Elevated-Temperature Performance of Recycled Aggregate Concrete: Separating Initial Strength Loss from Thermal Degradation

{ "cells": [ { "cell_type": "markdown", "id": "360c2dac-6bd9-45e3-80e6-141e8182a55d", "metadata": {}, "source": [ "#PRECHECKS" ] }, { "cell_type": "code", "execution_count": 2, "id": "575da0a7-959d-4baa-a4c1-50d13862144e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data file : C:\\Users\\sadal\\Desktop\\Yasin Hoca-RCA Thermal\\data.xlsx\n", "Exists : True\n", "Output dir: C:\\Users\\sadal\\Desktop\\Yasin Hoca-RCA Thermal\\analysis_outputs\\Step_01_Reliability\n" ] } ], "source": [ "from pathlib import Path\n", "import re\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "from scipy import stats\n", "\n", "DATA_PATH = Path(r\"C:\\Users\\sadal\\Desktop\\Yasin Hoca-RCA Thermal\\data.xlsx\")\n", "\n", "OUT_DIR = DATA_PATH.parent / \"analysis_outputs\" / \"Step_01_Reliability\"\n", "OUT_DIR.mkdir(parents=True, exist_ok=True)\n", "\n", "print(\"Data file :\", DATA_PATH)\n", "print(\"Exists :\", DATA_PATH.exists())\n", "print(\"Output dir:\", OUT_DIR)\n", "\n", "if not DATA_PATH.exists():\n", " raise FileNotFoundError(f\"Excel file not found:\\n{DATA_PATH}\")" ] }, { "cell_type": "code", "execution_count": 4, "id": "38b0eeef-a72f-44db-82f7-c7a11e4a291d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Raw data shape: (7, 16)\n" ] }, { "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>0</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " <th>4</th>\n", " <th>5</th>\n", " <th>6</th>\n", " <th>7</th>\n", " <th>8</th>\n", " <th>9</th>\n", " <th>10</th>\n", " <th>11</th>\n", " <th>12</th>\n", " <th>13</th>\n", " <th>14</th>\n", " <th>15</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>NaN</td>\n", " <td>24°C</td>\n", " <td>24°C</td>\n", " <td>24°C</td>\n", " <td>150 °C</td>\n", " <td>150 °C</td>\n", " <td>150 °C</td>\n", " <td>300 °C</td>\n", " <td>300 °C</td>\n", " <td>300 °C</td>\n", " <td>450 °C</td>\n", " <td>450 °C</td>\n", " <td>450 °C</td>\n", " <td>600 °C</td>\n", " <td>600 °C</td>\n", " <td>600 °C</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>RCA0%</td>\n", " <td>20.124</td>\n", " <td>20.612</td>\n", " <td>22.547</td>\n", " <td>19.015</td>\n", " <td>20.62</td>\n", " <td>20.822</td>\n", " <td>18.177</td>\n", " <td>16.806</td>\n", " <td>17.967</td>\n", " <td>15.726</td>\n", " <td>15.58</td>\n", " <td>15.193</td>\n", " <td>11.21</td>\n", " <td>11.662</td>\n", " <td>11.807</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>RCA10%</td>\n", " <td>20.805</td>\n", " <td>21.789</td>\n", " <td>20.548</td>\n", " <td>19.29</td>\n", " <td>19.951</td>\n", " <td>21.125</td>\n", " <td>17.983</td>\n", " <td>16.483</td>\n", " <td>17.451</td>\n", " <td>14.758</td>\n", " <td>15.467</td>\n", " <td>15.758</td>\n", " <td>11.758</td>\n", " <td>10.936</td>\n", " <td>10.952</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>RCA20%</td>\n", " <td>18.677</td>\n", " <td>21.273</td>\n", " <td>19.773</td>\n", " <td>20.16</td>\n", " <td>20.402</td>\n", " <td>19.08</td>\n", " <td>17.032</td>\n", " <td>17.064</td>\n", " <td>17.419</td>\n", " <td>13.548</td>\n", " <td>15.387</td>\n", " <td>14.097</td>\n", " <td>10.984</td>\n", " <td>10.42</td>\n", " <td>10.775</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>RCA30%</td>\n", " <td>16.709</td>\n", " <td>19.967</td>\n", " <td>21.386</td>\n", " <td>20.015</td>\n", " <td>17.999</td>\n", " <td>16.709</td>\n", " <td>17.177</td>\n", " <td>16.967</td>\n", " <td>16.903</td>\n", " <td>14.161</td>\n", " <td>14.042</td>\n", " <td>14.242</td>\n", " <td>9.904</td>\n", " <td>10.501</td>\n", " <td>9.565</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>RCA40%</td>\n", " <td>17.225</td>\n", " <td>15.468</td>\n", " <td>18.483</td>\n", " <td>16.774</td>\n", " <td>17.919</td>\n", " <td>16.258</td>\n", " <td>14.661</td>\n", " <td>17.275</td>\n", " <td>15.484</td>\n", " <td>14.226</td>\n", " <td>13.774</td>\n", " <td>13.984</td>\n", " <td>10.694</td>\n", " <td>9.872</td>\n", " <td>8.307</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>RCA50%</td>\n", " <td>17.273</td>\n", " <td>16.016</td>\n", " <td>17.08</td>\n", " <td>17.919</td>\n", " <td>16.032</td>\n", " <td>15.242</td>\n", " <td>15.355</td>\n", " <td>14.79</td>\n", " <td>13.758</td>\n", " <td>12.291</td>\n", " <td>11.742</td>\n", " <td>13.065</td>\n", " <td>9.452</td>\n", " <td>9.146</td>\n", " <td>8.791</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 0 1 2 3 4 5 6 7 8 \\\n", "0 NaN 24°C 24°C 24°C 150 °C 150 °C 150 °C 300 °C 300 °C \n", "1 RCA0% 20.124 20.612 22.547 19.015 20.62 20.822 18.177 16.806 \n", "2 RCA10% 20.805 21.789 20.548 19.29 19.951 21.125 17.983 16.483 \n", "3 RCA20% 18.677 21.273 19.773 20.16 20.402 19.08 17.032 17.064 \n", "4 RCA30% 16.709 19.967 21.386 20.015 17.999 16.709 17.177 16.967 \n", "5 RCA40% 17.225 15.468 18.483 16.774 17.919 16.258 14.661 17.275 \n", "6 RCA50% 17.273 16.016 17.08 17.919 16.032 15.242 15.355 14.79 \n", "\n", " 9 10 11 12 13 14 15 \n", "0 300 °C 450 °C 450 °C 450 °C 600 °C 600 °C 600 °C \n", "1 17.967 15.726 15.58 15.193 11.21 11.662 11.807 \n", "2 17.451 14.758 15.467 15.758 11.758 10.936 10.952 \n", "3 17.419 13.548 15.387 14.097 10.984 10.42 10.775 \n", "4 16.903 14.161 14.042 14.242 9.904 10.501 9.565 \n", "5 15.484 14.226 13.774 13.984 10.694 9.872 8.307 \n", "6 13.758 12.291 11.742 13.065 9.452 9.146 8.791 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Sheet name is expected to be \"data\"\n", "raw = pd.read_excel(\n", " DATA_PATH,\n", " sheet_name=\"data\",\n", " header=None,\n", " engine=\"openpyxl\"\n", ")\n", "\n", "print(\"Raw data shape:\", raw.shape)\n", "display(raw)" ] }, { "cell_type": "code", "execution_count": 6, "id": "50adbd62-a39c-4a30-940c-b59b4ecd72b5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total observations : 90\n", "RCA levels : [0, 10, 20, 30, 40, 50]\n", "Temperature levels : [24, 150, 300, 450, 600]\n", "Replicates/cell : [3]\n", "\n", "DATA STRUCTURE CHECK: PASSED\n" ] }, { "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>Mix</th>\n", " <th>RCA_pct</th>\n", " <th>Temperature_C</th>\n", " <th>Replicate</th>\n", " <th>CompressiveStrength_MPa</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>RCA0%</td>\n", " <td>0</td>\n", " <td>24</td>\n", " <td>1</td>\n", " <td>20.124</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>RCA0%</td>\n", " <td>0</td>\n", " <td>24</td>\n", " <td>2</td>\n", " <td>20.612</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>RCA0%</td>\n", " <td>0</td>\n", " <td>24</td>\n", " <td>3</td>\n", " <td>22.547</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>RCA0%</td>\n", " <td>0</td>\n", " <td>150</td>\n", " <td>1</td>\n", " <td>19.015</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>RCA0%</td>\n", " <td>0</td>\n", " <td>150</td>\n", " <td>2</td>\n", " <td>20.620</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>RCA0%</td>\n", " <td>0</td>\n", " <td>150</td>\n", " <td>3</td>\n", " <td>20.822</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>RCA0%</td>\n", " <td>0</td>\n", " <td>300</td>\n", " <td>1</td>\n", " <td>18.177</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>RCA0%</td>\n", " <td>0</td>\n", " <td>300</td>\n", " <td>2</td>\n", " <td>16.806</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>RCA0%</td>\n", " <td>0</td>\n", " <td>300</td>\n", " <td>3</td>\n", " <td>17.967</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>RCA0%</td>\n", " <td>0</td>\n", " <td>450</td>\n", " <td>1</td>\n", " <td>15.726</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>RCA0%</td>\n", " <td>0</td>\n", " <td>450</td>\n", " <td>2</td>\n", " <td>15.580</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>RCA0%</td>\n", " <td>0</td>\n", " <td>450</td>\n", " <td>3</td>\n", " <td>15.193</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>RCA0%</td>\n", " <td>0</td>\n", " <td>600</td>\n", " <td>1</td>\n", " <td>11.210</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>RCA0%</td>\n", " <td>0</td>\n", " <td>600</td>\n", " <td>2</td>\n", " <td>11.662</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>RCA0%</td>\n", " <td>0</td>\n", " <td>600</td>\n", " <td>3</td>\n", " <td>11.807</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Mix RCA_pct Temperature_C Replicate CompressiveStrength_MPa\n", "0 RCA0% 0 24 1 20.124\n", "1 RCA0% 0 24 2 20.612\n", "2 RCA0% 0 24 3 22.547\n", "3 RCA0% 0 150 1 19.015\n", "4 RCA0% 0 150 2 20.620\n", "5 RCA0% 0 150 3 20.822\n", "6 RCA0% 0 300 1 18.177\n", "7 RCA0% 0 300 2 16.806\n", "8 RCA0% 0 300 3 17.967\n", "9 RCA0% 0 450 1 15.726\n", "10 RCA0% 0 450 2 15.580\n", "11 RCA0% 0 450 3 15.193\n", "12 RCA0% 0 600 1 11.210\n", "13 RCA0% 0 600 2 11.662\n", "14 RCA0% 0 600 3 11.807" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# ---------------------------------------------------------\n", "# WIDE -> LONG FORMAT\n", "# ---------------------------------------------------------\n", "\n", "temperature_labels = raw.iloc[0, 1:]\n", "\n", "temperatures = (\n", " temperature_labels\n", " .astype(str)\n", " .str.extract(r\"(\\d+)\", expand=False)\n", " .astype(int)\n", " .tolist()\n", ")\n", "\n", "records = []\n", "\n", "for row_idx in range(1, len(raw)):\n", " \n", " mix_label = str(raw.iloc[row_idx, 0]).strip()\n", " \n", " rca_match = re.search(r\"(\\d+)\", mix_label)\n", " \n", " if rca_match is None:\n", " raise ValueError(f\"RCA percentage could not be parsed: {mix_label}\")\n", " \n", " rca_pct = int(rca_match.group(1))\n", " \n", " rep_counter = {}\n", " \n", " for col_idx, temperature in enumerate(temperatures, start=1):\n", " \n", " rep_counter[temperature] = rep_counter.get(temperature, 0) + 1\n", " replicate = rep_counter[temperature]\n", " \n", " value = pd.to_numeric(\n", " raw.iloc[row_idx, col_idx],\n", " errors=\"coerce\"\n", " )\n", " \n", " records.append({\n", " \"Mix\": mix_label,\n", " \"RCA_pct\": rca_pct,\n", " \"Temperature_C\": temperature,\n", " \"Replicate\": replicate,\n", " \"CompressiveStrength_MPa\": value\n", " })\n", "\n", "df = pd.DataFrame(records)\n", "\n", "# ---------------------------------------------------------\n", "# DATA INTEGRITY CHECKS\n", "# ---------------------------------------------------------\n", "\n", "if df[\"CompressiveStrength_MPa\"].isna().any():\n", " print(df[df[\"CompressiveStrength_MPa\"].isna()])\n", " raise ValueError(\"Missing or non-numeric strength value detected.\")\n", "\n", "cell_counts = (\n", " df.groupby([\"RCA_pct\", \"Temperature_C\"])\n", " .size()\n", ")\n", "\n", "print(\"Total observations :\", len(df))\n", "print(\"RCA levels :\", sorted(df[\"RCA_pct\"].unique()))\n", "print(\"Temperature levels :\", sorted(df[\"Temperature_C\"].unique()))\n", "print(\"Replicates/cell :\", sorted(cell_counts.unique()))\n", "\n", "assert len(df) == 90, \"Expected 90 observations.\"\n", "assert df[\"RCA_pct\"].nunique() == 6, \"Expected 6 RCA levels.\"\n", "assert df[\"Temperature_C\"].nunique() == 5, \"Expected 5 temperature levels.\"\n", "assert (cell_counts == 3).all(), \"Each design cell must contain exactly 3 replicates.\"\n", "\n", "print(\"\\nDATA STRUCTURE CHECK: PASSED\")\n", "\n", "display(df.head(15))" ] }, { "cell_type": "code", "execution_count": 8, "id": "42d731f4-a018-4eb9-a96d-dec84081a9bd", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Median CV (%) : 4.5\n", "Maximum CV (%): 12.6\n", "Cells CV >10% : 2\n" ] }, { "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>RCA_pct</th>\n", " <th>Temperature_C</th>\n", " <th>N</th>\n", " <th>Mean_MPa</th>\n", " <th>SD_MPa</th>\n", " <th>CV_pct</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>24</td>\n", " <td>3</td>\n", " <td>21.094</td>\n", " <td>1.281</td>\n", " <td>6.08</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0</td>\n", " <td>150</td>\n", " <td>3</td>\n", " <td>20.152</td>\n", " <td>0.990</td>\n", " <td>4.91</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0</td>\n", " <td>300</td>\n", " <td>3</td>\n", " <td>17.650</td>\n", " <td>0.738</td>\n", " <td>4.18</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0</td>\n", " <td>450</td>\n", " <td>3</td>\n", " <td>15.500</td>\n", " <td>0.275</td>\n", " <td>1.78</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0</td>\n", " <td>600</td>\n", " <td>3</td>\n", " <td>11.560</td>\n", " <td>0.311</td>\n", " <td>2.69</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>10</td>\n", " <td>24</td>\n", " <td>3</td>\n", " <td>21.047</td>\n", " <td>0.655</td>\n", " <td>3.11</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>10</td>\n", " <td>150</td>\n", " <td>3</td>\n", " <td>20.122</td>\n", " <td>0.929</td>\n", " <td>4.62</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>10</td>\n", " <td>300</td>\n", " <td>3</td>\n", " <td>17.306</td>\n", " <td>0.760</td>\n", " <td>4.39</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>10</td>\n", " <td>450</td>\n", " <td>3</td>\n", " <td>15.328</td>\n", " <td>0.514</td>\n", " <td>3.36</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>10</td>\n", " <td>600</td>\n", " <td>3</td>\n", " <td>11.215</td>\n", " <td>0.470</td>\n", " <td>4.19</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>20</td>\n", " <td>24</td>\n", " <td>3</td>\n", " <td>19.908</td>\n", " <td>1.303</td>\n", " <td>6.55</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>20</td>\n", " <td>150</td>\n", " <td>3</td>\n", " <td>19.881</td>\n", " <td>0.704</td>\n", " <td>3.54</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>20</td>\n", " <td>300</td>\n", " <td>3</td>\n", " <td>17.172</td>\n", " <td>0.215</td>\n", " <td>1.25</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>20</td>\n", " <td>450</td>\n", " <td>3</td>\n", " <td>14.344</td>\n", " <td>0.944</td>\n", " <td>6.58</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>20</td>\n", " <td>600</td>\n", " <td>3</td>\n", " <td>10.726</td>\n", " <td>0.285</td>\n", " <td>2.66</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>30</td>\n", " <td>24</td>\n", " <td>3</td>\n", " <td>19.354</td>\n", " <td>2.398</td>\n", " <td>12.39</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>30</td>\n", " <td>150</td>\n", " <td>3</td>\n", " <td>18.241</td>\n", " <td>1.666</td>\n", " <td>9.13</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>30</td>\n", " <td>300</td>\n", " <td>3</td>\n", " <td>17.016</td>\n", " <td>0.143</td>\n", " <td>0.84</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>30</td>\n", " <td>450</td>\n", " <td>3</td>\n", " <td>14.148</td>\n", " <td>0.101</td>\n", " <td>0.71</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>30</td>\n", " <td>600</td>\n", " <td>3</td>\n", " <td>9.990</td>\n", " <td>0.474</td>\n", " <td>4.74</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>40</td>\n", " <td>24</td>\n", " <td>3</td>\n", " <td>17.059</td>\n", " <td>1.514</td>\n", " <td>8.88</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>40</td>\n", " <td>150</td>\n", " <td>3</td>\n", " <td>16.984</td>\n", " <td>0.850</td>\n", " <td>5.01</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>40</td>\n", " <td>300</td>\n", " <td>3</td>\n", " <td>15.807</td>\n", " <td>1.337</td>\n", " <td>8.46</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>40</td>\n", " <td>450</td>\n", " <td>3</td>\n", " <td>13.995</td>\n", " <td>0.226</td>\n", " <td>1.62</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>40</td>\n", " <td>600</td>\n", " <td>3</td>\n", " <td>9.624</td>\n", " <td>1.213</td>\n", " <td>12.60</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>50</td>\n", " <td>24</td>\n", " <td>3</td>\n", " <td>16.790</td>\n", " <td>0.677</td>\n", " <td>4.03</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>50</td>\n", " <td>150</td>\n", " <td>3</td>\n", " <td>16.398</td>\n", " <td>1.375</td>\n", " <td>8.39</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>50</td>\n", " <td>300</td>\n", " <td>3</td>\n", " <td>14.634</td>\n", " <td>0.810</td>\n", " <td>5.53</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>50</td>\n", " <td>450</td>\n", " <td>3</td>\n", " <td>12.366</td>\n", " <td>0.665</td>\n", " <td>5.38</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>50</td>\n", " <td>600</td>\n", " <td>3</td>\n", " <td>9.130</td>\n", " <td>0.331</td>\n", " <td>3.62</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " RCA_pct Temperature_C N Mean_MPa SD_MPa CV_pct\n", "0 0 24 3 21.094 1.281 6.08\n", "1 0 150 3 20.152 0.990 4.91\n", "2 0 300 3 17.650 0.738 4.18\n", "3 0 450 3 15.500 0.275 1.78\n", "4 0 600 3 11.560 0.311 2.69\n", "5 10 24 3 21.047 0.655 3.11\n", "6 10 150 3 20.122 0.929 4.62\n", "7 10 300 3 17.306 0.760 4.39\n", "8 10 450 3 15.328 0.514 3.36\n", "9 10 600 3 11.215 0.470 4.19\n", "10 20 24 3 19.908 1.303 6.55\n", "11 20 150 3 19.881 0.704 3.54\n", "12 20 300 3 17.172 0.215 1.25\n", "13 20 450 3 14.344 0.944 6.58\n", "14 20 600 3 10.726 0.285 2.66\n", "15 30 24 3 19.354 2.398 12.39\n", "16 30 150 3 18.241 1.666 9.13\n", "17 30 300 3 17.016 0.143 0.84\n", "18 30 450 3 14.148 0.101 0.71\n", "19 30 600 3 9.990 0.474 4.74\n", "20 40 24 3 17.059 1.514 8.88\n", "21 40 150 3 16.984 0.850 5.01\n", "22 40 300 3 15.807 1.337 8.46\n", "23 40 450 3 13.995 0.226 1.62\n", "24 40 600 3 9.624 1.213 12.60\n", "25 50 24 3 16.790 0.677 4.03\n", "26 50 150 3 16.398 1.375 8.39\n", "27 50 300 3 14.634 0.810 5.53\n", "28 50 450 3 12.366 0.665 5.38\n", "29 50 600 3 9.130 0.331 3.62" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# ---------------------------------------------------------\n", "# DESCRIPTIVE STATISTICS\n", "# ---------------------------------------------------------\n", "\n", "summary = (\n", " df.groupby([\"RCA_pct\", \"Temperature_C\"])\n", " [\"CompressiveStrength_MPa\"]\n", " .agg(\n", " N=\"count\",\n", " Mean_MPa=\"mean\",\n", " SD_MPa=\"std\"\n", " )\n", " .reset_index()\n", ")\n", "\n", "summary[\"CV_pct\"] = (\n", " summary[\"SD_MPa\"] /\n", " summary[\"Mean_MPa\"] * 100\n", ")\n", "\n", "summary[\"Mean_MPa\"] = summary[\"Mean_MPa\"].round(3)\n", "summary[\"SD_MPa\"] = summary[\"SD_MPa\"].round(3)\n", "summary[\"CV_pct\"] = summary[\"CV_pct\"].round(2)\n", "\n", "print(\"Median CV (%) :\", round(summary[\"CV_pct\"].median(), 2))\n", "print(\"Maximum CV (%):\", round(summary[\"CV_pct\"].max(), 2))\n", "print(\"Cells CV >10% :\", int((summary[\"CV_pct\"] > 10).sum()))\n", "\n", "display(summary)" ] }, { "cell_type": "code", "execution_count": 10, "id": "409b6ef7-8522-4a9f-bf53-1e6939df1837", "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>RCA_pct</th>\n", " <th>Temperature_C</th>\n", " <th>Replicate</th>\n", " <th>CompressiveStrength_MPa</th>\n", " <th>Fitted_CellMean</th>\n", " <th>Residual</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>24</td>\n", " <td>1</td>\n", " <td>20.124</td>\n", " <td>21.094333</td>\n", " <td>-0.970333</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0</td>\n", " <td>24</td>\n", " <td>2</td>\n", " <td>20.612</td>\n", " <td>21.094333</td>\n", " <td>-0.482333</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0</td>\n", " <td>24</td>\n", " <td>3</td>\n", " <td>22.547</td>\n", " <td>21.094333</td>\n", " <td>1.452667</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0</td>\n", " <td>150</td>\n", " <td>1</td>\n", " <td>19.015</td>\n", " <td>20.152333</td>\n", " <td>-1.137333</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0</td>\n", " <td>150</td>\n", " <td>2</td>\n", " <td>20.620</td>\n", " <td>20.152333</td>\n", " <td>0.467667</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>0</td>\n", " <td>150</td>\n", " <td>3</td>\n", " <td>20.822</td>\n", " <td>20.152333</td>\n", " <td>0.669667</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>0</td>\n", " <td>300</td>\n", " <td>1</td>\n", " <td>18.177</td>\n", " <td>17.650000</td>\n", " <td>0.527000</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>0</td>\n", " <td>300</td>\n", " <td>2</td>\n", " <td>16.806</td>\n", " <td>17.650000</td>\n", " <td>-0.844000</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>0</td>\n", " <td>300</td>\n", " <td>3</td>\n", " <td>17.967</td>\n", " <td>17.650000</td>\n", " <td>0.317000</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>0</td>\n", " <td>450</td>\n", " <td>1</td>\n", " <td>15.726</td>\n", " <td>15.499667</td>\n", " <td>0.226333</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>0</td>\n", " <td>450</td>\n", " <td>2</td>\n", " <td>15.580</td>\n", " <td>15.499667</td>\n", " <td>0.080333</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>0</td>\n", " <td>450</td>\n", " <td>3</td>\n", " <td>15.193</td>\n", " <td>15.499667</td>\n", " <td>-0.306667</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>0</td>\n", " <td>600</td>\n", " <td>1</td>\n", " <td>11.210</td>\n", " <td>11.559667</td>\n", " <td>-0.349667</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>0</td>\n", " <td>600</td>\n", " <td>2</td>\n", " <td>11.662</td>\n", " <td>11.559667</td>\n", " <td>0.102333</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>0</td>\n", " <td>600</td>\n", " <td>3</td>\n", " <td>11.807</td>\n", " <td>11.559667</td>\n", " <td>0.247333</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " RCA_pct Temperature_C Replicate CompressiveStrength_MPa \\\n", "0 0 24 1 20.124 \n", "1 0 24 2 20.612 \n", "2 0 24 3 22.547 \n", "3 0 150 1 19.015 \n", "4 0 150 2 20.620 \n", "5 0 150 3 20.822 \n", "6 0 300 1 18.177 \n", "7 0 300 2 16.806 \n", "8 0 300 3 17.967 \n", "9 0 450 1 15.726 \n", "10 0 450 2 15.580 \n", "11 0 450 3 15.193 \n", "12 0 600 1 11.210 \n", "13 0 600 2 11.662 \n", "14 0 600 3 11.807 \n", "\n", " Fitted_CellMean Residual \n", "0 21.094333 -0.970333 \n", "1 21.094333 -0.482333 \n", "2 21.094333 1.452667 \n", "3 20.152333 -1.137333 \n", "4 20.152333 0.467667 \n", "5 20.152333 0.669667 \n", "6 17.650000 0.527000 \n", "7 17.650000 -0.844000 \n", "8 17.650000 0.317000 \n", "9 15.499667 0.226333 \n", "10 15.499667 0.080333 \n", "11 15.499667 -0.306667 \n", "12 11.559667 -0.349667 \n", "13 11.559667 0.102333 \n", "14 11.559667 0.247333 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# ---------------------------------------------------------\n", "# RESIDUALS FROM FULL FACTORIAL CELL-MEAN MODEL\n", "# ---------------------------------------------------------\n", "\n", "df[\"Fitted_CellMean\"] = (\n", " df.groupby([\"RCA_pct\", \"Temperature_C\"])\n", " [\"CompressiveStrength_MPa\"]\n", " .transform(\"mean\")\n", ")\n", "\n", "df[\"Residual\"] = (\n", " df[\"CompressiveStrength_MPa\"]\n", " - df[\"Fitted_CellMean\"]\n", ")\n", "\n", "display(\n", " df[\n", " [\n", " \"RCA_pct\",\n", " \"Temperature_C\",\n", " \"Replicate\",\n", " \"CompressiveStrength_MPa\",\n", " \"Fitted_CellMean\",\n", " \"Residual\"\n", " ]\n", " ].head(15)\n", ")" ] }, { "cell_type": "code", "execution_count": 12, "id": "5bfba0b7-a49b-4943-9d29-e6b43095eb1c", "metadata": {}, "outputs": [], "source": [ "# ---------------------------------------------------------\n", "# ASSUMPTION TESTS\n", "# ---------------------------------------------------------\n", "\n", "# 1. Shapiro-Wilk test on ANOVA residuals\n", "shapiro_stat, shapiro_p = stats.shapiro(df[\"Residual\"])\n", "\n", "# 2. Brown-Forsythe test\n", "#" ] }, { "cell_type": "code", "execution_count": 14, "id": "ebf59ce6-f2d7-4355-b077-c9339c25f87d", "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>Mix</th>\n", " <th>RCA_pct</th>\n", " <th>Temperature_C</th>\n", " <th>Replicate</th>\n", " <th>CompressiveStrength_MPa</th>\n", " <th>Fitted_CellMean</th>\n", " <th>Residual</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>RCA0%</td>\n", " <td>0</td>\n", " <td>24</td>\n", " <td>1</td>\n", " <td>20.124</td>\n", " <td>21.094333</td>\n", " <td>-0.970333</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>RCA0%</td>\n", " <td>0</td>\n", " <td>24</td>\n", " <td>2</td>\n", " <td>20.612</td>\n", " <td>21.094333</td>\n", " <td>-0.482333</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>RCA0%</td>\n", " <td>0</td>\n", " <td>24</td>\n", " <td>3</td>\n", " <td>22.547</td>\n", " <td>21.094333</td>\n", " <td>1.452667</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>RCA0%</td>\n", " <td>0</td>\n", " <td>150</td>\n", " <td>1</td>\n", " <td>19.015</td>\n", " <td>20.152333</td>\n", " <td>-1.137333</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>RCA0%</td>\n", " <td>0</td>\n", " <td>150</td>\n", " <td>2</td>\n", " <td>20.620</td>\n", " <td>20.152333</td>\n", " <td>0.467667</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>RCA0%</td>\n", " <td>0</td>\n", " <td>150</td>\n", " <td>3</td>\n", " <td>20.822</td>\n", " <td>20.152333</td>\n", " <td>0.669667</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>RCA0%</td>\n", " <td>0</td>\n", " <td>300</td>\n", " <td>1</td>\n", " <td>18.177</td>\n", " <td>17.650000</td>\n", " <td>0.527000</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>RCA0%</td>\n", " <td>0</td>\n", " <td>300</td>\n", " <td>2</td>\n", " <td>16.806</td>\n", " <td>17.650000</td>\n", " <td>-0.844000</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>RCA0%</td>\n", " <td>0</td>\n", " <td>300</td>\n", " <td>3</td>\n", " <td>17.967</td>\n", " <td>17.650000</td>\n", " <td>0.317000</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>RCA0%</td>\n", " <td>0</td>\n", " <td>450</td>\n", " <td>1</td>\n", " <td>15.726</td>\n", " <td>15.499667</td>\n", " <td>0.226333</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>RCA0%</td>\n", " <td>0</td>\n", " <td>450</td>\n", " <td>2</td>\n", " <td>15.580</td>\n", " <td>15.499667</td>\n", " <td>0.080333</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>RCA0%</td>\n", " <td>0</td>\n", " <td>450</td>\n", " <td>3</td>\n", " <td>15.193</td>\n", " <td>15.499667</td>\n", " <td>-0.306667</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Mix RCA_pct Temperature_C Replicate CompressiveStrength_MPa \\\n", "0 RCA0% 0 24 1 20.124 \n", "1 RCA0% 0 24 2 20.612 \n", "2 RCA0% 0 24 3 22.547 \n", "3 RCA0% 0 150 1 19.015 \n", "4 RCA0% 0 150 2 20.620 \n", "5 RCA0% 0 150 3 20.822 \n", "6 RCA0% 0 300 1 18.177 \n", "7 RCA0% 0 300 2 16.806 \n", "8 RCA0% 0 300 3 17.967 \n", "9 RCA0% 0 450 1 15.726 \n", "10 RCA0% 0 450 2 15.580 \n", "11 RCA0% 0 450 3 15.193 \n", "\n", " Fitted_CellMean Residual \n", "0 21.094333 -0.970333 \n", "1 21.094333 -0.482333 \n", "2 21.094333 1.452667 \n", "3 20.152333 -1.137333 \n", "4 20.152333 0.467667 \n", "5 20.152333 0.669667 \n", "6 17.650000 0.527000 \n", "7 17.650000 -0.844000 \n", "8 17.650000 0.317000 \n", "9 15.499667 0.226333 \n", "10 15.499667 0.080333 \n", "11 15.499667 -0.306667 " ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Mean residual: 0.0\n" ] } ], "source": [ "# ============================================================\n", "# CELL-CENTERED RESIDUALS\n", "# ============================================================\n", "\n", "df[\"Fitted_CellMean\"] = (\n", " df.groupby([\"RCA_pct\", \"Temperature_C\"])\n", " [\"CompressiveStrength_MPa\"]\n", " .transform(\"mean\")\n", ")\n", "\n", "df[\"Residual\"] = (\n", " df[\"CompressiveStrength_MPa\"]\n", " - df[\"Fitted_CellMean\"]\n", ")\n", "\n", "display(df.head(12))\n", "\n", "print(\"Mean residual:\",\n", " round(df[\"Residual\"].mean(), 10))" ] }, { "cell_type": "code", "execution_count": 16, "id": "d075ffa7-66a2-41d0-b585-bc06446446d1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shapiro-Wilk normality test\n", "--------------------------------\n", "W statistic = 0.9832\n", "p-value = 0.2994\n", "Result = Normality assumption not violated.\n" ] } ], "source": [ "# ============================================================\n", "# SHAPIRO-WILK NORMALITY TEST\n", "# ============================================================\n", "\n", "shapiro_stat, shapiro_p = stats.shapiro(df[\"Residual\"])\n", "\n", "print(\"Shapiro-Wilk normality test\")\n", "print(\"--------------------------------\")\n", "print(f\"W statistic = {shapiro_stat:.4f}\")\n", "print(f\"p-value = {shapiro_p:.4f}\")\n", "\n", "if shapiro_p > 0.05:\n", " print(\"Result = Normality assumption not violated.\")\n", "else:\n", " print(\"Result = Evidence of departure from normality.\")" ] }, { "cell_type": "code", "execution_count": 18, "id": "162b7191-c40f-4d09-90c8-ab2d91ffba8d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Brown-Forsythe homogeneity test\n", "--------------------------------\n", "F statistic = 0.8390\n", "p-value = 0.6923\n", "Result = Homogeneity of variance assumption not violated.\n" ] } ], "source": [ "# ============================================================\n", "# BROWN-FORSYTHE TEST\n", "# ============================================================\n", "\n", "groups = [\n", " group[\"CompressiveStrength_MPa\"].values\n", " for _, group in\n", " df.groupby([\"RCA_pct\", \"Temperature_C\"])\n", "]\n", "\n", "bf_stat, bf_p = stats.levene(\n", " *groups,\n", " center=\"median\"\n", ")\n", "\n", "print(\"Brown-Forsythe homogeneity test\")\n", "print(\"--------------------------------\")\n", "print(f\"F statistic = {bf_stat:.4f}\")\n", "print(f\"p-value = {bf_p:.4f}\")\n", "\n", "if bf_p > 0.05:\n", " print(\"Result = Homogeneity of variance assumption not violated.\")\n", "else:\n", " print(\"Result = Evidence of heterogeneous variances.\")" ] }, { "cell_type": "code", "execution_count": 22, "id": "ce299aff-4e8b-4f08-bcac-2da5841815d1", "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>RCA_pct</th>\n", " <th>Temperature_C</th>\n", " <th>N</th>\n", " <th>Mean_MPa</th>\n", " <th>SD_MPa</th>\n", " <th>CV_pct</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>24</td>\n", " <td>3</td>\n", " <td>21.094</td>\n", " <td>1.281</td>\n", " <td>6.08</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0</td>\n", " <td>150</td>\n", " <td>3</td>\n", " <td>20.152</td>\n", " <td>0.990</td>\n", " <td>4.91</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0</td>\n", " <td>300</td>\n", " <td>3</td>\n", " <td>17.650</td>\n", " <td>0.738</td>\n", " <td>4.18</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0</td>\n", " <td>450</td>\n", " <td>3</td>\n", " <td>15.500</td>\n", " <td>0.275</td>\n", " <td>1.78</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0</td>\n", " <td>600</td>\n", " <td>3</td>\n", " <td>11.560</td>\n", " <td>0.311</td>\n", " <td>2.69</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>10</td>\n", " <td>24</td>\n", " <td>3</td>\n", " <td>21.047</td>\n", " <td>0.655</td>\n", " <td>3.11</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>10</td>\n", " <td>150</td>\n", " <td>3</td>\n", " <td>20.122</td>\n", " <td>0.929</td>\n", " <td>4.62</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>10</td>\n", " <td>300</td>\n", " <td>3</td>\n", " <td>17.306</td>\n", " <td>0.760</td>\n", " <td>4.39</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>10</td>\n", " <td>450</td>\n", " <td>3</td>\n", " <td>15.328</td>\n", " <td>0.514</td>\n", " <td>3.36</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>10</td>\n", " <td>600</td>\n", " <td>3</td>\n", " <td>11.215</td>\n", " <td>0.470</td>\n", " <td>4.19</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>20</td>\n", " <td>24</td>\n", " <td>3</td>\n", " <td>19.908</td>\n", " <td>1.303</td>\n", " <td>6.55</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>20</td>\n", " <td>150</td>\n", " <td>3</td>\n", " <td>19.881</td>\n", " <td>0.704</td>\n", " <td>3.54</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>20</td>\n", " <td>300</td>\n", " <td>3</td>\n", " <td>17.172</td>\n", " <td>0.215</td>\n", " <td>1.25</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>20</td>\n", " <td>450</td>\n", " <td>3</td>\n", " <td>14.344</td>\n", " <td>0.944</td>\n", " <td>6.58</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>20</td>\n", " <td>600</td>\n", " <td>3</td>\n", " <td>10.726</td>\n", " <td>0.285</td>\n", " <td>2.66</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>30</td>\n", " <td>24</td>\n", " <td>3</td>\n", " <td>19.354</td>\n", " <td>2.398</td>\n", " <td>12.39</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>30</td>\n", " <td>150</td>\n", " <td>3</td>\n", " <td>18.241</td>\n", " <td>1.666</td>\n", " <td>9.13</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>30</td>\n", " <td>300</td>\n", " <td>3</td>\n", " <td>17.016</td>\n", " <td>0.143</td>\n", " <td>0.84</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>30</td>\n", " <td>450</td>\n", " <td>3</td>\n", " <td>14.148</td>\n", " <td>0.101</td>\n", " <td>0.71</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>30</td>\n", " <td>600</td>\n", " <td>3</td>\n", " <td>9.990</td>\n", " <td>0.474</td>\n", " <td>4.74</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>40</td>\n", " <td>24</td>\n", " <td>3</td>\n", " <td>17.059</td>\n", " <td>1.514</td>\n", " <td>8.88</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>40</td>\n", " <td>150</td>\n", " <td>3</td>\n", " <td>16.984</td>\n", " <td>0.850</td>\n", " <td>5.01</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>40</td>\n", " <td>300</td>\n", " <td>3</td>\n", " <td>15.807</td>\n", " <td>1.337</td>\n", " <td>8.46</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>40</td>\n", " <td>450</td>\n", " <td>3</td>\n", " <td>13.995</td>\n", " <td>0.226</td>\n", " <td>1.62</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>40</td>\n", " <td>600</td>\n", " <td>3</td>\n", " <td>9.624</td>\n", " <td>1.213</td>\n", " <td>12.60</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>50</td>\n", " <td>24</td>\n", " <td>3</td>\n", " <td>16.790</td>\n", " <td>0.677</td>\n", " <td>4.03</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>50</td>\n", " <td>150</td>\n", " <td>3</td>\n", " <td>16.398</td>\n", " <td>1.375</td>\n", " <td>8.39</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>50</td>\n", " <td>300</td>\n", " <td>3</td>\n", " <td>14.634</td>\n", " <td>0.810</td>\n", " <td>5.53</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>50</td>\n", " <td>450</td>\n", " <td>3</td>\n", " <td>12.366</td>\n", " <td>0.665</td>\n", " <td>5.38</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>50</td>\n", " <td>600</td>\n", " <td>3</td>\n", " <td>9.130</td>\n", " <td>0.331</td>\n", " <td>3.62</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " RCA_pct Temperature_C N Mean_MPa SD_MPa CV_pct\n", "0 0 24 3 21.094 1.281 6.08\n", "1 0 150 3 20.152 0.990 4.91\n", "2 0 300 3 17.650 0.738 4.18\n", "3 0 450 3 15.500 0.275 1.78\n", "4 0 600 3 11.560 0.311 2.69\n", "5 10 24 3 21.047 0.655 3.11\n", "6 10 150 3 20.122 0.929 4.62\n", "7 10 300 3 17.306 0.760 4.39\n", "8 10 450 3 15.328 0.514 3.36\n", "9 10 600 3 11.215 0.470 4.19\n", "10 20 24 3 19.908 1.303 6.55\n", "11 20 150 3 19.881 0.704 3.54\n", "12 20 300 3 17.172 0.215 1.25\n", "13 20 450 3 14.344 0.944 6.58\n", "14 20 600 3 10.726 0.285 2.66\n", "15 30 24 3 19.354 2.398 12.39\n", "16 30 150 3 18.241 1.666 9.13\n", "17 30 300 3 17.016 0.143 0.84\n", "18 30 450 3 14.148 0.101 0.71\n", "19 30 600 3 9.990 0.474 4.74\n", "20 40 24 3 17.059 1.514 8.88\n", "21 40 150 3 16.984 0.850 5.01\n", "22 40 300 3 15.807 1.337 8.46\n", "23 40 450 3 13.995 0.226 1.62\n", "24 40 600 3 9.624 1.213 12.60\n", "25 50 24 3 16.790 0.677 4.03\n", "26 50 150 3 16.398 1.375 8.39\n", "27 50 300 3 14.634 0.810 5.53\n", "28 50 450 3 12.366 0.665 5.38\n", "29 50 600 3 9.130 0.331 3.62" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "--- REPEATABILITY SUMMARY ---\n", "Design cells : 30\n", "Median CV : 4.50%\n", "Maximum CV : 12.60%\n", "CV > 10% : 2 cells\n", "\n", "✓ 'descriptive' dataframe successfully created.\n" ] } ], "source": [ "# ============================================================\n", "# STEP 1A — DESCRIPTIVE STATISTICS\n", "# Safe / reproducible version\n", "# ============================================================\n", "\n", "# First check whether long-format dataframe exists\n", "if \"df\" not in globals():\n", " raise RuntimeError(\n", " \"'df' is not defined. Please rerun the WIDE → LONG FORMAT cell first.\"\n", " )\n", "\n", "required_columns = [\n", " \"RCA_pct\",\n", " \"Temperature_C\",\n", " \"CompressiveStrength_MPa\"\n", "]\n", "\n", "missing_columns = [\n", " col for col in required_columns\n", " if col not in df.columns\n", "]\n", "\n", "if missing_columns:\n", " raise RuntimeError(\n", " f\"Missing columns in df: {missing_columns}\"\n", " )\n", "\n", "# ------------------------------------------------------------\n", "# Calculate descriptive statistics\n", "# ------------------------------------------------------------\n", "\n", "descriptive = (\n", " df.groupby(\n", " [\"RCA_pct\", \"Temperature_C\"],\n", " as_index=False\n", " )\n", " .agg(\n", " N=(\"CompressiveStrength_MPa\", \"count\"),\n", " Mean_MPa=(\"CompressiveStrength_MPa\", \"mean\"),\n", " SD_MPa=(\"CompressiveStrength_MPa\", \"std\")\n", " )\n", ")\n", "\n", "# Coefficient of variation\n", "descriptive[\"CV_pct\"] = (\n", " descriptive[\"SD_MPa\"]\n", " / descriptive[\"Mean_MPa\"]\n", " * 100\n", ")\n", "\n", "# Round only for presentation\n", "descriptive[\"Mean_MPa\"] = descriptive[\"Mean_MPa\"].round(3)\n", "descriptive[\"SD_MPa\"] = descriptive[\"SD_MPa\"].round(3)\n", "descriptive[\"CV_pct\"] = descriptive[\"CV_pct\"].round(2)\n", "\n", "# ------------------------------------------------------------\n", "# Checks\n", "# ------------------------------------------------------------\n", "\n", "assert len(descriptive) == 30, (\n", " f\"Expected 30 RCA × Temperature cells, found {len(descriptive)}\"\n", ")\n", "\n", "assert (descriptive[\"N\"] == 3).all(), (\n", " \"Some experimental cells do not contain exactly 3 replicates.\"\n", ")\n", "\n", "display(descriptive)\n", "\n", "print(\"\\n--- REPEATABILITY SUMMARY ---\")\n", "print(f\"Design cells : {len(descriptive)}\")\n", "print(f\"Median CV : {descriptive['CV_pct'].median():.2f}%\")\n", "print(f\"Maximum CV : {descriptive['CV_pct'].max():.2f}%\")\n", "print(f\"CV > 10% : {(descriptive['CV_pct'] > 10).sum()} cells\")\n", "\n", "print(\"\\n✓ 'descriptive' dataframe successfully created.