{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8eea57ed-3c18-4637-b3d0-eb536da9c380",
   "metadata": {},
   "source": [
    "\n",
    "****\n",
    "# **Code for submitted paper**:\n",
    "## **The economic value of load-shifting and its viability as a substitute for grid expansion**\n",
    "\n",
    "### Creating the plots of the paper\n",
    "\n",
    "    - 1. Figure 1:  Included, Comparison of LDCs\n",
    "    - 2. Figure 2:  Not included, is a Power-Point path diagram\n",
    "    - 3. Figure 3:  Not included, Code was written in R and is supplied in the provided R-File\n",
    "    - 4. Figure 4:  Included, Temporal distribution of exceedances/ oversupply (= also Figure Appendix B.7, if the Threshold is set from 95 to 85)\n",
    "    - 5. Figure 5:  Included,  Classification results false negatives only\n",
    "    - 6. Figure 6:  Included, Post-intervention capacity requirements\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4fc5e18e-3db5-4996-9669-6540f8c6fa91",
   "metadata": {},
   "outputs": [],
   "source": [
    "%reset\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "from sklearn.metrics import (mean_absolute_error, mean_absolute_percentage_error, r2_score)\n",
    "from matplotlib.colors import (BoundaryNorm, LinearSegmentedColormap, ListedColormap)\n",
    "from matplotlib.lines import Line2D\n",
    "from matplotlib.patches import Patch\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "from pathlib import Path\n",
    "\n",
    "plt.style.use(\"seaborn-v0_8-whitegrid\")\n",
    "mpl.rcParams[\"axes.linewidth\"] = 1\n",
    "\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "276a3a60",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ======================================================================\n",
    "# Reading-In the created dataframes\n",
    "# ======================================================================\n",
    "\n",
    "DATA_DIR = next(\n",
    "    p for p in [\n",
    "        Path(\"Data\"),\n",
    "        Path(\"../Data\"),\n",
    "        Path(\"ReadHandIn/Data\"),\n",
    "    ]\n",
    "    if p.is_dir()\n",
    ")\n",
    "\n",
    "# Initial Dataframes\n",
    "df_EV20_PV25_5 = pd.read_csv(DATA_DIR / \"df_EV20_PV25_5.csv\", parse_dates=[\"clock_local\"])\n",
    "df_EV40_PV41_5 = pd.read_csv(DATA_DIR / \"df_EV40_PV41_5.csv\", parse_dates=[\"clock_local\"])\n",
    "df_EV75_PV75 = pd.read_csv(DATA_DIR / \"df_EV75_PV75.csv\", parse_dates=[\"clock_local\"])\n",
    "\n",
    "df_EV20_PV25_5['clock_local'] = pd.to_datetime(df_EV20_PV25_5['clock_local'])\n",
    "df_EV40_PV41_5['clock_local'] = pd.to_datetime(df_EV40_PV41_5['clock_local'])\n",
    "df_EV75_PV75['clock_local'] = pd.to_datetime(df_EV75_PV75['clock_local'])\n",
    "\n",
    "df_EV20_PV25_5_last = df_EV20_PV25_5.tail(35040)\n",
    "df_EV40_PV41_5_last = df_EV40_PV41_5.tail(35040)\n",
    "df_EV75_PV75_last = df_EV75_PV75.tail(35040)\n",
    "\n",
    "# Forecast Results \n",
    "res_EV20_PV25_5 = pd.read_csv(DATA_DIR / \"forecast_EV20_PV25_5_day_ahead.csv\", parse_dates=[\"clock_local\"])\n",
    "res_EV40_PV_41_5 = pd.read_csv(DATA_DIR / \"forecast_EV40_PV41_5_day_ahead.csv\", parse_dates=[\"clock_local\"])\n",
    "res_EV75_PV_75 = pd.read_csv(DATA_DIR / \"forecast_EV75_PV75_day_ahead.csv\", parse_dates=[\"clock_local\"])\n",
    "\n",
    "res_EV20_PV25_5 = res_EV20_PV25_5.rename(columns={\"Adjusted_constumption_to_model\": \"ytrue_cont\", \"prediction\": \"ypred_cont\"})\n",
    "res_EV40_PV_41_5 = res_EV40_PV_41_5.rename(columns={\"Adjusted_constumption_to_model\": \"ytrue_cont\", \"prediction\": \"ypred_cont\"})\n",
    "res_EV75_PV_75 = res_EV75_PV_75.rename(columns={\"Adjusted_constumption_to_model\": \"ytrue_cont\", \"prediction\": \"ypred_cont\"})\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1e7dc7ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'MAE': 111.1995442711142, 'MAPE': 1.3021586528921434, 'R2': 0.8394410656599851}\n",
      "{'MAE': 181.25587459225804, 'MAPE': 1.214594033373569, 'R2': 0.8310177106209196}\n",
      "{'MAE': 312.4924824503887, 'MAPE': 1.1471094064980665, 'R2': 0.8427129090978996}\n"
     ]
    }
   ],
   "source": [
    "# ======================================================================\n",
    "# General Forecasting performance\n",
    "# ======================================================================\n",
    "\n",
    "def calc_metrics(df):\n",
    "    y_true = df[\"ytrue_cont\"]\n",
    "    y_pred = df[\"ypred_cont\"]\n",
    "\n",
    "    return {\n",
    "        \"MAE\": mean_absolute_error(y_true, y_pred),\n",
    "        \"MAPE\": mean_absolute_percentage_error(y_true, y_pred),\n",
    "        \"R2\": r2_score(y_true, y_pred),\n",
    "    }\n",
    "\n",
    "print(calc_metrics(res_EV20_PV25_5))\n",
    "print(calc_metrics(res_EV40_PV_41_5))\n",
    "print(calc_metrics(res_EV75_PV_75))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8e487051",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 9600x4000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ============================================================\n",
    "# FIGURE 1: \n",
    "# ============================================================\n",
    "\n",
    "\n",
    "\n",
    "df_list = [\n",
    "    (\"df_EV20_PV25_5_last\", \"RowSum_with_imputation\"),\n",
    "    (\"df_EV20_PV25_5_last\", \"Adjusted_constumption_to_model\"),\n",
    "    (\"df_EV40_PV41_5_last\", \"Adjusted_constumption_to_model\"),\n",
