{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import interpolate as ipl\n",
    "ipl.RBFInterpolator\n",
    "colormap = plt.get_cmap('viridis')\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "import cycler\n",
    "color = plt.cm.viridis(np.linspace(0, 1,5))\n",
    "mpl.rcParams['axes.prop_cycle'] = cycler.cycler('color', color)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=np.loadtxt('./sorted.csv', skiprows=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "database = {}\n",
    "database['description'] = '''The Idea was to studie the relation between the propultion force of the \n",
    "under water robotic fish to the head pivot point, frequency and amplitude of the propultion.\n",
    "The experiment has been done at 24th of november for head pivot positions of K1 = 111.5 mm for K2 = 101.5 mm, K3 = 91.5 mm and K4 = 121.5 mm and k5 = 131.5mm. During the study the propuktion force in mN has been measured\n",
    "by changing the frequency from 2 Hz to 7 Hz and varing the amplitude from 0.2 to 1. For apmlitude 1 the frequencies\n",
    "has been varied from 0 to 1.5 in 0.5 steps.\n",
    "\n",
    "unterschiedlische Kop,.... folgende Variante.... Datum , welche Fisch wo \n",
    "'''\n",
    "ident = data.T[0]\n",
    "kp = data.T[1]\n",
    "prop = data.T[2]\n",
    "cons = data.T[3]\n",
    "amp = data.T[4]\n",
    "freq = data.T[5]\n",
    "\n",
    "\n",
    "\n",
    "for i,i_d in enumerate(ident):\n",
    "    database[str(int(i_d))] = {\n",
    "                        'kp':kp[i],\n",
    "                        'prop':prop[i],\n",
    "                        'cons':cons[i],\n",
    "                        'amp':amp[i],\n",
    "                        'freq':freq[i]        \n",
    "        \n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "amp_1 = {}\n",
    "propforce_k1 = []\n",
    "frequency_k1 = []\n",
    "propforce_k2 = []\n",
    "frequency_k2 = []\n",
    "propforce_k3 = []\n",
    "frequency_k3 = []\n",
    "propforce_k4 = []\n",
    "frequency_k4 = []\n",
    "propforce_k5 = []\n",
    "frequency_k5 = []\n",
    "\n",
    "\n",
    "\n",
    "for key in database.keys():\n",
    "    if ('kp' in database[key] and database[key]['kp']==1.0 and database[key]['amp']==1.0):\n",
    "        propforce_k1.append(database[key]['prop'])\n",
    "        frequency_k1.append(database[key]['freq'])\n",
    "        #print(database[key])\n",
    "        #amp_1[key]=database[key]\n",
    "\n",
    "    \n",
    "    if ('kp' in database[key] and database[key]['kp']==2.0 and database[key]['amp']==1.0):\n",
    "        propforce_k2.append(database[key]['prop'])\n",
    "        frequency_k2.append(database[key]['freq'])\n",
    "        #print(database[key])\n",
    "        #amp_1[key]=database[key]\n",
    "    \n",
    "    if ('kp' in database[key] and database[key]['kp']==3.0 and database[key]['amp']==1.0):\n",
    "        propforce_k3.append(database[key]['prop'])\n",
    "        frequency_k3.append(database[key]['freq'])\n",
    "        #print(database[key])\n",
    "        #amp_1[key]=database[key]\n",
    "    \n",
    "    if ('kp' in database[key] and database[key]['kp']==4.0 and database[key]['amp']==1.0):\n",
    "        propforce_k4.append(database[key]['prop'])\n",
    "        frequency_k4.append(database[key]['freq'])\n",
    "        #print(database[key])\n",
    "        #amp_1[key]=database[key]\n",
    "\n",
    "    if ('kp' in database[key] and database[key]['kp']==5.0 and database[key]['amp']==1.0):\n",
    "        propforce_k5.append(database[key]['prop'])\n",
    "        frequency_k5.append(database[key]['freq'])\n",
    "        #print(database[key])\n",
    "        #amp_1[key]=database[key]\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lJcQri4KEeEKIEhPiASUmxNNoNIUJ8aoFy17QdkEIz/zpCZU3+apW+NkPc3XYaLfcxCdFTMAmbSyIc9/6zaV8vXM7G86e4cUhwwoVQnk09vXj49FjSMzJ4bX1az0mm0L9QlEKtZDLNSGetGeB9RhC182j48Tk+9Jcn8VVDUtPd1ERGhoaMix0COuSNhCfl+CWPouy43wsH23dxDWt2nBjh04VurZLw0bc16sPf8UcYuWJ426XTaH+oSgFN1FSQrzMzExGjBjBihUreOSRR3jppZeAepwQz7IfkE7XUc+Qak5jXUY+GiHR2w67rd9x4deiU2mZF7fAbX0CpOfnMWPZYiL8/HnjipGV+n++v1df2oeE8uy/K0nNU1xVFaqIlLLObj169JCXEh0dXaztcuSjjz6SPXv2lAsXLnRbn/PmzZM9evSQn3322UXt7vpO7VmfSVt8a2m3Zbmlv5L489w8ec/226Q1vrW0Z33h1r5/P/unvG3bnTIxP9FtfT7370rZ8tMP5P4LCVXqJyYpUbb+7EP5yLLFbpJM4XIG2ClLua8qM4U6Sl1MiCfNe0DTCqHy8Uj/FruFNYnraOPfA6FphTS7GjfpGiMbXoFKqFiRsMot/R1MvMCvB/ZxS5dudGpQtcysbUNCmdatBwuPxHAkJdkt8inUTxSloFAtSGkHyz7QdvXYGNtSd5BlzWJkw+EOE5Vlr2NcNxGoC6RPUG/WJ20gx1o1M41dSl5eu5ogLyMz+rgnB9TdPXrhrdPx0dbqDbarClJKduw7zbxle1iz5Qh7D50jPVMxgdUkSjlOherBdhJkJsJD6wlSSlYmrKKxIYz2fu0gvzsy73ewHgdta7eNM7rRKDanbGFd0nquDhtd6X7mH45md0I8740c7TZX0gCDF3d268En27ZwIPFClWcfnsRssbJqw2F++3snJ89ePLPxMmh56I5hXDO80+WxllbHUGYKCtWDh4PWTmSf4HTuGUY2GuG4kWh7OA5Ydrt1nKbekbT3a8vKC6uw2q3lX1ACmSYTb29cT/dGYUxo296t8k3r2oMAg4EPt9Te2UJiShZ3PvEzb36xDAE898Bo5n97Nz98cBsfPH8d7VuF8c5XK3jqrfkkp2XXtLj1DmWmoFAtOILW/EHdzCP9r0/ehF6lp3+wM8eRugmogpHm3QjjjW4da3SjK/nw6CfsSN1JvwrmVAL4auc2UvNymTluIio3Pwn76vXc3aMX72zawM7zcfRsXCzRcI1yOjaFR1/7k5xcE28/PZ4BPVsUzgZCg31pGRVKry5R/LV0D1/9sp6bH56FQa8lJS2bBsF+3D11IKMGu1eRKlyMMlNQqB48GLRmtlvYkbqDHoHdMagdphjHbKG722cKAJ38OxJmCGP5hYovOCdkZ/HD3j2Ma9POY+adWzp3I8Ro5JNtmz3Sf2U5ePQ89z33Kzarnc9fvZGBvVqWaB5SqQSTx3Tntuv6kJ1jIjk1GynhQnIm73y9ghXro2tA+vqDohTcRElZUn/66Sf69OnD4MGDufHGGzGZHIXh61uWVGnPButxhIdMR/vT95NryyuWCVXouoPtLNLmXm8clVBxRYOhnMo5xdncc+VfUITPtm/FLu080neAW2UqilGr5Y6u3dl07myt8USKS0jn8df/ws/HwJdv3ESrZuUn8Vu08kCxNpPJyjezN5ZwtoK7uGzNR7PP/FrhH2xpRBqbMLXpTWWeU1KW1IEDBzJ16lTUajVPPvkkv/zyC3feeWf9y5JqjcYRtFaxaF1X2ZyyFX+tv2OBuSgFmVgtu0E9yq1j9g/py9xzf7A2cT23Rk116ZqTaanMPXSAmzt3pYm/v1vluZQbO3Tms+1b+XHvbt4c7t7PXlHyTRaee28hKiH46KXJhDVw7bMnpmRWqF3BPSgzBTdRUpbU5s2bo1Y7ykzrdDo0GocOrndZUi3OJz4PKIVsazb70vfTL7gP6ktLems7ADqk2f0mJB+NDz2DerAlZQtmu9mlaz7Ysgm9RsP9vdxfS+JSAr28GN+mHfMPx5CWV3PV46SUvP/tSk6cSeLFGWNcVggADYL9KtSu4B4u25lCeU/21UlMTAxLlixh82bXbbyXU5ZUadnvqLSmKp5GvKrsSN2FVVr/W2AughA6pLajR9YVAIaEDmZLyjZ2pO5kQEj/Ms/ddyGBpceP8mDvvoQYjR6R51Ju69qd3w4d4NeD+7mvV59qGfNSFq7Yx7K10Uy7oT99u1XMyeDuqQN55+sVmEz/eXmp1YK7pw50t5gKRVBmCh4mNjaW22+/nT/++KPc2UFRLqssqZYDoCndVFYVNqdsobFXYyKNkSWfoOsBlkNIaXL72G1929BQ34B1SeXXTP5g80YCDQb+162n2+UojTbBIfRvEskv+/diqa4suEU4dz6NT2etoW+3Ztw+qeIBeqMGt+epe0bRMMQPIcCg16ASKnp0auoBaRUKUJSCB0lOTua6667jq6++okUL1+oDw+WVJVXa08AWi/CA6SjJlMzRrGP0D+5bapCTo8Kb5T8TlhsRQjAkdBBHso6WmT11a+w5Np47w709++Cr17tdjrK4o0t3EnKyWX7iWLWOK6XkvW9WoNNqePr+K1GpKud6O2pwe/76Zjob/nycHz64Dbvdzk9/bXWztApFKVUpCCEmurBdXZ3C1mZKypL68ssvExcXx6OPPsrQoUP5/vvvgXqWJdVy0LH3gFLYluKo1tovuAzTSEGabvMet48PMCBkAGqhZn0pswUpJR9s2UhDbx9u7tzFIzKUxbBmzWnqH8CP+zzz+Utj2dpD7D54jntuGUxIoHtyXUWEBTJmeCcWrtzH+QvpbulToQRKy5QHpAAzgVllbCdKu746NiVLau3PkmrP+kLa4ltJuy2zquIV46WDr8mXD75W7nm2xFHSlnqP28cv4OOjn8kHds2QVru12LF/T52QzT55X87ev9dj45fHt7u2y2afvC+PpSRXy3ip6Tnyqls/k/c8O0fabHa39p2YnCmH3fChfP3TJW7tt75BGVlSy1poXiqlnFaWQhFC/OIGvaRQCWbMmMGMGTPc2ueECROYMGGCW/uUlgOgboZQ+bq13xRTKqdyTnF9xHXln6zrBqa1SCk9MuMaGNKf3Wl7OJQRTeeA/2ZEdin5YPNGIv38mdzeM2sqrjChbQfe27yRuYcO8OygoR4bZ8X6aL6ZvZELyQ6X0YE9m1fabFQaocG+TLyqG3P/2cXtk/sR3ijArf0rlGE+klLeXN7FrpyjUM+xHPCI6Wh3msMc0iOwe7nnCm03sKeC7Yzb5QDo7N8Jb7WRzSkX27qXHjtKdHISM/r2R6tWl3K15wkxGhnerAXzD0dj9tCC84r10bzz9YpChQAw848tHok+vuGaHghgwfK9bu9boew1hcFlbdUppELdRNougD3RI4vMu9J209irMY28GpV/ctEgNg+gVWnpHdSLXWm7ybc5HAQsNhvvb9lI6+AQrm3d1iPjVoTrO3QkJS+P1adOeKT/b2ZvvMh1FDwXfRwa7MugPq1Y/O9BTCaL2/uvCLKIg8bqORuYGnUvo9TXMzXqXlbPKd8rrTZSlvnoiRLaJNAFiABq7tFHoW7goUXmLEsWh7OOcG3jMa5doGkBwhdp3oPwmuhWWQroH9KPNUnreGzvU2TbsjHgS67Glxf634FaVfNOfoMjo2jk7cPcQwe5qqX7UokXUN3RxxNHd2XtlqOs2nSYMVd4JlK+JGxWGwc3HWbr37vYtngXsUfj0XvpUGlU5GXlFyqJxLPJfDT9awCGTxlUbfK5g7LMR9cW3YB3AC0QD4yvJvkU6jDScgBQg7ZduedWhD3p+5BIl0xHgCMJn7YrWDzngZOUnwRAts2R6jmfLNq0SEDvc8FjY1YEtUrFpPYd2XD2NPFZWW7vPzSo5DUjT0Ufd+vQhGZNgpm3bK/H3KkvZf/6aKZ3eYzHh73Mgs+W0KBpKDc+PZ4xd4/EbpfF5DDlmpn57Jxqkc2dlPsII4QYLoRYC7wGfCil7Cul/NvjktUxSkqIt27dOgYMGMCQIUMYNmwY5845cjHVm4R4lgOgaYkQXu7pz8mutN2E6IJpWlrAWgkIXQ+wHkPaPfPk+lfcguJjquz8FTffI+NVhkntO2CXkr9iyo6OrwzdOzUp1qbXazwWfSyEYMLorhw5cYHoY6XHiLiDzJQsPrjzSx4b+hKmXDPP/PIQfyXP4u1lzzPtjSnc88FtmHJKDo5MPFs7EhJWhFLNR0KIMcBzQAbwnJSy9lbtKAF75htgiXFPZ9p2qPyeK/OUkhLi9evXj02bHF/bzJkz+fTTT3nvvffqRUI8KaVDKRhGuEGy/8iz5XEo4xDDGgyrmCeRthsgHSm89e5fEksxp1SovSaI9A+gX0Qkf0Qf5P5efdzmiZWbZ2br7lM0jQgiL89CUmpWtdQ+uHJwe776eT3zlu2hQ+swj4wRe/Q8T1/5OslxqVz/xDhufnESXt7FH+ZCmwSXqgC+eGgmd39wKxpt3cgqVJaUfwOxOOIVniohAtZ9d6PLgJIS4ul0usLXmZmZdO7cGXAkxCsP6UyIp9frefvtt+natWuZ57ds2ZKbbrqJVatWMXDgQAICAli+fDmBgYEsWLCAM2fOcP3119OhQwd27NjBM888w4oVKzhw4ACTJ092f+4jWxzIdLcvMh/IOIhFWukRWMGyntrOgMqxruABpeAl/MiTxWchXqJ2JW+b1K4Dj61cys74OHo1Lv43Wxn+XLKb9Mw83n12Iu1beebmXBLeRj2jh3bgn1UHePD2oQT4uTen1JEdx3luzJsIIfhk0+u06dWy1HOnvTmFj6Z/jSn3v+SIOi8tnQd3YMHnS0k+n8qzcx5Gq9O6VUZPUJZSGFZtUniA8p7sq4vFixfz0ksvkZmZyZIlS1y+rs4nxLMWLDK71z9/T9pefDU+tPZtVaHrhMobqWnrMQ+k02dDCWmchVr9n13ZZhOcPh8KvTwyZKUY1aIlXms0LDgc4xalkJNr4tdFO+nfo3m1KoQCxo/qwvxle1m98TDXXe3aGpMr7F61n5cmvEtAqB9vLX+BiHI+W8Fi8sxn55B0LoXQJsFMe3MKw6cMYt7Hi/nq0R94dfIHvDD3MXT62q0YSlUKUsp11SnI5cqYMWMYM2YMc+fO5dlnn2Xu3LkuXVdSQrzAwMBSzy9IiAeUmBAvMDCwxIR4QGFCPLUbfeml5SCgBU0bt/Vpl3b2Zxyks38nVJWp4KbrBnkLkNKKEO6dyh+N05OWH0ZU5AUMehsWq4oTpxqRnFK9uY7Kw1un48oWrVh87AgvDh6GXlO17+GPxbvJys5n2g1lZ4n1FC2ahtIyKpRl66LdphSO7DzBS+PfJaxFQ95c+hwhjV3L7jt8yqASPY0mzhiDRqfhswe+4+UJ7/LyvCfQGXQl9FA7cGWh+RohxB4hRKoQIlMIkSWEUKpcuEBBUjuAgIAAjC6mTL4sEuJZDoGmFUK474//ZM4psq3ZdAmonElKaHuAzAHrUbfJVECAwUBSSgA79rQhI9MLs1lLUkoAYb7ujeR2B+PbtifTZOLf0yer1E9WTj6//b2Tgb1a0LaFC/EiHmL0kA7EHE/gTGzV128Szybx4ti38Q/1450VL7isEMpj7H1X8si397Bj2V7ev/NL7Ha7W/r1BK48bn0M3AYESyn9pJS+UsraZSitBZSUEO+XX34p9Dz64IMPeOWVV4DLPyGeY5H5kNtNR/vS9yMQdPKvZL+FyfF2uU8oINNkwmq3U/A/lJTij7fRRKCPlSf61z4f9f5NIgk1erPwcNUcMf5YvJvsHBPTrq+ZWUIBIwe1Q6USLFtXtejpnMxcnr/2bfJzTbz+zzMENgxwj4BOrv7fcKa9MYU1v27ixxd/d2vfbqW0pEgFG7AGUJV3Xk1sSkK82pkQz24560iCl/Or22STUsoXDrwiXz/0VpX6sF0YLG1pD7lJIgevrF0tm3/yvvxs2xY5YOY3ss2Xb8tbtk6Tb+79P7eO405eX79Gtv7sQ5mam1up6zOz8+SVN38qn3lngZslqxyPvfannDD960on4LPZbPK5a96UozTXy50rPJe80G63y/fv/FKOEJPk0u9Xe2yc8qCMhHiuzBSeBJYIIZ4RQjxasHlSUSmUz4wZM9ixY4fbXVJ37tzJAw88ULWOrM5FcW2HqgvlJN2czpncM3QJ6Fy1jnQ9wbzLbeayHedj+XHfHm7u3JUHevdl4x3TOXzvU7T3a0uGOFVtgVUVZULb9ljsdhYfO1Kp6/9asofsXBN3XF/x4jme4Moh7UlMzmLvocrVZZ/38WK2Ld7NfR/fQY+RnktxLoTg4a/uovvIznx8z7fsW+v+mJGq4opSeAPIBQyAb5Gt1lJbf4h1kcp8l55YZN6X4SiSU1WlIHQ9wZ4ItrNVlinPYuGpVSsI9/PjyUvMRH2CexGfn8C5vKrHfHiCdiGhtA4KZsHhiptccvPMzP1nFwN6tqBVVAMPSFdxBvVuidFLx7JKJOA7tvsk3z8zmwETejP2vis9IN3FaLQaXpz7KI1bNuK16z/gwpkkj49ZEVxRCkFSyolSypeklK8UbOVdJIQwCCG2CyH2CSEOCSFecbYHCSFWCiGOOfeBRa55RghxXAhxRAhRqf8dg8FASkqKohjcRH5+PlptBV3oLAdB09qti8z70/cTpAskwiu8ah1pneUwzTurLNOHWzdxOj2Nt4dfibfu4s/aM7AHKlSFhYBqG0IIxrdtz+6EeM5mpFfo2gUr9pGZnc+t1xWvi11TGPRahvVrzZrNR8ivQJK8vJx83pzyMQEN/Hn023uqbe3O29+bVxc8icVs5eWJ75Gf6/5ysZXFFX+0VUKIUVLKFRXs2wRcIaXMFkJogY1CiKXARGC1lPJtIcTTwNM4guPaAzcCHYDGznFbSykrlOs3IiKC2NhYkpJql/aty4SFue5/LgsWmQ3ue+Ky2q0czIimX7AbonA1LUAEIC07EbhQi6EUdsXHMXPPLqZ06kL/JsXTbfhqfWnv145tqTuYFDGxVjoKXNu6Le9u3sDfR49wf68yqtcVwWSy8NuiHfTs3NRjUcSV5coh7Vn870E27jjBiIFlZ6ZdPWcDM5+dUxiFfNOzE/ELrl4DSETrxjw7+2FeGPsOH03/mqd/fqhW/J24ohTuB54UQpgACyAAKcvxQHIuZmQ732qdmwTGAUOd7T8Ca4GnnO2/SUeF9VNCiONAb2BLBT4PWq2WZs2aVeQSBXdiiwWZgXCT59Hm5C38enYu+fZ8dqbtprVvK/qHVN6OLYQKqetRpZlCpsnEY8uXEubry9MDSo+O7h3ci5mnfuBM7lmivGtfsflwPz96hDXm7yMxLiuFf/49SGp6Li8/4tr51UmXdhGEBPmweuPhMpXC6jkbikUfz/v4H5q2j6j2jKZ9xvTg9tduZNbzv9KyazMmP17ziSLKNR9JhwuqSkrpJSvokiqEUAsh9gKJwEop5TagoZQy3tl3PFBglAwHiq4SxTrbLu1zuhBipxBipzIbqIUULjJXXSlsTt7CrNM/kWl1hMVkWbOYdfonNidX6DmhGELXE2xnkLaK//1IKXlm9QrisjL5+Mox+OhKN5H1COiGChXbU6tuqvIU17Zuy9HUFA4nl/9dWCw2Zi/YTqe24XTrUDwBXk2jVqsY3r8NW/ecIisnv9TzZj475yKFADWb0fSmZyYweHI/vnv6F3bUgsJBZRXZKTcapbxzpJQ2KWVXHPUXegshyrpTlDRvKrYwIKX8VkrZU0rZMzQ0tDwRFaoZR7psLWiqnrP/z9j5mO0X/3jNdjN/xlYt86i05zn2SQOwJw7Fnut6ptmf9+9l6fGjPNF/ED0bl72+4aP1oZ1fW3am7qy1a1xXt2qDWggWHTlc7rnL10eTmJzFrde5L5meuxk+sC0Wq411W4+Vek7SuZKD3Epr9zRCCB6feR9RHSN586aPiT0WXyNyFFDWTMGVRD0uJfORUqbjMBONBi4IIcIAnPtE52mxQNHHjwjgvCv9K9QiLIfctsjsicyj9txFkPNNkYbzkPm8S4rhQOIF3tywjmFRzflf954ujdc7qCcXTIm11gspxGhkQJOm/HPscJmKy2az88v87bRu1oC+3WqvebZdy0aENwpg9abSlVxAQ/8S20OblJ+o0lN4eRt4ZcGTqNQqXhr/DjmZuTUmS1lKoYszrUVpWxbQsLSLhRChQogA52svYARwGFiEI0Ia536h8/Ui4EYhhF4I0QxoBdRO1w2FYthzF2G/MATMm8F6qkJP36URrCv5R1pau0tkf4jDB6Io+c720knOzeX+JYsINhp5f+RoVC4+KXcP7I5AsD11R+XkrQbGtmlLbGYmexJKf0Jdu/UosfFp3FKLZwngeOoePqAtuw6cJTU9p9hxm9WGVq8pZpfQG3VMe3NKNUlZMo2iGvDC3EeJPRrPa9d/iNViLf8iD1BW5TW1cw2htM1XSlnW/DkMWCOE2A/swLGm8A/wNjBSCHEMGOl8j5TyEDAXiAaWAfdX1PNIoWaw5y6CzOdBFtxUcl1++i6LSRETUF3yJ6pT6ZgUMaHyndpLufGV1g6YrFbuXbyQpJxcvhozlkAv14sG+Wl9aefXhh212IQ0snlL9GoNi46UnPZCSsnP87YR2TiIwb0rlp22JhgxsA12u2TNluI5rv75ZiWJZ5IZ/+BVNIgMQQhBg8gQHvn2nlpRNrPrsI7M+Ho6u1bs4+O7v62RvxmPFY+VUu6XUnaTUnaWUnaUUr7qbE+RUg6XUrZy7lOLXPOGlLKFlLKNlHKpp2RTcDPZHwKXLuyV//RdHn2D+6BT6dCpHKaoYF0wd0TdWiXvI1SluFGW0i6l5Ll/V7Ir/jzvjxxN54YVT/zWK6gXCfkXiM2Lq/C11YGvXs+wqGYsPnYUawmJ2rbuPsXx00ncMrE3anXN15suj+aRoTSPDGHVxotNSBnJmfz44m90G96J+z66g9mnv2KFbS6zT39VKxRCAVfdOZybX5jE8h/W8Murf1b7+HWjFJBC7aYST9+ucCb3LPn2fO5ufhf9Q9wUKOXzqGNWc5ESMzjaS+DrXduZdziaGX36M6Z15SK0ewR246fTv7AjdSdNjO4pbONuxrZpx7ITx9hy7iyDmkYVtksp+emvrTQM8WXkIPfW2vYkwwe05f9+3UhCYgaNGjjWEH544TdyMvO47+M7arUJDODWl68n8VwyP70ylwtnk9iz+kCxOg2eovarfYXaTwWfvl3lYIbDvbWDv/tuRirjWPB7HVSNnS1a8Hvd0X4JCw7H8N7mjVzTug0P9q68UvLX+tPGtzU7arFr6rCoZvjodPx97OKn673RsRw4cp4p43uj0biv3oanKYhTWL3Zkdvp+J5TLP52FePuH01ULXSnvRQhBI98czctukaxfNYaEs8mI6Uk8WwyH03/mtVzNnhsbJeUghBioBDiDufrUOdCsIKCA59HgUuLyZT+9O0qBzMOEWlsgr+2ZG+RyqIyjkXVYC3C52HAijAUD0DbcOY0T65aRt/wJrw3YnSVnyx7BfXkfH48cXm106FOr9FwZYtWLD9+HJP1vwXOH/7YQlCAkWuucG8adE8T3iiAdq0asWqjw6vqi4dn4hfsw60vX1/TormMRqshMyWrWLunYypcKbLzEo6I44J6jVrgF49JpFDnUBnHgveMIg2NS336dpU8Wx7Hso/T0d99mVaLoesHSDBvu6j5QOIF7luyiJZBwXx9zbgqVycDhwlJINiZ6t5aDu7k2tZtyTKbWHfmFAAHj5xn14Gz3DSuF/paXkKyJEYObMexU4n8+f1qDm48zLQ3puAT4F3TYlWI5NjUEts9GVPhykxhAjAWyAGQUp6nlmdJVah+hMbhnSyCF6FqsLZKCgHgSOZRbNJW+YI6rqDtBMIbafovQvpUehrTFs4jwODFrLET8dO7p5xmoC6QVj4ta7UJqX+TSIK9vPj7qMOE9OOfW/D39WL8KM+lkvYkVwxog0oIfpy1huadm3LltLpXdr602Ing8NJL81YVV5SC2ZnHSAIIIeqWqlWoFhyRzHrQtHRLfwcyDqJT6Wjl457+SkIILeh6gdmhFBKys7h1/p9IKflx/HU09PFx63i9gnpwLi+WhLwEt/brLjQqFVe1bM3qUyfZc+QcW3af4oZre+BVi+sJl0VIoA+NfQ1k+Hgx/b1b3FqDvLqY9uYU9Mbi37/NYuPUwaqnfy8JV5TCXCHEN0CAEOIuYBXwfx6RRqHuYjkI2nYI4R6HtkOZMbTxbY1W5VmzhdD1A9spMnJPcfuCv0jPz2PW+OtoHuie2rxF6RHYA4AdabXYhNSmLflWK+/+vRYfbz3XXdWtpkWqNBnJmaRvO4b0MeDdrNQ421rN8CmDeOTbey6KqZj6wiSEEMx8zjPrCuX+gqWU7wshRgKZQBvgRSnlSo9Io1AnkdIG1mjwqkJQWRFSzanE58czNLQafMd1jpiHn3Z8yen0cGaNm0inBp65gQTrg2jh3Zydqbu4tvEYj4xRVXqEhRNqMLI35QKPj+mHt9E95rOa4OdX/sB+OhF1x0hWboyhbcuKx5jUBoZPGVTMBfXae0ah1njGedSVheZmwAYp5RNSysdx1EWI8og0CnUT2ymQOW5Llx2d4Yisbe/f3i39lYVN1ZJMizfh+mg+Gn01/UqojeBOegb14HTuGZJMtTPDr0oIgrN1mAIFI6/w/PfvKc4dieOfb1Zyze3D6Ne9Gas3HcFmKx6YV1cJDgskINS9XnkFuKJq/gCKfps2Z5uCggPLQcde08kt3R3KjMZP41f1KmulsPBIDANnfUvzTz+g67dfsj6+EaOaJDO6hedTOPQKcpqQaqkX0uETCaTsSwMVbEzwjM26Ovj+mdnovXTc8vL1jBzUjuTUbPZF186khLUNV5SCRkpZmL/Y+bpurjwpeARpOQjCCzTNq96XlBzKjKG9XztUwv3T44VHYnh29QrOZzn8v3MtFrYlhmNUp4HthNvHu5RQfShRxqa11jX1+982EYSe5gGBLqXTro3sXx/NpgU7uOGp8QQ28GdAzxZ4GbSVqt9cH3HlV5ckhCj0LxRCjAOSPSeSQp3DchA07RGi6t4dcXnnybBkuDWKuSjvbd5AnvXi7JMbLzhnJKbNHhnzUnoF9eREzklSTCX7oNcUB4+cZ8vuU0wZ15vxbduz/XwscVmZNS1WhbDb7Xz7xE+ERgQzcYZj3cag13JF/zas2XyEvHxzOT0ouKIU7gGeFUKcFUKcxRHINt2zYinUFaS0giXa4fPvBqIzHU9zHfw8Y8+OzyoeIXoux4/TWX5I03qPjHkphSaktNoVs/B/v24k0N/IpKu7MbaNI03EP0fr1mxh3dwtHNlxgjtevwlDkUXyq4d1JC/fUmbxHQUHZSoF4Xj0u0dK2RdoD3SQUvaXUnp+nq1QN7CeAPIRWvdEHh/KjKGhviHBes8UPAn1LjnMZkdKSzBvRdo9X9ykoaEhTY2RtSqQbffBs+w6cJabJ/TGy6Aj0j+Abo3CWFiHTEjmfDMzn51Ni65RDL/5Ym+dzu3CadzQn6VrD9WQdHWHMpWCs55BD+frbCll8ccshfqNZb9jr+1c5a6sdiuHMw97zHSUbTaXWBzHS6MhImQcYAbzVo+MfSm9gnpyPPtErTAh2e2SL39aR4Ng34uil8e1acfh5CSOpNQNa/H8T5eScDqJ6e/egkp18a1NCMFVQzuw++BZEhIzakjCuoEr5qM9QohFQohbhBATCzaPS6ZQJ5CW/SD8QB1V5b5O5pwi327yiOlISsnTq5aTmJPDvT1709jXFwE09vXlzeGj6N/iOmfKi7VuH7skegc5ynnWBhPSyg0xHD5xgelTBl6U4+i/+s0lF9+pTaQlZjDnjb/oe00Puo8o+QFl9NAOSImy4FwOroSfBgEpwBVF2iQwzyMSKdQtLPtB29kt+ekPZUYjELTza+sGwS5m1t7dLDl+lKcGDOLuHr15on/xwDipGwCmtUgpPZ5vv8CEtD1lB6MbjfLoWGWRb7LwzewNtGnRkFGDL1bGIUYjAyObsujIYR7rN9DlEqQ1wY8v/o4pz8z0924p9ZywBv5069CEZWsOcdt1fWt9TYWaotyZgpTyjhK2adUhnELtRso8sB51i+kIHEFrUd5N8da4N73WjvOxvL1pPaOat2R6916lnif0Q8GeANbqsaP3Durl9ELyXMbL8vj9710kpmTx4G1DUamK3yTHtm5HXFYmO8/XzqpxAKcOnGHpd6u49t5RNGlTdmzL1cM6EJuQzv7Dtffz1DSuRDRHCCHmCyEShRAXhBB/CSFqZ/koherFcgiwIdygFPJseZzIOUl7N5uOknNzeXDpP4T7+vHuyHLqIuiHOPbVZELqVWBCquYF5xXro7nu7m8ZeN37fPfbRtq0aEjXUgrPjGrREqNWy/zDtdPkIqXk68d+xNvfyC0vTS73/CF9W+Nl0PL3qv3VIF3dxJU1hVnAIqAxEA787WxTqO+4cZH5SJYjVXYHP/ctMkspeWrVctLz8/ny6mvLTYMt1KGg6Vht6woNDQ1oamzK9mpUCivWR/PO1yu4kOyIP5ASTp5NZkUpdnZvnY6rWrZm8bEj5Fst1Sanq2z9Zxe7Vx3g5hcn4xdUfkZ/o5eOq4Z2YPXGI6RleN7TrC7iilIIlVLOklJandsPQKiH5VKoA0jLflCFI9QhVe4rOiMGrdDSytd9qSZ+ObCPNadP8szAwbQLbeDSNcIwDCx7kfbq8Qrq7QxkSzZVj4fPN7M3YjJdHLxnsdj4ZvbGUq+Z2LY92WYzK04c97R4FcKUZ+LLGbOIbBfOtfe6vi4z8apuWKw2bnrgewZNep/r7v62VKVYH3FFKSQLIW4WQqid2804Fp4V6juW/aBzz3rCocwYWvm2ROemVNnHUlJ4c8M6hjSN4tbOFUj/rB8GSKimQLbeQY41juqaLSSmlByhXFo7QJ+IJjT29WVeTO26cf7+zkISTiXywGd3otW5/ndz9OQFhBBk55qQEi4kZ/LO1ysUxeDEFaUwDbgeSADigUnONoV6jLSngi3WLesJGZYMYvNi3eaKarJambF8MT46Le9WtL6ypj2oGiDzqyc7fANDKC28m7M1ZVv5J7tjvGC/CrWDI3PqhLbt2XjuDBeysz0lWoWIP3mB395ZwNAbB9DtiopF038zeyOOumH/YTJZy5wt1SdKVQpCiHecL/tIKcdKKUOllA2klOOllGeqST6F2op5n2PvBqUQnenw9mnvpvWEz3dsJSY5ibeHX1lqBHNpCKECw5VgWo+0V88NsG9wb87knuV8XrzHx5o4unhpTb1ew91TB5Z53YS27bFLyYIjteNp+ssZs9Bo1dxdhgtqaVRmtlSfKGumcLUQQgs8U13CKNQdpGU/oHI8WVeR6MwYjGojUd5Nq9zXocQLfL1zO9e168Dw5i0q1YcwXAWYwLSmyvK4Qu+gXgiEx2cLFouNtVuP4WXQEhrkgxDQMMSPp+4ZVSxG4VKaBwbRvVEY82Kiiz1lVzebF+5g6z+7uOWl6wkJr3g6lMrMluoTZQWvLcORDdVbCJEJCBxBawKQUkrlG6zPWPaDphVCVbWYAikl0RnRtPNrW+VU2RabjadWLSfIy8hzg4ZUviNtd1A1ROYvQXhdWyWZXCFAF0A7vzZsTdnOhPBxHguq+r/fNhJzPIHXHx/L0H6tK3z9xHYdeH7NKg4kXqBzw5qpYpaVls0n9/0fzTpFMuGhqyrVx91TB/LO1ysuWnB3ZbZUXyj1V+istOYPLJZS+kkpfYvuq1FGhVqGlBIsB9xiOko0JZFsTnGLK+q3u3cSnZzEq8OGE2DwqnQ/DhPSVdVqQuoT1IcLpguczvWMZXbHvjPMWbCDsSM7V0ohAFzTui0GjYbfDx1ws3Su8/VjP5KemMETs+5Ho61cPfBRg9vz1D2jaBjy321s+k0Dy50t1RdciWgeVx2CKNQhbGdBprtlkbkgVXZVg9aOp6bw2bYtXN2yNVe6oYKaw4RkAdPqKvflCr2CeqAWao+YkNIycnn90yVERQTx0B3DKt2Pn17PmFZtWHQkhmxz9dcl2L50Dyt+WMsNT46jVfeqFXQaNbg9f30znfnf3o1Oq+bUOcWhsgDPVH5WuLyxuG+R+VBGNEG6QBoZGla6DyklL65ZjZdWy0tDryj/AlfQdgFVGDJ/iXv6KwdvjTed/TuxLWUHdum+WsJWm51XP1lMZnY+Lz1yDQZ91Vx+p3TsTI7FUm11FlbP2cDUqHsZqZrMC2PfJiQ8iJtfLD9y2VVCg30ZO7IzS9ceIi4h3W391mUUpaBQYaRlNwhv0FTODFGAXdqJzjxMe792VbKj/330MFvjzvF4/4GEGt2TN8lhQhoNpo1Ie/V4pfQN7k2aJY0jWUfd0p+Uko+/W82OfWd47K4RtIpyLYCvLLo2CqNNcAi/HvR8mojVczbw0fSvSTzrCOyz2+xkJGey4S/3pje/eUIf1CrBT27ut66iKAWFimPeDdquVS6/eSbnLDm2HDr6Vb5AT5bJxJsb19GpQUNu7OCe6m8FCMPVOExIq9zab2l0C+iKQWVgU7J7yoL+sXg3C1bsY8r4Xlwzwj3fjRCCmzp25kDiBQ4mXnBLn6Ux89k5mHIvNlNZTFZmPjvHreOEBPkwdmQXlimzBcC1hHgDhBArhRBHhRAnhRCnhBAnq0M4hdqHtGc7M6NWIEq4FA5mOqpgdfCv/HrCx9s2k5STw2vDRqBWufkZR9sZVOHIvL/d228p6NV6egf3YkfqTkw2U5X62rjjBJ/9sIYhfVpxz9TBbpLQwfi27dCrNfzm4QXnpFLs/KW1V4WbJ/ZGrVHzf78qAWyu/Iq+Bz4EBgK9gJ7OvUJ9xLIPsCN03avc1aGMaCKNTfDTVs6Z7XByEj/t28NNnbp4xEVSCAFe48G8GWmrnlTLA0P6k283sTNtV6X72LLrJC9+sIg2zRvywsNXl5gSuyr46Q1c07oNiw7HkOPBBeeQ8KAS20ObuL9Ua0igD1PH9WLVxsPs2Hfa7f3XJVxRChlSyqVSykQpZUrB5nHJFGonlj2AcCzEVgGTzcSx7OOVTm0hpeS19Wvx0+t5vN+AKslSFsLrOkBC3nyPjVGU1j6tCNWHsjF5U6Wu37jjBM++u5CoJiF8+MKkKi8sl8aNHTuRbTGzwENV2ex2O8ElKAW9Uce0N6d4ZMybJ/YhIiyQD75dhclU+zLCVheuKIU1Qoj3hBD9hBDdC7byLhJCNBFCrBFCxAghDgkhHna2BznNUcec+8Ai1zwjhDguhDgihLiyCp9LwUNI8x7QtEaoyk9TXBZHso5ildZKm47+PXWSLbFnebhP/yrFJJSH0ESArh8ybx7SjV5BpY4nBAND+hOTeaTCmVPXbTvG8+8vpGVUKB+/NBk/X899L90bNaZjaAN+2LsbuwcinL9/Zg6Htx1j2I0DaBAZghCCBpEhPPLtPQyfUrxqnjvQ6zQ8cfdIYhPS+Wle9eSiqo24Ev3Rx7nvWaRNcnF5zpKwAo9JKXcLIXyBXUKIlcDtwGop5dtCiKeBp4GnhBDtgRuBDjhqN6wSQrSWUtpc/zgKnkRKu2OmYLimyn0dyoxGKzS08a24B5PZZuPNjetoERjETR3dk6W1LITXJGTGY2DeBvp+Hh9vQEh/5sctZFPyFsaFlx9RLaXkl/nb+XbOBtq3CuOD5yfh41127YiqIoRgWreePLpiCevPnGZoVDO39f331yuY+95Crr1nFA9+8b9qLZvZo1MkVw5pz+wF2xk5qB1REe43VdV2XAleG1bCVq4zuJQyXkq52/k6C4jBUaRnHPCj87QfgfHO1+OA36SUJinlKeA40LvCn0jBc1iPg8xG6NywyJwRTSvfVuhUugpfO+fAPk6lp/HsoCFo1VXzgHIJw0gQvsi8Pz0/FhCqD6Gdb1s2JW8uN89QXr6Zlz78h29mb2D4gLZ88vL1HlcIBVzdqjUNvX2Yuafy6x+XsuXvnXz+wHf0GdOd+z+dViN1lB+4bShGg47XP12CyWwt/4LLDFe8j/yFEB8KIXY6tw+EEP4VGUQIEQV0A7YBDaWU8eBQHECB83Q4cK7IZbHOtkv7ml4gS1JSUkXEUKgqlt2OvbZqi8zp5nRi82LpWIn1hPT8PD7ZtoWBTZoytKn7nk7LQggDeF0L+cuR9oxqGXNgSH8umBI5ll16YZvDxxO4+5k5rN16lPtuGcxLM8Z4bA2hJHRqNbd07srGc2c4nFz13+LmRTt4bfIHtOzenOd+nYFaUw0KvwQC/Y08c/9oDp+4wPvfrKzxBIDVjStrCjOBLBw1Fa4HMqlAOU4hhA/wFzBDSllWFFBJjwTF/jeklN9KKXtKKXuGhioF4KoTad4DqiBQR1apn0PO1BYd/Csen/DFjm1kmU08O2hItT5FCq9JgBnyF1fLeD2DemBQGViTuK7YMZPJwpc/r2P6M7PJzM7n/eeuY8r43jXyVD2lU2cMGg2z9u6uUj/r/9zCq5M+oEXXKN5Z8QJePp5bD3GFQb1bMu2G/ixde4g/Flfts9U1XFlTaCGlvK7I+1eEEHtd6dyZevsvYLaUcp6z+YIQIkxKGS+ECAMSne2xQNHq4RHAeVfGUagmLHtA263KN5+DGdH4anyINJZcLL404rIy+XnfXia2a0/bkGp+INB0AE1bZO6v4HWTx2/ABrWBASH9WHNhPSs+MZF4Po/QIF8G9GzO9n1niEtI59oRnbjv1iH4ehs8KktZBBi8mNiuA39GH+SJ/oMIMRor3MfKn9bx/rQvaNevNW8sfhZvv4r34Qlun9SPYycT+eLHtbRoGkqPTlV7GKoruDJTyBNCFOaUFUIMAPLKu0g4fjXfAzFSyg+LHFoE3OZ8fRuwsEj7jUIIvRCiGdAK2O6CfArVgKPS2mlEFU1HUkqiM6Np79euwqmyP9m2GQQ83Kd/lWSoDEIIhHEqWI+AZUe1jGk4F4ld2MgPj0VKSEzJYv7yfVitNj5+aTJP3XtljSqEAqZ17Y7FZmPW3oqtLdjtdr5/Zjbv3v45nYe0562lz9UahQCgUgmef+gqmjQO5Om357P7wNmaFqlacOVXeS/whRDitBDiDPA5cI8L1w0AbgGuEELsdW5XA28DI4UQx4CRzvdIKQ8Bc4FoHLUc7lc8j2oRZucUWte1St2cy4sl3ZJBJ/+OFbruWEoK82KiuaVzV8J9ayhzu9dYEAHInB/LP9cNzPkxGnOKjuDBiTR/MobIe47h0y4DKaFn56oXJHIXzQODGNOqDT/t20NqXq5L1+Rk5vLShHf57Z0FjLlrBG8ufa7GTUYl4W3U8/FL19MwxI/H3/iLjTtKX+O5XCjXfCSl3At0EUL4Od+7lB1MSrmRktcJAIaXcs0bwBuu9K9QvUjzDkBX5cyo+9MdqRFcVQoLj8Tw3uYNnM/KQgBRAYHlXuMphPBCGq+HnO+Q1lhHDIMbsdslJ84ksXHHcTbuPEF+ozh8/c0UpJjS+lsJvSqepGVuHdYtPNSnH4uPHWHoj9+TYzYT5uvLE/0HMa5N8ToZR3ae4O2bP+H8iQs8+Pn/uPbeUTWyHuIqIUE+fPH6jTz++l889+5Cnrx3FGMqWBe6LlGqUhBC3Cyl/EUI8egl7QBcYhJSuNwxb3cmwauau+P+jANEGpsQoAso99yFR2J4dvUK8qwOt0AJvLlhLT46XYk3m+pAGKcic75H5v6C8Hu6Sn3lmywcPZXIoaPn2Rcdy76YOLKy8xECOrZuTMjQJFSX/EJVWkno0IoFtVUHh5ISUQlRWGfhfFYWz65eAVD4f2Wz2vjtnQX8/MofBDUK4N1VL9JlSOWTIVYn/r5efPLy9TzzzgLe+mI52/ee5tG7RuDvwQDBmqKsmUJBDuKqha4q1HmkPROsMeB9X5X6ybXmcjz7BFc1ci1Y/b3NGwoVQgF5Vivvbd5Qc0pBHYY0jIK8P5A+D15UjnTF+mi+mb2RxJRMGgT7cffU/6p52Wx2Tp1LIeZ4PNHHEog5Hs+ps8nY7A4Hu4iwQIb0aUWX9hH07RZFoL83t20rucCPyqf6C9yUx3ubN2C7xHWz6P/ViX2n+eTeb4nZeoyhNw7goS/+h2+gTw1JWzmMXjo+eGEScxZsZ+bczew9FMuj00cwuHfLWj3TqSilKgUp5TfO/SvVJ45CrcS8C0cSvKrFEkZnxmCTNpdNR/FZWRVqry6E8TZk/lLIXwDGqYBDIRSt+3shOZO3vlzOpp0nyM0zs/9wHDnONNC+PgbatWjEwIktaNsyjPatGhEUULwORLA+mBRz8TRjwfraF2Vb1v/VV4/8wILPluAX7MszvzzEFR5KU1EdaNQqbr2uL/16NOf1T5fy3LsLad2sAbde15fBfVq5PflgTVDumoIQ4l3gdRweR8uALjhiDn7xsGwKtQRp3gZoq7zIvD/jIF5qL1r6tHDp/EY+PsRnF6+RHOZbw5NXbTfQdkLm/ABeNyKEmm9mb7yoEDyAxWJj9aYjREUEMWJgOzq3DadD6zDCGwW49GQ5KWICs07/hNn+38xAK7RMipjg7k9UZcJ8fTlfgmLQZliY/+kSxkwfwR1v3IRf0OVheGgV1YDv372Z5euj+Xnedp5/fxERjQIYPrAtQ/u1pmXTUI/NHqSUpKTlYLXaaNSgQnHELuFKnMIoKeWTQogJOGIJJgNrAEUp1BcK1xMq7/4opeRAxgE6+LVHc6mhvBQmtG3Plzsv9kr20mh4on/NPmkKIcB7OjL9QchfyumUflxILt3/4pdPplVqnP4hjjxLf8bOL5wx9ArqUdhem7hKhjLTnI7UFYlCtthovi2dD7e8QdveVa+bXdvQaNSMuaITo4d0YM2WoyxauY+f523jxz+3EtbAj/atGtO2ZUPaNGtIowZ+hAb5otWWH6VttdlJz8wlJS2HlLRsLiRlEZ+UQfyFDOIS0jkXn0ZevoURA9vy8iNVz0NW7HO5cE5B3PzVwK9SytTLyX6mUDbSngXWaPC+t0r9xOXFkWpOY1xj10xHUkq2xcXirzdg1GpIyM4u06OlupG6EeRZI0k/9Q63vDiW0hztGoZUzX22f0i/QiXwevRbHM06hk3aUFex6p27OfTKckJDIfWaSKyBOpCgSTfjfyD9slQIRVGrVYwY2JYRA9uSlpHLhu3H2bbnFAeOxLF603+1rIWAAD8jXgYtXnotOp0GKSU2u8RqtZGTZyY7x0RuXvE1I61GTcNQPyIaBdClfQRNGgfSprn7a4iAa0rhbyHEYRzmo/uEEKFAvkekUah9WNyznrA/4yAAnQNcUwobz55hV/x5Xh06nJs7d63S2O5ESsmmnSf5/vdNRIW04IXb1/DyvUYyLf35/Kd1F5mQ9HoNd08dWEZvFePqsNF8cuxztqfupF9wn/IvqAZsNhub5m8n6VwKvufAd7djRpPVM4TEm1tyKrx2KS9PE+hvZOzIzowd6XDdTk3P4cSZJC4kZ5GYkkVyajb5Jgv5JismswWVEGjUatRqgdGox8eox9uoI8jfm+BAb4IDfWgY6kuQv3e1rVe4EqfwtBDiHSBTSmkTQuTgyGiqUA+Q5u24Yz3hQMYBIrzCCdKVXE3rojGl5ONtm2ns68vk9hULcvMkO/ef4Zs5G4g5lkB4owB6974PqTrOsK7/IoLuw9uoL9X7yB10DehCmKERS+OX0TeoZnIdFWDKM7H6lw3MfX8RccfiUWtU2Kz/1Zvw2ZlMZr8GpI5tSnp+nkdrXtRmggK8S3QiqM2UFacwsYS2om/nXXpc4TLEvA20nRGi8j/qPFseR7KOcWXDkS6dv/HcGfYkxPP6sBHoNa6tP3iSuIR0Pv9hLRt2HKdhiC9P33slo4e2R6NRI3OnIzNfAPNmRg0e4FYlcCkqoWJ0oyuZdfpHojNjqlTburIkn0/ln69W8M83K8hIzqJVj+a8MPdRTPlmPrnnW0xODysBNF4cx6mH2vHGhnW8N3J0tcuqUDnK+sWVVd1DoiiFyx5pzwZLNHhPr1I/+9MPYpM2ugV2LX9MKfl02xbCfHy4rl3NBjaZTBZ++HMrvy3aiUaj4u6pg7jh2h7otEV+Nl4TIPsLZPYXoOvv8af3/iH9mB+3gAXnF9Her121zBaklOxfF83fXy9n47zt2G12+o3tyYSHrqbL0A6FMqhUKmY+O4ekcymENglm2qtT2NdMxec7tjK4aRTXtm7rcVkVqk5ZcQp3VKcgCrUQyy7AVuX1hD3pe/DV+Ljkirol9hy74s/z8pAranSWsGPfGd7/diVxCelcOaQ99948mJCg4sFWQujA525k5itg3gh6z3pG6VRaxja+hp/OzOZgxiE6ubhGUxlSE9JY/csGls78l3OH4/AN9Gb8A6MZe/9oGrcovsg5fMqgYqUyh9jtbD53huf+XUnXhmE08Xe/C6WCe3ElTuHFktqllK+6XxyF2oQ0bQG0Dr/8SmK1W9mXfoDugd1cyor62fYtNPT24YYONZNbJisnn89mrWHJmkNENArg05evp3t5KZO9JkPO98isD0A3AFHB7K8VZUjoYJbEL+evuPl09O9Q4dnC6jkbLn6if3NK4c08LyefrX/v4t85G9i+dA92m512fVvx+Mz7GHpDf/ReFUtzolGp+Hj0GMbM+ZmHly3m90k3VE+1PIVK48qjWE6R1wbgGhylNRUud8wbQNcToap8OuMjWUfJteXS3QXT0bbYc2yLi+XFwcNqZJawdc8p3vlyOanpOdwysQ+3T+qL3oVKZo7ZwsPIjCeQif2QMh1UYeDzKCrjWLfLqVFpGB9+Ld+dmsWutD30DHI9nfnqORv4aPrXhbb/xLPJfHjXV8RsPUp6YgZb/96FKc9McONAJj92LaNuH0Zk22IFECtEhJ8/bw0fyQNL/+GdTRt4fvDQKvWn4Flc8T76oOh7IcT7OGofKFzGSFs8WI8hfIv5G1SIPel70QotHf3KXx/4dPtWQo3e3NixemcJuXlmPv9xLYtW7icqIpi3nhpP25YV8wGXdgkIkGmOBvt5yHweO3hEMfQP6cfi+KX8FTef7oFdXa5NMfPZOYUKoQBznoWFny8jINSPUbcNZcgN/ek4sC1qNz7RX92qDbedj2Pm3l008vHhf917uq1vBfdSmccxI9Dc3YIo1DJMGx17XeVt5FJKdqftoYN/e/Tqss0OO8/HsSX2LM8OHIJB49k6w0UT1wX6eYOQpGXkctPYnvzvpoHodZX4WeR8TPHqsfmQ/SF4QCmohZqJEeP54vjXbEnZyoCQ0gsP5eXkc2zXSaK3HCXxbOkZVn87/61bFcGlPD9oKIk5Oby5cR0hRm/Gt635IESF4riypnCA//7a1UAooKwnXOZI0wZQNQRN5aNRz+aeI8Wcyrjw8m+Kn2/fSpDBiymdulR6PFe4NHFdaobDOnrrdX2YXpVEbfb4irW7SFn2/56BPYgyNuWPc/PoEdgdrdQSf/ICZ6JjORsTx8kDZzix9zSxR84XFp+/NJ6ggAaRIR5VCABqlYoPR11Fen4eT65aRoDBwNCoZh4dU6HiuPJIVDS5hhW4IKW0lnayQt1HSiuYN4Hhyiq5PO5O34NA0DWg7MI8+xLiWX/2NE/0H4hR69lZQkmJ6wCWr4upmlJQhTlMRiW1V5LS7P/JcSlEtW9C/MlEvDMDOD3yDHe/PIPEd9OxWv4rVtggMoSW3Zox7IYBtOzejHZ9W7Fr5f6L+gTQG3VMe3NKpeWsCHqNhq/GjGPqvLnc/c8C3h5+JRPaVX+8hULpuLKmcEYI0R0YiGPGsBHY42nBFGoQy36QWYgqulfuSdtLC58W+GvLdkP8fMdWAgwGbulceS8nVyktcV1iiksFBUvH51HIfJ6LM8DoHe0VwGqxkng2mYRTiXzx0MwS7f/fPTW78L3BqCfE2xf71WauNgyndXhLmraPoEnb8BLrHRfMMkqbfVQHfno9sydO5t7Ff/PYyqXEZmXwQK++l1VNgrqMqy6pk/kvWO0HIcQfUsrXPSqZQo0hTRsAFehKt1OXR5IpiTO5Z7m+yaQyzzuUeIHVp07ySN/++Oh0lR6vPKw2OzN/31zq8QbBVUtcpzKO5cCWwzQM+oWgho7qaZmZ4QQ2Km46y8vJ5/zxBM4fTyDOuY8/dYH4ExdIOpeM3X7p2kRxPtn8BmHNGxIQ6keGJZOnDzyHaVI2o1oPLffmWlI8QXXjpzcwa9xEnl29go+2buZkWhqvDB2On75qlf0Uqo4r5qObgG5SynwAIcTbwG4cNRYULkfMG0DbBaGqfKDR1hRHyuveQb3KPO/zHdvw0em4rYvnZgnJadm8/NE/7D0US9f2EUQfj8ds/s/M4o7EdQ5Tzz5MuW0AuPWJBKY+cpJ5H76G1daZ2KPxxB47T9yxBFLj0y66NiDUj7AWDekwoA1hzQbTqHlDwpo14K2bPyHlfFqxsRpEhtC+b+v/rtf5MyF8HHPO/lZhF9WaRKdW897I0UQFBPLxts1sjzvH28OvZFDTqJoWrV7jilI4jSM+oWBerAdOeEoghZpF2lPBcgDh82CV+tmasp2WPi0I1YeUek5MchLLTxzjwd598dNXvlZDWew+cJaXPvqH3Dwzzz0wmquGdSyzbGZFyUzJ4uzhuGKmnt8/D2XUDam07/AnD4/Zh1+wP+Gtw+h5ZRfCW4YR3rIR4a3CCGvRsEQzD8Bd797isv1/eINhrE/awM9nZtPOrw3emrqRhE0IwQO9+zIosimPr1zGbQv/YnL7jszo07/miynVQuxSkpiTzen0dLy0Wro0dH/6bFeUggk4JIRYiWNNYSSwUQjxKYCU8iG3S6VQc5g2A7JKrqixuXHE5sVyc2TZi5efb9+Kj1bHHV3d/2Rrt0t+mb+N737bRJOwQD55+XqaRzoUlDo2BcPyPXidS8HQJBj1kLJdI202G4lnkjkbE8vZw+c5dziOc0fiOHc4jozkkstQmvLUfP9GGE9/cZYFCWPwCql41piK2P81Kg3/azaNV6Pf4Jczc7i7xV0VHq8m6dIojH9uuoWPtm5i1t7dLDwSw82dunJPz96EGCsfPFkXsdrtxGdlcSYjnbMZ6ZzJSOdMunOfkU6+s275VS1b88XVZaWoqxxCyrLtl0KI28o6LqX80a0SVYCePXvKnTt31tTwlyX29EfAtBnRYDOikoVc/oydxz/nl/BJtw9KXWQ+kpLMVbN/5P5efXisn/tqDgBkZufz+qdL2LzrJMMHtOWpe0dh9HKsV1zq0QOOp+9Hvr2H/uN6EXvkPGdjnDf9Iw4FEHs0HovJUnh+QKgfTdqGE9G6MZHtwmnSNpwP7/q6mFkIJB8sPEfH3lZEyDKEOtStn7Mk5scuZMH5RTzY8j56BvXw+HieIDYzg0+3b2FeTDQalYpRLVpyQ4dO9IuIRHUZLEZLKUnPz+dcZgaxmRmcy8zgXGYm5zLSOZuRQVxWJlb7f27DOrWaSD9/Iv0DiAoIpGlAAFEBAbQMDK70bEoIsUtKWWIEoSveRz8KIXRAgRHziJTSUtY1CnUTKU1gWguGMZVWCFJKtqZsp71fuzK9jj7fvgVvrZZpXd174zp8IoEX3l9EUmo2j/xvOBNHd71o4bWkiF5Trpl3b/scu+2/H6JKJWjUvCGRbcPpMbJL4c2/SZvG+JdQTW36eyWZevRkWx8H+TIy6y1EwIdu/awlcW3jMexJ38sPp3+mtW8r/LRVW0CvCSL8/Hl3xGju6dGbn/fvZcHhGP45eoTGvr5cEdWcoVHN6RfRBC8Puy9XFovNRmJODuezM4nPyuJ8VhZxWZmF+7jMDHIsF99CAwwGmvj506lBQ65q2Zqm/g4lEBkQQJiPb7UqQ1e8j4YCP+JYWxBAEyHEbVLK9R6VTKH6MW0EmYMwXFnpLk7mnCLJlMTYxmNKPedoSjJLjh3lnp69CfRyT/EVKSX/rD7AR9+tJsDfyBev3USH1o4YgbTEDA5uiOHAhphSI3rtNju3v3ojTdo2pknbcMJbhaFzIe9RAaWZevpPGITMTkBmf4Y0TUTo3TsruhSNSsP05nfy0qHXmHnqRx5u9UCddfVsHhjES0Ou4OkBg1l+4hh/Hz3MXzGH+OXAPrQqFW1CQuncsBGdGjSkeWAgUQGBhHgZPfJ5pZRkm82k5eeRkptLcm4uyXm5JOXkcCEnu3CfkJ1Ncm5Osdh2f72BcF9fIv386d8kkghfPyL8/Gji50+Enz++tcjryhXz0S5gipTyiPN9axy1mmt8bqqYj9yLPf0JMK11mo4q9hS2OXnLRQXmb296C8MaDi3x3IeW/sO/p0+y/vb/EeRVdXtxvsnCh/+3iiVrDtGrS1NemjGG7IR0Ns7bxqYF2zm8/TgAei8ddru8yBRUQIPIEGaf/qrKspSElGZk8rWADRHyd5UKFrnK8oSVzDn7G9c3mcSYsKs8Pl51YbJa2X4+ls3nzrL/wgUOJCaQbf5vduat1dLA24dQozchRiM+Oh3eOh0+Oh1alRqNSoVG5cgTZZN27FJittkwWW3k26zkms3kWMxkmy1km01k5OeTYXLszXZbiTIFGbwI9famobcPjXx8aOTjSyMfH8J8fGns60eYr69H3a0rQ5XMR4C2QCEASCmPioreMRRqPVKawfQvGEZWSiHMOv0TZvt/P845535Hr9YXFp0v4HByEouPHeHuHr3dohBi49N4/v1FHD+dxK3X9aGNVsOr177NgQ2ORL6te7bg9tdupNvwTrTq3oz1f26t9oheIXTg9yoy7RZk1kcIv2c9NlYBoxqO4FjWMf449xfNvZvRzu/yKHCj12gYFBnFoMgowOGNcy4jg1PpaZxOT+NsRjpJuTkk5+ZyOCWJbLOZHLO5mLnmUnQqNTq1GqNOi7fWoUh8dXpaBgXjbzDgbzAQ7OVFoMGLIC8jod7ehHgZCTYa0V1mqcBdUQq7hBDfAz87308FdnlOJIUawbzFGcVccdPRn7HzL1IIAGa7mT9j5xdTCh9v24y3Tsf0HlXPkrlh+3He+GwpQsBNfVqy/rlf+eNMEo2iQrnrnZsZeuMAGjS52CW2piJ6hb4P0jgVcn9EGkYidGXHb1R5PCG4s/kdxB6K48vj3/BqxxcJ1AV6dMyaQCUETQMCaBoQAJSeR0lKidVuxybtWGx2hBCohUAlBFq1+rJYwHYXrpiP9MD9ONJcCGA98KWU0uR58cpGMR+5D3vGs5C/FNFgm+PJtgLctv3OUo/92Pv7wtf7LyQw/vfZzOjTn4f69Cv1mvKw2ux8O2cDcxbsoGmjAHz2n+bkpiO07d2SG5+eQN9re3g8uVtlkPYcZIojwlkE/12lOhWuEpd3nlcOvU6EVzhPt3sCnap2mTEUaoayzEdlJmEXjhJSu6SUH0opJ0opJ0gpP6oNCkHBfUhpgfxVoL+iwgoBIFgX7FL7R1s3EWAwVCkuISUthxmvzGXOgh208fci8duVpB1P4MkfHuCTzW8wYHzvWqkQAITKG+H/NtjOIbPfr5Yxw70ac1fzaZzMOcU3J77DLotnSFVQKEqZ5iMppV0IsU8IESmlPFtdQilUM+btINMr7XU0KWIC352ahU3+txCnU+mYFDGh8P2O87GsO3OapwYMqpCnRdHo4wBfIxarDbPFSsucfM7O38bV/xvO9Pduwdu/jkTw6nohjbc5zEj6oQj9YI+P2SuoJzc2mcyv5+by69m5TG16o8fHVKi7uLKmEIYjonk7RUpzSindXzlEoUaQ+UtAeFW66Hzf4D7MPvMrJrsZi7QQrAtmUsSEwvUEKSUfbtlEiNHIrRXIhHpp7YO0zFwA/M4mkXToHI/PvI8rbx9WKZlrEuH7GNK8BZnxJAQvRKgbenzMKxuNItmcwooLKwnWBzG60SiPj6lQN3FFKbzicSkUagxpz4b8xWC4utKukvvTD5Bty+GBlvfSK6i4mXLN6VNsi4vl5SFXVCjgqLTaBzkNA/j+23to2a1uFmgRwgABHyOTJyIznoDAWZUOFnR9TMGUyBtJM6fx69nfWRT3Dzm2nGIKXEGh1DUFIYRBCDEDR9rstsAmKeW6gq28joUQM4UQiUKIg0XagoQQK4UQx5z7wCLHnhFCHBdCHBFCVD56SqFi5P8DMhfhdUOlu1iTtA5/rT/dAroWO2a123l74zqiAgK5qWPZxXYupbTaB3a9ts4qhAKEpiXC70Uwb4Wcr6tlTJVQ0TWgCwJBjs0x6U8xpzDr9E9sTt5SLTIo1H7KWmj+EegJHACuAj6oYN8/AKMvaXsaWC2lbAWsdr5HCNEeuBHo4LzmS+HpRycFAGTuXNC0AW3lymAmm5LZl76fwaED0aiKTzznHjrA8bRUnhowCK2LC8BSSn7/u3SvMj+vy8SDxus6MFzrjHbeVC1Dzo9bhLwk3rbAfVhBAcpWCu2llDdLKb8BJgEVMjg702CkXtI8DoeywbkfX6T9NymlSUp5CjgO9K7IeAoVR1oOgfUgwuv6SqcGWBK/HJVQMSx0aLFj2WYzH2/bTM/G4Yxq3tKl/rJy8nnuvUV89sNatDn5YL0kitRqQ3PoXKVkrW0IIRB+r4CmBTL9YaT1tMfHLIg4d7Vdof5RllIoDAF0Y03mhlLKeGef8UADZ3s4UPSXHutsK4YQYroQYqcQYmdSUpKbxKqfyNy5gB68xlXq+nRzOuuT1jMwpD/B+qBix/9v9w6Sc3N5duAQl5TO4eMJTHv8ZzbtPMGDtw9FvWIf2j2nELkmkBKRa0K75xR5+89USt7aiFD5IAK+AlTItLuR9iqWBS2H0tyH9Sq94q6qAJStFLoIITKdWxbQueC1EMLdf7kl3TFKjKqTUn4rpewppewZGur5VMSXI/bcRdgvDIa8XwEVMn9tpfpZlrACq7QxJuzqYsfisjL5v907uaZ1G7o2Krt4vZSSecv2cO9zv2Kz2/ni9RuZPKY7ei8dmtgUDMv34rVgO4ble9HEOqKQLyeEJhIR+IUjfiH9Edz3DFacSRETigWwqVBhspv47PiX5NvyS7lSob5QqlKQUqqllH7OzVdKqSnyurL5eC8IIcIAnPtEZ3ss0KTIeRHA+UqOoVAG9txFjgLzMsHZkgeZzzvaK0CWJYt/E9fSL7gPDQ0Nih1/bd0aBPBU/7L98HPzzLz80WI+/L/V9Owcyaz3b6Vj68b89PJczHlmNNqL1yE8naeophC6Xgi/l8G8AZn5PNJDT+39Q/pxR9SthTOGYF0wdzWfxtTIm9iTtpdXot8gIS+hnF4ULmdccUl1J4uA24C3nfuFRdrnCCE+BBoDrYDt1Sxb/SD7Q/6rrFpAvqPd6HroyYoLqzDZTVxTQorsf0+dZMXJ4zzZfxDhfqU/P5w8m8wL7y/iXHwad08dxNTxvVGpBHv+PcCcN+Yx+o5hdLmiI7Oe+7Va8xTVFMJ4PdgvOBaehRf4vuiRNND9Q/qV6IIa7tWYL098w8vRrzO9+Z10D/Rc3WyF2ovHlIIQ4ldgKBAihIgFXsKhDOYKIe4EzuJwd0VKeUgIMReIBqzA/VLKkvPUKlQNe3zF2ksg15rLqgur6RnYg3CvxgAsPBLDe5s3cD4rC7UQNPT2YVq30rOrr9wQwztfLcfopePjlybTvWMkABnJmbx9y2dEtGnMfZ9Ow8vbwIipno/6rTV4PwD2XMj9HimM4PN4tdVD6ODfnlc6vMBnx77kk2OfM6LBFVzfZBJ6de3J9a/geTymFKSUN5VyaHgp578BvOEpeRSciEYgS1AAqrLt/kVZdP4f8mz5hYV0Fh6J4dnVK8hz1o61SUlaXi5Ljx9lXJuL6x9bLDY+/3Etfy3dQ+d24bz66LWEBPkAjrWF96d9SVZKFm8ueRYvb0MlP2TdRQgBvk8iZR7k/B/ScgRpOeYw96nCwOdRVBWY0VWUEH0Iz7V/hj/P/cXyCys5lBnN9Ob/o7lP3Y4LUXCdMhPiKVyG6IeW0GgAn0dduvx83nlWXFjFoNCBNPVuCsB7mzcUKoQCzHY7723ecFFbSloOD708l7+W7uGGa3rw6cvXFyoEgIVfLGPrP7u4691baNElqiKf6rLC4ar6ImgHgHm9U4lLsJ+v1PpPRdGptExpeiNPtnkMk93Ea9Fv8uvZ38mz5Xl0XIXagaIU6hFSWsGyGVSNQYQBwvHa73WXnj6llPxy5lf0Kh2TIyYWtsdnZZV4ftH2g0fPc+eTP3P05AVefuQaHrxjGBrNf4vIsUfP839P/kzvq7sx/sHLp1JYZRFCBdaTJRxxrv9UAx382/N6x1cZFDqAZQkreGb/C+xI3Ul56fYV6jbVvdCsUJPkLQTbGUTAVwhDiVa8MtmVtodDmdHcHDnlooLwYb6+nC9BMYT5+gKw+N8DvP/NKkKDffjmram0jLrYldhms/HeHV+g99LxyLf31Nmawm5HluIFVIH1n6rirTEyrdntDA4ZyI9nfuHz41/Ryqcl1zeZRGvfVtUmh0L1oSiFeoK0pyKzPwBNR9BfUeHrTTYTv579jQivCK64pPby5PYd+WTbxblzvDQaHu07gM9mreH3f3bRs3NTXn30Gvx8iyfdm/fxEqK3HOWpnx4kpHHxILh6iyrMYTIqhjdSWipcNrUqtPRtycsdXmBd0gYWxi3ijZi36eLfibGNr6GlryNavWidbiXRXt1FUQr1BJn5CtgzEIEzK/Uk/mfsPJLNKTzd9gnURdJS5VosLDxymECDAb1Gw4XsbMJ8fXmwe182/hnD9r2nmXR1dx64fSgadXFr5dnDccx6/lf6j+vF8KmXp6tppfF51BFTcpELsRrIRiZfC37PIPRDqk0ctVBzRYOhDAjux6rEf1l8fgmvxbxFS58WNDNGsTZpPRbpSIRQkGgPUBRDHUNRCvUAmfePo9Smz6MIbcULuO9N28eKC6sY2XD4RQXgpZQ8tWoZZzPSebh5L9YuikGdIgkOsPPb5s2kZuTy5D2jGDuy5OyoNpuN96d9gcFbz8Nf3aWYjS5BZRyLHRxrCPb4Qu8jofJFZr2JTLsLqRuE8H0MoW1fbXLp1XrGhF3F8AbD2JC8ieUJK1mZuLrYeaXV6Vao3ShK4TJH2i44ZgnaruD9vwpfn2ZO47tTM4k0NuGGJpMvOvbt7h0sPnaUCWFtWDB7V2Htg+S0bABumdinVIUAsODTpcRsPcbTPz9EUKPLr6i8O1AZx5YcVKgfALmzkdmfI1PGI3X9Ed7TQDeo2pSrQW1gZMPhDG8wjDt23FXiOSnmFOzSjkooPi0VQUqJyW4i15ZLjjWXHGuO83WOY7PlEmYIo39IX7ePrSiFyxgpzY4iLtKE8H8XISr2322Xdr458R0mu5n7WtyDVvWfDXvDmdO8t3kjY1q15tSCuBKL4axYH8PdpZiE4o7HM+v5X+l7bQ+umDKwYh9MwVFL2/sO8JoIub8jc39Cpv0P1E0dCQ4NYxGayGqRRSVUBOuCS820et/uh2jl05LWvq1o4dOcSK8m+Gh9Sjz3ckFKidluJs+WR64tj1xbLnlWxz7XlkuuNZccZ3uutWhbDjnO97Yy4ncFgn7BfRSloOA6UpqR6Q+BeSvC/x2EJqrCffwZO4+YrMPc2ewOwrwaFbafSk/joWWLaRUUzDsjRnPl95+UeH1iSilFcux2PrzrazQ6DQ9/qZiNqoJQ+YPPdPC+HfKXIPPmIbM/g+xPkdouCP1wMIwAdYvC79meu6iYSaqqAXGTIiYw6/RPmO3mwjat0DI4dCB2aedI1lH2ZxwoPBaoDaSxVxgNDA1ooA8lVB+Cv9afQF0Afho/dCpdtf9d2KUdk92EyWZ27O0m8m35mGwm8u355Nnyybflk2937PNseeQ59wXvc215zva8Mm/q4FijMaqNeGuMeKmNeKuNhOiDMaqNGDXeeKu9HHuNEW+1N0aNER+NN0a1N15qg8dmX4pSuAyR0opMfxxM/yL8XkZ4TahwHysSVrE4finDQocwKGRAYXtcVia3zP8DtRB8PWYcepUaL72O3HxzsT4aBJec92jxNyvZvy6ax767l5DwyyvjaU0hhA68xiO8xiNt8ZC3CJm/HJn9oUMBqCOR+sEgdZA3GzA5LiwIiIMqKYaCdYOyvI+yLFmcyT3L2dxznMs9R3x+AttTtpNjyy3Wn1ZoMGq8MaqNGNR69Co9epUOjUqLRmhQCzUqoUIlVAgEBUmVJY6be8Fmk1Zs0oZN2rBIK1a7FYu0YLFbMNstmO1mLHazY1+B7LSOG7oXBrUBL7UXXmovAnWBNFY3drZ74a32wktjxEvt5bj5O98bne9rQvG5gqjLgSg9e/aUO3eWXqGrPiLt2cjM5xwLy77PILzvqHAf21K289WJb+kW2JUHW95X+ESSlJvDjX/+TnJuLnMmTqalfxCvfbqEtVuPoVYLbLb//pb0eg1P3TOKUYMvXgCNP3WBu7s8Trt+rXl72fO18kdxOSFtCWBajTStA9NWiidDdKJqjKrB2uoUrZAcaw4p5lTSzemkW9LJtGQ5zSg55FpzMdnN5NvzMdnM2KQVq7RitduwY0dKiR27M/e+41+VUKEWKlSoUAt14aZVadGqNGiEFp1K63gvtOhUOnRqHTqVzql89OjVegwqAwa1HoNKj15twKA2YFDpMagNF5lS6yJCiF1SyuIF1VFmCpcV0rwXmfEY2OIQvk9VSiEcSD/INye/o5VvS+5tMb1QIaTn53Hbgr9IyM7ix/GTiPDyY8arf3LwSBwP3j6UQH8j38zeSGJKJg2C/bh76sBiCsFut/PeHV8gVILH/k8JUqsOhLoRGKcijFOR0oS80KnkE+3nkfnLQTcAoapee7+3xhtvjTeRxibln6zgcRSlcBkg7dnInO8g5xtQNUIE/YLQlfgQUCZbUrbxfye/J9yrMTNaPVhYjCU2M4M7Fs7jXEYG/3fteBqrvbn32V9JSMrglUev5Yr+bQCKKYFLmf/JEg6sj+HxmffRIFIpkFTdCKFHqhqXEhAnkOkPAlqkridCPwz0wxCaptUtpkINoyiFOoy0Z0DuT8icH0FmOjxO/F5CqHwr3NfyhJXMOfsbbX3b8HCrBzBqjAAcSrzAtEXzMdms/DRhEt45au5+ZQ4Wq42PXpxMl/YRLvV/JiaW75+dQ7+xPRl129AKy6fgJkoMiDOA7ysIbTjStBZMa5FZb0LWm0h1M4dy0A8FXXfH2oVCtSKlFexpYE91bimOvToCYah4doLyUJRCLaYkLxHhdQ2YtyLzFoBpBchc0A9H+NyP0HZ0qd+i6QiCtEE09mrEwcxoegZ25+4W09E57aXLTxzj8RVL8TcY+GXCTZw/lsLTnywhOMCbz1+9gaYRri0SW8wW3rv9c7x8DMz4erpiNqpBSguIK1hkFrrejtTd1nNgWuNQErk/I3NngvBG6gYi9ANBPwihbnxR357warockTLfeXNPu2gvC2/6ac4bv/O4zCi5I/0ojygFZaG5llJYNvPSFAfCx/FHInzAcBXCeDNC2660boqxOXlLMddBgE5+HXm0zcOohIp8q4XXN6xjzoF9dGrQkG/GjGPVqmi+mb2Bdi3DeOeZ8QT6e7s85teP/sBfHy/mxT8fZ9DEPi5fp1A7kPYcMG92LlavA/sFxwF1M9D1Reh6IW1pkP0exWYgLmbgras4bvDpzhu4cy8de1nYXmSTqSBLS0GuBlUgqIKcW6BjE0EIdbCjTQRB4Wt/hFCX0lfZKAvNdZGsDyjuKWIDmY/w/xAMIxCi4kVo/oydX0whAJzPj0clVBxMvMDjK5ZyNDWF6d17cn/3Pnz47WpWbojhiv5teO6B0ej1rntebFqwnb8+Xsz4B65SFEIdRai8wTASYRjpSJttOwGmjUjTRshfhMz7tZQr8x1/x7VcKUgpHTNumQn2jP/29gyQGQ4zrT3DebMvssn0Mm7wOB7cVIGgCnDcyDUtQRWAuOimX+S18HOkTK9hFKVQy5CWIw7TUEnV0QAwO0xIlaS0qNMUcwrP/buS3w7uJ9ho5Mdx19HKGMSMl//gyIkLTJ8ykFsm9qmQ6Sf+1AXeu+MLWvdswV3v3VJpmRVqD0IIx81N0xLhfbvD3m2NQaZcV/IFMh578jhQhzts4OqGoAp2bgEgfB03T+EFQl9u1L3DsmEDLCBNRba8Iluuc8sBmYO0ZztfZ4E9C2Q22DOd7517yopRUIPKH0SA8wYfBtp2IAIcwYOFT/QB/ykBVUCdXX9RlEItQNqzHE9cuXPBGoPjv8VAiT7lFSibWRSz3cLKhJWlHzdrmXvoALd37c7Dffqzd+9Z7vjyJ6xWG28/PZ6BvVpWaDxTnonXb/gIgOd/fwRdBWYXCnUHITSg7VSGV5M3qBuA7QyYNznKjJaBRAVoQaj4rwaYBGmnUBlUXEoQRocCUjmVkCoIVE2dbX4I4Q8qP+fN37lXBYDwB+Fdr9bB6qVSkFJy+uBZojpG1uh/trREI3NnQ/4/jiccTXuE7/PgdQ0yf2PJXiIuls0swC7tbEnZyp+x80k1p+KvakCqJRF1EVOkzSbIS2nO4im30tQ3gC9/WsdfS/fQpkVDXp5xDU0aVyxZnc1m462pn3Bs10lenvcEYc0aVuh6hTpIaV5Nfq8Urik4zDQ5YE92bpmOp3aZ7XyyNyOlCccsQAJ2HDHKKqeSEIDOOZvQgjCA0Dk3o3O24eV87e3cO2YhtcEsU1eol0rhyI7jPNj3WZp1imTkLUMYNmVgtRV3kdIC+SuRuT+BZTdgAK9rEMYbQdOpUEmJcrxEysMu7exI3cWi8/8QmxdLlLEpdzS9nWl/rkNt1BAVmYheZ8Vk1nD6bAO05gAyYrO57dsFxCakc8O1Pbhn6mC0WtcWslbP2cDMZ+eQeDYZLx8Dedn53P/JNPqP61W5L0qhTlGeVxM4TU/CB1Q+QFSJ/dSf5/HaS730Pjp7JonX311IxtE4kvaeQZOVS49hHRl561AGjO+F3kvvdlmlLRnyfkfm/gr2RFA3QRingtd1Drukm7DarWxL3c7f55cQnx9PI30jmmt7czRWy5rTp0jPLyXNgYTG6yURYYE8dtcIenVxPWhp9ZwNfDT9a0y5/y1gqzUqnvjhAYZPUQrnKCjUNsryPqqXSiHmeDzPvLOQ5NTswjZNvgV7ShZ6k5kunaMYPakvQ0Z3Rat1bTK1b89XNPL6nmD/TFIy/EjIu5POXe8By05k7m+QvwywgG4gwngz6IeU6062Yn10uakjCsi15rI2aT0rElaSZknHiyCykiPYedKGxS7x1xu4ollzVh47RratuF1WnQ/PRPVj6vje6HUVm0BOjbqXxLPJxdobRIYw+/RXFepLQUHB8yhKoRRS0nI4evICR05e4NipRKIPx5GUUSRjo5T4G3Q0b96AZk1DaRTqR2iwLw2CfQkMMOLv64WPUc/B/d/QKvgzDLr/PBgsVhU2GYBBm+pYzPIajzBOQWhaFJ5jtdmxWm3Y7RKb3Y60S9RqFWq1ijWbj/D+/626qE5BSUnmYnPimXvmHw5k78KOhcxMH86eDyQ93YcOoQ3pH9mUIZFR9AqPQKNSMeTxz4kNz0eq/5uoC5skIk7PuvcfrNT3OFI1ucR2IQQrbHMr1aeCgoLnUOIUSiE40Jt+PZrTr0fzwra8fDNHjsWzcuEOdmw+QsL5VPZdSGd/dCy2EhalhYDfX5lzkUIA0GrsSGs6//fPGDYdbEOeSYXZvAKz1YbZbMXiVAZlkRsKWc3Bpge1CXxPWnn7q+VsOHKShIA4sgKPo/NNRUpISvFHlR1FR79W3NQ5nA6BDVBbISvbRFx0Kh8vOcKx04nYTuXjnw1ZzWWRfsGebKrUd5iRnInOoMWcX3z2EdpESYutoFDXqNdKoSS8DDq6dmpK104Om/r5Ewms+GEtK35aS2J8Ot5hAXS+qhvthrYiLCwOX91+GgZll9iXRm3nXPowmkWq0GhU6LQadFo1Wq268LVGo0KlUqFWCYQQhbOGD5atRzsgg+5R/y0InwkPJV9KYiKO4W00oTKriT8cQuaeAFTxWtRWM5s5xGYOFZPFx6inZVQoRi8dJJkxJl18vEFIybUPSkNKyZpfN/LljFlYLTY0OjVW839FRfRGHdPenFKhPhUUFGqeem0+chVpT8duPsWZA/+SfHoj3sbTtOiYi04vkVJgtQm0Gnux65LS/WjYtnLy9fvxBVq0jket/u//R0rHzMRXhHJF6DD6ePciL8dGZlYemdn5mMxWrDYbFqsdvVaNr48BX28DDUN8aRjq5zDnrI/mna9XlGuWKotju0/yw4u/sX3JHtr2acWj397NyQNnmfnsHJLOpRDaJJhpb05RFpkVFGopivnoEqQ9F2ynnNGP+Q4faXu2M6Q9E+xJjs2WCLZYkBkIIKoJREV6Y5WtORXjz7JfslnzVz69b4QZz8Rg0P/3pJxvUrNz31WMaVsx2dLz8/grJprI5okXKQRwKASTWc0PA976L76ighmoC278ri5gF2DKM7H1n90s+GwJBzcexsvHwL0f3c64B0ajVqtp1qmpogQUFC4D6qVSwHoMmVry4igIZ7RjqGPTdnEUQFdHgqYVqJugEyraNILWQyUj7jzK01e+hs3WhjsfPkOD4BwSU7z5/pOmHF6ZzJgbXBNp/4UEftq3h8XHjmDwzqRTu5LD7nVaW5UD7tSxKRiW78HrXAqGJsGohxRPqJeTkcOpA2c5vP04O1fs48D6aMz5Fho1a8A9H9zG6GnD8K5AUjwFBYW6Qb00H0l7Jpi3O6MfDc69jzPE3afCmQdHqa+ntO/xzaXP0X1EJ9Tq4n1a7XZWnDjOrL272BUfR3hoLm2jsjCrU6FI3dmieAk/vu71UYXkK0pJMQUanYZBE/tg9DMSfzKB2KPxF7mYRrYLp+eorvQc3bXUz6KgoFB3UFxSPUxpfvpCJZB2SUh4EFdMGcSwmwbQoksU2WYzc6MP8sPe3ZzPTqddEzMRjVPII4OG+gZcFTYaNSp+OP0LtiKJutRo+F/z2y8qhu4KFrOF0wfPcWzXSb5+7EfysksOYPMN8qFxi4Y0btmI5p2a0qxTJC26RhESrngRKShcTihrCh5m2ptTij196406Hvzifxh9vFjx41r++ugfZs9chu3aFiR39sems9CzpY32gQnkyxwaeTdjTNgUegR2L6yLfGTrCTaoN0CwhBRBP1s/+vcuWyFIKUk4nUjMlqPEbD1GzLajnNx3Bou5rCyQDuYlz6raF6GgoFDnUZSCGyhYYC3J+8YuJfRqRNzOlqyLPYNBZ6KZ5iTBrXNRGQTq074MtQxjSKdBRAU0KVQIq+dsYOH0lRcpmoXGlTT/tsVFC7qpCWkc33Oa43tOcXj7MWK2HiM90VGpyWDU06Z3SyY8PIbWPZrTsnsznhj+CknniqfPbhAZ4smvSEFBoY6gmI/cxMIjMby3eQPxWVmE+fpyd4/e5JjN/H7oAGcy0ogMtdCxeR5ZIg41aqIym2FbJDj021EykrMA8PIxEN4qjMYtG7Fz2V5ys4qnGTb6edFnTHfiTyaScPIC6UmZhcciWofRrm9r59aKZh0jUWsutv+XtKagN+p45Nt7FO8hBYV6Qp1aUxBCjAY+AdTAd1LKt0s7t7YohYVHYnh/z+80Do+/KPNoRpY3PZpL/AKTyJUZ+Gv9GRY6hGENhhCgCwAc5p7zJxKI3nyUoztPEHcigfPHE4g7VlqRHWjUrAFhzRvSKKoBUR2b0KJrFC27RrnsDVSQ0VSJKVBQqJ/UGaUgHG4/R4GRQCywA7hJShld0vk1rRSsdjuHk5O4f83XNIk8VyzQDByxBe392jI4dDC9AnugUblmsZvS9J4SzTyhTYKZc+Zrt8ivoKBQP6lLC829geNSypMAQojfgHFAiUqhOsk2mzmVnsbxlBSOpiaz/8IF9l2IJ9dipnf3uBIDzaxWwUfd36KBoYIRZsCdb00t0cxz51tTq/xZFBQUFEqjtimFcOBckfexwEXV3oUQ04HpAJGRkZUaJD4ri5/270EtnDmHENikHYvdjsVmI8dsJtNkItNsIiknh4TsbLLMjoRxWo0VP28zUQ0Eg7qYsWlTMUlbieOo1bJSCgHKXrxWUFBQ8BS1TSmUFKp70SO4lPJb4FtwmI8qM0hibg6z9uzGJu3YnHYelQCDVqDXSHyN4OclMOrtRATYaKm3IDR5mEUmJvlfam0/Q0Na+nRnW/IuLCXUUzaqKpZk7lKGTxmkKAEFBYVqpbYphVigSZH3EUBJ1cCrRKCvhQmDkzHbLVikGZPNjMluQpYQQWxDoNMFEKILoZFXKyK8wonwCifS2ARfrS8AHfza8d3JH4oFmt3a7Hp3i66goKDgUWqbUtgBtBJCNAPigBsBt+df1qv0hHs1RqvSoVfp0Kp0eKkNGNVeGNRe+Gl98dP64a/1I0AbgFalLbO/ggjjP2Pnk2JOIVgXzKSICRWOPFZQUFCoaWqV9xGAEOJq4GMcLqkzpZRvlHZuTXsfKSgoKNRF6pL3EVLKJcCSmpZDQUFBoT6iqmkBFBQUFBRqD4pSUFBQUFAoRFEKCgoKCgqFKEpBQUFBQaEQRSkoKCgoKBSiKAUFBQUFhUIUpaCgoKCgUEitC16rCEKIJOBMFboIAYoXV6591BU5QZHVUyiyeob6KmtTKWWJ2TrrtFKoKkKInaVF9dUm6oqcoMjqKRRZPYMia3EU85GCgoKCQiGKUlBQUFBQKKS+K4Vva1oAF6krcoIiq6dQZPUMiqyXUK/XFBQUFBQULqa+zxQUFBQUFIqgKAUFBQUFhULqpVIQQowWQhwRQhwXQjxd0/KUhhBiphAiUQhxsKZlKQ8hRBMhxBohRIwQ4pAQ4uGalqk0hBAGIcR2IcQ+p6yv1LRMZSGEUAsh9ggh/qlpWcpDCHFaCHFACLFXCFFrK2AJIQKEEH8KIQ47/2ZrZZlEIUQb53dZsGUKIWZ4dMz6tqYghFADR4GROGpC7wBuklJG16hgJSCEGAxkAz9JKTvWtDxlIYQIA8KklLuFEL7ALmB8Lf1eBeAtpcwWQmiBjcDDUsqtNSxaiQghHgV6An5SymtqWp6yEEKcBnpKKWt1QJgQ4kdgg5TyOyGEDjBKKdNrWKwycd674oA+UsqqBO2WSX2cKfQGjkspT0opzcBvwLgalqlEpJTrgdSalsMVpJTxUsrdztdZQAwQXrNSlYx0kO18q3VutfLpSAgRAYwBvqtpWS4XhBB+wGDgewAppbm2KwQnw4ETnlQIUD+VQjhwrsj7WGrpzauuIoSIAroB22pYlFJxmmT2AonASillbZX1Y+BJwF7DcriKBFYIIXYJIabXtDCl0BxIAmY5zXLfCSG8a1ooF7gR+NXTg9RHpSBKaKuVT4l1ESGED/AXMENKmVnT8pSGlNImpewKRAC9hRC1zjwnhLgGSJRS7qppWSrAAClld+Aq4H6nCbS2oQG6A19JKbsBOUCtXVsEcJq4xgJ/eHqs+qgUYoEmRd5HAOdrSJbLCqd9/i9gtpRyXk3L4wpOs8FaYHTNSlIiA4CxTjv9b8AVQohfalakspFSnnfuE4H5OMy1tY1YILbI7PBPHEqiNnMVsFtKecHTA9VHpbADaCWEaObUvjcCi2pYpjqPc/H2eyBGSvlhTctTFkKIUCFEgPO1FzACOFyjQpWAlPIZKWWElDIKx9/pv1LKm2tYrFIRQng7nQxwmmNGAbXOc05KmQCcE0K0cTYNB2qdQ8Ql3EQ1mI7AMY2qV0gprUKIB4DlgBqYKaU8VMNilYgQ4ldgKBAihIgFXpJSfl+zUpXKAOAW4IDTVg/wrJRySc2JVCphwI9Obw4VMFdKWevdPesADYH5jucDNMAcKeWymhWpVB4EZjsfDE8Cd9SwPKUihDDi8Ja8u1rGq28uqQoKCgoKpVMfzUcKCgoKCqWgKAUFBQUFhUIUpaCgoKCgUIiiFBQUFBQUClGUgoKCgoJCIYpSUPAYQgibM7PjQSHEH07XOk+O94MQYlIZx+8RQtzqprHec2ZYfc8d/dUGimQ47el8v7bgtfN9VFkZe4UQLZz/39mlnaNQ+6l3cQoK1UqeM5UEQojZwD1AYWCbEEItpbRVlzBSyq/d2N3dQKiU0lS0UQihkVJa3ThOdTOsshlOpZQngK6KUqjbKDMFhepiA9BSCDHUWXdhDo5AN4MQYpbzCXWPEGIYgBDidiHEQiHEMmfti5ec7Rc9rQohHhdCvHzpYEKIt4UQ0UKI/UKI951tLwshHne+7iqE2Oo8Pl8IEehsXyuEeEc46i0cFUIMKqHvRYA3sE0IcYNzhvKhEGIN8I7ziXmZMyncBiFEW+d1zYQQW4QQO4QQrxXcPJ3fyT9F+v9cCHG783UPIcQ6Z1/LhSNFealyOhP9ve/8PvcLIR4UQgwXQswv0v9IIUSV0pAIRxK5ghz/SQX/Pwp1H2WmoOBxhBAaHLlbCqJbewMdpZSnhBCPAUgpOzlvniuEEK2LngfkAjuEEIuBcp9ihRBBwASgrZRSFqS0uISfgAellOuEEK8CLwEznMc0UsreQoirne0jil4opRwrhMguMgu6CmgNjJBS2oQQq4F7pJTHhBB9gC+BK4BPcCRh+0kIcb8Ln0MLfAaMk1ImCSFuAN4AppUh53SgGdDNGb0fBKQBXwghQqWUSTiid2eVN76T2UKIPOdrHc5srVLK/zllbIojO8APLvanUMtRZgoKnsRLOFJe7ATO4sxfD2yXUp5yvh4I/AwgpTwMnMFxgwVHSusUKWUeMM95ritkAvnAd0KIiTiUSiFCCH8gQEq5ztn0I478+gUUPEXvAqJcHPMPp0LwAfoDfzg/+zc40mqAIxVIQf6an13osw0OpbjS2dfzOBI4liXnCODrAhOWlDJVOtIW/Azc7FSQ/YClLn6uqVLKrk4FeHXRA0IIA46snQ94Ose/QvWhzBQUPEnhmkIBwpEXJ6doUxnXX5qDRQJWLn6YMRS7yPGE3BtHorMbgQdwPKm7SsE6gQ3XfyMFn0kFpF/6uYuKV0JbaZ9JAIeklKWViixJTlHKGLOAv3Eoyz/ctO7xNTBPSrnKDX0p1BKUmYJCTbMemArgNBtFAkecx0YKIYKEI5PpeGATcAFoIIQIFkLogWLlKZ1P6/7OZHwzgK5Fj0spM4C0IusFtwDrcAPOGhKnhBCTnbIIIUQX5+FNOJQUOD+zkzNAeyGE3jmLGe5sPwKECmf9YCGEVgjRoRwRVgD3OE12Baa0gpTW53HMNn6owkfE2e/9gK+U8u2q9qVQu1CUgkJN8yWgFkIcAH4Hbi/i0bMRh9ljL/CXlHKnlNICvIqjqts/lJzy2hf4RwixH8fN/pESzrkNeM95Tldnn+5iKnCnEGIfcIj/yr0+jKPwzA7Av+BkKeU5YC6wH5gN7HG2m4FJOBav9+H4HvqXM/Z3OEx1+53XTClybDZwzk11sx8HOhVZbL7HDX0q1AKULKkKtRKn901PKeUDNS2Lp3AuVvtU43ifA3tKS78uHMV8elbWJbVIP9X6uRTcizJTUFCoBwghdgGdgbIqtyUBq0WRgLUKjtHCuSDu8epgCp5DmSkoKCgoKBSizBQUFBQUFApRlIKCgoKCQiGKUlBQUFBQKERRCgoKCgoKhShKQUFBQUGhkP8HRcTNyBp5tKgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "color = plt.cm.viridis(np.linspace(0, 1,5))\n",
    "mpl.rcParams['axes.prop_cycle'] = cycler.cycler('color', color)\n",
    "\n",
    "f3 = np.linspace(0,7.1,100)\n",
    "intfunc = ipl.Rbf(frequency_k3, propforce_k3, funktion='cubic')\n",
    "p3 = intfunc(f3)\n",
    "plt.plot(frequency_k3, propforce_k3, 'o', color='C0')\n",
    "plt.plot(f3, p3, '-', color='C0', label='91.5 [mm]')\n",
    "\n",
    "\n",
    "f2 = np.linspace(0,7.1,100)\n",
    "intfunc = ipl.Rbf(frequency_k2, propforce_k2, funktion='cubic')\n",
    "p2 = intfunc(f2)\n",
    "plt.plot(frequency_k2, propforce_k2, 'o', color='C1')\n",
    "plt.plot(f2, p2, '-', color='C1', label='101.5 [mm]')\n",
    "\n",
    "\n",
    "f1 = np.linspace(0,7.1,100)\n",
    "intfunc = ipl.Rbf(frequency_k1, propforce_k1, funktion='cubic')\n",
    "p1 = intfunc(f1)\n",
    "plt.plot(frequency_k1, propforce_k1, 'o', color='C2')\n",
    "plt.plot(f1, p1, '-',color='C2', label='111.5 [mm]')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "f4 = np.linspace(0,7.1,100)\n",
    "intfunc = ipl.Rbf(frequency_k4, propforce_k4, funktion='cubic')\n",
    "p4 = intfunc(f4)\n",
    "plt.plot(frequency_k4, propforce_k4, 'o', color='C3' )\n",
    "plt.plot(f4, p4, '-', color='C3', label='121.5 [mm]')\n",
    "\n",
    "f5 = np.linspace(0,7.1,100)\n",
    "intfunc = ipl.Rbf(frequency_k5, propforce_k5, funktion='cubic')\n",
    "p5 = intfunc(f5)\n",
    "plt.plot(frequency_k5, propforce_k5, 'o', color='C4')\n",
    "plt.plot( f5, p5, '-', color='C4', label='131.5 [mm]')\n",
    "\n",
    "plt.legend(loc='upper left', fontsize=9)\n",
    "\n",
    "plt.xlabel('Propulsion frequency [Hz]')\n",
    "plt.ylabel('Propulsion force [mN]')\n",
    "\n",
    "plt.legend(loc='upper left', fontsize=9)\n",
    "plt.savefig(\"force_freq.pdf\", format=\"pdf\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/IPython/core/interactiveshell.py:3457: RankWarning: Polyfit may be poorly conditioned\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "/usr/lib/python3/dist-packages/IPython/core/interactiveshell.py:3457: RankWarning: Polyfit may be poorly conditioned\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "/usr/lib/python3/dist-packages/IPython/core/interactiveshell.py:3457: RankWarning: Polyfit may be poorly conditioned\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "/usr/lib/python3/dist-packages/IPython/core/interactiveshell.py:3457: RankWarning: Polyfit may be poorly conditioned\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "/usr/lib/python3/dist-packages/IPython/core/interactiveshell.py:3457: RankWarning: Polyfit may be poorly conditioned\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "propforce_f20 = []\n",
    "amplitude_f20 = []\n",
    "propforce_f25 = []\n",
    "amplitude_f25 = []\n",
    "propforce_f30 = []\n",
    "amplitude_f30 = []\n",
    "propforce_f35 = []\n",
    "amplitude_f35 = []\n",
    "propforce_f40 = []\n",
    "amplitude_f40 = []\n",
    "propforce_f45 = []\n",
    "amplitude_f45 = []\n",
    "propforce_f50 = []\n",
    "amplitude_f50 = []\n",
    "propforce_f55 = []\n",
    "amplitude_f55 = []\n",
    "propforce_f60 = []\n",
    "amplitude_f60 = []\n",
    "\n",
    "\n",
    "for key in database.keys():\n",
    "        \n",
    "    if ('kp' in database[key] and database[key]['kp']==1.0 and database[key]['freq']==4.0):\n",
    "        propforce_f40.append(database[key]['prop'])\n",
    "        amplitude_f40.append(database[key]['amp'])\n",
    "        coefficients1 = np.polyfit(amplitude_f40,propforce_f40, 1)\n",
    "        poly = np.poly1d(coefficients1)\n",
    "        x40 = np.linspace(amplitude_f40[0], amplitude_f40[-1], 100, endpoint=True)\n",
    "        y40 = poly(x40)\n",
    "        \n",
    "    if ('kp' in database[key] and database[key]['kp']==1.0 and database[key]['freq']==3.5):\n",
    "        propforce_f35.append(database[key]['prop'])\n",
    "        amplitude_f35.append(database[key]['amp'])\n",
    "        coefficients2 = np.polyfit(amplitude_f35,propforce_f35, 1)\n",
    "        poly = np.poly1d(coefficients2)\n",
    "        x35 = np.linspace(amplitude_f35[0], amplitude_f35[-1], 100, endpoint=True)\n",
    "        y35 = poly(x35) \n",
    "        \n",
    "    if ('kp' in database[key] and database[key]['kp']==1.0 and database[key]['freq']==3.0):\n",
    "        propforce_f30.append(database[key]['prop'])\n",
    "        amplitude_f30.append(database[key]['amp'])\n",
    "        coefficients3 = np.polyfit(amplitude_f30,propforce_f30, 1)\n",
    "        poly = np.poly1d(coefficients3)\n",
    "        x30 = np.linspace(amplitude_f30[0], amplitude_f30[-1], 100, endpoint=True)\n",
    "        y30 = poly(x30)\n",
    "        \n",
    "    if ('kp' in database[key] and database[key]['kp']==1.0 and database[key]['freq']==2.5):\n",
    "        propforce_f25.append(database[key]['prop'])\n",
    "        amplitude_f25.append(database[key]['amp'])\n",
    "        coefficients4 = np.polyfit(amplitude_f25,propforce_f25, 1)\n",
    "        poly = np.poly1d(coefficients4)\n",
    "        x25 = np.linspace(amplitude_f25[0], amplitude_f25[-1], 100, endpoint=True)\n",
    "        y25 = poly(x25)\n",
    "        \n",
    "    if ('kp' in database[key] and database[key]['kp']==1.0 and database[key]['freq']==2.0):\n",
    "        propforce_f20.append(database[key]['prop'])\n",
    "        amplitude_f20.append(database[key]['amp'])\n",
    "        coefficients5 = np.polyfit(amplitude_f20,propforce_f20, 1)\n",
    "        poly = np.poly1d(coefficients5)\n",
    "        x20 = np.linspace(amplitude_f20[0], amplitude_f20[-1], 100, endpoint=True)\n",
    "        y20 = poly(x20)\n",
    "\n",
    "        \n",
    "plt.plot(amplitude_f40,propforce_f40, 'o', color ='C4',label='4.0 Hz')\n",
    "p1p = plt.plot(x40, y40, color=\"C4\", linestyle='--')\n",
    "\n",
    "plt.plot(amplitude_f35,propforce_f35, 'o', color ='C3',label='3.5 Hz')\n",
    "p2p = plt.plot(x35, y35, color=\"C3\", linestyle='--')\n",
    "\n",
    "plt.plot(amplitude_f30,propforce_f30, 'o', color ='C2',label='3.0 Hz')\n",
    "p3p = plt.plot(x30, y30, color=\"C2\", linestyle='--')\n",
    "\n",
    "plt.plot(amplitude_f25,propforce_f25, 'o', color ='C1', label='2.5 Hz')\n",
    "p4p = plt.plot(x25, y25, color=\"C1\", linestyle='--')\n",
    "\n",
    "plt.plot(amplitude_f20,propforce_f20, 'o', color ='C0',label='2.0 Hz')\n",
    "p5p = plt.plot(x20, y20, color=\"C0\", linestyle='--')\n",
    "\n",
    "\n",
    "plt.title('pivot point on 111.5 [mm]')\n",
    "plt.xlabel('amplitude [-]')\n",
    "plt.ylabel('propulsion force [mN]')\n",
    "plt.legend()       \n",
    "plt.savefig(\"force_amp_111.pdf\", format=\"pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5.78827488113754, 37.2661259591875, 103.310542195924, 178.623128964876, 237.704181470934]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/IPython/core/interactiveshell.py:3457: RankWarning: Polyfit may be poorly conditioned\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "/usr/lib/python3/dist-packages/IPython/core/interactiveshell.py:3457: RankWarning: Polyfit may be poorly conditioned\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "/usr/lib/python3/dist-packages/IPython/core/interactiveshell.py:3457: RankWarning: Polyfit may be poorly conditioned\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "/usr/lib/python3/dist-packages/IPython/core/interactiveshell.py:3457: RankWarning: Polyfit may be poorly conditioned\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "/usr/lib/python3/dist-packages/IPython/core/interactiveshell.py:3457: RankWarning: Polyfit may be poorly conditioned\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "/usr/lib/python3/dist-packages/IPython/core/interactiveshell.py:3457: RankWarning: Polyfit may be poorly conditioned\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "/usr/lib/python3/dist-packages/IPython/core/interactiveshell.py:3457: RankWarning: Polyfit may be poorly conditioned\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "/usr/lib/python3/dist-packages/IPython/core/interactiveshell.py:3457: RankWarning: Polyfit may be poorly conditioned\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "/usr/lib/python3/dist-packages/IPython/core/interactiveshell.py:3457: RankWarning: Polyfit may be poorly conditioned\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "propforce_f20 = []\n",
    "amplitude_f20 = []\n",
    "propforce_f25 = []\n",
    "amplitude_f25 = []\n",
    "propforce_f30 = []\n",
    "amplitude_f30 = []\n",
    "propforce_f35 = []\n",
    "amplitude_f35 = []\n",
    "propforce_f40 = []\n",
    "amplitude_f40 = []\n",
