four_arch_condition_factor.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "scour_depth_sample_number = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "condition_factor = []\n",
    "pier_local_scour_depth = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def condition_factor_calculation():\n",
    "    \n",
    "    import numpy as np\n",
    "    import matplotlib.pyplot as plt\n",
    "\n",
    "    F_w = 1\n",
    "    F_d = 0.8\n",
    "    F_mo = 0.9\n",
    "\n",
    "    F_j = F_w * F_d * F_mo\n",
    "\n",
    "    d_s_loc_min = 1.20\n",
    "    d_s_loc_max = 2.60\n",
    "\n",
    "    F_CM_max = 0.90\n",
    "    F_CM_min = 0.30\n",
    "\n",
    "    x = [d_s_loc_min, d_s_loc_max]\n",
    "    y = [F_CM_max, F_CM_min]\n",
    "\n",
    "    slope_intercept = np.polyfit(x, y, 1) #https://www.w3schools.com/datascience/ds_linear_slope.asp\n",
    "\n",
    "    m = slope_intercept[0]\n",
    "    q = slope_intercept[1]\n",
    "\n",
    "    d_s_loc_no_damage = 0\n",
    "    F_CM_no_damage = 1\n",
    "\n",
    "    d_s_loc_vector = np.linspace(d_s_loc_no_damage, d_s_loc_max, scour_depth_sample_number)\n",
    "\n",
    "    #https://www.pythonpool.com/numpy-piecewise/\n",
    "    F_CM_function = np.piecewise(d_s_loc_vector, [d_s_loc_vector <= 1.00, d_s_loc_vector > 1.00], [lambda d_s_loc_vector: 1.00, lambda d_s_loc_vector: q + m*d_s_loc_vector])\n",
    "\n",
    "    plt.scatter(d_s_loc_vector, F_CM_function, color = 'black')\n",
    "    plt.plot(d_s_loc_vector, F_CM_function, color = 'black')\n",
    "\n",
    "    plt.xlabel('Pier local scour depth')\n",
    "    plt.ylabel('Investigated cracks factor')\n",
    "\n",
    "    F_C = F_CM_function * F_j\n",
    "\n",
    "    plt.plot(d_s_loc_vector, F_C, color = 'black')\n",
    "    plt.xlabel('Pier local scour depth')\n",
    "    plt.ylabel('Condition factor')\n",
    "\n",
    "    condition_factor.append(list(F_C))\n",
    "    pier_local_scour_depth.append(list(d_s_loc_vector))\n",
    "\n",
    "    print(len(condition_factor))\n",
    "\n",
    "    print(condition_factor)\n",
    "    print(pier_local_scour_depth)\n",
    "\n",
    "    %store condition_factor\n",
    "    %store pier_local_scour_depth\n",
    "\n",
    "    # return F_C,d_s_loc_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "[[0.7200000000000001, 0.7200000000000001, 0.7200000000000001, 0.7200000000000001, 0.6617142857142858, 0.5725714285714285, 0.4834285714285714, 0.3942857142857141, 0.3051428571428569, 0.21599999999999975]]\n",
