diff --git a/main.py b/main.py index 0669d7f..956e02c 100644 --- a/main.py +++ b/main.py @@ -1,38 +1,54 @@ +import pdfkit from jinja2 import Environment, FileSystemLoader -from weasyprint import HTML -import matplotlib.pyplot as plt -import os -# 1. Generate a chart with matplotlib -os.makedirs("static/charts", exist_ok=True) -chart_path = "static/charts/resp_chart.png" +env = Environment(loader=FileSystemLoader("report_gen")) -plt.plot([1, 2, 3, 4], [1, 4, 2, 5], label="Breath Volume") -plt.legend() -plt.title("Respiratory Chart") -plt.savefig(chart_path) -plt.close() +# Define templates and their unique contexts +pages = [ + ("page_1.html", {"name": "John Doe", "surname": "Moran", "date": "July 29, 2025"}), + ("page_2.html", {"content": "This is page 2 content"}), +] -# 2. Patient data (this would usually come from your DB) -patient_data = { - "name": "Keirstyn Moran", - "age": 34, - "height": 163, - "weight": 56 +# Render each template with its own context +html_pages = [] +for tpl, ctx in pages: + template = env.get_template(tpl) + html_pages.append(template.render(ctx)) + +# Combine with page breaks +final_html = "
".join(html_pages) + +# Wrap in full HTML document +html_doc = f""" + + + + + + + + + {final_html} + + +""" + +print(html_doc) +# Generate PDF +options = { + "page-size": "A4", + "encoding": "UTF-8", + "margin-top": "0mm", + "margin-bottom": "0mm", + "margin-left": "0mm", + "margin-right": "0mm", + "no-outline": None, } +pdfkit.from_string(html_doc, "multi_page_report.pdf", options=options) -context = { - "patient": patient_data, - "indications": "No Respiratory Capacity Limitation", - "chart_path": chart_path -} - -# 3. Render Jinja2 template -env = Environment(loader=FileSystemLoader("templates")) -template = env.get_template("report.html") -html_out = template.render(context) - -# 4. Generate PDF -HTML(string=html_out, base_url=".").write_pdf("lung_report.pdf") - -print("✅ Report generated: lung_report.pdf") \ No newline at end of file +print("✅ PDF generated: multi_page_report.pdf") diff --git a/multi_page_report.pdf b/multi_page_report.pdf new file mode 100644 index 0000000..b66ad50 Binary files /dev/null and b/multi_page_report.pdf differ diff --git a/notebook.ipynb b/notebook.ipynb index 7127967..afa9f57 100644 --- a/notebook.ipynb +++ b/notebook.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 10, + "execution_count": 1, "id": "63f43af5", "metadata": {}, "outputs": [], @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 2, "id": "b0ee2af1", "metadata": {}, "outputs": [ @@ -31,7 +31,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_71494/3054964805.py:3: FutureWarning: errors='ignore' is deprecated and will raise in a future version. Use to_numeric without passing `errors` and catch exceptions explicitly instead\n", + "/tmp/ipykernel_225163/3054964805.py:3: FutureWarning: errors='ignore' is deprecated and will raise in a future version. Use to_numeric without passing `errors` and catch exceptions explicitly instead\n", " df = df.apply(pd.to_numeric, errors='ignore')\n" ] } @@ -61,7 +61,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "id": "fbd292c3", "metadata": {}, "outputs": [ @@ -69,7 +69,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "15.634193703216638\n" + "22.369999999999997\n" ] } ], @@ -79,7 +79,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 4, "id": "ef8bc7ac", "metadata": {}, "outputs": [ @@ -138,7 +138,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 5, "id": "06244aa2", "metadata": {}, "outputs": [ @@ -252,7 +252,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 6, "id": "8a1878a0", "metadata": {}, "outputs": [ @@ -327,7 +327,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 7, "id": "7361fb05", "metadata": {}, "outputs": [ @@ -383,7 +383,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 8, "id": "c89478ff", "metadata": {}, "outputs": [ @@ -441,7 +441,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 22, "id": "1db16040", "metadata": {}, "outputs": [ @@ -509,6 +509,575 @@ "\n", "plt.show()" ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "52642f49", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
MeasurementDateCommentExternalDeviceIdExternalPatientIdFirstNameLastNameBirthDateAgeEthnicityGender...Child_XCChild_XC_UnitChild_BIVA_ZRhChild_BIVA_ZXcHChild_PhAChild_PhA_UnitChild_REE_KcalChild_REE_MJChild_TEE_KcalChild_TEE_MJ
02025-09-05T14:56:27.0000000ZNaN10000001583275_0055003f5631501320313557LD5163301170LucyDibenedetto1997-08-28T00:00:00.0000000Z28CaucasianFemale...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
12025-09-03T13:16:22.0000000ZNaN10000001583275_0055003f5631501320313557NS6479273340NiyantaShah1985-03-11T00:00:00.0000000Z40OtherFemale...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
22025-09-03T13:14:23.0000000ZNaN10000001583275_0055003f5631501320313557NaNNaNNaN1985-03-11T00:00:00.0000000Z40NaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
32025-08-27T20:57:32.0000000ZNaN10000001583275_0055003f5631501320313557NaNNaNNaN1996-04-05T00:00:00.0000000Z29NaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
42025-08-20T14:01:13.0000000ZNaN10000001583275_0055003f5631501320313557MW4167267833MonicaWong1985-02-17T00:00:00.0000000Z40AsianFemale...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
..................................................................
582025-04-10T14:10:28.0000000ZNaN10000001583275_0055003f56315013203135576473915195TristanWalsh1995-12-01T00:00:00.0000000Z29CaucasianFemale...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
592025-04-02T21:19:51.0000000ZNaN10000001583275_0055003f5631501320313557WSC4168020667ScottChristie1968-08-19T00:00:00.0000000Z56CaucasianMale...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
602025-04-02T15:23:54.0000000ZNaN10000001583275_0055003f56315013203135576478809838LaurenKaratanevski1984-07-07T00:00:00.0000000Z40CaucasianFemale...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
612025-03-26T15:47:42.0000000ZNaN10000001583275_0055003f5631501320313557NaNNaNNaN2002-07-10T00:00:00.0000000Z22NaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
622025-03-19T15:10:21.0000000ZNaN10000001583275_0055003f56315013203135576478809838LaurenKaratanevski1984-07-07T00:00:00.0000000Z40CaucasianFemale...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
\n", + "

