three_compressive_resistance_sampling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run -i code_time_measurement.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "tic()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import scipy\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "normalised mean compressive unit:  11.799999999999999\n",
      "characteristic_compressive_masonry = 4.691  MPa\n",
      "k = 1.645\n",
      "mean_compressive_masonry = 6.228  MPa\n",
      "Stored 'compressive_resistances' (list)\n"
     ]
    }
   ],
   "source": [
    "#file no. two has to be run from here, and the called function needs the return for the variable(s) of interest\n",
    "%run -i two_masonry_compressive_resistance.ipynb \n",
    "#https://www.acgeospatial.co.uk/running-functions-juypter/\n",
    "#https://github.com/ipython/ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4.691352777703492, 6.2279668592488475]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compressive_resistances #to check that the called function returns the variable(s) of interest. Therefore, storing variables would not be needed anymore."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "f_k = compressive_resistances[0] #it's not used by the code, it's shown oly for record\n",
    "f_mean = compressive_resistances[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.691352777703492\n",
      "6.2279668592488475\n"
     ]
    }
   ],
   "source": [
    "print(f_k)\n",
    "print(f_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "f_cov = 0.15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "f_std = f_mean * f_cov"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9341950288873271\n"
     ]
    }
   ],
   "source": [
    "print(f_std)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Monte Carlo simulations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "simulations_number = 100_000 #assumed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.4359949 , 0.02592623, 0.54966248, 0.43532239, 0.4203678 ])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "u = np.random.uniform(low=0, high=1, size=simulations_number) #probability realisations\n",
    "u[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.16113153, -1.9443566 ,  0.12480864, -0.16283953, -0.20095264])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = scipy.stats.norm.ppf(u) #inverse of the normal distribution\n",
    "x[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.07743858, 4.41155859, 6.34456247, 6.07584298, 6.0402379 ])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r = x*f_std + f_mean #vector of realisations\n",
    "r[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "compressive_resistance_distribution = scipy.stats.norm.pdf(np.sort(r), f_mean, f_std)\n",
    "#https://stackoverflow.com/questions/10138085/how-to-plot-normal-distribution\n",
    "#https://www.askpython.com/python/normal-distribution\n",
    "#https://vitalflux.com/normal-distribution-explained-python-examples/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# x_label_pdf = input()\n",
    "# y_label_pdf = input()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Probability density')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#https://realpython.com/python-histograms/\n",
    "n, bins, patches = plt.hist(x=r, bins='auto', color='black', alpha=0.7, rwidth=0.85, density=True)\n",
    "\n",
    "plt.plot(np.sort(r), compressive_resistance_distribution)\n",
    "plt.xlabel('Compressive resistance [kPa]')\n",
    "plt.ylabel('Probability density')\n",
    "# plt.xlabel(x_label_pdf)\n",
    "# plt.ylabel(y_label_pdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "realisations_vector = np.transpose(np.sort(r))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "realisations_series = pd.Series(realisations_vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "realisations_header = 'masonry_compressive_realisations'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "realisations_vector_file_name = 'masonry_compressive_realisations.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "realisations_series.to_csv(realisations_vector_file_name, header = [realisations_header], index = False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convergence assessment (see Ballio and Guadagnini, 2004)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "realisations_list = []\n",
    "mean_list = []\n",
    "var_list = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def random_choice(sample_vector, sample_size):\n",
    "    \n",
    "#     r_sample_size = np.random.choice(sample_vector, size=sample_size, replace=False, p=None)\n",
    "#     r_sample_size_mean = np.mean(r_sample_size)\n",
    "#     r_sample_size_std = np.std(r_sample_size)\n",
    "\n",
    "#     mean_list.append(r_sample_size_mean)\n",
    "#     std_list.append(r_sample_size_std)\n",
    "    \n",
    "#     return r_sample_size, mean_list, std_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# random_choice(r, simulations_number)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def random_choice(sample_vector, sample_size):\n",
    "    \n",
    "    for i in range(sample_size):\n",
    "        # print(i+1)\n",
    "        r_sample_size = np.random.choice(sample_vector, size=i+1, replace=False, p=None)\n",
    "        r_sample_size_mean = np.mean(r_sample_size)\n",
    "        r_sample_size_var = np.var(r_sample_size)\n",
    "\n",
    "        realisations_list.append(r_sample_size)\n",
    "        mean_list.append(r_sample_size_mean)\n",
    "        var_list.append(r_sample_size_var)\n",
    "    \n",
    "    #return realisations_list, mean_list, var_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_choice(r, simulations_number) #it takes 4m 32s on average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = np.arange(1,simulations_number+1, 1)\n",
    "\n",
    "#print(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(n, mean_list, color='black') #https://stackoverflow.com/questions/41709257/how-to-change-the-plot-line-color-from-blue-to-black#:~:text=The%20usual%20way%20to%20set,explicitely%20stating%20the%20color%20argument.\n",
    "#plt.xticks(range(1,iterations+1)) #https://stackoverflow.com/questions/52229875/how-to-force-matplotlib-to-show-values-on-x-axis-as-integers\n",
    "plt.xticks(n) #https://towardsdatascience.com/change-tick-frequency-matplotlib-axis-6af2c6bce1ea\n",
    "plt.xlabel(\"Number of simulations\")\n",
    "plt.ylabel(\"Compressive resistance sample mean\")\n",
    "\n",
    "plt.xscale('log') #https://stackoverflow.com/questions/773814/plot-logarithmic-axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(n, var_list, color='black')\n",
    "plt.xticks(range(1,simulations_number+1))\n",
    "plt.xlabel(\"Number of simulations\")\n",
    "plt.ylabel(\"Compressive resistance sample variance\")\n",
    "\n",
    "plt.xscale('log')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Moments calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "second_moment_list = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for i in range(simulations_number):\n",
    "#     print(i)\n",
    "#     print(realisations_list[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "def second_moment_calculation(realisations):\n",
    "    for i in range(simulations_number):\n",
    "        sample_second_moment=scipy.stats.moment(realisations[i], moment=2)\n",
    "\n",
    "        second_moment_list.append(sample_second_moment)\n",
    "\n",
    "        #print(second_moment_list)    #return second_moment_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "second_moment_calculation(realisations_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_mean_cov_list = []\n",
    "r_array = np.array(r)\n",
    "sample_mean = np.mean(r_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sample_mean_cov_calculation(second_moments):\n",
    "\n",
    "    for i in range(simulations_number):\n",
    "        sample_mean_cov = second_moments[i]/sample_mean\n",
    "        \n",
    "        sample_mean_cov_list.append(sample_mean_cov)\n",
    "\n",
    "    #return sample_mean_cov_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_mean_cov_calculation(second_moment_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(n, sample_mean_cov_list, color='black')\n",
    "plt.xticks(range(1,simulations_number+1))\n",
    "plt.xlabel(\"Number of simulations\")\n",
    "plt.ylabel(\"Coefficient of variation of sample mean\")\n",
    "\n",
    "plt.xscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "fourth_moment_list = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fourth_moment_calculation(realisations):\n",
    "    for i in range(simulations_number):\n",
    "        sample_fourth_moment=scipy.stats.moment(realisations[i], moment=4)\n",
    "\n",
    "        fourth_moment_list.append(sample_fourth_moment)\n",
    "\n",
    "        #print(second_moment_list)\n",
    "    #return fourth_moment_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "fourth_moment_calculation(realisations_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_variance_cov_list = []\n",
    "sample_variance = np.var(r_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sample_variance_cov_calculation(fourth_moments):\n",
    "    for i in range(simulations_number):\n",
    "        sample_variance_cov = fourth_moments[i]/sample_variance\n",
    "\n",
    "        sample_variance_cov_list.append(sample_variance_cov)\n",
    "\n",
    "    #return sample_mean_cov_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_variance_cov_calculation(fourth_moment_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(n, sample_variance_cov_list, color='black')\n",
    "plt.xticks(range(1,simulations_number+1))\n",
    "plt.xlabel(\"Number of simulations\")\n",
    "plt.ylabel(\"Coefficient of variation of sample variance\")\n",
    "\n",
    "plt.xscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "compressive_resistance_realisations = r\n",
    "convergence_simulation_number = 100_000 #it can be deducted from the horizontal asyntote on the right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stored 'compressive_resistance_realisations' (ndarray)\n",
      "Stored 'convergence_simulation_number' (int)\n"
     ]
    }
   ],
   "source": [
    "%store compressive_resistance_realisations\n",
    "%store convergence_simulation_number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed time: 840.440055 seconds.\n",
      "\n",
      "Elapsed time: 14.007334 minutes.\n",
      "\n",
      "Elapsed time: 0.233456 hours.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "toc()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.9.7"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "ad2bdc8ecc057115af97d19610ffacc2b4e99fae6737bb82f5d7fb13d2f2c186"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}