    "    (\"df_EV75_PV75_last\", \"Adjusted_constumption_to_model\"),\n",
    "]\n",
    "\n",
    "plot_titles = [\n",
    "    \"Unadjusted\",\n",
    "    \"Adjusted – EV 20%, PV 25.5%\",\n",
    "    \"Adjusted – EV 40%, PV 41.5%\",\n",
    "    \"Adjusted – EV 75%, PV 75%\",\n",
    "]\n",
    "\n",
    "colors = [\"blue\", \"darkorange\", \"seagreen\", \"purple\"]\n",
    "\n",
    "\n",
    "def format_with_spaces(x, pos):\n",
    "    return f\"{int(x):,}\".replace(\",\", \" \")\n",
    "\n",
    "\n",
    "space_formatter = FuncFormatter(format_with_spaces)\n",
    "\n",
    "\n",
    "fig, (combined_ax, zoom_ax) = plt.subplots(1, 2, figsize=(12/1.25, 5/1.25), dpi=1000)\n",
    "\n",
    "\n",
    "# Left plot: full load duration curves\n",
    "for i, (df_name, col_name) in enumerate(df_list):\n",
    "    df = globals()[df_name]\n",
    "    sorted_load = df[col_name].sort_values(ascending=False).reset_index(drop=True)\n",
    "\n",
    "    combined_ax.plot(sorted_load, label=plot_titles[i], color=colors[i], linewidth=1)\n",
    "\n",
    "combined_ax.axhline(y=0, color=\"black\", linestyle=\"--\", linewidth=0.8, label=\"_nolegend_\")\n",
    "combined_ax.set_title(\"Load duration curves for different adoption rates\", fontsize=11)\n",
    "combined_ax.set_xlabel(\"Time Interval\", fontsize=9)\n",
    "combined_ax.set_ylabel(\"Load [kW]\", fontsize=9)\n",
    "combined_ax.yaxis.set_major_formatter(space_formatter)\n",
    "combined_ax.tick_params(axis=\"both\", labelsize=8)\n",
    "combined_ax.grid(True, linestyle=\"--\", linewidth=0.5, alpha=0.1)\n",
    "combined_ax.legend(fontsize=8, loc=\"lower left\", frameon=False)\n",
    "\n",
    "\n",
    "# Right plot: zoomed-in blue-line plot\n",
    "df_name, col_name = df_list[0]\n",
    "df = globals()[df_name]\n",
    "sorted_load = df[col_name].sort_values(ascending=False).reset_index(drop=True)\n",
    "\n",
    "zoom_ax.plot(sorted_load, label=plot_titles[0], color=\"blue\", linewidth=1)\n",
    "\n",
    "zoom_ax.set_title(\" \", fontsize=11)\n",
    "zoom_ax.set_xlabel(\"Time Interval\", fontsize=9)\n",
    "zoom_ax.set_ylabel(\"Load [kW]\", fontsize=9)\n",
    "\n",
    "zoom_ax.set_xlim(-400, 6500)\n",
    "zoom_ax.set_ylim(600, 1400)\n",
    "\n",
    "zoom_ax.yaxis.set_major_formatter(space_formatter)\n",
    "zoom_ax.tick_params(axis=\"both\", labelsize=8)\n",
    "zoom_ax.grid(True, linestyle=\"--\", linewidth=0.5, alpha=0.1)\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "710625b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ======================================================================\n",
    "# Figure 2\n",
    "# ======================================================================\n",
    "\n",
    "# No Code needed for that figure. Was created in Power Point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55e11d3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ======================================================================\n",
    "# Figure 3\n",
    "# ======================================================================\n",
    "\n",
    "# See provided R-File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f1054181",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q95 EV20 PV25.5: 1147.210\n",
      "Q95 EV40 PV41.5: 1442.445\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\AK903859\\AppData\\Local\\Temp\\ipykernel_7560\\716800852.py:245: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  plt.tight_layout(rect=[0, 0.105, 1, 1])\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 6000x2400 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ======================================================================\n",
    "# Figure 4\n",
    "# ======================================================================\n",
    "\n",
    "grid_size = (365, 96)\n",
    "total_len = grid_size[0] * grid_size[1]\n",
    "\n",
    "FONT_SIZE = 14\n",
    "TITLE_SIZE = 18\n",
    "\n",
    "\n",
    "positive_ystar_EV20_PV25_5 = df_EV20_PV25_5_last.loc[\n",
    "    df_EV20_PV25_5_last[\"Adjusted_constumption_to_model\"] > 0,\n",
    "    \"Adjusted_constumption_to_model\"\n",
    "]\n",
    "\n",
    "positive_ystar_EV40_PV41_5 = df_EV40_PV41_5_last.loc[\n",
    "    df_EV40_PV41_5_last[\"Adjusted_constumption_to_model\"] > 0,\n",
    "    \"Adjusted_constumption_to_model\"\n",
    "]\n",
    "\n",
    "q95_EV20_PV25_5 = np.percentile(positive_ystar_EV20_PV25_5, 95)\n",
    "q95_EV40_PV41_5 = np.percentile(positive_ystar_EV40_PV41_5, 95)\n",
    "\n",
    "print(f\"Q95 EV20 PV25.5: {q95_EV20_PV25_5:.3f}\")\n",
    "print(f\"Q95 EV40 PV41.5: {q95_EV40_PV41_5:.3f}\")\n",
    "\n",
    "\n",
    "# Grids\n",
    "y_20_25_grid = (df_EV20_PV25_5_last[\"Adjusted_constumption_to_model\"].iloc[:total_len].to_numpy().reshape(grid_size).T)\n",
    "y_40_41_grid = (df_EV40_PV41_5_last[\"Adjusted_constumption_to_model\"].iloc[:total_len].to_numpy().reshape(grid_size).T)\n",
    "\n",
    "\n",
    "# Scenario-specific magnitudes\n",
    "red_vals_20_25 = np.where(y_20_25_grid < 0, np.maximum(0, -y_20_25_grid), np.nan)\n",
    "blue_vals_20_25 = np.where(y_20_25_grid > q95_EV20_PV25_5, y_20_25_grid - q95_EV20_PV25_5, np.nan)\n",
    "red_vals_40_41 = np.where(y_40_41_grid < 0, np.maximum(0, -y_40_41_grid), np.nan)\n",
    "blue_vals_40_41 = np.where(y_40_41_grid > q95_EV40_PV41_5, y_40_41_grid - q95_EV40_PV41_5, np.nan)\n",
    "\n",
    "\n",