63 rows × 147 columns

\n", + "
" + ], + "text/plain": [ + " MeasurementDate Comment \\\n", + "0 2025-09-05T14:56:27.0000000Z NaN \n", + "1 2025-09-03T13:16:22.0000000Z NaN \n", + "2 2025-09-03T13:14:23.0000000Z NaN \n", + "3 2025-08-27T20:57:32.0000000Z NaN \n", + "4 2025-08-20T14:01:13.0000000Z NaN \n", + ".. ... ... \n", + "58 2025-04-10T14:10:28.0000000Z NaN \n", + "59 2025-04-02T21:19:51.0000000Z NaN \n", + "60 2025-04-02T15:23:54.0000000Z NaN \n", + "61 2025-03-26T15:47:42.0000000Z NaN \n", + "62 2025-03-19T15:10:21.0000000Z NaN \n", + "\n", + " ExternalDeviceId ExternalPatientId FirstName \\\n", + "0 10000001583275_0055003f5631501320313557 LD5163301170 Lucy \n", + "1 10000001583275_0055003f5631501320313557 NS6479273340 Niyanta \n", + "2 10000001583275_0055003f5631501320313557 NaN NaN \n", + "3 10000001583275_0055003f5631501320313557 NaN NaN \n", + "4 10000001583275_0055003f5631501320313557 MW4167267833 Monica \n", + ".. ... ... ... \n", + "58 10000001583275_0055003f5631501320313557 6473915195 Tristan \n", + "59 10000001583275_0055003f5631501320313557 WSC4168020667 Scott \n", + "60 10000001583275_0055003f5631501320313557 6478809838 Lauren \n", + "61 10000001583275_0055003f5631501320313557 NaN NaN \n", + "62 10000001583275_0055003f5631501320313557 6478809838 Lauren \n", + "\n", + " LastName BirthDate Age Ethnicity Gender ... \\\n", + "0 Dibenedetto 1997-08-28T00:00:00.0000000Z 28 Caucasian Female ... \n", + "1 Shah 1985-03-11T00:00:00.0000000Z 40 Other Female ... \n", + "2 NaN 1985-03-11T00:00:00.0000000Z 40 NaN NaN ... \n", + "3 NaN 1996-04-05T00:00:00.0000000Z 29 NaN NaN ... \n", + "4 Wong 1985-02-17T00:00:00.0000000Z 40 Asian Female ... \n", + ".. ... ... ... ... ... ... \n", + "58 Walsh 1995-12-01T00:00:00.0000000Z 29 Caucasian Female ... \n", + "59 Christie 1968-08-19T00:00:00.0000000Z 56 Caucasian Male ... \n", + "60 Karatanevski 1984-07-07T00:00:00.0000000Z 40 Caucasian Female ... \n", + "61 NaN 2002-07-10T00:00:00.0000000Z 22 NaN NaN ... \n", + "62 Karatanevski 1984-07-07T00:00:00.0000000Z 40 Caucasian Female ... \n", + "\n", + " Child_XC Child_XC_Unit Child_BIVA_ZRh Child_BIVA_ZXcH Child_PhA \\\n", + "0 NaN NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN NaN \n", + ".. ... ... ... ... ... \n", + "58 NaN NaN NaN NaN NaN \n", + "59 NaN NaN NaN NaN NaN \n", + "60 NaN NaN NaN NaN NaN \n", + "61 NaN NaN NaN NaN NaN \n", + "62 NaN NaN NaN NaN NaN \n", + "\n", + " Child_PhA_Unit Child_REE_Kcal Child_REE_MJ Child_TEE_Kcal Child_TEE_MJ \n", + "0 NaN NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN NaN \n", + ".. ... ... ... ... ... \n", + "58 NaN NaN NaN NaN NaN \n", + "59 NaN NaN NaN NaN NaN \n", + "60 NaN NaN NaN NaN NaN \n", + "61 NaN NaN NaN NaN NaN \n", + "62 NaN NaN NaN NaN NaN \n", + "\n", + "[63 rows x 147 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_2 = pd.read_excel('data/SECA body comp for all patients.xlsx')\n", + "df_2" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "2056096d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "body_fat_chart = {\n", + " \"male\": {\n", + " \"20-39\": {\n", + " \"bad\": [(0, 5), (20, 50)],\n", + " \"okay\": [(5, 10), (15, 20)],\n", + " \"good\": [(10, 15)]\n", + " },\n", + " \"40-59\": {\n", + " \"bad\": [(0, 10), (25, 50)],\n", + " \"okay\": [(10, 15), (20, 25)],\n", + " \"good\": [(15, 20)]\n", + " },\n", + " \"60-79\": {\n", + " \"bad\": [(0, 15), (25, 50)],\n", + " \"okay\": [(15, 20), (20, 25)],\n", + " \"good\": []\n", + " }\n", + " },\n", + " \"female\": {\n", + " \"20-39\": {\n", + " \"bad\": [(0, 15), (40, 50)],\n", + " \"okay\": [(15, 20), (35, 40)],\n", + " \"good\": [(20, 35)]\n", + " },\n", + " \"40-59\": {\n", + " \"bad\": [(0, 20), (40, 50)],\n", + " \"okay\": [(20, 25), (35, 40)],\n", + " \"good\": [(25, 35)]\n", + " },\n", + " \"60-79\": {\n", + " \"bad\": [(0, 20), (40, 50)],\n", + " \"okay\": [(20, 25), (35, 40)],\n", + " \"good\": [(25, 35)]\n", + " }\n", + " }\n", + "}\n", + "def create_body_fat_visualization(gender, age, body_fat_percentage):\n", + " # Determine age group\n", + " if 20 <= age <= 39:\n", + " age_group = \"20-39\"\n", + " elif 40 <= age <= 59:\n", + " age_group = \"40-59\"\n", + " elif 60 <= age <= 79:\n", + " age_group = \"60-79\"\n", + " else:\n", + " return \"Age out of range (20-79)\"\n", + " \n", + " # Get ranges for the specific gender and age group\n", + " ranges = body_fat_chart[gender.lower()][age_group]\n", + " \n", + " # Create figure\n", + " fig, ax = plt.subplots(figsize=(12, 3))\n", + " \n", + " # Define colors for different categories\n", + " colors = {'bad': '#ff6b6b', 'okay': '#ffeb3b', 'good': '#4caf50'}\n", + " \n", + " # Create the horizontal segments\n", + " bar_height = 0.4\n", + " y_position = 0\n", + " \n", + " # Plot each category's ranges\n", + " for category, ranges_list in ranges.items():\n", + " for range_tuple in ranges_list:\n", + " start, end = range_tuple\n", + " width = end - start\n", + " ax.barh(y_position, width, left=start, height=bar_height, \n", + " color=colors[category], alpha=0.8, edgecolor='white', linewidth=1)\n", + " \n", + " # Add the user's body fat percentage marker (triangle pointing down)\n", + " ax.plot(body_fat_percentage, y_position, 'v', markersize=15, color='black', \n", + " markeredgecolor='white', markeredgewidth=2)\n", + " \n", + " # Customize the chart\n", + " ax.set_xlim(0, 50)\n", + " ax.set_ylim(-0.4, 0.4)\n", + " ax.set_xlabel('', fontsize=12)\n", + " ax.set_title(f'Body Fat Percent - {body_fat_percentage:.1f}%', fontsize=16, fontweight='bold', pad=20)\n", + " \n", + " # Add age group and gender label below the bar\n", + " ax.text(25, -0.25, f'{age_group}\\n({gender[0]})', ha='center', va='center', fontsize=12)\n", + " \n", + " # Remove y-axis and top/right spines\n", + " ax.set_yticks([])\n", + " ax.spines['left'].set_visible(False)\n", + " ax.spines['right'].set_visible(False)\n", + " ax.spines['top'].set_visible(False)\n", + " \n", + " # Set x-axis ticks every 5%\n", + " ax.set_xticks([0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50])\n", + " ax.set_xticklabels(['0%', '5%', '10%', '15%', '20%', '25%', '30%', '35%', '40%', '45%', '50%'])\n", + " \n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "# Create the chart using Keirstyn's data\n", + "gender = 'female'\n", + "age = 60\n", + "fat_percentage = 28.7\n", + "create_body_fat_visualization(gender, age, fat_percentage)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bf55717b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Filter df_2 for Keirstyn Moran\n", + "keirstyn_data = df_2[df_2['LastName'].str.contains('Moran', case=False, na=False)]\n", + "# Get the fat mass percentage for Keirstyn\n", + "fat_percentage = keirstyn_data['Adult_FMP'].iloc[0]\n", + "weight_kg = keirstyn_data['Weight_kg'].iloc[0]\n", + "age = keirstyn_data['Age'].iloc[0]\n", + "gender = keirstyn_data['Gender'].iloc[0]\n", + "lean_percentage = 100 - fat_percentage\n", + "\n", + "# Create donut chart\n", + "plt.figure(figsize=(8, 8))\n", + "sizes = [fat_percentage, lean_percentage]\n", + "labels = ['Fat Mass', 'Lean Body Mass']\n", + "colors = ['#ff9999', '#66b3ff']\n", + "\n", + "# Create the donut chart with a wedge\n", + "wedges, texts, autotexts = plt.pie(sizes, labels=labels, colors=colors, autopct='%1.1f%%',\n", + " startangle=90, wedgeprops=dict(width=0.5))\n", + "\n", + "plt.title(f'Body Composition - {keirstyn_data[\"FirstName\"].iloc[0]} {keirstyn_data[\"LastName\"].iloc[0]}', \n", + " fontsize=14, fontweight='bold')\n", + "plt.