nineenteen_pier_compressive_limit_state_equation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run -i code_time_measurement.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "tic()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %run -i zero_reset_stored_variables.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stored 'arch_capacity_geometry_variables' (dict)\n",
      "Stored 'pier_capacity_geometry_variables' (dict)\n",
      "pier\n",
      "{'b_pier': 1, 't_pier': 1.8, 'e_pier': 0}\n",
      "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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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stored variables and their in-db values:\n",
      "arch_capacity_geometry_variables                   -> {'b': 1, 't': 0.5, 'e': 0.08333333333333333}\n",
      "arch_compressive_demand_horizontal                 -> array([354.03374496, 334.49348592, 357.16702591, .\n",
      "arch_compressive_demand_thrust                     -> array([525.16363953, 492.96620384, 530.32649903, .\n",
      "arch_compressive_demand_vertical                   -> array([384.84532383, 359.54270092, 388.90259995, .\n",
      "arch_compressive_failure_points_number             -> [69, 69, 69, 69, 259, 1880, 15286, 74760, 99995, 1\n",
      "compressive_demand_from_arch                       -> {'H_a': array([40.52707914, 33.18183373, 41.704889\n",
      "compressive_demand_from_backfill                   -> {'H_a': array([246.2211967, 246.2211967, 246.22119\n",
      "compressive_demand_from_parapet                    -> {'H_a': array([11.18458987,  9.15746235, 11.509639\n",
      "compressive_demand_from_spandrel_walls             -> {'H_a': array([56.10087926, 45.93299314, 57.731300\n",
      "compressive_resistance_realisations                -> array([6.07743858, 4.41155859, 6.34456247, ..., 6.\n",
      "compressive_resistances                            -> [4.691352777703492, 6.2279668592488475]\n",
      "condition_factor                                   -> [[0.7200000000000001, 0.7200000000000001, 0.720000\n",
      "convergence_simulation_number                      -> 100000\n",
      "df_arch_compressive_capacity                       ->    scour depth  condition factor           0      \n",
      "df_pier_compressive_capacity                       ->    scour depth  condition factor            0     \n",
      "masonry_unit_weight_realisations                   -> array([21.64551063, 17.72241548, 22.27457902, ...,\n",
      "pier_capacity_geometry_variables                   -> {'b_pier': 1, 't_pier': 1.8, 'e_pier': 0}\n",
      "pier_compressive_demand_from_pier                  -> array([ 97.40479781,  79.75086966, 100.23560557, .\n",
      "pier_compressive_failure_points_number             -> [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "pier_compressive_total_vertical_demand             -> array([482.25012164, 439.29357058, 489.13820553, .\n",
      "pier_local_scour_depth                             -> [[0.0, 0.2888888888888889, 0.5777777777777778, 0.8\n",
      "Stored variables and their in-db values:\n",
      "arch_capacity_geometry_variables                   -> {'b': 1, 't': 0.5, 'e': 0.08333333333333333}\n",
      "arch_compressive_demand_horizontal                 -> array([354.03374496, 334.49348592, 357.16702591, .\n",
      "arch_compressive_demand_thrust                     -> array([525.16363953, 492.96620384, 530.32649903, .\n",
      "arch_compressive_demand_vertical                   -> array([384.84532383, 359.54270092, 388.90259995, .\n",
      "arch_compressive_failure_points_number             -> [69, 69, 69, 69, 259, 1880, 15286, 74760, 99995, 1\n",
      "compressive_demand_from_arch                       -> {'H_a': array([40.52707914, 33.18183373, 41.704889\n",
      "compressive_demand_from_backfill                   -> {'H_a': array([246.2211967, 246.2211967, 246.22119\n",
      "compressive_demand_from_parapet                    -> {'H_a': array([11.18458987,  9.15746235, 11.509639\n",
      "compressive_demand_from_spandrel_walls             -> {'H_a': array([56.10087926, 45.93299314, 57.731300\n",
      "compressive_resistance_realisations                -> array([6.07743858, 4.41155859, 6.34456247, ..., 6.\n",
      "compressive_resistances                            -> [4.691352777703492, 6.2279668592488475]\n",
