diff --git a/graphs/body_composition_chart.png b/graphs/body_composition_chart.png
index af4574c..337db2f 100644
Binary files a/graphs/body_composition_chart.png and b/graphs/body_composition_chart.png differ
diff --git a/graphs/body_fat_percent_chart.png b/graphs/body_fat_percent_chart.png
index 644cff8..3d01923 100644
Binary files a/graphs/body_fat_percent_chart.png and b/graphs/body_fat_percent_chart.png differ
diff --git a/graphs/spirometry_chart.png b/graphs/spirometry_chart.png
index 9729fd3..fe3c5e7 100644
Binary files a/graphs/spirometry_chart.png and b/graphs/spirometry_chart.png differ
diff --git a/notebook.ipynb b/notebook.ipynb
index 8a911ef..b3f13ca 100644
--- a/notebook.ipynb
+++ b/notebook.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "63f43af5",
    "metadata": {},
    "outputs": [],
@@ -15,7 +15,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 2,
    "id": "b0ee2af1",
    "metadata": {},
    "outputs": [
@@ -31,7 +31,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_226264/622539462.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_1280137/622539462.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": 40,
+   "execution_count": 3,
    "id": "fbd292c3",
    "metadata": {},
    "outputs": [
@@ -79,13 +79,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 4,
    "id": "ef8bc7ac",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1800x500 with 2 Axes>"
       ]
@@ -138,7 +138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 5,
    "id": "06244aa2",
    "metadata": {},
    "outputs": [
@@ -253,7 +253,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 6,
    "id": "8a1878a0",
    "metadata": {},
    "outputs": [
@@ -331,7 +331,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 7,
    "id": "7361fb05",
    "metadata": {},
    "outputs": [
@@ -390,7 +390,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 8,
    "id": "c89478ff",
    "metadata": {},
    "outputs": [
@@ -449,7 +449,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 9,
    "id": "1db16040",
    "metadata": {},
    "outputs": [
@@ -522,7 +522,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 10,
    "id": "52642f49",
    "metadata": {},
    "outputs": [
@@ -909,7 +909,7 @@
        "[63 rows x 147 columns]"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -921,7 +921,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "2056096d",
    "metadata": {},
    "outputs": [
@@ -1022,7 +1022,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 12,
    "id": "bf55717b",
    "metadata": {},
    "outputs": [
@@ -1087,7 +1087,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 13,
    "id": "21c1c0a5",
    "metadata": {},
    "outputs": [
@@ -1157,7 +1157,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 16,
    "id": "10687f82",
    "metadata": {},
    "outputs": [
@@ -1183,14 +1183,12 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Parameters</th>\n",
+       "      <th>Pre</th>\n",
        "      <th>Best</th>\n",
        "      <th>LLN</th>\n",
        "      <th>Pred.</th>\n",
        "      <th>%Pred.</th>\n",
        "      <th>ZScore</th>\n",
-       "      <th>PRE#1</th>\n",
-       "      <th>PRE#2</th>\n",
-       "      <th>PRE#3</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -1198,97 +1196,81 @@
        "      <th>0</th>\n",
        "      <td>FVC</td>\n",
        "      <td>4.24</td>\n",
+       "      <td>4.24</td>\n",
        "      <td>3.03</td>\n",
        "      <td>3.79</td>\n",
        "      <td>112.0</td>\n",
        "      <td>0.95</td>\n",
-       "      <td>4.24</td>\n",
-       "      <td>4.17</td>\n",
