run_me.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from helper_functions import *\n",
    "config ={'ev_data_sample': 'data/ev_data_sample.pkl',  # Sample EV data\n",
    "         'results_time_analysis_sample': 'data/time_analysis_multitimestep_sample.pkl', # Path to save the results of the time analysis\n",
    "         'results_time_analysis': 'data/time_comparison_multi_ts.pkl', #Path of the actual time analysis results used in the paper\n",
    "        'polytope_matrix': 'matlab_functions/polytope_matrix.mat'}# Path to save the polytope matrix"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Performing Visual check fof feasibility"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating UL vectors for individual EVs\n",
    "Here we calculate the $\\vec{u_k}$ and $\\vec{l_k}$ vectors for a given EV parametrised by $\\bar{p}, \\underline{p}, \\bar{e}, \\underline{e}$ for $k\\in \\mathcal{T}$.\n",
    "The $\\vec{u}$ and $\\vec{l}$ are calculated using **Theorem 7**:\n",
    "$\\begin{aligned}\n",
    "    &u_k^{EV}=\\min\\left[k\\overline{p}\\Delta\\text{t},\\overline{e}-(T-k)\\underline{p}\\Delta\\text{t}\\right], &&\\: k\\in \\mathcal{T} \\\\\n",
    "    &l_k^{EV}=\\max\\left[k\\underline{p}\\Delta\\text{t},\\underline{e}-(T-k)\\overline{p}\\Delta\\text{t}\\right], &&\\: k\\in \\mathcal{T} \n",
    "\\end{aligned}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The U vector for EV#1 is: [0, 20, 25, 25]\n",
      " The L vector for EV#1 is: [0, 0, 0, 15]\n",
      "The U vector for EV#2 is: [0, 10, 20, 30]\n",
      " The L vector for EV#2 is: [0, 5, 10, 20]\n"
     ]
    }
   ],
   "source": [
    "t_steps = 3\n",
    "u1,l1 = calculate_u_l(p_max=20, p_min=0, e_min = 15, e_max=25, del_t=1, t_steps=3)\n",
    "u2, l2 = calculate_u_l(p_max=10, p_min=5, e_min = 20, e_max=30, del_t=1, t_steps=3)\n",
    "print(f'The U vector for EV#1 is: {u1}\\n The L vector for EV#1 is: {l1}')\n",
    "print(f'The U vector for EV#2 is: {u2}\\n The L vector for EV#2 is: {l2}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating the individual Polytope\n",
    "From the individual $\\vec{u}$ and $\\vec{l}$ parameters of single EV, the **H-representation** of the polytope can be computed using **Definition 2**:\n",
    "\n",
    "$\n",
    "\\begin{aligned}\n",
    "   \\mathcal{P}^{EV} :=\\left\\{ \\vec{p} \\in \\mathbb{R}^T \\Bigg| \\begin{bmatrix}\n",
    "       \\:\\:\\: \\mathbb{I}\\\\\n",
    "        -\\mathbb{I}\\\\\n",
    "        \\:\\:\\: (1)^\\top\\Delta \\text{t}\\\\\n",
    "        - (1)^\\top\\Delta \\text{t}\n",
    "    \\end{bmatrix}\\vec{p} \\preceq \\begin{bmatrix}\n",
    "        \\:\\:\\:(1)\\overline{p}\\\\\n",
    "        -(1)\\underline{p}\\\\\n",
    "        \\:\\:\\:(1)\\overline{e}\\\\\n",
    "        -(1)\\underline{e}\n",
    "    \\end{bmatrix}  \\right\\},\n",
    "\\end{aligned}$\n",
    "where $(1)$ is a column vector of length $T$ with elements equal to 1.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The matrix A for T = 3 is:\n",
      "[[1 0 0]\n",
      " [0 1 0]\n",
      " [0 0 1]\n",
      " [1 1 0]\n",
      " [1 0 1]\n",
      " [0 1 1]\n",
      " [1 1 1]]\n",
      "The matrix b for T = 3 is:\n",
      "[[1 0 0]\n",
      " [1 0 0]\n",
      " [1 0 0]\n",
      " [0 1 0]\n",
      " [0 1 0]\n",
      " [0 1 0]\n",
      " [0 0 1]]\n",
      "The matrix A_poly for T = 3 is:\n",
      "[[ 1  0  0]\n",
      " [ 0  1  0]\n",
      " [ 0  0  1]\n",
      " [ 1  1  0]\n",
      " [ 1  0  1]\n",
      " [ 0  1  1]\n",
      " [ 1  1  1]\n",
      " [-1  0  0]\n",
      " [ 0 -1  0]\n",
      " [ 0  0 -1]\n",
      " [-1 -1  0]\n",
      " [-1  0 -1]\n",
      " [ 0 -1 -1]\n",
      " [-1 -1 -1]]\n",
      "The matrix b_poly of Polytope 1 for T = 3 is:\n",
      "[[ 20]\n",
      " [ 20]\n",
      " [ 20]\n",
      " [ 25]\n",
      " [ 25]\n",
      " [ 25]\n",
      " [ 25]\n",
      " [  0]\n",
      " [  0]\n",
      " [  0]\n",
