRcDqGp16PlzgAki0jZQWgbBgc++7KKOL1X1s9A6lgOvq6qKyGfAkN2W/Sfww24GjDH7xILAmP9V18V8Aa5U1Z6O+tjU7nag3f0Au/0tqmqriPwfcEMPX8OYHrNdQybmqGoVUCEih4QeOg94s4NFVwKFIjININQ/4CM47O+3Q9dTQERGhoYW7m0PEtwdlReGdRuzi7UITKy6ALhHRJIJXgD8ot0XUNVmETkTuDN07YAGgh/MfyW4K+ej0HUVSvnvtWZ7Tej1/0jw4i3GhI0NQ21MGISO+39OVceHYd0LgOtUdUlvr9vEJts1ZEx4+IGMnp5Q1hUReYPgRU5aenO9JrZZi8AYY2KctQiMMSbGWRAYY0yMsyAwxpgYZ0FgjDEx7v8B6FCmXetZNAYAAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib\n", "import numpy as np \n", "\n", "cmap = matplotlib.cm.plasma\n", "normalize = matplotlib.colors.Normalize(vmin=2900, vmax=df['Sensor3'].max())\n", "\n", "\n", "fig, ax = plt.subplots(figsize=(6,10))\n", "n_values = 10000\n", "\n", "for idx in df.index[0:-1*shift]:\n", " d = [df['force'].iloc[idx], df['force'].iloc[idx+1]]\n", " indicies = [df['time'].iloc[idx], df['time'].iloc[idx+1]]\n", " ax.fill_betweenx(indicies, d,\n", " color=cmap(normalize(df['Sensor3'].iloc[idx])))\n", "\n", " \n", "# Plot up the force profile \n", "ax.plot(df['force'], df['time'], color='k', label='Force')\n", "ax.set_title(\"Lyte Probe Force cropped w/ NIR Fill\")\n", "ax.set_xlabel('Force [mN]')\n", "ax.set_ylabel('Time [s]')\n", "\n", "# Guess at bottom stop\n", "ax.set_ylim(df['time'].iloc[surface], df['time'].iloc[21000])\n", "ax.set_xlim(0,df['force'].max())\n", "ax.legend()\n", "ax.invert_yaxis()\n" ] }, { "cell_type": "code", "execution_count": null, "id": "ba1a6878", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 5 }