      "condition_factor                                   -> [[0.7200000000000001, 0.7200000000000001, 0.720000\n",
      "convergence_simulation_number                      -> 100000\n",
      "df_arch_compressive_capacity                       ->    scour depth  condition factor           0      \n",
      "df_pier_compressive_capacity                       ->    scour depth  condition factor            0     \n",
      "masonry_unit_weight_realisations                   -> array([21.64551063, 17.72241548, 22.27457902, ...,\n",
      "pier_capacity_geometry_variables                   -> {'b_pier': 1, 't_pier': 1.8, 'e_pier': 0}\n",
      "pier_compressive_demand_from_pier                  -> array([ 97.40479781,  79.75086966, 100.23560557, .\n",
      "pier_compressive_failure_points_number             -> [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "pier_compressive_total_vertical_demand             -> array([482.25012164, 439.29357058, 489.13820553, .\n",
      "pier_local_scour_depth                             -> [[0.0, 0.2888888888888889, 0.5777777777777778, 0.8\n",
      "[array([4725.81624081, 3430.42795909, 4933.53177957, ..., 4697.10889463,\n",
      "       4478.32693308, 5661.94929421]), array([4725.81624081, 3430.42795909, 4933.53177957, ..., 4697.10889463,\n",
      "       4478.32693308, 5661.94929421]), array([4725.81624081, 3430.42795909, 4933.53177957, ..., 4697.10889463,\n",
      "       4478.32693308, 5661.94929421]), array([4725.81624081, 3430.42795909, 4933.53177957, ..., 4697.10889463,\n",
      "       4478.32693308, 5661.94929421]), array([4343.25016417, 3152.72664812, 4534.15063551, ..., 4316.86674602,\n",
      "       4115.79570516, 5203.60101801])]\n",
      "Stored 'df_pier_compressive_capacity' (DataFrame)\n",
      "Stored variables and their in-db values:\n",
      "arch_capacity_geometry_variables                   -> {'b': 1, 't': 0.5, 'e': 0.08333333333333333}\n",
      "arch_compressive_demand_horizontal                 -> array([354.03374496, 334.49348592, 357.16702591, .\n",
      "arch_compressive_demand_thrust                     -> array([525.16363953, 492.96620384, 530.32649903, .\n",
      "arch_compressive_demand_vertical                   -> array([384.84532383, 359.54270092, 388.90259995, .\n",
      "arch_compressive_failure_points_number             -> [69, 69, 69, 69, 259, 1880, 15286, 74760, 99995, 1\n",
      "compressive_demand_from_arch                       -> {'H_a': array([40.52707914, 33.18183373, 41.704889\n",
      "compressive_demand_from_backfill                   -> {'H_a': array([246.2211967, 246.2211967, 246.22119\n",
      "compressive_demand_from_parapet                    -> {'H_a': array([11.18458987,  9.15746235, 11.509639\n",
      "compressive_demand_from_spandrel_walls             -> {'H_a': array([56.10087926, 45.93299314, 57.731300\n",
      "compressive_resistance_realisations                -> array([6.07743858, 4.41155859, 6.34456247, ..., 6.\n",
      "compressive_resistances                            -> [4.691352777703492, 6.2279668592488475]\n",
      "condition_factor                                   -> [[0.7200000000000001, 0.7200000000000001, 0.720000\n",
      "convergence_simulation_number                      -> 100000\n",
      "df_arch_compressive_capacity                       ->    scour depth  condition factor           0      \n",
      "df_pier_compressive_capacity                       ->    scour depth  condition factor            0     \n",
      "masonry_unit_weight_realisations                   -> array([21.64551063, 17.72241548, 22.27457902, ...,\n",
      "pier_capacity_geometry_variables                   -> {'b_pier': 1, 't_pier': 1.8, 'e_pier': 0}\n",
      "pier_compressive_demand_from_pier                  -> array([ 97.40479781,  79.75086966, 100.23560557, .\n",
      "pier_compressive_failure_points_number             -> [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "pier_compressive_total_vertical_demand             -> array([482.25012164, 439.29357058, 489.13820553, .\n",
      "pier_local_scour_depth                             -> [[0.0, 0.2888888888888889, 0.5777777777777778, 0.8\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%run -i sixteen_pier_capacity.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stored 'pier_compressive_demand_from_pier' (ndarray)\n"