-       "      <td>4.15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>FEV1</td>\n",
        "      <td>3.26</td>\n",
+       "      <td>3.26</td>\n",
        "      <td>2.53</td>\n",
        "      <td>3.16</td>\n",
        "      <td>103.3</td>\n",
        "      <td>0.28</td>\n",
-       "      <td>3.26</td>\n",
-       "      <td>3.21</td>\n",
-       "      <td>3.14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>FEV1/FVC%</td>\n",
-       "      <td>76.89</td>\n",
+       "      <td>76.90</td>\n",
+       "      <td>76.90</td>\n",
        "      <td>72.47</td>\n",
        "      <td>83.78</td>\n",
        "      <td>91.8</td>\n",
        "      <td>-1.05</td>\n",
-       "      <td>76.90</td>\n",
-       "      <td>77.00</td>\n",
-       "      <td>75.70</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>PEF</td>\n",
-       "      <td>684.00</td>\n",
+       "      <td>444.00</td>\n",
+       "      <td>444.00</td>\n",
        "      <td>222.00</td>\n",
        "      <td>384.00</td>\n",
        "      <td>178.7</td>\n",
-       "      <td>-</td>\n",
-       "      <td>444.00</td>\n",
-       "      <td>438.00</td>\n",
-       "      <td>684.00</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>FEF2575</td>\n",
        "      <td>2.74</td>\n",
+       "      <td>2.74</td>\n",
        "      <td>2.15</td>\n",
        "      <td>3.42</td>\n",
        "      <td>80.2</td>\n",
        "      <td>-0.84</td>\n",
-       "      <td>2.74</td>\n",
-       "      <td>2.68</td>\n",
-       "      <td>2.48</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>FEF25</td>\n",
        "      <td>6.08</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-</td>\n",
        "      <td>6.08</td>\n",
-       "      <td>6.00</td>\n",
-       "      <td>5.53</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>FEF50</td>\n",
        "      <td>3.06</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-</td>\n",
        "      <td>3.06</td>\n",
-       "      <td>3.10</td>\n",
-       "      <td>2.77</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>FEF75</td>\n",
        "      <td>1.06</td>\n",
+       "      <td>1.06</td>\n",
        "      <td>0.71</td>\n",
        "      <td>1.41</td>\n",
        "      <td>75.1</td>\n",
        "      <td>-0.72</td>\n",
-       "      <td>1.06</td>\n",
-       "      <td>1.12</td>\n",
-       "      <td>0.94</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
@@ -1296,11 +1278,9 @@
        "      <td>79.00</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>49.0</td>\n",
-       "      <td>-</td>\n",
-       "      <td>79.00</td>\n",
-       "      <td>40.00</td>\n",
-       "      <td>39.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
@@ -1308,44 +1288,40 @@
        "      <td>78.00</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>77.0</td>\n",
-       "      <td>-</td>\n",
-       "      <td>78.00</td>\n",
-       "      <td>77.00</td>\n",
-       "      <td>197.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>FEV6</td>\n",
        "      <td>4.22</td>\n",
+       "      <td>4.22</td>\n",
        "      <td>3.03</td>\n",
        "      <td>3.79</td>\n",
        "      <td>111.4</td>\n",
-       "      <td>-</td>\n",
-       "      <td>4.22</td>\n",
-       "      <td>4.17</td>\n",
-       "      <td>4.13</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "   Parameters    Best     LLN   Pred.  %Pred. ZScore   PRE#1   PRE#2   PRE#3\n",
-       "0         FVC    4.24    3.03    3.79   112.0   0.95    4.24    4.17    4.15\n",
-       "1        FEV1    3.26    2.53    3.16   103.3   0.28    3.26    3.21    3.14\n",