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "21c1c0a5", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/pdf_generation.ipynb b/pdf_generation.ipynb index 4bcc195..2749ec1 100644 --- a/pdf_generation.ipynb +++ b/pdf_generation.ipynb @@ -2,83 +2,855 @@ "cells": [ { "cell_type": "code", - "execution_count": 8, + "execution_count": 2, "id": "6eee3ddd", "metadata": {}, "outputs": [], "source": [ - "import camelot\n", "import pandas as pd" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 4, "id": "d95cd8b1", "metadata": {}, "outputs": [], "source": [ - "tables = camelot.read_pdf('data/KM6479696509 _ Keirstyn_Moran_SingleViewWithExplanation_2025-07-29.pdf')" + "df = pd.read_excel('data/SECA body comp for all patients.xlsx')" ] }, { "cell_type": "code", - "execution_count": 11, - "id": "759fd46e", - "metadata": {}, - "outputs": [], - "source": [ - "all_dfs = []\n", - "\n", - "# Append each table's dataframe to the list\n", - "for table in tables:\n", - " all_dfs.append(table.df)\n", - "\n", - "# Concatenate all dataframes into one\n", - "combined_df = pd.concat(all_dfs, ignore_index=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "e9c3b9f0", + "execution_count": 5, + "id": "6bbc907f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " 0 1 2 3 \\\n", - "0 ← FM →\\nLow Fat\\nHigh Fat\\nBody Composition Ch... \n", - "1 \n", - "2 \n", - "3 \n", - "4 \n", - "5 \n", - "6 \n", + "Data shape: (63, 147)\n", + "Columns: ['MeasurementDate', 'Comment', 'ExternalDeviceId', 'ExternalPatientId', 'FirstName', 'LastName', 'BirthDate', 'Age', 'Ethnicity', 'Gender', 'Height', 'Height_Unit', 'Weight', 'Weight_Unit', 'WaistCircumference', 'WaistCircumference_Unit', 'PAL', 'Adult_BMI', 'Adult_BMI_Unit', 'Adult_FM', 'Adult_FM_Unit', 'Adult_FMP', 'Adult_FMP_Unit', 'Adult_FMI', 'Adult_FMI_Unit', 'Adult_ZFMI', 'Adult_FFM', 'Adult_FFM_Unit', 'Adult_FFMP', 'Adult_FFMP_Unit', 'Adult_FFMI', 'Adult_FFMI_Unit', 'Adult_TBW', 'Adult_TBW_Unit', 'Adult_TBWP', 'Adult_TBWP_Unit', 'Adult_ECW', 'Adult_ECW_Unit', 'Adult_ECWP', 'Adult_ECWP_Unit', 'Adult_ECWbyTBW', 'Adult_ECWbyTBW_Unit', 'Adult_SMM', 'Adult_SMM_Unit', 'Adult_SMMBMIIndependant', 'Adult_SMMBMIIndependant_Unit', 'Adult_SMMP', 'Adult_SMMP_Unit', 'Adult_SMI', 'Adult_SMI_Unit', 'Adult_SMoA', 'Adult_SMoA_Unit', 'Adult_SMoABMIIndependant', 'Adult_SMoABMIIndependant_Unit', 'Adult_ZSMI', 'Adult_SSMMRightArm', 'Adult_SSMMRightArm_Unit', 'Adult_SSMMLeftArm', 'Adult_SSMMLeftArm_Unit', 'Adult_SSMMRightLeg', 'Adult_SSMMRightLeg_Unit', 'Adult_SSMMLeftLeg', 'Adult_SSMMLeftLeg_Unit', 'Adult_SSMMTorso', 'Adult_SSMMTorso_Unit', 'Adult_SSMMRightArmBMIIndependant', 'Adult_SSMMRightArmBMIIndependant_Unit', 'Adult_SSMMLeftArmBMIIndependant', 'Adult_SSMMLeftArmBMIIndependant_Unit', 'Adult_SSMMRightLegBMIIndependant', 'Adult_SSMMRightLegBMIIndependant_Unit', 'Adult_SSMMLeftLegBMIIndependant', 'Adult_SSMMLeftLegBMIIndependant_Unit', 'Adult_SSMMTorsoBMIIndependant', 'Adult_SSMMTorsoBMIIndependant_Unit', 'Adult_ASMM', 'Adult_ASMM_Unit', 'Adult_ASMI', 'Adult_ASMI_Unit', 'Adult_ASMP', 'Adult_ASMP_Unit', 'Adult_R', 'Adult_R_Unit', 'Adult_XC', 'Adult_XC_Unit', 'Adult_BIVA_ZRh', 'Adult_BIVA_ZXcH', 'Adult_PhA', 'Adult_PhA_Unit', 'Adult_VAT', 'Adult_VAT_Unit', 'Adult_TBS', 'Adult_TBS_Unit', 'Adult_TBS_MuscleScore', 'Adult_TBS_MuscleScore_Unit', 'Adult_TBS_FatScore', 'Adult_TBS_FatScore_Unit', 'Adult_REE_Kcal', 'Adult_REE_MJ', 'Adult_TEE_Kcal', 'Adult_TEE_MJ', 'Child_BMI', 'Child_BMI_Unit', 'Child_FM', 'Child_FM_Unit', 'Child_FMP', 'Child_FMP_Unit', 'Child_FMI', 'Child_FMI_Unit', 'Child_ZFMI', 'Child_FFM', 'Child_FFM_Unit', 'Child_FFMP', 'Child_FFMP_Unit', 'Child_FFMI', 'Child_FFMI_Unit', 'Child_ZFFMI', 'Child_TBW', 'Child_TBW_Unit', 'Child_TBWP', 'Child_TBWP_Unit', 'Child_SMMByKim', 'Child_SMMByKim_Unit', 'Child_SMMP', 'Child_SMMP_Unit', 'Child_SMI', 'Child_SMI_Unit', 'Child_LSTLeftArm', 'Child_LSTLeftArm_Unit', 'Child_LSTRightArm', 'Child_LSTRightArm_Unit', 'Child_LSTLeftLeg', 'Child_LSTLeftLeg_Unit', 'Child_LSTRightLeg', 'Child_LSTRightLeg_Unit', 'Child_R', 'Child_R_Unit', 'Child_XC', 'Child_XC_Unit', 'Child_BIVA_ZRh', 'Child_BIVA_ZXcH', 'Child_PhA', 'Child_PhA_Unit', 'Child_REE_Kcal', 'Child_REE_MJ', 'Child_TEE_Kcal', 'Child_TEE_MJ']\n", "\n", - " 4 5 \n", - "0 muscle mass in a coordinate system. A d... \n", - "1 \n", - "2 \n", - "3 \n", - "4 \n", - "5 \n", - "6 \n" + "First few rows:\n" ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
MeasurementDateCommentExternalDeviceIdExternalPatientIdFirstNameLastNameBirthDateAgeEthnicityGender...Child_XCChild_XC_UnitChild_BIVA_ZRhChild_BIVA_ZXcHChild_PhAChild_PhA_UnitChild_REE_KcalChild_REE_MJChild_TEE_KcalChild_TEE_MJ
02025-09-05T14:56:27.0000000ZNaN10000001583275_0055003f5631501320313557LD5163301170LucyDibenedetto1997-08-28T00:00:00.0000000Z28CaucasianFemale...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
12025-09-03T13:16:22.0000000ZNaN10000001583275_0055003f5631501320313557NS6479273340NiyantaShah1985-03-11T00:00:00.0000000Z40OtherFemale...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
22025-09-03T13:14:23.0000000ZNaN10000001583275_0055003f5631501320313557NaNNaNNaN1985-03-11T00:00:00.0000000Z40NaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
32025-08-27T20:57:32.0000000ZNaN10000001583275_0055003f5631501320313557NaNNaNNaN1996-04-05T00:00:00.0000000Z29NaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
42025-08-20T14:01:13.0000000ZNaN10000001583275_0055003f5631501320313557MW4167267833MonicaWong1985-02-17T00:00:00.0000000Z40AsianFemale...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
\n", + "