      "[[0.0, 0.2888888888888889, 0.5777777777777778, 0.8666666666666667, 1.1555555555555557, 1.4444444444444446, 1.7333333333333334, 2.0222222222222226, 2.3111111111111113, 2.6]]\n",
      "Stored 'condition_factor' (list)\n",
      "Stored 'pier_local_scour_depth' (list)\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ro0aMHTuWWrVq0adPH9auXet0iCHlVH2iOnXq0KVLF955x+d/ORNh/EkE4u1WAL+Vpvbn/QNfzwEWdqH3NiBHVb/3tVBV01XVraru6tWr+/HRJtxERUXRsWNHFixYwNatW7n//vuZO3cujRs35vrrr2fevHmcOHHC6TBDwqn6RE2bNrX6RAbwLxHsEJGhIlLOOz0M7PBju91ArXzzNYE9hazbA7ssZPxUr149Xn75ZXbv3s2kSZPYtWsXd911F3Xr1mXSpEn897//dTrEoFelShWysrKsPpHxKOzmgf5+E/dSPNf3vwX2A28Cl/qxXVk8CaM2v98sjvexXiXge6BCcftUu1lsfDh27Ji+88472rZtWwW0QoUKOmjQIN26davToQW9o0ePaq9evRTQxx57TE+ePOl0SCZAKOJmcbFfvOczAZ2BL/A8PTTG2zYQGJhvnT7AHH/3aYnAFGXNmjXap08fLV++vALaqVMn/fDDD+0LrggnTpzQhx56SAFt27atxsTEqIioy+XS2bNnOx2eKSFFJQJ/Xii7EOgPxAO/FSxR1X7n3g85d/ZCmfHH/v37efXVV5kyZQr79u3jmmuuYejQodx7771UqFDB6fCCjqqSlJTEvHnzTmuPjo4mPT2d5ORkhyIzJeWcXijLZxZwOdARWIrnWv9PJReeMSXvsssu44knnmDXrl3MmjWLChUqMGjQIGrWrMmIESPYtWuX0yEGFRFh9erVZ7QfPnyYMWPGOBCRKU3+JIIrVfVx4BdV/QfQBagf2LCMKRnly5fnnnvuYdWqVSxbtoybbrqJ5557jjp16pCUlMSyZcvsJqnX119/fVbtJnz4kwiOef/8r4gk8Psbx8aEDBGhVatWvP322+zYsYPHHnuMJUuW0KZNG9xuNzNnzuTYsWPF7yiMFVbX6Y9//GMpR2JKmz+JIF1EqgB/Bd4DNgMTAxqVMQEUExPDxIkT+eabb5g6dSpHjhzhvvvuo1mzZqxfv97p8ByTmprqs+rrkSNHrD5RmCuq6NzD3h+3qOoPqpqtqnVU9VJVfbWU4jMmYCpUqMADDzxAXl4ec+fOZe/evbjdbsaPH8/Ro0edDq/UJScnk56ejsvlQkRwuVw8/fTTlC9fnjZt2rBy5UqnQzQBUuhTQyKyTlUb+ipD7SR7asgEysGDB3n44YfJyMigQYMGzJgxg4YNGzodluO+/PJLbr75Zvbv38/8+fO58cYbnQ7JnINzfWpoi4jsBOoVKEO9UUQ2BCRSYxx0ySWXMHv2bObPn8/+/ftp2rQpY8eOjcjeQX5Wnyj8FVV9tCfQAtiOpxbQqelW75/GhKWuXbuSl5dHz549+dvf/kbTpk0jvrid1ScKb0XeLFbVfaraQL0lqPNPpRWgMU6oWrUqM2fO5L333uPAgQM0bdqUJ554IqJ7B1afKHz589SQMRHrtttuIy8vj+TkZJ588kncbrfPF68iRXR0NJmZmfTq1YvRo0czYsQISwZhwBKBMcWoUqUK//jHP3j//fc5ePAgzZs3569//Su//vqr06E5oly5csyaNYuHHnqISZMm0b9/f44fP+50WOY8WCIwxk+33normzZtonfv3qSmptKkSRMi9Qm2qKgo0tLSGDduHNOnT6dbt2783//9n9NhmXNUbCIQkatF5DURyRKRJaem0gjOmGBTpUoVpk+fzgcffMB///tfWrRowV/+8peI7B2ICGPHjmXy5MnMnz+fW265hUOHDjkdljkH/lQfXQ9MBVYDvw0BpaqOXCi19whMsPjvf//Lo48+yhtvvEFcXBwzZsygadOmTofliDfffJP77ruPBg0asHDhQmwkweBzvtVHj6vqFFVdqaqrT00lHKMxIady5cq8/vrrLFy4kEOHDtGiRQtGjRoVkZdIevXqRWZmJps3b6Z169ZWqC7E