diff --git a/0_basic_chart.ipynb b/0_basic_chart.ipynb new file mode 100644 index 0000000..fea4e24 --- /dev/null +++ b/0_basic_chart.ipynb @@ -0,0 +1,101 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "from matplotlib.patches import FancyBboxPatch\n", + "\n", + "# Data\n", + "categories = [\"Revenue\", \"Profit\", \"Customer Growth\"]\n", + "values = [75, 50, 90] # Actual values\n", + "targets = [90, 70, 85] # Target values\n", + "ranges = [(0, 30, 70, 100), (0, 40, 60, 100), (0, 50, 80, 100)] # Performance thresholds\n", + "\n", + "# Color scheme\n", + "green = '#8DC58D'\n", + "yellow = '#FBBC78'\n", + "red = '#F77F7F'\n", + "gray = '#DDDDDD'\n", + "light_blue = '#D9E8F5'\n", + "\n", + "# Colors for different ranges\n", + "colors = [green, yellow, red, gray, light_blue]\n", + "\n", + "# Create figure\n", + "fig, ax = plt.subplots(figsize=(8, 5))\n", + "\n", + "for i, (category, value, target, (low, mid, high, max_val)) in enumerate(zip(categories, values, targets, ranges)):\n", + " y = len(categories) - i # Reverse order for better alignment\n", + " \n", + " # Background performance ranges\n", + " ax.add_patch(FancyBboxPatch((low, y - 0.3), mid - low, 0.6, boxstyle=\"round,pad=0.1\", facecolor=colors[0])) # Low\n", + " ax.add_patch(FancyBboxPatch((mid, y - 0.3), high - mid, 0.6, boxstyle=\"round,pad=0.1\", facecolor=colors[1])) # Mid\n", + " ax.add_patch(FancyBboxPatch((high, y - 0.3), max_val - high, 0.6, boxstyle=\"round,pad=0.1\", facecolor=colors[2])) # High\n", + " \n", + " # Actual performance bar\n", + " ax.add_patch(FancyBboxPatch((low, y - 0.1), value - low, 0.2, boxstyle=\"round,pad=0.1\", facecolor=\"blue\"))\n", + " \n", + " # Target marker (a vertical line)\n", + " ax.plot([target, target], [y - 0.25, y + 0.25], color=\"red\", linewidth=3, linestyle=\"-\", label=\"Target\" if i == 0 else \"\")\n", + "\n", + " # Labels\n", + " ax.text(max_val + 5, y, f\"{value}%\", va=\"center\", fontsize=10, weight=\"bold\")\n", + "\n", + "# Format plot\n", + "ax.set_xlim(0, 110)\n", + "ax.set_yticks(range(1, len(categories) + 1))\n", + "ax.set_yticklabels(categories)\n", + "ax.set_xlabel(\"Performance (%)\")\n", + "ax.set_title(\"Bullet Chart Example\")\n", + "ax.legend()\n", + "\n", + "plt.grid(axis=\"x\", linestyle=\"--\", alpha=0.5)\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "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 +} diff --git a/1_bp_biometrik.ipynb b/1_bp_biometrik.ipynb new file mode 100644 index 0000000..7910053 --- /dev/null +++ b/1_bp_biometrik.ipynb @@ -0,0 +1,133 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "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 +} diff --git a/1_bp_biometrik.png b/1_bp_biometrik.png new file mode 100644 index 0000000..4421f9d Binary files /dev/null and b/1_bp_biometrik.png differ diff --git a/1_bp_biometrik.py b/1_bp_biometrik.py new file mode 100644 index 0000000..148f5f4 --- /dev/null +++ b/1_bp_biometrik.py @@ -0,0 +1,85 @@ +import matplotlib.pyplot as plt +import numpy as np +from matplotlib.patches import FancyBboxPatch, Rectangle +from matplotlib.colors import LinearSegmentedColormap + +# Color scheme +green = '#8DC58D' +yellow = '#FBBC78' +red = '#F77F7F' +gray = '#DDDDDD' +light_blue = '#D9E8F5' +light_gray = '#F5F5F5' + +# Data +categories = ["Resiko Hipertensi dalam 4 tahun (Hypertension Risk in 4 years)", + "Lingkar Pinggang (Waist Circumference)", "Tekanan Darah Sistolik (Systolic BP)", + "Tekanan Darah Diastolik (Diastolic BP)", "Indeks Massa Tubuh (BMI)"] +values = [51.17, 93.0, 132.0, 88.0, 31.11] +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])] +colors = [[green, yellow, red], # Hypertension + [green, red], # Waist + [yellow, green, yellow, red], # Systolic BP + [yellow, green, yellow, red], # Diastolic BP + [yellow, green, yellow, red, red]] # BMI + +fig, axes = plt.subplots(len(categories), 1, figsize=(12, 7)) + +for i, ax in enumerate(axes): + min_val, max_val, thresholds = ranges[i] + value = values[i] + + # First create a background bar (gray) with rounded corners + bg_rect = FancyBboxPatch((min_val, 0), max_val - min_val, 0.1, + boxstyle="round,pad=0.01", + facecolor=gray, alpha=0.3, linewidth=0) + ax.add_patch(bg_rect) + + # Create segments + y_gap = 0.01 # Define the gap between segments on the y-axis + ax.set_ylim(-y_gap, 0.1 + y_gap) # Adjust the y-axis limits to include the gap + all_points = [min_val] + thresholds + [max_val] + for j in range(len(all_points)-1): + start = all_points[j] + end = all_points[j+1] + + if i == 0: # Apply gradient only for the first chart's first segment + # Define the gradient colormap + gradient_cmap = LinearSegmentedColormap.from_list("gradient", [gray, colors[i][j]], N=256) + + # Create a gradient rectangle usingimshow + gradient_width = value - start + if gradient_width > 0: + gradient_rect = plt.Rectangle((start, y_gap), gradient_width, 0.1 - 2*y_gap, facecolor=gradient_cmap(0.9), linewidth=0) + ax.add_patch(gradient_rect) + + # Fill the remaining part with gray + remaining_width = end - max(value, start) + if remaining_width > 0: + remaining_rect = plt.Rectangle((max(value, start), y_gap), remaining_width, 0.1 - 2*y_gap, facecolor=gray, linewidth=0) + ax.add_patch(remaining_rect) + else: + segment_width = end - start + rect = Rectangle((start, y_gap), segment_width, 0.1 - 2*y_gap, + facecolor=colors[i][j % len(colors[i])], linewidth=0) + ax.add_patch(rect) + + # Plot value marker + ax.scatter([value], [0.06], color="white", marker="o", s=100, zorder=3, edgecolor='gray') + ax.text(value, 0.02, f"{value:.1f}", ha="center", fontsize=10, color="black", weight="bold", + fontfamily='monospace') + + # Axis formatting - properly align ticks with segment boundaries + ax.set_xlim(min_val, max_val) + ax.set_yticks([]) + ax.set_xticks(all_points) # Set ticks at exact boundary points + ax.set_title(categories[i], fontsize=12, weight="normal", fontfamily='sans-serif') + ax.spines['top'].set_visible(False) + ax.spines['right'].set_visible(False) + ax.spines['left'].set_visible(False) + +filename = '1_bp_biometrik.png' + +plt.tight_layout() +plt.savefig(filename, dpi=300, bbox_inches='tight') +print(f"Image saved as {filename}") \ No newline at end of file diff --git a/1_chart_bp_biometrik.png b/1_chart_bp_biometrik.png new file mode 100644 index 0000000..a00a1f4 Binary files /dev/null and b/1_chart_bp_biometrik.png differ diff --git a/1_chart_bp_biometrik_v2.png b/1_chart_bp_biometrik_v2.png new file mode 100644 index 0000000..7691ebc Binary files /dev/null and b/1_chart_bp_biometrik_v2.png differ diff --git a/2_urinalis.ipynb b/2_urinalis.ipynb new file mode 100644 index 0000000..e488fcc --- /dev/null +++ b/2_urinalis.ipynb @@ -0,0 +1,104 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "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", + "SEGMENT_HEIGHT = 0.1\n", + "Y_MARKER = 0.6 * SEGMENT_HEIGHT\n", + "Y_LABEL = 0.2 * SEGMENT_HEIGHT\n", + "\n", + "# Data\n", + "categories = [\"pH\", \n", + " \"Berat Jenis Urin (Urine Specific Gravity)\"]\n", + "values = [8.0, 1.010]\n", + "ranges = [(0, 14, [4.8, 7.4]), (1.005, 1.030, [1.015, 1.025])]\n", + "colors = [[yellow, green, red], # pH\n", + " [yellow, green, red]] # Urin]\n", + "\n", + "fig, axes = plt.subplots(len(categories), 1, figsize=(12, 2.6))\n", + "\n", + "for i, ax in