5 rows × 147 columns

\n", + "
" + ], + "text/plain": [ + " MeasurementDate Comment \\\n", + "0 2025-09-05T14:56:27.0000000Z NaN \n", + "1 2025-09-03T13:16:22.0000000Z NaN \n", + "2 2025-09-03T13:14:23.0000000Z NaN \n", + "3 2025-08-27T20:57:32.0000000Z NaN \n", + "4 2025-08-20T14:01:13.0000000Z NaN \n", + "\n", + " ExternalDeviceId ExternalPatientId FirstName \\\n", + "0 10000001583275_0055003f5631501320313557 LD5163301170 Lucy \n", + "1 10000001583275_0055003f5631501320313557 NS6479273340 Niyanta \n", + "2 10000001583275_0055003f5631501320313557 NaN NaN \n", + "3 10000001583275_0055003f5631501320313557 NaN NaN \n", + "4 10000001583275_0055003f5631501320313557 MW4167267833 Monica \n", + "\n", + " LastName BirthDate Age Ethnicity Gender ... \\\n", + "0 Dibenedetto 1997-08-28T00:00:00.0000000Z 28 Caucasian Female ... \n", + "1 Shah 1985-03-11T00:00:00.0000000Z 40 Other Female ... \n", + "2 NaN 1985-03-11T00:00:00.0000000Z 40 NaN NaN ... \n", + "3 NaN 1996-04-05T00:00:00.0000000Z 29 NaN NaN ... \n", + "4 Wong 1985-02-17T00:00:00.0000000Z 40 Asian Female ... \n", + "\n", + " Child_XC Child_XC_Unit Child_BIVA_ZRh Child_BIVA_ZXcH Child_PhA \\\n", + "0 NaN NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN NaN \n", + "\n", + " Child_PhA_Unit Child_REE_Kcal Child_REE_MJ Child_TEE_Kcal Child_TEE_MJ \n", + "0 NaN NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN NaN \n", + "\n", + "[5 rows x 147 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "print(combined_df)" + "print(\"Data shape:\", df.shape)\n", + "print(\"Columns:\", df.columns.tolist())\n", + "print(\"\\nFirst few rows:\")\n", + "df.head()" ] }, { "cell_type": "code", - "execution_count": null, - "id": "6bbc907f", + "execution_count": 6, + "id": "77d3b19a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking for body composition related columns:\n", + "Relevant columns: ['Adult_TBS_FatScore', 'Adult_TBS_FatScore_Unit']\n", + "\n", + "Looking for patient data:\n", + "Patient column names: ['ExternalPatientId', 'FirstName', 'LastName']\n" + ] + } + ], + "source": [ + "# Let's look for relevant columns for body composition\n", + "print(\"Looking for body composition related columns:\")\n", + "relevant_cols = [col for col in df.columns if any(word in col.lower() for word in ['fat', 'lean', 'mass', 'percent', 'composition'])]\n", + "print(\"Relevant columns:\", relevant_cols)\n", + "\n", + "# Let's also check if we have specific data for a patient like Keirstyn\n", + "print(\"\\nLooking for patient data:\")\n", + "if 'Name' in df.columns or 'Patient' in df.columns:\n", + " print(df[df.columns[df.columns.str.contains('name|patient', case=False, na=False)]].head())\n", + "else:\n", + " print(\"Patient column names:\", [col for col in df.columns if 'name' in col.lower() or 'patient' in col.lower()])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "0bd23e00", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from matplotlib.patches import Rectangle\n", + "import seaborn as sns\n", + "\n", + "# Set the style for better-looking plots\n", + "plt.style.use('default')\n", + "sns.set_palette(\"husl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "ebe2ef60", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Body composition visualization created!\n", + "Fat Mass: 27.6 lbs (22.4%)\n", + "Lean Mass: 95.4 lbs (77.6%)\n", + "Total Body Mass: 123.0 lbs\n", + "Body Fat Percentage: 22.4%\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Sample data based on the image (you can replace with actual data from your dataset)\n", + "fat_mass = 27.6 # lbs\n", + "lean_mass = 95.4 # lbs\n", + "body_fat_percent = 22.4\n", + "\n", + "# Calculate total body mass\n", + "total_mass = fat_mass + lean_mass\n", + "\n", + "# Data for pie chart\n", + "sizes = [fat_mass, lean_mass]\n", + "labels = [f'Fat Mass ({fat_mass}lbs)\\n{fat_mass/total_mass*100:.1f}%', \n", + " f'Lean Mass ({lean_mass}lbs)\\n{lean_mass/total_mass*100:.1f}%']\n", + "colors = ['#ff9999', '#ffcc99'] # Light red/orange colors\n", + "\n", + "# Create the figure\n", + "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(15, 12))\n", + "\n", + "# 1. Body Composition Pie Chart\n", + "wedges, texts, autotexts = ax1.pie(sizes, labels=labels, colors=colors, autopct='', \n", + " startangle=90, pctdistance=0.85)\n", + "\n", + "# Add a circle at the center to create a donut chart\n", + "centre_circle = plt.Circle((0,0), 0.70, fc='white')\n", + "ax1.add_artist(centre_circle)\n", + "\n", + "ax1.set_title('Body Composition', fontsize=16, fontweight='bold', pad=20)\n", + "ax1.axis('equal')\n", + "\n", + "# 2. Body Fat Percentage Bar Chart\n", + "ax2.barh([0], [body_fat_percent], color='orange', height=0.3, alpha=0.7)\n", + "ax2.set_xlim(0, 50)\n", + "ax2.set_ylim(-0.5, 0.5)\n", + "ax2.set_xlabel('Body Fat Percentage (%)', fontsize=12)\n", + "ax2.set_title(f'Body Fat Percent - {body_fat_percent}%', fontsize=16, fontweight='bold')\n", + "ax2.set_yticks([])\n", + "\n", + "# Add percentage ranges for context\n", + "ranges = [(0, 15, 'red'), (15, 20, 'yellow'), (20, 25, 'lightgreen'), \n", + " (25, 30, 'yellow'), (30, 50, 'red')]\n", + "for i, (start, end, color) in enumerate(ranges):\n", + " ax2.axvspan(start, end, alpha=0.3, color=color, ymin=0.3, ymax=0.7)\n", + "\n", + "# Add a marker for current value\n", + "ax2.axvline(x=body_fat_percent, color='black', linestyle='--', linewidth=2)\n", + "ax2.text(body_fat_percent, 0.2, f'{body_fat_percent}%', ha='center', va='bottom', \n", + " fontsize=12, fontweight='bold')\n", + "\n", + "print(f\"Body composition visualization created!