-       "2   FEV1/FVC%   76.89   72.47   83.78    91.8  -1.05   76.90   77.00   75.70\n",
-       "3         PEF  684.00  222.00  384.00   178.7      -  444.00  438.00  684.00\n",
-       "4     FEF2575    2.74    2.15    3.42    80.2  -0.84    2.74    2.68    2.48\n",
-       "5       FEF25    6.08     NaN     NaN     0.0      -    6.08    6.00    5.53\n",
-       "6       FEF50    3.06     NaN     NaN     0.0      -    3.06    3.10    2.77\n",
-       "7       FEF75    1.06    0.71    1.41    75.1  -0.72    1.06    1.12    0.94\n",
-       "8     PEFTime   79.00     NaN     NaN    49.0      -   79.00   40.00   39.00\n",
-       "9        EVol   78.00     NaN     NaN    77.0      -   78.00   77.00  197.00\n",
-       "10       FEV6    4.22    3.03    3.79   111.4      -    4.22    4.17    4.13"
+       "   Parameters     Pre    Best     LLN   Pred.  %Pred.  ZScore\n",
+       "0         FVC    4.24    4.24    3.03    3.79   112.0    0.95\n",
+       "1        FEV1    3.26    3.26    2.53    3.16   103.3    0.28\n",
+       "2   FEV1/FVC%   76.90   76.90   72.47   83.78    91.8   -1.05\n",
+       "3         PEF  444.00  444.00  222.00  384.00   178.7     NaN\n",
+       "4     FEF2575    2.74    2.74    2.15    3.42    80.2   -0.84\n",
+       "5       FEF25    6.08    6.08    0.00    0.00     0.0     NaN\n",
+       "6       FEF50    3.06    3.06    0.00    0.00     0.0     NaN\n",
+       "7       FEF75    1.06    1.06    0.71    1.41    75.1   -0.72\n",
+       "8     PEFTime   79.00     NaN     NaN     NaN     NaN     NaN\n",
+       "9        EVol   78.00     NaN     NaN     NaN     NaN     NaN\n",
+       "10       FEV6    4.22    4.22    3.03    3.79   111.4     NaN"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1357,13 +1333,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 17,
    "id": "d468d687",
    "metadata": {},
    "outputs": [
     {
      "data": {
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",
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       "text/plain": [
        "<Figure size 1150x360 with 4 Axes>"
       ]
@@ -1500,6 +1476,409 @@
     "plt.savefig('graphs/spirometry_chart.png', dpi=300, bbox_inches='tight')\n",
     "plt.show()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "26c7b01e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Resting Metabolic Rate (RMR): 14741 kcal\n",
+      "Fuel Source: Fats 15.7%, Carbs 84.1%\n",
+      "Estimated Caloric Intake: 15080 kcal/day\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_1280137/3162302372.py:31: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all Axes decorations.\n",
+      "  plt.tight_layout()\n"
+     ]
+    },
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 800x150 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x150 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "--- Summary ---\n",
+      "RMR: 14741 kcal\n",
+      "NEAT: 762 kcal\n",
+      "Deficit: 423 kcal\n",
+      "Caloric Intake: 15080 kcal/day\n"
+     ]
+    }
+   ],
+   "source": [
+    "# --- Extract Key Values ---\n",
+    "# Average RMR (EE per day)\n",
+    "rmr = df['EE(kcal/day)'].mean()\n",
+    "\n",
+    "# Average fuel source ratios\n",
+    "fat_pct = df['FAT(%)'].mean()\n",
+    "carb_pct = df['CARBS(%)'].mean()\n",
+    "\n",
+    "# --- Constants ---\n",
+    "NEAT = 762\n",
+    "DEFICIT = 423\n",
+    "\n",
+    "# --- Caloric Intake Calculation ---\n",
+    "caloric_intake = rmr + NEAT - DEFICIT\n",
+    "\n",
+    "print(f\"Resting Metabolic Rate (RMR): {rmr:.0f} kcal\")\n",
+    "print(f\"Fuel Source: Fats {fat_pct:.1f}%, Carbs {carb_pct:.1f}%\")\n",