    "# Regular demand masks\n",
    "regular_20_25 = np.ma.array(np.zeros_like(y_20_25_grid), mask=(~np.isnan(red_vals_20_25)) | (~np.isnan(blue_vals_20_25)))\n",
    "regular_40_41 = np.ma.array(np.zeros_like(y_40_41_grid), mask=(~np.isnan(red_vals_40_41)) | (~np.isnan(blue_vals_40_41)))\n",
    "\n",
    "\n",
    "# Colormaps\n",
    "cmap_regular = ListedColormap([\"lightblue\"])\n",
    "cmap_red = LinearSegmentedColormap.from_list(\"red_gradient\", [\"#ffd6d6\", \"#ff3333\"])\n",
    "cmap_blue = LinearSegmentedColormap.from_list(\"blue_gradient\", [\"#66acfd\", \"#161c22\"])\n",
    "\n",
    "\n",
    "# Separate scaling per panel\n",
    "red_vmax_20_25 = (\n",
    "    np.nanmax(red_vals_20_25)\n",
    "    if np.isfinite(red_vals_20_25).any()\n",
    "    else 1\n",
    ")\n",
    "\n",
    "blue_vmax_20_25 = (\n",
    "    np.nanmax(blue_vals_20_25)\n",
    "    if np.isfinite(blue_vals_20_25).any()\n",
    "    else 1\n",
    ")\n",
    "\n",
    "red_vmax_40_41 = (\n",
    "    np.nanmax(red_vals_40_41)\n",
    "    if np.isfinite(red_vals_40_41).any()\n",
    "    else 1\n",
    ")\n",
    "\n",
    "blue_vmax_40_41 = (\n",
    "    np.nanmax(blue_vals_40_41)\n",
    "    if np.isfinite(blue_vals_40_41).any()\n",
    "    else 1\n",
    ")\n",
    "\n",
    "\n",
    "# Axis labels\n",
    "date_range = pd.date_range(\"2017-10-01\", periods=365, freq=\"D\")\n",
    "month_starts = pd.date_range(start=\"2017-10-01\", end=\"2018-09-30\", freq=\"MS\")\n",
    "\n",
    "month_labels = [d.strftime(\"%b\") for d in month_starts]\n",
    "\n",
    "month_positions = [\n",
    "    (d - date_range[0]).days\n",
    "    for d in month_starts\n",
    "]\n",
    "\n",
    "time_labels = [\n",
    "    f\"{i // 4:02}:{(i % 4) * 15:02}\"\n",
    "    for i in range(96)\n",
    "]\n",
    "\n",
    "\n",
    "negative_formatter = FuncFormatter(lambda x, pos: f\"-{x:g}\" if x != 0 else \"0\")\n",
    "\n",
    "# Plot\n",
    "fig, axes = plt.subplots(1, 2, figsize=(20, 8), dpi=300, sharey=True)\n",
    "\n",
    "titles = [\n",
    "    \"Exceedances of $Q_{95}$ and PV oversupply $B_{0}$ - EV 20%, PV 25.5%\",\n",
    "    \"Exceedances of $Q_{95}$ and PV oversupply $B_{0}$ - EV 40%, PV 41.5%\"\n",
    "]\n",
    "\n",
    "plot_specs = [\n",
    "    {\n",
    "        \"ax\": axes[0],\n",
    "        \"regular\": regular_20_25,\n",
    "        \"red_vals\": red_vals_20_25,\n",
    "        \"blue_vals\": blue_vals_20_25,\n",
    "        \"red_vmax\": red_vmax_20_25,\n",
    "        \"blue_vmax\": blue_vmax_20_25,\n",
    "        \"title\": titles[0],\n",
    "        \"q95\": q95_EV20_PV25_5,\n",
    "    },\n",
    "    {\n",
    "        \"ax\": axes[1],\n",
    "        \"regular\": regular_40_41,\n",
    "        \"red_vals\": red_vals_40_41,\n",
    "        \"blue_vals\": blue_vals_40_41,\n",
    "        \"red_vmax\": red_vmax_40_41,\n",
    "        \"blue_vmax\": blue_vmax_40_41,\n",
    "        \"title\": titles[1],\n",
    "        \"q95\": q95_EV40_PV41_5,\n",
    "    }\n",
    "]\n",
    "\n",
    "red_overlays = []\n",
    "blue_overlays = []\n",
    "\n",
    "for spec in plot_specs:\n",
    "    ax = spec[\"ax\"]\n",
    "\n",
    "    ax.pcolormesh(\n",
    "        spec[\"regular\"],\n",
    "        cmap=cmap_regular,\n",
    "        shading=\"auto\",\n",
    "        edgecolors=\"white\",\n",
    "        linewidth=0.3\n",
    "    )\n",
    "\n",
    "    red_overlay = ax.pcolormesh(\n",
    "        spec[\"red_vals\"],\n",
    "        cmap=cmap_red,\n",
    "        shading=\"auto\",\n",
    "        edgecolors=\"none\",\n",
    "        vmin=0,\n",
    "        vmax=spec[\"red_vmax\"]\n",
    "    )\n",
    "\n",
    "    blue_overlay = ax.pcolormesh(\n",
    "        spec[\"blue_vals\"],\n",
    "        cmap=cmap_blue,\n",
    "        shading=\"auto\",\n",
    "        edgecolors=\"none\",\n",
    "        vmin=0,\n",
    "        vmax=spec[\"blue_vmax\"]\n",
    "    )\n",
    "\n",
    "    red_overlays.append(red_overlay)\n",
    "    blue_overlays.append(blue_overlay)\n",
    "\n",
    "    ax.set_title(spec[\"title\"], fontsize=TITLE_SIZE, pad=15)\n",
    "\n",
    "    ax.set_yticks(np.arange(0, 96, 12))\n",
    "    ax.set_yticklabels(time_labels[::12], fontsize=FONT_SIZE)\n",
    "    ax.tick_params(axis=\"y\", labelleft=True, labelsize=FONT_SIZE)\n",
    "\n",
    "    ax.set_xticks(month_positions)\n",
    "    ax.set_xticklabels(month_labels, fontsize=FONT_SIZE)\n",
    "    ax.tick_params(axis=\"x\", which=\"major\", pad=6, labelsize=FONT_SIZE)\n",
    "\n",
    "    ax.grid(axis=\"y\", linestyle=\"--\", alpha=0.4)\n",
    "\n",
    "\n",
    "\n",
    "legend_left = [Patch(facecolor=\"lightblue\", edgecolor=\"white\", label=\"Regular demand\"),]\n",
    "\n",
    "fig.legend(\n",
    "    handles=legend_left,\n",
    "    loc=\"center left\",\n",
    "    bbox_to_anchor=(0.065, 0.073),\n",
    "    fontsize=FONT_SIZE,\n",
    "    frameon=False,\n",
    "    handlelength=2.2,\n",
    "    handleheight=1.2,\n",
    "    handletextpad=0.5,\n",
    "    borderaxespad=0.0\n",
    ")\n",
    "\n",
    "cbar_ax_red_left = fig.add_axes([0.235, 0.067, 0.11, 0.014])\n",
    "cb_red_left = fig.colorbar(\n",
    "    red_overlays[0],\n",
    "    cax=cbar_ax_red_left,\n",
    "    orientation=\"horizontal\"\n",
    ")\n",
    "cb_red_left.ax.xaxis.set_major_formatter(negative_formatter)\n",
    "cb_red_left.ax.set_xlabel(\"Negative magnitude [kW]\", fontsize=FONT_SIZE, labelpad=2)\n",