\")\n", + "print(f\"Fat Mass: {fat_mass} lbs ({fat_mass/total_mass*100:.1f}%)\")\n", + "print(f\"Lean Mass: {lean_mass} lbs ({lean_mass/total_mass*100:.1f}%)\")\n", + "print(f\"Total Body Mass: {total_mass} lbs\")\n", + "print(f\"Body Fat Percentage: {body_fat_percent}%\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "622f0788", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Body Fat Percent Master Chart created!\n", + "Patient's body fat percentage (22.4%) is marked on the chart.\n" + ] + } + ], + "source": [ + "# Create the Body Fat Percent Master Chart\n", + "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 8))\n", + "\n", + "# Age groups and corresponding y-positions\n", + "age_groups = ['20-39', '40-59', '60-79']\n", + "y_positions = [2, 1, 0]\n", + "\n", + "# Body fat percentage ranges and colors (similar to the image)\n", + "# Red: 0-10%, 40-50% (high risk)\n", + "# Yellow: 10-15%, 15-20%, 35-40% (moderate risk) \n", + "# Green: 20-35% (healthy range)\n", + "\n", + "ranges_male = [\n", + " (0, 5, '#ff6b6b'), # Red - too low\n", + " (5, 10, '#ffd93d'), # Yellow - low\n", + " (10, 15, '#6bcf7f'), # Green - healthy\n", + " (15, 20, '#6bcf7f'), # Green - healthy\n", + " (20, 25, '#ffd93d'), # Yellow - moderate\n", + " (25, 30, '#ff6b6b'), # Red - high\n", + " (30, 35, '#ff6b6b'), # Red - very high\n", + " (35, 40, '#ff6b6b'), # Red - extremely high\n", + " (40, 45, '#ff6b6b'), # Red - extremely high\n", + " (45, 50, '#ff6b6b') # Red - extremely high\n", + "]\n", + "\n", + "ranges_female = [\n", + " (0, 10, '#ff6b6b'), # Red - too low\n", + " (10, 15, '#ffd93d'), # Yellow - low\n", + " (15, 20, '#ffd93d'), # Yellow - moderate\n", + " (20, 25, '#6bcf7f'), # Green - healthy\n", + " (25, 30, '#6bcf7f'), # Green - healthy\n", + " (30, 35, '#6bcf7f'), # Green - healthy\n", + " (35, 40, '#ffd93d'), # Yellow - moderate\n", + " (40, 45, '#ff6b6b'), # Red - high\n", + " (45, 50, '#ff6b6b') # Red - very high\n", + "]\n", + "\n", + "# Plot Male chart\n", + "ax1.set_title('Body Fat Percent Master Chart', fontsize=16, fontweight='bold', pad=20)\n", + "for i, age_group in enumerate(age_groups):\n", + " y = y_positions[i]\n", + " for start, end, color in ranges_male:\n", + " width = end - start\n", + " rect = Rectangle((start, y-0.3), width, 0.6, facecolor=color, alpha=0.7, edgecolor='black', linewidth=0.5)\n", + " ax1.add_patch(rect)\n", + "\n", + "ax1.set_xlim(0, 50)\n", + "ax1.set_ylim(-0.5, 2.5)\n", + "ax1.set_xlabel('Body Fat Percentage (%)', fontsize=12)\n", + "ax1.set_ylabel('Age (M)', fontsize=12)\n", + "ax1.set_yticks(y_positions)\n", + "ax1.set_yticklabels(age_groups)\n", + "ax1.set_xticks(range(0, 51, 5))\n", + "ax1.grid(True, alpha=0.3)\n", + "\n", + "# Add vertical lines for reference\n", + "for x in range(0, 51, 5):\n", + " ax1.axvline(x=x, color='black', linewidth=0.5, alpha=0.3)\n", + "\n", + "# Plot Female chart \n", + "for i, age_group in enumerate(age_groups):\n", + " y = y_positions[i]\n", + " for start, end, color in ranges_female:\n", + " width = end - start\n", + " rect = Rectangle((start, y-0.3), width, 0.6, facecolor=color, alpha=0.7, edgecolor='black', linewidth=0.5)\n", + " ax2.add_patch(rect)\n", + "\n", + "ax2.set_xlim(0, 50)\n", + "ax2.set_ylim(-0.5, 2.5)\n", + "ax2.set_xlabel('Body Fat Percentage (%)', fontsize=12)\n", + "ax2.set_ylabel('Age (F)', fontsize=12)\n", + "ax2.set_yticks(y_positions)\n", + "ax2.set_yticklabels(age_groups)\n", + "ax2.set_xticks(range(0, 51, 5))\n", + "ax2.grid(True, alpha=0.3)\n", + "\n", + "# Add vertical lines for reference\n", + "for x in range(0, 51, 5):\n", + " ax2.axvline(x=x, color='black', linewidth=0.5, alpha=0.3)\n", + "\n", + "# Mark the current patient's value (assuming female, 20-39 age group)\n", + "patient_age_group = 0 # 20-39 age group\n", + "ax2.axvline(x=body_fat_percent, color='black', linestyle='--', linewidth=2)\n", + "ax2.plot(body_fat_percent, y_positions[patient_age_group], 'v', color='black', markersize=10)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "print(f\"Body Fat Percent Master Chart created!\")\n", + "print(f\"Patient's body fat percentage ({body_fat_percent}%) is marked on the chart.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "732c3859", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_221522/2454516635.py:109: 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": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Complete body composition analysis visualization created!\n" + ] + } + ], + "source": [ + "# Create a combined visualization matching the original image more closely\n", + "fig = plt.figure(figsize=(12, 10))\n", + "\n", + "# Create custom grid layout\n", + "gs = fig.add_gridspec(3, 2, height_ratios=[2, 1, 2], width_ratios=[1, 1], hspace=0.3, wspace=0.3)\n", + "\n", + "# 1. Body Composition Pie Chart (top left)\n", + "ax1 = fig.add_subplot(gs[0, 0])\n", + "\n", + "# Create donut chart\n", + "sizes = [fat_mass, lean_mass]\n", + "colors = ['#ff9999', '#ffcc99'] # Light coral and peach colors\n", + "wedges, texts = ax1.pie(sizes, colors=colors, startangle=90, counterclock=False, \n", + " wedgeprops=dict(width=0.5))\n", + "\n", + "# Add labels inside the chart\n", + "ax1.text(0, 0.2, f'Fat Mass ({fat_mass}lbs)', ha='center', va='center', fontsize=10, fontweight='bold')\n", + "ax1.text(0, 0, f'{fat_mass/total_mass*100:.1f}%', ha='center', va='center', fontsize=12, fontweight='bold')\n", + "ax1.text(0, -0.2, f'Lean Mass ({lean_mass}lbs)', ha='center', va='center', fontsize=10, fontweight='bold')\n", + "ax1.text(0, -0.4, f'{lean_mass/total_mass*100:.1f}%', ha='center', va='center', fontsize=12, fontweight='bold')\n", + "\n", + "ax1.set_title('Body