+    "print(f\"Estimated Caloric Intake: {caloric_intake:.0f} kcal/day\")\n",
+    "\n",
+    "# --- Plot 1: Slow vs Fast Metabolism ---\n",
+    "fig, ax = plt.subplots(figsize=(8, 1.5))\n",
+    "ax.barh([0], [rmr], color='lightgreen')\n",
+    "ax.set_xlim(1000, 2500)\n",
+    "ax.set_yticks([])\n",
+    "ax.set_xlabel('Metabolic Rate (kcal/day)')\n",
+    "ax.set_title('Slow vs Fast Metabolism', fontsize=12, fontweight='bold')\n",
+    "ax.axvline(rmr, color='green', linewidth=4)\n",
+    "ax.text(rmr + 20, 0, f'{rmr:.0f} kcal', va='center', fontsize=10, fontweight='bold', color='green')\n",
+    "ax.text(1050, 0.1, 'Very Slow', fontsize=8)\n",
+    "ax.text(2450, 0.1, 'Very Fast', fontsize=8, ha='right')\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# --- Plot 2: Fuel Source Ratio ---\n",
+    "fig, ax = plt.subplots(figsize=(8, 1.5))\n",
+    "\n",
+    "ax.barh([0], [fat_pct], color='#f4d35e', label=f'Fats {fat_pct:.0f}%')\n",
+    "ax.barh([0], [carb_pct], left=[fat_pct], color='#9ad0f5', label=f'Carbs {carb_pct:.0f}%')\n",
+    "ax.set_xlim(0, 100)\n",
+    "ax.set_yticks([])\n",
+    "ax.set_xlabel('Fuel Source (%)')\n",
+    "ax.set_title('Fuel Source Ratio', fontsize=12, fontweight='bold')\n",
+    "\n",
+    "ax.axvline(70, color='black', linestyle='--', label='Optimal')\n",
+    "ax.legend(loc='upper center', ncol=3, bbox_to_anchor=(0.5, -0.3))\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# --- Summary Box ---\n",
+    "print(f\"\\n--- Summary ---\\nRMR: {rmr:.0f} kcal\\nNEAT: {NEAT} kcal\\nDeficit: {DEFICIT} kcal\\nCaloric Intake: {caloric_intake:.0f} kcal/day\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "55cfd2d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>MeasurementDate</th>\n",
+       "      <th>Comment</th>\n",
+       "      <th>ExternalDeviceId</th>\n",
+       "      <th>ExternalPatientId</th>\n",
+       "      <th>FirstName</th>\n",
+       "      <th>LastName</th>\n",
+       "      <th>BirthDate</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Ethnicity</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Child_XC</th>\n",
+       "      <th>Child_XC_Unit</th>\n",
+       "      <th>Child_BIVA_ZRh</th>\n",
+       "      <th>Child_BIVA_ZXcH</th>\n",
+       "      <th>Child_PhA</th>\n",
+       "      <th>Child_PhA_Unit</th>\n",
+       "      <th>Child_REE_Kcal</th>\n",
+       "      <th>Child_REE_MJ</th>\n",
+       "      <th>Child_TEE_Kcal</th>\n",
+       "      <th>Child_TEE_MJ</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>2025-07-29T18:58:54.0000000Z</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10000001583275_0055003f5631501320313557</td>\n",
+       "      <td>KM6479696509</td>\n",
+       "      <td>Keirstyn</td>\n",
+       "      <td>Moran</td>\n",
+       "      <td>1991-02-01T00:00:00.0000000Z</td>\n",
+       "      <td>34</td>\n",
+       "      <td>Caucasian</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 147 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 MeasurementDate  Comment  \\\n",
+       "13  2025-07-29T18:58:54.0000000Z      NaN   \n",
+       "\n",
+       "                           ExternalDeviceId ExternalPatientId FirstName  \\\n",
+       "13  10000001583275_0055003f5631501320313557      KM6479696509  Keirstyn   \n",
+       "\n",
+       "   LastName                     BirthDate  Age  Ethnicity  Gender  ...  \\\n",
+       "13    Moran  1991-02-01T00:00:00.0000000Z   34  Caucasian  Female  ...   \n",
+       "\n",
+       "    Child_XC Child_XC_Unit  Child_BIVA_ZRh Child_BIVA_ZXcH  Child_PhA  \\\n",
+       "13       NaN           NaN             NaN             NaN        NaN   \n",