+JMI3heRQSJSQ0SqnpoCHpkxIaJTp05s2rSJvn37MnHiRBo3bsyKFSucDqvUde7cmaysLPbv30+rVq2sPlEI8ScR3AcMBz7Hc3loNeDXtRkR6SQi20Rku4iMKmSddiKyTkTyRGSpv4EbE0wqVarEtGnT+PDDD/npp59o2bIlI0eOjLjeQevWrcnOzub48eNWnyiEFJsIVLW2j6lOcduJSBngZeAWIA7oKSJxBdapDLwC/ElV44Fu53IQxgSLjh07smnTJvr3788zzzxDo0aNWL58udNhlarExESWLVtGpUqV6NChA4sXL3Y6JFMMf54aKiciQ0Vkrnd6SETK+bHvZsB2Vd2hqkeBOUDXAuv0Auap6tcAqvrt2R6AMcGmUqVKpKen89FHH/HLL7/QqlUrhg8fzpEjR5wOrdRYfaLQ4s+loSlAEzy/ub/i/XmKH9tdAXyTb363ty2/q4EqIvKpiKwWkXt97UhEUkQkV0RyDxw44MdHG+O8m2++mU2bNnH//fczadIkGjVqxP/8z/84HVapOVWfyO12W32iIOdPImiqqvep6hLv1Bfw5xk58dFW8FnVsngSSxc8YyI/LiJXn7GRarqqulXVbY+lmVBSsWJFXn31VRYtWsSRI0do1aoVjz32WMT0DqpUqcKiRYt+q080caKNaRWM/EkEJ0Sk7qkZEalDvvcJirAbqJVvviawx8c6H6rqL6r6HZANNPBj38aElBtvvJFNmzbxwAMP8Oyzz9KwYUNycnKcDqtU5K9PNGrUKIYPH271iYKMP4lgOPCJ9/LNUmAJ8Kgf260CrhKR2iJSHuiBZ6jL/DKBNiJSVkSigeaAPXNmwtLFF1/MlClTWLx4Mb/++itt2rThz3/+M4cPH3Y6tICz+kRBTlWLnYALgEQ8v61f4M823u06A18AXwJjvG0DgYH51hmOZxzkTcCw4vbZpEkTNSbUHTp0SAcNGqSAXnnllfrZZ585HVKpOHnypI4bN04Bvf322/XIkSNOhxQxgFwt5Hu1qKEqO6jqEhG5s5AEMq+kktHZsBITJpx88skn9OvXj127djF06FBSU1OpUKGC02EF3OTJkxk6dCjt27dn/vz5VKxY0emQwt65lpi43vvnbT6mW0s0QmMiVPv27dm4cSODBg3ixRdfpEGDBmRnZzsdVsANGTKE2bNn89lnn9GhQwfsaUBn+VN0rraqflVcW2mxHoEJV59++in9+vXjq6++YsiQIfy///f/wr53sGDBApKSkqhVqxaLFi0iJibG6ZDC1vkWnfP1Jsjc8wvJGFNQu3bt2LBhA0OGDGHy5MkkJiaydGl4V12x+kTBodBEICLXiMhdQCURuTPf1Ae4sNQiNCaCXHTRRaSlpfHpp58CnuQwZMgQfv75Z2cDC6BT9YmOHTtm9YkcUlSPoB6eewGVOf3+QGNgQMAjMyaCXX/99WzYsIGhQ4fy0ksvkZiY+FtyCEeJiYnk5ORQsWJFOnTowOjRo4mNjSUqKorY2FgyMjKcDjGs+XOP4DpVDZr34u0egYk02dnZ9OvXjy+//JLBgwczYcIELrroIqfDCoi9e/fSvHlzvvnmm9Pao6OjSU9PJzk52aHIQt853SMQkRHeH3uJSFrBKSCRGmPO0LZtWzZs2MCwYcN45ZVXqF+/PkuWhOdosTVq1PD51vHhw4cZM2aMAxFFhqIuDZ26a5PL7+MQ5J+MMaUkOjqa559/nuzsbMqVK8cNN9zAoEGD+Omnn5wOrcT95z//8dluo54FTrGXhoKNXRoyke7w4cM8/vjjPP/888TExPD6669zww03OB1WiYmNjWXXrl1ntMfExPhsN/4510tD74vIe4VNgQvXGFOU6Ohonn32WZYtW8YFF1zAjTfeyMCBA8Omd5Camkp0dPQZ7bVr17b6RAFS1KWhScCzwFfAEeA17/QznrpAxhgHtWzZknXr1vHoo4+Snp5OQkJCWIwGlpycTHp6Oi6XCxEhJiaGO+64g6VLl9KtW7eIG/6zVBRWhEh/LwqX7U9baU1WdM6YM+Xk5OjVV1+tgKakpOiPP/7odEglLi0tTQFt3759WB5foFFE0Tl/3iyu7h2DAPCUlwBsdBhjgsip3sHw4cOZNm0aCQkJZGVlOR1WibL6RIHjTyJ4BPjUOx7Bp8AnwLBABmWMOXt/+MMfeOaZZ8jJyaFChQp07NiRAQMG8OOPPzodWolJTk4mMzOTzZs307p1a3uSqIQUmwhU9UPgKuBh71RPVT8KdGDGmHPTokUL1q5dy4gRI3jjjTdISEjgo4/C57+s1Scqef70CMAzrnA8noFp7i5skHljTHC48MILmThxIp9//jkXX3wxnTp14v777w+b3oHVJypZxSYCEZmF5wmi1ngGrW8K+HwW1ce2nURkm4hsF5FRPpa3E5EfRWSdd3riLOM3xhShefPmrFmzhlGjRjF9+nQSEhJYuHCh02GViIL1iT7++GOnQwpdhd1FPjXhecNYilvPx3Zl8AxRWQcoD6wH4gqs0w7499ns154aMubcrFixQuPi4hTQvn376g8//OB0SCViz549Wr9+fS1fvrzOnTvX6XCCFuf51NAm4PJzyDHNgO2qukNVjwJzgK7nsB9jTAlo1qwZa9as4S9/+QszZ84kISGBBQsWOB3WeatRowZLly7F7XbTvXt3XnvtNadDCjn+JIJqwGYR+egs3yy+AshfQnC3t62g60RkvYgsFJF4XzsSkRQRyRWRXHtkzJhzd8EFF5Camsry5cupXLkyXbp0oU+fPvzwww9Oh3ZeqlSpQlZWFh07diQlJYWJEyf6LF5nfPMnEYwDbgeexvOm8ampOOKjreCZWQO4VLUBMBmY72tHqpquqm5VdVevbq8wGHO+3G43q1evZsyYMcyePZuEhAQ++OADp8M6LxUqVCAzM5NevXoxatQoRowYYcnAT/48ProU2Apc7J22eNuKsxuolW++JrCnwL4PqerP3p8XAOVEpJqfsRtjzsMFF1zAU089xYoVK6hatSq33nor9913X0j3DsqVK8esWbMYPHgwkyZNon///lafyA/+PDXUHVgJdAO6AytEJMmPfa8CrhKR2iJSHugBnHZJSUQuFxHx/tzMG8/BszsEY8z5aNKkCbm5uTz++ONkZGQQHx/P+++/73RY5ywqKorJkyczduxYpk+fbvWJ/FHYXWT9/cme9cCl+earA+uL2867bmfgCzxPD43xtg0EBnp/fgjI837GcqBlcfu0p4aMCZzVq1drYmKiAtq7d289ePCg0yGdl1P1idq1axfx9Yko4qkhf4aq3Kiq9fPNR3kTQf0iNgsYG4/AmMA6evQoqampPP3001SrVo1XX32VP/3pT06Hdc4yMjLo06cPDRo0YOHChUTqfcZzGo8gnw+9Twz1EZE+wAdAeLyRYow5Q/ny5Rk/fjwrV67ksssuo2vXrtxzzz0cPBiaV22tPlHx/LlZPBx4FUjEU2IiXVVHFL2VMSbUNWrUiJUrVzJu3Dj++c9/Eh8fz/z5850O65xYfaKiFTVC2ZUi0gpAVeep6p9V9RHgoIjULbUIjTGOKV++PGPHjmXVqlVcfvnl3HHHHfTq1SskewdWn6hwRfUIXgB8jX132LvMGBMhGjZsyKpVqxg/fjxz584lLi6Od9991+mwzlrB+kThMKJbSSgqEcSq6oaCjaqaC8QGLCJjTFAqV64cTzzxBLm5uVxxxRXceeed9OzZk++++87p0M5K3bp1ycnJoU6dOnTp0oV33nnH6ZAcV1QiuLCIZX8o6UCMMaEhMTGRFStW8OSTT/LOO+8QFxcXcl+mVp/odEUlglUiMqBgo4j0B1YHLiRjTLArV64cf/3rX1m9ejW1atUiKSmJu+++O6SGjyxYn2jChAkRW5KiqEQwDOjrHaLyWe+0FLgfz0hlxpgIV79+fZYvX85TTz3Fu+++S3x8PHPnznU6LL/lr080evToiK1PVGgiUNX9qtoSGA/s9E7jVfU6Vd1XOuEZY4JduXLlGDNmDGvWrCEmJoZu3brRvXt3vv32W6dD84vVJ/LvPYJPVHWyd1pSGkEZY0JPQkICy5cv5+mnnyYzM5P4+HjefvvtkPgNu2B9oqSkpIiqT+TvmMXGGFOssmXLMnr0aNasWUPt2rW5++676datG/v373c6tGKJCOPGjSMtLY3MzExuueUWDh065HRYpcISgTGmxMXHx/P5558zYcIE3n//feLj45kzZ05I9A6GDBnC7NmzWbZsGQ0aNKBWrVpERUURGxtLRkaG0+EFhCUCY0xAlC1blpEjR7J27Vrq1q1Lz549SUpKConeQXJyMsOGDWPnzp3s3r0bVWXXrl2kpKSEZTKwRGCMCai4uDhycnKYOHEiH3zwAXFxcbz11ltB3zv417/+dUbb4cOHGTNmjAPRBJYlAmNMwJUtW5YRI0awdu1arrrqKnr16sWdd97Jvn3B+wBiYVVKw7F6qSUCY0ypufbaa8nJyeHvf/87CxcuJC4ujoyMjKDsHcTExPhsD8fxDAKaCESkk4hsE5HtIjKqiPWaisgJP4fANMaEsDJlyvDYY4+xbt06rrnmGu655x5uv/129u7d63Rop0lNTSU6Ovq0NhHh+++/D7mSGsUJWCIQkTLAy8AtQBzQU0TiCllvIvBRoGIxxgSfa665hs8++4xJkyaRlZVFfHw8s2fPDpreQXJyMunp6bhcLkQEl8vF1KlTadasWdjVJwpkj6AZsF1Vd6jqUWAO0NXHekOAd4DQeA3RGFNiypQpw6OPPsq6deu49tpr6d27N