enumerate(axes):\n", + " min_val, max_val, thresholds = ranges[i]\n", + " value = values[i]\n", + " \n", + " # Create segments\n", + " # y_gap = 0.01 # {{ Removed y_gap definition }}\n", + " ax.set_ylim(0, 0.1) # {{ Updated y-axis limits to remove y_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", + " segment_width = end - start\n", + "\n", + " rect = Rectangle((start, 0), segment_width, SEGMENT_HEIGHT, # {{ Updated y-coordinate and height to remove 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], [Y_MARKER], color=\"white\", marker=\"o\", s=100, zorder=3, edgecolor='gray')\n", + " ax.text(value, Y_LABEL, 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=\"bold\", fontfamily='monospace')\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()" + ] + } + ], + "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 +} diff --git a/2_urinalis.png b/2_urinalis.png new file mode 100644 index 0000000..52e1dbd Binary files /dev/null and b/2_urinalis.png differ diff --git a/2_urinalis.py b/2_urinalis.py new file mode 100644 index 0000000..ef64b70 --- /dev/null +++ b/2_urinalis.py @@ -0,0 +1,63 @@ +import matplotlib.pyplot as plt +import numpy as np +from matplotlib.patches import FancyBboxPatch, Rectangle +from matplotlib.colors import LinearSegmentedColormap + +# Color scheme +green = '#8DC58D' +yellow = '#FBBC78' +red = '#F77F7F' +gray = '#DDDDDD' +light_blue = '#D9E8F5' +light_gray = '#F5F5F5' + +SEGMENT_HEIGHT = 0.1 +Y_MARKER = 0.6 * SEGMENT_HEIGHT +Y_LABEL = 0.2 * SEGMENT_HEIGHT + +# Data +categories = ["pH", + "Berat Jenis Urin (Urine Specific Gravity)"] +values = [8.0, 1.010] +ranges = [(0, 14, [4.8, 7.4]), (1.005, 1.030, [1.015, 1.025])] +colors = [[yellow, green, red], # pH + [yellow, green, red]] # Urin] + +fig, axes = plt.subplots(len(categories), 1, figsize=(12, 2.6)) + +for i, ax in enumerate(axes): + min_val, max_val, thresholds = ranges[i] + value = values[i] + + # Create segments + # y_gap = 0.01 # {{ Removed y_gap definition }} + ax.set_ylim(0, 0.1) # {{ Updated y-axis limits to remove y_gap }} + all_points = [min_val] + thresholds + [max_val] + for j in range(len(all_points)-1): + start = all_points[j] + end = all_points[j+1] + segment_width = end - start + + rect = Rectangle((start, 0), segment_width, SEGMENT_HEIGHT, # {{ Updated y-coordinate and height to remove y_gap }} + facecolor=colors[i][j % len(colors[i])], linewidth=0) + ax.add_patch(rect) + + # Plot value marker + ax.scatter([value], [Y_MARKER], color="white", marker="o", s=100, zorder=3, edgecolor='gray') + ax.text(value, Y_LABEL, f"{value:.1f}", ha="center", fontsize=10, color="black", weight="bold", + fontfamily='monospace') + + # Axis formatting - properly align ticks with segment boundaries + ax.set_xlim(min_val, max_val) + ax.set_yticks([]) + ax.set_xticks(all_points) # Set ticks at exact boundary points + ax.set_title(categories[i], fontsize=12, weight="bold", fontfamily='monospace') + ax.spines['top'].set_visible(False) + ax.spines['right'].set_visible(False) + ax.spines['left'].set_visible(False) + +filename = '2_urinalis.png' + +plt.tight_layout() +plt.savefig(filename, dpi=300, bbox_inches='tight') +print(f"Image saved as {filename}") \ No newline at end of file diff --git a/3_pie_suggested_lifestyle.ipynb b/3_pie_suggested_lifestyle.ipynb new file mode 100644 index 0000000..7ab05cb --- /dev/null +++ b/3_pie_suggested_lifestyle.ipynb @@ -0,0 +1,149 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import numpy as np\n", + "from matplotlib.patches import Rectangle\n", + "\n", + "# Data for the pie chart\n", + "labels = ['Tekanan Darah Sistolik', 'Berat Badan', 'Tekanan Darah