     ]
    }
   ],
   "source": [
    "%run -i seventeen_pier_compression_demand_from_pier.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stored 'pier_compressive_total_vertical_demand' (ndarray)\n"
     ]
    }
   ],
   "source": [
    "%run -i eighteen_pier_compressive_total_demand.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "%store -r convergence_simulation_number\n",
    "%store -r df_pier_compressive_capacity\n",
    "%store -r pier_compressive_total_vertical_demand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "%store -r pier_local_scour_depth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[867.09544547 798.8362715  878.04080548 ... 865.58274093 854.05424697\n",
      " 916.42402892]\n"
     ]
    }
   ],
   "source": [
    "print(pier_compressive_total_vertical_demand)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #Variables declared for code developing purposes\n",
    "# example_number = 2\n",
    "# pier_compressive_capacity = np.zeros((example_number, convergence_simulation_number)) #https://numpy.org/doc/stable/reference/generated/numpy.zeros.html\n",
    "# pier_compressive_demand_thrust = np.random.normal(200, 100, convergence_simulation_number)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "#five_pier_capacity gives a dataframe as capacity output\n",
    "pier_compressive_capacity = df_pier_compressive_capacity.iloc[:,2:] #https://towardsdatascience.com/extract-rows-columns-from-a-dataframe-in-python-r-678e5b6743d6\n",
    "print(type(pier_compressive_capacity))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 100000)\n",
      "[[4725.81624081 3430.42795909 4933.53177957 ... 4697.10889463\n",
      "  4478.32693308 5661.94929421]\n",
      " [4725.81624081 3430.42795909 4933.53177957 ... 4697.10889463\n",
      "  4478.32693308 5661.94929421]\n",
      " [4725.81624081 3430.42795909 4933.53177957 ... 4697.10889463\n",
      "  4478.32693308 5661.94929421]\n",
      " [4725.81624081 3430.42795909 4933.53177957 ... 4697.10889463\n",
      "  4478.32693308 5661.94929421]\n",
      " [4343.25016417 3152.72664812 4534.15063551 ... 4316.86674602\n",
      "  4115.79570516 5203.60101801]]\n"
     ]
    }
   ],
   "source": [
    "pier_compressive_capacity_array = np.array(pier_compressive_capacity)\n",
    "print(pier_compressive_capacity_array.shape)\n",
    "print(pier_compressive_capacity_array[:5,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100000,)\n",
      "(100000, 1)\n",
      "[[867.09544547]\n",
      " [798.8362715 ]\n",
      " [878.04080548]\n",
      " [867.03006605]\n",
      " [865.5711533 ]\n",
      " [856.45948664]\n",
      " [841.67854753]\n",
      " [884.88339292]\n",
      " [853.15202734]\n",
      " [849.4373655 ]]\n"
     ]
    }
   ],
   "source": [
    "print(pier_compressive_total_vertical_demand.shape)\n",
    "pier_compressive_demand_thrust_expanded = np.expand_dims(pier_compressive_total_vertical_demand, 1)\n",
    "print(pier_compressive_demand_thrust_expanded.shape)\n",
    "print(pier_compressive_demand_thrust_expanded[0:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100000, 10)\n"
     ]
    }
   ],
   "source": [
    "pier_compressive_capacity_array_transposed = pier_compressive_capacity_array.T\n",
    "print(pier_compressive_capacity_array_transposed.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "pier_compressive_limit_state_equation = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "[array([[3858.72079534],\n",
      "       [2631.5916876 ],\n",
      "       [4055.49097409],\n",
      "       ...,\n",
      "       [3831.5261537 ],\n",
      "       [3624.2726861 ],\n",
      "       [4745.52526529]]), array([[3858.72079534],\n",
      "       [2631.5916876 ],\n",
      "       [4055.49097409],\n",
      "       ...,\n",
      "       [3831.5261537 ],\n",
      "       [3624.2726861 ],\n",
      "       [4745.52526529]]), array([[3858.72079534],\n",
      "       [2631.5916876 ],\n",
      "       [4055.49097409],\n",
      "       ...,\n",
      "       [3831.5261537 ],\n",
      "       [3624.2726861 ],\n",
      "       [4745.52526529]]), array([[3858.72079534],\n",
      "       [2631.5916876 ],\n",
      "       [4055.49097409],\n",
      "       ...,\n",
      "       [3831.5261537 ],\n",