    "cb_red_left.ax.tick_params(labelsize=FONT_SIZE, pad=4)\n",
    "\n",
    "cbar_ax_blue_left = fig.add_axes([0.365, 0.067, 0.11, 0.014])\n",
    "cb_blue_left = fig.colorbar(\n",
    "    blue_overlays[0],\n",
    "    cax=cbar_ax_blue_left,\n",
    "    orientation=\"horizontal\"\n",
    ")\n",
    "cb_blue_left.ax.set_xlabel(\"Positive magnitude [kW]\", fontsize=FONT_SIZE, labelpad=2)\n",
    "cb_blue_left.ax.tick_params(labelsize=FONT_SIZE, pad=4)\n",
    "\n",
    "\n",
    "legend_right = [Patch(facecolor=\"lightblue\", edgecolor=\"white\", label=\"Regular demand\"),]\n",
    "\n",
    "fig.legend(\n",
    "    handles=legend_right,\n",
    "    loc=\"center left\",\n",
    "    bbox_to_anchor=(0.545, 0.073),\n",
    "    fontsize=FONT_SIZE,\n",
    "    frameon=False,\n",
    "    handlelength=2.2,\n",
    "    handleheight=1.2,\n",
    "    handletextpad=0.5,\n",
    "    borderaxespad=0.0\n",
    ")\n",
    "\n",
    "cbar_ax_red_right = fig.add_axes([0.715, 0.067, 0.11, 0.014])\n",
    "cb_red_right = fig.colorbar(\n",
    "    red_overlays[1],\n",
    "    cax=cbar_ax_red_right,\n",
    "    orientation=\"horizontal\"\n",
    ")\n",
    "cb_red_right.ax.xaxis.set_major_formatter(negative_formatter)\n",
    "cb_red_right.ax.set_xlabel(\"Negative magnitude [kW]\", fontsize=FONT_SIZE, labelpad=2)\n",
    "cb_red_right.ax.tick_params(labelsize=FONT_SIZE, pad=4)\n",
    "\n",
    "cbar_ax_blue_right = fig.add_axes([0.845, 0.067, 0.11, 0.014])\n",
    "cb_blue_right = fig.colorbar(\n",
    "    blue_overlays[1],\n",
    "    cax=cbar_ax_blue_right,\n",
    "    orientation=\"horizontal\"\n",
    ")\n",
    "cb_blue_right.ax.set_xlabel(\"Positive magnitude [kW]\", fontsize=FONT_SIZE, labelpad=2)\n",
    "cb_blue_right.ax.tick_params(labelsize=FONT_SIZE, pad=4)\n",
    "\n",
    "\n",
    "plt.tight_layout(rect=[0, 0.105, 1, 1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "865c6398",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q95 threshold: 1147.210\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\AK903859\\AppData\\Local\\Temp\\ipykernel_7560\\4033559836.py:330: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  plt.tight_layout(rect=[0, 0.105, 1, 1])\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 6000x2400 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ======================================================================\n",
    "# Figure 5 & Figure Appendix B.7\n",
    "# ======================================================================\n",
    "\n",
    "\n",
    "threshold_quantile = 95   # change to 85 for Figure B.7\n",
    "\n",
    "grid_size = (365, 96)\n",
    "total_len = grid_size[0] * grid_size[1]\n",
    "\n",
    "FONT_SIZE = 14\n",
    "TITLE_SIZE = 18\n",
    "\n",
    "full_time = pd.date_range(start=\"2017-10-01 00:00:00\", end=\"2018-09-30 23:45:00\", freq=\"15min\")\n",
    "\n",
    "\n",
    "# RIGHT PANEL DATA:\n",
    "df_actual = df_EV20_PV25_5_last.copy()\n",
    "\n",
    "positive_ystar = df_actual.loc[\n",
    "    df_actual[\"Adjusted_constumption_to_model\"] > 0,\n",
    "    \"Adjusted_constumption_to_model\"\n",
    "]\n",
    "\n",
    "q_threshold = np.percentile(positive_ystar, threshold_quantile)\n",
    "\n",
    "print(f\"Q{threshold_quantile} threshold: {q_threshold:.3f}\")\n",
    "\n",
    "actual_y = (df_actual[\"Adjusted_constumption_to_model\"].iloc[:total_len].to_numpy())\n",
    "\n",
    "actual_below0 = actual_y < 0\n",
    "actual_above_q = actual_y > q_threshold\n",
    "\n",
    "combined_actual = np.where(\n",
    "    actual_below0,\n",
    "    -1,\n",
    "    np.where(actual_above_q, 1, 0)\n",
    ").reshape(grid_size).T\n",
    "\n",
    "red_vals_actual = np.where(\n",
    "    combined_actual == -1,\n",
    "    np.maximum(0, -actual_y.reshape(grid_size).T),\n",
    "    np.nan\n",
    ")\n",
    "\n",
    "blue_vals_actual = np.where(\n",
    "    combined_actual == 1,\n",
    "    np.maximum(\n",
    "        0,\n",
    "        actual_y.reshape(grid_size).T - q_threshold\n",
    "    ),\n",
    "    np.nan\n",
    ")\n",
    "\n",
    "\n",
    "# LEFT PANEL DATA:\n",
    "df_missed = res_EV20_PV25_5.copy()\n",
    "\n",
    "df_missed[\"clock_local\"] = pd.to_datetime(df_missed[\"clock_local\"])\n",
    "df_missed = (df_missed.sort_values(\"clock_local\").reset_index(drop=True))\n",
    "\n",
    "df_missed = (\n",
    "    pd.DataFrame({\"clock_local\": full_time})\n",
    "    .merge(df_missed, on=\"clock_local\", how=\"left\")\n",
    ")\n",
    "\n",
    "df_missed[\"missed_below0\"] = (\n",
    "    (df_missed[\"ytrue_cont\"] < 0) &\n",
    "    (df_missed[\"ypred_cont\"] >= 0)\n",
    ")\n",
    "\n",
    "df_missed[\"missed_above_q\"] = (\n",
    "    (df_missed[\"ytrue_cont\"] > q_threshold) &\n",
    "    (df_missed[\"ypred_cont\"] <= q_threshold)\n",
    ")\n",
    "\n",
    "missed_ytrue = (df_missed[\"ytrue_cont\"].iloc[:total_len].to_numpy())\n",
    "missed_ypred = (df_missed[\"ypred_cont\"].iloc[:total_len].to_numpy())\n",
    "\n",
    "missing_mask = (pd.isna(missed_ytrue) | pd.isna(missed_ypred))\n",
    "missed_below0 = (df_missed[\"missed_below0\"].iloc[:total_len].to_numpy())\n",
    "missed_above_q = (df_missed[\"missed_above_q\"].iloc[:total_len].to_numpy())\n",
    "\n",
    "combined_missed = np.where(\n",
    "    missing_mask,\n",
    "    -3,\n",
    "    np.where(\n",
    "        missed_below0,\n",
    "        -1,\n",