    "propforce_f45 = []\n",
    "amplitude_f45 = []\n",
    "propforce_f50 = []\n",
    "amplitude_f50 = []\n",
    "propforce_f55 = []\n",
    "amplitude_f55 = []\n",
    "propforce_f60 = []\n",
    "amplitude_f60 = []\n",
    "\n",
    "for key in database.keys():\n",
    "    if ('kp' in database[key] and database[key]['kp']==2.0 and database[key]['freq']==6.0):\n",
    "        propforce_f60.append(database[key]['prop'])\n",
    "        amplitude_f60.append(database[key]['amp'])\n",
    "        coefficients1 = np.polyfit(amplitude_f60,propforce_f60, 1)\n",
    "        poly = np.poly1d(coefficients1)\n",
    "        x60 = np.linspace(amplitude_f60[0], amplitude_f60[-1], 100, endpoint=True)\n",
    "        y60 = poly(x60)\n",
    "        \n",
    "    if ('kp' in database[key] and database[key]['kp']==2.0 and database[key]['freq']==5.5):\n",
    "        propforce_f55.append(database[key]['prop'])\n",
    "        amplitude_f55.append(database[key]['amp'])\n",
    "        coefficients2 = np.polyfit(amplitude_f55,propforce_f55, 1)\n",
    "        poly = np.poly1d(coefficients2)\n",
    "        x55 = np.linspace(amplitude_f55[0], amplitude_f55[-1], 100, endpoint=True)\n",
    "        y55 = poly(x55)\n",
    "        \n",
    "        \n",
    "    if ('kp' in database[key] and database[key]['kp']==2.0 and database[key]['freq']==5.0):\n",
    "        propforce_f50.append(database[key]['prop'])\n",
    "        amplitude_f50.append(database[key]['amp'])\n",
    "        coefficients3 = np.polyfit(amplitude_f50,propforce_f50, 1)\n",
    "        poly = np.poly1d(coefficients3)\n",
    "        x50 = np.linspace(amplitude_f50[0], amplitude_f50[-1], 100, endpoint=True)\n",
    "        y50 = poly(x50)\n",
    "        \n",
    "    if ('kp' in database[key] and database[key]['kp']==2.0 and database[key]['freq']==4.5):\n",
    "        propforce_f45.append(database[key]['prop'])\n",
    "        amplitude_f45.append(database[key]['amp'])\n",
    "        coefficients4 = np.polyfit(amplitude_f45,propforce_f45, 1)\n",
    "        poly = np.poly1d(coefficients4)\n",
    "        x45 = np.linspace(amplitude_f45[0], amplitude_f45[-1], 100, endpoint=True)\n",
    "        y45 = poly(x45)\n",
    "        \n",
    "    if ('kp' in database[key] and database[key]['kp']==2.0 and database[key]['freq']==4.0):\n",
    "        propforce_f40.append(database[key]['prop'])\n",
    "        amplitude_f40.append(database[key]['amp'])\n",
    "        coefficients5 = np.polyfit(amplitude_f40,propforce_f40, 1)\n",
    "        poly = np.poly1d(coefficients5)\n",
    "        x40 = np.linspace(amplitude_f40[0], amplitude_f40[-1], 100, endpoint=True)\n",
    "        y40 = poly(x40)\n",
    "        \n",
    "    if ('kp' in database[key] and database[key]['kp']==2.0 and database[key]['freq']==3.5):\n",
    "        propforce_f35.append(database[key]['prop'])\n",
    "        amplitude_f35.append(database[key]['amp'])\n",
    "        coefficients6 = np.polyfit(amplitude_f35,propforce_f35, 1)\n",
    "        poly = np.poly1d(coefficients6)\n",
    "        x35 = np.linspace(amplitude_f35[0], amplitude_f35[-1], 100, endpoint=True)\n",
    "        y35 = poly(x35) \n",
    "        \n",
    "    if ('kp' in database[key] and database[key]['kp']==2.0 and database[key]['freq']==3.0):\n",
    "        propforce_f30.append(database[key]['prop'])\n",
    "        amplitude_f30.append(database[key]['amp'])\n",
    "        coefficients7 = np.polyfit(amplitude_f30,propforce_f30, 1)\n",
    "        poly = np.poly1d(coefficients7)\n",
    "        x30 = np.linspace(amplitude_f30[0], amplitude_f30[-1], 100, endpoint=True)\n",
    "        y30 = poly(x30)\n",
    "        \n",
    "    if ('kp' in database[key] and database[key]['kp']==2.0 and database[key]['freq']==2.5):\n",
    "        propforce_f25.append(database[key]['prop'])\n",
    "        amplitude_f25.append(database[key]['amp'])\n",
    "        coefficients8 = np.polyfit(amplitude_f25,propforce_f25, 1)\n",
    "        poly = np.poly1d(coefficients8)\n",
    "        x25 = np.linspace(amplitude_f25[0], amplitude_f25[-1], 100, endpoint=True)\n",
    "        y25 = poly(x25)\n",
    "        \n",
    "    if ('kp' in database[key] and database[key]['kp']==2.0 and database[key]['freq']==2.0):\n",
    "        propforce_f20.append(database[key]['prop'])\n",
    "        amplitude_f20.append(database[key]['amp'])\n",
    "        coefficients9 = np.polyfit(amplitude_f20,propforce_f20, 1)\n",
    "        poly = np.poly1d(coefficients9)\n",
    "        x20 = np.linspace(amplitude_f20[0], amplitude_f20[-1], 100, endpoint=True)\n",
    "        y20 = poly(x20)\n",
    "        \n",
    "        \n",
    "\n",
    "print(propforce_f45)\n",
    "\n",
    "\n",
    "plt.plot(amplitude_f60,propforce_f60,  'o', color ='b',label='6.0 Hz')\n",
    "p1p = plt.plot(x60, y60, color=\"b\", linestyle='--')\n",
    "\n",
    "plt.plot(amplitude_f55,propforce_f55,  'o', color ='teal', label='5.5 Hz')\n",
    "p2p = plt.plot(x55, y55, color=\"teal\", linestyle='--')\n",
    "\n",
    "plt.plot(amplitude_f50,propforce_f50,  'o', color ='r',label='5.0 Hz')\n",
    "p3p = plt.plot(x50, y50, color=\"r\", linestyle='--')\n",
    "\n",
    "plt.plot(amplitude_f45,propforce_f45,  'o', color ='gray', label='4.5 Hz')\n",
    "p4p = plt.plot(x45, y45, color=\"gray\", linestyle='--')\n",
    "\n",
    "plt.plot(amplitude_f40,propforce_f40,  'o', color ='y', label='4.0 Hz')\n",
    "p5p = plt.plot(x40, y40, color=\"y\", linestyle='--')\n",
    "\n",
    "plt.plot(amplitude_f35,propforce_f35, 'o',  color ='royalblue', label='3.5 Hz')\n",
    "p6p = plt.plot(x35, y35, color=\"royalblue\", linestyle='--')\n",
    "\n",
    "plt.plot(amplitude_f30,propforce_f30, 'o',  color ='mediumorchid', label='3.0 Hz')\n",
    "p7p = plt.plot(x30, y30, color=\"mediumorchid\", linestyle='--')\n",
    "\n",
    "plt.plot(amplitude_f25,propforce_f25, 'o',  color ='k', label='2.5 Hz')\n",
    "p8p = plt.plot(x25, y25, color=\"k\", linestyle='--')\n",
    "\n",
    "plt.plot(amplitude_f20,propforce_f20, 'o', color ='teal',label='2.0 Hz')\n",
    "p9p = plt.plot(x20, y20, color=\"teal\", linestyle='--')\n",
    "\n",
    "plt.title('pivot point on 101.5 [mm]')\n",
    "plt.xlabel('amplitude [-]')\n",
    "plt.ylabel('propulsion force [mN]')\n",
    "plt.legend()\n",
    "plt.savefig(\"force_amp_K101.pdf\", format=\"pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4.86084161664322,\n",
       " 35.2707188261834,\n",
       " 70.697616407879,\n",
       " 131.876977137056,\n",
       " 175.091112735643]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "propforce_f35"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/IPython/core/interactiveshell.py:3457: RankWarning: Polyfit may be poorly conditioned\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "/usr/lib/python3/dist-packages/IPython/core/interactiveshell.py:3457: RankWarning: Polyfit may be poorly conditioned\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "/usr/lib/python3/dist-packages/IPython/core/interactiveshell.py:3457: RankWarning: Polyfit may be poorly conditioned\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "/usr/lib/python3/dist-packages/IPython/core/interactiveshell.py:3457: RankWarning: Polyfit may be poorly conditioned\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "/usr/lib/python3/dist-packages/IPython/core/interactiveshell.py:3457: RankWarning: Polyfit may be poorly conditioned\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "/usr/lib/python3/dist-packages/IPython/core/interactiveshell.py:3457: RankWarning: Polyfit may be poorly conditioned\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "/usr/lib/python3/dist-packages/IPython/core/interactiveshell.py:3457: RankWarning: Polyfit may be poorly conditioned\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "/usr/lib/python3/dist-packages/IPython/core/interactiveshell.py:3457: RankWarning: Polyfit may be poorly conditioned\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "/usr/lib/python3/dist-packages/IPython/core/interactiveshell.py:3457: RankWarning: Polyfit may be poorly conditioned\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "/usr/lib/python3/dist-packages/IPython/core/interactiveshell.py:3457: RankWarning: Polyfit may be poorly conditioned\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "/usr/lib/python3/dist-packages/IPython/core/interactiveshell.py:3457: RankWarning: Polyfit may be poorly conditioned\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "propforce_f20 = []\n",
    "amplitude_f20 = []\n",
    "propforce_f25 = []\n",
    "amplitude_f25 = []\n",
    "propforce_f30 = []\n",
    "amplitude_f30 = []\n",
    "propforce_f35 = []\n",
    "amplitude_f35 = []\n",
    "propforce_f40 = []\n",
    "amplitude_f40 = []\n",
    "propforce_f45 = []\n",
    "amplitude_f45 = []\n",
    "propforce_f50 = []\n",
    "amplitude_f50 = []\n",
    "propforce_f55 = []\n",
    "amplitude_f55 = []\n",
    "propforce_f60 = []\n",
    "amplitude_f60 = []\n",
    "propforce_f65 = []\n",
    "amplitude_f65 = []\n",
    "propforce_f70 = []\n",
    "amplitude_f70 = []\n",
    "\n",
    "for key in database.keys():\n",
    "    if ('kp' in database[key] and database[key]['kp']==3.0 and database[key]['freq']==7.0):\n",
    "        propforce_f70.append(database[key]['prop'])\n",
    "        amplitude_f70.append(database[key]['amp'])        \n",
    "        coefficients1 = np.polyfit(amplitude_f70,propforce_f70, 1)\n",
    "        poly = np.poly1d(coefficients1)\n",
    "        x70 = np.linspace(amplitude_f70[0], amplitude_f70[-1], 100, endpoint=True)\n",
    "        y70 = poly(x70)\n",
    "        \n",
    "    if ('kp' in database[key] and database[key]['kp']==3.0 and database[key]['freq']==6.5):\n",
    "        propforce_f65.append(database[key]['prop'])\n",
    "        amplitude_f65.append(database[key]['amp'])\n",
    "        coefficients2 = np.polyfit(amplitude_f65,propforce_f65, 1)\n",
    "        poly = np.poly1d(coefficients2)\n",
    "        x65 = np.linspace(amplitude_f65[0], amplitude_f65[-1], 100, endpoint=True)\n",
    "        y65 = poly(x65)\n",
    "        \n",
    "    if ('kp' in database[key] and database[key]['kp']==3.0 and database[key]['freq']==6.0):\n",
    "        propforce_f60.append(database[key]['prop'])\n",