127dmXPnj1OhwZ4ksHOnTs5efIkO3fuJCUlJSzrEwUyEVwBfJNvfre37TcicgVwBzA1gHEYY4JcvXr1yM7O5rnnnmPRokXEx8czc+bMoPySDcf6RIFMBOKjreDf1gvASFU9UeSORFJEJFdEckOpuqExxn9lypThkUceYf369cTHx3Pfffdx22238Z///Mfp0M4QbvWJApkIdgO18s3XBAr299zAHBHZCSQBr4jI7QV3pKrpqupWVXc43rE3xvzu6quvZunSpTz//PMsWbKE+Ph4ZsyYEXS/dYdTfaJAJoJVwFUiUltEygM9gPfyr6CqtVU1VlVjgbnAIFWdH8CYjDEhoEyZMgwbNoz169dTv359+vbty6233hp0vYNwqU8UsESgqseBh/A8DbQFeFtV80RkoIgMDNTnGmPCx1VXXcXSpUt58cUX+eSTT4iPj2f69OlB1zvIX5+offv2IVOC+xQJtr/Q4rjdbs3NzXU6DGNMKdu+fTv9+/cnOzubTp068dprr1GzZk2nwzrNggULSEpKolatWmRlZeFyuZwO6TcislpV3b6W2ZvFxpiQcOWVV/LJJ5+QlpZGdnY28fHxvP7660HVO+jcuTNZWVns37+f1q1bs2XLFqdD8oslAmNMyIiKimLIkCFs2LCBRo0acf/999OpU6egqv/TunVrsrOzOXbsGG3atGHlypVOh1QsSwTGmJBTt25dlixZwksvvcSyZctISEhg2rRpQdM7SExMJCcnh4oVK9KhQwcWL17sdEhFskRgjAlJUVFRDB48mI0bN9KkSRMGDBhAx44dg6Z3ULduXXJycqhTpw5dunQJ6vpElgiMMSGtTp06fPzxx7zyyit8/vnnJCQkkJ6eHhS9gxo1arB06VLcbndQ1yeyRGCMCXlRUVE8+OCDbNy4EbfbzQMPPMDNN9/Mrl27nA6NKlWqnFafaOLEiUGRpPKzRGCMCRu1a9dm8eLFTJkyheXLl5OQkMCrr77q+Bdv/vpEo0aNCrr6RJYIjDFhJSoqioEDB7Jx40aaN2/OwIEDuemmm9i5c6ejcQVzfSJLBMaYsBQbG8uiRYuYOnUqK1asICEhgSlTpnDy5EnHYipYn6hbt25BUZ/IEoExJmyJCA888ACbNm3iuuuuY9CgQdx444189dVXjsZ0qj7R/Pnzg6I+kSUCY0zYc7lcZGVlkZ6eTm5uLvXr1+eVV15xtHeQvz5Rhw4dcLLEviUCY0xEEBEGDBjApk2baNWqFYMHD+aGG25gx44djsWUnJxMZmYmmzdvpnXr1o69A2GJwBgTUWJiYvjwww+ZNm0aa9asoX79+rz00kuO9Q7y1ydq1aqVI/WJLBEYYyKOiNC/f382bdpEmzZtGDJkCB06dODLL790JB6n6xNZIjDGRKxatWqxcOFCXn/9ddauXUtiYiJpaWmO9A4K1if6+OOPS+2zLREYYyKaiNCvXz/y8vJo27YtDz/8MO3atWP79u2lHkv++kSdO3cutfpElgiMMQaoWbMmCxYsYPr06WzYsIHExERefPHFUu8dOFGfKKCJQEQ6icg2EdkuIqN8LO8qIhtEZJ2I5IpI60DGY4wxRRER+vTpQ15eHu3bt2fYsGFcf/31/O///m+pxlGwPlGPHj2IjY0lKiqK2NhYMjIySvYDVTUgE1AG+BKoA5QH1gNxBda5iN+Hy0wEtha33yZNmqgxxgTayZMndcaMGVqpUiX9wx/+oM8//7weP368VGM4evSotmzZUoHTpujoaJ09e/ZZ7QvI1UK+VwPZI2gGbFfVHap6FJgDdC2QhH72BghQwXuQxhjjOBHhvvvuIy8vjw4dOvDII49w/fXX88UXX5RaDOXKlWP37t1ntB8+fJgxY8aU2OcEMhFcAXyTb363t+00InKHiGwFPgD6+dqRiKR4Lx3lOvn2nTEm8lxxxRW8//77zJw5k7y8PBo0aMBzzz3HiRMnSuXzv/nmG5/tJfnyWSATgfhoO+M3flV9V1WvAW4HnvS1I1VNV1W3qrqrV69eslEaY0wxRITevXuTl5fHTTfdxKOPPkqbNm3Ytm1bwD87JibmrNrPRSATwW6gVr75msCewlZW1WygrohUC2BMxhhzzv74xz+SmZnJ7Nmz2bp1Kw0bNmTSpEkB7R2kpqYSHR19Wlt0dDSpqakl9hmBTASrgKtEpLaIlAd6AO/lX0FErhQR8f7cGM9N5YMBjMkYY86LiJCcnExeXh4333wzw4cPp3Xr1mzdujUgn5ecnEx6ejoulwsRweVykZ6eTnJycol9hvx+r7bkiUhn4AU8TxC9oaqpIjIQQFWnishI4F7gGHAEGK6qy4rap9vt1tzc3IDFbIwx/lJV3nrrLYYMGcIvv/zCk08+yZ///GfKlCnjdGhnEJHVqur2uSyQiSAQLBEYY4LNvn37ePDBB5k/fz7Nmzdn+vTpXHvttU6HdZqiEoG9WWyMMefp8ssvZ968ebz11lts376dRo0aMXHixKAZirI4lgiMMaYEiAg9evQgLy+PLl26MGrUKFq2bMnmzZudDq1YlgiMMaYEXXbZZcydO5c5c+awY8cOGjVqxIQJE4K6d2CJwBhjSpiIcPfdd7N582Zuu+02Ro8ezXXXXUdeXp7ToflkicAYYwLk0ksvZe7cubz99tvs3LmTxo0b8/TTTwdd78ASgTHGBFi3bt3YvHkzt99+O2PGjKFFixZs3LjR6bB+Y4nAGGNKQfXq1fnnP//Jv/71L77++muaNGnCU089xbFjx5wOzRKBMcaUpqSkJPLy8rjzzjt5/PHHadGiBRs2bHA0JksExhhTyqpXr86cOXOYO3cuu3fvxu128+STTzrWO7BEYIwxDrnrrrvIy8sjKSmJJ554gubNm7N+/fpSj8MSgTHGOKhatWq8+eabzJs3jz179uB2uxk/fjxHjx4ttRgsERhjTBC44447yMvLo3v37owbN45mzZqxbt26UvlsSwTGGBMkLrnkEjIyMnj33XfZt28fTZs2Zdy4cQHvHVgiMMaYIHP77bezefNmevTowfjx42natClr164N2OdZIjDGmCBUtWpVZs2aRWZmJt9++y3NmjXjhRdeCMhnWSIwxpgg9qc//Ym8vDx69uzJlVdeGZDPKBuQvXqJSCfgRTwjlE1T1QkFlicDI72zPwMPqmrpPztljDFBrGrVqsycOTNg+w9Yj0BEygAvA7cAcUBPEYkrsNpXwPWqmgg8CaQHKh5jjDG+BfLSUDNgu6ruUNWjwByga/4VVPVzVf3BO7scqBnAeIwxxvgQyERwBfBNvvnd3rbC9AcW+logIikikisiuQcOHCjBEI0xxgQyEYiPNvW5okh7PIlgpK/lqpquqm5VdVevXr0EQzTGGBPIm8W7gVr55msCewquJCKJwDTgFlU9GMB4jDHG+BDIHsEq4CoRqS0i5YEewHv5VxCRGGAe0FtVvwhgLMYYYwoRsB6Bqh4XkYeAj/A8PvqGquaJyEDv8qnAE8AlwCsiAnBcVd2BiskYY8yZRNXnZfug5Xa7NTc31+kwjDEmpIjI6sJ+0Q65RCAiB4Bd57h5NeC7EgwnmEXKsUbKcYIdazgqzeN0qarPp21CLhGcDxHJjZRLT5FyrJFynGDHGo6C5Tit1pAxxkQ4SwTGGBPhIi0RRFIto0g51kg5TrBjDUdBcZwRdY/AGGPMmSKtR2CMMaYASwTGGBPhwjIRiEgnEdkmIttFZJSP5SIiad7lG0SksRNxlgQ/jrWdiPwoIuu80xNOxHm+ROQNEflWRDYVsjwszqkfxxku57OWiHwiIltEJE9EHvaxTricU3+O1dnzqqphNeEpZ/ElUAcoD6wH4gqs0xlPyWsBWgArnI47gMfaDvi307GWwLG2BRoDmwpZHi7ntLjjDJfzWQNo7P35YuCLMP5/6s+xOnpew7FHUOyAON75meqxHKgsIjVKO9AS4M+xhgVVzQa+L2KVsDinfhxnWFDVvaq6xvvzT8AWzhyvJFzOqT/H6qhwTAT+DIhztoPmBCt/j+M6EVkvIgtFJL50Qit14XJO/RFW51NEYoFGwIoCi8LunBZxrODgeQ3o4PUO8WdAHL8HzQly/hzHGjw1Rn4Wkc7AfOCqQAfmgHA5p8UJq/MpIhcB7wDDVPVQwcU+NgnZc1rMsTp6XsOxR+DPgDh+DZoTAoo9DlU9pKo/e39eAJQTkWqlF2KpCZdzWqRwOp8iUg7PF2OGqs7zsUrYnNPijtXp8xqOiaDYAXG88/d6n0poAfyoqntLO9AS4M/gP5eLd7AHEWmG55yH40hw4XJOixQu59N7DK8DW1T1uUJWC4tz6s+xOn1ew+7SkPo3IM4CPE8kbAcOA32divd8+HmsScCDInIcOAL0UO9jCqFERN7C82RFNRHZDYwFykF4nVM/jjMszifQCugNbBSRdd62vwAxEF7nFP+O1dHzaiUmjDEmwoXjpSFjjDFnwRKBMcZEOEsExhgT4SwRGGNMhLNEYIwxEc4SgXGMiJzwVlrcJCL/EpFoEXGLSNp57vfnkorRu79xIvJYSe6zJInIDBFJOsdt24lIy5LYlwldlgiMk46oakNVTQCOAgNVNVdVh/q7AxEJu3dhCgrwMbYDWha3kglvlghMsPgMuNL7G+q/AUSkgnjq868SkbUi0tXb3sfbg3gfyCpsh943Uv/u7XFsFJG78y0b4W1bLyITvG0DvJ+1XkTeEZHoogIWkW7efa8XkWxvWxkRmeTd9wYRGeJtv8F7DBu9x3SBt33nqVIC3t7Qp96fx4lIuohkATN9HNdLIrJZRD4ALs23rImILBWR1SLykXirdYrIpyLygoh87o25mXgKoA0EHvH2zNp4d9PWu94O6x1EhrD/bcoEP+9vvLcAHxZYNAZYoqr9RKQysFJEFnuXXQckqmpRJZvvBBoCDYBqwCrvF3ZD4HaguaoeFpGq3vXnqepr3pieAvoDk4vY/xNAR1X9jzc+gBSgNtDI++Z3VRG5EJgB3KCqX4jITOBB4IUi9g3QBGitqkcKtN8B1APqA5cBm4E3xFPPZjLQVVUPeBNfKtDPu10FVW0pIm3xvIWeICJTgZ9VdZL3uPvjqZ/fGrgGT5mHucXEaUKc9QiMk/7gfeU+F/gaTz2W/G4GRnnX+RS4EO9r+cCiYpIAeL7M3lLVE6q6H1gKNAVuBKar6mGAfPtJEJHPRGQjkAwUVwo4B5ghIgPwlPjAu++pqno8377rAV+p6hfedf6BZwCa4rznIwng3fbUce0Blnjb6wEJwCLv39lf8RRqO+Utb0zZQMV8yaug+ap6UlU340k0JsxZj8A46YiqNszfIHJa5WEB7lLVbQXWaQ784sf+fZUxPtXuq7bKDOB2VV0vIn3wXD8vlKoO9MbSBVgnIg0L2XdhcQAc5/dfyC4ssKyoY/QVvwB5qnqdn9sUVl/m1wL7NGHOegQmmH0EDBH5rSpjo7PcPhu423vdvjqe36RX4rmv0O/UPYB8l4YuBvZ6L7EkF7dzEamrqitU9QngOzwlk7OAgadu8Hr3vRWIFZErvZv2xtM7AdiJ5xIQwF1ncVw9vMdVA2jvbd8GVBeR67yfXU5OH+Dkbm97azyVPH8EfvIet4lglghMMHsST+XNDeIZzP3Js9z+XWADnrGclwAjVHWfqn6I59p3rvcSyqlHQx/HM3LUIjxf3sX5u/fm7yY8X87rgWl4LnNtEJH1QC9V/T88lTP/5b3sdBKY6t3HeOBFEfkMOHEWx/W/wEZgCt6k4h2uNAmY6P3sdZz+RNAPIvK597P7e9veB+4ocLPYRBirPmpMBPA+jfSYquY6HYsJPtYjMMaYCGc9AmOMiXDWIzDGmAhnicAYYyKcJQJjjIlwlgiMMSbCWSIwxpgI9/8BBYlaTBEPgvIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "condition_factor_calculation()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0.7200000000000001,\n",
       "  0.7200000000000001,\n",
       "  0.7200000000000001,\n",
       "  0.7200000000000001,\n",
       "  0.6617142857142858,\n",
       "  0.5725714285714285,\n",
       "  0.4834285714285714,\n",
       "  0.3942857142857141,\n",
       "  0.3051428571428569,\n",
       "  0.21599999999999975]]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "condition_factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(condition_factor[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0.0,\n",
       "  0.2888888888888889,\n",
       "  0.5777777777777778,\n",
       "  0.8666666666666667,\n",
       "  1.1555555555555557,\n",
       "  1.4444444444444446,\n",
       "  1.7333333333333334,\n",
       "  2.0222222222222226,\n",
       "  2.3111111111111113,\n",
       "  2.6]]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pier_local_scour_depth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pier_local_scour_depth[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stored variables and their in-db values:\n",
      "condition_factor                   -> [[0.7200000000000001, 0.7200000000000001, 0.720000\n",