    "        np.where(missed_above_q, 1, 0)\n",
    "    )\n",
    ").reshape(grid_size).T\n",
    "\n",
    "red_vals_missed = np.where(\n",
    "    combined_missed == -1,\n",
    "    np.maximum(\n",
    "        0,\n",
    "        -missed_ytrue.reshape(grid_size).T\n",
    "    ),\n",
    "    np.nan\n",
    ")\n",
    "\n",
    "blue_vals_missed = np.where(\n",
    "    combined_missed == 1,\n",
    "    np.maximum(\n",
    "        0,\n",
    "        missed_ytrue.reshape(grid_size).T - q_threshold\n",
    "    ),\n",
    "    np.nan\n",
    ")\n",
    "\n",
    "\n",
    "# COLORMAPS\n",
    "cmap_base_actual = ListedColormap([\"red\", \"lightblue\", \"white\"])\n",
    "cmap_base_missed = ListedColormap([\"grey\", \"red\", \"lightblue\", \"white\"])\n",
    "\n",
    "boundaries_actual = [-1.5, -0.5, 0.5, 1.5]\n",
    "boundaries_missed = [-3.5, -2, -0.5, 0.5, 1.5]\n",
    "\n",
    "norm_actual = BoundaryNorm(boundaries_actual, cmap_base_actual.N)\n",
    "norm_missed = BoundaryNorm(boundaries_missed, cmap_base_missed.N)\n",
    "\n",
    "cmap_red = LinearSegmentedColormap.from_list(\"red_gradient\", [\"#ffd6d6\", \"#ff3333\"])\n",
    "cmap_blue = LinearSegmentedColormap.from_list(\"blue_gradient\", [\"#66acfd\", \"#161c22\"])\n",
    "\n",
    "red_vmax = np.nanpercentile(\n",
    "    np.concatenate([\n",
    "        red_vals_actual[np.isfinite(red_vals_actual)],\n",
    "        red_vals_missed[np.isfinite(red_vals_missed)]\n",
    "    ]),\n",
    "    99\n",
    ")\n",
    "\n",
    "blue_vmax = np.nanpercentile(\n",
    "    np.concatenate([\n",
    "        blue_vals_actual[np.isfinite(blue_vals_actual)],\n",
    "        blue_vals_missed[np.isfinite(blue_vals_missed)]\n",
    "    ]),\n",
    "    99\n",
    ")\n",
    "\n",
    "\n",
    "# AXIS LABELS\n",
    "date_range = pd.date_range(\"2017-10-01\", periods=365, freq=\"D\")\n",
    "month_starts = pd.date_range(start=\"2017-10-01\", end=\"2018-09-30\", freq=\"MS\")\n",
    "\n",
    "month_labels = [\n",
    "    d.strftime(\"%b\")\n",
    "    for d in month_starts\n",
    "]\n",
    "\n",
    "month_positions = [\n",
    "    (d - date_range[0]).days\n",
    "    for d in month_starts\n",
    "]\n",
    "\n",
    "time_labels = [\n",
    "    f\"{i // 4:02}:{(i % 4) * 15:02}\"\n",
    "    for i in range(96)\n",
    "]\n",
    "\n",
    "negative_formatter = FuncFormatter(lambda x, pos: f\"-{x:g}\" if x != 0 else \"0\")\n",
    "\n",
    "\n",
    "# FIGURE\n",
    "fig, axes = plt.subplots(1, 2, figsize=(20, 8), dpi=300, sharey=True)\n",
    "\n",
    "titles = [\n",
    "    f\"Missed exceedances of $Q_{{{threshold_quantile}}}$ and PV oversupply $B_0$ - EV 20%, PV 25.5%\",\n",
    "    f\"All exceedances of $Q_{{{threshold_quantile}}}$ and PV oversupply $B_0$ - EV 20%, PV 25.5%\"\n",
    "]\n",
    "\n",
    "plot_specs = [\n",
    "    {\n",
    "        \"ax\": axes[0],\n",
    "        \"combined\": combined_missed,\n",
    "        \"red_vals\": red_vals_missed,\n",
    "        \"blue_vals\": blue_vals_missed,\n",
    "        \"title\": titles[0],\n",
    "        \"cmap\": cmap_base_missed,\n",
    "        \"norm\": norm_missed\n",
    "    },\n",
    "    {\n",
    "        \"ax\": axes[1],\n",
    "        \"combined\": combined_actual,\n",
    "        \"red_vals\": red_vals_actual,\n",
    "        \"blue_vals\": blue_vals_actual,\n",
    "        \"title\": titles[1],\n",
    "        \"cmap\": cmap_base_actual,\n",
    "        \"norm\": norm_actual\n",
    "    }\n",
    "]\n",
    "\n",
    "red_overlays = []\n",
    "blue_overlays = []\n",
    "\n",
    "for spec in plot_specs:\n",
    "\n",
    "    ax = spec[\"ax\"]\n",
    "\n",
    "    combined = spec[\"combined\"]\n",
    "\n",
    "    red_vals = spec[\"red_vals\"]\n",
    "\n",
    "    blue_vals = spec[\"blue_vals\"]\n",
    "\n",
    "    mask_overlay = (\n",
    "        (~np.isnan(red_vals)) |\n",
    "        (~np.isnan(blue_vals))\n",
    "    )\n",
    "\n",
    "    base = np.ma.array(\n",
    "        combined,\n",
    "        mask=mask_overlay\n",
    "    )\n",
    "\n",
    "    ax.pcolormesh(\n",
    "        base,\n",
    "        cmap=spec[\"cmap\"],\n",
    "        norm=spec[\"norm\"],\n",
    "        shading=\"auto\",\n",
    "        edgecolors=\"white\",\n",
    "        linewidth=0.3\n",
    "    )\n",
    "\n",
    "    red_overlay = ax.pcolormesh(\n",
    "        red_vals,\n",
    "        cmap=cmap_red,\n",
    "        shading=\"auto\",\n",
    "        edgecolors=\"none\",\n",
    "        vmin=0,\n",
    "        vmax=red_vmax\n",
    "    )\n",
    "\n",
    "    blue_overlay = ax.pcolormesh(\n",
    "        blue_vals,\n",
    "        cmap=cmap_blue,\n",
    "        shading=\"auto\",\n",
    "        edgecolors=\"none\",\n",
    "        vmin=0,\n",
    "        vmax=blue_vmax\n",
    "    )\n",
    "\n",
    "    red_overlays.append(red_overlay)\n",
    "\n",
    "    blue_overlays.append(blue_overlay)\n",
    "\n",
    "    ax.set_title(spec[\"title\"], fontsize=TITLE_SIZE, pad=15)\n",
    "    ax.set_yticks(np.arange(0, 96, 12))\n",
    "    ax.set_yticklabels(time_labels[::12], fontsize=FONT_SIZE)\n",
    "    ax.tick_params(axis=\"y\", labelleft=True, labelsize=FONT_SIZE)\n",
    "    ax.set_xticks(month_positions)\n",
    "    ax.set_xticklabels(month_labels, fontsize=FONT_SIZE)\n",
    "    ax.tick_params(axis=\"x\", which=\"major\", pad=6, labelsize=FONT_SIZE)\n",