\")" ] }, { "cell_type": "code", "execution_count": 24, "id": "ab0beaca-e2d7-4022-8cbe-38572685aa15", "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>24 °C</th>\n", " <th>150 °C</th>\n", " <th>300 °C</th>\n", " <th>450 °C</th>\n", " <th>600 °C</th>\n", " </tr>\n", " <tr>\n", " <th>RCA (%)</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>21.09 ± 1.28 [6.1%]</td>\n", " <td>20.15 ± 0.99 [4.9%]</td>\n", " <td>17.65 ± 0.74 [4.2%]</td>\n", " <td>15.50 ± 0.28 [1.8%]</td>\n", " <td>11.56 ± 0.31 [2.7%]</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>21.05 ± 0.66 [3.1%]</td>\n", " <td>20.12 ± 0.93 [4.6%]</td>\n", " <td>17.31 ± 0.76 [4.4%]</td>\n", " <td>15.33 ± 0.51 [3.4%]</td>\n", " <td>11.21 ± 0.47 [4.2%]</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>19.91 ± 1.30 [6.5%]</td>\n", " <td>19.88 ± 0.70 [3.5%]</td>\n", " <td>17.17 ± 0.21 [1.2%]</td>\n", " <td>14.34 ± 0.94 [6.6%]</td>\n", " <td>10.73 ± 0.28 [2.7%]</td>\n", " </tr>\n", " <tr>\n", " <th>30</th>\n", " <td>19.35 ± 2.40 [12.4%]</td>\n", " <td>18.24 ± 1.67 [9.1%]</td>\n", " <td>17.02 ± 0.14 [0.8%]</td>\n", " <td>14.15 ± 0.10 [0.7%]</td>\n", " <td>9.99 ± 0.47 [4.7%]</td>\n", " </tr>\n", " <tr>\n", " <th>40</th>\n", " <td>17.06 ± 1.51 [8.9%]</td>\n", " <td>16.98 ± 0.85 [5.0%]</td>\n", " <td>15.81 ± 1.34 [8.5%]</td>\n", " <td>13.99 ± 0.23 [1.6%]</td>\n", " <td>9.62 ± 1.21 [12.6%]</td>\n", " </tr>\n", " <tr>\n", " <th>50</th>\n", " <td>16.79 ± 0.68 [4.0%]</td>\n", " <td>16.40 ± 1.38 [8.4%]</td>\n", " <td>14.63 ± 0.81 [5.5%]</td>\n", " <td>12.37 ± 0.67 [5.4%]</td>\n", " <td>9.13 ± 0.33 [3.6%]</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 24 °C 150 °C 300 °C \\\n", "RCA (%) \n", "0 21.09 ± 1.28 [6.1%] 20.15 ± 0.99 [4.9%] 17.65 ± 0.74 [4.2%] \n", "10 21.05 ± 0.66 [3.1%] 20.12 ± 0.93 [4.6%] 17.31 ± 0.76 [4.4%] \n", "20 19.91 ± 1.30 [6.5%] 19.88 ± 0.70 [3.5%] 17.17 ± 0.21 [1.2%] \n", "30 19.35 ± 2.40 [12.4%] 18.24 ± 1.67 [9.1%] 17.02 ± 0.14 [0.8%] \n", "40 17.06 ± 1.51 [8.9%] 16.98 ± 0.85 [5.0%] 15.81 ± 1.34 [8.5%] \n", "50 16.79 ± 0.68 [4.0%] 16.40 ± 1.38 [8.4%] 14.63 ± 0.81 [5.5%] \n", "\n", " 450 °C 600 °C \n", "RCA (%) \n", "0 15.50 ± 0.28 [1.8%] 11.56 ± 0.31 [2.7%] \n", "10 15.33 ± 0.51 [3.4%] 11.21 ± 0.47 [4.2%] \n", "20 14.34 ± 0.94 [6.6%] 10.73 ± 0.28 [2.7%] \n", "30 14.15 ± 0.10 [0.7%] 9.99 ± 0.47 [4.7%] \n", "40 13.99 ± 0.23 [1.6%] 9.62 ± 1.21 [12.6%] \n", "50 12.37 ± 0.67 [5.4%] 9.13 ± 0.33 [3.6%] " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# ============================================================\n", "# TABLE 5\n", "# ============================================================\n", "\n", "publication_data = descriptive.copy()\n", "\n", "publication_data[\"Summary\"] = publication_data.apply(\n", " lambda r:\n", " f\"{r['Mean_MPa']:.2f} ± {r['SD_MPa']:.2f} [{r['CV_pct']:.1f}%]\",\n", " axis=1\n", ")\n", "\n", "table5_panelA = publication_data.pivot(\n", " index=\"RCA_pct\",\n", " columns=\"Temperature_C\",\n", " values=\"Summary\"\n", ")\n", "\n", "table5_panelA.columns = [\n", " f\"{int(c)} °C\" for c in table5_panelA.columns\n", "]\n", "\n", "table5_panelA.index.name = \"RCA (%)\"\n", "\n", "display(table5_panelA)" ] }, { "cell_type": "code", "execution_count": 26, "id": "50a5c739-a228-42cb-a13d-2f8457baff69", "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>Diagnostic</th>\n", " <th>Statistic</th>\n", " <th>p_value</th>\n", " <th>Interpretation</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Shapiro-Wilk normality test</td>\n", " <td>0.9832</td>\n", " <td>0.2994</td>\n", " <td>Normality assumption not violated</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Brown-Forsythe homogeneity test</td>\n", " <td>0.8390</td>\n", " <td>0.6923</td>\n", " <td>Variance homogeneity assumption not violated</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Diagnostic Statistic p_value \\\n", "0 Shapiro-Wilk normality test 0.9832 0.2994 \n", "1 Brown-Forsythe homogeneity test 0.8390 0.6923 \n", "\n", " Interpretation \n", "0 Normality assumption not violated \n", "1 Variance homogeneity assumption not violated " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# ============================================================\n", "# ASSUMPTION DIAGNOSTICS TABLE\n", "# ============================================================\n", "\n", "diagnostics = pd.DataFrame({\n", " \"Diagnostic\": [\n", " \"Shapiro-Wilk normality test\",\n", " \"Brown-Forsythe homogeneity test\"\n", " ],\n", " \"Statistic\": [\n", " shapiro_stat,\n", " bf_stat\n", " ],\n", " \"p_value\": [\n", " shapiro_p,\n", " bf_p\n", " ],\n", " \"Interpretation\": [\n", " (\n", " \"Normality assumption not violated\"\n", " if shapiro_p > 0.05\n", " else \"Evidence of non-normality\"\n", " ),\n", " (\n", " \"Variance homogeneity assumption not violated\"\n", " if bf_p > 0.05\n", " else \"Evidence of unequal variances\"\n", " )\n", " ]\n", "})\n", "\n", "diagnostics[\"Statistic\"] = diagnostics[\"Statistic\"].round(4)\n", "diagnostics[\"p_value\"] = diagnostics[\"p_value\"].round(4)\n", "\n", "display(diagnostics)" ] }, { "cell_type": "code", "execution_count": 30, "id": "9bee776b-8f06-4ec4-874e-ebaf6324080c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Output directory:\n", "C:\\Users\\sadal\\Desktop\\Yasin Hoca-RCA Thermal\\Analysis_Outputs\\Step_01_Reliability\n", "\n", "✓ TABLE 5 SAVED SUCCESSFULLY\n", "\n", "File:\n", "C:\\Users\\sadal\\Desktop\\Yasin Hoca-RCA Thermal\\Analysis_Outputs\\Step_01_Reliability\\Table_5_Descriptive_and_Assumption_Diagnostics.xlsx\n", "\n", "File exists: True\n", "File size: 7.4 KB\n" ] } ], "source": [ "# ============================================================\n", "#SAVE TABLE 5\n", "# Self-contained path version\n", "# ============================================================\n", "\n", "from pathlib import Path\n", "import pandas as pd\n", "\n", "# ------------------------------------------------------------\n", "# Define paths again to avoid kernel-state problems\n", "# ------------------------------------------------------------\n", "\n", "DATA_PATH = Path(\n", " r\"C:\\Users\\sadal\\Desktop\\Yasin Hoca-RCA Thermal\\data.xlsx\"\n", ")\n", "\n", "OUTPUT_DIR = (\n", " DATA_PATH.parent\n", " / \"Analysis_Outputs\"\n", " / \"Step_01_Reliability\"\n", ")\n", "\n", "OUTPUT_DIR.mkdir(\n", " parents=True,\n", " exist_ok=True\n", ")\n", "\n", "print(\"Output directory:\")\n", "print(OUTPUT_DIR)\n", "\n", "\n", "# ------------------------------------------------------------\n", "# Check whether required tables exist\n", "# ------------------------------------------------------------\n", "\n", "required_objects = [\n", " \"table5_panelA\",\n", " \"descriptive\",\n", " \"diagnostics\"\n", "]\n", "\n", "missing_objects = [\n", " obj for obj in required_objects\n", " if obj not in globals()\n", "]\n", "\n", "if missing_objects:\n", " raise RuntimeError(\n", " f\"Missing objects: {missing_objects}. \"\n", " \"Please rerun the corresponding previous cells.\"\n", " )\n", "\n", "\n", "# ------------------------------------------------------------\n", "# Save Table 5\n", "# ------------------------------------------------------------\n", "\n", "table5_path = (\n", " OUTPUT_DIR\n", " / \"Table_5_Descriptive_and_Assumption_Diagnostics.xlsx\"\n", ")\n", "\n", "with pd.ExcelWriter(\n", " table5_path,\n", " engine=\"openpyxl\"\n", ") as writer:\n", "\n", " # Publication-ready compact version\n", " table5_panelA.to_excel(\n", " writer,\n", " sheet_name=\"Publication_Table\"\n", " )\n", "\n", " # Full numerical statistics\n", " descriptive.to_excel(\n", " writer,\n", " sheet_name=\"Numeric_Descriptives\",\n", " index=False\n", " )\n", "\n", " # Statistical assumption tests\n", " diagnostics.to_excel(\n", " writer,\n", " sheet_name=\"Assumption_Diagnostics\",\n", " index=False\n", " )\n", "\n", "\n", "# ------------------------------------------------------------\n", "# Also save CSV backups\n", "# ------------------------------------------------------------\n", "\n", "descriptive.to_csv(\n", " OUTPUT_DIR / \"Table_5A_Numeric_Descriptives.csv\",\n", " index=False\n", ")\n", "\n", "diagnostics.to_csv(\n", " OUTPUT_DIR / \"Table_5B_Assumption_Diagnostics.csv\",\n", " index=False\n", ")\n", "\n", "\n", "# ------------------------------------------------------------\n", "# Final verification\n", "# ------------------------------------------------------------\n", "\n", "print(\"\\n✓ TABLE 5 SAVED SUCCESSFULLY\")\n", "print(\"\\nFile:\")\n", "print(table5_path)\n", "\n", "print(\"\\nFile exists:\", table5_path.exists())\n", "print(\n", " \"File size:\",\n", " round(table5_path.stat().st_size / 1024, 1),\n", " \"KB\"\n", ")" ] }, { "cell_type": "code", "execution_count": 34, "id": "4d78120a-79b6-4298-8deb-edc4b9dd026b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "✓ Figure dependencies ready.\n", "Output directory:\n", "C:\\Users\\sadal\\Desktop\\Yasin Hoca-RCA Thermal\\Analysis_Outputs\\Step_01_Reliability\n" ] }, { "data": { "image/png": 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", "text/plain": [ "<Figure size 1520x520 with 4 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# ============================================================\n", "# FIGURE S1 — EXPERIMENTAL RELIABILITY DIAGNOSTICS\n", "# ============================================================\n", "\n", "# ============================================================\n", "# FIGURE S1\n", "# REQUIRED IMPORTS AND PATH CHECK\n", "# ============================================================\n", "\n", "from pathlib import Path\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "from scipy import stats\n", "from matplotlib.colors import LinearSegmentedColormap\n", "\n", "\n", "# ------------------------------------------------------------\n", "# Output directory — define locally for robustness\n", "# ------------------------------------------------------------\n", "\n", "DATA_PATH = Path(\n", " r\"C:\\Users\\sadal\\Desktop\\Yasin Hoca-RCA Thermal\\data.xlsx\"\n", ")\n", "\n", "OUTPUT_DIR = (\n", " DATA_PATH.parent\n", " / \"Analysis_Outputs\"\n", " / \"Step_01_Reliability\"\n", ")\n", "\n", "OUTPUT_DIR.mkdir(\n", " parents=True,\n", " exist_ok=True\n", ")\n", "\n", "\n", "# ------------------------------------------------------------\n", "# Check required analytical objects\n", "# ------------------------------------------------------------\n", "\n", "required_objects = [\n", " \"df\",\n", " \"descriptive\",\n", " \"shapiro_p\",\n", " \"bf_p\"\n", "]\n", "\n", "missing_objects = [\n", " obj for obj in required_objects\n", " if obj not in globals()\n", "]\n", "\n", "if missing_objects:\n", " raise RuntimeError(\n", " f\"Missing analytical objects: {missing_objects}. \"\n", " \"Please rerun the relevant previous cells.\"\n", " )\n", "\n", "print(\"✓ Figure dependencies ready.\")\n", "print(\"Output directory:\")\n", "print(OUTPUT_DIR)\n", "\n", "\n", "\n", "# ---------- Global visual style ----------\n", "\n", "plt.rcParams.update({\n", " \"font.family\": \"DejaVu Sans\",\n", " \"font.size\": 10.5,\n", " \"axes.labelsize\": 11,\n", " \"axes.titlesize\": 12,\n", " \"xtick.labelsize\": 9.5,\n", " \"ytick.labelsize\": 9.5,\n", " \"axes.linewidth\": 0.8,\n", " \"pdf.fonttype\": 42,\n", " \"ps.fonttype\": 42,\n", "})\n", "\n", "background = \"#F7F5F0\"\n", "navy = \"#20364B\"\n", "teal = \"#2A9D8F\"\n", "coral = \"#E76F51\"\n", "gold = \"#E9C46A\"\n", "gray = \"#66737F\"\n", "\n", "temperature_colors = {\n", " 24: \"#264653\",\n", " 150: \"#287271\",\n", " 300: \"#2A9D8F\",\n", " 450: \"#E9C46A\",\n", " 600: \"#E76F51\"\n", "}\n", "\n", "cv_cmap = LinearSegmentedColormap.from_list(\n", " \"custom_cv\",\n", " [\n", " \"#EDF5F3\",\n", " \"#A8D5CC\",\n", " \"#E9C46A\",\n", " \"#E76F51\"\n", " ]\n", ")\n", "\n", "# ---------- Figure layout ----------\n", "\n", "fig = plt.figure(\n", " figsize=(15.2, 5.2),\n", " facecolor=background\n", ")\n", "\n", "gs = fig.add_gridspec(\n", " 1, 3,\n", " width_ratios=[1.35, 1.0, 1.25],\n", " wspace=0.36\n", ")\n", "\n", "ax1 = fig.add_subplot(gs[0, 0])\n", "ax2 = fig.add_subplot(gs[0, 1])\n", "ax3 = fig.add_subplot(gs[0, 2])\n", "\n", "for ax in [ax1, ax2, ax3]:\n", " ax.set_facecolor(background)\n", "\n", "\n", "# ============================================================\n", "# PANEL A — CV HEATMAP\n", "# ============================================================\n", "\n", "cv_matrix = descriptive.pivot(\n", " index=\"RCA_pct\",\n", " columns=\"Temperature_C\",\n", " values=\"CV_pct\"\n", ")\n", "\n", "sns.heatmap(\n", " cv_matrix,\n", " ax=ax1,\n", " cmap=cv_cmap,\n", " annot=True,\n", " fmt=\".1f\",\n", " linewidths=2.2,\n", " linecolor=background,\n", " cbar_kws={\n", " \"label\": \"Coefficient of variation (%)\",\n", " \"shrink\": 0.78\n", " }\n", ")\n", "\n", "ax1.set_xlabel(\"Exposure temperature (°C)\")\n", "ax1.set_ylabel(\"RCA replacement (%)\")\n", "ax1.set_title(\n", " \"Replicate variability across the design space\",\n", " loc=\"left\",\n", " color=navy,\n", " fontweight=\"bold\",\n", " pad=12\n", ")\n", "\n", "ax1.tick_params(axis=\"y\", rotation=0)\n", "\n", "ax1.text(\n", " -0.13, 1.08,\n", " \"A\",\n", " transform=ax1.transAxes,\n", " fontsize=17,\n", " fontweight=\"bold\",\n", " color=coral\n", ")\n", "\n", "\n", "# ============================================================\n", "# PANEL B — Q-Q PLOT\n", "# ============================================================\n", "\n", "(osm, osr), (slope, intercept, r) = stats.probplot(\n", " df[\"Residual\"],\n", " dist=\"norm\"\n", ")\n", "\n", "ax2.scatter(\n", " osm,\n", " osr,\n", " s=38,\n", " color=teal,\n", " edgecolor=\"white\",\n", " linewidth=0.7,\n", " alpha=0.90,\n", " zorder=3\n", ")\n", "\n", "xline = np.linspace(min(osm), max(osm), 100)\n", "\n", "ax2.plot(\n", " xline,\n", " slope * xline + intercept,\n", " color=navy,\n", " linewidth=1.8\n", ")\n", "\n", "ax2.set_xlabel(\"Theoretical normal quantiles\")\n", "ax2.set_ylabel(\"Observed residual quantiles\")\n", "\n", "ax2.set_title(\n", " \"Normality of cell-centered residuals\",\n", " loc=\"left\",\n", " color=navy,\n", " fontweight=\"bold\",\n", " pad=12\n", ")\n", "\n", "ax2.text(\n", " 0.05, 0.93,\n", " f\"Shapiro–Wilk\\np = {shapiro_p:.3f}\",\n", " transform=ax2.transAxes,\n", " va=\"top\",\n", " bbox=dict(\n", " boxstyle=\"round,pad=0.45\",\n", " facecolor=\"white\",\n", " edgecolor=\"#D8D4CB\",\n", " alpha=0.95\n", " )\n", ")\n", "\n", "ax2.text(\n", " -0.16, 1.08,\n", " \"B\",\n", " transform=ax2.transAxes,\n", " fontsize=17,\n", " fontweight=\"bold\",\n", " color=coral\n", ")\n", "\n", "\n", "# ============================================================\n", "# PANEL C — RESIDUAL vs FITTED\n", "# ============================================================\n", "\n", "for temp in sorted(df[\"Temperature_C\"].unique()):\n", "\n", " sub = df[df[\"Temperature_C\"] == temp]\n", "\n", " ax3.scatter(\n", " sub[\"Fitted_CellMean\"],\n", " sub[\"Residual\"],\n", " s=42,\n", " color=temperature_colors[temp],\n", " label=f\"{temp} °C\",\n", " edgecolor=\"white\",\n", " linewidth=0.65,\n", " alpha=0.88\n", " )\n", "\n", "ax3.axhline(\n", " 0,\n", " color=navy,\n", " linewidth=1.1,\n", " linestyle=(0, (4, 3))\n", ")\n", "\n", "ax3.set_xlabel(\"Cell mean / fitted strength (MPa)\")\n", "ax3.set_ylabel(\"Residual (MPa)\")\n", "\n", "ax3.set_title(\n", " \"Residual dispersion across the response range\",\n", " loc=\"left\",\n", " color=navy,\n", " fontweight=\"bold\",\n", " pad=12\n", ")\n", "\n", "ax3.text(\n", " 0.05, 0.95,\n", " f\"Brown–Forsythe\\np = {bf_p:.3f}\",\n", " transform=ax3.transAxes,\n", " va=\"top\",\n", " bbox=dict(\n", " boxstyle=\"round,pad=0.45\",\n", " facecolor=\"white\",\n", " edgecolor=\"#D8D4CB\",\n", " alpha=0.95\n", " )\n", ")\n", "\n", "ax3.legend(\n", " title=\"Temperature\",\n", " frameon=False,\n", " fontsize=8.5,\n", " title_fontsize=9,\n", " loc=\"lower left\",\n", " ncol=2\n", ")\n", "\n", "ax3.text(\n", " -0.15, 1.08,\n", " \"C\",\n", " transform=ax3.transAxes,\n", " fontsize=17,\n", " fontweight=\"bold\",\n", " color=coral\n", ")\n", "\n", "\n", "# ---------- Clean journal-style axes ----------\n", "\n", "for ax in [ax2, ax3]:\n", "\n", " ax.spines[\"top\"].set_visible(False)\n", " ax.spines[\"right\"].set_visible(False)\n", "\n", " ax.spines[\"left\"].set_color(\"#9A9891\")\n", " ax.spines[\"bottom\"].set_color(\"#9A9891\")\n", "\n", " ax.grid(\n", " axis=\"y\",\n", " color=\"#DDD9D0\",\n", " linewidth=0.6,\n", " alpha=0.65,\n", " zorder=0\n", " )\n", "\n", "\n", "# ---------- Save ----------\n", "\n", "plt.savefig(\n", " OUTPUT_DIR / \"Figure_S1_Data_Reliability_Diagnostics.png\",\n", " dpi=600,\n", " bbox_inches=\"tight\",\n", " facecolor=fig.get_facecolor()\n", ")\n", "\n", "plt.savefig(\n", " OUTPUT_DIR / \"Figure_S1_Data_Reliability_Diagnostics.pdf\",\n", " bbox_inches=\"tight\",\n", " facecolor=fig.get_facecolor()\n", ")\n", "\n", "plt.savefig(\n", " OUTPUT_DIR / \"Figure_S1_Data_Reliability_Diagnostics.tiff\",\n", " dpi=600,\n", " bbox_inches=\"tight\",\n", " facecolor=fig.get_facecolor(),\n", " pil_kwargs={\"compression\": \"tiff_lzw\"}\n", ")\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 36, "id": "94d475be-b0d2-42ae-9e3b-a1d23f6121c2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "============================================================\n", "STEP 1 — EXPERIMENTAL DATA RELIABILITY\n", "============================================================\n", "\n", "Total observations : 90\n", "Design cells : 30\n", "Replicates/cell : 3\n", "\n", "Repeatability:\n", "Median CV : 4.50%\n", "Maximum CV : 12.60%\n", "CV > 10% cells : 2\n", "\n", "ANOVA assumptions:\n", "Shapiro-Wilk p : 0.2994\n", "Brown-Forsythe p : 0.6923\n", "\n", "Saved outputs:\n", " - Figure_S1_Data_Reliability_Diagnostics.pdf\n", " - Figure_S1_Data_Reliability_Diagnostics.png\n", " - Figure_S1_Data_Reliability_Diagnostics.tiff\n", " - Table_5_Descriptive_and_Assumption_Diagnostics.xlsx\n", " - Table_5A_Numeric_Descriptives.csv\n", " - Table_5B_Assumption_Diagnostics.csv\n", "============================================================\n" ] } ], "source": [ "# ============================================================\n", "# STEP 1 — FINAL CHECK\n", "# ============================================================\n", "\n", "print(\"=\" * 60)\n", "print(\"STEP 1 — EXPERIMENTAL DATA RELIABILITY\")\n", "print(\"=\" * 60)\n", "\n", "print(f\"\\nTotal observations : {len(df)}\")\n", "print(f\"Design cells : {df.groupby(['RCA_pct','Temperature_C']).ngroups}\")\n", "print(f\"Replicates/cell : 3\")\n", "\n", "print(\"\\nRepeatability:\")\n", "print(f\"Median CV : {descriptive['CV_pct'].median():.2f}%\")\n", "print(f\"Maximum CV : {descriptive['CV_pct'].max():.2f}%\")\n", "print(f\"CV > 10% cells : {(descriptive['CV_pct'] > 10).sum()}\")\n", "\n", "print(\"\\nANOVA assumptions:\")\n", "print(f\"Shapiro-Wilk p : {shapiro_p:.4f}\")\n", "print(f\"Brown-Forsythe p : {bf_p:.4f}\")\n", "\n", "print(\"\\nSaved outputs:\")\n", "for file in sorted(OUTPUT_DIR.iterdir()):\n", " print(\" -\", file.name)\n", "\n", "print(\"=\" * 60)" ] }, { "cell_type": "code", "execution_count": 40, "id": "3bcaed06-798c-41ab-9760-166aaaaba7d0", "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>RCA_pct</th>\n", " <th>Temperature_C</th>\n", " <th>N</th>\n", " <th>Mean_MPa</th>\n", " <th>SD_MPa</th>\n", " <th>CV_pct</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>15</th>\n", " <td>30</td>\n", " <td>24</td>\n", " <td>3</td>\n", " <td>19.354</td>\n", " <td>2.398</td>\n", " <td>12.39</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>40</td>\n", " <td>600</td>\n", " <td>3</td>\n", " <td>9.624</td>\n", " <td>1.213</td>\n", " <td>12.60</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " RCA_pct Temperature_C N Mean_MPa SD_MPa CV_pct\n", "15 30 24 3 19.354 2.398 12.39\n", "24 40 600 3 9.624 1.213 12.60" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "high_cv_cells = descriptive[\n", " descriptive[\"CV_pct\"] > 10\n", "].copy()\n", "\n", "display(high_cv_cells)" ] }, { "cell_type": "markdown", "id": "ba823ed3-04f9-4483-857b-146df99b9201", "metadata": {}, "source": [ "#STEP2:Two-way factorial ANOVA: RCA × Temperature" ] }, { "cell_type": "code", "execution_count": 42, "id": "e0a93c4e-cee8-4b16-9b79-393fea209b7a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "✓ STEP 2 DATA CHECK PASSED\n", "\n", "Observations : 90\n", "RCA levels : [0, 10, 20, 30, 40, 50]\n", "Temperatures: [24, 150, 300, 450, 600]\n", "Replicates : 3\n", "\n", "Output directory:\n", "C:\\Users\\sadal\\Desktop\\Yasin Hoca-RCA Thermal\\Analysis_Outputs\\Step_02_TwoWay_ANOVA\n" ] } ], "source": [ "# ============================================================\n", "# STEP 2 — TWO-WAY FACTORIAL ANOVA\n", "# — SETUP AND DATA RELOAD\n", "# ============================================================\n", "\n", "from pathlib import Path\n", "import re\n", "import warnings\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "from scipy import stats\n", "\n", "warnings.filterwarnings(\"ignore\")\n", "\n", "\n", "# ============================================================\n", "# PATHS\n", "# ============================================================\n", "\n", "DATA_PATH = Path(\n", " r\"C:\\Users\\sadal\\Desktop\\Yasin Hoca-RCA Thermal\\data.xlsx\"\n", ")\n", "\n", "OUTPUT_DIR = (\n", " DATA_PATH.parent\n", " / \"Analysis_Outputs\"\n", " / \"Step_02_TwoWay_ANOVA\"\n", ")\n", "\n", "OUTPUT_DIR.mkdir(\n", " parents=True,\n", " exist_ok=True\n", ")\n", "\n", "assert DATA_PATH.exists(), f\"Data file not found: {DATA_PATH}\"\n", "\n", "\n", "# ============================================================\n", "# READ RAW DATA\n", "# ============================================================\n", "\n", "raw = pd.read_excel(\n", " DATA_PATH,\n", " sheet_name=\"data\",\n", " header=None\n", ")\n", "\n", "\n", "# Temperature values from first row\n", "temperature_headers = raw.iloc[0, 1:].astype(str)\n", "\n", "temperatures = (\n", " temperature_headers\n", " .str.extract(r\"(\\d+)\")[0]\n", " .astype(int)\n", " .tolist()\n", ")\n", "\n", "\n", "# ============================================================\n", "# WIDE → LONG FORMAT\n", "# ============================================================\n", "\n", "records = []\n", "\n", "for _, row in raw.iloc[1:].iterrows():\n", "\n", " if pd.isna(row.iloc[0]):\n", " continue\n", "\n", " mix_label = str(row.iloc[0])\n", "\n", " rca_match = re.search(r\"(\\d+)\", mix_label)\n", "\n", " if rca_match is None:\n", " raise ValueError(\n", " f\"RCA percentage could not be parsed: {mix_label}\"\n", " )\n", "\n", " rca_pct = int(rca_match.group(1))\n", "\n", " replicate_counter = {}\n", "\n", " for col_index, temperature in enumerate(\n", " temperatures,\n", " start=1\n", " ):\n", "\n", " replicate_counter[temperature] = (\n", " replicate_counter.get(temperature, 0) + 1\n", " )\n", "\n", " value = pd.to_numeric(\n", " row.iloc[col_index],\n", " errors=\"coerce\"\n", " )\n", "\n", " if pd.isna(value):\n", " raise ValueError(\n", " f\"Missing value detected: \"\n", " f\"RCA={rca_pct}, T={temperature}\"\n", " )\n", "\n", " records.append({\n", " \"RCA_pct\": rca_pct,\n", " \"Temperature_C\": temperature,\n", " \"Replicate\": replicate_counter[temperature],\n", " \"CompressiveStrength_MPa\": float(value)\n", " })\n", "\n", "\n", "df_anova = pd.DataFrame(records)\n", "\n", "\n", "# ============================================================\n", "# DESIGN VALIDATION\n", "# ============================================================\n", "\n", "cell_counts = (\n", " df_anova.groupby(\n", " [\"RCA_pct\", \"Temperature_C\"]\n", " )\n", " .size()\n", ")\n", "\n", "assert len(df_anova) == 90\n", "assert df_anova[\"RCA_pct\"].nunique() == 6\n", "assert df_anova[\"Temperature_C\"].nunique() == 5\n", "assert cell_counts.nunique() == 1\n", "assert cell_counts.iloc[0] == 3\n", "\n", "print(\"✓ STEP 2 DATA CHECK PASSED\")\n", "print()\n", "print(\"Observations :\", len(df_anova))\n", "print(\"RCA levels :\", sorted(df_anova[\"RCA_pct\"].unique()))\n", "print(\"Temperatures:\", sorted(df_anova[\"Temperature_C\"].unique()))\n", "print(\"Replicates :\", cell_counts.iloc[0])\n", "print()\n", "print(\"Output directory:\")\n", "print(OUTPUT_DIR)" ] }, { "cell_type": "code", "execution_count": 44, "id": "aeb5f575-10cf-49a7-b0b7-7e2365253f31", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "✓ TWO-WAY ANOVA CALCULATED SUCCESSFULLY\n" ] } ], "source": [ "# ============================================================\n", "# — BALANCED TWO-WAY FACTORIAL ANOVA\n", "# ============================================================\n", "\n", "response = \"CompressiveStrength_MPa\"\n", "\n", "rca_levels = sorted(df_anova[\"RCA_pct\"].unique())\n", "temp_levels = sorted(df_anova[\"Temperature_C\"].unique())\n", "\n", "a = len(rca_levels) # RCA levels = 6\n", "b = len(temp_levels) # Temperature levels = 5\n", "n = int(cell_counts.iloc[0]) # Replicates = 3\n", "\n", "N = len(df_anova)\n", "\n", "\n", "# ============================================================\n", "# MEANS\n", "# ============================================================\n", "\n", "grand_mean = df_anova[response].mean()\n", "\n", "rca_means = (\n", " df_anova.groupby(\"RCA_pct\")[response]\n", " .mean()\n", ")\n", "\n", "temp_means = (\n", " df_anova.groupby(\"Temperature_C\")[response]\n", " .mean()\n", ")\n", "\n", "cell_means = (\n", " df_anova.groupby(\n", " [\"RCA_pct\", \"Temperature_C\"]\n", " )[response]\n", " .mean()\n", ")\n", "\n", "\n", "# ============================================================\n", "# SUMS OF SQUARES\n", "# ============================================================\n", "\n", "# RCA main effect\n", "SS_RCA = (\n", " b * n *\n", " np.sum((rca_means - grand_mean) ** 2)\n", ")\n", "\n", "# Temperature main effect\n", "SS_TEMP = (\n", " a * n *\n", " np.sum((temp_means - grand_mean) ** 2)\n", ")\n", "\n", "# Interaction effect\n", "SS_INTERACTION = 0.0\n", "\n", "for rca in rca_levels:\n", " for temp in temp_levels:\n", "\n", " interaction_component = (\n", " cell_means.loc[(rca, temp)]\n", " - rca_means.loc[rca]\n", " - temp_means.loc[temp]\n", " + grand_mean\n", " )\n", "\n", " SS_INTERACTION += (\n", " n * interaction_component ** 2\n", " )\n", "\n", "\n", "# Pure experimental error\n", "cell_fitted = (\n", " df_anova.groupby(\n", " [\"RCA_pct\", \"Temperature_C\"]\n", " )[response]\n", " .transform(\"mean\")\n", ")\n", "\n", "residuals = (\n", " df_anova[response]\n", " - cell_fitted\n", ")\n", "\n", "SS_ERROR = np.sum(residuals ** 2)\n", "\n", "# Total\n", "SS_TOTAL = np.sum(\n", " (df_anova[response] - grand_mean) ** 2\n", ")\n", "\n", "\n", "# ============================================================\n", "# DEGREES OF FREEDOM\n", "# ============================================================\n", "\n", "df_RCA = a - 1\n", "df_TEMP = b - 1\n", "df_INTERACTION = (a - 1) * (b - 1)\n", "df_ERROR = a * b * (n - 1)\n", "df_TOTAL = N - 1\n", "\n", "\n", "# ============================================================\n", "# SANITY CHECK — SS DECOMPOSITION\n", "# ============================================================\n", "\n", "SS_RECONSTRUCTED = (\n", " SS_RCA\n", " + SS_TEMP\n", " + SS_INTERACTION\n", " + SS_ERROR\n", ")\n", "\n", "assert np.isclose(\n", " SS_TOTAL,\n", " SS_RECONSTRUCTED,\n", " rtol=1e-10,\n", " atol=1e-10\n", "), \"ANOVA SS decomposition failed.\"\n", "\n", "\n", "# ============================================================\n", "# MEAN SQUARES\n", "# ============================================================\n", "\n", "MS_RCA = SS_RCA / df_RCA\n", "MS_TEMP = SS_TEMP / df_TEMP\n", "MS_INTERACTION = (\n", " SS_INTERACTION / df_INTERACTION\n", ")\n", "\n", "MS_ERROR = SS_ERROR / df_ERROR\n", "\n", "\n", "# ============================================================\n", "# F STATISTICS\n", "# ============================================================\n", "\n", "F_RCA = MS_RCA / MS_ERROR\n", "F_TEMP = MS_TEMP / MS_ERROR\n", "F_INTERACTION = MS_INTERACTION / MS_ERROR\n", "\n", "\n", "# ============================================================\n", "# P VALUES\n", "# ============================================================\n", "\n", "p_RCA = stats.f.sf(\n", " F_RCA,\n", " df_RCA,\n", " df_ERROR\n", ")\n", "\n", "p_TEMP = stats.f.sf(\n", " F_TEMP,\n", " df_TEMP,\n", " df_ERROR\n", ")\n", "\n", "p_INTERACTION = stats.f.sf(\n", " F_INTERACTION,\n", " df_INTERACTION,\n", " df_ERROR\n", ")\n", "\n", "\n", "print(\"✓ TWO-WAY ANOVA CALCULATED SUCCESSFULLY\")" ] }, { "cell_type": "code", "execution_count": 46, "id": "19a0ae50-9df0-4f09-a2e7-64b93599e708", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "VARIANCE CONTRIBUTIONS\n", "-----------------------------------------\n", "Temperature : 82.17%\n", "RCA : 11.49%\n", "RCA × Temperature : 1.50%\n", "Pure error : 4.84%\n", "-----------------------------------------\n", "Total : 100.00%\n" ] } ], "source": [ "# ============================================================\n", "# — EFFECT SIZE CALCULATION\n", "# ============================================================\n", "\n", "def eta_squared(ss_effect, ss_total):\n", " return ss_effect / ss_total\n", "\n", "\n", "def partial_eta_squared(ss_effect, ss_error):\n", " return ss_effect / (ss_effect + ss_error)\n", "\n", "\n", "def omega_squared(\n", " ss_effect,\n", " df_effect,\n", " ms_error,\n", " ss_total\n", "):\n", " omega = (\n", " ss_effect\n", " - df_effect * ms_error\n", " ) / (\n", " ss_total\n", " + ms_error\n", " )\n", "\n", " # Negative omega² has no practical interpretation\n", " return max(0.0, omega)\n", "\n", "\n", "# ============================================================\n", "# η²\n", "# ============================================================\n", "\n", "eta2_RCA = eta_squared(\n", " SS_RCA,\n", " SS_TOTAL\n", ")\n", "\n", "eta2_TEMP = eta_squared(\n", " SS_TEMP,\n", " SS_TOTAL\n", ")\n", "\n", "eta2_INTERACTION = eta_squared(\n", " SS_INTERACTION,\n", " SS_TOTAL\n", ")\n", "\n", "\n", "# ============================================================\n", "# PARTIAL η²\n", "# Kept for complete numerical record\n", "# ============================================================\n", "\n", "peta2_RCA = partial_eta_squared(\n", " SS_RCA,\n", " SS_ERROR\n", ")\n", "\n", "peta2_TEMP = partial_eta_squared(\n", " SS_TEMP,\n", " SS_ERROR\n", ")\n", "\n", "peta2_INTERACTION = partial_eta_squared(\n", " SS_INTERACTION,\n", " SS_ERROR\n", ")\n", "\n", "\n", "# ============================================================\n", "# ω²\n", "# ============================================================\n", "\n", "omega2_RCA = omega_squared(\n", " SS_RCA,\n", " df_RCA,\n", " MS_ERROR,\n", " SS_TOTAL\n", ")\n", "\n", "omega2_TEMP = omega_squared(\n", " SS_TEMP,\n", " df_TEMP,\n", " MS_ERROR,\n", " SS_TOTAL\n", ")\n", "\n", "omega2_INTERACTION = omega_squared(\n", " SS_INTERACTION,\n", " df_INTERACTION,\n", " MS_ERROR,\n", " SS_TOTAL\n", ")\n", "\n", "\n", "# ============================================================\n", "# VARIANCE CONTRIBUTIONS\n", "# ============================================================\n", "\n", "contrib_RCA = (\n", " SS_RCA / SS_TOTAL * 100\n", ")\n", "\n", "contrib_TEMP = (\n", " SS_TEMP / SS_TOTAL * 100\n", ")\n", "\n", "contrib_INTERACTION = (\n", " SS_INTERACTION / SS_TOTAL * 100\n", ")\n", "\n", "contrib_ERROR = (\n", " SS_ERROR / SS_TOTAL * 100\n", ")\n", "\n", "\n", "print(\"VARIANCE CONTRIBUTIONS\")\n", "print(\"-----------------------------------------\")\n", "print(f\"Temperature : {contrib_TEMP:.2f}%\")\n", "print(f\"RCA : {contrib_RCA:.2f}%\")\n", "print(f\"RCA × Temperature : {contrib_INTERACTION:.2f}%\")\n", "print(f\"Pure error : {contrib_ERROR:.2f}%\")\n", "print(\"-----------------------------------------\")\n", "print(\n", " \"Total :\",\n", " f\"{contrib_TEMP + contrib_RCA + contrib_INTERACTION + contrib_ERROR:.2f}%\"\n", ")" ] }, { "cell_type": "code", "execution_count": 48, "id": "ed839c4e-5dc1-4956-8d0a-507bcad36e92", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "====================================================================\n", "TWO-WAY FACTORIAL ANOVA — COMPRESSIVE STRENGTH\n", "====================================================================\n", "\n", "RCA EFFECT\n", "F(5, 60) = 28.4833\n", "p = 1.136492e-14\n", "η² = 0.1149\n", "ω² = 0.1108\n", "\n", "TEMPERATURE EFFECT\n", "F(4, 60) = 254.6396\n", "p = 6.707392e-37\n", "η² = 0.8217\n", "ω² = 0.8178\n", "\n", "RCA × TEMPERATURE INTERACTION\n", "F(20, 60) = 0.9273\n", "p = 0.5568\n", "η² = 0.0150\n", "ω² = 0.0000\n", "\n", "PURE ERROR\n", "MS error = 0.9191\n", "====================================================================\n" ] } ], "source": [ "# ============================================================\n", "# — INFERENTIAL RESULTS\n", "# ============================================================\n", "\n", "print(\"=\" * 68)\n", "print(\"TWO-WAY FACTORIAL ANOVA — COMPRESSIVE STRENGTH\")\n", "print(\"=\" * 68)\n", "\n", "print(\"\\nRCA EFFECT\")\n", "print(f\"F({df_RCA}, {df_ERROR}) = {F_RCA:.4f}\")\n", "print(f\"p = {p_RCA:.6e}\")\n", "print(f\"η² = {eta2_RCA:.4f}\")\n", "print(f\"ω² = {omega2_RCA:.4f}\")\n", "\n", "print(\"\\nTEMPERATURE EFFECT\")\n", "print(f\"F({df_TEMP}, {df_ERROR}) = {F_TEMP:.4f}\")\n", "print(f\"p = {p_TEMP:.6e}\")\n", "print(f\"η² = {eta2_TEMP:.4f}\")\n", "print(f\"ω² = {omega2_TEMP:.4f}\")\n", "\n", "print(\"\\nRCA × TEMPERATURE INTERACTION\")\n", "print(\n", " f\"F({df_INTERACTION}, {df_ERROR}) \"\n", " f\"= {F_INTERACTION:.4f}\"\n", ")\n", "print(f\"p = {p_INTERACTION:.4f}\")\n", "print(f\"η² = {eta2_INTERACTION:.4f}\")\n", "print(f\"ω² = {omega2_INTERACTION:.4f}\")\n", "\n", "print(\"\\nPURE ERROR\")\n", "print(f\"MS error = {MS_ERROR:.4f}\")\n", "\n", "print(\"=\" * 68)" ] }, { "cell_type": "code", "execution_count": 50, "id": "ba32146a-30c5-4023-ad49-4c2464c7810d", "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>Source</th>\n", " <th>SS</th>\n", " <th>df</th>\n", " <th>MS</th>\n", " <th>F</th>\n", " <th>p_value</th>\n", " <th>Eta_squared</th>\n", " <th>Partial_eta_squared</th>\n", " <th>Omega_squared</th>\n", " <th>Contribution_pct</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>RCA</td>\n", " <td>130.894127</td>\n", " <td>5</td>\n", " <td>26.178825</td>\n", " <td>28.483270</td>\n", " <td>1.136492e-14</td>\n", " <td>0.114896</td>\n", " <td>0.703581</td>\n", " <td>0.110773</td>\n", " <td>11.489627</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Temperature</td>\n", " <td>936.151891</td>\n", " <td>4</td>\n", " <td>234.037973</td>\n", " <td>254.639647</td>\n", " <td>6.707392e-37</td>\n", " <td>0.821736</td>\n", " <td>0.944370</td>\n", " <td>0.817849</td>\n", " <td>82.173559</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>RCA × Temperature</td>\n", " <td>17.045662</td>\n", " <td>20</td>\n", " <td>0.852283</td>\n", " <td>0.927307</td>\n", " <td>5.567725e-01</td>\n", " <td>0.014962</td>\n", " <td>0.236118</td>\n", " <td>0.000000</td>\n", " <td>1.496234</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Pure error</td>\n", " <td>55.145687</td>\n", " <td>60</td>\n", " <td>0.919095</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>4.840579</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Total</td>\n", " <td>1139.237368</td>\n", " <td>89</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>100.