Composition', fontsize=14, fontweight='bold', pad=20)\n", + "\n", + "# 2. Body Fat Percentage indicator (top right)\n", + "ax2 = fig.add_subplot(gs[0, 1])\n", + "ax2.text(0.5, 0.7, f'Body Fat Percent - {body_fat_percent}%', ha='center', va='center', \n", + " fontsize=16, fontweight='bold', transform=ax2.transAxes)\n", + "\n", + "# Create horizontal bar for current age group (20-39 F)\n", + "bar_ranges = [(0, 10, '#ff6b6b'), (10, 15, '#ff6b6b'), (15, 20, '#ffd93d'), \n", + " (20, 25, '#6bcf7f'), (25, 30, '#6bcf7f'), (30, 35, '#6bcf7f'),\n", + " (35, 40, '#ffd93d'), (40, 45, '#ff6b6b'), (45, 50, '#ff6b6b')]\n", + "\n", + "y_pos = 0.4\n", + "bar_height = 0.1\n", + "for start, end, color in bar_ranges:\n", + " width = (end - start) / 50 # Normalize to 0-1 range\n", + " x_start = start / 50\n", + " rect = plt.Rectangle((x_start, y_pos), width, bar_height, facecolor=color, \n", + " alpha=0.7, edgecolor='black', linewidth=0.5, transform=ax2.transAxes)\n", + " ax2.add_patch(rect)\n", + "\n", + "# Add percentage labels\n", + "for pct in [0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50]:\n", + " x_pos = pct / 50\n", + " ax2.text(x_pos, 0.3, f'{pct}%', ha='center', va='top', fontsize=8, \n", + " transform=ax2.transAxes)\n", + "\n", + "# Add arrow pointing to current value\n", + "current_x = body_fat_percent / 50\n", + "ax2.arrow(current_x, 0.6, 0, -0.05, head_width=0.02, head_length=0.02, \n", + " fc='black', ec='black', transform=ax2.transAxes)\n", + "\n", + "ax2.text(0.1, 0.2, '20-39\\n(F)', ha='center', va='center', fontsize=10, \n", + " transform=ax2.transAxes)\n", + "ax2.set_xlim(0, 1)\n", + "ax2.set_ylim(0, 1)\n", + "ax2.axis('off')\n", + "\n", + "# 3. Full Body Fat Percent Master Chart (bottom)\n", + "ax3 = fig.add_subplot(gs[2, :])\n", + "\n", + "age_groups = ['20-39', '40-59', '60-79']\n", + "y_positions = [2, 1, 0]\n", + "\n", + "# Male ranges (top)\n", + "for i, age_group in enumerate(age_groups):\n", + " y = y_positions[i] + 3\n", + " for start, end, color in ranges_male:\n", + " width = end - start\n", + " rect = Rectangle((start, y-0.3), width, 0.6, facecolor=color, alpha=0.7, \n", + " edgecolor='black', linewidth=0.5)\n", + " ax3.add_patch(rect)\n", + "\n", + "# Female ranges (bottom)\n", + "for i, age_group in enumerate(age_groups):\n", + " y = y_positions[i]\n", + " for start, end, color in ranges_female:\n", + " width = end - start\n", + " rect = Rectangle((start, y-0.3), width, 0.6, facecolor=color, alpha=0.7, \n", + " edgecolor='black', linewidth=0.5)\n", + " ax3.add_patch(rect)\n", + "\n", + "ax3.set_xlim(0, 50)\n", + "ax3.set_ylim(-0.5, 5.5)\n", + "ax3.set_xlabel('Body Fat Percentage (%)', fontsize=12)\n", + "ax3.set_title('Body Fat Percent Master Chart', fontsize=14, fontweight='bold')\n", + "\n", + "# Set y-axis labels\n", + "y_labels = ['20-39', '40-59', '60-79', '20-39', '40-59', '60-79']\n", + "y_tick_positions = [0, 1, 2, 3, 4, 5]\n", + "ax3.set_yticks(y_tick_positions)\n", + "ax3.set_yticklabels(y_labels)\n", + "\n", + "# Add age group labels\n", + "ax3.text(-3, 1, 'Age (F)', ha='center', va='center', fontsize=10, fontweight='bold')\n", + "ax3.text(-3, 4, 'Age (M)', ha='center', va='center', fontsize=10, fontweight='bold')\n", + "\n", + "ax3.set_xticks(range(0, 51, 5))\n", + "ax3.grid(True, alpha=0.3)\n", + "\n", + "# Mark patient's position (assuming 20-39 female)\n", + "ax3.axvline(x=body_fat_percent, color='black', linestyle='--', linewidth=2, alpha=0.8)\n", + "ax3.plot(body_fat_percent, 2, 'v', color='black', markersize=12) # Triangle pointing down\n", + "\n", + "plt.suptitle('Nutrition Guidelines\\nUltrasound & Body Composition Assessment', \n", + " fontsize=16, fontweight='bold', y=0.95)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "print(\"Complete body composition analysis visualization created!\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "cff16154", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\\n==================================================\n", + "SAMPLE USAGE:\n", + "==================================================\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_221522/3354465480.py:138: 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": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results: {'total_mass': 123.0, 'body_fat_percent': 22.439024390243905, 'fat_mass_percent': 22.439024390243905, 'lean_mass_percent': 77.5609756097561}\n" + ] + } + ], + "source": [ + "def create_body_composition_chart(fat_mass_lbs, lean_mass_lbs, age_group='20-39', gender='F'):\n", + " \"\"\"\n", + " Create a comprehensive body composition visualization\n", + " \n", + " Parameters:\n", + " - fat_mass_lbs: Fat mass in pounds\n", + " - lean_mass_lbs: Lean mass in pounds \n", + " - age_group: Age group ('20-39', '40-59', '60-79')\n", + " - gender: Gender ('M' or 'F')\n", + " \"\"\"\n", + " \n", + " # Calculate derived values\n", + " total_mass = fat_mass_lbs + lean_mass_lbs\n", + " body_fat_percent = (fat_mass_lbs / total_mass) * 100\n", + " \n", + " # Create the visualization\n", + " fig = plt.figure(figsize=(12, 10))\n", + " gs = fig.add_gridspec(3, 2, height_ratios=[2, 1, 2], width_ratios=[1, 1], \n", + " hspace=0.3, wspace=0.3)\n", + "\n", + " # 1. Body Composition Pie Chart\n", + " ax1 = fig.add_subplot(gs[0, 0])\n", + " sizes = [fat_mass_lbs, lean_mass_lbs]\n", + " colors = ['#ff9999', '#ffcc99']\n", + " wedges, texts = ax1.pie(sizes, colors=colors, startangle=90, counterclock=False, \n", + " wedgeprops=dict(width=0.5))\n", + "\n", + " # Add labels\n", + " ax1.text(0, 0.2, f'Fat Mass ({fat_mass_lbs}lbs)', ha='center', va='center', \n", + " fontsize=10, fontweight='bold')\n", + " ax1.text(0, 