      "       [3624.2726861 ],\n",
      "       [4745.52526529]]), array([[3476.1547187 ],\n",
      "       [2353.89037662],\n",
      "       [3656.10983003],\n",
      "       ...,\n",
      "       [3451.28400509],\n",
      "       [3261.74145819],\n",
      "       [4287.17698909]])]\n"
     ]
    }
   ],
   "source": [
    "for i in range(pier_compressive_capacity_array.shape[0]):\n",
    "    compressive_limit_state_equation = pier_compressive_capacity_array_transposed[:,[i]] - pier_compressive_demand_thrust_expanded\n",
    "    pier_compressive_limit_state_equation.append(compressive_limit_state_equation)\n",
    "\n",
    "print(len(pier_compressive_limit_state_equation))\n",
    "print(pier_compressive_limit_state_equation[0:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[3858.72079534]\n",
      "  [2631.5916876 ]\n",
      "  [4055.49097409]\n",
      "  ...\n",
      "  [3831.5261537 ]\n",
      "  [3624.2726861 ]\n",
      "  [4745.52526529]]\n",
      "\n",
      " [[3858.72079534]\n",
      "  [2631.5916876 ]\n",
      "  [4055.49097409]\n",
      "  ...\n",
      "  [3831.5261537 ]\n",
      "  [3624.2726861 ]\n",
      "  [4745.52526529]]\n",
      "\n",
      " [[3858.72079534]\n",
      "  [2631.5916876 ]\n",
      "  [4055.49097409]\n",
      "  ...\n",
      "  [3831.5261537 ]\n",
      "  [3624.2726861 ]\n",
      "  [4745.52526529]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[1720.85154354]\n",
      "  [1079.73142039]\n",
      "  [1823.65516905]\n",
      "  ...\n",
      "  [1706.64355851]\n",
      "  [1598.36288305]\n",
      "  [2184.16725124]]\n",
      "\n",
      " [[1135.75048516]\n",
      "  [ 655.01176831]\n",
      "  [1212.83694872]\n",
      "  ...\n",
      "  [1125.09674299]\n",
      "  [1043.903358  ]\n",
      "  [1483.16400529]]\n",
      "\n",
      " [[ 550.64942677]\n",
      "  [ 230.29211623]\n",
      "  [ 602.01872839]\n",
      "  ...\n",
      "  [ 543.54992746]\n",
      "  [ 489.44383295]\n",
      "  [ 782.16075934]]]\n"
     ]
    }
   ],
   "source": [
    "pier_compressive_limit_state_equation_array = np.array(pier_compressive_limit_state_equation)\n",
    "print(pier_compressive_limit_state_equation_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 100000, 1)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pier_compressive_limit_state_equation_array.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "100000\n"
     ]
    }
   ],
   "source": [
    "print(pier_compressive_limit_state_equation_array.shape[0])\n",
    "print(pier_compressive_limit_state_equation_array.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "pier_compressive_limit_state_equation_array_reshaped = pier_compressive_limit_state_equation_array.reshape(pier_compressive_limit_state_equation_array.shape[0], pier_compressive_limit_state_equation_array.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[False False False ... False False False]\n",
      " [False False False ... False False False]\n",
      " [False False False ... False False False]\n",
      " ...\n",
      " [False False False ... False False False]\n",
      " [False False False ... False False False]\n",
      " [False False False ... False False False]]\n",
      "(10, 100000)\n"
     ]
    }
   ],
   "source": [
    "pier_compressive_failure_points = pier_compressive_limit_state_equation_array_reshaped<=0\n",
    "print(pier_compressive_failure_points)\n",
    "print(pier_compressive_failure_points.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "pier_compressive_failure_points_number = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(pier_compressive_limit_state_equation_array.shape[0]):\n",
    "    compressive_failure_points_number = np.count_nonzero(pier_compressive_failure_points[i, :])\n",
    "    pier_compressive_failure_points_number.append(compressive_failure_points_number)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 0, 0, 0, 0, 0, 0, 0, 0, 52]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pier_compressive_failure_points_number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "pier_compressive_failure_probability_data = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(pier_compressive_failure_points_number)):\n",
    "    failure_probability_data = pier_compressive_failure_points_number[i]/convergence_simulation_number\n",