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Source SS df MS F p_value \\\n", "0 RCA 130.894127 5 26.178825 28.483270 1.136492e-14 \n", "1 Temperature 936.151891 4 234.037973 254.639647 6.707392e-37 \n", "2 RCA × Temperature 17.045662 20 0.852283 0.927307 5.567725e-01 \n", "3 Pure error 55.145687 60 0.919095 NaN NaN \n", "4 Total 1139.237368 89 NaN NaN NaN \n", "\n", " Eta_squared Partial_eta_squared Omega_squared Contribution_pct \n", "0 0.114896 0.703581 0.110773 11.489627 \n", "1 0.821736 0.944370 0.817849 82.173559 \n", "2 0.014962 0.236118 0.000000 1.496234 \n", "3 NaN NaN NaN 4.840579 \n", "4 NaN NaN NaN 100.000000 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# ============================================================\n", "# — TABLE 6\n", "# ============================================================\n", "\n", "anova_numeric = pd.DataFrame({\n", "\n", " \"Source\": [\n", " \"RCA\",\n", " \"Temperature\",\n", " \"RCA × Temperature\",\n", " \"Pure error\",\n", " \"Total\"\n", " ],\n", "\n", " \"SS\": [\n", " SS_RCA,\n", " SS_TEMP,\n", " SS_INTERACTION,\n", " SS_ERROR,\n", " SS_TOTAL\n", " ],\n", "\n", " \"df\": [\n", " df_RCA,\n", " df_TEMP,\n", " df_INTERACTION,\n", " df_ERROR,\n", " df_TOTAL\n", " ],\n", "\n", " \"MS\": [\n", " MS_RCA,\n", " MS_TEMP,\n", " MS_INTERACTION,\n", " MS_ERROR,\n", " np.nan\n", " ],\n", "\n", " \"F\": [\n", " F_RCA,\n", " F_TEMP,\n", " F_INTERACTION,\n", " np.nan,\n", " np.nan\n", " ],\n", "\n", " \"p_value\": [\n", " p_RCA,\n", " p_TEMP,\n", " p_INTERACTION,\n", " np.nan,\n", " np.nan\n", " ],\n", "\n", " \"Eta_squared\": [\n", " eta2_RCA,\n", " eta2_TEMP,\n", " eta2_INTERACTION,\n", " np.nan,\n", " np.nan\n", " ],\n", "\n", " \"Partial_eta_squared\": [\n", " peta2_RCA,\n", " peta2_TEMP,\n", " peta2_INTERACTION,\n", " np.nan,\n", " np.nan\n", " ],\n", "\n", " \"Omega_squared\": [\n", " omega2_RCA,\n", " omega2_TEMP,\n", " omega2_INTERACTION,\n", " np.nan,\n", " np.nan\n", " ],\n", "\n", " \"Contribution_pct\": [\n", " contrib_RCA,\n", " contrib_TEMP,\n", " contrib_INTERACTION,\n", " contrib_ERROR,\n", " 100\n", " ]\n", "})\n", "\n", "display(anova_numeric)" ] }, { "cell_type": "code", "execution_count": 52, "id": "7686ff0d-202b-4c88-8b53-404d0885cd2d", "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>Source</th>\n", " <th>SS</th>\n", " <th>df</th>\n", " <th>MS</th>\n", " <th>F</th>\n", " <th>p</th>\n", " <th>η²</th>\n", " <th>ω²</th>\n", " <th>Contribution (%)</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>RCA</td>\n", " <td>130.894</td>\n", " <td>5</td>\n", " <td>26.179</td>\n", " <td>28.483</td>\n", " <td>&lt;0.001</td>\n", " <td>0.1149</td>\n", " <td>0.1108</td>\n", " <td>11.49</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Temperature</td>\n", " <td>936.152</td>\n", " <td>4</td>\n", " <td>234.038</td>\n", " <td>254.640</td>\n", " <td>&lt;0.001</td>\n", " <td>0.8217</td>\n", " <td>0.8178</td>\n", " <td>82.17</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>RCA × Temperature</td>\n", " <td>17.046</td>\n", " <td>20</td>\n", " <td>0.852</td>\n", " <td>0.927</td>\n", " <td>0.557</td>\n", " <td>0.0150</td>\n", " <td>0.0000</td>\n", " <td>1.50</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Pure error</td>\n", " <td>55.146</td>\n", " <td>60</td>\n", " <td>0.919</td>\n", " <td>NaN</td>\n", " <td></td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>4.84</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Total</td>\n", " <td>1139.237</td>\n", " <td>89</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td></td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>100.00</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Source SS df MS F p η² ω² \\\n", "0 RCA 130.894 5 26.179 28.483 <0.001 0.1149 0.1108 \n", "1 Temperature 936.152 4 234.038 254.640 <0.001 0.8217 0.8178 \n", "2 RCA × Temperature 17.046 20 0.852 0.927 0.557 0.0150 0.0000 \n", "3 Pure error 55.146 60 0.919 NaN NaN NaN \n", "4 Total 1139.237 89 NaN NaN NaN NaN \n", "\n", " Contribution (%) \n", "0 11.49 \n", "1 82.17 \n", "2 1.50 \n", "3 4.84 \n", "4 100.00 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# ============================================================\n", "# — TABLE 6\n", "# ============================================================\n", "\n", "def format_p(p):\n", "\n", " if pd.isna(p):\n", " return \"\"\n", "\n", " if p < 0.001:\n", " return \"<0.001\"\n", "\n", " return f\"{p:.3f}\"\n", "\n", "\n", "table6_publication = pd.DataFrame({\n", "\n", " \"Source\": [\n", " \"RCA\",\n", " \"Temperature\",\n", " \"RCA × Temperature\",\n", " \"Pure error\",\n", " \"Total\"\n", " ],\n", "\n", " \"SS\": [\n", " SS_RCA,\n", " SS_TEMP,\n", " SS_INTERACTION,\n", " SS_ERROR,\n", " SS_TOTAL\n", " ],\n", "\n", " \"df\": [\n", " df_RCA,\n", " df_TEMP,\n", " df_INTERACTION,\n", " df_ERROR,\n", " df_TOTAL\n", " ],\n", "\n", " \"MS\": [\n", " MS_RCA,\n", " MS_TEMP,\n", " MS_INTERACTION,\n", " MS_ERROR,\n", " np.nan\n", " ],\n", "\n", " \"F\": [\n", " F_RCA,\n", " F_TEMP,\n", " F_INTERACTION,\n", " np.nan,\n", " np.nan\n", " ],\n", "\n", " \"p\": [\n", " format_p(p_RCA),\n", " format_p(p_TEMP),\n", " format_p(p_INTERACTION),\n", " \"\",\n", " \"\"\n", " ],\n", "\n", " \"η²\": [\n", " eta2_RCA,\n", " eta2_TEMP,\n", " eta2_INTERACTION,\n", " np.nan,\n", " np.nan\n", " ],\n", "\n", " \"ω²\": [\n", " omega2_RCA,\n", " omega2_TEMP,\n", " omega2_INTERACTION,\n", " np.nan,\n", " np.nan\n", " ],\n", "\n", " \"Contribution (%)\": [\n", " contrib_RCA,\n", " contrib_TEMP,\n", " contrib_INTERACTION,\n", " contrib_ERROR,\n", " 100\n", " ]\n", "})\n", "\n", "\n", "# Numerical formatting\n", "for col in [\"SS\", \"MS\", \"F\"]:\n", " table6_publication[col] = (\n", " table6_publication[col]\n", " .round(3)\n", " )\n", "\n", "for col in [\"η²\", \"ω²\"]:\n", " table6_publication[col] = (\n", " table6_publication[col]\n", " .round(4)\n", " )\n", "\n", "table6_publication[\"Contribution (%)\"] = (\n", " table6_publication[\"Contribution (%)\"]\n", " .round(2)\n", ")\n", "\n", "display(table6_publication)" ] }, { "cell_type": "code", "execution_count": 54, "id": "f5506fe5-37f7-4943-9a59-edbb6f9af754", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "✓ TABLE 6 SAVED\n", "C:\\Users\\sadal\\Desktop\\Yasin Hoca-RCA Thermal\\Analysis_Outputs\\Step_02_TwoWay_ANOVA\\Table_6_Two_Way_ANOVA_with_Effect_Sizes.xlsx\n", "File exists: True\n" ] } ], "source": [ "# ============================================================\n", "# — SAVE TABLE 6\n", "# ============================================================\n", "\n", "from pathlib import Path\n", "\n", "# Redefine for Jupyter robustness\n", "DATA_PATH = Path(\n", " r\"C:\\Users\\sadal\\Desktop\\Yasin Hoca-RCA Thermal\\data.xlsx\"\n", ")\n", "\n", "OUTPUT_DIR = (\n", " DATA_PATH.parent\n", " / \"Analysis_Outputs\"\n", " / \"Step_02_TwoWay_ANOVA\"\n", ")\n", "\n", "OUTPUT_DIR.mkdir(\n", " parents=True,\n", " exist_ok=True\n", ")\n", "\n", "\n", "table6_path = (\n", " OUTPUT_DIR\n", " / \"Table_6_Two_Way_ANOVA_with_Effect_Sizes.xlsx\"\n", ")\n", "\n", "\n", "with pd.ExcelWriter(\n", " table6_path,\n", " engine=\"openpyxl\"\n", ") as writer:\n", "\n", " table6_publication.to_excel(\n", " writer,\n", " sheet_name=\"Publication_Table\",\n", " index=False\n", " )\n", "\n", " anova_numeric.to_excel(\n", " writer,\n", " sheet_name=\"Full_Numeric_Results\",\n", " index=False\n", " )\n", "\n", "\n", "anova_numeric.to_csv(\n", " OUTPUT_DIR\n", " / \"Table_6_Two_Way_ANOVA_Numeric.csv\",\n", " index=False\n", ")\n", "\n", "\n", "print(\"✓ TABLE 6 SAVED\")\n", "print(table6_path)\n", "print(\"File exists:\", table6_path.exists())" ] }, { "cell_type": "code", "execution_count": 56, "id": "b0753082-17cc-47f3-a2d8-f3cb7bb21a1b", "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>Effect</th>\n", " <th>F_observed</th>\n", " <th>F_critical_alpha_0.05</th>\n", " <th>F_over_Fcritical</th>\n", " <th>p_value</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Temperature</td>\n", " <td>254.639647</td>\n", " <td>2.525215</td>\n", " <td>100.838795</td>\n", " <td>6.707392e-37</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>RCA</td>\n", " <td>28.483270</td>\n", " <td>2.368270</td>\n", " <td>12.027036</td>\n", " <td>1.136492e-14</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>RCA × Temperature</td>\n", " <td>0.927307</td>\n", " <td>1.747984</td>\n", " <td>0.530501</td>\n", " <td>5.567725e-01</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Effect F_observed F_critical_alpha_0.05 F_over_Fcritical \\\n", "0 Temperature 254.639647 2.525215 100.838795 \n", "1 RCA 28.483270 2.368270 12.027036 \n", "2 RCA × Temperature 0.927307 1.747984 0.530501 \n", "\n", " p_value \n", "0 6.707392e-37 \n", "1 1.136492e-14 \n", "2 5.567725e-01 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# ============================================================\n", "# — INFERENTIAL MARGIN\n", "# ============================================================\n", "\n", "effect_names = [\n", " \"Temperature\",\n", " \"RCA\",\n", " \"RCA × Temperature\"\n", "]\n", "\n", "effect_F = [\n", " F_TEMP,\n", " F_RCA,\n", " F_INTERACTION\n", "]\n", "\n", "effect_df = [\n", " df_TEMP,\n", " df_RCA,\n", " df_INTERACTION\n", "]\n", "\n", "effect_p = [\n", " p_TEMP,\n", " p_RCA,\n", " p_INTERACTION\n", "]\n", "\n", "\n", "F_critical = [\n", " stats.f.ppf(\n", " 0.95,\n", " effect_df_i,\n", " df_ERROR\n", " )\n", " for effect_df_i in effect_df\n", "]\n", "\n", "\n", "F_margin = (\n", " np.array(effect_F)\n", " / np.array(F_critical)\n", ")\n", "\n", "\n", "effect_evidence = pd.DataFrame({\n", " \"Effect\": effect_names,\n", " \"F_observed\": effect_F,\n", " \"F_critical_alpha_0.05\": F_critical,\n", " \"F_over_Fcritical\": F_margin,\n", " \"p_value\": effect_p\n", "})\n", "\n", "display(effect_evidence)" ] }, { "cell_type": "code", "execution_count": 58, "id": "6f32d754-cd96-4e6c-8bc5-13aa085dcde0", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "<Figure size 1280x520 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "✓ FIGURE S2 SAVED\n" ] } ], "source": [ "# ============================================================\n", "# — FIGURE S2\n", "# ANOVA EFFECT DECOMPOSITION\n", "# ============================================================\n", "\n", "from pathlib import Path\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "\n", "# ------------------------------------------------------------\n", "# Robust output path\n", "# ------------------------------------------------------------\n", "\n", "DATA_PATH = Path(\n", " r\"C:\\Users\\sadal\\Desktop\\Yasin Hoca-RCA Thermal\\data.xlsx\"\n", ")\n", "\n", "OUTPUT_DIR = (\n", " DATA_PATH.parent\n", " / \"Analysis_Outputs\"\n", " / \"Step_02_TwoWay_ANOVA\"\n", ")\n", "\n", "OUTPUT_DIR.mkdir(\n", " parents=True,\n", " exist_ok=True\n", ")\n", "\n", "\n", "# ------------------------------------------------------------\n", "# Visual identity\n", "# ------------------------------------------------------------\n", "\n", "background = \"#F7F5F0\"\n", "\n", "navy = \"#20364B\"\n", "teal = \"#2A9D8F\"\n", "coral = \"#E76F51\"\n", "gold = \"#E9C46A\"\n", "gray = \"#889198\"\n", "\n", "colors = {\n", " \"Temperature\": coral,\n", " \"RCA replacement\": teal,\n", " \"RCA × Temperature\": gold,\n", " \"Pure error\": gray\n", "}\n", "\n", "\n", "plt.rcParams.update({\n", " \"font.family\": \"DejaVu Sans\",\n", " \"font.size\": 10.5,\n", " \"axes.labelsize\": 11,\n", " \"axes.titlesize\": 12,\n", " \"xtick.labelsize\": 9.5,\n", " \"ytick.labelsize\": 10,\n", " \"axes.linewidth\": 0.8,\n", " \"pdf.fonttype\": 42,\n", " \"ps.fonttype\": 42\n", "})\n", "\n", "\n", "# ------------------------------------------------------------\n", "# Figure\n", "# ------------------------------------------------------------\n", "\n", "fig, (ax1, ax2) = plt.subplots(\n", " 1,\n", " 2,\n", " figsize=(12.8, 5.2),\n", " gridspec_kw={\n", " \"width_ratios\": [1.1, 1]\n", " },\n", " facecolor=background\n", ")\n", "\n", "ax1.set_facecolor(background)\n", "ax2.set_facecolor(background)\n", "\n", "\n", "# ============================================================\n", "# PANEL A — VARIANCE DECOMPOSITION\n", "# ============================================================\n", "\n", "labels_A = [\n", " \"Temperature\",\n", " \"RCA replacement\",\n", " \"RCA × Temperature\",\n", " \"Pure error\"\n", "]\n", "\n", "values_A = [\n", " contrib_TEMP,\n", " contrib_RCA,\n", " contrib_INTERACTION,\n", " contrib_ERROR\n", "]\n", "\n", "y_A = np.array([3, 2, 1, 0])\n", "\n", "\n", "for y, label, value in zip(\n", " y_A,\n", " labels_A,\n", " values_A\n", "):\n", "\n", " color = colors[label]\n", "\n", " ax1.hlines(\n", " y=y,\n", " xmin=0,\n", " xmax=value,\n", " color=color,\n", " linewidth=3.0,\n", " alpha=0.45\n", " )\n", "\n", " ax1.scatter(\n", " value,\n", " y,\n", " s=190,\n", " color=color,\n", " edgecolor=\"white\",\n", " linewidth=1.2,\n", " zorder=3\n", " )\n", "\n", " ax1.text(\n", " value + 1.6,\n", " y,\n", " f\"{value:.1f}%\",\n", " va=\"center\",\n", " ha=\"left\",\n", " color=navy,\n", " fontweight=\"bold\",\n", " fontsize=10.5\n", " )\n", "\n", "\n", "ax1.set_yticks(y_A)\n", "ax1.set_yticklabels(labels_A)\n", "\n", "ax1.set_xlim(0, 90)\n", "ax1.set_ylim(-0.7, 3.7)\n", "\n", "ax1.set_xlabel(\n", " \"Contribution to total observed variation (%)\"\n", ")\n", "\n", "ax1.set_title(\n", " \"Partitioning of compressive-strength variation\",\n", " loc=\"left\",\n", " color=navy,\n", " fontweight=\"bold\",\n", " pad=12\n", ")\n", "\n", "ax1.text(\n", " -0.11,\n", " 1.06,\n", " \"A\",\n", " transform=ax1.transAxes,\n", " fontsize=17,\n", " fontweight=\"bold\",\n", " color=coral\n", ")\n", "\n", "\n", "# ============================================================\n", "# PANEL B — F/Fcritical SIGNIFICANCE MARGIN\n", "# ============================================================\n", "\n", "labels_B = [\n", " \"Temperature\",\n", " \"RCA replacement\",\n", " \"RCA × Temperature\"\n", "]\n", "\n", "ratios_B = [\n", " F_margin[0],\n", " F_margin[1],\n", " F_margin[2]\n", "]\n", "\n", "p_B = [\n", " p_TEMP,\n", " p_RCA,\n", " p_INTERACTION\n", "]\n", "\n", "colors_B = [\n", " coral,\n", " teal,\n", " gold\n", "]\n", "\n", "y_B = np.array([2, 1, 0])\n", "\n", "\n", "# Background interpretation zones\n", "ax2.axvspan(\n", " 0.25,\n", " 1,\n", " color=\"#EEE9E1\",\n", " alpha=0.85,\n", " zorder=0\n", ")\n", "\n", "ax2.axvspan(\n", " 1,\n", " 200,\n", " color=\"#E6F0ED\",\n", " alpha=0.72,\n", " zorder=0\n", ")\n", "\n", "# Critical boundary\n", "ax2.axvline(\n", " 1,\n", " color=navy,\n", " linewidth=1.3,\n", " linestyle=(0, (4, 3))\n", ")\n", "\n", "\n", "for y, label, ratio, p, color in zip(\n", " y_B,\n", " labels_B,\n", " ratios_B,\n", " p_B,\n", " colors_B\n", "):\n", "\n", " xmin = min(1, ratio)\n", " xmax = max(1, ratio)\n", "\n", " ax2.hlines(\n", " y=y,\n", " xmin=xmin,\n", " xmax=xmax,\n", " color=color,\n", " linewidth=3,\n", " alpha=0.55\n", " )\n", "\n", " ax2.scatter(\n", " ratio,\n", " y,\n", " s=190,\n", " color=color,\n", " edgecolor=\"white\",\n", " linewidth=1.2,\n", " zorder=3\n", " )\n", "\n", " if p < 0.001:\n", " p_text = \"p < 0.001\"\n", " else:\n", " p_text = f\"p = {p:.3f}\"\n", "\n", " ax2.annotate(\n", " p_text,\n", " xy=(ratio, y),\n", " xytext=(8, 9),\n", " textcoords=\"offset points\",\n", " fontsize=9.5,\n", " color=navy,\n", " fontweight=\"bold\"\n", " )\n", "\n", "\n", "ax2.set_xscale(\"log\")\n", "\n", "ax2.set_xlim(0.25, 200)\n", "ax2.set_ylim(-0.7, 2.7)\n", "\n", "ax2.set_yticks(y_B)\n", "ax2.set_yticklabels(labels_B)\n", "\n", "ax2.set_xlabel(\n", " r\"Statistical evidence margin ($F/F_{critical}$)\"\n", ")\n", "\n", "ax2.set_title(\n", " \"Inferential strength relative to α = 0.05 threshold\",\n", " loc=\"left\",\n", " color=navy,\n", " fontweight=\"bold\",\n", " pad=12\n", ")\n", "\n", "\n", "ax2.text(\n", " 0.36,\n", " 2.48,\n", " \"non-significant\",\n", " color=gray,\n", " fontsize=9\n", ")\n", "\n", "ax2.text(\n", " 1.35,\n", " 2.48,\n", " \"significant\",\n", " color=teal,\n", " fontsize=9,\n", " fontweight=\"bold\"\n", ")\n", "\n", "ax2.text(\n", " -0.11,\n", " 1.06,\n", " \"B\",\n", " transform=ax2.transAxes,\n", " fontsize=17,\n", " fontweight=\"bold\",\n", " color=coral\n", ")\n", "\n", "\n", "# ============================================================\n", "# CLEAN \n", "# ============================================================\n", "\n", "for ax in [ax1, ax2]:\n", "\n", " ax.spines[\"top\"].set_visible(False)\n", " ax.spines[\"right\"].set_visible(False)\n", "\n", " ax.spines[\"left\"].set_color(\"#A09B92\")\n", " ax.spines[\"bottom\"].set_color(\"#A09B92\")\n", "\n", " ax.grid(\n", " axis=\"x\",\n", " color=\"#DDD9D0\",\n", " linewidth=0.6,\n", " alpha=0.65,\n", " zorder=0\n", " )\n", "\n", "\n", "# ============================================================\n", "# SAVE\n", "# ============================================================\n", "\n", "figure_base = (\n", " OUTPUT_DIR\n", " / \"Figure_S2_ANOVA_Effect_Decomposition\"\n", ")\n", "\n", "plt.savefig(\n", " str(figure_base) + \".png\",\n", " dpi=600,\n", " bbox_inches=\"tight\",\n", " facecolor=fig.get_facecolor()\n", ")\n", "\n", "plt.savefig(\n", " str(figure_base) + \".pdf\",\n", " bbox_inches=\"tight\",\n", " facecolor=fig.get_facecolor()\n", ")\n", "\n", "plt.savefig(\n", " str(figure_base) + \".tiff\",\n", " dpi=600,\n", " bbox_inches=\"tight\",\n", " facecolor=fig.get_facecolor(),\n", " pil_kwargs={\n", " \"compression\": \"tiff_lzw\"\n", " }\n", ")\n", "\n", "plt.show()\n", "\n", "print(\"✓ FIGURE S2 SAVED\")" ] }, { "cell_type": "code", "execution_count": 60, "id": "3e8a56eb-4af0-4781-a0f4-83efdcfd67f5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "======================================================================\n", "STEP 2 — TWO-WAY FACTORIAL ANOVA\n", "======================================================================\n", "\n", "DESIGN\n", "N : 90\n", "RCA levels : 6\n", "Temperature levels : 5\n", "Replicates/cell : 3\n", "Residual df : 60\n", "\n", "ANOVA\n", "RCA : F=28.4833, p=1.136492e-14\n", "Temperature : F=254.6396, p=6.707392e-37\n", "RCA × Temperature : F=0.9273, p=0.5568\n", "\n", "VARIANCE CONTRIBUTION\n", "Temperature : 82.17%\n", "RCA : 11.49%\n", "Interaction : 1.50%\n", "Pure error : 4.84%\n", "\n", "EFFECT SIZE — η²\n", "Temperature : 0.8217\n", "RCA : 0.1149\n", "Interaction : 0.0150\n", "\n", "EFFECT SIZE — ω²\n", "Temperature : 0.8178\n", "RCA : 0.1108\n", "Interaction : 0.0000\n", "\n", "SS CHECK\n", "SS decomposition : True\n", "\n", "SAVED OUTPUTS\n", " - Figure_S2_ANOVA_Effect_Decomposition.pdf\n", " - Figure_S2_ANOVA_Effect_Decomposition.png\n", " - Figure_S2_ANOVA_Effect_Decomposition.tiff\n", " - Table_6_Two_Way_ANOVA_Numeric.csv\n", " - Table_6_Two_Way_ANOVA_with_Effect_Sizes.xlsx\n", "======================================================================\n" ] } ], "source": [ "# ============================================================\n", "# — STEP 2 FINAL AUDIT\n", "# ============================================================\n", "\n", "print(\"=\" * 70)\n", "print(\"STEP 2 — TWO-WAY FACTORIAL ANOVA\")\n", "print(\"=\" * 70)\n", "\n", "print(\"\\nDESIGN\")\n", "print(f\"N : {N}\")\n", "print(f\"RCA levels : {a}\")\n", "print(f\"Temperature levels : {b}\")\n", "print(f\"Replicates/cell : {n}\")\n", "print(f\"Residual df : {df_ERROR}\")\n", "\n", "print(\"\\nANOVA\")\n", "print(\n", " f\"RCA : \"\n", " f\"F={F_RCA:.4f}, p={p_RCA:.6e}\"\n", ")\n", "\n", "print(\n", " f\"Temperature : \"\n", " f\"F={F_TEMP:.4f}, p={p_TEMP:.6e}\"\n", ")\n", "\n", "print(\n", " f\"RCA × Temperature : \"\n", " f\"F={F_INTERACTION:.4f}, \"\n", " f\"p={p_INTERACTION:.4f}\"\n", ")\n", "\n", "print(\"\\nVARIANCE CONTRIBUTION\")\n", "print(\n", " f\"Temperature : \"\n", " f\"{contrib_TEMP:.2f}%\"\n", ")\n", "\n", "print(\n", " f\"RCA : \"\n", " f\"{contrib_RCA:.2f}%\"\n", ")\n", "\n", "print(\n", " f\"Interaction : \"\n", " f\"{contrib_INTERACTION:.2f}%\"\n", ")\n", "\n", "print(\n", " f\"Pure error : \"\n", " f\"{contrib_ERROR:.2f}%\"\n", ")\n", "\n", "print(\"\\nEFFECT SIZE — η²\")\n", "print(f\"Temperature : {eta2_TEMP:.4f}\")\n", "print(f\"RCA : {eta2_RCA:.4f}\")\n", "print(\n", " f\"Interaction : \"\n", " f\"{eta2_INTERACTION:.4f}\"\n", ")\n", "\n", "print(\"\\nEFFECT SIZE — ω²\")\n", "print(f\"Temperature : {omega2_TEMP:.4f}\")\n", "print(f\"RCA : {omega2_RCA:.4f}\")\n", "print(\n", " f\"Interaction : \"\n", " f\"{omega2_INTERACTION:.4f}\"\n", ")\n", "\n", "print(\"\\nSS CHECK\")\n", "print(\n", " \"SS decomposition :\",\n", " np.isclose(\n", " SS_TOTAL,\n", " SS_RECONSTRUCTED\n", " )\n", ")\n", "\n", "print(\"\\nSAVED OUTPUTS\")\n", "\n", "for file in sorted(OUTPUT_DIR.iterdir()):\n", " print(\" -\", file.name)\n", "\n", "print(\"=\" * 70)" ] }, { "cell_type": "code", "execution_count": 62, "id": "83a00a87-5336-4efe-9a77-545831fc24c3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "======================================================================\n", "STEP 2 — TWO-WAY FACTORIAL ANOVA\n", "======================================================================\n", "\n", "DESIGN\n", "N : 90\n", "RCA levels : 6\n", "Temperature levels : 5\n", "Replicates/cell : 3\n", "Residual df : 60\n", "\n", "ANOVA\n", "RCA : F=28.4833, p=1.136492e-14\n", "Temperature : F=254.6396, p=6.707392e-37\n", "RCA × Temperature : F=0.9273, p=0.5568\n", "\n", "VARIANCE CONTRIBUTION\n", "Temperature : 82.17%\n", "RCA : 11.49%\n", "Interaction : 1.50%\n", "Pure error : 4.84%\n", "\n", "EFFECT SIZE — η²\n", "Temperature : 0.8217\n", "RCA : 0.1149\n", "Interaction : 0.0150\n", "\n", "EFFECT SIZE — ω²\n", "Temperature : 0.8178\n", "RCA : 0.1108\n", "Interaction : 0.0000\n", "\n", "SS CHECK\n", "SS decomposition : True\n", "\n", "SAVED OUTPUTS\n", " - Figure_S2_ANOVA_Effect_Decomposition.pdf\n", " - Figure_S2_ANOVA_Effect_Decomposition.png\n", " - Figure_S2_ANOVA_Effect_Decomposition.tiff\n", " - Table_6_Two_Way_ANOVA_Numeric.csv\n", " - Table_6_Two_Way_ANOVA_with_Effect_Sizes.xlsx\n", "======================================================================\n" ] } ], "source": [ "# ============================================================\n", "# — STEP 2 FINAL CHECK\n", "# ============================================================\n", "\n", "print(\"=\" * 70)\n", "print(\"STEP 2 — TWO-WAY FACTORIAL ANOVA\")\n", "print(\"=\" * 70)\n", "\n", "print(\"\\nDESIGN\")\n", "print(f\"N : {N}\")\n", "print(f\"RCA levels : {a}\")\n", "print(f\"Temperature levels : {b}\")\n", "print(f\"Replicates/cell : {n}\")\n", "print(f\"Residual df : {df_ERROR}\")\n", "\n", "print(\"\\nANOVA\")\n", "print(\n", " f\"RCA : \"\n", " f\"F={F_RCA:.4f}, p={p_RCA:.6e}\"\n", ")\n", "\n", "print(\n", " f\"Temperature : \"\n", " f\"F={F_TEMP:.4f}, p={p_TEMP:.6e}\"\n", ")\n", "\n", "print(\n", " f\"RCA × Temperature : \"\n", " f\"F={F_INTERACTION:.4f}, \"\n", " f\"p={p_INTERACTION:.4f}\"\n", ")\n", "\n", "print(\"\\nVARIANCE CONTRIBUTION\")\n", "print(\n", " f\"Temperature : \"\n", " f\"{contrib_TEMP:.2f}%\"\n", ")\n", "\n", "print(\n", " f\"RCA : \"\n", " f\"{contrib_RCA:.2f}%\"\n", ")\n", "\n", "print(\n", " f\"Interaction : \"\n", " f\"{contrib_INTERACTION:.2f}%\"\n", ")\n", "\n", "print(\n", " f\"Pure error : \"\n", " f\"{contrib_ERROR:.2f}%\"\n", ")\n", "\n", "print(\"\\nEFFECT SIZE — η²\")\n", "print(f\"Temperature : {eta2_TEMP:.4f}\")\n", "print(f\"RCA : {eta2_RCA:.4f}\")\n", "print(\n", " f\"Interaction : \"\n", " f\"{eta2_INTERACTION:.4f}\"\n", ")\n", "\n", "print(\"\\nEFFECT SIZE — ω²\")\n", "print(f\"Temperature : {omega2_TEMP:.4f}\")\n", "print(f\"RCA : {omega2_RCA:.4f}\")\n", "print(\n", " f\"Interaction : \"\n", " f\"{omega2_INTERACTION:.4f}\"\n", ")\n", "\n", "print(\"\\nSS CHECK\")\n", "print(\n", " \"SS decomposition :\",\n", " np.isclose(\n", " SS_TOTAL,\n", " SS_RECONSTRUCTED\n", " )\n", ")\n", "\n", "print(\"\\nSAVED OUTPUTS\")\n", "\n", "for file in sorted(OUTPUT_DIR.iterdir()):\n", " print(\" -\", file.name)\n", "\n", "print(\"=\" * 70)" ] }, { "cell_type": "markdown", "id": "d410752a-ca1a-4098-aa6d-93069945d672", "metadata": {}, "source": [ "#STEP 3" ] }, { "cell_type": "code", "execution_count": 64, "id": "d144f45f-24f1-4c9d-be5c-b9ad16562f6c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "✓ STEP 3 DATA CHECK PASSED\n", "N = 90\n", "Output: C:\\Users\\sadal\\Desktop\\Yasin Hoca-RCA Thermal\\Analysis_Outputs\\Step_03_Tukey_HSD\n" ] } ], "source": [ "# ============================================================\n", "# STEP 3 — TUKEY HSD POST-HOC\n", "# — SETUP AND DATA RELOAD\n", "# ============================================================\n", "\n", "from pathlib import Path\n", "from itertools import combinations\n", "import string\n", "import re\n", "import warnings\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "from scipy import stats\n", "from scipy.stats import studentized_range\n", "from matplotlib.colors import LinearSegmentedColormap\n", "\n", "warnings.filterwarnings(\"ignore\")\n", "\n", "\n", "# ============================================================\n", "# PATHS\n", "# ============================================================\n", "\n", "DATA_PATH = Path(\n", " r\"C:\\Users\\sadal\\Desktop\\Yasin Hoca-RCA Thermal\\data.xlsx\"\n", ")\n", "\n", "OUTPUT_DIR = (\n", " DATA_PATH.parent\n", " / \"Analysis_Outputs\"\n", " / \"Step_03_Tukey_HSD\"\n", ")\n", "\n", "OUTPUT_DIR.mkdir(\n", " parents=True,\n", " exist_ok=True\n", ")\n", "\n", "assert DATA_PATH.exists(), f\"Data file not found: {DATA_PATH}\"\n", "\n", "\n", "# ============================================================\n", "# READ DATA\n", "# ============================================================\n", "\n", "raw = pd.read_excel(\n", " DATA_PATH,\n", " sheet_name=\"data\",\n", " header=None\n", ")\n", "\n", "temperature_headers = raw.iloc[0, 1:].astype(str)\n", "\n", "temperatures = (\n", " temperature_headers\n", " .str.extract(r\"(\\d+)\")[0]\n", " .astype(int)\n", " .tolist()\n", ")\n", "\n", "\n", "records = []\n", "\n", "for _, row in raw.iloc[1:].iterrows():\n", "\n", " if pd.isna(row.iloc[0]):\n", " continue\n", "\n", " mix_label = str(row.iloc[0])\n", "\n", " rca_match = re.search(r\"(\\d+)\", mix_label)\n", "\n", " if rca_match is None:\n", " raise ValueError(\n", " f\"Could not identify RCA level: {mix_label}\"\n", " )\n", "\n", " rca_pct = int(rca_match.group(1))\n", "\n", " replicate_counter = {}\n", "\n", " for col_index, temperature in enumerate(\n", " temperatures,\n", " start=1\n", " ):\n", "\n", " replicate_counter[temperature] = (\n", " replicate_counter.get(temperature, 0) + 1\n", " )\n", "\n", " value = pd.to_numeric(\n", " row.iloc[col_index],\n", " errors=\"coerce\"\n", " )\n", "\n", " if pd.isna(value):\n", " raise ValueError(\n", " f\"Missing measurement: RCA={rca_pct}, T={temperature}\"\n", " )\n", "\n", " records.append({\n", " \"RCA_pct\": rca_pct,\n", " \"Temperature_C\": temperature,\n", " \"Replicate\": replicate_counter[temperature],\n", " \"CompressiveStrength_MPa\": float(value)\n", " })\n", "\n", "\n", "df_tukey = pd.DataFrame(records)\n", "\n", "\n", "# ============================================================\n", "# DESIGN CHECK\n", "# ============================================================\n", "\n", "cell_counts = (\n", " df_tukey.groupby(\n", " [\"RCA_pct\", \"Temperature_C\"]\n", " )\n", " .size()\n", ")\n", "\n", "assert len(df_tukey) == 90\n", "assert df_tukey[\"RCA_pct\"].nunique() == 6\n", "assert df_tukey[\"Temperature_C\"].nunique() == 5\n", "assert cell_counts.nunique() == 1\n", "assert cell_counts.iloc[0] == 3\n", "\n", "print(\"✓ STEP 3 DATA CHECK PASSED\")\n", "print(\"N =\", len(df_tukey))\n", "print(\"Output:\", OUTPUT_DIR)" ] }, { "cell_type": "code", "execution_count": 66, "id": "7bff8a3b-b8ac-4122-8ab2-f15564e797e9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "POOLED EXPERIMENTAL ERROR\n", "--------------------------------\n", "SS error = 55.1457\n", "df error = 60\n", "MS error = 0.9191\n" ] } ], "source": [ "# ============================================================\n", "# — POOLED ERROR VARIANCE\n", "# ============================================================\n", "\n", "response = \"CompressiveStrength_MPa\"\n", "\n", "rca_levels = sorted(\n", " df_tukey[\"RCA_pct\"].unique()\n", ")\n", "\n", "temp_levels = sorted(\n", " df_tukey[\"Temperature_C\"].unique()\n", ")\n", "\n", "a = len(rca_levels) # 6\n", "b = len(temp_levels) # 5\n", "n = int(cell_counts.iloc[0]) # 3\n", "\n", "\n", "# Cell means\n", "cell_fitted = (\n", " df_tukey.groupby(\n", " [\"RCA_pct\", \"Temperature_C\"]\n", " )[response]\n", " .transform(\"mean\")\n", ")\n", "\n", "residuals = (\n", " df_tukey[response]\n", " - cell_fitted\n", ")\n", "\n", "SS_ERROR = np.sum(\n", " residuals ** 2\n", ")\n", "\n", "df_ERROR = (\n", " a * b * (n - 1)\n", ")\n", "\n", "MS_ERROR = (\n", " SS_ERROR / df_ERROR\n", ")\n", "\n", "\n", "print(\"POOLED EXPERIMENTAL ERROR\")\n", "print(\"--------------------------------\")\n", "print(f\"SS error = {SS_ERROR:.4f}\")\n", "print(f\"df error = {df_ERROR}\")\n", "print(f\"MS error = {MS_ERROR:.4f}\")" ] }, { "cell_type": "code", "execution_count": 68, "id": "d524e47e-91f2-40d8-8edf-a929fbc6711c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RCA marginal means\n" ] }, { "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>Mean_MPa</th>\n", " </tr>\n", " <tr>\n", " <th>RCA_pct</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>17.191</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>17.004</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>16.406</td>\n", " </tr>\n", " <tr>\n", " <th>30</th>\n", " <td>15.750</td>\n", " </tr>\n", " <tr>\n", " <th>40</th>\n", " <td>14.694</td>\n", " </tr>\n", " <tr>\n", " <th>50</th>\n", " <td>13.863</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Mean_MPa\n", "RCA_pct \n", "0 17.191\n", "10 17.004\n", "20 16.406\n", "30 15.750\n", "40 14.694\n", "50 13.863" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Temperature marginal means\n" ] }, { "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>Mean_MPa</th>\n", " </tr>\n", " <tr>\n", " <th>Temperature_C</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>24</th>\n", " <td>19.209</td>\n", " </tr>\n", " <tr>\n", " <th>150</th>\n", " <td>18.630</td>\n", " </tr>\n", " <tr>\n", " <th>300</th>\n", " <td>16.597</td>\n", " </tr>\n", " <tr>\n", " <th>450</th>\n", " <td>14.280</td>\n", " </tr>\n", " <tr>\n", " <th>600</th>\n", " <td>10.374</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Mean_MPa\n", "Temperature_C \n", "24 19.209\n", "150 18.630\n", "300 16.597\n", "450 14.280\n", "600 10.374" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Pooled SE\n", "RCA mean SE = 0.2475 MPa\n", "Temperature mean SE = 0.2260 MPa\n" ] } ], "source": [ "# ============================================================\n", "# — MARGINAL MEANS\n", "# ============================================================\n", "\n", "rca_means = (\n", " df_tukey.groupby(\"RCA_pct\")[response]\n", " .mean()\n", ")\n", "\n", "temp_means = (\n", " df_tukey.groupby(\"Temperature_C\")[response]\n", " .mean()\n", ")\n", "\n", "\n", "n_RCA_mean = int(\n", " df_tukey.groupby(\"RCA_pct\")\n", " .size()\n", " .iloc[0]\n", ")\n", "\n", "n_TEMP_mean = int(\n", " df_tukey.groupby(\"Temperature_C\")\n", " .size()\n", " .iloc[0]\n", ")\n", "\n", "\n", "SE_RCA = np.sqrt(\n", " MS_ERROR / n_RCA_mean\n", ")\n", "\n", "SE_TEMP = np.sqrt(\n", " MS_ERROR / n_TEMP_mean\n", ")\n", "\n", "\n", "print(\"RCA marginal means\")\n", "display(\n", " rca_means\n", " .round(3)\n", " .to_frame(\"Mean_MPa\")\n", ")\n", "\n", "print(\"\\nTemperature marginal means\")\n", "display(\n", " temp_means\n", " .round(3)\n", " .to_frame(\"Mean_MPa\")\n", ")\n", "\n", "print(\"\\nPooled SE\")\n", "print(f\"RCA mean SE = {SE_RCA:.4f} MPa\")\n", "print(f\"Temperature mean SE = {SE_TEMP:.4f} MPa\")" ] }, { "cell_type": "code", "execution_count": 70, "id": "7d6cd22f-bd84-4171-b8a5-474fc423b723", "metadata": {}, "outputs": [], "source": [ "# ============================================================\n", "# — BALANCED TUKEY HSD FUNCTION\n", "# ============================================================\n", "\n", "def tukey_hsd_balanced(\n", " means,\n", " n_per_mean,\n", " mse,\n", " df_error,\n", " alpha=0.05\n", "):\n", "\n", " levels = list(means.index)\n", "\n", " k = len(levels)\n", "\n", " pooled_se = np.sqrt(\n", " mse / n_per_mean\n", " )\n", "\n", " q_critical = studentized_range.ppf(\n", " 1 - alpha,\n", " k,\n", " df_error\n", " )\n", "\n", " HSD = (\n", " q_critical * pooled_se\n", " )\n", "\n", " results = []\n", "\n", " for g1, g2 in combinations(levels, 2):\n", "\n", " mean1 = means.loc[g1]\n", " mean2 = means.loc[g2]\n", "\n", " difference = (\n", " mean1 - mean2\n", " )\n", "\n", " q_observed = (\n", " abs(difference)\n", " / pooled_se\n", " )\n", "\n", " p_adjusted = studentized_range.sf(\n", " q_observed,\n", " k,\n", " df_error\n", " )\n", "\n", " ci_lower = (\n", " difference - HSD\n", " )\n", "\n", " ci_upper = (\n", " difference + HSD\n", " )\n", "\n", " significant = (\n", " p_adjusted < alpha\n", " )\n", "\n", " results.append({\n", " \"Group_1\": g1,\n", " \"Group_2\": g2,\n", " \"Mean_1\": mean1,\n", " \"Mean_2\": mean2,\n", " \"Difference\": difference,\n", " \"HSD_threshold\": HSD,\n", " \"CI95_lower\": ci_lower,\n", " \"CI95_upper\": ci_upper,\n", " \"q_statistic\": q_observed,\n", " \"p_adjusted\": p_adjusted,\n", " \"Significant\": significant\n", " })\n", "\n", " results_df = pd.DataFrame(results)\n", "\n", " return results_df, HSD, q_critical" ] }, { "cell_type": "code", "execution_count": 72, "id": "3b6ce58c-ebc0-48a1-ab78-03f7f15bc155", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RCA TUKEY HSD\n", "-----------------------------------\n", "q critical = 4.1632\n", "HSD = 1.0305 MPa\n" ] }, { "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>Group_1</th>\n", " <th>Group_2</th>\n", " <th>Difference</th>\n", " <th>p_adjusted</th>\n", " <th>Significant</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>10</td>\n", " <td>0.1876</td>\n", " <td>0.9945</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0</td>\n", " <td>20</td>\n", " <td>0.7851</td>\n", " <td>0.2341</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0</td>\n", " <td>30</td>\n", " <td>1.4414</td>\n", " <td>0.0016</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0</td>\n", " <td>40</td>\n", " <td>2.4976</td>\n", " <td>0.0000</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0</td>\n", " <td>50</td>\n", " <td>3.3277</td>\n", " <td>0.0000</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>10</td>\n", " <td>20</td>\n", " <td>0.5975</td>\n", " <td>0.5328</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>10</td>\n", " <td>30</td>\n", " <td>1.2538</td>\n", " <td>0.0086</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>10</td>\n", " <td>40</td>\n", " <td>2.3100</td>\n", " <td>0.0000</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>10</td>\n", " <td>50</td>\n", " <td>3.1401</td>\n", " <td>0.0000</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>20</td>\n", " <td>30</td>\n", " <td>0.6563</td>\n", " <td>0.4275</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>20</td>\n", " <td>40</td>\n", " <td>1.7125</td>\n", " <td>0.0001</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>20</td>\n", " <td>50</td>\n", " <td>2.5426</td>\n", " <td>0.0000</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>30</td>\n", " <td>40</td>\n", " <td>1.0562</td>\n", " <td>0.0415</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>30</td>\n", " <td>50</td>\n", " <td>1.8863</td>\n", " <td>0.0000</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>40</td>\n", " <td>50</td>\n", " <td>0.8301</td>\n", " <td>0.1829</td>\n", " <td>False</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Group_1 Group_2 Difference p_adjusted Significant\n", "0 0 10 0.1876 0.9945 False\n", "1 0 20 0.7851 0.2341 False\n", "2 0 30 1.4414 0.0016 True\n", "3 0 40 2.4976 0.0000 True\n", "4 0 50 3.3277 0.0000 True\n", "5 10 20 0.5975 0.5328 False\n", "6 10 30 1.2538 0.0086 True\n", "7 10 40 2.3100 0.0000 True\n", "8 10 50 3.1401 0.0000 True\n", "9 20 30 0.6563 0.4275 False\n", "10 20 40 1.7125 0.0001 True\n", "11 20 50 2.5426 0.0000 True\n", "12 30 40 1.0562 0.0415 True\n", "13 30 50 1.8863 0.0000 True\n", "14 40 50 0.8301 0.1829 False" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# ============================================================\n", "# — TUKEY HSD FOR RCA\n", "# ============================================================\n", "\n", "tukey_RCA, HSD_RCA, qcrit_RCA = tukey_hsd_balanced(\n", " means=rca_means,\n", " n_per_mean=n_RCA_mean,\n", " mse=MS_ERROR,\n", " df_error=df_ERROR,\n", " alpha=0.05\n", ")\n", "\n", "print(\"RCA TUKEY HSD\")\n", "print(\"-----------------------------------\")\n", "print(f\"q critical = {qcrit_RCA:.4f}\")\n", "print(f\"HSD = {HSD_RCA:.4f} MPa\")\n", "\n", "display(\n", " tukey_RCA[\n", " [\n", " \"Group_1\",\n", " \"Group_2\",\n", " \"Difference\",\n", " \"p_adjusted\",\n", " \"Significant\"\n", " ]\n", " ].round(4)\n", ")" ] }, { "cell_type": "code", "execution_count": 74, "id": "56fa3939-77db-4894-be99-f05620157c93", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TEMPERATURE TUKEY HSD\n", "-----------------------------------\n", "q critical = 3.9774\n", "HSD = 0.8988 MPa\n" ] }, { "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>Group_1</th>\n", " <th>Group_2</th>\n", " <th>Difference</th>\n", " <th>p_adjusted</th>\n", " <th>Significant</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>24</td>\n", " <td>150</td>\n", " <td>0.5791</td>\n", " <td>0.3764</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>24</td>\n", " <td>300</td>\n", " <td>2.6113</td>\n", " <td>0.0000</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>24</td>\n", " <td>450</td>\n", " <td>4.9286</td>\n", " <td>0.0000</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>24</td>\n", " <td>600</td>\n", " <td>8.8344</td>\n", " <td>0.0000</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>150</td>\n", " <td>300</td>\n", " <td>2.0322</td>\n", " <td>0.0000</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>150</td>\n", " <td>450</td>\n", " <td>4.3495</td>\n", " <td>0.0000</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>150</td>\n", " <td>600</td>\n", " <td>8.2553</td>\n", " <td>0.0000</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>300</td>\n", " <td>450</td>\n", " <td>2.3173</td>\n", " <td>0.0000</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>300</td>\n", " <td>600</td>\n", " <td>6.2231</td>\n", " <td>0.0000</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>450</td>\n", " <td>600</td>\n", " <td>3.9058</td>\n", " <td>0.0000</td>\n", " <td>True</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Group_1 Group_2 Difference p_adjusted Significant\n", "0 24 150 0.5791 0.3764 False\n", "1 24 300 2.6113 0.0000 True\n", "2 24 450 4.9286 0.0000 True\n", "3 24 600 8.8344 0.0000 True\n", "4 150 300 2.0322 0.0000 True\n", "5 150 450 4.3495 0.0000 True\n", "6 150 600 8.2553 0.0000 True\n", "7 300 450 2.3173 0.0000 True\n", "8 300 600 6.2231 0.0000 True\n", "9 450 600 3.9058 0.0000 True" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# ============================================================\n", "# CELL 29 — TUKEY HSD FOR TEMPERATURE\n", "# ============================================================\n", "\n", "tukey_TEMP, HSD_TEMP, qcrit_TEMP = tukey_hsd_balanced(\n", " means=temp_means,\n", " n_per_mean=n_TEMP_mean,\n", " mse=MS_ERROR,\n", " df_error=df_ERROR,\n", " alpha=0.05\n", ")\n", "\n", "print(\"TEMPERATURE TUKEY HSD\")\n", "print(\"-----------------------------------\")\n", "print(f\"q critical = {qcrit_TEMP:.4f}\")\n", "print(f\"HSD = {HSD_TEMP:.4f} MPa\")\n", "\n", "display(\n", " tukey_TEMP[\n", " [\n", " \"Group_1\",\n", " \"Group_2\",\n", " \"Difference\",\n", " \"p_adjusted\",\n", " \"Significant\"\n", " ]\n", " ].round(4)\n", ")" ] }, { "cell_type": "code", "execution_count": 76, "id": "07b057d7-a307-44e5-a9a2-a8ebee139182", "metadata": {}, "outputs": [], "source": [ "# ============================================================\n", "# — COMPACT LETTER DISPLAY\n", "# ============================================================\n", "\n", "def create_compact_letters(\n", " means,\n", " pairwise_results\n", "):\n", "\n", " levels = list(means.index)\n", "\n", " # Store pairwise significance\n", " significance = {}\n", "\n", " for _, row in pairwise_results.iterrows():\n", "\n", " key = frozenset([\n", " row[\"Group_1\"],\n", " row[\"Group_2\"]\n", " ])\n", "\n", " significance[key] = bool(\n", " row[\"Significant\"]\n", " )\n", "\n", "\n", " def are_non_significant(g1, g2):\n", "\n", " if g1 == g2:\n", " return True\n", "\n", " key = frozenset([g1, g2])\n", "\n", " return not significance[key]\n", "\n", "\n", " # --------------------------------------------------------\n", " # Find all mutually non-significant subsets\n", " # --------------------------------------------------------\n", "\n", " candidate_cliques = []\n", "\n", " for size in range(\n", " 1,\n", " len(levels) + 1\n", " ):\n", "\n", " for combo in combinations(\n", " levels,\n", " size\n", " ):\n", "\n", " valid = all(\n", " are_non_significant(g1, g2)\n", " for g1, g2 in combinations(\n", " combo,\n", " 2\n", " )\n", " )\n", "\n", " if valid:\n", " candidate_cliques.append(\n", " set(combo)\n", " )\n", "\n", "\n", " # Keep only maximal cliques\n", " maximal_cliques = []\n", "\n", " for clique in candidate_cliques:\n", "\n", " is_subset = any(\n", " clique < other\n", " for other in candidate_cliques\n", " )\n", "\n", " if not is_subset:\n", " maximal_cliques.append(clique)\n", "\n", "\n", " # Highest mean cluster gets letter \"a\"\n", " maximal_cliques = sorted(\n", " maximal_cliques,\n", " key=lambda c:\n", " max(means.loc[level] for level in c),\n", " reverse=True\n", " )\n", "\n", "\n", " alphabet = list(\n", " string.ascii_lowercase\n", " )\n", "\n", " if len(maximal_cliques) > len(alphabet):\n", " raise RuntimeError(\n", " \"Too many letter groups.