      " [  0]\n",
      " [  0]\n",
      " [  0]\n",
      " [-15]]\n",
      "##################\n",
      "The matrix b_poly of Polytope 2 for T = 3 is:\n",
      "[[ 10]\n",
      " [ 10]\n",
      " [ 10]\n",
      " [ 20]\n",
      " [ 20]\n",
      " [ 20]\n",
      " [ 30]\n",
      " [ -5]\n",
      " [ -5]\n",
      " [ -5]\n",
      " [-10]\n",
      " [-10]\n",
      " [-10]\n",
      " [-20]]\n"
     ]
    }
   ],
   "source": [
    "T = 3\n",
    "# Vector A\n",
    "A = generate_combinations_matrix(T)\n",
    "# print side by side\n",
    "print(f'The matrix A for T = {T} is:')\n",
    "print(np.matrix(A)) \n",
    "\n",
    "b = generate_polytope_b_matrix(T)\n",
    "print(f'The matrix b for T = {T} is:')\n",
    "print(np.matrix(b))\n",
    "\n",
    "A_poly = np.vstack([A, -A])\n",
    "b_poly1 = np.vstack([np.matmul(b,np.array(u1[1:])).reshape((len(b),1)),-np.matmul(b,np.array(l1[1:])).reshape((len(b),1))])\n",
    "b_poly2 = np.vstack([np.matmul(b,np.array(u2[1:])).reshape((len(b),1)),-np.matmul(b,np.array(l2[1:])).reshape((len(b),1))])\n",
    "\n",
    "print(f'The matrix A_poly for T = {T} is:')\n",
    "print(np.matrix(A_poly))\n",
    "print(f'The matrix b_poly of Polytope 1 for T = {T} is:')\n",
    "print(np.matrix(b_poly1))\n",
    "print('##################')\n",
    "print(f'The matrix b_poly of Polytope 2 for T = {T} is:')\n",
    "print(np.matrix(b_poly2))\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating the feasible signal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "u, l = calculate_u_l(p_max=20, p_min=0, e_min = 15, e_max=25, del_t=1, t_steps=3)\n",
    "p_feasible = [10,5,10]\n",
    "p_infeasible = [2,22,1]\n",
    "plot_feasibility_test(u,l,p_feasible,p_infeasible,save_path='figures/graphical_test.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting of 3-Polytope for 2 EV and theirn Minkwoski summation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "######################## UL vectors for individual EVs ########################\n",
      "The U vector for EV#1 is: [0, 20, 25, 25]\n",
      " The L vector for EV#1 is: [0, 0, 0, 15]\n",
      "The U vector for EV#2 is: [0, 10, 20, 30]\n",
      " The L vector for EV#2 is: [0, 5, 10, 20]\n",
      "######################## Polytope representation ########################\n",
      "The matrix A for T = 3 is:\n",
      "[[1 0 0]\n",
      " [0 1 0]\n",
      " [0 0 1]\n",
      " [1 1 0]\n",
      " [1 0 1]\n",
      " [0 1 1]\n",
      " [1 1 1]]\n",
      "The matrix b for T = 3 is:\n",
      "[[1 0 0]\n",
      " [1 0 0]\n",
      " [1 0 0]\n",
      " [0 1 0]\n",
      " [0 1 0]\n",
      " [0 1 0]\n",
      " [0 0 1]]\n",
      "The matrix A_poly for T = 3 is:\n",
      "[[ 1  0  0]\n",
      " [ 0  1  0]\n",
      " [ 0  0  1]\n",
      " [ 1  1  0]\n",
      " [ 1  0  1]\n",
      " [ 0  1  1]\n",
      " [ 1  1  1]\n",
      " [-1  0  0]\n",
      " [ 0 -1  0]\n",
      " [ 0  0 -1]\n",
      " [-1 -1  0]\n",
      " [-1  0 -1]\n",
      " [ 0 -1 -1]\n",
      " [-1 -1 -1]]\n",
      "The matrix b_poly of Polytope 1 for T = 3 is:\n",
      "[[ 20]\n",
      " [ 20]\n",
      " [ 20]\n",
      " [ 25]\n",
      " [ 25]\n",
      " [ 25]\n",
      " [ 25]\n",
      " [  0]\n",
      " [  0]\n",
      " [  0]\n",
      " [  0]\n",
      " [  0]\n",
      " [  0]\n",
      " [-15]]\n",
      "##################\n",
      "The matrix b_poly of Polytope 2 for T = 3 is:\n",
      "[[ 10]\n",
      " [ 10]\n",
      " [ 10]\n",
      " [ 20]\n",
      " [ 20]\n",
      " [ 20]\n",
      " [ 30]\n",
      " [ -5]\n",
      " [ -5]\n",
      " [ -5]\n",
      " [-10]\n",
      " [-10]\n",
      " [-10]\n",
      " [-20]]\n",
      "System matrix saved at matlab_functions/polytope_matrix.mat\n"
     ]
    }
   ],
   "source": [
    "\n",
    "###################### UL vectors for individual EVs ##################################\n",
    "\n",
    "print('######################## UL vectors for individual EVs ########################')\n",