    "    ax.grid(axis=\"y\", linestyle=\"--\", alpha=0.4)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "legend_left = [Patch(facecolor=\"lightblue\", edgecolor=\"white\", label=\"Regular demand\"),]\n",
    "\n",
    "fig.legend(\n",
    "    handles=legend_left,\n",
    "    loc=\"center left\",\n",
    "    bbox_to_anchor=(0.065, 0.073),\n",
    "    fontsize=FONT_SIZE,\n",
    "    frameon=False,\n",
    "    handlelength=2.2,\n",
    "    handleheight=1.2,\n",
    "    handletextpad=0.5,\n",
    "    borderaxespad=0.0\n",
    ")\n",
    "\n",
    "cbar_ax_red_left = fig.add_axes([0.235, 0.067, 0.11, 0.014])\n",
    "cb_red_left = fig.colorbar(\n",
    "    red_overlays[0],\n",
    "    cax=cbar_ax_red_left,\n",
    "    orientation=\"horizontal\"\n",
    ")\n",
    "cb_red_left.ax.xaxis.set_major_formatter(negative_formatter)\n",
    "cb_red_left.ax.set_xlabel(\"Negative magnitude [kW]\", fontsize=FONT_SIZE, labelpad=2)\n",
    "cb_red_left.ax.tick_params(labelsize=FONT_SIZE, pad=4)\n",
    "\n",
    "cbar_ax_blue_left = fig.add_axes([0.365, 0.067, 0.11, 0.014])\n",
    "cb_blue_left = fig.colorbar(\n",
    "    blue_overlays[0],\n",
    "    cax=cbar_ax_blue_left,\n",
    "    orientation=\"horizontal\"\n",
    ")\n",
    "cb_blue_left.ax.set_xlabel(\"Positive magnitude [kW]\", fontsize=FONT_SIZE, labelpad=2)\n",
    "cb_blue_left.ax.tick_params(labelsize=FONT_SIZE, pad=4)\n",
    "\n",
    "\n",
    "legend_right = [\n",
    "    Patch(facecolor=\"lightblue\", edgecolor=\"white\", label=\"Regular demand\"),\n",
    "]\n",
    "\n",
    "fig.legend(\n",
    "    handles=legend_right,\n",
    "    loc=\"center left\",\n",
    "    bbox_to_anchor=(0.545, 0.073),\n",
    "    fontsize=FONT_SIZE,\n",
    "    frameon=False,\n",
    "    handlelength=2.2,\n",
    "    handleheight=1.2,\n",
    "    handletextpad=0.5,\n",
    "    borderaxespad=0.0\n",
    ")\n",
    "\n",
    "cbar_ax_red_right = fig.add_axes([0.715, 0.067, 0.11, 0.014])\n",
    "cb_red_right = fig.colorbar(\n",
    "    red_overlays[1],\n",
    "    cax=cbar_ax_red_right,\n",
    "    orientation=\"horizontal\"\n",
    ")\n",
    "cb_red_right.ax.xaxis.set_major_formatter(negative_formatter)\n",
    "cb_red_right.ax.set_xlabel(\"Negative magnitude [kW]\", fontsize=FONT_SIZE, labelpad=2)\n",
    "cb_red_right.ax.tick_params(labelsize=FONT_SIZE, pad=4)\n",
    "\n",
    "cbar_ax_blue_right = fig.add_axes([0.845, 0.067, 0.11, 0.014])\n",
    "cb_blue_right = fig.colorbar(\n",
    "    blue_overlays[1],\n",
    "    cax=cbar_ax_blue_right,\n",
    "    orientation=\"horizontal\"\n",
    ")\n",
    "cb_blue_right.ax.set_xlabel(\"Positive magnitude [kW]\", fontsize=FONT_SIZE, labelpad=2)\n",
    "cb_blue_right.ax.tick_params(labelsize=FONT_SIZE, pad=4)\n",
    "\n",
    "\n",
    "plt.tight_layout(rect=[0, 0.105, 1, 1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0e6911cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\AK903859\\AppData\\Local\\Temp\\ipykernel_7560\\471617687.py:361: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ============================================================\n",
    "# Figure 6\n",
    "# ============================================================\n",
    "\n",
    "start = 1\n",
    "\n",
    "datasets = {\n",
    "    \"EV20%  PV25.5%\": res_EV20_PV25_5.iloc[start:].copy(),\n",
    "    \"EV40%  PV41.5%\": res_EV40_PV_41_5.iloc[start:].copy(),\n",
    "    \"EV75%  PV75%\": res_EV75_PV_75.iloc[start:].copy(),\n",
    "}\n",
    "\n",
    "quantile_levels = [85, 95]\n",
    "\n",
    "\n",
    "# METRIC FUNCTIONS\n",
    "def get_positive_threshold(df, q):\n",
    "    positive = df.loc[df[\"ytrue_cont\"] > 0, \"ytrue_cont\"]\n",
    "    return np.percentile(positive, q)\n",
    "\n",
    "\n",
    "def max_load(df):\n",
    "    return df[\"ytrue_cont\"].max()\n",
    "\n",
    "\n",
    "def min_load(df):\n",
    "    return df[\"ytrue_cont\"].min()\n",
    "\n",
    "\n",
    "def nrc_pos(df, q_threshold):\n",
    "    positive_shift = np.maximum(df[\"ypred_cont\"] - q_threshold, 0)\n",
    "    post_intervention_load = (df[\"ytrue_cont\"] - positive_shift)\n",
    "    return max(post_intervention_load.max(), q_threshold)\n",
    "\n",
    "\n",
    "def nrc_neg(df):\n",
    "    negative_shift = np.minimum(df[\"ypred_cont\"], 0)\n",
    "    post_intervention_load = (df[\"ytrue_cont\"] - negative_shift)\n",
    "    return min(post_intervention_load.min(), 0.0)\n",
    "\n",
    "\n",
    "def fpr_over(df, q_threshold):\n",
    "    return ((df[\"ytrue_cont\"] <= q_threshold) & (df[\"ypred_cont\"] > q_threshold)).mean()\n",
    "\n",
    "\n",
    "def fpr_under(df):\n",
    "    return ((df[\"ytrue_cont\"] >= 0) & (df[\"ypred_cont\"] < 0)).mean()\n",
    "\n",
    "\n",
    "def build_df(datasets, q):\n",
    "    rows = []\n",
    "\n",
    "    for name, df in datasets.items():\n",
    "        df = df.dropna(subset=[\"ytrue_cont\", \"ypred_cont\"]).copy()\n",
    "\n",
    "        q_threshold = get_positive_threshold(df, q)\n",
    "\n",
    "        rows.append({\n",
    "            \"Case\": name,\n",
    "            \"Q\": q,\n",
    "            \"Q_threshold\": q_threshold,\n",
    "            \"MAX\": max_load(df),\n",
    "            \"MIN\": min_load(df),\n",
    "            \"NRC_pos\": nrc_pos(df, q_threshold),\n",
    "            \"NRC_neg\": nrc_neg(df),\n",
    "            \"FPR_over\": fpr_over(df, q_threshold),\n",
    "            \"FPR_under\": fpr_under(df),\n",