    "        amplitude_f60.append(database[key]['amp'])\n",
    "        coefficients3 = np.polyfit(amplitude_f60,propforce_f60, 1)\n",
    "        poly = np.poly1d(coefficients3)\n",
    "        x60 = np.linspace(amplitude_f60[0], amplitude_f60[-1], 100, endpoint=True)\n",
    "        y60 = poly(x60) \n",
    "        \n",
    "    if ('kp' in database[key] and database[key]['kp']==3.0 and database[key]['freq']==5.5):\n",
    "        propforce_f55.append(database[key]['prop'])\n",
    "        amplitude_f55.append(database[key]['amp'])\n",
    "        coefficients4 = np.polyfit(amplitude_f55,propforce_f55, 1)\n",
    "        poly = np.poly1d(coefficients4)\n",
    "        x55 = np.linspace(amplitude_f55[0], amplitude_f55[-1], 100, endpoint=True)\n",
    "        y55 = poly(x55) \n",
    "        \n",
    "    if ('kp' in database[key] and database[key]['kp']==3.0 and database[key]['freq']==5.0):\n",
    "        propforce_f50.append(database[key]['prop'])\n",
    "        amplitude_f50.append(database[key]['amp'])\n",
    "        coefficients5 = np.polyfit(amplitude_f50,propforce_f50, 1)\n",
    "        poly = np.poly1d(coefficients5)\n",
    "        x50 = np.linspace(amplitude_f50[0], amplitude_f50[-1], 100, endpoint=True)\n",
    "        y50 = poly(x50)\n",
    "\n",
    "    if ('kp' in database[key] and database[key]['kp']==3.0 and database[key]['freq']==4.5):\n",
    "        propforce_f45.append(database[key]['prop'])\n",
    "        amplitude_f45.append(database[key]['amp'])\n",
    "        coefficients6 = np.polyfit(amplitude_f45,propforce_f45, 1)\n",
    "        poly = np.poly1d(coefficients6)\n",
    "        x45 = np.linspace(amplitude_f45[0], amplitude_f45[-1], 100, endpoint=True)\n",
    "        y45 = poly(x45)\n",
    "        \n",
    "    if ('kp' in database[key] and database[key]['kp']==3.0 and database[key]['freq']==4.0):\n",
    "        propforce_f40.append(database[key]['prop'])\n",
    "        amplitude_f40.append(database[key]['amp'])\n",
    "        coefficients7 = np.polyfit(amplitude_f40,propforce_f40, 1)\n",
    "        poly = np.poly1d(coefficients7)\n",
    "        x40 = np.linspace(amplitude_f40[0], amplitude_f40[-1], 100, endpoint=True)\n",
    "        y340 = poly(x40)\n",
    "        \n",
    "    if ('kp' in database[key] and database[key]['kp']==3.0 and database[key]['freq']==3.5):\n",
    "        propforce_f35.append(database[key]['prop'])\n",
    "        amplitude_f35.append(database[key]['amp'])\n",
    "        coefficients8 = np.polyfit(amplitude_f35,propforce_f35, 1)\n",
    "        poly = np.poly1d(coefficients8)\n",
    "        x35 = np.linspace(amplitude_f35[0], amplitude_f35[-1], 100, endpoint=True)\n",
    "        y35 = poly(x35) \n",
    "        \n",
    "    if ('kp' in database[key] and database[key]['kp']==3.0 and database[key]['freq']==3.0):\n",
    "        propforce_f30.append(database[key]['prop'])\n",
    "        amplitude_f30.append(database[key]['amp'])\n",
    "        coefficients9 = np.polyfit(amplitude_f30,propforce_f30, 1)\n",
    "        poly = np.poly1d(coefficients9)\n",
    "        x30 = np.linspace(amplitude_f30[0], amplitude_f30[-1], 100, endpoint=True)\n",
    "        y30 = poly(x30)\n",
    "        \n",
    "    if ('kp' in database[key] and database[key]['kp']==3.0 and database[key]['freq']==2.5):\n",
    "        propforce_f25.append(database[key]['prop'])\n",
    "        amplitude_f25.append(database[key]['amp'])\n",
    "        coefficients10 = np.polyfit(amplitude_f25,propforce_f25, 1)\n",
    "        poly = np.poly1d(coefficients10)\n",
    "        x25 = np.linspace(amplitude_f25[0], amplitude_f25[-1], 100, endpoint=True)\n",
    "        y25 = poly(x25)\n",
    "        \n",
    "    if ('kp' in database[key] and database[key]['kp']==3.0 and database[key]['freq']==2.0):\n",
    "        propforce_f20.append(database[key]['prop'])\n",
    "        amplitude_f20.append(database[key]['amp'])\n",
    "        coefficients11 = np.polyfit(amplitude_f20,propforce_f20, 1)\n",
    "        poly = np.poly1d(coefficients11)\n",
    "        x20 = np.linspace(amplitude_f20[0], amplitude_f20[-1], 100, endpoint=True)\n",
    "        y20 = poly(x20)        \n",
    "        \n",
    "plt.plot(amplitude_f70,propforce_f70, 'o', color ='r',label='7.0 Hz')\n",
    "p1p = plt.plot(x70, y70, color=\"r\", linestyle='--')\n",
    "\n",
    "plt.plot(amplitude_f65,propforce_f65, 'o', color ='royalblue',label='6.5 Hz')\n",
    "p2p = plt.plot(x65, y65, color=\"royalblue\", linestyle='--')\n",
    "\n",
    "plt.plot(amplitude_f60,propforce_f60, 'o', color ='b',label='6.0 Hz')\n",
    "p3p = plt.plot(x60, y60, color=\"b\", linestyle='--')\n",
    "\n",
    "plt.plot(amplitude_f55,propforce_f55, 'o', color ='teal',label='5.5 Hz')\n",
    "p4p = plt.plot(x55, y55, color=\"teal\", linestyle='--')\n",
    "\n",
    "\n",
    "plt.plot(amplitude_f35,propforce_f35, 'o', color ='royalblue', label='3.5 Hz')\n",
    "p8p = plt.plot(x35, y35, color=\"royalblue\", linestyle='--')\n",
    "\n",
    "plt.plot(amplitude_f30,propforce_f30, 'o', color ='mediumorchid', label='3.0 Hz')\n",
    "p9p = plt.plot(x30, y30, color=\"mediumorchid\", linestyle='--')\n",
    "\n",
    "plt.plot(amplitude_f25,propforce_f25, 'o', color ='k', label='2.5 Hz')\n",
    "p10p = plt.plot(x25, y25, color=\"k\", linestyle='--')\n",
    "\n",
    "plt.plot(amplitude_f20,propforce_f20, 'o', color ='teal',label='2.0 Hz')\n",
    "p11p = plt.plot(x20, y20, color=\"teal\", linestyle='--')\n",
    "\n",
    "plt.title('pivot point on 91.5 [mm]')\n",
    "plt.xlabel('amplitude [-]')\n",
    "plt.ylabel('propulsion force [mN]')\n",
    "plt.legend()\n",
    "plt.savefig(\"force_amp_K91.pdf\", format=\"pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[9.49442777988793,\n",
       " 34.2628914311285,\n",
       " 66.9270855526298,\n",
       " 93.3440387674512,\n",
       " 136.101788176461]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "propforce_f35"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.70222129319039, 56.0624661670372, 122.424426476763, 208.524809876241, 295.374849526502]\n",
      "[19.3689633754071, 99.590967724957, 202.442119375943, 313.662305037507, 408.704218735329]\n",
      "[13.0520634106236, 90.2173149207771, 169.864225745443, 256.379124136023, 328.454202341645]\n",
      "[]\n",
      "[151.870075527288]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/IPython/core/interactiveshell.py:3457: RankWarning: Polyfit may be poorly conditioned\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "/usr/lib/python3/dist-packages/IPython/core/interactiveshell.py:3457: RankWarning: Polyfit may be poorly conditioned\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "/usr/lib/python3/dist-packages/IPython/core/interactiveshell.py:3457: RankWarning: Polyfit may be poorly conditioned\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "/usr/lib/python3/dist-packages/IPython/core/interactiveshell.py:3457: RankWarning: Polyfit may be poorly conditioned\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "propforce_f25 = []\n",
    "amplitude_f25 = []\n",
    "propforce_f30 = []\n",
    "amplitude_f30 = []\n",
    "propforce_f35 = []\n",
    "amplitude_f35 = []\n",
    "propforce_f40 = []\n",
    "amplitude_f40 = []\n",
    "propforce_f20 = []\n",
    "amplitude_f20 = []\n",
    "for key in database.keys():\n",
    "        \n",
    "    if ('kp' in database[key] and database[key]['kp']==4.0 and database[key]['freq']==3.5):\n",
    "        propforce_f35.append(database[key]['prop'])\n",
    "        amplitude_f35.append(database[key]['amp'])\n",
    "        coefficients1 = np.polyfit(amplitude_f35,propforce_f35, 1)\n",
    "        poly = np.poly1d(coefficients1)\n",
    "        x35 = np.linspace(amplitude_f35[0], amplitude_f35[-1], 100, endpoint=True)\n",
    "        y35 = poly(x35) \n",
    "        \n",
    "    if ('kp' in database[key] and database[key]['kp']==4.0 and database[key]['freq']==3.0):\n",
    "        propforce_f30.append(database[key]['prop'])\n",
    "        amplitude_f30.append(database[key]['amp'])\n",
    "        coefficients2 = np.polyfit(amplitude_f30,propforce_f30, 1)\n",
    "        poly = np.poly1d(coefficients2)\n",
    "        x30 = np.linspace(amplitude_f30[0], amplitude_f30[-1], 100, endpoint=True)\n",
    "        y30 = poly(x30)\n",
    "        \n",
    "    if ('kp' in database[key] and database[key]['kp']==4.0 and database[key]['freq']==2.5):\n",
    "        propforce_f25.append(database[key]['prop'])\n",
    "        amplitude_f25.append(database[key]['amp'])\n",
    "        coefficients3 = np.polyfit(amplitude_f25,propforce_f25, 1)\n",
    "        poly = np.poly1d(coefficients3)\n",
    "        x25 = np.linspace(amplitude_f25[0], amplitude_f25[-1], 100, endpoint=True)\n",
    "        y25 = poly(x25)\n",
    "        \n",
    "    if ('kp' in database[key] and database[key]['kp']==4.0 and database[key]['freq']==2.0):\n",
    "        propforce_f20.append(database[key]['prop'])\n",
    "        amplitude_f20.append(database[key]['amp'])\n",
    "        coefficients4 = np.polyfit(amplitude_f20,propforce_f20, 1)\n",
    "        poly = np.poly1d(coefficients4)\n",
    "        x20 = np.linspace(amplitude_f20[0], amplitude_f20[-1], 100, endpoint=True)\n",
    "        y20 = poly(x20)\n",
    "\n",
    "        \n",
    "plt.plot(amplitude_f35,propforce_f35,'o', label='3.5 Hz')\n",
    "p1p = plt.plot(x35, y35, linestyle='--')\n",
    "\n",
    "plt.plot(amplitude_f30,propforce_f30,'o', label='3.0 Hz')\n",
    "p2p = plt.plot(x30, y30, linestyle='--')\n",
    "\n",
    "plt.plot(amplitude_f25,propforce_f25,'o', label='2.5 Hz')\n",
    "p3p = plt.plot(x25, y25,  linestyle='--')\n",
    "\n",
    "plt.plot(amplitude_f20,propforce_f20,'o', label='2.0 Hz')\n",
    "p4p = plt.plot(x20, y20, linestyle='--')\n",
    "\n",
    "plt.title('pivot point on 121.5 [mm]')\n",
    "plt.xlabel('amplitude [-]')\n",
    "plt.ylabel('propulsion force [mN]')\n",
    "plt.legend()\n",
    "print(propforce_f25)\n",
    "print(propforce_f30)\n",
    "print(propforce_f35)\n",
    "print(propforce_f40)\n",
    "print(propforce_f45)\n",
    "plt.savefig(\"force_amp_K121.pdf\", format=\"pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[53.0081802336643, 160.695129355789, 260.871362010721, 375.671598829653, 457.913786372099]\n",