0, f'{fat_mass_lbs/total_mass*100:.1f}%', ha='center', va='center', \n", + " fontsize=12, fontweight='bold')\n", + " ax1.text(0, -0.2, f'Lean Mass ({lean_mass_lbs}lbs)', ha='center', va='center', \n", + " fontsize=10, fontweight='bold')\n", + " ax1.text(0, -0.4, f'{lean_mass_lbs/total_mass*100:.1f}%', ha='center', va='center', \n", + " fontsize=12, fontweight='bold')\n", + " ax1.set_title('Body Composition', fontsize=14, fontweight='bold', pad=20)\n", + "\n", + " # 2. Body Fat Percentage Bar\n", + " ax2 = fig.add_subplot(gs[0, 1])\n", + " ax2.text(0.5, 0.7, f'Body Fat Percent - {body_fat_percent:.1f}%', ha='center', va='center', \n", + " fontsize=16, fontweight='bold', transform=ax2.transAxes)\n", + "\n", + " # Color ranges for body fat percentage\n", + " if gender == 'F':\n", + " bar_ranges = [(0, 10, '#ff6b6b'), (10, 15, '#ff6b6b'), (15, 20, '#ffd93d'), \n", + " (20, 25, '#6bcf7f'), (25, 30, '#6bcf7f'), (30, 35, '#6bcf7f'),\n", + " (35, 40, '#ffd93d'), (40, 45, '#ff6b6b'), (45, 50, '#ff6b6b')]\n", + " else: # Male\n", + " bar_ranges = [(0, 5, '#ff6b6b'), (5, 10, '#ffd93d'), (10, 15, '#6bcf7f'), \n", + " (15, 20, '#6bcf7f'), (20, 25, '#ffd93d'), (25, 30, '#ff6b6b'),\n", + " (30, 35, '#ff6b6b'), (35, 40, '#ff6b6b'), (40, 50, '#ff6b6b')]\n", + "\n", + " y_pos = 0.4\n", + " bar_height = 0.1\n", + " for start, end, color in bar_ranges:\n", + " width = (end - start) / 50\n", + " x_start = start / 50\n", + " rect = plt.Rectangle((x_start, y_pos), width, bar_height, facecolor=color, \n", + " alpha=0.7, edgecolor='black', linewidth=0.5, transform=ax2.transAxes)\n", + " ax2.add_patch(rect)\n", + "\n", + " # Add percentage labels\n", + " for pct in [0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50]:\n", + " x_pos = pct / 50\n", + " ax2.text(x_pos, 0.3, f'{pct}%', ha='center', va='top', fontsize=8, \n", + " transform=ax2.transAxes)\n", + "\n", + " # Add arrow for current value\n", + " current_x = body_fat_percent / 50\n", + " ax2.arrow(current_x, 0.6, 0, -0.05, head_width=0.02, head_length=0.02, \n", + " fc='black', ec='black', transform=ax2.transAxes)\n", + "\n", + " ax2.text(0.1, 0.2, f'{age_group}\\\\n({gender})', ha='center', va='center', fontsize=10, \n", + " transform=ax2.transAxes)\n", + " ax2.set_xlim(0, 1)\n", + " ax2.set_ylim(0, 1)\n", + " ax2.axis('off')\n", + "\n", + " # 3. Master Chart\n", + " ax3 = fig.add_subplot(gs[2, :])\n", + " \n", + " age_groups = ['20-39', '40-59', '60-79']\n", + " y_positions = [2, 1, 0]\n", + " \n", + " # Male and female ranges\n", + " ranges_male = [(0, 5, '#ff6b6b'), (5, 10, '#ffd93d'), (10, 15, '#6bcf7f'), \n", + " (15, 20, '#6bcf7f'), (20, 25, '#ffd93d'), (25, 30, '#ff6b6b'),\n", + " (30, 35, '#ff6b6b'), (35, 40, '#ff6b6b'), (40, 50, '#ff6b6b')]\n", + " \n", + " ranges_female = [(0, 10, '#ff6b6b'), (10, 15, '#ff6b6b'), (15, 20, '#ffd93d'), \n", + " (20, 25, '#6bcf7f'), (25, 30, '#6bcf7f'), (30, 35, '#6bcf7f'),\n", + " (35, 40, '#ffd93d'), (40, 45, '#ff6b6b'), (45, 50, '#ff6b6b')]\n", + "\n", + " # Plot male ranges (top)\n", + " for i, age_group_name in enumerate(age_groups):\n", + " y = y_positions[i] + 3\n", + " for start, end, color in ranges_male:\n", + " width = end - start\n", + " rect = Rectangle((start, y-0.3), width, 0.6, facecolor=color, alpha=0.7, \n", + " edgecolor='black', linewidth=0.5)\n", + " ax3.add_patch(rect)\n", + "\n", + " # Plot female ranges (bottom)\n", + " for i, age_group_name in enumerate(age_groups):\n", + " y = y_positions[i]\n", + " for start, end, color in ranges_female:\n", + " width = end - start\n", + " rect = Rectangle((start, y-0.3), width, 0.6, facecolor=color, alpha=0.7, \n", + " edgecolor='black', linewidth=0.5)\n", + " ax3.add_patch(rect)\n", + "\n", + " ax3.set_xlim(0, 50)\n", + " ax3.set_ylim(-0.5, 5.5)\n", + " ax3.set_xlabel('Body Fat Percentage (%)', fontsize=12)\n", + " ax3.set_title('Body Fat Percent Master Chart', fontsize=14, fontweight='bold')\n", + "\n", + " # Set labels\n", + " y_labels = ['20-39', '40-59', '60-79', '20-39', '40-59', '60-79']\n", + " y_tick_positions = [0, 1, 2, 3, 4, 5]\n", + " ax3.set_yticks(y_tick_positions)\n", + " ax3.set_yticklabels(y_labels)\n", + "\n", + " ax3.text(-3, 1, 'Age (F)', ha='center', va='center', fontsize=10, fontweight='bold')\n", + " ax3.text(-3, 4, 'Age (M)', ha='center', va='center', fontsize=10, fontweight='bold')\n", + "\n", + " ax3.set_xticks(range(0, 51, 5))\n", + " ax3.grid(True, alpha=0.3)\n", + "\n", + " # Mark patient's position\n", + " patient_y = age_groups.index(age_group) + (0 if gender == 'F' else 3)\n", + " ax3.axvline(x=body_fat_percent, color='black', linestyle='--', linewidth=2, alpha=0.8)\n", + " ax3.plot(body_fat_percent, patient_y, 'v', color='black', markersize=12)\n", + "\n", + " plt.suptitle('Nutrition Guidelines\\\\nUltrasound & Body Composition Assessment', \n", + " fontsize=16, fontweight='bold', y=0.95)\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + " \n", + " return {\n", + " 'total_mass': total_mass,\n", + " 'body_fat_percent': body_fat_percent,\n", + " 'fat_mass_percent': (fat_mass_lbs/total_mass)*100,\n", + " 'lean_mass_percent': (lean_mass_lbs/total_mass)*100\n", + " }\n", + "\n", + "# Example usage with the sample data\n", + "print(\"\\\\n\" + \"=\"*50)\n", + "print(\"SAMPLE USAGE:\")\n", + "print(\"=\"*50)\n", + "result = create_body_composition_chart(fat_mass_lbs=27.6, lean_mass_lbs=95.4, \n", + " age_group='20-39', gender='F')\n", + "print(f\"Results: {result}\")" + ] } ], "metadata": { diff --git a/report_gen/page_1.html b/report_gen/page_1.html new file mode 100644 index 0000000..e3b7671 --- /dev/null +++ b/report_gen/page_1.html @@ -0,0 +1,104 @@ +
+ +
+