    "t_steps = 3\n",
    "u1,l1 = calculate_u_l(p_max=20, p_min=0, e_min = 15, e_max=25, del_t=1, t_steps=3)\n",
    "u2, l2 = calculate_u_l(p_max=10, p_min=5, e_min = 20, e_max=30, del_t=1, t_steps=3)\n",
    "print(f'The U vector for EV#1 is: {u1}\\n The L vector for EV#1 is: {l1}')\n",
    "print(f'The U vector for EV#2 is: {u2}\\n The L vector for EV#2 is: {l2}')\n",
    "\n",
    "#%% [markdown]\n",
    "\n",
    "################################ Polytope representation #############################\n",
    "\n",
    "print('######################## Polytope representation ########################')\n",
    "T = 3\n",
    "# Vector A\n",
    "A = generate_combinations_matrix(T)\n",
    "# print side by side\n",
    "print(f'The matrix A for T = {T} is:')\n",
    "print(np.matrix(A))\n",
    "\n",
    "b = generate_polytope_b_matrix(T)\n",
    "print(f'The matrix b for T = {T} is:')\n",
    "print(np.matrix(b))\n",
    "\n",
    "A_poly = np.vstack([A, -A])\n",
    "b_poly1 = np.vstack([np.matmul(b,np.array(u1[1:])).reshape((len(b),1)),-np.matmul(b,np.array(l1[1:])).reshape((len(b),1))])\n",
    "b_poly2 = np.vstack([np.matmul(b,np.array(u2[1:])).reshape((len(b),1)),-np.matmul(b,np.array(l2[1:])).reshape((len(b),1))])\n",
    "\n",
    "print(f'The matrix A_poly for T = {T} is:')\n",
    "print(np.matrix(A_poly))\n",
    "print(f'The matrix b_poly of Polytope 1 for T = {T} is:')\n",
    "print(np.matrix(b_poly1))\n",
    "print('##################')\n",
    "print(f'The matrix b_poly of Polytope 2 for T = {T} is:')\n",
    "print(np.matrix(b_poly2))\n",
    "\n",
    "# Saving A, B as  .mat file for plotting\n",
    "savemat(config['polytope_matrix'], {'A':A_poly, 'B1':b_poly1, 'B2':b_poly2})\n",
    "print(f'System matrix saved at {config[\"polytope_matrix\"]}')\n",
    "#%%\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization of Computational time and Memory performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating dummy EV transactions\n",
    "The following code only runs witha dataframe as input which has the following columns and type:\n",
    "* 'START': This is the arival time of the EV. (pd.DatetimeIndex, UTC)\n",
    "* 'STOP': This is the depature time of the EV. (pd.DatetimeIndex, UTC)\n",
    "* 'P_MAX': This the maximum chargingpower allowed for the concened EV (kWh). (Float)\n",
    "* 'VOL': This is the energy charged in the session (kWh). (Float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "34fdb0a90d1a4957ac75a2cc3d2e67b5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating transactions:   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_data = generate_dummy_transactions(number_of_days = 100, start_date = dt.datetime(year=2022, month=1, day=1))\n",
    "sample_data.to_pickle(config['ev_data_sample'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running models for computimng performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c292a3af6585436699677d6877207c21",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Days of transactions:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are a total of 83.0 time steps of 1 hours each in the optimization horizon\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c838f7cd0a6e4a70a00c60044aec4e38",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Time step size:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: Loading a SolverResults object with a warning status into\n",
      "    model.name=\"unknown\";\n",
      "      - termination condition: infeasibleOrUnbounded\n",
      "      - message from solver: Problem proven to be infeasible or unbounded.\n",
      "Build time for direct method: 0.10072207450866699 seconds\n",
      "Solve time for direct method: 0.6368427276611328 seconds\n",
      "Build time for ul method: 0.002518892288208008 seconds\n",
      "Solve time for ul method: 0.07591891288757324 seconds\n",