\"\n", " )\n", "\n", "\n", " letter_map = {\n", " level: \"\"\n", " for level in levels\n", " }\n", "\n", "\n", " for letter, clique in zip(\n", " alphabet,\n", " maximal_cliques\n", " ):\n", "\n", " for level in clique:\n", " letter_map[level] += letter\n", "\n", "\n", " return letter_map, maximal_cliques" ] }, { "cell_type": "code", "execution_count": 78, "id": "096586ea-90f5-4924-b7e3-b376f0b8a2a9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RCA TUKEY GROUPS\n", "--------------------------------\n", "RCA 0% : 17.191 MPa → a\n", "RCA 10% : 17.004 MPa → a\n", "RCA 20% : 16.406 MPa → ab\n", "RCA 30% : 15.750 MPa → b\n", "RCA 40% : 14.694 MPa → c\n", "RCA 50% : 13.863 MPa → c\n", "\n", "TEMPERATURE TUKEY GROUPS\n", "--------------------------------\n", " 24 °C : 19.209 MPa → a\n", "150 °C : 18.630 MPa → a\n", "300 °C : 16.597 MPa → b\n", "450 °C : 14.280 MPa → c\n", "600 °C : 10.374 MPa → d\n" ] } ], "source": [ "# ============================================================\n", "# — GENERATE TUKEY LETTER GROUPS\n", "# ============================================================\n", "\n", "RCA_letters, RCA_cliques = create_compact_letters(\n", " rca_means,\n", " tukey_RCA\n", ")\n", "\n", "TEMP_letters, TEMP_cliques = create_compact_letters(\n", " temp_means,\n", " tukey_TEMP\n", ")\n", "\n", "\n", "print(\"RCA TUKEY GROUPS\")\n", "print(\"--------------------------------\")\n", "\n", "for level in sorted(\n", " RCA_letters.keys()\n", "):\n", " print(\n", " f\"RCA {level:>2}% : \"\n", " f\"{rca_means.loc[level]:.3f} MPa \"\n", " f\"→ {RCA_letters[level]}\"\n", " )\n", "\n", "\n", "print(\"\\nTEMPERATURE TUKEY GROUPS\")\n", "print(\"--------------------------------\")\n", "\n", "for level in sorted(\n", " TEMP_letters.keys()\n", "):\n", " print(\n", " f\"{level:>3} °C : \"\n", " f\"{temp_means.loc[level]:.3f} MPa \"\n", " f\"→ {TEMP_letters[level]}\"\n", " )" ] }, { "cell_type": "code", "execution_count": 80, "id": "03bd19d8-c997-4a01-b1da-44751ceaa6c0", "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>Factor</th>\n", " <th>Level</th>\n", " <th>Marginal_Mean_MPa</th>\n", " <th>Pooled_SE_MPa</th>\n", " <th>Tukey_Letter</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>RCA</td>\n", " <td>0</td>\n", " <td>17.191</td>\n", " <td>0.2475</td>\n", " <td>a</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>RCA</td>\n", " <td>10</td>\n", " <td>17.004</td>\n", " <td>0.2475</td>\n", " <td>a</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>RCA</td>\n", " <td>20</td>\n", " <td>16.406</td>\n", " <td>0.2475</td>\n", " <td>ab</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>RCA</td>\n", " <td>30</td>\n", " <td>15.750</td>\n", " <td>0.2475</td>\n", " <td>b</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>RCA</td>\n", " <td>40</td>\n", " <td>14.694</td>\n", " <td>0.2475</td>\n", " <td>c</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>RCA</td>\n", " <td>50</td>\n", " <td>13.863</td>\n", " <td>0.2475</td>\n", " <td>c</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>Temperature</td>\n", " <td>24</td>\n", " <td>19.209</td>\n", " <td>0.2260</td>\n", " <td>a</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>Temperature</td>\n", " <td>150</td>\n", " <td>18.630</td>\n", " <td>0.2260</td>\n", " <td>a</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>Temperature</td>\n", " <td>300</td>\n", " <td>16.597</td>\n", " <td>0.2260</td>\n", " <td>b</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>Temperature</td>\n", " <td>450</td>\n", " <td>14.280</td>\n", " <td>0.2260</td>\n", " <td>c</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>Temperature</td>\n", " <td>600</td>\n", " <td>10.374</td>\n", " <td>0.2260</td>\n", " <td>d</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Factor Level Marginal_Mean_MPa Pooled_SE_MPa Tukey_Letter\n", "0 RCA 0 17.191 0.2475 a\n", "1 RCA 10 17.004 0.2475 a\n", "2 RCA 20 16.406 0.2475 ab\n", "3 RCA 30 15.750 0.2475 b\n", "4 RCA 40 14.694 0.2475 c\n", "5 RCA 50 13.863 0.2475 c\n", "6 Temperature 24 19.209 0.2260 a\n", "7 Temperature 150 18.630 0.2260 a\n", "8 Temperature 300 16.597 0.2260 b\n", "9 Temperature 450 14.280 0.2260 c\n", "10 Temperature 600 10.374 0.2260 d" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# ============================================================\n", "# — SUPPLEMENTARY TABLE S1\n", "# ============================================================\n", "\n", "RCA_summary = pd.DataFrame({\n", " \"Factor\": \"RCA\",\n", " \"Level\": rca_means.index,\n", " \"Marginal_Mean_MPa\": rca_means.values,\n", " \"Pooled_SE_MPa\": SE_RCA,\n", " \"Tukey_Letter\": [\n", " RCA_letters[level]\n", " for level in rca_means.index\n", " ]\n", "})\n", "\n", "\n", "TEMP_summary = pd.DataFrame({\n", " \"Factor\": \"Temperature\",\n", " \"Level\": temp_means.index,\n", " \"Marginal_Mean_MPa\": temp_means.values,\n", " \"Pooled_SE_MPa\": SE_TEMP,\n", " \"Tukey_Letter\": [\n", " TEMP_letters[level]\n", " for level in temp_means.index\n", " ]\n", "})\n", "\n", "\n", "marginal_summary = pd.concat(\n", " [\n", " RCA_summary,\n", " TEMP_summary\n", " ],\n", " ignore_index=True\n", ")\n", "\n", "\n", "# Round display values\n", "marginal_summary[\n", " \"Marginal_Mean_MPa\"\n", "] = marginal_summary[\n", " \"Marginal_Mean_MPa\"\n", "].round(3)\n", "\n", "marginal_summary[\n", " \"Pooled_SE_MPa\"\n", "] = marginal_summary[\n", " \"Pooled_SE_MPa\"\n", "].round(4)\n", "\n", "\n", "display(marginal_summary)" ] }, { "cell_type": "code", "execution_count": 82, "id": "dd8fe36c-3a1f-4c93-a32b-b54c8ae1738e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "✓ SUPPLEMENTARY TABLE S1 SAVED\n", "C:\\Users\\sadal\\Desktop\\Yasin Hoca-RCA Thermal\\Analysis_Outputs\\Step_03_Tukey_HSD\\Supplementary_Table_S1_Tukey_HSD.xlsx\n" ] } ], "source": [ "# ============================================================\n", "# — SAVE SUPPLEMENTARY TABLE S1\n", "# ============================================================\n", "\n", "from pathlib import Path\n", "\n", "DATA_PATH = Path(\n", " r\"C:\\Users\\sadal\\Desktop\\Yasin Hoca-RCA Thermal\\data.xlsx\"\n", ")\n", "\n", "OUTPUT_DIR = (\n", " DATA_PATH.parent\n", " / \"Analysis_Outputs\"\n", " / \"Step_03_Tukey_HSD\"\n", ")\n", "\n", "OUTPUT_DIR.mkdir(\n", " parents=True,\n", " exist_ok=True\n", ")\n", "\n", "\n", "table_s1_path = (\n", " OUTPUT_DIR\n", " / \"Supplementary_Table_S1_Tukey_HSD.xlsx\"\n", ")\n", "\n", "\n", "with pd.ExcelWriter(\n", " table_s1_path,\n", " engine=\"openpyxl\"\n", ") as writer:\n", "\n", " marginal_summary.to_excel(\n", " writer,\n", " sheet_name=\"Compact_Letter_Groups\",\n", " index=False\n", " )\n", "\n", " tukey_RCA.to_excel(\n", " writer,\n", " sheet_name=\"RCA_Pairwise\",\n", " index=False\n", " )\n", "\n", " tukey_TEMP.to_excel(\n", " writer,\n", " sheet_name=\"Temperature_Pairwise\",\n", " index=False\n", " )\n", "\n", "\n", "# CSV backups\n", "marginal_summary.to_csv(\n", " OUTPUT_DIR\n", " / \"Tukey_Compact_Letter_Groups.csv\",\n", " index=False\n", ")\n", "\n", "tukey_RCA.to_csv(\n", " OUTPUT_DIR\n", " / \"Tukey_RCA_Pairwise.csv\",\n", " index=False\n", ")\n", "\n", "tukey_TEMP.to_csv(\n", " OUTPUT_DIR\n", " / \"Tukey_Temperature_Pairwise.csv\",\n", " index=False\n", ")\n", "\n", "\n", "print(\"✓ SUPPLEMENTARY TABLE S1 SAVED\")\n", "print(table_s1_path)" ] }, { "cell_type": "code", "execution_count": 84, "id": "a5698e2a-2451-44b4-9f97-bf15d0c77c1b", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "<Figure size 1340x530 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "✓ FIGURE S3 SAVED\n" ] } ], "source": [ "# ============================================================\n", "# FIGURE S3\n", "# TUKEY HSD MARGINAL-MEAN SEPARATION\n", "# ============================================================\n", "\n", "from pathlib import Path\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "\n", "# ------------------------------------------------------------\n", "# Robust path\n", "# ------------------------------------------------------------\n", "\n", "DATA_PATH = Path(\n", " r\"C:\\Users\\sadal\\Desktop\\Yasin Hoca-RCA Thermal\\data.xlsx\"\n", ")\n", "\n", "OUTPUT_DIR = (\n", " DATA_PATH.parent\n", " / \"Analysis_Outputs\"\n", " / \"Step_03_Tukey_HSD\"\n", ")\n", "\n", "OUTPUT_DIR.mkdir(\n", " parents=True,\n", " exist_ok=True\n", ")\n", "\n", "\n", "# ------------------------------------------------------------\n", "# Visual identity\n", "# ------------------------------------------------------------\n", "\n", "background = \"#F7F5F0\"\n", "navy = \"#20364B\"\n", "teal = \"#2A9D8F\"\n", "coral = \"#E76F51\"\n", "gold = \"#E9C46A\"\n", "gray = \"#7C858C\"\n", "\n", "\n", "plt.rcParams.update({\n", " \"font.family\": \"DejaVu Sans\",\n", " \"font.size\": 10.5,\n", " \"axes.labelsize\": 11,\n", " \"axes.titlesize\": 12,\n", " \"xtick.labelsize\": 9.5,\n", " \"ytick.labelsize\": 9.5,\n", " \"axes.linewidth\": 0.8,\n", " \"pdf.fonttype\": 42,\n", " \"ps.fonttype\": 42\n", "})\n", "\n", "\n", "fig, (ax1, ax2) = plt.subplots(\n", " 1,\n", " 2,\n", " figsize=(13.4, 5.3),\n", " facecolor=background\n", ")\n", "\n", "for ax in [ax1, ax2]:\n", " ax.set_facecolor(background)\n", "\n", "\n", "# ============================================================\n", "# PANEL A — RCA MARGINAL MEANS\n", "# ============================================================\n", "\n", "x_rca = np.array(\n", " rca_levels,\n", " dtype=float\n", ")\n", "\n", "y_rca = np.array([\n", " rca_means.loc[x]\n", " for x in rca_levels\n", "])\n", "\n", "\n", "rca_point_colors = [\n", " \"#264653\",\n", " \"#287271\",\n", " \"#2A9D8F\",\n", " \"#84A98C\",\n", " \"#E9C46A\",\n", " \"#E76F51\"\n", "]\n", "\n", "\n", "ax1.plot(\n", " x_rca,\n", " y_rca,\n", " color=\"#A7AAA6\",\n", " linewidth=1.5,\n", " zorder=1\n", ")\n", "\n", "\n", "for x, y, color in zip(\n", " x_rca,\n", " y_rca,\n", " rca_point_colors\n", "):\n", "\n", " ax1.errorbar(\n", " x,\n", " y,\n", " yerr=SE_RCA,\n", " fmt=\"none\",\n", " ecolor=color,\n", " elinewidth=1.5,\n", " capsize=4,\n", " capthick=1.2,\n", " zorder=2\n", " )\n", "\n", " ax1.scatter(\n", " x,\n", " y,\n", " s=155,\n", " color=color,\n", " edgecolor=\"white\",\n", " linewidth=1.2,\n", " zorder=3\n", " )\n", "\n", " ax1.text(\n", " x,\n", " y + 0.55,\n", " RCA_letters[int(x)],\n", " ha=\"center\",\n", " va=\"center\",\n", " color=navy,\n", " fontweight=\"bold\",\n", " fontsize=11,\n", " bbox=dict(\n", " boxstyle=\"round,pad=0.28\",\n", " facecolor=\"white\",\n", " edgecolor=\"#D5D1C8\"\n", " )\n", " )\n", "\n", "\n", "ax1.set_xlabel(\n", " \"RCA replacement (%)\"\n", ")\n", "\n", "ax1.set_ylabel(\n", " \"Marginal mean compressive strength (MPa)\"\n", ")\n", "\n", "ax1.set_xticks(\n", " rca_levels\n", ")\n", "\n", "ax1.set_title(\n", " \"RCA main-effect separation\",\n", " loc=\"left\",\n", " color=navy,\n", " fontweight=\"bold\",\n", " pad=12\n", ")\n", "\n", "\n", "ax1.text(\n", " 0.03,\n", " 0.06,\n", " f\"Tukey HSD = {HSD_RCA:.3f} MPa\",\n", " transform=ax1.transAxes,\n", " color=gray,\n", " fontsize=9.5,\n", " bbox=dict(\n", " boxstyle=\"round,pad=0.38\",\n", " facecolor=\"white\",\n", " edgecolor=\"#D5D1C8\"\n", " )\n", ")\n", "\n", "ax1.text(\n", " -0.10,\n", " 1.07,\n", " \"A\",\n", " transform=ax1.transAxes,\n", " fontsize=17,\n", " fontweight=\"bold\",\n", " color=coral\n", ")\n", "\n", "\n", "# ============================================================\n", "# PANEL B — TEMPERATURE MARGINAL MEANS\n", "# ============================================================\n", "\n", "x_temp = np.array(\n", " temp_levels,\n", " dtype=float\n", ")\n", "\n", "y_temp = np.array([\n", " temp_means.loc[x]\n", " for x in temp_levels\n", "])\n", "\n", "\n", "temp_colors = [\n", " \"#264653\",\n", " \"#287271\",\n", " \"#2A9D8F\",\n", " \"#E9C46A\",\n", " \"#E76F51\"\n", "]\n", "\n", "\n", "ax2.plot(\n", " x_temp,\n", " y_temp,\n", " color=\"#A7AAA6\",\n", " linewidth=1.5,\n", " zorder=1\n", ")\n", "\n", "\n", "for x, y, color in zip(\n", " x_temp,\n", " y_temp,\n", " temp_colors\n", "):\n", "\n", " ax2.errorbar(\n", " x,\n", " y,\n", " yerr=SE_TEMP,\n", " fmt=\"none\",\n", " ecolor=color,\n", " elinewidth=1.5,\n", " capsize=4,\n", " capthick=1.2,\n", " zorder=2\n", " )\n", "\n", " ax2.scatter(\n", " x,\n", " y,\n", " s=155,\n", " color=color,\n", " edgecolor=\"white\",\n", " linewidth=1.2,\n", " zorder=3\n", " )\n", "\n", " ax2.text(\n", " x,\n", " y + 0.70,\n", " TEMP_letters[int(x)],\n", " ha=\"center\",\n", " va=\"center\",\n", " color=navy,\n", " fontweight=\"bold\",\n", " fontsize=11,\n", " bbox=dict(\n", " boxstyle=\"round,pad=0.28\",\n", " facecolor=\"white\",\n", " edgecolor=\"#D5D1C8\"\n", " )\n", " )\n", "\n", "\n", "ax2.set_xlabel(\n", " \"Exposure temperature (°C)\"\n", ")\n", "\n", "ax2.set_ylabel(\n", " \"Marginal mean compressive strength (MPa)\"\n", ")\n", "\n", "ax2.set_xticks(\n", " temp_levels\n", ")\n", "\n", "ax2.set_title(\n", " \"Temperature main-effect separation\",\n", " loc=\"left\",\n", " color=navy,\n", " fontweight=\"bold\",\n", " pad=12\n", ")\n", "\n", "\n", "ax2.text(\n", " 0.03,\n", " 0.06,\n", " f\"Tukey HSD = {HSD_TEMP:.3f} MPa\",\n", " transform=ax2.transAxes,\n", " color=gray,\n", " fontsize=9.5,\n", " bbox=dict(\n", " boxstyle=\"round,pad=0.38\",\n", " facecolor=\"white\",\n", " edgecolor=\"#D5D1C8\"\n", " )\n", ")\n", "\n", "ax2.text(\n", " -0.10,\n", " 1.07,\n", " \"B\",\n", " transform=ax2.transAxes,\n", " fontsize=17,\n", " fontweight=\"bold\",\n", " color=coral\n", ")\n", "\n", "\n", "# ============================================================\n", "# CLEAN JOURNAL STYLE\n", "# ============================================================\n", "\n", "for ax in [ax1, ax2]:\n", "\n", " ax.spines[\"top\"].set_visible(False)\n", " ax.spines[\"right\"].set_visible(False)\n", "\n", " ax.spines[\"left\"].set_color(\"#A09B92\")\n", " ax.spines[\"bottom\"].set_color(\"#A09B92\")\n", "\n", " ax.grid(\n", " axis=\"y\",\n", " color=\"#DDD9D0\",\n", " linewidth=0.65,\n", " alpha=0.60,\n", " zorder=0\n", " )\n", "\n", "\n", "# ============================================================\n", "# SAVE\n", "# ============================================================\n", "\n", "figure_base = (\n", " OUTPUT_DIR\n", " / \"Figure_S3_Tukey_Grouping_Map\"\n", ")\n", "\n", "\n", "plt.savefig(\n", " str(figure_base) + \".png\",\n", " dpi=600,\n", " bbox_inches=\"tight\",\n", " facecolor=fig.get_facecolor()\n", ")\n", "\n", "plt.savefig(\n", " str(figure_base) + \".pdf\",\n", " bbox_inches=\"tight\",\n", " facecolor=fig.get_facecolor()\n", ")\n", "\n", "plt.savefig(\n", " str(figure_base) + \".tiff\",\n", " dpi=600,\n", " bbox_inches=\"tight\",\n", " facecolor=fig.get_facecolor(),\n", " pil_kwargs={\n", " \"compression\": \"tiff_lzw\"\n", " }\n", ")\n", "\n", "plt.show()\n", "\n", "print(\"✓ FIGURE S3 SAVED\")" ] }, { "cell_type": "code", "execution_count": 86, "id": "a41d737a-9ef9-4a3f-a06a-9f9268ae5bf5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "====================================================================\n", "STEP 3 — TUKEY HSD POST-HOC\n", "====================================================================\n", "\n", "POOLED ERROR\n", "MS error : 0.9191\n", "Residual df : 60\n", "\n", "TUKEY CRITICAL DIFFERENCES\n", "RCA HSD : 1.0305 MPa\n", "Temperature HSD : 0.8988 MPa\n", "\n", "RCA COMPACT LETTER DISPLAY\n", "RCA 0% : 17.191 MPa → a\n", "RCA 10% : 17.004 MPa → a\n", "RCA 20% : 16.406 MPa → ab\n", "RCA 30% : 15.750 MPa → b\n", "RCA 40% : 14.694 MPa → c\n", "RCA 50% : 13.863 MPa → c\n", "\n", "TEMPERATURE COMPACT LETTER DISPLAY\n", " 24 °C : 19.209 MPa → a\n", "150 °C : 18.630 MPa → a\n", "300 °C : 16.597 MPa → b\n", "450 °C : 14.280 MPa → c\n", "600 °C : 10.374 MPa → d\n", "\n", "NON-SIGNIFICANT RCA PAIRS\n", "0% vs 10% : p_adj=0.9945\n", "0% vs 20% : p_adj=0.2341\n", "10% vs 20% : p_adj=0.5328\n", "20% vs 30% : p_adj=0.4275\n", "40% vs 50% : p_adj=0.1829\n", "\n", "NON-SIGNIFICANT TEMPERATURE PAIRS\n", "24°C vs 150°C : p_adj=0.3764\n", "\n", "SAVED OUTPUTS\n", " - Figure_S3_Tukey_Grouping_Map.pdf\n", " - Figure_S3_Tukey_Grouping_Map.png\n", " - Figure_S3_Tukey_Grouping_Map.tiff\n", " - Supplementary_Table_S1_Tukey_HSD.xlsx\n", " - Tukey_Compact_Letter_Groups.csv\n", " - Tukey_RCA_Pairwise.csv\n", " - Tukey_Temperature_Pairwise.csv\n", "====================================================================\n" ] } ], "source": [ "# ============================================================\n", "# — STEP 3 FINAL AUDIT\n", "# ============================================================\n", "\n", "print(\"=\" * 68)\n", "print(\"STEP 3 — TUKEY HSD POST-HOC\")\n", "print(\"=\" * 68)\n", "\n", "\n", "print(\"\\nPOOLED ERROR\")\n", "print(f\"MS error : {MS_ERROR:.4f}\")\n", "print(f\"Residual df : {df_ERROR}\")\n", "\n", "\n", "print(\"\\nTUKEY CRITICAL DIFFERENCES\")\n", "print(\n", " f\"RCA HSD : \"\n", " f\"{HSD_RCA:.4f} MPa\"\n", ")\n", "\n", "print(\n", " f\"Temperature HSD : \"\n", " f\"{HSD_TEMP:.4f} MPa\"\n", ")\n", "\n", "\n", "print(\"\\nRCA COMPACT LETTER DISPLAY\")\n", "\n", "for level in rca_levels:\n", "\n", " print(\n", " f\"RCA {level:>2}% : \"\n", " f\"{rca_means.loc[level]:.3f} MPa \"\n", " f\"→ {RCA_letters[level]}\"\n", " )\n", "\n", "\n", "print(\"\\nTEMPERATURE COMPACT LETTER DISPLAY\")\n", "\n", "for level in temp_levels:\n", "\n", " print(\n", " f\"{level:>3} °C : \"\n", " f\"{temp_means.loc[level]:.3f} MPa \"\n", " f\"→ {TEMP_letters[level]}\"\n", " )\n", "\n", "\n", "print(\"\\nNON-SIGNIFICANT RCA PAIRS\")\n", "\n", "ns_RCA = tukey_RCA[\n", " ~tukey_RCA[\"Significant\"]\n", "]\n", "\n", "for _, row in ns_RCA.iterrows():\n", "\n", " print(\n", " f\"{int(row['Group_1'])}% vs \"\n", " f\"{int(row['Group_2'])}% : \"\n", " f\"p_adj={row['p_adjusted']:.4f}\"\n", " )\n", "\n", "\n", "print(\"\\nNON-SIGNIFICANT TEMPERATURE PAIRS\")\n", "\n", "ns_TEMP = tukey_TEMP[\n", " ~tukey_TEMP[\"Significant\"]\n", "]\n", "\n", "for _, row in ns_TEMP.iterrows():\n", "\n", " print(\n", " f\"{int(row['Group_1'])}°C vs \"\n", " f\"{int(row['Group_2'])}°C : \"\n", " f\"p_adj={row['p_adjusted']:.4f}\"\n", " )\n", "\n", "\n", "print(\"\\nSAVED OUTPUTS\")\n", "\n", "for file in sorted(\n", " OUTPUT_DIR.iterdir()\n", "):\n", " print(\" -\", file.name)\n", "\n", "\n", "print(\"=\" * 68)" ] }, { "cell_type": "markdown", "id": "a5077548-6d81-4257-985b-2baef2117aae", "metadata": {}, "source": [ "#STEP 4" ] }, { "cell_type": "code", "execution_count": 88, "id": "50ef0331-5970-4ba3-ab22-7e00b1ca6e0c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "✓ STEP 4 DATA CHECK PASSED\n", "N = 90\n", "Output: C:\\Users\\sadal\\Desktop\\Yasin Hoca-RCA Thermal\\Analysis_Outputs\\Step_04_Absolute_Normalized_Strength\n" ] } ], "source": [ "# ============================================================\n", "# STEP 4 — ABSOLUTE + NORMALIZED RESIDUAL STRENGTH\n", "# — SETUP AND DATA RELOAD\n", "# ============================================================\n", "\n", "from pathlib import Path\n", "import re\n", "import warnings\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "from scipy import stats\n", "\n", "warnings.filterwarnings(\"ignore\")\n", "\n", "\n", "# ============================================================\n", "# PATHS\n", "# ============================================================\n", "\n", "DATA_PATH = Path(\n", " r\"C:\\Users\\sadal\\Desktop\\Yasin Hoca-RCA Thermal\\data.xlsx\"\n", ")\n", "\n", "OUTPUT_DIR = (\n", " DATA_PATH.parent\n", " / \"Analysis_Outputs\"\n", " / \"Step_04_Absolute_Normalized_Strength\"\n", ")\n", "\n", "STEP3_DIR = (\n", " DATA_PATH.parent\n", " / \"Analysis_Outputs\"\n", " / \"Step_03_Tukey_HSD\"\n", ")\n", "\n", "OUTPUT_DIR.mkdir(\n", " parents=True,\n", " exist_ok=True\n", ")\n", "\n", "assert DATA_PATH.exists(), \\\n", " f\"Data file not found: {DATA_PATH}\"\n", "\n", "\n", "# ============================================================\n", "# READ RAW DATA\n", "# ============================================================\n", "\n", "raw = pd.read_excel(\n", " DATA_PATH,\n", " sheet_name=\"data\",\n", " header=None\n", ")\n", "\n", "temperature_headers = raw.iloc[0, 1:].astype(str)\n", "\n", "temperatures = (\n", " temperature_headers\n", " .str.extract(r\"(\\d+)\")[0]\n", " .astype(int)\n", " .tolist()\n", ")\n", "\n", "\n", "records = []\n", "\n", "for _, row in raw.iloc[1:].iterrows():\n", "\n", " if pd.isna(row.iloc[0]):\n", " continue\n", "\n", " mix_label = str(row.iloc[0])\n", "\n", " rca_match = re.search(\n", " r\"(\\d+)\",\n", " mix_label\n", " )\n", "\n", " if rca_match is None:\n", " raise ValueError(\n", " f\"RCA level could not be identified: {mix_label}\"\n", " )\n", "\n", " rca_pct = int(\n", " rca_match.group(1)\n", " )\n", "\n", " replicate_counter = {}\n", "\n", " for col_index, temperature in enumerate(\n", " temperatures,\n", " start=1\n", " ):\n", "\n", " replicate_counter[temperature] = (\n", " replicate_counter.get(temperature, 0) + 1\n", " )\n", "\n", " value = pd.to_numeric(\n", " row.iloc[col_index],\n", " errors=\"coerce\"\n", " )\n", "\n", " if pd.isna(value):\n", " raise ValueError(\n", " f\"Missing measurement: \"\n", " f\"RCA={rca_pct}, T={temperature}\"\n", " )\n", "\n", " records.append({\n", " \"RCA_pct\": rca_pct,\n", " \"Temperature_C\": temperature,\n", " \"Replicate\": replicate_counter[temperature],\n", " \"CompressiveStrength_MPa\": float(value)\n", " })\n", "\n", "\n", "df_strength = pd.DataFrame(records)\n", "\n", "\n", "# ============================================================\n", "# DESIGN CHECK\n", "# ============================================================\n", "\n", "cell_counts = (\n", " df_strength.groupby(\n", " [\"RCA_pct\", \"Temperature_C\"]\n", " )\n", " .size()\n", ")\n", "\n", "assert len(df_strength) == 90\n", "assert df_strength[\"RCA_pct\"].nunique() == 6\n", "assert df_strength[\"Temperature_C\"].nunique() == 5\n", "assert cell_counts.nunique() == 1\n", "assert cell_counts.iloc[0] == 3\n", "\n", "print(\"✓ STEP 4 DATA CHECK PASSED\")\n", "print(\"N =\", len(df_strength))\n", "print(\"Output:\", OUTPUT_DIR)" ] }, { "cell_type": "code", "execution_count": 90, "id": "a6a8a88b-ffd6-4209-bacf-044e756d993d", "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>RCA_pct</th>\n", " <th>Temperature_C</th>\n", " <th>N</th>\n", " <th>Mean_MPa</th>\n", " <th>SD_MPa</th>\n", " <th>SE_MPa</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>24</td>\n", " <td>3</td>\n", " <td>21.094</td>\n", " <td>1.281</td>\n", " <td>0.740</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0</td>\n", " <td>150</td>\n", " <td>3</td>\n", " <td>20.152</td>\n", " <td>0.990</td>\n", " <td>0.572</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0</td>\n", " <td>300</td>\n", " <td>3</td>\n", " <td>17.650</td>\n", " <td>0.738</td>\n", " <td>0.426</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0</td>\n", " <td>450</td>\n", " <td>3</td>\n", " <td>15.500</td>\n", " <td>0.275</td>\n", " <td>0.159</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0</td>\n", " <td>600</td>\n", " <td>3</td>\n", " <td>11.560</td>\n", " <td>0.311</td>\n", " <td>0.180</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>10</td>\n", " <td>24</td>\n", " <td>3</td>\n", " <td>21.047</td>\n", " <td>0.655</td>\n", " <td>0.378</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>10</td>\n", " <td>150</td>\n", " <td>3</td>\n", " <td>20.122</td>\n", " <td>0.929</td>\n", " <td>0.537</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>10</td>\n", " <td>300</td>\n", " <td>3</td>\n", " <td>17.306</td>\n", " <td>0.760</td>\n", " <td>0.439</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>10</td>\n", " <td>450</td>\n", " <td>3</td>\n", " <td>15.328</td>\n", " <td>0.514</td>\n", " <td>0.297</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>10</td>\n", " <td>600</td>\n", " <td>3</td>\n", " <td>11.215</td>\n", " <td>0.470</td>\n", " <td>0.271</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>20</td>\n", " <td>24</td>\n", " <td>3</td>\n", " <td>19.908</td>\n", " <td>1.303</td>\n", " <td>0.752</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>20</td>\n", " <td>150</td>\n", " <td>3</td>\n", " <td>19.881</td>\n", " <td>0.704</td>\n", " <td>0.406</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>20</td>\n", " <td>300</td>\n", " <td>3</td>\n", " <td>17.172</td>\n", " <td>0.215</td>\n", " <td>0.124</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>20</td>\n", " <td>450</td>\n", " <td>3</td>\n", " <td>14.344</td>\n", " <td>0.944</td>\n", " <td>0.545</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>20</td>\n", " <td>600</td>\n", " <td>3</td>\n", " <td>10.726</td>\n", " <td>0.285</td>\n", " <td>0.165</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>30</td>\n", " <td>24</td>\n", " <td>3</td>\n", " <td>19.354</td>\n", " <td>2.398</td>\n", " <td>1.384</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>30</td>\n", " <td>150</td>\n", " <td>3</td>\n", " <td>18.241</td>\n", " <td>1.666</td>\n", " <td>0.962</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>30</td>\n", " <td>300</td>\n", " <td>3</td>\n", " <td>17.016</td>\n", " <td>0.143</td>\n", " <td>0.083</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>30</td>\n", " <td>450</td>\n", " <td>3</td>\n", " <td>14.148</td>\n", " <td>0.101</td>\n", " <td>0.058</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>30</td>\n", " <td>600</td>\n", " <td>3</td>\n", " <td>9.990</td>\n", " <td>0.474</td>\n", " <td>0.274</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>40</td>\n", " <td>24</td>\n", " <td>3</td>\n", " <td>17.059</td>\n", " <td>1.514</td>\n", " <td>0.874</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>40</td>\n", " <td>150</td>\n", " <td>3</td>\n", " <td>16.984</td>\n", " <td>0.850</td>\n", " <td>0.491</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>40</td>\n", " <td>300</td>\n", " <td>3</td>\n", " <td>15.807</td>\n", " <td>1.337</td>\n", " <td>0.772</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>40</td>\n", " <td>450</td>\n", " <td>3</td>\n", " <td>13.995</td>\n", " <td>0.226</td>\n", " <td>0.131</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>40</td>\n", " <td>600</td>\n", " <td>3</td>\n", " <td>9.624</td>\n", " <td>1.213</td>\n", " <td>0.700</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>50</td>\n", " <td>24</td>\n", " <td>3</td>\n", " <td>16.790</td>\n", " <td>0.677</td>\n", " <td>0.391</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>50</td>\n", " <td>150</td>\n", " <td>3</td>\n", " <td>16.398</td>\n", " <td>1.375</td>\n", " <td>0.794</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>50</td>\n", " <td>300</td>\n", " <td>3</td>\n", " <td>14.634</td>\n", " <td>0.810</td>\n", " <td>0.468</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>50</td>\n", " <td>450</td>\n", " <td>3</td>\n", " <td>12.366</td>\n", " <td>0.665</td>\n", " <td>0.384</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>50</td>\n", " <td>600</td>\n", " <td>3</td>\n", " <td>9.130</td>\n", " <td>0.331</td>\n", " <td>0.191</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " RCA_pct Temperature_C N Mean_MPa SD_MPa SE_MPa\n", "0 0 24 3 21.094 1.281 0.740\n", "1 0 150 3 20.152 0.990 0.572\n", "2 0 300 3 17.650 0.738 0.426\n", "3 0 450 3 15.500 0.275 0.159\n", "4 0 600 3 11.560 0.311 0.180\n", "5 10 24 3 21.047 0.655 0.378\n", "6 10 150 3 20.122 0.929 0.537\n", "7 10 300 3 17.306 0.760 0.439\n", "8 10 450 3 15.328 0.514 0.297\n", "9 10 600 3 11.215 0.470 0.271\n", "10 20 24 3 19.908 1.303 0.752\n", "11 20 150 3 19.881 0.704 0.406\n", "12 20 300 3 17.172 0.215 0.124\n", "13 20 450 3 14.344 0.944 0.545\n", "14 20 600 3 10.726 0.285 0.165\n", "15 30 24 3 19.354 2.398 1.384\n", "16 30 150 3 18.241 1.666 0.962\n", "17 30 300 3 17.016 0.143 0.083\n", "18 30 450 3 14.148 0.101 0.058\n", "19 30 600 3 9.990 0.474 0.274\n", "20 40 24 3 17.059 1.514 0.874\n", "21 40 150 3 16.984 0.850 0.491\n", "22 40 300 3 15.807 1.337 0.772\n", "23 40 450 3 13.995 0.226 0.131\n", "24 40 600 3 9.624 1.213 0.700\n", "25 50 24 3 16.790 0.677 0.391\n", "26 50 150 3 16.398 1.375 0.794\n", "27 50 300 3 14.634 0.810 0.468\n", "28 50 450 3 12.366 0.665 0.384\n", "29 50 600 3 9.130 0.331 0.191" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# ============================================================\n", "# ABSOLUTE COMPRESSIVE STRENGTH SUMMARY\n", "# ============================================================\n", "\n", "absolute_summary = (\n", " df_strength\n", " .groupby(\n", " [\"RCA_pct\", \"Temperature_C\"],\n", " as_index=False\n", " )\n", " .agg(\n", " N=(\"CompressiveStrength_MPa\", \"count\"),\n", " Mean_MPa=(\"CompressiveStrength_MPa\", \"mean\"),\n", " SD_MPa=(\"CompressiveStrength_MPa\", \"std\")\n", " )\n", ")\n", "\n", "\n", "absolute_summary[\"SE_MPa\"] = (\n", " absolute_summary[\"SD_MPa\"]\n", " / np.sqrt(absolute_summary[\"N\"])\n", ")\n", "\n", "\n", "display(\n", " absolute_summary.round(3)\n", ")" ] }, { "cell_type": "code", "execution_count": 92, "id": "a17e4d9b-90b1-4718-aff9-4ada4bcf0aee", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "✓ 30 experimental cells summarized.\n" ] } ], "source": [ "assert len(absolute_summary) == 30\n", "assert (absolute_summary[\"N\"] == 3).all()\n", "\n", "print(\"✓ 30 experimental cells summarized.\")" ] }, { "cell_type": "code", "execution_count": 94, "id": "c773346d-22d0-4913-a776-8811b02e7b87", "metadata": {}, "outputs": [], "source": [ "# ============================================================\n", "# CELL 38 — NORMALIZED RESIDUAL STRENGTH\n", "# ============================================================\n", "\n", "BASELINE_TEMP = 24\n", "\n", "assert BASELINE_TEMP in absolute_summary[\"Temperature_C\"].unique()\n", "\n", "\n", "# ============================================================\n", "# BASELINE VALUES FOR EACH RCA\n", "# ============================================================\n", "\n", "baseline = (\n", " absolute_summary[\n", " absolute_summary[\"Temperature_C\"] == BASELINE_TEMP\n", " ][\n", " [\n", " \"RCA_pct\",\n", " \"Mean_MPa\",\n", " \"SE_MPa\"\n", " ]\n", " ]\n", " .rename(\n", " columns={\n", " \"Mean_MPa\": \"Baseline_Mean_MPa\",\n", " \"SE_MPa\": \"Baseline_SE_MPa\"\n", " }\n", " )\n", ")\n", "\n", "\n", "# Merge baseline into all temperature points\n", "normalized_summary = absolute_summary.merge(\n", " baseline,\n", " on=\"RCA_pct\",\n", " how=\"left\"\n", ")\n", "\n", "\n", "# ============================================================\n", "# RELATIVE RESIDUAL STRENGTH\n", "# ============================================================\n", "\n", "normalized_summary[\"Residual_Strength_pct\"] = (\n", " normalized_summary[\"Mean_MPa\"]\n", " / normalized_summary[\"Baseline_Mean_MPa\"]\n", " * 100\n", ")\n", "\n", "\n", "normalized_summary[\"Thermal_Loss_pct\"] = (\n", " 100\n", " - normalized_summary[\"Residual_Strength_pct\"]\n", ")\n", "\n", "\n", "normalized_summary[\"Absolute_Loss_MPa\"] = (\n", " normalized_summary[\"Baseline_Mean_MPa\"]\n", " - normalized_summary[\"Mean_MPa\"]\n", ")" ] }, { "cell_type": "code", "execution_count": 96, "id": "f8976439-133f-4c60-9dd2-83e55c34386c", "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>RCA_pct</th>\n", " <th>Temperature_C</th>\n", " <th>Mean_MPa</th>\n", " <th>SD_MPa</th>\n", " <th>Residual_Strength_pct</th>\n", " <th>Thermal_Loss_pct</th>\n", " <th>Residual_Strength_SE_pct</th>\n", " <th>Residual_CI95_Lower_pct</th>\n", " <th>Residual_CI95_Upper_pct</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>24</td>\n", " <td>21.09</td>\n", " <td>1.28</td>\n", " <td>100.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>100.00</td>\n", " <td>100.00</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0</td>\n", " <td>150</td>\n", " <td>20.15</td>\n", " <td>0.99</td>\n", " <td>95.53</td>\n", " <td>4.47</td>\n", " <td>4.31</td>\n", " <td>87.09</td>\n", " <td>103.98</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0</td>\n", " <td>300</td>\n", " <td>17.65</td>\n", " <td>0.74</td>\n", " <td>83.67</td>\n", " <td>16.33</td>\n", " <td>3.56</td>\n", " <td>76.69</td>\n", " <td>90.66</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0</td>\n", " <td>450</td>\n", " <td>15.50</td>\n", " <td>0.28</td>\n", " <td>73.48</td>\n", " <td>26.52</td>\n", " <td>2.69</td>\n", " <td>68.22</td>\n", " <td>78.74</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0</td>\n", " <td>600</td>\n", " <td>11.56</td>\n", " <td>0.31</td>\n", " <td>54.80</td>\n", " <td>45.20</td>\n", " <td>2.10</td>\n", " <td>50.68</td>\n", " <td>58.92</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>10</td>\n", " <td>24</td>\n", " <td>21.05</td>\n", " <td>0.66</td>\n", " <td>100.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>100.00</td>\n", " <td>100.00</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>10</td>\n", " <td>150</td>\n", " <td>20.12</td>\n", " <td>0.93</td>\n", " <td>95.60</td>\n", " <td>4.40</td>\n", " <td>3.07</td>\n", " <td>89.58</td>\n", " <td>101.63</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>10</td>\n", " <td>300</td>\n", " <td>17.31</td>\n", " <td>0.76</td>\n", " <td>82.22</td>\n", " <td>17.78</td>\n", " <td>2.56</td>\n", " <td>77.21</td>\n", " <td>87.23</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>10</td>\n", " <td>450</td>\n", " <td>15.33</td>\n", " <td>0.51</td>\n", " <td>72.82</td>\n", " <td>27.18</td>\n", " <td>1.92</td>\n", " <td>69.05</td>\n", " <td>76.60</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>10</td>\n", " <td>600</td>\n", " <td>11.22</td>\n", " <td>0.47</td>\n", " <td>53.29</td>\n", " <td>46.71</td>\n", " <td>1.61</td>\n", " <td>50.14</td>\n", " <td>56.43</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>20</td>\n", " <td>24</td>\n", " <td>19.91</td>\n", " <td>1.30</td>\n", " <td>100.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>100.00</td>\n", " <td>100.00</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>20</td>\n", " <td>150</td>\n", " <td>19.88</td>\n", " <td>0.70</td>\n", " <td>99.86</td>\n", " <td>0.14</td>\n", " <td>4.29</td>\n", " <td>91.45</td>\n", " <td>108.27</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>20</td>\n", " <td>300</td>\n", " <td>17.17</td>\n", " <td>0.21</td>\n", " <td>86.26</td>\n", " <td>13.74</td>\n", " <td>3.32</td>\n", " <td>79.75</td>\n", " <td>92.76</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>20</td>\n", " <td>450</td>\n", " <td>14.34</td>\n", " <td>0.94</td>\n", " <td>72.05</td>\n", " <td>27.95</td>\n", " <td>3.86</td>\n", " <td>64.48</td>\n", " <td>79.62</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>20</td>\n", " <td>600</td>\n", " <td>10.73</td>\n", " <td>0.29</td>\n", " <td>53.88</td>\n", " <td>46.12</td>\n", " <td>2.20</td>\n", " <td>49.57</td>\n", " <td>58.19</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>30</td>\n", " <td>24</td>\n", " <td>19.35</td>\n", " <td>2.40</td>\n", " <td>100.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>100.00</td>\n", " <td>100.00</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>30</td>\n", " <td>150</td>\n", " <td>18.24</td>\n", " <td>1.67</td>\n", " <td>94.25</td>\n", " <td>5.75</td>\n", " <td>8.38</td>\n", " <td>77.83</td>\n", " <td>110.67</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>30</td>\n", " <td>300</td>\n", " <td>17.02</td>\n", " <td>0.14</td>\n", " <td>87.92</td>\n", " <td>12.08</td>\n", " <td>6.30</td>\n", " <td>75.56</td>\n", " <td>100.27</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>30</td>\n", " <td>450</td>\n", " <td>14.15</td>\n", " <td>0.10</td>\n", " <td>73.10</td>\n", " <td>26.90</td>\n", " <td>5.24</td>\n", " <td>62.84</td>\n", " <td>83.37</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>30</td>\n", " <td>600</td>\n", " <td>9.99</td>\n", " <td>0.47</td>\n", " <td>51.62</td>\n", " <td>48.38</td>\n", " <td>3.95</td>\n", " <td>43.87</td>\n", " <td>59.37</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>40</td>\n", " <td>24</td>\n", " <td>17.06</td>\n", " <td>1.51</td>\n", " <td>100.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>100.00</td>\n", " <td>100.00</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>40</td>\n", " <td>150</td>\n", " <td>16.98</td>\n", " <td>0.85</td>\n", " <td>99.56</td>\n", " <td>0.44</td>\n", " <td>5.86</td>\n", " <td>88.08</td>\n", " <td>111.04</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>40</td>\n", " <td>300</td>\n", " <td>15.81</td>\n", " <td>1.34</td>\n", " <td>92.66</td>\n", " <td>7.34</td>\n", " <td>6.56</td>\n", " <td>79.81</td>\n", " <td>105.52</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>40</td>\n", " <td>450</td>\n", " <td>13.99</td>\n", " <td>0.23</td>\n", " <td>82.04</td>\n", " <td>17.96</td>\n", " <td>4.27</td>\n", " <td>73.66</td>\n", " <td>90.42</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>40</td>\n", " <td>600</td>\n", " <td>9.62</td>\n", " <td>1.21</td>\n", " <td>56.42</td>\n", " <td>43.58</td>\n", " <td>5.02</td>\n", " <td>46.58</td>\n", " <td>66.26</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>50</td>\n", " <td>24</td>\n", " <td>16.79</td>\n", " <td>0.68</td>\n", " <td>100.