pyplot-health-analysis/1_bp_biometrik.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x700 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from matplotlib.patches import FancyBboxPatch, Rectangle\n",
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "\n",
    "# Color scheme\n",
    "green = '#8DC58D'\n",
    "yellow = '#FBBC78'\n",
    "red = '#F77F7F'\n",
    "gray = '#DDDDDD'\n",
    "light_blue = '#D9E8F5'\n",
    "light_gray = '#F5F5F5'\n",
    "\n",
    "# Data\n",
    "categories = [\"Resiko Hipertensi dalam 4 tahun (Hypertension Risk in 4 years)\", \n",
    "              \"Lingkar Pinggang (Waist Circumference)\", \"Tekanan Darah Sistolik (Systolic BP)\", \n",
    "              \"Tekanan Darah Diastolik (Diastolic BP)\", \"Indeks Massa Tubuh (BMI)\"]\n",
    "values = [51.17, 93.0, 132.0, 88.0, 31.11]\n",
    "ranges = [(0, 100, [5, 10]), (60, 120, [80]), (70, 180, [90, 130, 140]), (50, 120, [60, 85, 90]), (15, 40, [18.5, 23, 25, 30])]\n",
    "colors = [[green, yellow, red],  # Hypertension\n",
    "          [green, red],          # Waist\n",
    "          [yellow, green, yellow, red],   # Systolic BP\n",
    "          [yellow, green, yellow, red],   # Diastolic BP\n",
    "          [yellow, green, yellow, red, red]]   # BMI\n",
    "\n",
    "fig, axes = plt.subplots(len(categories), 1, figsize=(12, 7))\n",
    "\n",
    "for i, ax in enumerate(axes):\n",
    "    min_val, max_val, thresholds = ranges[i]\n",
    "    value = values[i]\n",
    "    \n",
    "    # First create a background bar (gray) with rounded corners\n",
    "    bg_rect = FancyBboxPatch((min_val, 0), max_val - min_val, 0.1,\n",
    "                          boxstyle=\"round,pad=0.01\", \n",
    "                          facecolor=gray, alpha=0.3, linewidth=0)\n",
    "    ax.add_patch(bg_rect)\n",
    "    \n",
    "    # Create segments\n",
    "    y_gap = 0.01  # Define the gap between segments on the y-axis\n",
    "    ax.set_ylim(-y_gap, 0.1 + y_gap)  # Adjust the y-axis limits to include the gap\n",
    "    all_points = [min_val] + thresholds + [max_val]\n",
    "    for j in range(len(all_points)-1):\n",
    "        start = all_points[j]\n",
    "        end = all_points[j+1]\n",
    "\n",
    "        if i == 0:  # Apply gradient only for the first chart's first segment\n",
    "            # Define the gradient colormap\n",
    "            gradient_cmap = LinearSegmentedColormap.from_list(\"gradient\", [gray, colors[i][j]], N=256)\n",
    "            \n",
    "            # Create a gradient rectangle usingimshow\n",
    "            gradient_width = value - start\n",
    "            if gradient_width > 0:\n",
    "                gradient_rect = plt.Rectangle((start, y_gap), gradient_width, 0.1 - 2*y_gap, facecolor=gradient_cmap(0.9), linewidth=0)\n",
    "                ax.add_patch(gradient_rect)\n",
    "            \n",
    "            # Fill the remaining part with gray\n",
    "            remaining_width = end - max(value, start)\n",
    "            if remaining_width > 0:\n",
    "                remaining_rect = plt.Rectangle((max(value, start), y_gap), remaining_width, 0.1 - 2*y_gap, facecolor=gray, linewidth=0)\n",
    "                ax.add_patch(remaining_rect)\n",
    "        else:\n",
    "            segment_width = end - start\n",
    "            rect = Rectangle((start, y_gap), segment_width, 0.1 - 2*y_gap,\n",
    "                            facecolor=colors[i][j % len(colors[i])], linewidth=0)\n",
    "            ax.add_patch(rect)\n",
    "    \n",
    "    # Plot value marker\n",
    "    ax.scatter([value], [0.06], color=\"white\", marker=\"o\", s=100, zorder=3, edgecolor='gray')\n",
    "    ax.text(value, 0.02, f\"{value:.1f}\", ha=\"center\", fontsize=10, color=\"black\", weight=\"bold\", \n",
    "            fontfamily='monospace')\n",
    "    \n",
    "    # Axis formatting - properly align ticks with segment boundaries\n",
    "    ax.set_xlim(min_val, max_val)\n",
    "    ax.set_yticks([])\n",
    "    ax.set_xticks(all_points)  # Set ticks at exact boundary points\n",
    "    ax.set_title(categories[i], fontsize=12, weight=\"normal\", fontfamily='sans-serif')\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['left'].set_visible(False)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ai-inacbg",
   "language": "python",
   "name": "ai-inacbg"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}