    "    pier_compressive_failure_probability_data.append(failure_probability_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://queirozf.com/entries/add-labels-and-text-to-matplotlib-plots-annotation-examples\n",
    "xs = []\n",
    "ys = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(pier_compressive_failure_probability_data)):\n",
    "    rounded_xs = round(pier_local_scour_depth[0][i], 2)\n",
    "    rounded_ys = round(pier_compressive_failure_probability_data[i], 4) #https://stackoverflow.com/questions/3400965/getting-only-1-decimal-place\n",
    "    xs.append(rounded_xs)\n",
    "    ys.append(rounded_ys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(xs, ys, color = 'black')\n",
    "plt.ylim([-0.05, 1.05])\n",
    "plt.xlabel('Pier local scour depth')\n",
    "plt.ylabel('Annual probability of failure')\n",
    "\n",
    "for x,y in zip(xs, ys): #https://realpython.com/python-zip-function/#using-zip-in-python\n",
    "    label = f\"({x},{y})\"\n",
    "    plt.annotate(label,\n",
    "                (x,y),\n",
    "                textcoords=\"offset points\",\n",
    "                xytext=(0,10),\n",
    "                ha='center',\n",
    "                rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stored 'pier_compressive_failure_points_number' (list)\n"
     ]
    }
   ],
   "source": [
    "%store pier_compressive_failure_points_number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://www.geeksforgeeks.org/different-ways-to-create-pandas-dataframe/\n",
    "pier_compressive_failure_probability_data = {'pier_local_scour_depth': pier_local_scour_depth[0],\n",
    "                                                'number_of_analyses': convergence_simulation_number,\n",
    "                                                'number_of_collapses': pier_compressive_failure_points_number}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pier_compressive_failure_probability_data = pd.DataFrame(pier_compressive_failure_probability_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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>pier_local_scour_depth</th>\n",
       "      <th>number_of_analyses</th>\n",
       "      <th>number_of_collapses</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.288889</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.577778</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.866667</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.155556</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.444444</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.733333</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.022222</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.311111</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.600000</td>\n",
       "      <td>100000</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pier_local_scour_depth  number_of_analyses  number_of_collapses\n",
       "0                0.000000              100000                    0\n",
       "1                0.288889              100000                    0\n",
       "2                0.577778              100000                    0\n",
       "3                0.866667              100000                    0\n",
       "4                1.155556              100000                    0\n",
       "5                1.444444              100000                    0\n",
       "6                1.733333              100000                    0\n",
       "7                2.022222              100000                    0\n",
       "8                2.311111              100000                    0\n",
       "9                2.600000              100000                   52"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pier_compressive_failure_probability_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pier_compressive_failure_probability_data.to_excel(\"twenty_pier_compressive_failure_probability_data.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Elapsed time: 104.765398 seconds.\n",
      "\n",
      "Elapsed time: 1.746090 minutes.\n",
      "\n",
      "Elapsed time: 0.029101 hours.\n",
      "\n"
     ]
    }
   ],
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    "toc()"
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