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>100.00</td>\n", " <td>100.00</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>50</td>\n", " <td>150</td>\n", " <td>16.40</td>\n", " <td>1.38</td>\n", " <td>97.67</td>\n", " <td>2.33</td>\n", " <td>5.25</td>\n", " <td>87.38</td>\n", " <td>107.95</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>50</td>\n", " <td>300</td>\n", " <td>14.63</td>\n", " <td>0.81</td>\n", " <td>87.16</td>\n", " <td>12.84</td>\n", " <td>3.45</td>\n", " <td>80.41</td>\n", " <td>93.92</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>50</td>\n", " <td>450</td>\n", " <td>12.37</td>\n", " <td>0.66</td>\n", " <td>73.65</td>\n", " <td>26.35</td>\n", " <td>2.86</td>\n", " <td>68.05</td>\n", " <td>79.25</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>50</td>\n", " <td>600</td>\n", " <td>9.13</td>\n", " <td>0.33</td>\n", " <td>54.38</td>\n", " <td>45.62</td>\n", " <td>1.70</td>\n", " <td>51.04</td>\n", " <td>57.71</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " RCA_pct Temperature_C Mean_MPa SD_MPa Residual_Strength_pct \\\n", "0 0 24 21.09 1.28 100.00 \n", "1 0 150 20.15 0.99 95.53 \n", "2 0 300 17.65 0.74 83.67 \n", "3 0 450 15.50 0.28 73.48 \n", "4 0 600 11.56 0.31 54.80 \n", "5 10 24 21.05 0.66 100.00 \n", "6 10 150 20.12 0.93 95.60 \n", "7 10 300 17.31 0.76 82.22 \n", "8 10 450 15.33 0.51 72.82 \n", "9 10 600 11.22 0.47 53.29 \n", "10 20 24 19.91 1.30 100.00 \n", "11 20 150 19.88 0.70 99.86 \n", "12 20 300 17.17 0.21 86.26 \n", "13 20 450 14.34 0.94 72.05 \n", "14 20 600 10.73 0.29 53.88 \n", "15 30 24 19.35 2.40 100.00 \n", "16 30 150 18.24 1.67 94.25 \n", "17 30 300 17.02 0.14 87.92 \n", "18 30 450 14.15 0.10 73.10 \n", "19 30 600 9.99 0.47 51.62 \n", "20 40 24 17.06 1.51 100.00 \n", "21 40 150 16.98 0.85 99.56 \n", "22 40 300 15.81 1.34 92.66 \n", "23 40 450 13.99 0.23 82.04 \n", "24 40 600 9.62 1.21 56.42 \n", "25 50 24 16.79 0.68 100.00 \n", "26 50 150 16.40 1.38 97.67 \n", "27 50 300 14.63 0.81 87.16 \n", "28 50 450 12.37 0.66 73.65 \n", "29 50 600 9.13 0.33 54.38 \n", "\n", " Thermal_Loss_pct Residual_Strength_SE_pct Residual_CI95_Lower_pct \\\n", "0 0.00 0.00 100.00 \n", "1 4.47 4.31 87.09 \n", "2 16.33 3.56 76.69 \n", "3 26.52 2.69 68.22 \n", "4 45.20 2.10 50.68 \n", "5 0.00 0.00 100.00 \n", "6 4.40 3.07 89.58 \n", "7 17.78 2.56 77.21 \n", "8 27.18 1.92 69.05 \n", "9 46.71 1.61 50.14 \n", "10 0.00 0.00 100.00 \n", "11 0.14 4.29 91.45 \n", "12 13.74 3.32 79.75 \n", "13 27.95 3.86 64.48 \n", "14 46.12 2.20 49.57 \n", "15 0.00 0.00 100.00 \n", "16 5.75 8.38 77.83 \n", "17 12.08 6.30 75.56 \n", "18 26.90 5.24 62.84 \n", "19 48.38 3.95 43.87 \n", "20 0.00 0.00 100.00 \n", "21 0.44 5.86 88.08 \n", "22 7.34 6.56 79.81 \n", "23 17.96 4.27 73.66 \n", "24 43.58 5.02 46.58 \n", "25 0.00 0.00 100.00 \n", "26 2.33 5.25 87.38 \n", "27 12.84 3.45 80.41 \n", "28 26.35 2.86 68.05 \n", "29 45.62 1.70 51.04 \n", "\n", " Residual_CI95_Upper_pct \n", "0 100.00 \n", "1 103.98 \n", "2 90.66 \n", "3 78.74 \n", "4 58.92 \n", "5 100.00 \n", "6 101.63 \n", "7 87.23 \n", "8 76.60 \n", "9 56.43 \n", "10 100.00 \n", "11 108.27 \n", "12 92.76 \n", "13 79.62 \n", "14 58.19 \n", "15 100.00 \n", "16 110.67 \n", "17 100.27 \n", "18 83.37 \n", "19 59.37 \n", "20 100.00 \n", "21 111.04 \n", "22 105.52 \n", "23 90.42 \n", "24 66.26 \n", "25 100.00 \n", "26 107.95 \n", "27 93.92 \n", "28 79.25 \n", "29 57.71 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# ============================================================\n", "# — UNCERTAINTY PROPAGATION FOR NORMALIZED STRENGTH\n", "# ============================================================\n", "\n", "def propagated_ratio_se(row):\n", "\n", " # Baseline is defined as exactly 100% in normalized scale\n", " if row[\"Temperature_C\"] == BASELINE_TEMP:\n", " return 0.0\n", "\n", " relative_uncertainty = np.sqrt(\n", " (row[\"SE_MPa\"] / row[\"Mean_MPa\"]) ** 2\n", " +\n", " (\n", " row[\"Baseline_SE_MPa\"]\n", " / row[\"Baseline_Mean_MPa\"]\n", " ) ** 2\n", " )\n", "\n", " return (\n", " row[\"Residual_Strength_pct\"]\n", " * relative_uncertainty\n", " )\n", "\n", "\n", "normalized_summary[\n", " \"Residual_Strength_SE_pct\"\n", "] = normalized_summary.apply(\n", " propagated_ratio_se,\n", " axis=1\n", ")\n", "\n", "\n", "# Approximate 95% uncertainty interval\n", "z95 = stats.norm.ppf(0.975)\n", "\n", "normalized_summary[\n", " \"Residual_CI95_Lower_pct\"\n", "] = (\n", " normalized_summary[\"Residual_Strength_pct\"]\n", " - z95\n", " * normalized_summary[\"Residual_Strength_SE_pct\"]\n", ")\n", "\n", "normalized_summary[\n", " \"Residual_CI95_Upper_pct\"\n", "] = (\n", " normalized_summary[\"Residual_Strength_pct\"]\n", " + z95\n", " * normalized_summary[\"Residual_Strength_SE_pct\"]\n", ")\n", "\n", "\n", "# Ensure baseline = exactly 100\n", "baseline_mask = (\n", " normalized_summary[\"Temperature_C\"]\n", " == BASELINE_TEMP\n", ")\n", "\n", "normalized_summary.loc[\n", " baseline_mask,\n", " \"Residual_Strength_pct\"\n", "] = 100.0\n", "\n", "normalized_summary.loc[\n", " baseline_mask,\n", " \"Residual_CI95_Lower_pct\"\n", "] = 100.0\n", "\n", "normalized_summary.loc[\n", " baseline_mask,\n", " \"Residual_CI95_Upper_pct\"\n", "] = 100.0\n", "\n", "\n", "display(\n", " normalized_summary[\n", " [\n", " \"RCA_pct\",\n", " \"Temperature_C\",\n", " \"Mean_MPa\",\n", " \"SD_MPa\",\n", " \"Residual_Strength_pct\",\n", " \"Thermal_Loss_pct\",\n", " \"Residual_Strength_SE_pct\",\n", " \"Residual_CI95_Lower_pct\",\n", " \"Residual_CI95_Upper_pct\"\n", " ]\n", " ].round(2)\n", ")" ] }, { "cell_type": "code", "execution_count": 98, "id": "1f6cd2ff-e5a0-48c3-911f-915aea24f5a7", "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>RCA_pct</th>\n", " <th>Baseline_Mean_MPa</th>\n", " <th>Strength_600C_MPa</th>\n", " <th>Residual_Strength_pct</th>\n", " <th>Thermal_Loss_pct</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>4</th>\n", " <td>0</td>\n", " <td>21.09</td>\n", " <td>11.56</td>\n", " <td>54.80</td>\n", " <td>45.20</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>10</td>\n", " <td>21.05</td>\n", " <td>11.22</td>\n", " <td>53.29</td>\n", " <td>46.71</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>20</td>\n", " <td>19.91</td>\n", " <td>10.73</td>\n", " <td>53.88</td>\n", " <td>46.12</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>30</td>\n", " <td>19.35</td>\n", " <td>9.99</td>\n", " <td>51.62</td>\n", " <td>48.38</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>40</td>\n", " <td>17.06</td>\n", " <td>9.62</td>\n", " <td>56.42</td>\n", " <td>43.58</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>50</td>\n", " <td>16.79</td>\n", " <td>9.13</td>\n", " <td>54.38</td>\n", " <td>45.62</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " RCA_pct Baseline_Mean_MPa Strength_600C_MPa Residual_Strength_pct \\\n", "4 0 21.09 11.56 54.80 \n", "9 10 21.05 11.22 53.29 \n", "14 20 19.91 10.73 53.88 \n", "19 30 19.35 9.99 51.62 \n", "24 40 17.06 9.62 56.42 \n", "29 50 16.79 9.13 54.38 \n", "\n", " Thermal_Loss_pct \n", "4 45.20 \n", "9 46.71 \n", "14 46.12 \n", "19 48.38 \n", "24 43.58 \n", "29 45.62 " ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "600 °C retention range:\n", "51.62% – 56.42%\n" ] } ], "source": [ "# ============================================================\n", "# CELL 40 — 600 °C RETENTION CHECK\n", "# ============================================================\n", "\n", "retention_600 = (\n", " normalized_summary[\n", " normalized_summary[\"Temperature_C\"] == 600\n", " ][\n", " [\n", " \"RCA_pct\",\n", " \"Baseline_Mean_MPa\",\n", " \"Mean_MPa\",\n", " \"Residual_Strength_pct\",\n", " \"Thermal_Loss_pct\"\n", " ]\n", " ]\n", " .copy()\n", ")\n", "\n", "\n", "retention_600 = retention_600.rename(\n", " columns={\n", " \"Mean_MPa\": \"Strength_600C_MPa\"\n", " }\n", ")\n", "\n", "\n", "display(\n", " retention_600.round(2)\n", ")\n", "\n", "\n", "print(\n", " \"\\n600 °C retention range:\"\n", ")\n", "\n", "print(\n", " f\"{retention_600['Residual_Strength_pct'].min():.2f}%\"\n", " \" – \"\n", " f\"{retention_600['Residual_Strength_pct'].max():.2f}%\"\n", ")" ] }, { "cell_type": "code", "execution_count": 100, "id": "e67cfb63-cf25-4ed3-b7e3-9cdb2701332b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Temperature Tukey groups:\n", "{24: 'a', 150: 'a', 300: 'b', 450: 'c', 600: 'd'}\n", "\n", "RCA Tukey groups:\n", "{0: 'a', 10: 'a', 20: 'ab', 30: 'b', 40: 'c', 50: 'c'}\n" ] } ], "source": [ "# ============================================================\n", "# CELL 41 — LOAD VALIDATED TUKEY LETTERS FROM STEP 3\n", "# ============================================================\n", "\n", "letters_path = (\n", " STEP3_DIR\n", " / \"Tukey_Compact_Letter_Groups.csv\"\n", ")\n", "\n", "assert letters_path.exists(), (\n", " \"Step 3 Tukey letter file not found. \"\n", " \"Please make sure Step 3 outputs were saved.\"\n", ")\n", "\n", "\n", "letters_df = pd.read_csv(\n", " letters_path\n", ")\n", "\n", "\n", "# Temperature letters\n", "temp_letters_df = letters_df[\n", " letters_df[\"Factor\"] == \"Temperature\"\n", "].copy()\n", "\n", "temp_letters = {\n", " int(row[\"Level\"]): str(row[\"Tukey_Letter\"])\n", " for _, row in temp_letters_df.iterrows()\n", "}\n", "\n", "\n", "# RCA letters — retain for later use/caption\n", "rca_letters_df = letters_df[\n", " letters_df[\"Factor\"] == \"RCA\"\n", "].copy()\n", "\n", "rca_letters = {\n", " int(row[\"Level\"]): str(row[\"Tukey_Letter\"])\n", " for _, row in rca_letters_df.iterrows()\n", "}\n", "\n", "\n", "print(\"Temperature Tukey groups:\")\n", "print(temp_letters)\n", "\n", "print(\"\\nRCA Tukey groups:\")\n", "print(rca_letters)" ] }, { "cell_type": "code", "execution_count": 102, "id": "53bf5907-c8d0-4520-a6e5-a4e0749e1112", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "✓ STEP 4 NUMERICAL DATA SAVED\n", "C:\\Users\\sadal\\Desktop\\Yasin Hoca-RCA Thermal\\Analysis_Outputs\\Step_04_Absolute_Normalized_Strength\\Step_4_Absolute_and_Normalized_Strength_Data.xlsx\n" ] } ], "source": [ "# ============================================================\n", "# — SAVE STEP 4 NUMERICAL DATA\n", "# ============================================================\n", "\n", "from pathlib import Path\n", "\n", "DATA_PATH = Path(\n", " r\"C:\\Users\\sadal\\Desktop\\Yasin Hoca-RCA Thermal\\data.xlsx\"\n", ")\n", "\n", "OUTPUT_DIR = (\n", " DATA_PATH.parent\n", " / \"Analysis_Outputs\"\n", " / \"Step_04_Absolute_Normalized_Strength\"\n", ")\n", "\n", "OUTPUT_DIR.mkdir(\n", " parents=True,\n", " exist_ok=True\n", ")\n", "\n", "\n", "step4_table_path = (\n", " OUTPUT_DIR\n", " / \"Step_4_Absolute_and_Normalized_Strength_Data.xlsx\"\n", ")\n", "\n", "\n", "with pd.ExcelWriter(\n", " step4_table_path,\n", " engine=\"openpyxl\"\n", ") as writer:\n", "\n", " absolute_summary.to_excel(\n", " writer,\n", " sheet_name=\"Absolute_Strength\",\n", " index=False\n", " )\n", "\n", " normalized_summary.to_excel(\n", " writer,\n", " sheet_name=\"Normalized_Strength\",\n", " index=False\n", " )\n", "\n", " retention_600.to_excel(\n", " writer,\n", " sheet_name=\"600C_Retention\",\n", " index=False\n", " )\n", "\n", "\n", "absolute_summary.to_csv(\n", " OUTPUT_DIR\n", " / \"Absolute_Strength_Summary.csv\",\n", " index=False\n", ")\n", "\n", "normalized_summary.to_csv(\n", " OUTPUT_DIR\n", " / \"Normalized_Residual_Strength.csv\",\n", " index=False\n", ")\n", "\n", "\n", "print(\"✓ STEP 4 NUMERICAL DATA SAVED\")\n", "print(step4_table_path)" ] }, { "cell_type": "code", "execution_count": 104, "id": "be1dd56d-c497-46b2-992e-2faddee556cb", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "<Figure size 1430x570 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "✓ FIGURE 7 SAVED\n" ] } ], "source": [ "# ============================================================\n", "#— FIGURE 7\n", "# ABSOLUTE + RELATIVE RESIDUAL STRENGTH\n", "# ============================================================\n", "\n", "from pathlib import Path\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "\n", "# ============================================================\n", "# ROBUST PATH\n", "# ============================================================\n", "\n", "DATA_PATH = Path(\n", " r\"C:\\Users\\sadal\\Desktop\\Yasin Hoca-RCA Thermal\\data.xlsx\"\n", ")\n", "\n", "OUTPUT_DIR = (\n", " DATA_PATH.parent\n", " / \"Analysis_Outputs\"\n", " / \"Step_04_Absolute_Normalized_Strength\"\n", ")\n", "\n", "OUTPUT_DIR.mkdir(\n", " parents=True,\n", " exist_ok=True\n", ")\n", "\n", "\n", "# ============================================================\n", "# VISUAL IDENTITY\n", "# ============================================================\n", "\n", "background = \"#F7F5F0\"\n", "navy = \"#20364B\"\n", "gray = \"#7C858C\"\n", "light_gray = \"#D9D6CF\"\n", "\n", "rca_colors = {\n", " 0: \"#264653\",\n", " 10: \"#287271\",\n", " 20: \"#2A9D8F\",\n", " 30: \"#8AB17D\",\n", " 40: \"#E9C46A\",\n", " 50: \"#E76F51\"\n", "}\n", "\n", "rca_markers = {\n", " 0: \"o\",\n", " 10: \"s\",\n", " 20: \"^\",\n", " 30: \"D\",\n", " 40: \"P\",\n", " 50: \"X\"\n", "}\n", "\n", "\n", "plt.rcParams.update({\n", " \"font.family\": \"DejaVu Sans\",\n", " \"font.size\": 10.5,\n", " \"axes.labelsize\": 11,\n", " \"axes.titlesize\": 12,\n", " \"xtick.labelsize\": 9.5,\n", " \"ytick.labelsize\": 9.5,\n", " \"axes.linewidth\": 0.8,\n", " \"pdf.fonttype\": 42,\n", " \"ps.fonttype\": 42\n", "})\n", "\n", "\n", "# ============================================================\n", "# FIGURE\n", "# ============================================================\n", "\n", "fig, (ax1, ax2) = plt.subplots(\n", " 1,\n", " 2,\n", " figsize=(14.3, 5.7),\n", " facecolor=background,\n", " gridspec_kw={\n", " \"width_ratios\": [1, 1]\n", " }\n", ")\n", "\n", "for ax in [ax1, ax2]:\n", " ax.set_facecolor(background)\n", "\n", "\n", "rca_levels = sorted(\n", " absolute_summary[\"RCA_pct\"].unique()\n", ")\n", "\n", "temp_levels = sorted(\n", " absolute_summary[\"Temperature_C\"].unique()\n", ")\n", "\n", "\n", "# ============================================================\n", "# PANEL A — ABSOLUTE COMPRESSIVE STRENGTH\n", "# ============================================================\n", "\n", "for rca in rca_levels:\n", "\n", " sub = (\n", " absolute_summary[\n", " absolute_summary[\"RCA_pct\"] == rca\n", " ]\n", " .sort_values(\"Temperature_C\")\n", " )\n", "\n", " ax1.errorbar(\n", " sub[\"Temperature_C\"],\n", " sub[\"Mean_MPa\"],\n", " yerr=sub[\"SD_MPa\"],\n", " color=rca_colors[rca],\n", " marker=rca_markers[rca],\n", " markersize=6.8,\n", " linewidth=2.0,\n", " elinewidth=1.0,\n", " capsize=3.2,\n", " capthick=1.0,\n", " markeredgecolor=\"white\",\n", " markeredgewidth=0.7,\n", " label=f\"{rca}%\",\n", " alpha=0.96,\n", " zorder=3\n", " )\n", "\n", "\n", "ax1.set_xlabel(\n", " \"Exposure temperature (°C)\"\n", ")\n", "\n", "ax1.set_ylabel(\n", " \"Compressive strength (MPa)\"\n", ")\n", "\n", "ax1.set_xticks(\n", " temp_levels\n", ")\n", "\n", "\n", "ax1.set_title(\n", " \"Absolute strength capacity\",\n", " loc=\"left\",\n", " color=navy,\n", " fontweight=\"bold\",\n", " pad=12\n", ")\n", "\n", "\n", "# ============================================================\n", "# TUKEY TEMPERATURE LETTER STRIP\n", "# ============================================================\n", "\n", "# Find suitable height automatically\n", "upper_strength = np.max(\n", " absolute_summary[\"Mean_MPa\"]\n", " + absolute_summary[\"SD_MPa\"]\n", ")\n", "\n", "letter_y = (\n", " upper_strength + 1.20\n", ")\n", "\n", "\n", "for temp in temp_levels:\n", "\n", " ax1.text(\n", " temp,\n", " letter_y,\n", " temp_letters[temp],\n", " ha=\"center\",\n", " va=\"center\",\n", " fontsize=10.5,\n", " fontweight=\"bold\",\n", " color=navy,\n", " bbox=dict(\n", " boxstyle=\"round,pad=0.25\",\n", " facecolor=\"white\",\n", " edgecolor=\"#D2CEC5\"\n", " )\n", " )\n", "\n", "\n", "ax1.text(\n", " 0.015,\n", " 0.965,\n", " \"Temperature Tukey groups\",\n", " transform=ax1.transAxes,\n", " va=\"top\",\n", " color=gray,\n", " fontsize=8.8\n", ")\n", "\n", "\n", "ax1.set_ylim(\n", " 7,\n", " letter_y + 1.1\n", ")\n", "\n", "\n", "ax1.text(\n", " -0.09,\n", " 1.07,\n", " \"A\",\n", " transform=ax1.transAxes,\n", " fontsize=17,\n", " fontweight=\"bold\",\n", " color=\"#E76F51\"\n", ")\n", "\n", "\n", "# ============================================================\n", "# PANEL B — RELATIVE RESIDUAL STRENGTH\n", "# ============================================================\n", "\n", "for rca in rca_levels:\n", "\n", " sub = (\n", " normalized_summary[\n", " normalized_summary[\"RCA_pct\"] == rca\n", " ]\n", " .sort_values(\"Temperature_C\")\n", " )\n", "\n", " ci_half = (\n", " sub[\"Residual_CI95_Upper_pct\"]\n", " - sub[\"Residual_Strength_pct\"]\n", " )\n", "\n", " ax2.errorbar(\n", " sub[\"Temperature_C\"],\n", " sub[\"Residual_Strength_pct\"],\n", " yerr=ci_half,\n", " color=rca_colors[rca],\n", " marker=rca_markers[rca],\n", " markersize=6.8,\n", " linewidth=2.0,\n", " elinewidth=0.9,\n", " capsize=3,\n", " capthick=0.9,\n", " markeredgecolor=\"white\",\n", " markeredgewidth=0.7,\n", " alpha=0.94,\n", " zorder=3\n", " )\n", "\n", "\n", "# 100% normalization reference\n", "ax2.axhline(\n", " 100,\n", " color=\"#999A97\",\n", " linewidth=1.1,\n", " linestyle=(0, (4, 3)),\n", " zorder=1\n", ")\n", "\n", "\n", "ax2.set_xlabel(\n", " \"Exposure temperature (°C)\"\n", ")\n", "\n", "ax2.set_ylabel(\n", " \"Relative residual strength (%)\"\n", ")\n", "\n", "ax2.set_xticks(\n", " temp_levels\n", ")\n", "\n", "\n", "ax2.set_title(\n", " \"Within-mixture thermal strength retention\",\n", " loc=\"left\",\n", " color=navy,\n", " fontweight=\"bold\",\n", " pad=12\n", ")\n", "\n", "\n", "ax2.set_ylim(\n", " 35,\n", " 108\n", ")\n", "\n", "\n", "# ============================================================\n", "# 600 °C RETENTION RANGE ANNOTATION\n", "# ============================================================\n", "\n", "ret_min = (\n", " retention_600[\"Residual_Strength_pct\"]\n", " .min()\n", ")\n", "\n", "ret_max = (\n", " retention_600[\"Residual_Strength_pct\"]\n", " .max()\n", ")\n", "\n", "ret_mid = (\n", " ret_min + ret_max\n", ") / 2\n", "\n", "\n", "ax2.annotate(\n", " \"\",\n", " xy=(632, ret_min),\n", " xytext=(632, ret_max),\n", " arrowprops=dict(\n", " arrowstyle=\"<->\",\n", " color=\"#E76F51\",\n", " linewidth=1.5\n", " )\n", ")\n", "\n", "\n", "ax2.text(\n", " 642,\n", " ret_mid,\n", " f\"{ret_min:.1f}–{ret_max:.1f}%\",\n", " va=\"center\",\n", " ha=\"left\",\n", " color=\"#E76F51\",\n", " fontweight=\"bold\",\n", " fontsize=9.5\n", ")\n", "\n", "\n", "ax2.text(\n", " 642,\n", " ret_mid - 5.0,\n", " \"retained at 600 °C\",\n", " va=\"center\",\n", " ha=\"left\",\n", " color=gray,\n", " fontsize=8.7\n", ")\n", "\n", "\n", "ax2.set_xlim(\n", " 0,\n", " 735\n", ")\n", "\n", "\n", "ax1.set_xlim(\n", " 0,\n", " 630\n", ")\n", "\n", "\n", "ax2.text(\n", " -0.09,\n", " 1.07,\n", " \"B\",\n", " transform=ax2.transAxes,\n", " fontsize=17,\n", " fontweight=\"bold\",\n", " color=\"#E76F51\"\n", ")\n", "\n", "\n", "# ============================================================\n", "# "# ============================================================\n", "\n", "for ax in [ax1, ax2]:\n", "\n", " ax.spines[\"top\"].set_visible(False)\n", " ax.spines[\"right\"].set_visible(False)\n", "\n", " ax.spines[\"left\"].set_color(\"#A09B92\")\n", " ax.spines[\"bottom\"].set_color(\"#A09B92\")\n", "\n", " ax.grid(\n", " axis=\"y\",\n", " color=\"#DDD9D0\",\n", " linewidth=0.65,\n", " alpha=0.60,\n", " zorder=0\n", " )\n", "\n", "\n", "# ============================================================\n", "# SHARED LEGEND\n", "# ============================================================\n", "\n", "handles, labels = ax1.get_legend_handles_labels()\n", "\n", "legend = fig.legend(\n", " handles,\n", " labels,\n", " title=\"RCA replacement\",\n", " loc=\"upper center\",\n", " bbox_to_anchor=(0.5, 1.035),\n", " ncol=6,\n", " frameon=False,\n", " columnspacing=1.8,\n", " handletextpad=0.5\n", ")\n", "\n", "legend.get_title().set_fontweight(\n", " \"bold\"\n", ")\n", "\n", "\n", "fig.subplots_adjust(\n", " top=0.82,\n", " wspace=0.24\n", ")\n", "\n", "\n", "# ============================================================\n", "# SAVE FIGURE 7\n", "# ============================================================\n", "\n", "figure_base = (\n", " OUTPUT_DIR\n", " / \"Figure_7_Absolute_and_Normalized_Residual_Strength\"\n", ")\n", "\n", "\n", "plt.savefig(\n", " str(figure_base) + \".png\",\n", " dpi=600,\n", " bbox_inches=\"tight\",\n", " facecolor=fig.get_facecolor()\n", ")\n", "\n", "plt.savefig(\n", " str(figure_base) + \".pdf\",\n", " bbox_inches=\"tight\",\n", " facecolor=fig.get_facecolor()\n", ")\n", "\n", "plt.savefig(\n", " str(figure_base) + \".tiff\",\n", " dpi=600,\n", " bbox_inches=\"tight\",\n", " facecolor=fig.get_facecolor(),\n", " pil_kwargs={\n", " \"compression\": \"tiff_lzw\"\n", " }\n", ")\n", "\n", "\n", "plt.show()\n", "\n", "print(\"✓ FIGURE 7 SAVED\")" ] }, { "cell_type": "code", "execution_count": 106, "id": "894eab8f-d218-40ef-9665-4ce9dc66f8c8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "========================================================================\n", "STEP 4 — ABSOLUTE + NORMALIZED RESIDUAL STRENGTH\n", "========================================================================\n", "\n", "DESIGN\n", "Experimental cells : 30\n", "Baseline temperature : 24 °C\n", "\n", "NORMALIZATION CHECK\n", "All 24 °C values = 100% : True\n", "\n", "600 °C RETENTION\n", "RCA 0% : 11.560 MPa | 54.80% retained\n", "RCA 10% : 11.215 MPa | 53.29% retained\n", "RCA 20% : 10.726 MPa | 53.88% retained\n", "RCA 30% : 9.990 MPa | 51.62% retained\n", "RCA 40% : 9.624 MPa | 56.42% retained\n", "RCA 50% : 9.130 MPa | 54.38% retained\n", "\n", "RETENTION RANGE AT 600 °C\n", "Minimum : 51.62%\n", "Maximum : 56.42%\n", "Range width : 4.80 percentage points\n", "\n", "THERMAL SUSCEPTIBILITY CHECK\n", "Retention decreases monotonically with RCA: False\n", "\n", "SAVED OUTPUTS\n", " - Absolute_Strength_Summary.csv\n", " - Figure_7_Absolute_and_Normalized_Residual_Strength.pdf\n", " - Figure_7_Absolute_and_Normalized_Residual_Strength.png\n", " - Figure_7_Absolute_and_Normalized_Residual_Strength.tiff\n", " - Normalized_Residual_Strength.csv\n", " - Step_4_Absolute_and_Normalized_Strength_Data.xlsx\n", "========================================================================\n" ] } ], "source": [ "# ============================================================\n", "# STEP 4 FINAL AUDIT\n", "# ============================================================\n", "\n", "print(\"=\" * 72)\n", "print(\"STEP 4 — ABSOLUTE + NORMALIZED RESIDUAL STRENGTH\")\n", "print(\"=\" * 72)\n", "\n", "\n", "print(\"\\nDESIGN\")\n", "print(\n", " f\"Experimental cells : \"\n", " f\"{len(absolute_summary)}\"\n", ")\n", "\n", "print(\n", " f\"Baseline temperature : \"\n", " f\"{BASELINE_TEMP} °C\"\n", ")\n", "\n", "\n", "# ============================================================\n", "# BASELINE NORMALIZATION CHECK\n", "# ============================================================\n", "\n", "baseline_check = (\n", " normalized_summary[\n", " normalized_summary[\"Temperature_C\"]\n", " == BASELINE_TEMP\n", " ][\"Residual_Strength_pct\"]\n", ")\n", "\n", "print(\"\\nNORMALIZATION CHECK\")\n", "\n", "print(\n", " \"All 24 °C values = 100% :\",\n", " np.allclose(\n", " baseline_check,\n", " 100\n", " )\n", ")\n", "\n", "\n", "# ============================================================\n", "# 600 °C RESULTS\n", "# ============================================================\n", "\n", "print(\"\\n600 °C RETENTION\")\n", "\n", "for _, row in retention_600.iterrows():\n", "\n", " print(\n", " f\"RCA {int(row['RCA_pct']):>2}% : \"\n", " f\"{row['Strength_600C_MPa']:.3f} MPa | \"\n", " f\"{row['Residual_Strength_pct']:.2f}% retained\"\n", " )\n", "\n", "\n", "print(\"\\nRETENTION RANGE AT 600 °C\")\n", "\n", "print(\n", " f\"Minimum : \"\n", " f\"{ret_min:.2f}%\"\n", ")\n", "\n", "print(\n", " f\"Maximum : \"\n", " f\"{ret_max:.2f}%\"\n", ")\n", "\n", "print(\n", " f\"Range width : \"\n", " f\"{ret_max - ret_min:.2f} percentage points\"\n", ")\n", "\n", "\n", "# ============================================================\n", "# CHECK WHETHER 600°C RETENTION IS MONOTONIC WITH RCA\n", "# ============================================================\n", "\n", "ret_values = (\n", " retention_600\n", " .sort_values(\"RCA_pct\")\n", " [\"Residual_Strength_pct\"]\n", " .values\n", ")\n", "\n", "monotonic_decrease = np.all(\n", " np.diff(ret_values) <= 0\n", ")\n", "\n", "print(\"\\nTHERMAL SUSCEPTIBILITY CHECK\")\n", "\n", "print(\n", " \"Retention decreases monotonically with RCA:\",\n", " monotonic_decrease\n", ")\n", "\n", "\n", "print(\"\\nSAVED OUTPUTS\")\n", "\n", "for file in sorted(\n", " OUTPUT_DIR.iterdir()\n", "):\n", " print(\" -\", file.name)\n", "\n", "\n", "print(\"=\" * 72)" ] }, { "cell_type": "markdown", "id": "1bce9a2d-08f4-4ec8-9d7b-8a48fa16f212", "metadata": {}, "source": [ "#STEP5" ] }, { "cell_type": "code", "execution_count": 111, "id": "4bf07e2b-9959-4ff6-9ce9-d4288235fc4b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "✓ STEP 5 DATA CHECK PASSED\n", "N = 90\n", "Output: C:\\Users\\sadal\\Desktop\\Yasin Hoca-RCA Thermal\\Analysis_Outputs\\Step_05_ETD_Decomposition\n" ] } ], "source": [ "# ============================================================\n", "# STEP 5 — STRENGTH-LOSS DECOMPOSITION / ETD\n", "# — SETUP AND DATA RELOAD\n", "# ============================================================\n", "\n", "from pathlib import Path\n", "import re\n", "import warnings\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "from scipy import stats\n", "from matplotlib.colors import LinearSegmentedColormap\n", "from matplotlib.patches import Patch\n", "from matplotlib.lines import Line2D\n", "\n", "warnings.filterwarnings(\"ignore\")\n", "\n", "\n", "# ============================================================\n", "# PATHS\n", "# ============================================================\n", "\n", "DATA_PATH = Path(\n", " r\"C:\\Users\\sadal\\Desktop\\Yasin Hoca-RCA Thermal\\data.xlsx\"\n", ")\n", "\n", "OUTPUT_DIR = (\n", " DATA_PATH.parent\n", " / \"Analysis_Outputs\"\n", " / \"Step_05_ETD_Decomposition\"\n", ")\n", "\n", "OUTPUT_DIR.mkdir(\n", " parents=True,\n", " exist_ok=True\n", ")\n", "\n", "assert DATA_PATH.exists()\n", "\n", "\n", "# ============================================================\n", "# READ RAW DATA\n", "# ============================================================\n", "\n", "raw = pd.read_excel(\n", " DATA_PATH,\n", " sheet_name=\"data\",\n", " header=None\n", ")\n", "\n", "temperature_headers = raw.iloc[0, 1:].astype(str)\n", "\n", "temperatures = (\n", " temperature_headers\n", " .str.extract(r\"(\\d+)\")[0]\n", " .astype(int)\n", " .tolist()\n", ")\n", "\n", "\n", "records = []\n", "\n", "for _, row in raw.iloc[1:].iterrows():\n", "\n", " if pd.isna(row.iloc[0]):\n", " continue\n", "\n", " mix_label = str(row.iloc[0])\n", "\n", " rca_match = re.search(r\"(\\d+)\", mix_label)\n", "\n", " if rca_match is None:\n", " raise ValueError(\n", " f\"RCA level could not be identified: {mix_label}\"\n", " )\n", "\n", " rca_pct = int(rca_match.group(1))\n", "\n", " replicate_counter = {}\n", "\n", " for col_index, temperature in enumerate(\n", " temperatures,\n", " start=1\n", " ):\n", "\n", " replicate_counter[temperature] = (\n", " replicate_counter.get(temperature, 0) + 1\n", " )\n", "\n", " value = pd.to_numeric(\n", " row.iloc[col_index],\n", " errors=\"coerce\"\n", " )\n", "\n", " if pd.isna(value):\n", " raise ValueError(\n", " f\"Missing value: RCA={rca_pct}, T={temperature}\"\n", " )\n", "\n", " records.append({\n", " \"RCA_pct\": rca_pct,\n", " \"Temperature_C\": temperature,\n", " \"Replicate\": replicate_counter[temperature],\n", " \"CompressiveStrength_MPa\": float(value)\n", " })\n", "\n", "\n", "df_etd = pd.DataFrame(records)\n", "\n", "\n", "# ============================================================\n", "# DATA CHECK\n", "# ============================================================\n", "\n", "cell_counts = (\n", " df_etd.groupby(\n", " [\"RCA_pct\", \"Temperature_C\"]\n", " ).size()\n", ")\n", "\n", "assert len(df_etd) == 90\n", "assert cell_counts.nunique() == 1\n", "assert cell_counts.iloc[0] == 3\n", "\n", "print(\"✓ STEP 5 DATA CHECK PASSED\")\n", "print(\"N =\", len(df_etd))\n", "print(\"Output:\", OUTPUT_DIR)" ] }, { "cell_type": "code", "execution_count": 113, "id": "837e1ffc-0448-46dc-b467-b7dc5b934908", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "✓ STEP 5 DATA CHECK PASSED\n", "N = 90\n", "Output: C:\\Users\\sadal\\Desktop\\Yasin Hoca-RCA Thermal\\Analysis_Outputs\\Step_05_ETD_Decomposition\n" ] } ], "source": [ "# ============================================================\n", "# STEP 5 — STRENGTH-LOSS DECOMPOSITION / ETD\n", "# CELL 45 — SETUP AND DATA RELOAD\n", "# ============================================================\n", "\n", "from pathlib import Path\n", "import re\n", "import warnings\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "from scipy import stats\n", "from matplotlib.colors import LinearSegmentedColormap\n", "from matplotlib.patches import Patch\n", "from matplotlib.lines import Line2D\n", "\n", "warnings.filterwarnings(\"ignore\")\n", "\n", "\n", "# ============================================================\n", "# PATHS\n", "# ============================================================\n", "\n", "DATA_PATH = Path(\n", " r\"C:\\Users\\sadal\\Desktop\\Yasin Hoca-RCA Thermal\\data.xlsx\"\n", ")\n", "\n", "OUTPUT_DIR = (\n", " DATA_PATH.parent\n", " / \"Analysis_Outputs\"\n", " / \"Step_05_ETD_Decomposition\"\n", ")\n", "\n", "OUTPUT_DIR.mkdir(\n", " parents=True,\n", " exist_ok=True\n", ")\n", "\n", "assert DATA_PATH.exists()\n", "\n", "\n", "# ============================================================\n", "# READ DATA\n", "# ============================================================\n", "\n", "raw = pd.read_excel(\n", " DATA_PATH,\n", " sheet_name=\"data\",\n", " header=None\n", ")\n", "\n", "temperature_headers = raw.iloc[0, 1:].astype(str)\n", "\n", "temperatures = (\n", " temperature_headers\n", " .str.extract(r\"(\\d+)\")[0]\n", " .astype(int)\n", " .tolist()\n", ")\n", "\n", "records = []\n", "\n", "for _, row in raw.iloc[1:].iterrows():\n", "\n", " if pd.isna(row.iloc[0]):\n", " continue\n", "\n", " mix_label = str(row.iloc[0])\n", "\n", " rca_match = re.search(\n", " r\"(\\d+)\",\n", " mix_label\n", " )\n", "\n", " if rca_match is None:\n", " raise ValueError(\n", " f\"RCA level could not be read: {mix_label}\"\n", " )\n", "\n", " rca_pct = int(\n", " rca_match.group(1)\n", " )\n", "\n", " replicate_counter = {}\n", "\n", " for col_index, temperature in enumerate(\n", " temperatures,\n", " start=1\n", " ):\n", "\n", " replicate_counter[temperature] = (\n", " replicate_counter.get(temperature, 0) + 1\n", " )\n", "\n", " value = pd.to_numeric(\n", " row.iloc[col_index],\n", " errors=\"coerce\"\n", " )\n", "\n", " if pd.isna(value):\n", " raise ValueError(\n", " f\"Missing value: RCA={rca_pct}, T={temperature}\"\n", " )\n", "\n", " records.append({\n", " \"RCA_pct\": rca_pct,\n", " \"Temperature_C\": temperature,\n", " \"Replicate\": replicate_counter[temperature],\n", " \"CompressiveStrength_MPa\": float(value)\n", " })\n", "\n", "\n", "df_etd = pd.DataFrame(records)\n", "\n", "\n", "# ============================================================\n", "# CHECK\n", "# ============================================================\n", "\n", "cell_counts = (\n", " df_etd.groupby(\n", " [\"RCA_pct\", \"Temperature_C\"]\n", " )\n", " .size()\n", ")\n", "\n", "assert len(df_etd) == 90\n", "assert cell_counts.nunique() == 1\n", "assert cell_counts.iloc[0] == 3\n", "\n", "print(\"✓ STEP 5 DATA CHECK PASSED\")\n", "print(\"N =\", len(df_etd))\n", "print(\"Output:\", OUTPUT_DIR)" ] }, { "cell_type": "code", "execution_count": 115, "id": "d7267636-a0dc-4695-b331-389b3a457b36", "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>RCA_pct</th>\n", " <th>Temperature_C</th>\n", " <th>Mean_MPa</th>\n", " <th>Retention_pct</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>24</td>\n", " <td>21.09</td>\n", " <td>100.00</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0</td>\n", " <td>150</td>\n", " <td>20.15</td>\n", " <td>95.53</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0</td>\n", " <td>300</td>\n", " <td>17.65</td>\n", " <td>83.67</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0</td>\n", " <td>450</td>\n", " <td>15.50</td>\n", " <td>73.48</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0</td>\n", " <td>600</td>\n", " <td>11.56</td>\n", " <td>54.80</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>10</td>\n", " <td>24</td>\n", " <td>21.05</td>\n", " <td>100.00</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>10</td>\n", " <td>150</td>\n", " <td>20.12</td>\n", " <td>95.60</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>10</td>\n", " <td>300</td>\n", " <td>17.31</td>\n", " <td>82.22</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>10</td>\n", " <td>450</td>\n", " <td>15.33</td>\n", " <td>72.82</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>10</td>\n", " <td>600</td>\n", " <td>11.22</td>\n", " <td>53.29</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>20</td>\n", " <td>24</td>\n", " <td>19.91</td>\n", " <td>100.00</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>20</td>\n", " <td>150</td>\n", " <td>19.88</td>\n", " <td>99.86</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>20</td>\n", " <td>300</td>\n", " <td>17.17</td>\n", " <td>86.26</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>20</td>\n", " <td>450</td>\n", " <td>14.34</td>\n", " <td>72.05</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>20</td>\n", " <td>600</td>\n", " <td>10.73</td>\n", " <td>53.88</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>30</td>\n", " <td>24</td>\n", " <td>19.35</td>\n", " <td>100.00</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>30</td>\n", " <td>150</td>\n", " <td>18.24</td>\n", " <td>94.25</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>30</td>\n", " <td>300</td>\n", " <td>17.02</td>\n", " <td>87.92</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>30</td>\n", " <td>450</td>\n", " <td>14.15</td>\n", " <td>73.10</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>30</td>\n", " <td>600</td>\n", " <td>9.99</td>\n", " <td>51.62</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>40</td>\n", " <td>24</td>\n", " <td>17.06</td>\n", " <td>100.00</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>40</td>\n", " <td>150</td>\n", " <td>16.98</td>\n", " <td>99.56</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>40</td>\n", " <td>300</td>\n", " <td>15.81</td>\n", " <td>92.66</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>40</td>\n", " <td>450</td>\n", " <td>13.99</td>\n", " <td>82.04</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>40</td>\n", " <td>600</td>\n", " <td>9.62</td>\n", " <td>56.42</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>50</td>\n", " <td>24</td>\n", " <td>16.79</td>\n", " <td>100.00</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>50</td>\n", " <td>150</td>\n", " <td>16.40</td>\n", " <td>97.67</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>50</td>\n", " <td>300</td>\n", " <td>14.63</td>\n", " <td>87.16</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>50</td>\n", " <td>450</td>\n", " <td>12.37</td>\n", " <td>73.65</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>50</td>\n", " <td>600</td>\n", " <td>9.13</td>\n", " <td>54.38</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " RCA_pct Temperature_C Mean_MPa Retention_pct\n", "0 0 24 21.09 100.00\n", "1 0 150 20.15 95.53\n", "2 0 300 17.65 83.67\n", "3 0 450 15.50 73.48\n", "4 0 600 11.56 54.80\n", "5 10 24 21.05 100.00\n", "6 10 150 20.12 95.60\n", "7 10 300 17.31 82.22\n", "8 10 450 15.33 72.82\n", "9 10 600 11.22 53.29\n", "10 20 24 19.91 100.00\n", "11 20 150 19.88 99.86\n", "12 20 300 17.17 86.26\n", "13 20 450 14.34 72.05\n", "14 20 600 10.73 53.88\n", "15 30 24 19.35 100.00\n", "16 30 150 18.24 94.25\n", "17 30 300 17.02 87.92\n", "18 30 450 14.15 73.10\n", "19 30 600 9.99 51.62\n", "20 40 24 17.06 100.00\n", "21 40 150 16.98 99.56\n", "22 40 300 15.81 92.66\n", "23 40 450 13.99 82.04\n", "24 40 600 9.62 56.42\n", "25 50 24 16.79 100.00\n", "26 50 150 16.40 97.67\n", "27 50 300 14.63 87.16\n", "28 50 450 12.37 73.65\n", "29 50 600 9.13 54.38" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# ============================================================\n", "# — STRENGTH SUMMARY AND NORMALIZATION\n", "# ============================================================\n", "\n", "BASELINE_TEMP = 24\n", "CONTROL_RCA = 0\n", "\n", "\n", "absolute_summary = (\n", " df_etd\n", " .groupby(\n", " [\"RCA_pct\", \"Temperature_C\"],\n", " as_index=False\n", " )\n", " .agg(\n", " N=(\"CompressiveStrength_MPa\", \"count\"),\n", " Mean_MPa=(\"CompressiveStrength_MPa\", \"mean\"),\n", " SD_MPa=(\"CompressiveStrength_MPa\", \"std\")\n", " )\n", ")\n", "\n", "\n", "absolute_summary[\"SE_MPa\"] = (\n", " absolute_summary[\"SD_MPa\"]\n", " / np.sqrt(absolute_summary[\"N\"])\n", ")\n", "\n", "\n", "# ============================================================\n", "# BASELINE FOR EACH RCA\n", "# ============================================================\n", "\n", "baseline = (\n", " absolute_summary[\n", " absolute_summary[\"Temperature_C\"] == BASELINE_TEMP\n", " ][\n", " [\n", " \"RCA_pct\",\n", " \"Mean_MPa\",\n", " \"SE_MPa\"\n", " ]\n", " ]\n", " .rename(\n", " columns={\n", " \"Mean_MPa\": \"Baseline_Mean_MPa\",\n", " \"SE_MPa\": \"Baseline_SE_MPa\"\n", " }\n", " )\n", ")\n", "\n", "\n", "normalized = absolute_summary.merge(\n", " baseline,\n", " on=\"RCA_pct\",\n", " how=\"left\"\n", ")\n", "\n", "\n", "normalized[\"Retention_pct\"] = (\n", " normalized[\"Mean_MPa\"]\n", " / normalized[\"Baseline_Mean_MPa\"]\n", " * 100\n", ")\n", "\n", "\n", "# ============================================================\n", "# UNCERTAINTY OF RETENTION\n", "# ============================================================\n", "\n", "def ratio_se(row):\n", "\n", " if row[\"Temperature_C\"] == BASELINE_TEMP:\n", " return 0.0\n", "\n", " relative_se = np.sqrt(\n", " (row[\"SE_MPa\"] / row[\"Mean_MPa\"]) ** 2\n", " +\n", " (\n", " row[\"Baseline_SE_MPa\"]\n", " / row[\"Baseline_Mean_MPa\"]\n", " ) ** 2\n", " )\n", "\n", " return (\n", " row[\"Retention_pct\"]\n", " * relative_se\n", " )\n", "\n", "\n", "normalized[\"Retention_SE_pct\"] = (\n", " normalized.apply(\n", " ratio_se,\n", " axis=1\n", " )\n", ")\n", "\n", "\n", "# Baseline exactly 100 by definition\n", "normalized.loc[\n", " normalized[\"Temperature_C\"] == BASELINE_TEMP,\n", " \"Retention_pct\"\n", "] = 100.0\n", "\n", "\n", "display(\n", " normalized[\n", " [\n", " \"RCA_pct\",\n", " \"Temperature_C\",\n", " \"Mean_MPa\",\n", " \"Retention_pct\"\n", " ]\n", " ].round(2)\n", ")" ] }, { "cell_type": "code", "execution_count": 117, "id": "4fabd464-8c75-4c6e-84fb-95924a29dc4f", "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>RCA_pct</th>\n", " <th>Temperature_C</th>\n", " <th>Retention_pct</th>\n", " <th>Control_Retention_pct</th>\n", " <th>ETD_pp</th>\n", " <th>ETD_SE_pp</th>\n", " <th>ETD_CI95_Lower_pp</th>\n", " <th>ETD_CI95_Upper_pp</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>24</td>\n", " <td>100.00</td>\n", " <td>100.