ISHP

+
+ + +
+
+
+ + +
+ +
+

BIO-PERFORMX

+

Endurance

+
+ + +
+

+ {{ name|upper }} +

+

+ {{ surname|upper }} +

+

{{ date }}

+
+
+ + +
+
+ +
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ +
+ +
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ +
+ +
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ + diff --git a/report_gen/page_10.html b/report_gen/page_10.html new file mode 100644 index 0000000..e69de29 diff --git a/report_gen/page_11.html b/report_gen/page_11.html new file mode 100644 index 0000000..e69de29 diff --git a/report_gen/page_12.html b/report_gen/page_12.html new file mode 100644 index 0000000..e69de29 diff --git a/report_gen/page_13.html b/report_gen/page_13.html new file mode 100644 index 0000000..e69de29 diff --git a/report_gen/page_14.html b/report_gen/page_14.html new file mode 100644 index 0000000..e69de29 diff --git a/report_gen/page_15.html b/report_gen/page_15.html new file mode 100644 index 0000000..e69de29 diff --git a/report_gen/page_16.html b/report_gen/page_16.html new file mode 100644 index 0000000..e69de29 diff --git a/report_gen/page_17.html b/report_gen/page_17.html new file mode 100644 index 0000000..e69de29 diff --git a/report_gen/page_18.html b/report_gen/page_18.html new file mode 100644 index 0000000..e69de29 diff --git a/report_gen/page_19.html b/report_gen/page_19.html new file mode 100644 index 0000000..e69de29 diff --git a/report_gen/page_2.html b/report_gen/page_2.html new file mode 100644 index 0000000..3189a1f --- /dev/null +++ b/report_gen/page_2.html @@ -0,0 +1,121 @@ +
+
+ +
+

+ TABLE OF CONTENTS +

+
+
+ + +
+ +
+
+ 3 +
+
+

+ Lung Analysis +

+
+

+ Pulse Oximetry Assessment +

+

+ Spirometry Assessment +

+
+
+
+ + +
+
+ 4 +
+
+

+ Cardio Metrics +

+

+ Active Metabolic Rate Assessment +

+
+
+ + +
+
+ 5 +
+
+

+ Fuel Utilization +

+
+
+ + +
+
+ 9 +
+
+

+ Local Muscle Activity +

+
+
+ + +
+
+ 10 +
+
+

+ Training Recommendations +

+
+
+ + +
+
+ 12 +
+
+

+ Next Steps +

+
+
+ + +
+
+ 13 +
+
+

Glossary

+
+
+
+
+
diff --git a/report_gen/page_3.html b/report_gen/page_3.html new file mode 100644 index 0000000..e69de29 diff --git a/report_gen/page_4.html b/report_gen/page_4.html new file mode 100644 index 0000000..e69de29 diff --git a/report_gen/page_5.html b/report_gen/page_5.html new file mode 100644 index 0000000..e69de29 diff --git a/report_gen/page_6.html b/report_gen/page_6.html new file mode 100644 index 0000000..e69de29 diff --git a/report_gen/page_7.html b/report_gen/page_7.html new file mode 100644 index 0000000..e69de29 diff --git a/report_gen/page_8.html b/report_gen/page_8.html new file mode 100644 index 0000000..e69de29 diff --git a/report_gen/page_9.html b/report_gen/page_9.html new file mode 100644 index 0000000..e69de29 diff --git a/templates/base.html b/templates/base.html index 27ae3ca..bb8a28a 100644 --- a/templates/base.html +++ b/templates/base.html @@ -1,39 +1,128 @@ - - Lung Report - - + - -
-

Lung Analysis Report

-
+ +
+ +
+ +
+
ISHP
+
-
-
Name: {{ patient.name }}
-
Age: {{ patient.age }}
-
Height: {{ patient.height }} cm
-
Weight: {{ patient.weight }} kg
-
+ +
+ + + +
-
-

Lung Function

-
- + +
+ +
+
+ +
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ + +
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ +
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
-
-
- Indications: {{ indications }} -
+ +
+ +
+

BIO-PERFORMX

+

Endurance

+
-
-

Respiratory Chart

- -
+ +
+

John Doe

+

Moran

+
+ + +
+

July 29, 2025

+
+
+
\ No newline at end of file