Diastolik']\n", + "en_labels = ['Systolic Blood Pressure', 'Weight', 'Diastolic Blood Pressure']\n", + "values = [66.8, 18.9, 14.2]\n", + "colors = ['#F77F7F', '#FFB84C', '#5ECCC4'] # Red, Orange, Teal\n", + "\n", + "# Data for the table\n", + "current_readings = [132, 70, 88]\n", + "target_values = ['< 130', '41,6-51,5', '< 85']\n", + "units = ['mmHg', 'kg', 'mmHg']\n", + "\n", + "# Create figure with subplots\n", + "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 10), \n", + " gridspec_kw={'height_ratios': [1, 0.8]})\n", + "plt.subplots_adjust(hspace=0.3)\n", + "\n", + "# Pie Chart\n", + "wedges, texts, autotexts = ax1.pie(values, colors=colors, autopct='', \n", + " startangle=90, wedgeprops={'edgecolor': 'white', 'linewidth': 1})\n", + "\n", + "# Title\n", + "ax1.set_title('Kontributor Risiko Relatif\\nRelative Risk Contributors', fontsize=16, fontweight='bold', pad=20)\n", + "\n", + "# Add custom labels outside the pie\n", + "for i, p in enumerate(wedges):\n", + " ang = (p.theta2 - p.theta1)/2. + p.theta1\n", + " ang_rad = ang * np.pi / 180\n", + " x = 1.05 * np.cos(ang_rad)\n", + " y = 1.05 * np.sin(ang_rad)\n", + " \n", + " # Alignment based on angle\n", + " horizontalalignment = 'left' if x > 0 else 'right'\n", + " \n", + " # Create the label text with percentage and name\n", + " label = f\"{values[i]}% {labels[i]}\"\n", + " \n", + " # Add the label\n", + " ax1.annotate(label, xy=(x, y), xytext=(1*x, 1*y),\n", + " horizontalalignment=horizontalalignment, fontsize=12)\n", + "\n", + "# Equal aspect ratio ensures the pie chart is circular\n", + "ax1.set_aspect('equal')\n", + "ax1.axis('off')\n", + "\n", + "# Table\n", + "# Hide axes for the table subplot\n", + "ax2.axis('off')\n", + "\n", + "# Create table headers\n", + "col_labels = ['Kontributor Risiko\\nRisk Contributor', 'Bacaan saat ini\\nCurrent reading', \n", + " 'Nilai Target\\nTarget value', 'Satuan\\nUnits']\n", + "table_data = []\n", + "\n", + "# Prepare colored boxes for the legend\n", + "for i, (label, en_label, value, target, unit) in enumerate(zip(labels, en_labels, current_readings, target_values, units)):\n", + " color_box = f'\\n{label}\\n{en_label}'\n", + " table_data.append([color_box, value, target, unit])\n", + "\n", + "# Create table\n", + "table = ax2.table(cellText=table_data, colLabels=col_labels, loc='center',\n", + " cellLoc='center', colWidths=[0.4, 0.2, 0.2, 0.2])\n", + "\n", + "# Format table\n", + "table.auto_set_font_size(False)\n", + "table.set_fontsize(10)\n", + "table.scale(1, 2.15)\n", + "\n", + "# Add color boxes to the first column of each data row\n", + "for i in range(len(table_data)):\n", + " cell = table[i+1, 0] # +1 because header is row 0\n", + " cell.get_text().set_color('black')\n", + " cell.set_facecolor(colors[i])\n", + " cell.set_text_props(weight='bold')\n", + "\n", + "# Add horizontal lines\n", + "for i in range(len(table_data)+1):\n", + " for j in range(len(col_labels)):\n", + " table[i, j].set_edgecolor('black')\n", + "\n", + "# Add risk information at the bottom\n", + "risk_text = 'Risiko Hipertensi dalam 4 Tahun\\nHypertension Risk in 4 Years'\n", + "current_risk = '51,2 %'\n", + "target_risk = '8,3 %'\n", + "\n", + "ax2.text(0.00, 0.1, risk_text, transform=ax2.transAxes, \n", + " color='red', fontsize=12, fontweight='bold')\n", + "ax2.text(0.45, 0.15, current_risk, transform=ax2.transAxes, \n", + " color='red', fontsize=12, fontweight='bold')\n", + "ax2.text(0.68, 0.15, target_risk, transform=ax2.transAxes, \n", + " color='black', fontsize=12)\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 +} diff --git a/3_pie_suggested_lifestyle.png b/3_pie_suggested_lifestyle.png new file mode 