    "        })\n",
    "\n",
    "    return pd.DataFrame(rows)\n",
    "\n",
    "\n",
    "df_q85 = build_df(datasets, 85)\n",
    "df_q95 = build_df(datasets, 95)\n",
    "\n",
    "\n",
    "n_cases = len(df_q95)\n",
    "group_spacing = 2.8\n",
    "bar_w = 0.65\n",
    "offset = 0.6\n",
    "sub_w = 0.4\n",
    "\n",
    "x_base = np.arange(n_cases) * group_spacing\n",
    "\n",
    "fig, (ax_load, ax_fpr) = plt.subplots(2, 1, figsize=(10, 9),\n",
    "    gridspec_kw={\"height_ratios\": [4, 1], \"hspace\": 0.25},\n",
    "    sharex=True\n",
    ")\n",
    "\n",
    "\n",
    "# LOAD PANEL\n",
    "def plot_bar(ax, x_pos, df, color_gray=\"#B3B3B3\", alpha=0.35, label_suffix=\"\"):\n",
    "    for i in range(len(df)):\n",
    "        left = x_pos[i] - bar_w / 2\n",
    "        right = x_pos[i] + bar_w / 2\n",
    "\n",
    "        max_i = df.loc[i, \"MAX\"]\n",
    "        min_i = df.loc[i, \"MIN\"]\n",
    "        nrc_pos_i = df.loc[i, \"NRC_pos\"]\n",
    "        nrc_neg_i = df.loc[i, \"NRC_neg\"]\n",
    "\n",
    "        ax.bar(x_pos[i], max_i - min_i, bottom=min_i, width=bar_w, color=color_gray, alpha=alpha,\n",
    "            label=f\"Post-intervention capacity req. {label_suffix}\" if i == 0 else None\n",
    "        )\n",
    "\n",
    "        ax.fill_between([left, right], nrc_pos_i, max_i, color=\"red\", alpha=0.15, hatch=\"/\", edgecolor=\"red\")\n",
    "        ax.fill_between([left, right], min_i, nrc_neg_i, color=\"red\", alpha=0.15, hatch=\"/\", edgecolor=\"red\")\n",
    "\n",
    "        x_left_bracket = left - 0.05\n",
    "\n",
    "        ax.plot([x_left_bracket, x_left_bracket], [min_i, max_i], color=\"red\", linewidth=1.2, alpha=0.35)\n",
    "        ax.plot([x_left_bracket, left], [max_i, max_i], color=\"red\", linewidth=1.2, alpha=0.35)\n",
    "        ax.plot([x_left_bracket, left], [min_i, min_i], color=\"red\", linewidth=1.2, alpha=0.35)\n",
    "\n",
    "        ax.text(x_left_bracket - 0.1, (min_i + max_i) / 2, \"Pre-interv.\",\n",
    "            rotation=90, va=\"center\", ha=\"center\", fontsize=9, color=\"black\"\n",
    "        )\n",
    "\n",
    "        x_right_bracket = right + 0.05\n",
    "\n",
    "        ax.plot(\n",
    "            [x_right_bracket, x_right_bracket],\n",
    "            [nrc_neg_i, nrc_pos_i],\n",
    "            color=color_gray, linewidth=1.2\n",
    "        )\n",
    "\n",
    "        ax.plot(\n",
    "            [right, x_right_bracket],\n",
    "            [nrc_pos_i, nrc_pos_i],\n",
    "            color=color_gray, linewidth=1.2\n",
    "        )\n",
    "\n",
    "        ax.plot(\n",
    "            [right, x_right_bracket],\n",
    "            [nrc_neg_i, nrc_neg_i],\n",
    "            color=color_gray, linewidth=1.2\n",
    "        )\n",
    "\n",
    "        ax.text(\n",
    "            x_right_bracket + 0.1,\n",
    "            (nrc_neg_i + nrc_pos_i) / 2,\n",
    "            \"Post-interv.\",\n",
    "            rotation=90,\n",
    "            va=\"center\",\n",
    "            ha=\"center\",\n",
    "            fontsize=9,\n",
    "            color=\"black\"\n",
    "        )\n",
    "\n",
    "        pos_range = max_i\n",
    "        extra_over = max_i - nrc_pos_i\n",
    "        pct_over = (extra_over / pos_range) * 100 if pos_range != 0 else 0\n",
    "\n",
    "        neg_range = 0 - min_i\n",
    "        extra_under = nrc_neg_i - min_i\n",
    "        pct_under = (extra_under / neg_range) * 100 if neg_range != 0 else 0\n",
    "\n",
    "        ax.text(\n",
    "            x_pos[i],\n",
    "            (max_i + nrc_pos_i) / 2,\n",
    "            f\"-{int(extra_over)} kW (-{pct_over:.1f}%)\",\n",
    "            ha=\"center\",\n",
    "            va=\"center\",\n",
    "            fontsize=9,\n",
    "            color=\"black\"\n",
    "        )\n",
    "\n",
    "        ax.text(\n",
    "            x_pos[i],\n",
    "            (min_i + nrc_neg_i) / 2,\n",
    "            f\"-{int(extra_under)} kW (-{pct_under:.1f}%)\",\n",
    "            ha=\"center\",\n",
    "            va=\"center\",\n",
    "            fontsize=9,\n",
    "            color=\"black\"\n",
    "        )\n",
    "\n",
    "    ax.scatter(x_pos, df[\"NRC_pos\"], color=\"black\", s=40, marker=\"^\")\n",
    "    ax.scatter(x_pos, df[\"NRC_neg\"], color=\"black\", s=40, marker=\"v\")\n",
    "\n",
    "\n",
    "plot_bar(ax_load, x_base - offset, df_q85, color_gray=\"#B3B3B3\", alpha=0.35, label_suffix=\"Q85\")\n",
    "plot_bar(ax_load, x_base + offset, df_q95, color_gray=\"#B3B3B3\", alpha=0.35, label_suffix=\"Q95\")\n",
    "\n",
    "ax_load.axhline(0, color=\"black\", linewidth=0.5, linestyle=\"--\", dashes=(10, 8), alpha=0.5)\n",
    "\n",
    "y_top = max(df_q95[\"MAX\"].max(), df_q85[\"MAX\"].max())\n",
    "y_bottom = min(df_q95[\"MIN\"].min(), df_q85[\"MIN\"].min())\n",
    "\n",
    "ax_load.set_ylabel(\"Load [kW]\")\n",
    "ax_load.set_ylim(y_bottom - 400, y_top + 900)\n",
    "ax_load.spines[\"top\"].set_visible(False)\n",
    "ax_load.grid(False)\n",
    "\n",
    "\n",
    "for i, case in enumerate(df_q95[\"Case\"]):\n",
    "    ax_load.text(\n",
    "        x_base[i],\n",
    "        y_top + 650,\n",
    "        case,\n",
    "        ha=\"center\",\n",
    "        va=\"bottom\",\n",
    "        fontsize=10,\n",
    "        color=\"black\",\n",
    "        fontweight=\"bold\"\n",
    "    )\n",
    "\n",
    "    ax_load.text(\n",