+       "\n",
+       "   Child_PhA_Unit  Child_REE_Kcal  Child_REE_MJ Child_TEE_Kcal  Child_TEE_MJ  \n",
+       "13            NaN             NaN           NaN            NaN           NaN  \n",
+       "\n",
+       "[1 rows x 147 columns]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "keirstyn_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e94d5f23",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Estimated RMR from data: 1385 kcal/day\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Step 1: Filter resting phase (usually lowest VO2 or MET values)\n",
+    "rest_phase = df[df['MET'] <= 1.1]  # assuming <1.1 MET means rest\n",
+    "\n",
+    "# Step 2: Compute resting metabolic rate\n",
+    "rmr = rest_phase['EE(kcal/day)'].mean()\n",
+    "\n",
+    "print(f\"Estimated RMR from data: {rmr:.0f} kcal/day\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "03fbb87e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Resting phase fuel mix: Fats 0.8%, Carbs 82.1%\n"
+     ]
+    }
+   ],
+   "source": [
+    "rest_phase = df[df['MET'] <= 1.1]  # filter rest data\n",
+    "fat_rest = rest_phase['FAT(%)'].mean()\n",
+    "carb_rest = rest_phase['CARBS(%)'].mean()\n",
+    "\n",
+    "print(f\"Resting phase fuel mix: Fats {fat_rest:.1f}%, Carbs {carb_rest:.1f}%\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "bc6610a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Resting RER: 1.00\n",
+      "Estimated Fuel Mix → Fats: 0.9%, Carbs: 99.1%\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# 1. Filter to resting phase\n",
+    "rest_data = df[df['MET'] < 1.3]\n",
+    "\n",
+    "# 2. Compute mean RER\n",
+    "mean_rer = df['RER'].mean()\n",
+    "\n",
+    "# 3. Compute fuel mix\n",
+    "fat_pct = (1.0 - mean_rer) / 0.3 * 100\n",
+    "carb_pct = 100 - fat_pct\n",
+    "\n",
+    "print(f\"Resting RER: {mean_rer:.2f}\")\n",
+    "print(f\"Estimated Fuel Mix → Fats: {fat_pct:.1f}%, Carbs: {carb_pct:.1f}%\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "3a9c9e66",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- RMR Fuel Usage Calculation Confirmation ---\n",
+      "1. Average RER Measured during Resting Phase: 0.997\n",
+      "2. Calculated Carbohydrate Usage: 99.1%\n",
+      "3. Calculated Fat Usage: 0.9%\n",
+      "\n",
+      "Note: The calculated value differs from 33%. This is likely due to the actual measured RER being slightly different from the exact 0.80 benchmark.\n"
+     ]
+    }
+   ],
+   "source": [
+    "def calculate_fuel_usage_from_rer(df, phase_name=\"RMR\"):\n",
+    "    \"\"\"\n",
+    "    Calculates the percentage of Carbohydrate and Fat oxidation using the\n",
+    "    measured Respiratory Exchange Ratio (RER) during a specified phase.\n",
+    "\n",
+    "    This function uses the standard non-protein RER calculation (the RER\n",
+    "    is assumed to be the Non-Protein Respiratory Quotient, or non-protein RQ).\n",
+    "    \n",
+    "    Args:\n",
+    "        df (pd.DataFrame): The DataFrame containing the metabolic data.\n",
+    "        phase_name (str): The name of the phase to analyze (e.g., 'RMR').\n",
+    "                          The RMR phase is usually the initial resting period.\n",
+    "\n",
+    "    Returns:\n",
+    "        tuple: (average_rer, carbs_percent, fat_percent)\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    # 1. Clean the data columns\n",
+    "    # We must ensure RER is numeric and handle potential missing values\n",
+    "    df['RER'] = pd.to_numeric(df['RER'], errors='coerce')\n",
+    "    df['PHASE'] = df['PHASE'].astype(str).str.strip()\n",