100644 index 0000000..1042739 Binary files /dev/null and b/3_pie_suggested_lifestyle.png differ diff --git a/3_pie_suggested_lifestyle.py b/3_pie_suggested_lifestyle.py new file mode 100644 index 0000000..36cfa7d --- /dev/null +++ b/3_pie_suggested_lifestyle.py @@ -0,0 +1,100 @@ +import matplotlib.pyplot as plt +import pandas as pd +import numpy as np +from matplotlib.patches import Rectangle + +# Data for the pie chart +labels = ['Tekanan Darah Sistolik', 'Berat Badan', 'Tekanan Darah Diastolik'] +en_labels = ['Systolic Blood Pressure', 'Weight', 'Diastolic Blood Pressure'] +values = [66.8, 18.9, 14.2] +colors = ['#F77F7F', '#FFB84C', '#5ECCC4'] # Red, Orange, Teal + +# Data for the table +current_readings = [132, 70, 88] +target_values = ['< 130', '41,6-51,5', '< 85'] +units = ['mmHg', 'kg', 'mmHg'] + +# Create figure with subplots +fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 10), + gridspec_kw={'height_ratios': [1, 0.8]}) +plt.subplots_adjust(hspace=0.3) + +# Pie Chart +wedges, texts, autotexts = ax1.pie(values, colors=colors, autopct='', + startangle=90, wedgeprops={'edgecolor': 'white', 'linewidth': 1}) + +# Title +ax1.set_title('Kontributor Risiko Relatif\nRelative Risk Contributors', fontsize=16, fontweight='bold', pad=20) + +# Add custom labels outside the pie +for i, p in enumerate(wedges): + ang = (p.theta2 - p.theta1)/2. + p.theta1 + ang_rad = ang * np.pi / 180 + x = 1.05 * np.cos(ang_rad) + y = 1.05 * np.sin(ang_rad) + + # Alignment based on angle + horizontalalignment = 'left' if x > 0 else 'right' + + # Create the label text with percentage and name + label = f"{values[i]}% {labels[i]}" + + # Add the label + ax1.annotate(label, xy=(x, y), xytext=(1*x, 1*y), + horizontalalignment=horizontalalignment, fontsize=12) + +# Equal aspect ratio ensures the pie chart is circular +ax1.set_aspect('equal') +ax1.axis('off') + +# Table +# Hide axes for the table subplot +ax2.axis('off') + +# Create table headers +col_labels = ['Kontributor Risiko\nRisk Contributor', 'Bacaan saat ini\nCurrent reading', + 'Nilai Target\nTarget value', 'Satuan\nUnits'] +table_data = [] + +# Prepare colored boxes for the legend +for i, (label, en_label, value, target, unit) in enumerate(zip(labels, en_labels, current_readings, target_values, units)): + color_box = f'\n{label}\n{en_label}' + table_data.append([color_box, value, target, unit]) + +# Create table +table = ax2.table(cellText=table_data, colLabels=col_labels, loc='center', + cellLoc='center', colWidths=[0.4, 0.2, 0.2, 0.2]) + +# Format table +table.auto_set_font_size(False) +table.set_fontsize(10) +table.scale(1, 2.15) + +# Add color boxes to the first column of each data row +for i in range(len(table_data)): + cell = table[i+1, 0] # +1 because header is row 0 + cell.get_text().set_color('black') + cell.set_facecolor(colors[i]) + cell.set_text_props(weight='bold') + +# Add horizontal lines +for i in range(len(table_data)+1): + for j in range(len(col_labels)): + table[i, j].set_edgecolor('black') + +# Add risk information at the bottom +risk_text = 'Risiko Hipertensi dalam 4 Tahun\nHypertension Risk in 4 Years' +current_risk = '51,2 %' +target_risk = '8,3 %' + +ax2.text(0.00, 0.1, risk_text, transform=ax2.transAxes, + color='red', fontsize=12, fontweight='bold') +ax2.text(0.45, 0.15, current_risk, transform=ax2.transAxes, + color='red', fontsize=12, fontweight='bold') +ax2.text(0.68, 0.15, target_risk, transform=ax2.transAxes, + color='black', fontsize=12) + +filename = '3_pie_suggested_lifestyle.png' +plt.tight_layout() +plt.savefig(filename, dpi=300, bbox_inches='tight') +print(f"Image saved as {filename}") \ No newline at end of file