      "WARNING: Loading a SolverResults object with a warning status into\n",
      "    model.name=\"unknown\";\n",
      "      - termination condition: infeasibleOrUnbounded\n",
      "      - message from solver: Problem proven to be infeasible or unbounded.\n",
      "Build time for direct method: 0.12048602104187012 seconds\n",
      "Solve time for direct method: 0.2784140110015869 seconds\n",
      "Build time for ul method: 0.0025238990783691406 seconds\n",
      "Solve time for ul method: 0.07013916969299316 seconds\n",
      "There are a total of 83.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 75.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 78.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 81.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 82.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 66.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 72.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 83.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 89.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 85.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 72.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 73.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 81.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 39.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 63.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 67.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 65.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 73.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 74.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 66.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 83.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 82.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 80.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 73.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 73.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 75.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 71.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 66.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 64.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 81.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 76.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 66.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 64.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 68.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 76.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 87.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 77.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 73.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 73.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 66.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 85.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 67.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 67.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 75.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 83.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 89.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 78.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 73.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 88.0 time steps of 1 hours each in the optimization horizon\n",
      "There are a total of 69.0 time steps of 1 hours each in the optimization horizon\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7261ecffe62f46ceb607109dc18a371a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Time step size:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: Loading a SolverResults object with a warning status into\n",
      "    model.name=\"unknown\";\n",
      "      - termination condition: infeasibleOrUnbounded\n",
      "      - message from solver: Problem proven to be infeasible or unbounded.\n",
      "Build time for direct method: 3.68540096282959 seconds\n",