      "[30.5479092277679, 81.8292643853534, 154.501513886986, 216.556570953894, 291.421341952816]\n",
      "[13.2148146149747, 40.5556061306801, 79.7994323540219, 106.450122430895, 151.271973931633]\n",
      "[151.870075527288]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/IPython/core/interactiveshell.py:3457: RankWarning: Polyfit may be poorly conditioned\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "/usr/lib/python3/dist-packages/IPython/core/interactiveshell.py:3457: RankWarning: Polyfit may be poorly conditioned\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "/usr/lib/python3/dist-packages/IPython/core/interactiveshell.py:3457: RankWarning: Polyfit may be poorly conditioned\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "/usr/lib/python3/dist-packages/IPython/core/interactiveshell.py:3457: RankWarning: Polyfit may be poorly conditioned\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "propforce_f25 = []\n",
    "amplitude_f25 = []\n",
    "propforce_f30 = []\n",
    "amplitude_f30 = []\n",
    "propforce_f35 = []\n",
    "amplitude_f35 = []\n",
    "propforce_f20 = []\n",
    "amplitude_f20 = []\n",
    "\n",
    "for key in database.keys():\n",
    "\n",
    "    if ('kp' in database[key] and database[key]['kp']==5.0 and database[key]['freq']==2.0):\n",
    "        propforce_f20.append(database[key]['prop'])\n",
    "        amplitude_f20.append(database[key]['amp'])\n",
    "        coefficients4 = np.polyfit(amplitude_f20,propforce_f20, 1)\n",
    "        poly = np.poly1d(coefficients4)\n",
    "        x20 = np.linspace(amplitude_f20[0], amplitude_f20[-1], 100, endpoint=True)\n",
    "        y20 = poly(x20)\n",
    "        \n",
    "    if ('kp' in database[key] and database[key]['kp']==5.0 and database[key]['freq']==3.5):\n",
    "        propforce_f35.append(database[key]['prop'])\n",
    "        amplitude_f35.append(database[key]['amp']) \n",
    "        coefficients1 = np.polyfit(amplitude_f35,propforce_f35, 1)\n",
    "        poly = np.poly1d(coefficients1)\n",
    "        x35 = np.linspace(amplitude_f35[0], amplitude_f35[-1], 100, endpoint=True)\n",
    "        y35 = poly(x35) \n",
    "        \n",
    "    if ('kp' in database[key] and database[key]['kp']==5.0 and database[key]['freq']==3.0):\n",
    "        propforce_f30.append(database[key]['prop'])\n",
    "        amplitude_f30.append(database[key]['amp'])\n",
    "        coefficients2 = np.polyfit(amplitude_f30,propforce_f30, 1)\n",
    "        poly = np.poly1d(coefficients2)\n",
    "        x30 = np.linspace(amplitude_f30[0], amplitude_f30[-1], 100, endpoint=True)\n",
    "        y30 = poly(x30)\n",
    "        \n",
    "        \n",
    "    if ('kp' in database[key] and database[key]['kp']==5.0 and database[key]['freq']==2.5):\n",
    "        propforce_f25.append(database[key]['prop'])\n",
    "        amplitude_f25.append(database[key]['amp'])\n",
    "        coefficients3 = np.polyfit(amplitude_f25,propforce_f25, 1)\n",
    "        poly = np.poly1d(coefficients3)\n",
    "        x25 = np.linspace(amplitude_f25[0], amplitude_f25[-1], 100, endpoint=True)\n",
    "        y25 = poly(x25)\n",
    "        \n",
    "\n",
    "        \n",
    "plt.plot(amplitude_f35,propforce_f35,'o', color ='C0', label='3.5 Hz')\n",
    "p1p = plt.plot(x35, y35,  linestyle='--', color ='C0')\n",
    "\n",
    "plt.plot(amplitude_f30,propforce_f30,'o',color ='C1', label='3.0 Hz')\n",
    "p2p = plt.plot(x30, y30,  linestyle=':', color ='C1')\n",
    "\n",
    "plt.plot(amplitude_f25,propforce_f25,'o', color ='C2',label='2.5 Hz')\n",
    "p3p = plt.plot(x25, y25, linestyle='--', color ='C2')\n",
    "\n",
    "plt.plot(amplitude_f20,propforce_f20,'o',color ='C3', label='2.0 Hz')\n",
    "p4p = plt.plot(x20, y20, linestyle='-.', color ='C3')\n",
    "\n",
    "\n",
    "\n",
    "plt.title('Pivot Point at 131.5 [mm]')\n",
    "plt.xlabel('Normalised tail beat amplitudes [-]')\n",
    "plt.ylabel('Propulsion force [mN]')\n",
    "plt.legend()\n",
    "print(propforce_f25)\n",
    "print(propforce_f30)\n",
    "print(propforce_f35)\n",
    "print(propforce_f45)\n",
    "plt.savefig(\"force_amp_K131.pdf\", format=\"pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the article we want to have one diagram with four Ordinate axis (maximum Tail fin deflection, mximum static force, maximum head angle and maximum propulsion frequency) and the Abscissa axis for head halter (k1-k5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=np.loadtxt('./gesamtdiagram.csv', skiprows=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "database = {}\n",
    "database['description'] = '''unterschiedlische Kop,.... folgende Variante.... Datum , welche Fisch wo \n",
    "'''\n",
    "ident = data.T[0]\n",
    "kp = data.T[1]\n",
    "prop = data.T[2]\n",
    "cons = data.T[3]\n",
    "amp = data.T[4]\n",
    "freq = data.T[5]\n",
    "alpha = data.T[6]\n",
    "tail = data.T[7]\n",
    "\n",
    "\n",
    "\n",
    "for i,i_d in enumerate(ident):\n",
    "    database[str(int(i_d))] = {\n",
    "                        'kp':kp[i],\n",
    "                        'prop':prop[i],\n",
    "                        'cons':cons[i],\n",
    "                        'amp':amp[i],\n",
    "                        'freq':freq[i],\n",
    "                        'alpha': alpha[i],\n",
    "                        'tail':tail[i]\n",
    "    }\n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[33.284      36.26088889 39.23777778 42.21466667 45.19155556 48.16844444\n",
      " 51.14533333 54.12222222 57.09911111 60.076     ]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.close('all')\n",
    "fig, ax = plt.subplots()\n",
    "fig.subplots_adjust(right=0.75)\n",
    "\n",
    "#Polynom alpha\n",
    "coefficients = np.polyfit(kp, alpha, 1)\n",
    "poly = np.poly1d(coefficients)\n",
    "xa = np.linspace(kp[0], kp[-1], 100, endpoint=True)\n",
    "ya = poly(xa)\n",
    "R2_alpha = r2_score(alpha, poly(kp))\n",
    "\n",
    "\n",
    "#Polynom force\n",
    "coefficients = np.polyfit(kp, prop, 1)\n",
    "poly = np.poly1d(coefficients)\n",
    "xp = np.linspace(91.5, 131.5,10)\n",
    "yp = poly(xp)\n",
    "R2_prop = r2_score(prop, poly(kp))\n",
    "\n",
    "\n",
    "#Polynom freq\n",
    "coefficients = np.polyfit(kp, freq, 1)\n",
    "poly = np.poly1d(coefficients)\n",
    "xf = np.linspace(91.5, 131.5,10)\n",
    "yf = poly(xf)\n",
    "R2_freq = r2_score(freq, poly(kp))\n",
    "\n",
    "\n",
    "#Polynom freq\n",
    "coefficients = np.polyfit(kp, tail, 1)\n",
    "poly = np.poly1d(coefficients)\n",
    "xt = np.linspace(91.5, 131.5,10)\n",
    "yt = poly(xt)\n",
    "print(yt)\n",
    "\n",
    "R2_tail = r2_score(tail, poly(kp))\n",
    "\n",
    "\n",
    "twin1 = ax.twinx()\n",
    "twin2 = ax.twinx()\n",
    "twin3 = ax.twinx()\n",
    "\n",
    "twin2.spines.right.set_position((\"axes\", 1.2))\n",
    "twin3.spines.right.set_position((\"axes\", 1.4))\n",
    "\n",
    "\n",
    "p1p = ax.plot(xa, ya, color=\"C0\", linestyle='-.')\n",
    "p1p = ax.plot(kp, alpha, \"o\", color=\"C0\", label=\"Head angle\")\n",
    "ax.text(120,27,r'R² = %0.3f'%R2_alpha, color='C0')\n",
    "\n",
    "p2p = twin1.plot(xp, yp, color=\"C1\", linestyle='-')\n",
    "p2p = twin1.plot(kp, prop, \"o\", color=\"C1\", label=\"Static force\")\n",
    "ax.text(120,26,r'R² = %0.3f'%R2_prop, color='C1')\n",
    "\n",
    "p3p = twin2.plot(xf, yf, color=\"C2\", linestyle=':')\n",
    "p3p = twin2.plot(kp, freq, \"o\", color=\"C2\", label=\"Beat frequency\")\n",
    "\n",
    "ax.text(95,27,r'R² = %0.3f'%R2_freq, color='C2')\n",
    "\n",
    "p4p = twin3.plot(xt, yt, color=\"C3\", linestyle='--')\n",
    "p4p = twin3.plot(kp, tail, \"o\", color=\"C3\", label=\"Tail deflection\")\n",
    "ax.text(120,25,r'R² = %0.3f'%R2_tail, color='C3')\n",
    "\n",
    "ax.set_xlim(90, 133)\n",
    "ax.set_ylim(10, 28)\n",
    "twin1.set_ylim(310, 480)\n",
    "twin2.set_ylim(2, 6.7)\n",
    "twin3.set_ylim(30, 65)\n",
    "\n",
    "\n",
    "ax.set_xlabel(\"K [mm]\")\n",
    "ax.set_ylabel(r\"$\\alpha$ [$^{\\circ}$]\")\n",
    "twin1.set_ylabel(\"$F_{static_{max}}$ [mN]\")\n",
    "twin2.set_ylabel(\"$f_{max}$ [Hz]\")\n",
    "twin3.set_ylabel(\"$D_{max}$ [mm]\")\n",
    "\n",
    "\n",
    "tkw = dict(size=4, width=1.5)\n",
    "\n",
    "ax.tick_params(axis='x', **tkw)\n",
    "\n",
    "plt.savefig(\"max_values_kp.svg\", format=\"svg\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$D_{max}$ [mm]: maximum tail deflection\n",
    "\n",
    "$K$ [mm]:pivot point distance [mm]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "pivot_point=[91.5,101.5,111.5,121.5,131.5]\n",
    "y_tail_max=[20.737427913345,19.8277111700291,22.9129468427142,28.9272214940338,31.2514845778251]\n",
    "head_amp=[5.66834071880653, 4.18115228773412, 6.88558758178341, 16.8485222667991, 22.7803060696502]\n",
    "alpha=[-3.54489066824317, -2.35888681560755, -3.5337645560503, -7.89491252973676, -9.82805333286524]\n",
    "force=[331.961996057662, 358.754057563684, 395.469982858502, 404.830382605589, 451.193645986589]\n",
    "ratio= [3.65846531499808,4.74216431393707,3.32766762031071,1.71689962098554,1.37186412167925]\n",
    "coefficient_of_determination= 0.99805332# 5959568\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "fig.subplots_adjust(right=0.75)\n",
    "\n",
    "coefficients = np.polyfit(y_tail_max, head_amp, 2)\n",
    "poly = np.poly1d(coefficients)\n",
    "xa = np.linspace(y_tail_max[1], y_tail_max[-1], 100, endpoint=True)\n",
    "ya = poly(xa)\n",
    "\n",
    "p1p = ax.plot(xa, ya, linestyle=':', color='C0', label=r'R²= 0.998')\n",
    "p1p = ax.plot(y_tail_max,head_amp, 'o', color='C3')\n",
    "for i, pivot in enumerate(pivot_point):\n",
    "    p1p = ax.text(y_tail_max[i]+0.55, head_amp[i]-0.6, str(pivot)+' mm',\n",
    "                 bbox=dict(boxstyle=\"square\",\n",
    "                   edgecolor='None',\n",
    "                   fc=(1., 1., 1.),\n",
    "                   )\n",
    "                 )\n",
    "\n",
    "ax.set_xlabel(\"D [mm]\")\n",
    "ax.set_ylabel(\"d [mm]\")\n",
    "ax.legend()\n",
    "plt.savefig(\"tail_to_head_ratio.svg\", format=\"svg\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "d:head ampltude"
   ]
  }
 ],
 "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": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