+    "\n",
+    "    # The RMR phase is usually recorded in the 'PHASE' column.\n",
+    "    # If the RMR phase isn't explicitly labeled, we'll try to use the very start of the test.\n",
+    "    \n",
+    "    # First, let's identify the resting data. PNOE often uses an empty string or 'Rest'\n",
+    "    # for the initial RMR phase, or simply the first block of data.\n",
+    "    resting_data = df[df['PHASE'].str.contains(phase_name, case=False, na=False)].copy()\n",
+    "    \n",
+    "    if resting_data.empty:\n",
+    "        # Fallback: If no explicit phase name is found, assume the first 10 minutes (600 seconds)\n",
+    "        # or the first 50 rows of data are the resting phase, which is standard RMR protocol.\n",
+    "        print(f\"Warning: Phase '{phase_name}' not found. Assuming first 50 data points (approx. 5-10 min) are RMR.\")\n",
+    "        resting_data = df.head(50).copy()\n",
+    "        \n",
+    "    if resting_data.empty:\n",
+    "        print(\"Error: Could not find any valid resting data to analyze.\")\n",
+    "        return None, None, None\n",
+    "\n",
+    "    # Calculate the average RER during the identified resting period\n",
+    "    average_rer = resting_data['RER'].mean()\n",
+    "\n",
+    "    # 2. Apply the RER to Fuel Conversion Formulas\n",
+    "    # These formulas are based on the non-protein respiratory exchange:\n",
+    "    # RER of 0.70 = 100% Fat, 0% Carb\n",
+    "    # RER of 1.00 = 0% Fat, 100% Carb\n",
+    "    \n",
+    "    # Non-Protein Fat Oxidation Formula:\n",
+    "    # %Fat = ((1.00 - RER) / (1.00 - 0.70)) * 100\n",
+    "    # %Fat = ((1.00 - RER) / 0.30) * 100\n",
+    "    \n",
+    "    # Non-Protein Carbohydrate Oxidation Formula:\n",
+    "    # %Carb = 100 - %Fat\n",
+    "    \n",
+    "    if average_rer is None or pd.isna(average_rer):\n",
+    "        print(\"Error: Average RER calculation resulted in NaN.\")\n",
+    "        return None, None, None\n",
+    "        \n",
+    "    # We constrain the RER to the physiological range [0.70, 1.00] for fuel calculation\n",
+    "    constrained_rer = max(0.70, min(1.00, average_rer))\n",
+    "\n",
+    "    # Calculate the percentages\n",
+    "    fat_percent = ((1.00 - constrained_rer) / 0.30) * 100\n",
+    "    carbs_percent = 100.0 - fat_percent\n",
+    "    \n",
+    "    return average_rer, carbs_percent, fat_percent\n",
+    "\n",
+    "# --- Execution ---\n",
+    "# file_path = 'Pnoe_20250729_1550-Moran_Keirstyn.csv'\n",
+    "# df = pd.read_csv(file_path, delimiter=';')\n",
+    "\n",
+    "# Run the calculation. Since the RMR is the resting phase, we look for the\n",
+    "# beginning of the test or a 'Rest' phase.\n",
+    "avg_rer, carbs, fat = calculate_fuel_usage_from_rer(df, phase_name='')\n",
+    "\n",
+    "if avg_rer is not None:\n",
+    "    print(f\"--- RMR Fuel Usage Calculation Confirmation ---\")\n",
+    "    print(f\"1. Average RER Measured during Resting Phase: {avg_rer:.3f}\")\n",
+    "    print(f\"2. Calculated Carbohydrate Usage: {carbs:.1f}%\")\n",
+    "    print(f\"3. Calculated Fat Usage: {fat:.1f}%\")\n",
+    "    \n",
+    "    # This comparison confirms the 33% calculation\n",
+    "    if 32.5 <= carbs <= 34.5:\n",
+    "        print(\"\\n✅ This confirms the reported ~33% fuel usage based on the RER = 0.80 standard conversion.\")\n",
+    "    else:\n",
+    "        print(\"\\nNote: The calculated value differs from 33%. This is likely due to the actual measured RER being slightly different from the exact 0.80 benchmark.\")"
+   ]
   }
  ],
  "metadata": {