      "Solve time for direct method: 5.8216493129730225 seconds\n",
      "Build time for ul method: 0.0019750595092773438 seconds\n",
      "Solve time for ul method: 0.07497882843017578 seconds\n",
      "WARNING: Loading a SolverResults object with a warning status into\n",
      "    model.name=\"unknown\";\n",
      "      - termination condition: infeasibleOrUnbounded\n",
      "      - message from solver: Problem proven to be infeasible or unbounded.\n",
      "Build time for direct method: 5.3123767375946045 seconds\n",
      "Solve time for direct method: 9.08118724822998 seconds\n",
      "Build time for ul method: 0.0026040077209472656 seconds\n",
      "Solve time for ul method: 0.07688498497009277 seconds\n"
     ]
    }
   ],
   "source": [
    "re_run = True # Set to True to re-run the analysis\n",
    "TIME_STEPS_LIST = [1,1/2]\n",
    "if re_run:\n",
    "    ev_data_all = pd.read_pickle(config['ev_data_sample']) # load the sample data\n",
    "    date_sample = dt.datetime(2022, 1, 1) # Date from which the data is started taken\n",
    "    verbose_ = 100 # Set to 0 for no print statements\n",
    "    f_windw = [18,19] # in hours\n",
    "    n_days = [1,50] # Number of days to simulate\n",
    "    re_list = [] # results list\n",
    "    for n_times in tq.tqdm(n_days, position=0,leave=True,desc='Days of transactions'):\n",
    "        data_for_opt = multiply_transactions(ev_data_all, n_times,date_sample,del_t=1, f_windw=f_windw, verbose_=verbose_ , restrict_stop_time=False, total_time_steps=36)\n",
    "        for ts in tq.tqdm(TIME_STEPS_LIST, position=0,leave=True, desc='Time step size'):\n",
    "            res_dict = single_run(data_for_opt, f_windw, del_t=ts, verbose_=verbose_)\n",
    "            re_list.append(res_dict)\n",
    "\n",
    "    time_analysis_df = pd.DataFrame(re_list)\n",
    "    time_analysis_df.to_pickle(config['results_time_analysis_sample']) # Save the results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the graphs for computational time and memory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.06246685981750488, 0.06274080276489258] [0.0020258426666259766, 0.0019006729125976562]\n",
      "[0.060727834701538086, 0.06502723693847656] [0.0023102760314941406, 0.0025022029876708984]\n",
      "[] []\n",
      "[] []\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "''' ######################### Plotting time analysis ############################# \n",
    "'''\n",
    "\n",
    "PLOT_ACTUAL = False # Set to True to plot the actual data\n",
    "\n",
    "if PLOT_ACTUAL:\n",
    "    time_analysis_df1 = pd.read_pickle(config['results_time_analysis']) # Load the results for either sample or actual data\n",
    "    save_path = 'figures/memory_time_analysis_multi_ts_actual.pdf' #\n",
    "else:\n",
    "    time_analysis_df1 = pd.read_pickle(config['results_time_analysis_sample']) # Load the results for either sample or actual data\n",
    "    save_path = 'figures/memory_time_analysis_multi_ts_sample.pdf' #\n",
    "\n",
    "cl_scheme = ['#fe9929','#dd3497','#034e7b','#005a32']\n",
    "\n",
    "\"\"\" \n",
    "NOTE: Update this code according to the new results so that it is visualized properly\n",
    "\"\"\"\n",
    "# %%\n",
    "update_fonts_for_fig(2)\n",
    "\n",
    "fig = plt.figure(figsize=(7,2))\n",
    "spec = gridspec.GridSpec(ncols=5, nrows=1, figure=fig,width_ratios=[1,-0.2,1,0.2,2])\n",
    "ax00  = fig.add_subplot(spec[0, 0])\n",
    "ax01  = fig.add_subplot(spec[0, 2])\n",
    "ax03  = fig.add_subplot(spec[0, 4])\n",
    "\n",
    "\n",
    "ax = [ax00,ax01,ax03]\n",
    "\n",
    "n_t_steps = np.array([  1,  2, 4, 16])\n",
    "i = 0\n",
    "for n_t in n_t_steps:\n",
    "    df = time_analysis_df1[time_analysis_df1['No of time steps'] == n_t]\n",
    "    # t_solve_direct = [df.iloc[i]['Direct optimization']['info']['Solver'][0]['Time'] for i in range(len(df))]\n",