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0</td>\n", " <td>150</td>\n", " <td>95.53</td>\n", " <td>95.53</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0</td>\n", " <td>300</td>\n", " <td>83.67</td>\n", " <td>83.67</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0</td>\n", " <td>450</td>\n", " <td>73.48</td>\n", " <td>73.48</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0</td>\n", " <td>600</td>\n", " <td>54.80</td>\n", " <td>54.80</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>10</td>\n", " <td>24</td>\n", " <td>100.00</td>\n", " <td>100.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>10</td>\n", " <td>150</td>\n", " <td>95.60</td>\n", " <td>95.53</td>\n", " <td>-0.07</td>\n", " <td>5.29</td>\n", " <td>-10.44</td>\n", " <td>10.31</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>10</td>\n", " <td>300</td>\n", " <td>82.22</td>\n", " <td>83.67</td>\n", " <td>1.45</td>\n", " <td>4.39</td>\n", " <td>-7.15</td>\n", " <td>10.04</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>10</td>\n", " <td>450</td>\n", " <td>72.82</td>\n", " <td>73.48</td>\n", " <td>0.65</td>\n", " <td>3.30</td>\n", " <td>-5.82</td>\n", " <td>7.13</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>10</td>\n", " <td>600</td>\n", " <td>53.29</td>\n", " <td>54.80</td>\n", " <td>1.51</td>\n", " <td>2.65</td>\n", " <td>-3.67</td>\n", " <td>6.70</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>20</td>\n", " <td>24</td>\n", " <td>100.00</td>\n", " <td>100.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>20</td>\n", " <td>150</td>\n", " <td>99.86</td>\n", " <td>95.53</td>\n", " <td>-4.33</td>\n", " <td>6.08</td>\n", " <td>-16.25</td>\n", " <td>7.59</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>20</td>\n", " <td>300</td>\n", " <td>86.26</td>\n", " <td>83.67</td>\n", " <td>-2.58</td>\n", " <td>4.87</td>\n", " <td>-12.13</td>\n", " <td>6.96</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>20</td>\n", " <td>450</td>\n", " <td>72.05</td>\n", " <td>73.48</td>\n", " <td>1.43</td>\n", " <td>4.70</td>\n", " <td>-7.79</td>\n", " <td>10.64</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>20</td>\n", " <td>600</td>\n", " <td>53.88</td>\n", " <td>54.80</td>\n", " <td>0.92</td>\n", " <td>3.04</td>\n", " <td>-5.04</td>\n", " <td>6.88</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>30</td>\n", " <td>24</td>\n", " <td>100.00</td>\n", " <td>100.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>30</td>\n", " <td>150</td>\n", " <td>94.25</td>\n", " <td>95.53</td>\n", " <td>1.29</td>\n", " <td>9.42</td>\n", " <td>-17.18</td>\n", " <td>19.75</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>30</td>\n", " <td>300</td>\n", " <td>87.92</td>\n", " <td>83.67</td>\n", " <td>-4.25</td>\n", " <td>7.24</td>\n", " <td>-18.44</td>\n", " <td>9.95</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>30</td>\n", " <td>450</td>\n", " <td>73.10</td>\n", " <td>73.48</td>\n", " <td>0.37</td>\n", " <td>5.89</td>\n", " <td>-11.16</td>\n", " <td>11.91</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>30</td>\n", " <td>600</td>\n", " <td>51.62</td>\n", " <td>54.80</td>\n", " <td>3.18</td>\n", " <td>4.48</td>\n", " <td>-5.59</td>\n", " <td>11.96</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>40</td>\n", " <td>24</td>\n", " <td>100.00</td>\n", " <td>100.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>40</td>\n", " <td>150</td>\n", " <td>99.56</td>\n", " <td>95.53</td>\n", " <td>-4.03</td>\n", " <td>7.27</td>\n", " <td>-18.28</td>\n", " <td>10.23</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>40</td>\n", " <td>300</td>\n", " <td>92.66</td>\n", " <td>83.67</td>\n", " <td>-8.99</td>\n", " <td>7.46</td>\n", " <td>-23.62</td>\n", " <td>5.64</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>40</td>\n", " <td>450</td>\n", " <td>82.04</td>\n", " <td>73.48</td>\n", " <td>-8.56</td>\n", " <td>5.05</td>\n", " <td>-18.45</td>\n", " <td>1.33</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>40</td>\n", " <td>600</td>\n", " <td>56.42</td>\n", " <td>54.80</td>\n", " <td>-1.62</td>\n", " <td>5.44</td>\n", " <td>-12.29</td>\n", " <td>9.05</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>50</td>\n", " <td>24</td>\n", " <td>100.00</td>\n", " <td>100.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>50</td>\n", " <td>150</td>\n", " <td>97.67</td>\n", " <td>95.53</td>\n", " <td>-2.13</td>\n", " <td>6.79</td>\n", " <td>-15.44</td>\n", " <td>11.18</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>50</td>\n", " <td>300</td>\n", " <td>87.16</td>\n", " <td>83.67</td>\n", " <td>-3.49</td>\n", " <td>4.96</td>\n", " <td>-13.21</td>\n", " <td>6.22</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>50</td>\n", " <td>450</td>\n", " <td>73.65</td>\n", " <td>73.48</td>\n", " <td>-0.17</td>\n", " <td>3.92</td>\n", " <td>-7.86</td>\n", " <td>7.51</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>50</td>\n", " <td>600</td>\n", " <td>54.38</td>\n", " <td>54.80</td>\n", " <td>0.42</td>\n", " <td>2.70</td>\n", " <td>-4.88</td>\n", " <td>5.72</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " RCA_pct Temperature_C Retention_pct Control_Retention_pct ETD_pp \\\n", "0 0 24 100.00 100.00 0.00 \n", "1 0 150 95.53 95.53 0.00 \n", "2 0 300 83.67 83.67 0.00 \n", "3 0 450 73.48 73.48 0.00 \n", "4 0 600 54.80 54.80 0.00 \n", "5 10 24 100.00 100.00 0.00 \n", "6 10 150 95.60 95.53 -0.07 \n", "7 10 300 82.22 83.67 1.45 \n", "8 10 450 72.82 73.48 0.65 \n", "9 10 600 53.29 54.80 1.51 \n", "10 20 24 100.00 100.00 0.00 \n", "11 20 150 99.86 95.53 -4.33 \n", "12 20 300 86.26 83.67 -2.58 \n", "13 20 450 72.05 73.48 1.43 \n", "14 20 600 53.88 54.80 0.92 \n", "15 30 24 100.00 100.00 0.00 \n", "16 30 150 94.25 95.53 1.29 \n", "17 30 300 87.92 83.67 -4.25 \n", "18 30 450 73.10 73.48 0.37 \n", "19 30 600 51.62 54.80 3.18 \n", "20 40 24 100.00 100.00 0.00 \n", "21 40 150 99.56 95.53 -4.03 \n", "22 40 300 92.66 83.67 -8.99 \n", "23 40 450 82.04 73.48 -8.56 \n", "24 40 600 56.42 54.80 -1.62 \n", "25 50 24 100.00 100.00 0.00 \n", "26 50 150 97.67 95.53 -2.13 \n", "27 50 300 87.16 83.67 -3.49 \n", "28 50 450 73.65 73.48 -0.17 \n", "29 50 600 54.38 54.80 0.42 \n", "\n", " ETD_SE_pp ETD_CI95_Lower_pp ETD_CI95_Upper_pp \n", "0 0.00 0.00 0.00 \n", "1 0.00 0.00 0.00 \n", "2 0.00 0.00 0.00 \n", "3 0.00 0.00 0.00 \n", "4 0.00 0.00 0.00 \n", "5 0.00 0.00 0.00 \n", "6 5.29 -10.44 10.31 \n", "7 4.39 -7.15 10.04 \n", "8 3.30 -5.82 7.13 \n", "9 2.65 -3.67 6.70 \n", "10 0.00 0.00 0.00 \n", "11 6.08 -16.25 7.59 \n", "12 4.87 -12.13 6.96 \n", "13 4.70 -7.79 10.64 \n", "14 3.04 -5.04 6.88 \n", "15 0.00 0.00 0.00 \n", "16 9.42 -17.18 19.75 \n", "17 7.24 -18.44 9.95 \n", "18 5.89 -11.16 11.91 \n", "19 4.48 -5.59 11.96 \n", "20 0.00 0.00 0.00 \n", "21 7.27 -18.28 10.23 \n", "22 7.46 -23.62 5.64 \n", "23 5.05 -18.45 1.33 \n", "24 5.44 -12.29 9.05 \n", "25 0.00 0.00 0.00 \n", "26 6.79 -15.44 11.18 \n", "27 4.96 -13.21 6.22 \n", "28 3.92 -7.86 7.51 \n", "29 2.70 -4.88 5.72 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# ============================================================\n", "# — EXCESS THERMAL DAMAGE (ETD)\n", "# ============================================================\n", "\n", "control_retention = (\n", " normalized[\n", " normalized[\"RCA_pct\"] == CONTROL_RCA\n", " ][\n", " [\n", " \"Temperature_C\",\n", " \"Retention_pct\",\n", " \"Retention_SE_pct\"\n", " ]\n", " ]\n", " .rename(\n", " columns={\n", " \"Retention_pct\":\n", " \"Control_Retention_pct\",\n", "\n", " \"Retention_SE_pct\":\n", " \"Control_Retention_SE_pct\"\n", " }\n", " )\n", ")\n", "\n", "\n", "etd = normalized.merge(\n", " control_retention,\n", " on=\"Temperature_C\",\n", " how=\"left\"\n", ")\n", "\n", "\n", "# ============================================================\n", "# ETD\n", "# Positive = more thermal damage than control\n", "# Negative = less thermal damage than control\n", "# ============================================================\n", "\n", "etd[\"ETD_pp\"] = (\n", " etd[\"Control_Retention_pct\"]\n", " - etd[\"Retention_pct\"]\n", ")\n", "\n", "\n", "# ============================================================\n", "# APPROXIMATE UNCERTAINTY\n", "# ============================================================\n", "\n", "etd[\"ETD_SE_pp\"] = np.sqrt(\n", " etd[\"Control_Retention_SE_pct\"] ** 2\n", " +\n", " etd[\"Retention_SE_pct\"] ** 2\n", ")\n", "\n", "\n", "# For control vs itself uncertainty difference is zero\n", "control_mask = (\n", " etd[\"RCA_pct\"] == CONTROL_RCA\n", ")\n", "\n", "etd.loc[\n", " control_mask,\n", " \"ETD_SE_pp\"\n", "] = 0.0\n", "\n", "\n", "z95 = stats.norm.ppf(0.975)\n", "\n", "etd[\"ETD_CI95_Lower_pp\"] = (\n", " etd[\"ETD_pp\"]\n", " - z95 * etd[\"ETD_SE_pp\"]\n", ")\n", "\n", "etd[\"ETD_CI95_Upper_pp\"] = (\n", " etd[\"ETD_pp\"]\n", " + z95 * etd[\"ETD_SE_pp\"]\n", ")\n", "\n", "\n", "# Descriptive direction\n", "etd[\"ETD_Interpretation\"] = np.select(\n", " [\n", " etd[\"ETD_pp\"] > 0,\n", " etd[\"ETD_pp\"] < 0\n", " ],\n", " [\n", " \"Higher fractional thermal loss than control\",\n", " \"Lower fractional thermal loss than control\"\n", " ],\n", " default=\"Equal to control/reference\"\n", ")\n", "\n", "\n", "display(\n", " etd[\n", " [\n", " \"RCA_pct\",\n", " \"Temperature_C\",\n", " \"Retention_pct\",\n", " \"Control_Retention_pct\",\n", " \"ETD_pp\",\n", " \"ETD_SE_pp\",\n", " \"ETD_CI95_Lower_pp\",\n", " \"ETD_CI95_Upper_pp\"\n", " ]\n", " ].round(2)\n", ")" ] }, { "cell_type": "code", "execution_count": 119, "id": "b9a545ed-e612-4fde-b656-23b0c9747df5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Control ambient strength = 21.094 MPa\n" ] }, { "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>RCA_pct</th>\n", " <th>Baseline_Mean_MPa</th>\n", " <th>Baseline_Strength_Ratio</th>\n", " <th>Initial_Strength_Penalty_pct</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>21.094</td>\n", " <td>1.000</td>\n", " <td>0.000</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>10</td>\n", " <td>21.047</td>\n", " <td>0.998</td>\n", " <td>0.223</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>20</td>\n", " <td>19.908</td>\n", " <td>0.944</td>\n", " <td>5.626</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>30</td>\n", " <td>19.354</td>\n", " <td>0.917</td>\n", " <td>8.250</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>40</td>\n", " <td>17.059</td>\n", " <td>0.809</td>\n", " <td>19.132</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>50</td>\n", " <td>16.790</td>\n", " <td>0.796</td>\n", " <td>20.407</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " RCA_pct Baseline_Mean_MPa Baseline_Strength_Ratio \\\n", "0 0 21.094 1.000 \n", "5 10 21.047 0.998 \n", "10 20 19.908 0.944 \n", "15 30 19.354 0.917 \n", "20 40 17.059 0.809 \n", "25 50 16.790 0.796 \n", "\n", " Initial_Strength_Penalty_pct \n", "0 0.000 \n", "5 0.223 \n", "10 5.626 \n", "15 8.250 \n", "20 19.132 \n", "25 20.407 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# ============================================================\n", "# — INITIAL RCA STRENGTH PENALTY\n", "# ============================================================\n", "\n", "control_ambient_strength = float(\n", " absolute_summary.loc[\n", " (\n", " (absolute_summary[\"RCA_pct\"] == CONTROL_RCA)\n", " &\n", " (\n", " absolute_summary[\"Temperature_C\"]\n", " == BASELINE_TEMP\n", " )\n", " ),\n", " \"Mean_MPa\"\n", " ].iloc[0]\n", ")\n", "\n", "\n", "baseline_penalty = baseline.copy()\n", "\n", "\n", "baseline_penalty[\n", " \"Baseline_Strength_Ratio\"\n", "] = (\n", " baseline_penalty[\"Baseline_Mean_MPa\"]\n", " / control_ambient_strength\n", ")\n", "\n", "\n", "baseline_penalty[\n", " \"Initial_Strength_Penalty_pct\"\n", "] = (\n", " 100\n", " * (\n", " 1\n", " - baseline_penalty[\"Baseline_Strength_Ratio\"]\n", " )\n", ")\n", "\n", "\n", "print(\n", " f\"Control ambient strength = \"\n", " f\"{control_ambient_strength:.3f} MPa\"\n", ")\n", "\n", "display(\n", " baseline_penalty[\n", " [\n", " \"RCA_pct\",\n", " \"Baseline_Mean_MPa\",\n", " \"Baseline_Strength_Ratio\",\n", " \"Initial_Strength_Penalty_pct\"\n", " ]\n", " ].round(3)\n", ")" ] }, { "cell_type": "code", "execution_count": 121, "id": "349edb34-6243-4d49-9134-37e0996b3328", "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>RCA_pct</th>\n", " <th>Initial_Strength_Penalty_pct</th>\n", " <th>ETD_pp</th>\n", " <th>Baseline_Related_Component_pct</th>\n", " <th>Excess_Thermal_Component_pct</th>\n", " <th>Observed_Control_RCA_Gap_pct</th>\n", " <th>Reconstructed_Gap_pct</th>\n", " <th>Closure_Error</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>10</td>\n", " <td>0.2228</td>\n", " <td>1.5136</td>\n", " <td>0.1221</td>\n", " <td>1.5103</td>\n", " <td>1.6323</td>\n", " <td>1.6323</td>\n", " <td>-0.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>20</td>\n", " <td>5.6255</td>\n", " <td>0.9195</td>\n", " <td>3.0828</td>\n", " <td>0.8677</td>\n", " <td>3.9505</td>\n", " <td>3.9505</td>\n", " <td>-0.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>30</td>\n", " <td>8.2502</td>\n", " <td>3.1826</td>\n", " <td>4.5211</td>\n", " <td>2.9201</td>\n", " <td>7.4412</td>\n", " <td>7.4412</td>\n", " <td>-0.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>40</td>\n", " <td>19.1315</td>\n", " <td>-1.6192</td>\n", " <td>10.4840</td>\n", " <td>-1.3094</td>\n", " <td>9.1747</td>\n", " <td>9.1747</td>\n", " <td>-0.0</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>50</td>\n", " <td>20.4067</td>\n", " <td>0.4232</td>\n", " <td>11.1829</td>\n", " <td>0.3368</td>\n", " <td>11.5197</td>\n", " <td>11.5197</td>\n", " <td>-0.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " RCA_pct Initial_Strength_Penalty_pct ETD_pp \\\n", "0 0 0.0000 0.0000 \n", "1 10 0.2228 1.5136 \n", "2 20 5.6255 0.9195 \n", "3 30 8.2502 3.1826 \n", "4 40 19.1315 -1.6192 \n", "5 50 20.4067 0.4232 \n", "\n", " Baseline_Related_Component_pct Excess_Thermal_Component_pct \\\n", "0 0.0000 0.0000 \n", "1 0.1221 1.5103 \n", "2 3.0828 0.8677 \n", "3 4.5211 2.9201 \n", "4 10.4840 -1.3094 \n", "5 11.1829 0.3368 \n", "\n", " Observed_Control_RCA_Gap_pct Reconstructed_Gap_pct Closure_Error \n", "0 0.0000 0.0000 0.0 \n", "1 1.6323 1.6323 -0.0 \n", "2 3.9505 3.9505 -0.0 \n", "3 7.4412 7.4412 -0.0 \n", "4 9.1747 9.1747 -0.0 \n", "5 11.5197 11.5197 -0.0 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# ============================================================\n", "# — EXACT GAP DECOMPOSITION AT 600 °C\n", "# ============================================================\n", "\n", "TARGET_TEMP = 600\n", "\n", "\n", "decomp_600 = (\n", " etd[\n", " etd[\"Temperature_C\"] == TARGET_TEMP\n", " ]\n", " .copy()\n", ")\n", "\n", "\n", "decomp_600 = decomp_600.merge(\n", " baseline_penalty[\n", " [\n", " \"RCA_pct\",\n", " \"Baseline_Strength_Ratio\",\n", " \"Initial_Strength_Penalty_pct\"\n", " ]\n", " ],\n", " on=\"RCA_pct\",\n", " how=\"left\"\n", ")\n", "\n", "\n", "# ============================================================\n", "# CONTROL VALUES AT 600 °C\n", "# ============================================================\n", "\n", "control_600_strength = float(\n", " absolute_summary.loc[\n", " (\n", " (absolute_summary[\"RCA_pct\"] == CONTROL_RCA)\n", " &\n", " (\n", " absolute_summary[\"Temperature_C\"]\n", " == TARGET_TEMP\n", " )\n", " ),\n", " \"Mean_MPa\"\n", " ].iloc[0]\n", ")\n", "\n", "\n", "control_retention_600 = float(\n", " decomp_600.loc[\n", " decomp_600[\"RCA_pct\"] == CONTROL_RCA,\n", " \"Control_Retention_pct\"\n", " ].iloc[0]\n", ")\n", "\n", "\n", "r0_600 = (\n", " control_retention_600\n", " / 100\n", ")\n", "\n", "\n", "# ============================================================\n", "# BASELINE-RELATED COMPONENT\n", "# Common basis = % of control strength at 24 °C\n", "# ============================================================\n", "\n", "decomp_600[\n", " \"Baseline_Related_Component_pct\"\n", "] = (\n", " 100\n", " * r0_600\n", " * (\n", " 1\n", " - decomp_600[\"Baseline_Strength_Ratio\"]\n", " )\n", ")\n", "\n", "\n", "# ============================================================\n", "# EXCESS THERMAL COMPONENT\n", "# ============================================================\n", "\n", "decomp_600[\n", " \"Excess_Thermal_Component_pct\"\n", "] = (\n", " decomp_600[\"Baseline_Strength_Ratio\"]\n", " * decomp_600[\"ETD_pp\"]\n", ")\n", "\n", "\n", "# ============================================================\n", "# OBSERVED TOTAL GAP ON THE SAME REFERENCE BASIS\n", "# ============================================================\n", "\n", "decomp_600[\n", " \"Observed_Control_RCA_Gap_pct\"\n", "] = (\n", " 100\n", " * (\n", " control_600_strength\n", " - decomp_600[\"Mean_MPa\"]\n", " )\n", " / control_ambient_strength\n", ")\n", "\n", "\n", "# ============================================================\n", "# RECONSTRUCTED GAP\n", "# ============================================================\n", "\n", "decomp_600[\n", " \"Reconstructed_Gap_pct\"\n", "] = (\n", " decomp_600[\n", " \"Baseline_Related_Component_pct\"\n", " ]\n", " +\n", " decomp_600[\n", " \"Excess_Thermal_Component_pct\"\n", " ]\n", ")\n", "\n", "\n", "decomp_600[\n", " \"Closure_Error\"\n", "] = (\n", " decomp_600[\n", " \"Observed_Control_RCA_Gap_pct\"\n", " ]\n", " -\n", " decomp_600[\n", " \"Reconstructed_Gap_pct\"\n", " ]\n", ")\n", "\n", "\n", "display(\n", " decomp_600[\n", " [\n", " \"RCA_pct\",\n", " \"Initial_Strength_Penalty_pct\",\n", " \"ETD_pp\",\n", " \"Baseline_Related_Component_pct\",\n", " \"Excess_Thermal_Component_pct\",\n", " \"Observed_Control_RCA_Gap_pct\",\n", " \"Reconstructed_Gap_pct\",\n", " \"Closure_Error\"\n", " ]\n", " ].round(4)\n", ")" ] }, { "cell_type": "code", "execution_count": 123, "id": "b56d71a9-519e-4ecf-86d6-0a8e3df5a02b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Maximum decomposition closure error: 0.000000000000\n", "✓ EXACT DECOMPOSITION CHECK PASSED\n" ] } ], "source": [ "max_closure_error = (\n", " decomp_600[\"Closure_Error\"]\n", " .abs()\n", " .max()\n", ")\n", "\n", "print(\n", " \"Maximum decomposition closure error:\",\n", " f\"{max_closure_error:.12f}\"\n", ")\n", "\n", "assert max_closure_error < 1e-10\n", "\n", "print(\"✓ EXACT DECOMPOSITION CHECK PASSED\")" ] }, { "cell_type": "code", "execution_count": 125, "id": "a4e9b9f8-2bdf-4872-bf06-3e1290afca61", "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>Temperature_C</th>\n", " <th>24</th>\n", " <th>150</th>\n", " <th>300</th>\n", " <th>450</th>\n", " <th>600</th>\n", " </tr>\n", " <tr>\n", " <th>RCA_pct</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0.0</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>0.0</td>\n", " <td>-0.07</td>\n", " <td>1.45</td>\n", " <td>0.65</td>\n", " <td>1.51</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>0.0</td>\n", " <td>-4.33</td>\n", " <td>-2.58</td>\n", " <td>1.43</td>\n", " <td>0.92</td>\n", " </tr>\n", " <tr>\n", " <th>30</th>\n", " <td>0.0</td>\n", " <td>1.29</td>\n", " <td>-4.25</td>\n", " <td>0.37</td>\n", " <td>3.18</td>\n", " </tr>\n", " <tr>\n", " <th>40</th>\n", " <td>0.0</td>\n", " <td>-4.03</td>\n", " <td>-8.99</td>\n", " <td>-8.56</td>\n", " <td>-1.62</td>\n", " </tr>\n", " <tr>\n", " <th>50</th>\n", " <td>0.0</td>\n", " <td>-2.13</td>\n", " <td>-3.49</td>\n", " <td>-0.17</td>\n", " <td>0.42</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "Temperature_C 24 150 300 450 600\n", "RCA_pct \n", "0 0.0 0.00 0.00 0.00 0.00\n", "10 0.0 -0.07 1.45 0.65 1.51\n", "20 0.0 -4.33 -2.58 1.43 0.92\n", "30 0.0 1.29 -4.25 0.37 3.18\n", "40 0.0 -4.03 -8.99 -8.56 -1.62\n", "50 0.0 -2.13 -3.49 -0.17 0.42" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# ============================================================\n", "# — ETD MATRIX\n", "# ============================================================\n", "\n", "etd_matrix = (\n", " etd.pivot(\n", " index=\"RCA_pct\",\n", " columns=\"Temperature_C\",\n", " values=\"ETD_pp\"\n", " )\n", ")\n", "\n", "\n", "display(\n", " etd_matrix.round(2)\n", ")" ] }, { "cell_type": "code", "execution_count": 127, "id": "a74a3c3c-67e8-4534-9a5a-12180cd4dc0d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "✓ SUPPLEMENTARY TABLE S2 SAVED\n", "C:\\Users\\sadal\\Desktop\\Yasin Hoca-RCA Thermal\\Analysis_Outputs\\Step_05_ETD_Decomposition\\Supplementary_Table_S2_ETD_Decomposition.xlsx\n" ] } ], "source": [ "# ============================================================\n", "# — SAVE SUPPLEMENTARY TABLE S2\n", "# ============================================================\n", "\n", "from pathlib import Path\n", "\n", "DATA_PATH = Path(\n", " r\"C:\\Users\\sadal\\Desktop\\Yasin Hoca-RCA Thermal\\data.xlsx\"\n", ")\n", "\n", "OUTPUT_DIR = (\n", " DATA_PATH.parent\n", " / \"Analysis_Outputs\"\n", " / \"Step_05_ETD_Decomposition\"\n", ")\n", "\n", "OUTPUT_DIR.mkdir(\n", " parents=True,\n", " exist_ok=True\n", ")\n", "\n", "\n", "table_s2_path = (\n", " OUTPUT_DIR\n", " / \"Supplementary_Table_S2_ETD_Decomposition.xlsx\"\n", ")\n", "\n", "\n", "with pd.ExcelWriter(\n", " table_s2_path,\n", " engine=\"openpyxl\"\n", ") as writer:\n", "\n", " etd.to_excel(\n", " writer,\n", " sheet_name=\"ETD_All_Temperatures\",\n", " index=False\n", " )\n", "\n", " etd_matrix.to_excel(\n", " writer,\n", " sheet_name=\"ETD_Matrix\"\n", " )\n", "\n", " baseline_penalty.to_excel(\n", " writer,\n", " sheet_name=\"Initial_Penalty\",\n", " index=False\n", " )\n", "\n", " decomp_600.to_excel(\n", " writer,\n", " sheet_name=\"600C_Decomposition\",\n", " index=False\n", " )\n", "\n", "\n", "etd.to_csv(\n", " OUTPUT_DIR\n", " / \"ETD_All_Temperatures.csv\",\n", " index=False\n", ")\n", "\n", "decomp_600.to_csv(\n", " OUTPUT_DIR\n", " / \"600C_Strength_Gap_Decomposition.csv\",\n", " index=False\n", ")\n", "\n", "\n", "print(\"✓ SUPPLEMENTARY TABLE S2 SAVED\")\n", "print(table_s2_path)" ] }, { "cell_type": "code", "execution_count": 129, "id": "3bfc2ac8-1961-4afc-8a13-91ade2e88ce6", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "<Figure size 1470x580 with 3 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "✓ FIGURE 8 SAVED\n" ] } ], "source": [ "# ============================================================\n", "# — FIGURE 8\n", "# ETD + EXACT STRENGTH-GAP DECOMPOSITION\n", "# ============================================================\n", "\n", "from pathlib import Path\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "from matplotlib.colors import LinearSegmentedColormap\n", "from matplotlib.patches import Patch\n", "from matplotlib.lines import Line2D\n", "\n", "\n", "# ============================================================\n", "# ROBUST OUTPUT PATH\n", "# ============================================================\n", "\n", "DATA_PATH = Path(\n", " r\"C:\\Users\\sadal\\Desktop\\Yasin Hoca-RCA Thermal\\data.xlsx\"\n", ")\n", "\n", "OUTPUT_DIR = (\n", " DATA_PATH.parent\n", " / \"Analysis_Outputs\"\n", " / \"Step_05_ETD_Decomposition\"\n", ")\n", "\n", "OUTPUT_DIR.mkdir(\n", " parents=True,\n", " exist_ok=True\n", ")\n", "\n", "\n", "# ============================================================\n", "# VISUAL IDENTITY\n", "# ============================================================\n", "\n", "background = \"#F7F5F0\"\n", "\n", "navy = \"#264653\"\n", "teal = \"#2A9D8F\"\n", "cream = \"#F7F5F0\"\n", "gold = \"#E9C46A\"\n", "coral = \"#E76F51\"\n", "gray = \"#7A838A\"\n", "\n", "\n", "etd_cmap = LinearSegmentedColormap.from_list(\n", " \"ETD_diverging\",\n", " [\n", " \"#264653\",\n", " \"#65A7A0\",\n", " \"#F7F5F0\",\n", " \"#E9C46A\",\n", " \"#E76F51\"\n", " ]\n", ")\n", "\n", "\n", "plt.rcParams.update({\n", " \"font.family\": \"DejaVu Sans\",\n", " \"font.size\": 10.5,\n", " \"axes.labelsize\": 11,\n", " \"axes.titlesize\": 12,\n", " \"xtick.labelsize\": 9.5,\n", " \"ytick.labelsize\": 9.5,\n", " \"axes.linewidth\": 0.8,\n", " \"pdf.fonttype\": 42,\n", " \"ps.fonttype\": 42\n", "})\n", "\n", "\n", "# ============================================================\n", "# FIGURE\n", "# ============================================================\n", "\n", "fig, (ax1, ax2) = plt.subplots(\n", " 1,\n", " 2,\n", " figsize=(14.7, 5.8),\n", " facecolor=background,\n", " gridspec_kw={\n", " \"width_ratios\": [1.15, 1]\n", " }\n", ")\n", "\n", "ax1.set_facecolor(background)\n", "ax2.set_facecolor(background)\n", "\n", "\n", "# ============================================================\n", "# PANEL A — ETD MAP\n", "# ============================================================\n", "\n", "vmax = np.nanmax(\n", " np.abs(etd_matrix.values)\n", ")\n", "\n", "sns.heatmap(\n", " etd_matrix,\n", " ax=ax1,\n", " cmap=etd_cmap,\n", " center=0,\n", " vmin=-vmax,\n", " vmax=vmax,\n", " annot=True,\n", " fmt=\"+.1f\",\n", " linewidths=2.0,\n", " linecolor=background,\n", " cbar_kws={\n", " \"label\":\n", " \"Excess thermal damage (percentage points)\",\n", " \"shrink\": 0.82\n", " }\n", ")\n", "\n", "\n", "ax1.set_xlabel(\n", " \"Exposure temperature (°C)\"\n", ")\n", "\n", "ax1.set_ylabel(\n", " \"RCA replacement (%)\"\n", ")\n", "\n", "ax1.set_title(\n", " \"Excess thermal damage relative to RCA0\",\n", " loc=\"left\",\n", " color=navy,\n", " fontweight=\"bold\",\n", " pad=12\n", ")\n", "\n", "ax1.tick_params(\n", " axis=\"y\",\n", " rotation=0\n", ")\n", "\n", "\n", "ax1.text(\n", " 0.01,\n", " -0.15,\n", " \"Positive: greater fractional thermal loss than control | \"\n", " \"Negative: lower fractional thermal loss than control\",\n", " transform=ax1.transAxes,\n", " fontsize=8.6,\n", " color=gray\n", ")\n", "\n", "\n", "ax1.text(\n", " -0.10,\n", " 1.07,\n", " \"A\",\n", " transform=ax1.transAxes,\n", " fontsize=17,\n", " fontweight=\"bold\",\n", " color=coral\n", ")\n", "\n", "\n", "# ============================================================\n", "# PANEL B — EXACT 600 °C DECOMPOSITION\n", "# ============================================================\n", "\n", "plot_data = (\n", " decomp_600[\n", " decomp_600[\"RCA_pct\"] != CONTROL_RCA\n", " ]\n", " .sort_values(\"RCA_pct\")\n", " .copy()\n", ")\n", "\n", "\n", "x = np.arange(\n", " len(plot_data)\n", ")\n", "\n", "\n", "baseline_component = (\n", " plot_data[\n", " \"Baseline_Related_Component_pct\"\n", " ].values\n", ")\n", "\n", "thermal_component = (\n", " plot_data[\n", " \"Excess_Thermal_Component_pct\"\n", " ].values\n", ")\n", "\n", "observed_gap = (\n", " plot_data[\n", " \"Observed_Control_RCA_Gap_pct\"\n", " ].values\n", ")\n", "\n", "\n", "# Baseline-related contribution\n", "ax2.bar(\n", " x,\n", " baseline_component,\n", " width=0.62,\n", " color=teal,\n", " alpha=0.82,\n", " edgecolor=\"white\",\n", " linewidth=0.8,\n", " zorder=2\n", ")\n", "\n", "\n", "# Excess thermal contribution\n", "for i in range(\n", " len(plot_data)\n", "):\n", "\n", " thermal_color = (\n", " coral\n", " if thermal_component[i] >= 0\n", " else navy\n", " )\n", "\n", " ax2.bar(\n", " x[i],\n", " thermal_component[i],\n", " bottom=baseline_component[i],\n", " width=0.62,\n", " color=thermal_color,\n", " alpha=0.88,\n", " edgecolor=\"white\",\n", " linewidth=0.8,\n", " zorder=3\n", " )\n", "\n", "\n", "# Observed total gap\n", "ax2.scatter(\n", " x,\n", " observed_gap,\n", " marker=\"D\",\n", " s=62,\n", " color=\"#202A35\",\n", " edgecolor=\"white\",\n", " linewidth=0.8,\n", " zorder=5\n", ")\n", "\n", "\n", "# Annotate total gap\n", "for xi, total in zip(\n", " x,\n", " observed_gap\n", "):\n", "\n", " ax2.text(\n", " xi,\n", " total + 0.65,\n", " f\"{total:.1f}\",\n", " ha=\"center\",\n", " va=\"bottom\",\n", " fontsize=8.8,\n", " color=\"#202A35\",\n", " fontweight=\"bold\"\n", " )\n", "\n", "\n", "ax2.axhline(\n", " 0,\n", " color=\"#999A97\",\n", " linewidth=0.9\n", ")\n", "\n", "\n", "ax2.set_xticks(\n", " x\n", ")\n", "\n", "ax2.set_xticklabels([\n", " f\"{int(v)}%\"\n", " for v in plot_data[\"RCA_pct\"]\n", "])\n", "\n", "\n", "ax2.set_xlabel(\n", " \"RCA replacement\"\n", ")\n", "\n", "ax2.set_ylabel(\n", " \"Strength-gap contribution\\n(% of RCA0 strength at 24 °C)\"\n", ")\n", "\n", "\n", "ax2.set_title(\n", " \"Decomposition of the 600 °C control–RCA strength gap\",\n", " loc=\"left\",\n", " color=navy,\n", " fontweight=\"bold\",\n", " pad=12\n", ")\n", "\n", "\n", "# ============================================================\n", "# CUSTOM LEGEND\n", "# ============================================================\n", "\n", "legend_handles = [\n", "\n", " Patch(\n", " facecolor=teal,\n", " alpha=0.82,\n", " label=\"Baseline-related component\"\n", " ),\n", "\n", " Patch(\n", " facecolor=coral,\n", " label=\"Positive excess thermal component\"\n", " ),\n", "\n", " Patch(\n", " facecolor=navy,\n", " label=\"Negative excess thermal component\"\n", " ),\n", "\n", " Line2D(\n", " [0],\n", " [0],\n", " marker=\"D\",\n", " color=\"none\",\n", " markerfacecolor=\"#202A35\",\n", " markeredgecolor=\"white\",\n", " markersize=7,\n", " label=\"Observed total gap\"\n", " )\n", "]\n", "\n", "\n", "ax2.legend(\n", " handles=legend_handles,\n", " frameon=False,\n", " fontsize=8.4,\n", " loc=\"upper left\"\n", ")\n", "\n", "\n", "ax2.text(\n", " -0.10,\n", " 1.07,\n", " \"B\",\n", " transform=ax2.transAxes,\n", " fontsize=17,\n", " fontweight=\"bold\",\n", " color=coral\n", ")\n", "\n", "\n", "# ============================================================\n", "# CLEAN STYLE\n", "# ============================================================\n", "\n", "for ax in [ax2]:\n", "\n", " ax.spines[\"top\"].set_visible(False)\n", " ax.spines[\"right\"].set_visible(False)\n", "\n", " ax.spines[\"left\"].set_color(\"#A09B92\")\n", " ax.spines[\"bottom\"].set_color(\"#A09B92\")\n", "\n", " ax.grid(\n", " axis=\"y\",\n", " color=\"#DDD9D0\",\n", " linewidth=0.65,\n", " alpha=0.60,\n", " zorder=0\n", " )\n", "\n", "\n", "fig.subplots_adjust(\n", " wspace=0.30,\n", " bottom=0.18\n", ")\n", "\n", "\n", "# ============================================================\n", "# SAVE FIGURE 8\n", "# ============================================================\n", "\n", "figure_base = (\n", " OUTPUT_DIR\n", " / \"Figure_8_ETD_and_Strength_Loss_Decomposition\"\n", ")\n", "\n", "\n", "plt.savefig(\n", " str(figure_base) + \".png\",\n", " dpi=600,\n", " bbox_inches=\"tight\",\n", " facecolor=fig.get_facecolor()\n", ")\n", "\n", "plt.savefig(\n", " str(figure_base) + \".pdf\",\n", " bbox_inches=\"tight\",\n", " facecolor=fig.get_facecolor()\n", ")\n", "\n", "plt.savefig(\n", " str(figure_base) + \".tiff\",\n", " dpi=600,\n", " bbox_inches=\"tight\",\n", " facecolor=fig.get_facecolor(),\n", " pil_kwargs={\n", " \"compression\": \"tiff_lzw\"\n", " }\n", ")\n", "\n", "\n", "plt.show()\n", "\n", "print(\"✓ FIGURE 8 SAVED\")" ] }, { "cell_type": "code", "execution_count": 131, "id": "a2b2a603-0488-4853-9d74-c882b4f73807", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "============================================================================\n", "STEP 5 — STRENGTH-LOSS DECOMPOSITION / ETD\n", "============================================================================\n", "\n", "INITIAL STRENGTH PENALTY AT 24 °C\n", "RCA 0% : 0.00%\n", "RCA 10% : 0.22%\n", "RCA 20% : 5.63%\n", "RCA 30% : 8.25%\n", "RCA 40% : 19.13%\n", "RCA 50% : 20.41%\n", "\n", "ETD AT 600 °C\n", "RCA 0% : +0.00 percentage points\n", "RCA 10% : +1.51 percentage points\n", "RCA 20% : +0.92 percentage points\n", "RCA 30% : +3.18 percentage points\n", "RCA 40% : -1.62 percentage points\n", "RCA 50% : +0.42 percentage points\n", "\n", "ETD MONOTONICITY\n", "ETD increases monotonically with RCA: False\n", "\n", "600 °C EXACT DECOMPOSITION\n", "RCA 10% | Baseline-related=0.12 | Excess-thermal=+1.51 | Total gap=1.63\n", "RCA 20% | Baseline-related=3.08 | Excess-thermal=+0.87 | Total gap=3.95\n", "RCA 30% | Baseline-related=4.52 | Excess-thermal=+2.92 | Total gap=7.44\n", "RCA 40% | Baseline-related=10.48 | Excess-thermal=-1.31 | Total gap=9.17\n", "RCA 50% | Baseline-related=11.18 | Excess-thermal=+0.34 | Total gap=11.52\n", "\n", "CLOSURE CHECK\n", "Maximum closure error : 1.065814103640e-14\n", "Exact decomposition : True\n", "\n", "LARGEST OBSERVED |ETD| — DESCRIPTIVE ONLY\n", "RCA 40% at 300 °C : -8.99 pp\n", "\n", "SAVED OUTPUTS\n", " - 600C_Strength_Gap_Decomposition.csv\n", " - ETD_All_Temperatures.csv\n", " - Figure_8_ETD_and_Strength_Loss_Decomposition.pdf\n", " - Figure_8_ETD_and_Strength_Loss_Decomposition.png\n", " - Figure_8_ETD_and_Strength_Loss_Decomposition.tiff\n", " - Supplementary_Table_S2_ETD_Decomposition.xlsx\n", "============================================================================\n" ] } ], "source": [ "# ============================================================\n", "# — STEP 5 FINAL AUDIT\n", "# ============================================================\n", "\n", "print(\"=\" * 76)\n", "print(\"STEP 5 — STRENGTH-LOSS DECOMPOSITION / ETD\")\n", "print(\"=\" * 76)\n", "\n", "\n", "# ============================================================\n", "# INITIAL PENALTY\n", "# ============================================================\n", "\n", "print(\"\\nINITIAL STRENGTH PENALTY AT 24 °C\")\n", "\n", "for _, row in (\n", " baseline_penalty\n", " .sort_values(\"RCA_pct\")\n", " .iterrows()\n", "):\n", "\n", " print(\n", " f\"RCA {int(row['RCA_pct']):>2}% : \"\n", " f\"{row['Initial_Strength_Penalty_pct']:.2f}%\"\n", " )\n", "\n", "\n", "# ============================================================\n", "# 600 °C ETD\n", "# ============================================================\n", "\n", "print(\"\\nETD AT 600 °C\")\n", "\n", "etd_600 = (\n", " etd[\n", " etd[\"Temperature_C\"] == 600\n", " ]\n", " .sort_values(\"RCA_pct\")\n", ")\n", "\n", "for _, row in etd_600.iterrows():\n", "\n", " print(\n", " f\"RCA {int(row['RCA_pct']):>2}% : \"\n", " f\"{row['ETD_pp']:+.2f} percentage points\"\n", " )\n", "\n", "\n", "# ============================================================\n", "# MONOTONICITY\n", "# ============================================================\n", "\n", "etd_noncontrol = (\n", " etd_600[\n", " etd_600[\"RCA_pct\"] > 0\n", " ][\"ETD_pp\"]\n", " .values\n", ")\n", "\n", "etd_monotonic = np.all(\n", " np.diff(etd_noncontrol) >= 0\n", ")\n", "\n", "print(\"\\nETD MONOTONICITY\")\n", "\n", "print(\n", " \"ETD increases monotonically with RCA:\",\n", " etd_monotonic\n", ")\n", "\n", "\n", "# ============================================================\n", "# EXACT DECOMPOSITION CHECK\n", "# ============================================================\n", "\n", "print(\"\\n600 °C EXACT DECOMPOSITION\")\n", "\n", "for _, row in (\n", " decomp_600[\n", " decomp_600[\"RCA_pct\"] > 0\n", " ]\n", " .sort_values(\"RCA_pct\")\n", " .iterrows()\n", "):\n", "\n", " print(\n", " f\"RCA {int(row['RCA_pct']):>2}% | \"\n", " f\"Baseline-related={row['Baseline_Related_Component_pct']:.2f} | \"\n", " f\"Excess-thermal={row['Excess_Thermal_Component_pct']:+.2f} | \"\n", " f\"Total gap={row['Observed_Control_RCA_Gap_pct']:.2f}\"\n", " )\n", "\n", "\n", "print(\"\\nCLOSURE CHECK\")\n", "\n", "print(\n", " f\"Maximum closure error : \"\n", " f\"{max_closure_error:.12e}\"\n", ")\n", "\n", "print(\n", " \"Exact decomposition :\",\n", " max_closure_error < 1e-10\n", ")\n", "\n", "\n", "# ============================================================\n", "# DESCRIPTIVE EXTREME — DO NOT INTERPRET AS SIGNIFICANCE\n", "# ============================================================\n", "\n", "nonbaseline_etd = etd[\n", " etd[\"Temperature_C\"] > BASELINE_TEMP\n", "]\n", "\n", "max_abs_idx = (\n", " nonbaseline_etd[\"ETD_pp\"]\n", " .abs()\n", " .idxmax()\n", ")\n", "\n", "extreme_row = (\n", " nonbaseline_etd.loc[\n", " max_abs_idx\n", " ]\n", ")\n", "\n", "print(\"\\nLARGEST OBSERVED |ETD| — DESCRIPTIVE ONLY\")\n", "\n", "print(\n", " f\"RCA {int(extreme_row['RCA_pct'])}% at \"\n", " f\"{int(extreme_row['Temperature_C'])} °C : \"\n", " f\"{extreme_row['ETD_pp']:+.2f} pp\"\n", ")\n", "\n", "\n", "print(\"\\nSAVED OUTPUTS\")\n", "\n", "for file in sorted(\n", " OUTPUT_DIR.iterdir()\n", "):\n", " print(\" -\", file.name)\n", "\n", "\n", "print(\"=\" * 76)" ] }, { "cell_type": "markdown", "id": "a9ef14d3-9092-405d-a3b4-f3a84488c502", "metadata": {}, "source": [ "#STEP6" ] }, { "cell_type": "code", "execution_count": 133, "id": "0b8a32f4-fb4a-49c6-bac3-53a8dbc2c7b3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "✓ STEP 6 DATA CHECK PASSED\n", "N = 90\n", "Output: C:\\Users\\sadal\\Desktop\\Yasin Hoca-RCA Thermal\\Analysis_Outputs\\Step_06_Thermal_Resistance_AUC\n" ] } ], "source": [ "# ============================================================\n", "# STEP 6 — HEAT RESISTANCE / NORMALIZED AUC\n", "# — SETUP AND DATA RELOAD\n", "# ============================================================\n", "\n", "from pathlib import Path\n", "import re\n", "import warnings\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "from matplotlib.patches import Patch\n", "\n", "warnings.filterwarnings(\"ignore\")\n", "\n", "\n", "DATA_PATH = Path(\n", " r\"C:\\Users\\sadal\\Desktop\\Yasin Hoca-RCA Thermal\\data.xlsx\"\n", ")\n", "\n", "OUTPUT_DIR = (\n", " DATA_PATH.parent\n", " / \"Analysis_Outputs\"\n", " / \"Step_06_Thermal_Resistance_AUC\"\n", ")\n", "\n", "OUTPUT_DIR.mkdir(\n", " parents=True,\n", " exist_ok=True\n", ")\n", "\n", "assert DATA_PATH.exists()\n", "\n", "\n", "# ============================================================\n", "# READ\n", "# ============================================================\n", "\n", "raw = pd.read_excel(\n", " DATA_PATH,\n", " sheet_name=\"data\",\n", " header=None\n", ")\n", "\n", "temperature_headers = raw.iloc[0, 1:].astype(str)\n", "\n", "temperatures = (\n", " temperature_headers\n", " .str.extract(r\"(\\d+)\")[0]\n", " .astype(int)\n", " .tolist()\n", ")\n", "\n", "\n", "records = []\n", "\n", "for _, row in raw.iloc[1:].iterrows():\n", "\n", " if pd.isna(row.iloc[0]):\n", " continue\n", "\n", " mix_label = str(row.iloc[0])\n", "\n", " rca_match = re.search(\n", " r\"(\\d+)\",\n", " mix_label\n", " )\n", "\n", " if rca_match is None:\n", " raise ValueError(\n", " f\"RCA level could not be identified: {mix_label}\"\n", " )\n", "\n", " rca_pct = int(\n", " rca_match.group(1)\n", " )\n", "\n", " replicate_counter = {}\n", "\n", " for col_index, temperature in enumerate(\n", " temperatures,\n", " start=1\n", " ):\n", "\n", " replicate_counter[temperature] = (\n", " replicate_counter.get(temperature, 0) + 1\n", " )\n", "\n", " value = pd.to_numeric(\n", " row.iloc[col_index],\n", " errors=\"coerce\"\n", " )\n", "\n", " if