    "        x_base[i] - offset,\n",
    "        df_q85.loc[i, \"MAX\"] + 150,\n",
    "        r\"$Q_{85}$ thres.\",\n",
    "        ha=\"center\",\n",
    "        fontsize=9,\n",
    "        va=\"bottom\"\n",
    "    )\n",
    "\n",
    "    ax_load.text(\n",
    "        x_base[i] + offset,\n",
    "        df_q95.loc[i, \"MAX\"] + 150,\n",
    "        r\"$Q_{95}$ thres.\",\n",
    "        ha=\"center\",\n",
    "        fontsize=9,\n",
    "        va=\"bottom\"\n",
    "    )\n",
    "\n",
    "\n",
    "for i in range(len(x_base) - 1):\n",
    "    mid_x = (x_base[i] + x_base[i + 1]) / 2\n",
    "    ax_load.axvline(\n",
    "        x=mid_x,\n",
    "        color=\"black\",\n",
    "        linestyle=\"-\",\n",
    "        alpha=0.5,\n",
    "        linewidth=1\n",
    "    )\n",
    "\n",
    "\n",
    "# FPR PANEL\n",
    "c_over = \"#0072B2\"\n",
    "c_under = \"#B3E5FF\"\n",
    "\n",
    "for i in range(n_cases):\n",
    "    x_q85_ov = x_base[i] - offset - sub_w / 2\n",
    "    x_q85_un = x_base[i] - offset + sub_w / 2\n",
    "\n",
    "    ax_fpr.bar(\n",
    "        x_q85_ov,\n",
    "        df_q85.loc[i, \"FPR_over\"] * 100,\n",
    "        width=sub_w,\n",
    "        color=c_over,\n",
    "        edgecolor=\"black\",\n",
    "        lw=0.6\n",
    "    )\n",
    "\n",
    "    ax_fpr.bar(\n",
    "        x_q85_un,\n",
    "        df_q85.loc[i, \"FPR_under\"] * 100,\n",
    "        width=sub_w,\n",
    "        color=c_under,\n",
    "        edgecolor=\"black\",\n",
    "        lw=0.6\n",
    "    )\n",
    "\n",
    "    x_q95_ov = x_base[i] + offset - sub_w / 2\n",
    "    x_q95_un = x_base[i] + offset + sub_w / 2\n",
    "\n",
    "    ax_fpr.bar(\n",
    "        x_q95_ov,\n",
    "        df_q95.loc[i, \"FPR_over\"] * 100,\n",
    "        width=sub_w,\n",
    "        color=c_over,\n",
    "        edgecolor=\"black\",\n",
    "        lw=0.6\n",
    "    )\n",
    "\n",
    "    ax_fpr.bar(\n",
    "        x_q95_un,\n",
    "        df_q95.loc[i, \"FPR_under\"] * 100,\n",
    "        width=sub_w,\n",
    "        color=c_under,\n",
    "        edgecolor=\"black\",\n",
    "        lw=0.6\n",
    "    )\n",
    "\n",
    "    h85 = max(\n",
    "        df_q85.loc[i, \"FPR_over\"],\n",
    "        df_q85.loc[i, \"FPR_under\"]\n",
    "    ) * 100\n",
    "\n",
    "    h95 = max(\n",
    "        df_q95.loc[i, \"FPR_over\"],\n",
    "        df_q95.loc[i, \"FPR_under\"]\n",
    "    ) * 100\n",
    "\n",
    "    ax_fpr.text(x_base[i] - offset, h85 + 7, r\"$Q_{85}$ thres.\", ha=\"center\", fontsize=9)\n",
    "    ax_fpr.text(x_base[i] + offset, h95 + 7, r\"$Q_{95}$ thres.\", ha=\"center\", fontsize=9)\n",
    "\n",
    "    bar_data = [\n",
    "        (x_q85_ov, df_q85.loc[i, \"FPR_over\"] * 100),\n",
    "        (x_q85_un, df_q85.loc[i, \"FPR_under\"] * 100),\n",
    "        (x_q95_ov, df_q95.loc[i, \"FPR_over\"] * 100),\n",
    "        (x_q95_un, df_q95.loc[i, \"FPR_under\"] * 100),\n",
    "    ]\n",
    "\n",
    "    for x_pos, val in bar_data:\n",
    "        ax_fpr.text(x_pos, val + 2, f\"{val:.1f}%\", ha=\"center\", va=\"bottom\", fontsize=9)\n",
    "\n",
    "\n",
    "for i in range(len(x_base) - 1):\n",
    "    mid_x = (x_base[i] + x_base[i + 1]) / 2\n",
    "    ax_fpr.axvline(x=mid_x, color=\"black\", linestyle=\"-\", alpha=0.5, linewidth=1)\n",
    "\n",
    "fpr_top = max(\n",
    "    df_q85[[\"FPR_over\", \"FPR_under\"]].max().max(),\n",
    "    df_q95[[\"FPR_over\", \"FPR_under\"]].max().max()\n",
    ") * 100\n",
    "\n",
    "ax_fpr.set_ylim(0, max(30, fpr_top + 8))\n",
    "ax_fpr.set_ylabel(\"False positive freq. [%]\")\n",
    "ax_fpr.grid(False)\n",
    "ax_fpr.spines[\"top\"].set_visible(False)\n",
    "ax_fpr.spines[\"right\"].set_visible(False)\n",
    "\n",
    "ax_fpr.set_xticks(x_base)\n",
    "ax_fpr.set_xticklabels(\n",
    "    df_q95[\"Case\"],\n",
    "    fontsize=10,\n",
    "    fontweight=\"bold\"\n",
    ")\n",
    "\n",
    "\n",
    "# LEGENDS\n",
    "ax_load.legend(\n",
    "    handles=[\n",
    "        Patch(color=\"lightgray\", alpha=.35, label=\"Post-intervention capacity req.\"),\n",
    "        Patch(facecolor=\"red\", edgecolor=\"red\", hatch=\"///\", alpha=.4,\n",
    "              label=\"Reduction to Pre-intervention capacity req.\"),\n",
    "        Line2D([0], [0], marker=\"^\", color=\"black\", ls=\"None\", ms=8,\n",
    "               label=\"Post-intervention capacity req. upper end\"),\n",
    "        Line2D([0], [0], marker=\"v\", color=\"black\", ls=\"None\", ms=8,\n",
    "               label=\"Post-intervention capacity req. lower end\"),\n",
    "    ],\n",
    "    loc=\"lower left\", frameon=False, fontsize=9\n",
    ")\n",
    "\n",
    "fpr_legend_elements = [\n",
    "    Patch(facecolor=c_over, edgecolor=\"black\", label=\"False positive freq. above upper threshold\"),\n",
    "    Patch(facecolor=c_under,edgecolor=\"black\", label=\"False positive freq. below zero\"),\n",
    "]\n",
    "\n",
    "ax_fpr.legend(handles=fpr_legend_elements, loc=\"upper left\", bbox_to_anchor=(0, 1.3), frameon=False, fontsize=9, ncol=1)\n",
    "\n",
    "\n",
    "plt.suptitle(\n",
    "    \"Pre- and post intervention capacity requirement alongside false positive frequency\",\n",
    "    y=0.99,\n",
    "    fontsize=12,\n",
    "    color=\"black\",\n",
    ")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
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