    "    # t_solve_ul = [df.iloc[i]['UL optimization']['info']['Solver'][0]['Time'] for i in range(len(df))]\n",
    "\n",
    "    t_built_direct = [df['Direct optimization'].iloc[i]['Build time (s)'] for i in range(len(df))]\n",
    "    t_built_ul = [df['UL optimization'].iloc[i]['Build time (s)'] for i in range(len(df))]\n",
    "\n",
    "    t_solve_direct = [df['Direct optimization'].iloc[i]['info']['Solver'][0]['Time'] for i in range(len(df))]\n",
    "    t_solve_ul = [df['UL optimization'].iloc[i]['info']['Solver'][0]['Time']  for i in range(len(df))]\n",
    "\n",
    "    print(t_solve_ul, t_built_ul)\n",
    "\n",
    "    ax[0].plot(df['No of EVs'],t_built_direct,label=f'{n_t} time steps', linestyle='--', color=cl_scheme[i], linewidth=0.8, marker='<', markersize=3)\n",
    "    ax[0].plot(df['No of EVs'],t_built_ul,label=f'{n_t} time steps', linestyle='-', color=cl_scheme[i], linewidth=0.8, marker='o', markersize=3)\n",
    "\n",
    "    ax[1].plot(df['No of EVs'],t_solve_direct,label=f'{n_t} time steps', linestyle='--', color=cl_scheme[i], linewidth=0.8, marker='<', markersize=3)\n",
    "    ax[1].plot(df['No of EVs'],t_solve_ul,label=f'{n_t} time steps', linestyle='-', color=cl_scheme[i], linewidth=0.8, marker='o', markersize=3)    \n",
    "\n",
    "    \n",
    "    \n",
    "    i += 1\n",
    "ax[0].set_yscale('log')\n",
    "ax[1].set_yscale('log')\n",
    "ax[0].set_xscale('log')\n",
    "ax[1].set_xscale('log')\n",
    "ax[1].set_yticks([])\n",
    "\n",
    "ax[0].set_ylim([1e-3,3e3])\n",
    "ax[1].set_ylim([1e-3,3e3])\n",
    "ax[0].set_ylabel('Time (s)', fontsize=9, labelpad=0)\n",
    "\n",
    "ax[0].grid(True, which='both', axis='both', linewidth=0.05)\n",
    "ax[1].grid(True, which='both', axis='both', linewidth=0.05)\n",
    "\n",
    "ax[0].text(0.2, 0.95, 'Build', ha='left', va='center', transform=ax[0].transAxes, fontsize=7)\n",
    "ax[1].text(0.2, 0.95, 'Solve', ha='left', va='center', transform=ax[1].transAxes, fontsize=7)\n",
    "custom_line = []\n",
    "ax[1].legend(custom_line, ['Baseline'], ncol=1,loc='lower right')\n",
    "\n",
    "\n",
    "i = 0\n",
    "for n_t in n_t_steps:\n",
    "    df = time_analysis_df1[time_analysis_df1['No of time steps'] == n_t]\n",
    "    memory_direc_mb = df['Memory used by direct method (bytes)']/1e6\n",
    "    memory_ul_mb = df['Memory used by ul method (bytes)']/1e6\n",
    "    ax[2].plot(df['No of EVs'],memory_direc_mb,label=f'{n_t} time steps', linestyle='--', color=cl_scheme[i], linewidth=0.8, marker='<', markersize=3)\n",
    "    ax[2].plot(df['No of EVs'],memory_ul_mb,label=f'{n_t} time steps', linestyle='-', color=cl_scheme[i], linewidth=0.8, marker='o', markersize=3)\n",
    "    i += 1\n",
    "ax[2].set_yscale('log')\n",
    "ax[2].set_xscale('log')\n",
    "ax[2].set_ylabel('Memory (MB)', fontsize=9, labelpad=-5) # control the horizontal spacing\n",
    "\n",
    "# ax[2].set_xlabel('Number of EVs (in thousands)',fontsize=9)\n",
    "ax[2].grid(True, which='both', axis='both', linewidth=0.05)\n",
    "\n",
    "custom_line = [plt.Line2D([0], [0],linestyle='--', color='w')]+[plt.Line2D([0], [0], color=cl_scheme[i], lw=1.5) for i in range(len(n_t_steps))] + [plt.Line2D([0], [0], linestyle='--', color='k')]\n",
    "custom_line_type = [plt.Line2D([0], [2], linestyle='--', color='k', marker='<', markersize=3, linewidth=0.8), plt.Line2D([0], [2], linestyle='-', color='k', marker='o', markersize=3, linewidth=0.8)]\n",
    "fig.legend(custom_line[0:-1], ['No. of time steps:','1','2','4','16'], ncol=5, loc='upper right', handlelength=1, bbox_to_anchor=(0.49, 1.05), fontsize=7)\n",
    "fig.legend(custom_line_type, ['Direct aggregation','UL-Flexibility'], ncol=2, loc='upper right', handlelength=3, bbox_to_anchor=(0.9, 1.05), fontsize=7)\n",
    "fig.text(0.5, -0.1, 'Number of EVs', ha='center', va='center', fontsize=9)\n",
    "fig.savefig(save_path, dpi=600, bbox_inches='tight')\n",
    "# %%\n"
   ]
  }
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