pd.isna(value):\n", " raise ValueError(\n", " f\"Missing measurement: \"\n", " f\"RCA={rca_pct}, T={temperature}\"\n", " )\n", "\n", " records.append({\n", " \"RCA_pct\": rca_pct,\n", " \"Temperature_C\": temperature,\n", " \"Replicate\": replicate_counter[temperature],\n", " \"CompressiveStrength_MPa\": float(value)\n", " })\n", "\n", "\n", "df_auc = pd.DataFrame(records)\n", "\n", "\n", "cell_counts = (\n", " df_auc.groupby(\n", " [\"RCA_pct\", \"Temperature_C\"]\n", " )\n", " .size()\n", ")\n", "\n", "assert len(df_auc) == 90\n", "assert cell_counts.nunique() == 1\n", "assert cell_counts.iloc[0] == 3\n", "\n", "print(\"✓ STEP 6 DATA CHECK PASSED\")\n", "print(\"N =\", len(df_auc))\n", "print(\"Output:\", OUTPUT_DIR)" ] }, { "cell_type": "code", "execution_count": 135, "id": "09f8879b-063a-4a07-a972-ee9d812745dd", "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>RCA_pct</th>\n", " <th>Temperature_C</th>\n", " <th>Mean_MPa</th>\n", " <th>Retention_pct</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>24</td>\n", " <td>21.094</td>\n", " <td>100.000</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0</td>\n", " <td>150</td>\n", " <td>20.152</td>\n", " <td>95.534</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0</td>\n", " <td>300</td>\n", " <td>17.650</td>\n", " <td>83.672</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0</td>\n", " <td>450</td>\n", " <td>15.500</td>\n", " <td>73.478</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0</td>\n", " <td>600</td>\n", " <td>11.560</td>\n", " <td>54.800</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>10</td>\n", " <td>24</td>\n", " <td>21.047</td>\n", " <td>100.000</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>10</td>\n", " <td>150</td>\n", " <td>20.122</td>\n", " <td>95.604</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>10</td>\n", " <td>300</td>\n", " <td>17.306</td>\n", " <td>82.223</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>10</td>\n", " <td>450</td>\n", " <td>15.328</td>\n", " <td>72.825</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>10</td>\n", " <td>600</td>\n", " <td>11.215</td>\n", " <td>53.286</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>20</td>\n", " <td>24</td>\n", " <td>19.908</td>\n", " <td>100.000</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>20</td>\n", " <td>150</td>\n", " <td>19.881</td>\n", " <td>99.864</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>20</td>\n", " <td>300</td>\n", " <td>17.172</td>\n", " <td>86.257</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>20</td>\n", " <td>450</td>\n", " <td>14.344</td>\n", " <td>72.053</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>20</td>\n", " <td>600</td>\n", " <td>10.726</td>\n", " <td>53.880</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>30</td>\n", " <td>24</td>\n", " <td>19.354</td>\n", " <td>100.000</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>30</td>\n", " <td>150</td>\n", " <td>18.241</td>\n", " <td>94.249</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>30</td>\n", " <td>300</td>\n", " <td>17.016</td>\n", " <td>87.918</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>30</td>\n", " <td>450</td>\n", " <td>14.148</td>\n", " <td>73.103</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>30</td>\n", " <td>600</td>\n", " <td>9.990</td>\n", " <td>51.617</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>40</td>\n", " <td>24</td>\n", " <td>17.059</td>\n", " <td>100.000</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>40</td>\n", " <td>150</td>\n", " <td>16.984</td>\n", " <td>99.560</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>40</td>\n", " <td>300</td>\n", " <td>15.807</td>\n", " <td>92.661</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>40</td>\n", " <td>450</td>\n", " <td>13.995</td>\n", " <td>82.038</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>40</td>\n", " <td>600</td>\n", " <td>9.624</td>\n", " <td>56.419</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>50</td>\n", " <td>24</td>\n", " <td>16.790</td>\n", " <td>100.000</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>50</td>\n", " <td>150</td>\n", " <td>16.398</td>\n", " <td>97.665</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>50</td>\n", " <td>300</td>\n", " <td>14.634</td>\n", " <td>87.163</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>50</td>\n", " <td>450</td>\n", " <td>12.366</td>\n", " <td>73.652</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>50</td>\n", " <td>600</td>\n", " <td>9.130</td>\n", " <td>54.377</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " RCA_pct Temperature_C Mean_MPa Retention_pct\n", "0 0 24 21.094 100.000\n", "1 0 150 20.152 95.534\n", "2 0 300 17.650 83.672\n", "3 0 450 15.500 73.478\n", "4 0 600 11.560 54.800\n", "5 10 24 21.047 100.000\n", "6 10 150 20.122 95.604\n", "7 10 300 17.306 82.223\n", "8 10 450 15.328 72.825\n", "9 10 600 11.215 53.286\n", "10 20 24 19.908 100.000\n", "11 20 150 19.881 99.864\n", "12 20 300 17.172 86.257\n", "13 20 450 14.344 72.053\n", "14 20 600 10.726 53.880\n", "15 30 24 19.354 100.000\n", "16 30 150 18.241 94.249\n", "17 30 300 17.016 87.918\n", "18 30 450 14.148 73.103\n", "19 30 600 9.990 51.617\n", "20 40 24 17.059 100.000\n", "21 40 150 16.984 99.560\n", "22 40 300 15.807 92.661\n", "23 40 450 13.995 82.038\n", "24 40 600 9.624 56.419\n", "25 50 24 16.790 100.000\n", "26 50 150 16.398 97.665\n", "27 50 300 14.634 87.163\n", "28 50 450 12.366 73.652\n", "29 50 600 9.130 54.377" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# ============================================================\n", "# — NORMALIZED RESPONSE CURVES\n", "# ============================================================\n", "\n", "BASELINE_TEMP = 24\n", "CONTROL_RCA = 0\n", "\n", "\n", "strength_summary = (\n", " df_auc\n", " .groupby(\n", " [\"RCA_pct\", \"Temperature_C\"],\n", " as_index=False\n", " )\n", " .agg(\n", " N=(\"CompressiveStrength_MPa\", \"count\"),\n", " Mean_MPa=(\"CompressiveStrength_MPa\", \"mean\"),\n", " SD_MPa=(\"CompressiveStrength_MPa\", \"std\")\n", " )\n", ")\n", "\n", "\n", "baseline = (\n", " strength_summary[\n", " strength_summary[\"Temperature_C\"] == BASELINE_TEMP\n", " ][\n", " [\n", " \"RCA_pct\",\n", " \"Mean_MPa\"\n", " ]\n", " ]\n", " .rename(\n", " columns={\n", " \"Mean_MPa\":\n", " \"Baseline_Mean_MPa\"\n", " }\n", " )\n", ")\n", "\n", "\n", "normalized = strength_summary.merge(\n", " baseline,\n", " on=\"RCA_pct\",\n", " how=\"left\"\n", ")\n", "\n", "\n", "normalized[\"Retention_fraction\"] = (\n", " normalized[\"Mean_MPa\"]\n", " / normalized[\"Baseline_Mean_MPa\"]\n", ")\n", "\n", "\n", "normalized[\"Retention_pct\"] = (\n", " 100\n", " * normalized[\"Retention_fraction\"]\n", ")\n", "\n", "\n", "normalized[\"Thermal_Damage_fraction\"] = (\n", " 1\n", " - normalized[\"Retention_fraction\"]\n", ")\n", "\n", "\n", "display(\n", " normalized[\n", " [\n", " \"RCA_pct\",\n", " \"Temperature_C\",\n", " \"Mean_MPa\",\n", " \"Retention_pct\"\n", " ]\n", " ].round(3)\n", ")" ] }, { "cell_type": "code", "execution_count": 137, "id": "4c4ff2bc-9766-4512-afc6-a83fb04871c2", "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>RCA_pct</th>\n", " <th>AUC_fraction_degC</th>\n", " <th>TRI</th>\n", " <th>Integrated_Damage_Index</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>471.6617</td>\n", " <td>0.8189</td>\n", " <td>0.1811</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>10</td>\n", " <td>467.4686</td>\n", " <td>0.8116</td>\n", " <td>0.1884</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>20</td>\n", " <td>478.6869</td>\n", " <td>0.8311</td>\n", " <td>0.1689</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>30</td>\n", " <td>473.3084</td>\n", " <td>0.8217</td>\n", " <td>0.1783</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>40</td>\n", " <td>504.7562</td>\n", " <td>0.8763</td>\n", " <td>0.1237</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>50</td>\n", " <td>479.7833</td>\n", " <td>0.8330</td>\n", " <td>0.1670</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " RCA_pct AUC_fraction_degC TRI Integrated_Damage_Index\n", "0 0 471.6617 0.8189 0.1811\n", "1 10 467.4686 0.8116 0.1884\n", "2 20 478.6869 0.8311 0.1689\n", "3 30 473.3084 0.8217 0.1783\n", "4 40 504.7562 0.8763 0.1237\n", "5 50 479.7833 0.8330 0.1670" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# ============================================================\n", "# — AUC / THERMAL RESISTANCE INDEX\n", "# ============================================================\n", "\n", "temperature_axis = np.array(\n", " sorted(\n", " normalized[\"Temperature_C\"].unique()\n", " ),\n", " dtype=float\n", ")\n", "\n", "T_MIN = temperature_axis.min()\n", "T_MAX = temperature_axis.max()\n", "\n", "temperature_range = (\n", " T_MAX - T_MIN\n", ")\n", "\n", "\n", "# Compatibility between NumPy versions\n", "if hasattr(np, \"trapezoid\"):\n", " trapz_function = np.trapezoid\n", "else:\n", " trapz_function = np.trapz\n", "\n", "\n", "tri_records = []\n", "interval_records = []\n", "\n", "\n", "for rca in sorted(\n", " normalized[\"RCA_pct\"].unique()\n", "):\n", "\n", " sub = (\n", " normalized[\n", " normalized[\"RCA_pct\"] == rca\n", " ]\n", " .sort_values(\"Temperature_C\")\n", " )\n", "\n", " x = sub[\n", " \"Temperature_C\"\n", " ].values.astype(float)\n", "\n", " y = sub[\n", " \"Retention_fraction\"\n", " ].values.astype(float)\n", "\n", "\n", " # Raw area\n", " auc = trapz_function(\n", " y,\n", " x\n", " )\n", "\n", "\n", " # Normalized area\n", " TRI = (\n", " auc / temperature_range\n", " )\n", "\n", "\n", " tri_records.append({\n", " \"RCA_pct\": rca,\n", " \"AUC_fraction_degC\": auc,\n", " \"TRI\": TRI,\n", " \"Integrated_Damage_Index\":\n", " 1 - TRI\n", " })\n", "\n", "\n", " # ========================================================\n", " # CONTRIBUTION OF EACH TEMPERATURE INTERVAL\n", " # ========================================================\n", "\n", " for i in range(\n", " len(x) - 1\n", " ):\n", "\n", " delta_T = (\n", " x[i + 1] - x[i]\n", " )\n", "\n", " interval_area = (\n", " (y[i] + y[i + 1])\n", " / 2\n", " * delta_T\n", " )\n", "\n", " normalized_contribution = (\n", " interval_area\n", " / temperature_range\n", " )\n", "\n", " interval_records.append({\n", " \"RCA_pct\": rca,\n", " \"Interval\":\n", " f\"{int(x[i])}–{int(x[i+1])} °C\",\n", " \"T_start\": x[i],\n", " \"T_end\": x[i + 1],\n", " \"Interval_AUC\": interval_area,\n", " \"TRI_Contribution\":\n", " normalized_contribution\n", " })\n", "\n", "\n", "tri_summary = pd.DataFrame(\n", " tri_records\n", ")\n", "\n", "interval_contributions = pd.DataFrame(\n", " interval_records\n", ")\n", "\n", "\n", "display(\n", " tri_summary.round(4)\n", ")" ] }, { "cell_type": "code", "execution_count": 139, "id": "76b6e165-aef6-440f-9591-5fa09f602420", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AUC CLOSURE ERRORS\n", "RCA_pct\n", "0 0.000000e+00\n", "10 1.110223e-16\n", "20 0.000000e+00\n", "30 -1.110223e-16\n", "40 0.000000e+00\n", "50 0.000000e+00\n", "dtype: float64\n", "\n", "✓ AUC DECOMPOSITION CHECK PASSED\n" ] } ], "source": [ "# ============================================================\n", "# — AUC CLOSURE CHECK\n", "# ============================================================\n", "\n", "interval_sum = (\n", " interval_contributions\n", " .groupby(\"RCA_pct\")[\n", " \"TRI_Contribution\"\n", " ]\n", " .sum()\n", ")\n", "\n", "\n", "closure_check = (\n", " tri_summary\n", " .set_index(\"RCA_pct\")[\"TRI\"]\n", " - interval_sum\n", ")\n", "\n", "\n", "print(\"AUC CLOSURE ERRORS\")\n", "print(\n", " closure_check\n", ")\n", "\n", "max_auc_closure_error = (\n", " closure_check\n", " .abs()\n", " .max()\n", ")\n", "\n", "\n", "assert max_auc_closure_error < 1e-12\n", "\n", "print()\n", "print(\"✓ AUC DECOMPOSITION CHECK PASSED\")" ] }, { "cell_type": "code", "execution_count": 141, "id": "fb439153-18cf-4841-b9a0-799c35f4ad0d", "metadata": {}, "outputs": [], "source": [ "# ============================================================\n", "# — NONPARAMETRIC BOOTSTRAP FOR TRI\n", "# ============================================================\n", "\n", "N_BOOT = 20000\n", "RANDOM_SEED = 20260806\n", "\n", "rng = np.random.default_rng(\n", " RANDOM_SEED\n", ")\n", "\n", "\n", "def bootstrap_tri(\n", " data,\n", " rca_value,\n", " temperatures,\n", " n_boot,\n", " rng\n", "):\n", "\n", " sub = data[\n", " data[\"RCA_pct\"] == rca_value\n", " ]\n", "\n", " bootstrap_means = []\n", "\n", "\n", " for temp in temperatures:\n", "\n", " values = (\n", " sub[\n", " sub[\"Temperature_C\"] == temp\n", " ][\"CompressiveStrength_MPa\"]\n", " .values.astype(float)\n", " )\n", "\n", " n_obs = len(values)\n", "\n", " indices = rng.integers(\n", " low=0,\n", " high=n_obs,\n", " size=(n_boot, n_obs)\n", " )\n", "\n", " sampled_values = (\n", " values[indices]\n", " )\n", "\n", " sampled_means = (\n", " sampled_values.mean(axis=1)\n", " )\n", "\n", " bootstrap_means.append(\n", " sampled_means\n", " )\n", "\n", "\n", " # Shape:\n", " # n_boot × temperatures\n", " bootstrap_means = np.column_stack(\n", " bootstrap_means\n", " )\n", "\n", "\n", " # Same bootstrap baseline denominator\n", " bootstrap_retention = (\n", " bootstrap_means\n", " / bootstrap_means[:, [0]]\n", " )\n", "\n", "\n", " # Integrate each bootstrap curve\n", " bootstrap_auc = trapz_function(\n", " bootstrap_retention,\n", " temperatures,\n", " axis=1\n", " )\n", "\n", "\n", " bootstrap_tri_values = (\n", " bootstrap_auc\n", " / (\n", " temperatures[-1]\n", " - temperatures[0]\n", " )\n", " )\n", "\n", "\n", " return bootstrap_tri_values" ] }, { "cell_type": "code", "execution_count": 143, "id": "4ca721e0-d14b-4fc4-b28f-44ac2c6eacf2", "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>RCA_pct</th>\n", " <th>AUC_fraction_degC</th>\n", " <th>TRI</th>\n", " <th>Integrated_Damage_Index</th>\n", " <th>Bootstrap_Mean_TRI</th>\n", " <th>Bootstrap_SE</th>\n", " <th>CI95_Lower</th>\n", " <th>CI95_Upper</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>471.6617</td>\n", " <td>0.8189</td>\n", " <td>0.1811</td>\n", " <td>0.8197</td>\n", " <td>0.0214</td>\n", " <td>0.7760</td>\n", " <td>0.8564</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>10</td>\n", " <td>467.4686</td>\n", " <td>0.8116</td>\n", " <td>0.1884</td>\n", " <td>0.8116</td>\n", " <td>0.0126</td>\n", " <td>0.7866</td>\n", " <td>0.8351</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>20</td>\n", " <td>478.6869</td>\n", " <td>0.8311</td>\n", " <td>0.1689</td>\n", " <td>0.8320</td>\n", " <td>0.0235</td>\n", " <td>0.7865</td>\n", " <td>0.8776</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>30</td>\n", " <td>473.3084</td>\n", " <td>0.8217</td>\n", " <td>0.1783</td>\n", " <td>0.8236</td>\n", " <td>0.0433</td>\n", " <td>0.7540</td>\n", " <td>0.9264</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>40</td>\n", " <td>504.7562</td>\n", " <td>0.8763</td>\n", " <td>0.1237</td>\n", " <td>0.8774</td>\n", " <td>0.0345</td>\n", " <td>0.8167</td>\n", " <td>0.9504</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>50</td>\n", " <td>479.7833</td>\n", " <td>0.8330</td>\n", " <td>0.1670</td>\n", " <td>0.8329</td>\n", " <td>0.0182</td>\n", " <td>0.7999</td>\n", " <td>0.8709</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " RCA_pct AUC_fraction_degC TRI Integrated_Damage_Index \\\n", "0 0 471.6617 0.8189 0.1811 \n", "1 10 467.4686 0.8116 0.1884 \n", "2 20 478.6869 0.8311 0.1689 \n", "3 30 473.3084 0.8217 0.1783 \n", "4 40 504.7562 0.8763 0.1237 \n", "5 50 479.7833 0.8330 0.1670 \n", "\n", " Bootstrap_Mean_TRI Bootstrap_SE CI95_Lower CI95_Upper \n", "0 0.8197 0.0214 0.7760 0.8564 \n", "1 0.8116 0.0126 0.7866 0.8351 \n", "2 0.8320 0.0235 0.7865 0.8776 \n", "3 0.8236 0.0433 0.7540 0.9264 \n", "4 0.8774 0.0345 0.8167 0.9504 \n", "5 0.8329 0.0182 0.7999 0.8709 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# ============================================================\n", "# RUN BOOTSTRAP\n", "# ============================================================\n", "\n", "bootstrap_records = []\n", "\n", "bootstrap_distributions = {}\n", "\n", "\n", "for rca in sorted(\n", " df_auc[\"RCA_pct\"].unique()\n", "):\n", "\n", " boot_values = bootstrap_tri(\n", " data=df_auc,\n", " rca_value=rca,\n", " temperatures=temperature_axis,\n", " n_boot=N_BOOT,\n", " rng=rng\n", " )\n", "\n", "\n", " bootstrap_distributions[rca] = (\n", " boot_values\n", " )\n", "\n", "\n", " bootstrap_records.append({\n", " \"RCA_pct\": rca,\n", "\n", " \"Bootstrap_Mean_TRI\":\n", " np.mean(boot_values),\n", "\n", " \"Bootstrap_SE\":\n", " np.std(\n", " boot_values,\n", " ddof=1\n", " ),\n", "\n", " \"CI95_Lower\":\n", " np.percentile(\n", " boot_values,\n", " 2.5\n", " ),\n", "\n", " \"CI95_Upper\":\n", " np.percentile(\n", " boot_values,\n", " 97.5\n", " )\n", " })\n", "\n", "\n", "bootstrap_summary = pd.DataFrame(\n", " bootstrap_records\n", ")\n", "\n", "\n", "tri_summary = tri_summary.merge(\n", " bootstrap_summary,\n", " on=\"RCA_pct\",\n", " how=\"left\"\n", ")\n", "\n", "\n", "display(\n", " tri_summary.round(4)\n", ")" ] }, { "cell_type": "code", "execution_count": 145, "id": "4d3357e1-8214-4495-9b49-06608892164e", "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>RCA_pct</th>\n", " <th>Baseline_Mean_MPa</th>\n", " <th>Initial_Strength_Penalty_pct</th>\n", " <th>Retention_600C_pct</th>\n", " <th>ETD_600C_pp</th>\n", " <th>AUC_fraction_degC</th>\n", " <th>TRI</th>\n", " <th>Integrated_Damage_Index</th>\n", " <th>Bootstrap_Mean_TRI</th>\n", " <th>Bootstrap_SE</th>\n", " <th>CI95_Lower</th>\n", " <th>CI95_Upper</th>\n", " <th>Delta_TRI_vs_RCA0_pp</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>21.0943</td>\n", " <td>0.0000</td>\n", " <td>54.7999</td>\n", " <td>0.0000</td>\n", " <td>471.6617</td>\n", " <td>0.8189</td>\n", " <td>0.1811</td>\n", " <td>0.8197</td>\n", " <td>0.0214</td>\n", " <td>0.7760</td>\n", " <td>0.8564</td>\n", " <td>0.0000</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>10</td>\n", " <td>21.0473</td>\n", " <td>0.2228</td>\n", " <td>53.2862</td>\n", " <td>1.5136</td>\n", " <td>467.4686</td>\n", " <td>0.8116</td>\n", " <td>0.1884</td>\n", " <td>0.8116</td>\n", " <td>0.0126</td>\n", " <td>0.7866</td>\n", " <td>0.8351</td>\n", " <td>-0.7280</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>20</td>\n", " <td>19.9077</td>\n", " <td>5.6255</td>\n", " <td>53.8804</td>\n", " <td>0.9195</td>\n", " <td>478.6869</td>\n", " <td>0.8311</td>\n", " <td>0.1689</td>\n", " <td>0.8320</td>\n", " <td>0.0235</td>\n", " <td>0.7865</td>\n", " <td>0.8776</td>\n", " <td>1.2197</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>30</td>\n", " <td>19.3540</td>\n", " <td>8.2502</td>\n", " <td>51.6172</td>\n", " <td>3.1826</td>\n", " <td>473.3084</td>\n", " <td>0.8217</td>\n", " <td>0.1783</td>\n", " <td>0.8236</td>\n", " <td>0.0433</td>\n", " <td>0.7540</td>\n", " <td>0.9264</td>\n", " <td>0.2859</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>40</td>\n", " <td>17.0587</td>\n", " <td>19.1315</td>\n", " <td>56.4190</td>\n", " <td>-1.6192</td>\n", " <td>504.7562</td>\n", " <td>0.8763</td>\n", " <td>0.1237</td>\n", " <td>0.8774</td>\n", " <td>0.0345</td>\n", " <td>0.8167</td>\n", " <td>0.9504</td>\n", " <td>5.7456</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>50</td>\n", " <td>16.7897</td>\n", " <td>20.4067</td>\n", " <td>54.3767</td>\n", " <td>0.4232</td>\n", " <td>479.7833</td>\n", " <td>0.8330</td>\n", " <td>0.1670</td>\n", " <td>0.8329</td>\n", " <td>0.0182</td>\n", " <td>0.7999</td>\n", " <td>0.8709</td>\n", " <td>1.4100</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " RCA_pct Baseline_Mean_MPa Initial_Strength_Penalty_pct \\\n", "0 0 21.0943 0.0000 \n", "1 10 21.0473 0.2228 \n", "2 20 19.9077 5.6255 \n", "3 30 19.3540 8.2502 \n", "4 40 17.0587 19.1315 \n", "5 50 16.7897 20.4067 \n", "\n", " Retention_600C_pct ETD_600C_pp AUC_fraction_degC TRI \\\n", "0 54.7999 0.0000 471.6617 0.8189 \n", "1 53.2862 1.5136 467.4686 0.8116 \n", "2 53.8804 0.9195 478.6869 0.8311 \n", "3 51.6172 3.1826 473.3084 0.8217 \n", "4 56.4190 -1.6192 504.7562 0.8763 \n", "5 54.3767 0.4232 479.7833 0.8330 \n", "\n", " Integrated_Damage_Index Bootstrap_Mean_TRI Bootstrap_SE CI95_Lower \\\n", "0 0.1811 0.8197 0.0214 0.7760 \n", "1 0.1884 0.8116 0.0126 0.7866 \n", "2 0.1689 0.8320 0.0235 0.7865 \n", "3 0.1783 0.8236 0.0433 0.7540 \n", "4 0.1237 0.8774 0.0345 0.8167 \n", "5 0.1670 0.8329 0.0182 0.7999 \n", "\n", " CI95_Upper Delta_TRI_vs_RCA0_pp \n", "0 0.8564 0.0000 \n", "1 0.8351 -0.7280 \n", "2 0.8776 1.2197 \n", "3 0.9264 0.2859 \n", "4 0.9504 5.7456 \n", "5 0.8709 1.4100 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# ============================================================\n", "# — BUILD TABLE 7\n", "# ============================================================\n", "\n", "# Control strength at ambient\n", "fc_control_24 = float(\n", " strength_summary.loc[\n", " (\n", " (strength_summary[\"RCA_pct\"] == 0)\n", " &\n", " (\n", " strength_summary[\"Temperature_C\"]\n", " == 24\n", " )\n", " ),\n", " \"Mean_MPa\"\n", " ].iloc[0]\n", ")\n", "\n", "\n", "# Baseline penalty\n", "baseline_metrics = baseline.copy()\n", "\n", "baseline_metrics[\n", " \"Initial_Strength_Penalty_pct\"\n", "] = (\n", " 100\n", " * (\n", " 1\n", " - baseline_metrics[\n", " \"Baseline_Mean_MPa\"\n", " ]\n", " / fc_control_24\n", " )\n", ")\n", "\n", "\n", "# ============================================================\n", "# 600 °C RETENTION\n", "# ============================================================\n", "\n", "ret_600 = (\n", " normalized[\n", " normalized[\"Temperature_C\"] == 600\n", " ][\n", " [\n", " \"RCA_pct\",\n", " \"Retention_pct\"\n", " ]\n", " ]\n", " .rename(\n", " columns={\n", " \"Retention_pct\":\n", " \"Retention_600C_pct\"\n", " }\n", " )\n", ")\n", "\n", "\n", "control_ret_600 = float(\n", " ret_600.loc[\n", " ret_600[\"RCA_pct\"] == 0,\n", " \"Retention_600C_pct\"\n", " ].iloc[0]\n", ")\n", "\n", "\n", "ret_600[\n", " \"ETD_600C_pp\"\n", "] = (\n", " control_ret_600\n", " - ret_600[\n", " \"Retention_600C_pct\"\n", " ]\n", ")\n", "\n", "\n", "# ============================================================\n", "# MERGE\n", "# ============================================================\n", "\n", "table7_numeric = (\n", " baseline_metrics[\n", " [\n", " \"RCA_pct\",\n", " \"Baseline_Mean_MPa\",\n", " \"Initial_Strength_Penalty_pct\"\n", " ]\n", " ]\n", " .merge(\n", " ret_600,\n", " on=\"RCA_pct\"\n", " )\n", " .merge(\n", " tri_summary,\n", " on=\"RCA_pct\"\n", " )\n", ")\n", "\n", "\n", "# Difference from RCA0 TRI\n", "TRI_control = float(\n", " table7_numeric.loc[\n", " table7_numeric[\"RCA_pct\"] == 0,\n", " \"TRI\"\n", " ].iloc[0]\n", ")\n", "\n", "\n", "table7_numeric[\n", " \"Delta_TRI_vs_RCA0_pp\"\n", "] = (\n", " 100\n", " * (\n", " table7_numeric[\"TRI\"]\n", " - TRI_control\n", " )\n", ")\n", "\n", "\n", "display(\n", " table7_numeric.round(4)\n", ")" ] }, { "cell_type": "code", "execution_count": 155, "id": "7d6e4785-c311-47f1-a600-89c5e4f98eef", "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>RCA (%)</th>\n", " <th>fc,24 (MPa)</th>\n", " <th>Initial penalty (%)</th>\n", " <th>Retention at 600 °C (%)</th>\n", " <th>ETD at 600 °C (pp)</th>\n", " <th>AUC (fraction·°C)</th>\n", " <th>TRI</th>\n", " <th>TRI 95% bootstrap CI</th>\n", " <th>Integrated damage (1−TRI)</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>21.09</td>\n", " <td>0.00</td>\n", " <td>54.80</td>\n", " <td>0.00</td>\n", " <td>471.66</td>\n", " <td>0.819</td>\n", " <td>0.776–0.856</td>\n", " <td>0.181</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>10</td>\n", " <td>21.05</td>\n", " <td>0.22</td>\n", " <td>53.29</td>\n", " <td>1.51</td>\n", " <td>467.47</td>\n", " <td>0.812</td>\n", " <td>0.787–0.835</td>\n", " <td>0.188</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>20</td>\n", " <td>19.91</td>\n", " <td>5.63</td>\n", " <td>53.88</td>\n", " <td>0.92</td>\n", " <td>478.69</td>\n", " <td>0.831</td>\n", " <td>0.787–0.878</td>\n", " <td>0.169</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>30</td>\n", " <td>19.35</td>\n", " <td>8.25</td>\n", " <td>51.62</td>\n", " <td>3.18</td>\n", " <td>473.31</td>\n", " <td>0.822</td>\n", " <td>0.754–0.926</td>\n", " <td>0.178</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>40</td>\n", " <td>17.06</td>\n", " <td>19.13</td>\n", " <td>56.42</td>\n", " <td>-1.62</td>\n", " <td>504.76</td>\n", " <td>0.876</td>\n", " <td>0.817–0.950</td>\n", " <td>0.124</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>50</td>\n", " <td>16.79</td>\n", " <td>20.41</td>\n", " <td>54.38</td>\n", " <td>0.42</td>\n", " <td>479.78</td>\n", " <td>0.833</td>\n", " <td>0.800–0.871</td>\n", " <td>0.167</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " RCA (%) fc,24 (MPa) Initial penalty (%) Retention at 600 °C (%) \\\n", "0 0 21.09 0.00 54.80 \n", "1 10 21.05 0.22 53.29 \n", "2 20 19.91 5.63 53.88 \n", "3 30 19.35 8.25 51.62 \n", "4 40 17.06 19.13 56.42 \n", "5 50 16.79 20.41 54.38 \n", "\n", " ETD at 600 °C (pp) AUC (fraction·°C) TRI TRI 95% bootstrap CI \\\n", "0 0.00 471.66 0.819 0.776–0.856 \n", "1 1.51 467.47 0.812 0.787–0.835 \n", "2 0.92 478.69 0.831 0.787–0.878 \n", "3 3.18 473.31 0.822 0.754–0.926 \n", "4 -1.62 504.76 0.876 0.817–0.950 \n", "5 0.42 479.78 0.833 0.800–0.871 \n", "\n", " Integrated damage (1−TRI) \n", "0 0.181 \n", "1 0.188 \n", "2 0.169 \n", "3 0.178 \n", "4 0.124 \n", "5 0.167 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# ============================================================\n", "# — PUBLICATION TABLE 7\n", "# ============================================================\n", "\n", "table7_publication = pd.DataFrame({\n", "\n", " \"RCA (%)\":\n", " table7_numeric[\"RCA_pct\"],\n", "\n", " \"fc,24 (MPa)\":\n", " table7_numeric[\n", " \"Baseline_Mean_MPa\"\n", " ],\n", "\n", " \"Initial penalty (%)\":\n", " table7_numeric[\n", " \"Initial_Strength_Penalty_pct\"\n", " ],\n", "\n", " \"Retention at 600 °C (%)\":\n", " table7_numeric[\n", " \"Retention_600C_pct\"\n", " ],\n", "\n", " \"ETD at 600 °C (pp)\":\n", " table7_numeric[\n", " \"ETD_600C_pp\"\n", " ],\n", "\n", " \"AUC (fraction·°C)\":\n", " table7_numeric[\n", " \"AUC_fraction_degC\"\n", " ],\n", "\n", " \"TRI\":\n", " table7_numeric[\"TRI\"],\n", "\n", " \"TRI 95% bootstrap CI\":\n", " table7_numeric.apply(\n", " lambda r:\n", " (\n", " f\"{r['CI95_Lower']:.3f}\"\n", " f\"–\"\n", " f\"{r['CI95_Upper']:.3f}\"\n", " ),\n", " axis=1\n", " ),\n", "\n", " \"Integrated damage (1−TRI)\":\n", " table7_numeric[\n", " \"Integrated_Damage_Index\"\n", " ]\n", "})\n", "\n", "\n", "# Formatting\n", "for col in [\n", " \"fc,24 (MPa)\",\n", " \"Initial penalty (%)\",\n", " \"Retention at 600 °C (%)\",\n", " \"ETD at 600 °C (pp)\"\n", "]:\n", "\n", " table7_publication[col] = (\n", " table7_publication[col]\n", " .round(2)\n", " )\n", "\n", "\n", "table7_publication[\n", " \"AUC (fraction·°C)\"\n", "] = (\n", " table7_publication[\n", " \"AUC (fraction·°C)\"\n", " ]\n", " .round(2)\n", ")\n", "\n", "\n", "table7_publication[\n", " \"TRI\"\n", "] = (\n", " table7_publication[\"TRI\"]\n", " .round(3)\n", ")\n", "\n", "\n", "table7_publication[\n", " \"Integrated damage (1−TRI)\"\n", "] = (\n", " table7_publication[\n", " \"Integrated damage (1−TRI)\"\n", " ]\n", " .round(3)\n", ")\n", "\n", "\n", "display(\n", " table7_publication\n", ")" ] }, { "cell_type": "code", "execution_count": 149, "id": "12c4be06-6887-4882-9bb2-085cf7679d2a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "✓ TABLE 7 SAVED\n", "C:\\Users\\sadal\\Desktop\\Yasin Hoca-RCA Thermal\\Analysis_Outputs\\Step_06_Thermal_Resistance_AUC\\Table_7_Thermal_Resistance_Index_AUC.xlsx\n" ] } ], "source": [ "# ============================================================\n", "# — SAVE TABLE 7\n", "# ============================================================\n", "\n", "from pathlib import Path\n", "\n", "DATA_PATH = Path(\n", " r\"C:\\Users\\sadal\\Desktop\\Yasin Hoca-RCA Thermal\\data.xlsx\"\n", ")\n", "\n", "OUTPUT_DIR = (\n", " DATA_PATH.parent\n", " / \"Analysis_Outputs\"\n", " / \"Step_06_Thermal_Resistance_AUC\"\n", ")\n", "\n", "OUTPUT_DIR.mkdir(\n", " parents=True,\n", " exist_ok=True\n", ")\n", "\n", "\n", "table7_path = (\n", " OUTPUT_DIR\n", " / \"Table_7_Thermal_Resistance_Index_AUC.xlsx\"\n", ")\n", "\n", "\n", "with pd.ExcelWriter(\n", " table7_path,\n", " engine=\"openpyxl\"\n", ") as writer:\n", "\n", " table7_publication.to_excel(\n", " writer,\n", " sheet_name=\"Publication_Table\",\n", " index=False\n", " )\n", "\n", " table7_numeric.to_excel(\n", " writer,\n", " sheet_name=\"Full_Numeric\",\n", " index=False\n", " )\n", "\n", " interval_contributions.to_excel(\n", " writer,\n", " sheet_name=\"AUC_Intervals\",\n", " index=False\n", " )\n", "\n", " bootstrap_summary.to_excel(\n", " writer,\n", " sheet_name=\"Bootstrap_TRI\",\n", " index=False\n", " )\n", "\n", "\n", "table7_numeric.to_csv(\n", " OUTPUT_DIR\n", " / \"Table_7_Thermal_Resistance_Numeric.csv\",\n", " index=False\n", ")\n", "\n", "\n", "print(\"✓ TABLE 7 SAVED\")\n", "print(table7_path)" ] }, { "cell_type": "code", "execution_count": 157, "id": "d9d5b453-3d99-44c6-9911-7a61941d3a4b", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "<Figure size 1480x610 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "✓ FIGURE S4 SAVED — REVISED LAYOUT\n" ] } ], "source": [ "# ============================================================\n", "# — FIGURE S4 — REVISED\n", "# INTEGRATED THERMAL RESISTANCE PROFILE\n", "# ============================================================\n", "\n", "from pathlib import Path\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from matplotlib.patches import Patch\n", "\n", "\n", "DATA_PATH = Path(\n", " r\"C:\\Users\\sadal\\Desktop\\Yasin Hoca-RCA Thermal\\data.xlsx\"\n", ")\n", "\n", "OUTPUT_DIR = (\n", " DATA_PATH.parent\n", " / \"Analysis_Outputs\"\n", " / \"Step_06_Thermal_Resistance_AUC\"\n", ")\n", "\n", "OUTPUT_DIR.mkdir(\n", " parents=True,\n", " exist_ok=True\n", ")\n", "\n", "\n", "# ============================================================\n", "# VISUAL IDENTITY\n", "# ============================================================\n", "\n", "background = \"#F7F5F0\"\n", "navy = \"#20364B\"\n", "\n", "rca_colors = {\n", " 0: \"#264653\",\n", " 10: \"#287271\",\n", " 20: \"#2A9D8F\",\n", " 30: \"#8AB17D\",\n", " 40: \"#E9C46A\",\n", " 50: \"#E76F51\"\n", "}\n", "\n", "interval_colors = {\n", " \"24–150 °C\": \"#264653\",\n", " \"150–300 °C\": \"#2A9D8F\",\n", " \"300–450 °C\": \"#E9C46A\",\n", " \"450–600 °C\": \"#E76F51\"\n", "}\n", "\n", "\n", "plt.rcParams.update({\n", " \"font.family\": \"DejaVu Sans\",\n", " \"font.size\": 10.5,\n", " \"axes.labelsize\": 11,\n", " \"axes.titlesize\": 12,\n", " \"xtick.labelsize\": 9.5,\n", " \"ytick.labelsize\": 9.5,\n", " \"axes.linewidth\": 0.8,\n", " \"pdf.fonttype\": 42,\n", " \"ps.fonttype\": 42\n", "})\n", "\n", "\n", "# Slightly larger figure\n", "fig, (ax1, ax2) = plt.subplots(\n", " 1,\n", " 2,\n", " figsize=(14.8, 6.1),\n", " facecolor=background,\n", " gridspec_kw={\n", " \"width_ratios\": [1.0, 1.25]\n", " }\n", ")\n", "\n", "ax1.set_facecolor(background)\n", "ax2.set_facecolor(background)\n", "\n", "\n", "# ============================================================\n", "# PANEL A — TRI WITH BOOTSTRAP CI\n", "# ============================================================\n", "\n", "plot_tri = (\n", " table7_numeric\n", " .sort_values(\"RCA_pct\")\n", " .reset_index(drop=True)\n", ")\n", "\n", "y = np.arange(len(plot_tri))\n", "\n", "\n", "# Determine limits using CI, leaving space for labels\n", "ci_min = plot_tri[\"CI95_Lower\"].min()\n", "ci_max = plot_tri[\"CI95_Upper\"].max()\n", "\n", "x_padding_left = 0.018\n", "x_padding_right = 0.055\n", "\n", "ax1.set_xlim(\n", " ci_min - x_padding_left,\n", " ci_max + x_padding_right\n", ")\n", "\n", "\n", "for i, row in plot_tri.iterrows():\n", "\n", " tri = row[\"TRI\"]\n", " ci_lower = row[\"CI95_Lower\"]\n", " ci_upper = row[\"CI95_Upper\"]\n", "\n", " lower_error = tri - ci_lower\n", " upper_error = ci_upper - tri\n", "\n", " color = rca_colors[\n", " int(row[\"RCA_pct\"])\n", " ]\n", "\n", " # CI\n", " ax1.errorbar(\n", " tri,\n", " i,\n", " xerr=np.array([\n", " [lower_error],\n", " [upper_error]\n", " ]),\n", " fmt=\"none\",\n", " ecolor=color,\n", " elinewidth=2.0,\n", " capsize=4,\n", " capthick=1.4,\n", " zorder=2\n", " )\n", "\n", " # TRI point\n", " ax1.scatter(\n", " tri,\n", " i,\n", " s=145,\n", " color=color,\n", " edgecolor=\"white\",\n", " linewidth=1.1,\n", " zorder=3\n", " )\n", "\n", " # --------------------------------------------------------\n", " # VALUE LABEL\n", " # IMPORTANT: place AFTER the CI upper limit,\n", " # not directly after the TRI marker\n", " # --------------------------------------------------------\n", "\n", " ax1.text(\n", " ci_upper + 0.008,\n", " i,\n", " f\"{tri:.3f}\",\n", " va=\"center\",\n", " ha=\"left\",\n", " fontsize=9,\n", " color=navy,\n", " fontweight=\"bold\",\n", " zorder=5\n", " )\n", "\n", "\n", "# Control TRI reference\n", "ax1.axvline(\n", " TRI_control,\n", " color=\"#7F8587\",\n", " linewidth=1.0,\n", " linestyle=(0, (4, 3)),\n", " zorder=0\n", ")\n", "\n", "\n", "ax1.set_yticks(y)\n", "\n", "ax1.set_yticklabels([\n", " f\"RCA {int(v)}%\"\n", " for v in plot_tri[\"RCA_pct\"]\n", "])\n", "\n", "ax1.invert_yaxis()\n", "\n", "\n", "ax1.set_xlabel(\n", " \"Thermal Resistance Index (TRI)\",\n", " labelpad=10\n", ")\n", "\n", "ax1.set_title(\n", " \"Integrated thermal resistance\",\n", " loc=\"left\",\n", " color=navy,\n", " fontweight=\"bold\",\n", " pad=16\n", ")\n", "\n", "\n", "# Panel letter moved farther away from title\n", "ax1.text(\n", " -0.14,\n", " 1.08,\n", " \"A\",\n", " transform=ax1.transAxes,\n", " fontsize=17,\n", " fontweight=\"bold\",\n", " color=\"#E76F51\",\n", " va=\"bottom\"\n", ")\n", "\n", "\n", "# ============================================================\n", "# PANEL B — AUC INTERVAL FINGERPRINT\n", "# ============================================================\n", "\n", "rca_order = sorted(\n", " interval_contributions[\n", " \"RCA_pct\"\n", " ].unique()\n", ")\n", "\n", "interval_order = [\n", " \"24–150 °C\",\n", " \"150–300 °C\",\n", " \"300–450 °C\",\n", " \"450–600 °C\"\n", "]\n", "\n", "\n", "bar_totals = []\n", "\n", "\n", "for yi, rca in enumerate(rca_order):\n", "\n", " left = 0\n", "\n", " for interval in interval_order:\n", "\n", " value = float(\n", " interval_contributions.loc[\n", " (\n", " interval_contributions[\n", " \"RCA_pct\"\n", " ] == rca\n", " )\n", " &\n", " (\n", " interval_contributions[\n", " \"Interval\"\n", " ] == interval\n", " ),\n", " \"TRI_Contribution\"\n", " ].iloc[0]\n", " )\n", "\n", " ax2.barh(\n", " yi,\n", " value,\n", " left=left,\n", " height=0.54,\n", " color=interval_colors[interval],\n", " edgecolor=background,\n", " linewidth=1.2,\n", " zorder=2\n", " )\n", "\n", " left += value\n", "\n", " bar_totals.append(left)\n", "\n", " # Total label clearly outside the stacked bar\n", " ax2.text(\n", " left + 0.012,\n", " yi,\n", " f\"{left:.3f}\",\n", " va=\"center\",\n", " ha=\"left\",\n", " color=navy,\n", " fontweight=\"bold\",\n", " fontsize=9,\n", " zorder=4\n", " )\n", "\n", "\n", "# Give enough room for total labels\n", "ax2.set_xlim(\n", " 0,\n", " max(bar_totals) + 0.10\n", ")\n", "\n", "\n", "ax2.set_yticks(\n", " np.arange(len(rca_order))\n", ")\n", "\n", "ax2.set_yticklabels([\n", " f\"RCA {int(v)}%\"\n", " for v in rca_order\n", "])\n", "\n", "ax2.invert_yaxis()\n", "\n", "\n", "ax2.set_xlabel(\n", " \"Contribution to normalized thermal-resistance area\",\n", " labelpad=10\n", ")\n", "\n", "ax2.set_title(\n", " \"Temperature-interval contribution fingerprint\",\n", " loc=\"left\",\n", " color=navy,\n", " fontweight=\"bold\",\n", " pad=16\n", ")\n", "\n", "\n", "# ============================================================\n", "# LEGEND — BELOW PANEL B, OUTSIDE DATA AREA\n", "# ============================================================\n", "\n", "legend_handles = [\n", " Patch(\n", " facecolor=interval_colors[i],\n", " label=i\n", " )\n", " for i in interval_order\n", "]\n", "\n", "\n", "ax2.legend(\n", " handles=legend_handles,\n", " frameon=False,\n", " fontsize=8.4,\n", "\n", " # Two-column legend below graph\n", " loc=\"upper center\",\n", " bbox_to_anchor=(0.50, -0.18),\n", " ncol=2,\n", "\n", " columnspacing=1.5,\n", " handlelength=1.5,\n", " handletextpad=0.6\n", ")\n", "\n", "\n", "# Panel letter\n", "ax2.text(\n", " -0.13,\n", " 1.08,\n", " \"B\",\n", " transform=ax2.transAxes,\n", " fontsize=17,\n", " fontweight=\"bold\",\n", " color=\"#E76F51\",\n", " va=\"bottom\"\n", ")\n", "\n", "\n", "# ============================================================\n", "# CLEAN STYLE\n", "# ============================================================\n", "\n", "for ax in [ax1, ax2]:\n", "\n", " ax.spines[\"top\"].set_visible(False)\n", " ax.spines[\"right\"].set_visible(False)\n", "\n", " ax.spines[\"left\"].set_color(\"#A09B92\")\n", " ax.spines[\"bottom\"].set_color(\"#A09B92\")\n", "\n", " ax.grid(\n", " axis=\"x\",\n", " color=\"#DDD9D0\",\n", " linewidth=0.65,\n", " alpha=0.60,\n", " zorder=0\n", " )\n", "\n", "\n", "# More space between panels and below for legend\n", "fig.subplots_adjust(\n", " wspace=0.34,\n", " bottom=0.25,\n", " top=0.88,\n", " left=0.09,\n", " right=0.97\n", ")\n", "\n", "\n", "# ============================================================\n", "# SAVE\n", "# ============================================================\n", "\n", "figure_base = (\n", " OUTPUT_DIR\n", " / \"Figure_S4_Integrated_Thermal_Resistance\"\n", ")\n", "\n", "\n", "plt.savefig(\n", " str(figure_base) + \".png\",\n", " dpi=600,\n", " bbox_inches=\"tight\",\n", " facecolor=fig.get_facecolor()\n", ")\n", "\n", "plt.savefig(\n", " str(figure_base) + \".pdf\",\n", " bbox_inches=\"tight\",\n", " facecolor=fig.get_facecolor()\n", ")\n", "\n", "plt.savefig(\n", " str(figure_base) + \".tiff\",\n", " dpi=600,\n", " bbox_inches=\"tight\",\n", " facecolor=fig.get_facecolor(),\n", " pil_kwargs={\n", " \"compression\": \"tiff_lzw\"\n", " }\n", ")\n", "\n", "\n", "plt.show()\n", "\n", "print(\"✓ FIGURE S4 SAVED — REVISED LAYOUT\")" ] }, { "cell_type": "code", "execution_count": 