      "pier_local_scour_depth             -> [[0.0, 0.2888888888888889, 0.5777777777777778, 0.8\n"
     ]
    }
   ],
   "source": [
    "%store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(condition_factor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://www.geeksforgeeks.org/python-save-list-to-csv/\n",
    "condition_factor_dict = {\"scour_depth\": pier_local_scour_depth[0],\n",
    "                         \"condition_factor\": condition_factor[0]}\n",
    "condition_factor_df = pd.DataFrame.from_dict(condition_factor_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>scour_depth</th>\n",
       "      <th>condition_factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.288889</td>\n",
       "      <td>0.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.577778</td>\n",
       "      <td>0.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.866667</td>\n",
       "      <td>0.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.155556</td>\n",
       "      <td>0.661714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.444444</td>\n",
       "      <td>0.572571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.733333</td>\n",
       "      <td>0.483429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.022222</td>\n",
       "      <td>0.394286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.311111</td>\n",
       "      <td>0.305143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.216000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   scour_depth  condition_factor\n",
       "0     0.000000          0.720000\n",
       "1     0.288889          0.720000\n",
       "2     0.577778          0.720000\n",
       "3     0.866667          0.720000\n",
       "4     1.155556          0.661714\n",
       "5     1.444444          0.572571\n",
       "6     1.733333          0.483429\n",
       "7     2.022222          0.394286\n",
       "8     2.311111          0.305143\n",
       "9     2.600000          0.216000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "condition_factor_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://stackoverflow.com/questions/62290012/why-is-row-number-added-as-a-column-in-csv-file-saved-by-pandas\n",
    "condition_factor_df.to_csv(\"condition_factor_df.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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