159, "id": "014d025b-3848-4e75-9bad-eacc0c0d2ddf", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "============================================================================\n", "STEP 6 — HEAT RESISTANCE / NORMALIZED AUC\n", "============================================================================\n", "\n", "THERMAL RESISTANCE INDEX\n", "RCA 0% : TRI=0.8189 | 95% bootstrap CI [0.7760, 0.8564] | ΔvsRCA0=+0.00 pp\n", "RCA 10% : TRI=0.8116 | 95% bootstrap CI [0.7866, 0.8351] | ΔvsRCA0=-0.73 pp\n", "RCA 20% : TRI=0.8311 | 95% bootstrap CI [0.7865, 0.8776] | ΔvsRCA0=+1.22 pp\n", "RCA 30% : TRI=0.8217 | 95% bootstrap CI [0.7540, 0.9264] | ΔvsRCA0=+0.29 pp\n", "RCA 40% : TRI=0.8763 | 95% bootstrap CI [0.8167, 0.9504] | ΔvsRCA0=+5.75 pp\n", "RCA 50% : TRI=0.8330 | 95% bootstrap CI [0.7999, 0.8709] | ΔvsRCA0=+1.41 pp\n", "\n", "TRI SPREAD\n", "Minimum TRI : 0.8116\n", "Maximum TRI : 0.8763\n", "Full spread : 6.47 percentage points\n", "\n", "MONOTONICITY\n", "TRI decreases monotonically with RCA: False\n", "\n", "ABSOLUTE vs THERMAL-RESISTANCE SPREAD\n", "Maximum initial strength penalty : 20.41%\n", "TRI full spread : 6.47 pp\n", "\n", "AUC CLOSURE CHECK\n", "Maximum closure error : 1.110223024625e-16\n", "Exact AUC reconstruction: True\n", "\n", "BOOTSTRAP\n", "Resamples : 20000\n", "Random seed : 20260806\n", "\n", "SAVED OUTPUTS\n", " - Figure_S4_Integrated_Thermal_Resistance.pdf\n", " - Figure_S4_Integrated_Thermal_Resistance.png\n", " - Figure_S4_Integrated_Thermal_Resistance.tiff\n", " - Table_7_Thermal_Resistance_Index_AUC.xlsx\n", " - Table_7_Thermal_Resistance_Numeric.csv\n", "============================================================================\n" ] } ], "source": [ "# ============================================================\n", "# — STEP 6 FINAL AUDIT\n", "# ============================================================\n", "\n", "print(\"=\" * 76)\n", "print(\"STEP 6 — HEAT RESISTANCE / NORMALIZED AUC\")\n", "print(\"=\" * 76)\n", "\n", "\n", "print(\"\\nTHERMAL RESISTANCE INDEX\")\n", "\n", "for _, row in (\n", " table7_numeric\n", " .sort_values(\"RCA_pct\")\n", " .iterrows()\n", "):\n", "\n", " print(\n", " f\"RCA {int(row['RCA_pct']):>2}% : \"\n", " f\"TRI={row['TRI']:.4f} | \"\n", " f\"95% bootstrap CI \"\n", " f\"[{row['CI95_Lower']:.4f}, \"\n", " f\"{row['CI95_Upper']:.4f}] | \"\n", " f\"ΔvsRCA0=\"\n", " f\"{row['Delta_TRI_vs_RCA0_pp']:+.2f} pp\"\n", " )\n", "\n", "\n", "# ============================================================\n", "# TRI RANGE\n", "# ============================================================\n", "\n", "tri_min = (\n", " table7_numeric[\"TRI\"]\n", " .min()\n", ")\n", "\n", "tri_max = (\n", " table7_numeric[\"TRI\"]\n", " .max()\n", ")\n", "\n", "tri_range_pp = (\n", " 100\n", " * (tri_max - tri_min)\n", ")\n", "\n", "\n", "print(\"\\nTRI SPREAD\")\n", "\n", "print(\n", " f\"Minimum TRI : \"\n", " f\"{tri_min:.4f}\"\n", ")\n", "\n", "print(\n", " f\"Maximum TRI : \"\n", " f\"{tri_max:.4f}\"\n", ")\n", "\n", "print(\n", " f\"Full spread : \"\n", " f\"{tri_range_pp:.2f} percentage points\"\n", ")\n", "\n", "\n", "# ============================================================\n", "# MONOTONICITY\n", "# ============================================================\n", "\n", "tri_ordered = (\n", " table7_numeric\n", " .sort_values(\"RCA_pct\")[\"TRI\"]\n", " .values\n", ")\n", "\n", "tri_monotonic = np.all(\n", " np.diff(tri_ordered) <= 0\n", ")\n", "\n", "\n", "print(\"\\nMONOTONICITY\")\n", "\n", "print(\n", " \"TRI decreases monotonically with RCA:\",\n", " tri_monotonic\n", ")\n", "\n", "\n", "# ============================================================\n", "# COMPARE WITH INITIAL ABSOLUTE-STRENGTH PENALTY\n", "# ============================================================\n", "\n", "max_initial_penalty = (\n", " table7_numeric[\n", " \"Initial_Strength_Penalty_pct\"\n", " ].max()\n", ")\n", "\n", "\n", "print(\"\\nABSOLUTE vs THERMAL-RESISTANCE SPREAD\")\n", "\n", "print(\n", " f\"Maximum initial strength penalty : \"\n", " f\"{max_initial_penalty:.2f}%\"\n", ")\n", "\n", "print(\n", " f\"TRI full spread : \"\n", " f\"{tri_range_pp:.2f} pp\"\n", ")\n", "\n", "\n", "# ============================================================\n", "# AUC CLOSURE\n", "# ============================================================\n", "\n", "print(\"\\nAUC CLOSURE CHECK\")\n", "\n", "print(\n", " f\"Maximum closure error : \"\n", " f\"{max_auc_closure_error:.12e}\"\n", ")\n", "\n", "print(\n", " \"Exact AUC reconstruction:\",\n", " max_auc_closure_error < 1e-12\n", ")\n", "\n", "\n", "print(\"\\nBOOTSTRAP\")\n", "\n", "print(f\"Resamples : {N_BOOT}\")\n", "print(f\"Random seed : {RANDOM_SEED}\")\n", "\n", "\n", "print(\"\\nSAVED OUTPUTS\")\n", "\n", "for file in sorted(\n", " OUTPUT_DIR.iterdir()\n", "):\n", " print(\" -\", file.name)\n", "\n", "\n", "print(\"=\" * 76)" ] }, { "cell_type": "markdown", "id": "0977b648-c18b-4577-b04f-4e2cf92624b5", "metadata": {}, "source": [ "#STEP7" ] }, { "cell_type": "code", "execution_count": 161, "id": "5025ff07-0b16-4e87-a5a5-17811111bad3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "✓ STEP 7 DATA CHECK PASSED\n", "N observations : 90\n", "Design cells : 30\n", "Replicates/cell: 3\n", "Output : C:\\Users\\sadal\\Desktop\\Yasin Hoca-RCA Thermal\\Analysis_Outputs\\Step_07_Quadratic_Response_Surface\n" ] } ], "source": [ "# ============================================================\n", "# STEP 7 — VALIDATED QUADRATIC RESPONSE SURFACE\n", "# — SETUP\n", "# ============================================================\n", "\n", "from pathlib import Path\n", "import re\n", "import warnings\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "import statsmodels.api as sm\n", "\n", "from scipy.stats import f\n", "\n", "warnings.filterwarnings(\"ignore\")\n", "\n", "\n", "DATA_PATH = Path(\n", " r\"C:\\Users\\sadal\\Desktop\\Yasin Hoca-RCA Thermal\\data.xlsx\"\n", ")\n", "\n", "OUTPUT_DIR = (\n", " DATA_PATH.parent\n", " / \"Analysis_Outputs\"\n", " / \"Step_07_Quadratic_Response_Surface\"\n", ")\n", "\n", "OUTPUT_DIR.mkdir(\n", " parents=True,\n", " exist_ok=True\n", ")\n", "\n", "assert DATA_PATH.exists()\n", "\n", "\n", "# ============================================================\n", "# RELOAD RAW DATA\n", "# ============================================================\n", "\n", "raw = pd.read_excel(\n", " DATA_PATH,\n", " sheet_name=\"data\",\n", " header=None\n", ")\n", "\n", "temperature_headers = raw.iloc[0, 1:].astype(str)\n", "\n", "temperatures = (\n", " temperature_headers\n", " .str.extract(r\"(\\d+)\")[0]\n", " .astype(int)\n", " .tolist()\n", ")\n", "\n", "\n", "records = []\n", "\n", "for _, row in raw.iloc[1:].iterrows():\n", "\n", " if pd.isna(row.iloc[0]):\n", " continue\n", "\n", " mix_label = str(row.iloc[0])\n", "\n", " rca_match = re.search(\n", " r\"(\\d+)\",\n", " mix_label\n", " )\n", "\n", " if rca_match is None:\n", " raise ValueError(\n", " f\"RCA level could not be identified: {mix_label}\"\n", " )\n", "\n", " rca_pct = int(\n", " rca_match.group(1)\n", " )\n", "\n", " replicate_counter = {}\n", "\n", " for col_index, temperature in enumerate(\n", " temperatures,\n", " start=1\n", " ):\n", "\n", " replicate_counter[temperature] = (\n", " replicate_counter.get(temperature, 0) + 1\n", " )\n", "\n", " value = pd.to_numeric(\n", " row.iloc[col_index],\n", " errors=\"coerce\"\n", " )\n", "\n", " if pd.isna(value):\n", " raise ValueError(\n", " f\"Missing observation: \"\n", " f\"RCA={rca_pct}, T={temperature}\"\n", " )\n", "\n", " records.append({\n", " \"RCA_pct\": rca_pct,\n", " \"Temperature_C\": temperature,\n", " \"Replicate\": replicate_counter[temperature],\n", " \"CompressiveStrength_MPa\": float(value)\n", " })\n", "\n", "\n", "df_rsm = pd.DataFrame(records)\n", "\n", "\n", "cell_counts = (\n", " df_rsm.groupby(\n", " [\"RCA_pct\", \"Temperature_C\"]\n", " )\n", " .size()\n", ")\n", "\n", "assert len(df_rsm) == 90\n", "assert len(cell_counts) == 30\n", "assert cell_counts.nunique() == 1\n", "assert cell_counts.iloc[0] == 3\n", "\n", "print(\"✓ STEP 7 DATA CHECK PASSED\")\n", "print(\"N observations :\", len(df_rsm))\n", "print(\"Design cells :\", len(cell_counts))\n", "print(\"Replicates/cell:\", cell_counts.iloc[0])\n", "print(\"Output :\", OUTPUT_DIR)" ] }, { "cell_type": "code", "execution_count": 163, "id": "9153fee4-d35f-4b92-85de-eacc3dc4cd72", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: y R-squared: 0.942\n", "Model: OLS Adj. R-squared: 0.938\n", "Method: Least Squares F-statistic: 270.5\n", "Date: Thu, 06 Aug 2026 Prob (F-statistic): 3.18e-50\n", "Time: 10:03:03 Log-Likelihood: -114.16\n", "No. Observations: 90 AIC: 240.3\n", "Df Residuals: 84 BIC: 255.3\n", "Df Model: 5 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "Intercept 16.8857 0.185 91.241 0.000 16.518 17.254\n", "R -1.7153 0.137 -12.476 0.000 -1.989 -1.442\n", "T -4.3987 0.132 -33.431 0.000 -4.660 -4.137\n", "R2 -0.5633 0.235 -2.395 0.019 -1.031 -0.096\n", "T2 -1.7953 0.227 -7.907 0.000 -2.247 -1.344\n", "R_T 0.6018 0.193 3.124 0.002 0.219 0.985\n", "==============================================================================\n", "Omnibus: 1.898 Durbin-Watson: 1.980\n", "Prob(Omnibus): 0.387 Jarque-Bera (JB): 1.287\n", "Skew: -0.189 Prob(JB): 0.525\n", "Kurtosis: 3.447 Cond. No. 3.78\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "# ============================================================\n", "# — QUADRATIC RESPONSE SURFACE MODEL\n", "# ============================================================\n", "\n", "R_MIN = df_rsm[\"RCA_pct\"].min()\n", "R_MAX = df_rsm[\"RCA_pct\"].max()\n", "\n", "T_MIN = df_rsm[\"Temperature_C\"].min()\n", "T_MAX = df_rsm[\"Temperature_C\"].max()\n", "\n", "R_CENTER = (R_MIN + R_MAX) / 2\n", "R_HALF_RANGE = (R_MAX - R_MIN) / 2\n", "\n", "T_CENTER = (T_MIN + T_MAX) / 2\n", "T_HALF_RANGE = (T_MAX - T_MIN) / 2\n", "\n", "\n", "def build_rsm_matrix(data):\n", "\n", " R = (\n", " (data[\"RCA_pct\"].values - R_CENTER)\n", " / R_HALF_RANGE\n", " )\n", "\n", " T = (\n", " (data[\"Temperature_C\"].values - T_CENTER)\n", " / T_HALF_RANGE\n", " )\n", "\n", " X = pd.DataFrame({\n", " \"Intercept\": np.ones(len(data)),\n", " \"R\": R,\n", " \"T\": T,\n", " \"R2\": R**2,\n", " \"T2\": T**2,\n", " \"R_T\": R*T\n", " })\n", "\n", " return X\n", "\n", "\n", "X = build_rsm_matrix(df_rsm)\n", "\n", "y = (\n", " df_rsm[\n", " \"CompressiveStrength_MPa\"\n", " ].values\n", ")\n", "\n", "\n", "rsm_model = sm.OLS(\n", " y,\n", " X\n", ").fit()\n", "\n", "\n", "print(rsm_model.summary())" ] }, { "cell_type": "code", "execution_count": 165, "id": "bd3d60f3-9e9e-4890-96b7-a5cf57975140", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "QUADRATIC RESPONSE SURFACE\n", "R² : 0.941531\n", "Adj. R² : 0.938051\n", "RMSE : 0.8603 MPa\n", "MAE : 0.6585 MPa\n" ] } ], "source": [ "# ============================================================\n", "# — MODEL FIT METRICS\n", "# ============================================================\n", "\n", "df_rsm[\"Predicted_RSM\"] = (\n", " rsm_model.predict(X)\n", ")\n", "\n", "df_rsm[\"Residual_RSM\"] = (\n", " df_rsm[\"CompressiveStrength_MPa\"]\n", " - df_rsm[\"Predicted_RSM\"]\n", ")\n", "\n", "\n", "R2 = rsm_model.rsquared\n", "ADJ_R2 = rsm_model.rsquared_adj\n", "\n", "RMSE = np.sqrt(\n", " np.mean(\n", " df_rsm[\"Residual_RSM\"]**2\n", " )\n", ")\n", "\n", "MAE = np.mean(\n", " np.abs(\n", " df_rsm[\"Residual_RSM\"]\n", " )\n", ")\n", "\n", "\n", "print(\"QUADRATIC RESPONSE SURFACE\")\n", "print(f\"R² : {R2:.6f}\")\n", "print(f\"Adj. R² : {ADJ_R2:.6f}\")\n", "print(f\"RMSE : {RMSE:.4f} MPa\")\n", "print(f\"MAE : {MAE:.4f} MPa\")" ] }, { "cell_type": "code", "execution_count": 167, "id": "c91ca642-fb25-43d2-acd0-335dba09eae9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CELL-OUT CROSS-VALIDATION\n", "Q² : 0.936522\n", "RMSE-CV : 0.8964 MPa\n", "MAE-CV : 0.6904 MPa\n", "PRESS : 72.3163\n" ] } ], "source": [ "# ============================================================\n", "# — LEAVE-ONE-CELL-OUT CROSS-VALIDATION\n", "# ============================================================\n", "\n", "cell_predictions = []\n", "\n", "\n", "unique_cells = (\n", " df_rsm[\n", " [\"RCA_pct\", \"Temperature_C\"]\n", " ]\n", " .drop_duplicates()\n", " .sort_values(\n", " [\"RCA_pct\", \"Temperature_C\"]\n", " )\n", ")\n", "\n", "\n", "for _, cell in unique_cells.iterrows():\n", "\n", " rca_hold = cell[\"RCA_pct\"]\n", " temp_hold = cell[\"Temperature_C\"]\n", "\n", " test_mask = (\n", " (df_rsm[\"RCA_pct\"] == rca_hold)\n", " &\n", " (df_rsm[\"Temperature_C\"] == temp_hold)\n", " )\n", "\n", " train = df_rsm.loc[\n", " ~test_mask\n", " ].copy()\n", "\n", " test = df_rsm.loc[\n", " test_mask\n", " ].copy()\n", "\n", "\n", " X_train = build_rsm_matrix(train)\n", "\n", " y_train = train[\n", " \"CompressiveStrength_MPa\"\n", " ].values\n", "\n", "\n", " cv_model = sm.OLS(\n", " y_train,\n", " X_train\n", " ).fit()\n", "\n", "\n", " X_test = build_rsm_matrix(test)\n", "\n", " predictions = cv_model.predict(\n", " X_test\n", " )\n", "\n", "\n", " for actual, predicted, rep in zip(\n", " test[\"CompressiveStrength_MPa\"],\n", " predictions,\n", " test[\"Replicate\"]\n", " ):\n", "\n", " cell_predictions.append({\n", " \"RCA_pct\": rca_hold,\n", " \"Temperature_C\": temp_hold,\n", " \"Replicate\": rep,\n", " \"Observed_MPa\": actual,\n", " \"Predicted_CellOut_MPa\": predicted\n", " })\n", "\n", "\n", "cellout = pd.DataFrame(\n", " cell_predictions\n", ")\n", "\n", "\n", "PRESS = np.sum(\n", " (\n", " cellout[\"Observed_MPa\"]\n", " - cellout[\"Predicted_CellOut_MPa\"]\n", " )**2\n", ")\n", "\n", "\n", "grand_mean = (\n", " cellout[\"Observed_MPa\"].mean()\n", ")\n", "\n", "SST = np.sum(\n", " (\n", " cellout[\"Observed_MPa\"]\n", " - grand_mean\n", " )**2\n", ")\n", "\n", "\n", "Q2_CELL_OUT = (\n", " 1 - PRESS / SST\n", ")\n", "\n", "\n", "RMSE_CELL_OUT = np.sqrt(\n", " np.mean(\n", " (\n", " cellout[\"Observed_MPa\"]\n", " - cellout[\"Predicted_CellOut_MPa\"]\n", " )**2\n", " )\n", ")\n", "\n", "\n", "MAE_CELL_OUT = np.mean(\n", " np.abs(\n", " cellout[\"Observed_MPa\"]\n", " - cellout[\"Predicted_CellOut_MPa\"]\n", " )\n", ")\n", "\n", "\n", "print(\"CELL-OUT CROSS-VALIDATION\")\n", "print(f\"Q² : {Q2_CELL_OUT:.6f}\")\n", "print(f\"RMSE-CV : {RMSE_CELL_OUT:.4f} MPa\")\n", "print(f\"MAE-CV : {MAE_CELL_OUT:.4f} MPa\")\n", "print(f\"PRESS : {PRESS:.4f}\")" ] }, { "cell_type": "code", "execution_count": 169, "id": "17c84ea3-f792-4abd-9c5a-e24e4ef50c29", "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>Source</th>\n", " <th>SS</th>\n", " <th>df</th>\n", " <th>MS</th>\n", " <th>F</th>\n", " <th>p</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Lack of fit</td>\n", " <td>11.464078</td>\n", " <td>24</td>\n", " <td>0.477670</td>\n", " <td>0.519718</td>\n", " <td>0.96067</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Pure error</td>\n", " <td>55.145687</td>\n", " <td>60</td>\n", " <td>0.919095</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Residual</td>\n", " <td>66.609765</td>\n", " <td>84</td>\n", " <td>0.792973</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Source SS df MS F p\n", "0 Lack of fit 11.464078 24 0.477670 0.519718 0.96067\n", "1 Pure error 55.145687 60 0.919095 NaN NaN\n", "2 Residual 66.609765 84 0.792973 NaN NaN" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Lack-of-fit F : 0.51972\n", "Lack-of-fit p : 0.960670\n" ] } ], "source": [ "# ============================================================\n", "# — PURE ERROR / LACK-OF-FIT TEST\n", "# ============================================================\n", "\n", "cell_means = (\n", " df_rsm\n", " .groupby(\n", " [\"RCA_pct\", \"Temperature_C\"]\n", " )[\n", " \"CompressiveStrength_MPa\"\n", " ]\n", " .transform(\"mean\")\n", ")\n", "\n", "\n", "# Pure experimental error\n", "SS_PE = np.sum(\n", " (\n", " df_rsm[\"CompressiveStrength_MPa\"]\n", " - cell_means\n", " )**2\n", ")\n", "\n", "\n", "# Model residual error\n", "SS_RES = np.sum(\n", " rsm_model.resid**2\n", ")\n", "\n", "\n", "# Lack-of-fit component\n", "SS_LOF = SS_RES - SS_PE\n", "\n", "\n", "N = len(df_rsm)\n", "\n", "G = (\n", " df_rsm[\n", " [\"RCA_pct\", \"Temperature_C\"]\n", " ]\n", " .drop_duplicates()\n", " .shape[0]\n", ")\n", "\n", "P = len(\n", " rsm_model.params\n", ")\n", "\n", "\n", "DF_PE = N - G\n", "DF_LOF = G - P\n", "DF_RES = N - P\n", "\n", "\n", "MS_PE = (\n", " SS_PE / DF_PE\n", ")\n", "\n", "MS_LOF = (\n", " SS_LOF / DF_LOF\n", ")\n", "\n", "\n", "F_LOF = (\n", " MS_LOF / MS_PE\n", ")\n", "\n", "\n", "P_LOF = (\n", " 1\n", " - f.cdf(\n", " F_LOF,\n", " DF_LOF,\n", " DF_PE\n", " )\n", ")\n", "\n", "\n", "lof_table = pd.DataFrame({\n", " \"Source\": [\n", " \"Lack of fit\",\n", " \"Pure error\",\n", " \"Residual\"\n", " ],\n", "\n", " \"SS\": [\n", " SS_LOF,\n", " SS_PE,\n", " SS_RES\n", " ],\n", "\n", " \"df\": [\n", " DF_LOF,\n", " DF_PE,\n", " DF_RES\n", " ],\n", "\n", " \"MS\": [\n", " MS_LOF,\n", " MS_PE,\n", " SS_RES / DF_RES\n", " ],\n", "\n", " \"F\": [\n", " F_LOF,\n", " np.nan,\n", " np.nan\n", " ],\n", "\n", " \"p\": [\n", " P_LOF,\n", " np.nan,\n", " np.nan\n", " ]\n", "})\n", "\n", "\n", "display(\n", " lof_table.round(6)\n", ")\n", "\n", "print()\n", "print(f\"Lack-of-fit F : {F_LOF:.5f}\")\n", "print(f\"Lack-of-fit p : {P_LOF:.6f}\")" ] }, { "cell_type": "code", "execution_count": 171, "id": "424d80c6-ebb7-4529-9122-9f9a5b2c3958", "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>Term</th>\n", " <th>Coefficient</th>\n", " <th>SE</th>\n", " <th>t</th>\n", " <th>p</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Intercept</td>\n", " <td>21.347898</td>\n", " <td>0.356338</td>\n", " <td>59.909047</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>RCA</td>\n", " <td>-0.049624</td>\n", " <td>0.021230</td>\n", " <td>-2.337484</td>\n", " <td>0.021793</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Temperature</td>\n", " <td>-0.003857</td>\n", " <td>0.001889</td>\n", " <td>-2.041491</td>\n", " <td>0.044340</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>RCA2</td>\n", " <td>-0.000901</td>\n", " <td>0.000376</td>\n", " <td>-2.395183</td>\n", " <td>0.018835</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Temperature2</td>\n", " <td>-0.000022</td>\n", " <td>0.000003</td>\n", " <td>-7.907308</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>RCA_x_T</td>\n", " <td>0.000084</td>\n", " <td>0.000027</td>\n", " <td>3.124355</td>\n", " <td>0.002446</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Term Coefficient SE t p\n", "0 Intercept 21.347898 0.356338 59.909047 0.000000\n", "1 RCA -0.049624 0.021230 -2.337484 0.021793\n", "2 Temperature -0.003857 0.001889 -2.041491 0.044340\n", "3 RCA2 -0.000901 0.000376 -2.395183 0.018835\n", "4 Temperature2 -0.000022 0.000003 -7.907308 0.000000\n", "5 RCA_x_T 0.000084 0.000027 3.124355 0.002446" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "NATURAL-UNIT RESPONSE EQUATION\n", "\n", "fc = 21.347898 -0.049624·R -0.003857·T -0.00090131·R² -0.0000216446·T² +0.00008358·R·T\n" ] } ], "source": [ "# ============================================================\n", "# — RESPONSE EQUATION IN NATURAL UNITS\n", "# ============================================================\n", "\n", "R = df_rsm[\"RCA_pct\"].values.astype(float)\n", "T = df_rsm[\"Temperature_C\"].values.astype(float)\n", "\n", "X_natural = pd.DataFrame({\n", " \"Intercept\": np.ones(len(df_rsm)),\n", " \"RCA\": R,\n", " \"Temperature\": T,\n", " \"RCA2\": R**2,\n", " \"Temperature2\": T**2,\n", " \"RCA_x_T\": R*T\n", "})\n", "\n", "\n", "natural_model = sm.OLS(\n", " y,\n", " X_natural\n", ").fit()\n", "\n", "\n", "coefficient_table = pd.DataFrame({\n", " \"Term\": natural_model.params.index,\n", " \"Coefficient\": natural_model.params.values,\n", " \"SE\": natural_model.bse.values,\n", " \"t\": natural_model.tvalues.values,\n", " \"p\": natural_model.pvalues.values\n", "})\n", "\n", "\n", "display(\n", " coefficient_table.round(8)\n", ")\n", "\n", "\n", "b = natural_model.params\n", "\n", "\n", "print(\"\\nNATURAL-UNIT RESPONSE EQUATION\\n\")\n", "\n", "print(\n", " \"fc = \"\n", " f\"{b['Intercept']:.6f} \"\n", " f\"{b['RCA']:+.6f}·R \"\n", " f\"{b['Temperature']:+.6f}·T \"\n", " f\"{b['RCA2']:+.8f}·R² \"\n", " f\"{b['Temperature2']:+.10f}·T² \"\n", " f\"{b['RCA_x_T']:+.8f}·R·T\"\n", ")" ] }, { "cell_type": "code", "execution_count": 179, "id": "04897c63-1e22-4b98-8531-b731e752b800", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "<Figure size 1620x680 with 3 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "✓ FIGURE 9 SAVED — REVISED 3D VIEW AND AXIS LABELS\n", "3D view: elev=24°, azim=-58°\n", "Strength scale: 9.01–21.24 MPa\n" ] } ], "source": [ "# ============================================================\n", "# — FIGURE 9\n", "# VALIDATED QUADRATIC RCA–TEMPERATURE RESPONSE SURFACE\n", "# REVISED FULL VERSION\n", "# ============================================================\n", "\n", "from mpl_toolkits.mplot3d import Axes3D\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "\n", "# ============================================================\n", "# STYLE\n", "# ============================================================\n", "\n", "background = \"#F7F5F0\"\n", "navy = \"#20364B\"\n", "orange = \"#E76F51\"\n", "\n", "\n", "plt.rcParams.update({\n", " \"font.family\": \"DejaVu Sans\",\n", " \"font.size\": 10.5,\n", " \"axes.labelsize\": 11,\n", " \"axes.titlesize\": 12,\n", " \"xtick.labelsize\": 9,\n", " \"ytick.labelsize\": 9,\n", " \"pdf.fonttype\": 42,\n", " \"ps.fonttype\": 42\n", "})\n", "\n", "\n", "# ============================================================\n", "# CONTINUOUS DESIGN GRID\n", "# Interpolation only within experimental domain\n", "# ============================================================\n", "\n", "R_grid = np.linspace(\n", " R_MIN,\n", " R_MAX,\n", " 151\n", ")\n", "\n", "T_grid = np.linspace(\n", " T_MIN,\n", " T_MAX,\n", " 181\n", ")\n", "\n", "\n", "RR, TT = np.meshgrid(\n", " R_grid,\n", " T_grid\n", ")\n", "\n", "\n", "grid_data = pd.DataFrame({\n", " \"RCA_pct\": RR.ravel(),\n", " \"Temperature_C\": TT.ravel()\n", "})\n", "\n", "\n", "X_grid = build_rsm_matrix(\n", " grid_data\n", ")\n", "\n", "\n", "ZZ = (\n", " rsm_model\n", " .predict(X_grid)\n", " .values\n", " .reshape(RR.shape)\n", ")\n", "\n", "\n", "# ============================================================\n", "# EXPERIMENTAL CELL MEANS\n", "# ============================================================\n", "\n", "observed_means = (\n", " df_rsm\n", " .groupby(\n", " [\"RCA_pct\", \"Temperature_C\"],\n", " as_index=False\n", " )\n", " .agg(\n", " Mean_MPa=(\n", " \"CompressiveStrength_MPa\",\n", " \"mean\"\n", " )\n", " )\n", ")\n", "\n", "\n", "# ============================================================\n", "# COMMON COLOR SCALE\n", "# ============================================================\n", "\n", "vmin = min(\n", " float(ZZ.min()),\n", " float(observed_means[\"Mean_MPa\"].min())\n", ")\n", "\n", "vmax = max(\n", " float(ZZ.max()),\n", " float(observed_means[\"Mean_MPa\"].max())\n", ")\n", "\n", "\n", "# ============================================================\n", "# CREATE FIGURE\n", "# ============================================================\n", "\n", "fig = plt.figure(\n", " figsize=(16.2, 6.8),\n", " facecolor=background\n", ")\n", "\n", "\n", "# Give Panel A slightly more space\n", "gs = fig.add_gridspec(\n", " 1,\n", " 2,\n", " width_ratios=[1.08, 1.00],\n", " wspace=0.24\n", ")\n", "\n", "\n", "ax1 = fig.add_subplot(\n", " gs[0, 0],\n", " projection=\"3d\"\n", ")\n", "\n", "ax2 = fig.add_subplot(\n", " gs[0, 1]\n", ")\n", "\n", "\n", "ax1.set_facecolor(background)\n", "ax2.set_facecolor(background)\n", "\n", "\n", "# ============================================================\n", "# PANEL A — 3D QUADRATIC RESPONSE SURFACE\n", "# ============================================================\n", "\n", "surface = ax1.plot_surface(\n", " RR,\n", " TT,\n", " ZZ,\n", " cmap=\"viridis\",\n", " vmin=vmin,\n", " vmax=vmax,\n", " linewidth=0,\n", " antialiased=True,\n", " alpha=0.92,\n", " rcount=80,\n", " ccount=80,\n", " zorder=1\n", ")\n", "\n", "\n", "# Experimental cell means\n", "ax1.scatter(\n", " observed_means[\"RCA_pct\"],\n", " observed_means[\"Temperature_C\"],\n", " observed_means[\"Mean_MPa\"],\n", " s=38,\n", " c=\"white\",\n", " edgecolors=\"#17252A\",\n", " linewidth=1.0,\n", " depthshade=False,\n", " label=\"Experimental cell mean\",\n", " zorder=5\n", ")\n", "\n", "\n", "# ============================================================\n", "# PANEL A — AXIS LABELS\n", "# ============================================================\n", "\n", "ax1.set_xlabel(\n", " \"RCA replacement (%)\",\n", " fontsize=10.5,\n", " fontweight=\"bold\",\n", " color=navy,\n", " labelpad=15\n", ")\n", "\n", "ax1.set_ylabel(\n", " \"Temperature (°C)\",\n", " fontsize=10.5,\n", " fontweight=\"bold\",\n", " color=navy,\n", " labelpad=17\n", ")\n", "\n", "ax1.set_zlabel(\n", " \"Compressive strength (MPa)\",\n", " fontsize=10.5,\n", " fontweight=\"bold\",\n", " color=navy,\n", " labelpad=14\n", ")\n", "\n", "\n", "# ============================================================\n", "# PANEL A — LIMITS AND TICKS\n", "# ============================================================\n", "\n", "ax1.set_xlim(\n", " R_MIN,\n", " R_MAX\n", ")\n", "\n", "ax1.set_ylim(\n", " T_MIN,\n", " T_MAX\n", ")\n", "\n", "\n", "ax1.set_xticks(\n", " sorted(\n", " df_rsm[\"RCA_pct\"].unique()\n", " )\n", ")\n", "\n", "ax1.set_yticks(\n", " sorted(\n", " df_rsm[\"Temperature_C\"].unique()\n", " )\n", ")\n", "\n", "\n", "ax1.tick_params(\n", " axis=\"x\",\n", " labelsize=8.5,\n", " pad=1\n", ")\n", "\n", "ax1.tick_params(\n", " axis=\"y\",\n", " labelsize=8.5,\n", " pad=1\n", ")\n", "\n", "ax1.tick_params(\n", " axis=\"z\",\n", " labelsize=8.5,\n", " pad=2\n", ")\n", "\n", "\n", "# ============================================================\n", "# PANEL A — CAMERA ANGLE\n", "# ============================================================\n", "\n", "# Revised viewing angle:\n", "# RCA axis toward front,\n", "# temperature axis toward depth.\n", "ax1.view_init(\n", " elev=24,\n", " azim=-58\n", ")\n", "\n", "\n", "# Balanced proportions\n", "ax1.set_box_aspect(\n", " (1.25, 1.35, 0.85)\n", ")\n", "\n", "\n", "# ============================================================\n", "# PANEL A — GRID / PANE STYLE\n", "# ============================================================\n", "\n", "ax1.xaxis.pane.set_facecolor(background)\n", "ax1.yaxis.pane.set_facecolor(background)\n", "ax1.zaxis.pane.set_facecolor(background)\n", "\n", "ax1.xaxis.pane.set_alpha(0.45)\n", "ax1.yaxis.pane.set_alpha(0.45)\n", "ax1.zaxis.pane.set_alpha(0.45)\n", "\n", "\n", "ax1.xaxis._axinfo[\"grid\"][\"color\"] = \"#D8D4CB\"\n", "ax1.yaxis._axinfo[\"grid\"][\"color\"] = \"#D8D4CB\"\n", "ax1.zaxis._axinfo[\"grid\"][\"color\"] = \"#D8D4CB\"\n", "\n", "ax1.xaxis._axinfo[\"grid\"][\"linewidth\"] = 0.55\n", "ax1.yaxis._axinfo[\"grid\"][\"linewidth\"] = 0.55\n", "ax1.zaxis._axinfo[\"grid\"][\"linewidth\"] = 0.55\n", "\n", "\n", "# ============================================================\n", "# PANEL A — TITLE AND PANEL LETTER\n", "# ============================================================\n", "\n", "ax1.set_title(\n", " \"Quadratic response surface\",\n", " color=navy,\n", " fontweight=\"bold\",\n", " pad=19\n", ")\n", "\n", "\n", "ax1.text2D(\n", " -0.035,\n", " 1.035,\n", " \"A\",\n", " transform=ax1.transAxes,\n", " fontsize=17,\n", " fontweight=\"bold\",\n", " color=orange\n", ")\n", "\n", "\n", "# Legend moved away from the surface\n", "ax1.legend(\n", " loc=\"upper left\",\n", " bbox_to_anchor=(0.01, 0.94),\n", " frameon=False,\n", " fontsize=8.2\n", ")\n", "\n", "\n", "# ============================================================\n", "# PANEL B — CONTOUR MAP\n", "# ============================================================\n", "\n", "levels = np.linspace(\n", " vmin,\n", " vmax,\n", " 18\n", ")\n", "\n", "\n", "contour_filled = ax2.contourf(\n", " RR,\n", " TT,\n", " ZZ,\n", " levels=levels,\n", " cmap=\"viridis\",\n", " vmin=vmin,\n", " vmax=vmax\n", ")\n", "\n", "\n", "# Contour isolines\n", "contour_levels = np.linspace(\n", " vmin,\n", " vmax,\n", " 10\n", ")\n", "\n", "\n", "contour_lines = ax2.contour(\n", " RR,\n", " TT,\n", " ZZ,\n", " levels=contour_levels,\n", " colors=navy,\n", " linewidths=0.55,\n", " alpha=0.62\n", ")\n", "\n", "\n", "ax2.clabel(\n", " contour_lines,\n", " inline=True,\n", " fontsize=7.5,\n", " fmt=\"%.0f\"\n", ")\n", "\n", "\n", "# ============================================================\n", "# PANEL B — EXPERIMENTAL DESIGN LOCATIONS\n", "# ============================================================\n", "\n", "ax2.scatter(\n", " observed_means[\"RCA_pct\"],\n", " observed_means[\"Temperature_C\"],\n", " s=31,\n", " facecolor=\"white\",\n", " edgecolor=\"#17252A\",\n", " linewidth=0.9,\n", " zorder=4\n", ")\n", "\n", "\n", "# ============================================================\n", "# PANEL B — AXES\n", "# ============================================================\n", "\n", "ax2.set_xlabel(\n", " \"RCA replacement (%)\",\n", " fontsize=10.5,\n", " fontweight=\"bold\",\n", " color=navy,\n", " labelpad=10\n", ")\n", "\n", "ax2.set_ylabel(\n", " \"Temperature (°C)\",\n", " fontsize=10.5,\n", " fontweight=\"bold\",\n", " color=navy,\n", " labelpad=10\n", ")\n", "\n", "\n", "ax2.set_xlim(\n", " R_MIN,\n", " R_MAX\n", ")\n", "\n", "ax2.set_ylim(\n", " T_MIN,\n", " T_MAX\n", ")\n", "\n", "\n", "ax2.set_xticks(\n", " sorted(\n", " df_rsm[\"RCA_pct\"].unique()\n", " )\n", ")\n", "\n", "ax2.set_yticks(\n", " sorted(\n", " df_rsm[\"Temperature_C\"].unique()\n", " )\n", ")\n", "\n", "\n", "# ============================================================\n", "# PANEL B — TITLE\n", "# ============================================================\n", "\n", "ax2.set_title(\n", " \"Continuous RCA–temperature strength map\",\n", " loc=\"left\",\n", " color=navy,\n", " fontweight=\"bold\",\n", " pad=19\n", ")\n", "\n", "\n", "ax2.text(\n", " -0.12,\n", " 1.035,\n", " \"B\",\n", " transform=ax2.transAxes,\n", " fontsize=17,\n", " fontweight=\"bold\",\n", " color=orange\n", ")\n", "\n", "\n", "# ============================================================\n", "# PANEL B — CLEAN STYLE\n", "# ============================================================\n", "\n", "ax2.spines[\"top\"].set_visible(False)\n", "ax2.spines[\"right\"].set_visible(False)\n", "\n", "ax2.spines[\"left\"].set_color(\"#A09B92\")\n", "ax2.spines[\"bottom\"].set_color(\"#A09B92\")\n", "\n", "\n", "# ============================================================\n", "# COLORBAR\n", "# ============================================================\n", "\n", "cbar = fig.colorbar(\n", " contour_filled,\n", " ax=ax2,\n", " fraction=0.046,\n", " pad=0.035\n", ")\n", "\n", "\n", "cbar.set_label(\n", " \"Predicted compressive strength (MPa)\",\n", " rotation=90,\n", " labelpad=13,\n", " fontsize=10,\n", " fontweight=\"bold\",\n", " color=navy\n", ")\n", "\n", "cbar.ax.tick_params(\n", " labelsize=8.5\n", ")\n", "\n", "\n", "# ============================================================\n", "# LAYOUT\n", "# ============================================================\n", "\n", "fig.subplots_adjust(\n", " left=0.045,\n", " right=0.945,\n", " bottom=0.17,\n", " top=0.88\n", ")\n", "\n", "\n", "# ============================================================\n", "# SAVE\n", "# ============================================================\n", "\n", "figure_base = (\n", " OUTPUT_DIR\n", " / \"Figure_9_RCA_Temperature_Response_Surface\"\n", ")\n", "\n", "\n", "plt.savefig(\n", " str(figure_base) + \".png\",\n", " dpi=600,\n", " bbox_inches=\"tight\",\n", " facecolor=fig.get_facecolor()\n", ")\n", "\n", "\n", "plt.savefig(\n", " str(figure_base) + \".pdf\",\n", " bbox_inches=\"tight\",\n", " facecolor=fig.get_facecolor()\n", ")\n", "\n", "\n", "plt.savefig(\n", " str(figure_base) + \".tiff\",\n", " dpi=600,\n", " bbox_inches=\"tight\",\n", " facecolor=fig.get_facecolor(),\n", " pil_kwargs={\n", " \"compression\": \"tiff_lzw\"\n", " }\n", ")\n", "\n", "\n", "plt.show()\n", "\n", "\n", "print(\"✓ FIGURE 9 SAVED — REVISED 3D VIEW AND AXIS LABELS\")\n", "print(f\"3D view: elev=24°, azim=-58°\")\n", "print(f\"Strength scale: {vmin:.2f}–{vmax:.2f} MPa\")" ] }, { "cell_type": "code", "execution_count": 175, "id": "085451bd-3b27-48f4-af27-5f368d7f2948", "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>Metric</th>\n", " <th>Value</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>R2</td>\n", " <td>0.941531</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Adjusted_R2</td>\n", " <td>0.938051</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>RMSE_MPa</td>\n", " <td>0.860296</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>MAE_MPa</td>\n", " <td>0.658515</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>CellOut_Q2</td>\n", " <td>0.936522</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>CellOut_RMSE_MPa</td>\n", " <td>0.896390</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>CellOut_MAE_MPa</td>\n", " <td>0.690432</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>LackOfFit_F</td>\n", " <td>0.519718</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>LackOfFit_p</td>\n", " <td>0.960670</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Metric Value\n", "0 R2 0.941531\n", "1 Adjusted_R2 0.938051\n", "2 RMSE_MPa 0.860296\n", "3 MAE_MPa 0.658515\n", "4 CellOut_Q2 0.936522\n", "5 CellOut_RMSE_MPa 0.896390\n", "6 CellOut_MAE_MPa 0.690432\n", "7 LackOfFit_F 0.519718\n", "8 LackOfFit_p 0.960670" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "✓ STEP 7 TABLES SAVED\n" ] } ], "source": [ "# ============================================================\n", "# — SAVE STEP 7 NUMERIC OUTPUTS\n", "# ============================================================\n", "\n", "model_metrics = pd.DataFrame({\n", " \"Metric\": [\n", " \"R2\",\n", " \"Adjusted_R2\",\n", " \"RMSE_MPa\",\n", " \"MAE_MPa\",\n", " \"CellOut_Q2\",\n", " \"CellOut_RMSE_MPa\",\n", " \"CellOut_MAE_MPa\",\n", " \"LackOfFit_F\",\n", " \"LackOfFit_p\"\n", " ],\n", "\n", " \"Value\": [\n", " R2,\n", " ADJ_R2,\n", " RMSE,\n", " MAE,\n", " Q2_CELL_OUT,\n", " RMSE_CELL_OUT,\n", " MAE_CELL_OUT,\n", " F_LOF,\n", " P_LOF\n", " ]\n", "})\n", "\n", "\n", "cell_summary_export = (\n", " observed_means.copy()\n", ")\n", "\n", "\n", "display(\n", " model_metrics.round(6)\n", ")\n", "\n", "\n", "model_metrics.to_csv(\n", " OUTPUT_DIR\n", " / \"Table_RSM_Model_Validation.csv\",\n", " index=False\n", ")\n", "\n", "coefficient_table.to_csv(\n", " OUTPUT_DIR\n", " / \"Table_RSM_Coefficients.csv\",\n", " index=False\n", ")\n", "\n", "lof_table.to_csv(\n", " OUTPUT_DIR\n", " / \"Table_RSM_Lack_of_Fit.csv\",\n", " index=False\n", ")\n", "\n", "cellout.to_csv(\n", " OUTPUT_DIR\n", " / \"Table_RSM_CellOut_Predictions.csv\",\n", " index=False\n", ")\n", "\n", "cell_summary_export.to_csv(\n", " OUTPUT_DIR\n", " / \"Table_RSM_Experimental_Cell_Means.csv\",\n", " index=False\n", ")\n", "\n", "\n", "print(\"✓ STEP 7 TABLES SAVED\")" ] }, { "cell_type": "code", "execution_count": 177, "id": "c03ab89a-1098-4ed7-a306-0122016e733a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "============================================================================\n", "STEP 7 — VALIDATED QUADRATIC RESPONSE SURFACE\n", "============================================================================\n", "\n", "MODEL\n", "fc = β0 + β1·R + β2·T + β11·R² + β22·T² + β12·R·T\n", "\n", "MODEL FIT\n", "R² : 0.941531\n", "Adjusted R² : 0.938051\n", "RMSE : 0.8603 MPa\n", "MAE : 0.6585 MPa\n", "\n", "CELL-OUT VALIDATION\n", "Cell-out Q² : 0.936522\n", "Cell-out RMSE : 0.8964 MPa\n", "Cell-out MAE : 0.6904 MPa\n", "R² − Q² : 0.005009\n", "\n", "LACK-OF-FIT\n", "SS pure error : 55.145687\n", "SS lack of fit : 11.464078\n", "df pure error : 60\n", "df lack of fit : 24\n", "F lack of fit : 0.519718\n", "p lack of fit : 0.960670\n", "Detectable lack of fit: False\n", "\n", "DESIGN DOMAIN\n", "RCA : 0–50%\n", "Temperature : 24–600 °C\n", "Extrapolation used : False\n", "\n", "TARGET CONSISTENCY\n", "R² approximately 0.942 : True\n", "Adj.R² approx. 0.938 : True\n", "Cell-out Q² ~ 0.937 : True\n", "LOF non-significant : True\n", "\n", "SAVED OUTPUTS\n", " - Figure_9_RCA_Temperature_Response_Surface.pdf\n", " - Figure_9_RCA_Temperature_Response_Surface.png\n", " - Figure_9_RCA_Temperature_Response_Surface.tiff\n", " - Table_RSM_CellOut_Predictions.csv\n", " - Table_RSM_Coefficients.csv\n", " - Table_RSM_Experimental_Cell_Means.csv\n", " - Table_RSM_Lack_of_Fit.csv\n", " - Table_RSM_Model_Validation.csv\n", "============================================================================\n" ] } ], "source": [ "# ============================================================\n", "# — STEP 7 FINAL AUDIT\n", "# ============================================================\n", "\n", "print(\"=\" * 76)\n", "print(\"STEP 7 — VALIDATED QUADRATIC RESPONSE SURFACE\")\n", "print(\"=\" * 76)\n", "\n", "\n", "print(\"\\nMODEL\")\n", "print(\n", " \"fc = β0 + β1·R + β2·T \"\n", " \"+ β11·R² + β22·T² + β12·R·T\"\n", ")\n", "\n", "\n", "print(\"\\nMODEL FIT\")\n", "print(f\"R² : {R2:.6f}\")\n", "print(f\"Adjusted R² : {ADJ_R2:.6f}\")\n", "print(f\"RMSE : {RMSE:.4f} MPa\")\n", "print(f\"MAE : {MAE:.4f} MPa\")\n", "\n", "\n", "print(\"\\nCELL-OUT VALIDATION\")\n", "print(f\"Cell-out Q² : {Q2_CELL_OUT:.6f}\")\n", "print(f\"Cell-out RMSE : {RMSE_CELL_OUT:.4f} MPa\")\n", "print(f\"Cell-out MAE : {MAE_CELL_OUT:.4f} MPa\")\n", "print(f\"R² − Q² : {R2 - Q2_CELL_OUT:.6f}\")\n", "\n", "\n", "print(\"\\nLACK-OF-FIT\")\n", "print(f\"SS pure error : {SS_PE:.6f}\")\n", "print(f\"SS lack of fit : {SS_LOF:.6f}\")\n", "print(f\"df pure error : {DF_PE}\")\n", "print(f\"df lack of fit : {DF_LOF}\")\n", "print(f\"F lack of fit : {F_LOF:.6f}\")\n", "print(f\"p lack of fit : {P_LOF:.6f}\")\n", "print(\n", " \"Detectable lack of fit:\",\n", " P_LOF < 0.05\n", ")\n", "\n", "\n", "print(\"\\nDESIGN DOMAIN\")\n", "print(\n", " f\"RCA : \"\n", " f\"{R_MIN:.0f}–{R_MAX:.0f}%\"\n", ")\n", "print(\n", " f\"Temperature : \"\n", " f\"{T_MIN:.0f}–{T_MAX:.0f} °C\"\n", ")\n", "print(\"Extrapolation used : False\")\n", "\n", "\n", "print(\"\\nTARGET CONSISTENCY\")\n", "print(\n", " \"R² approximately 0.942 :\",\n", " abs(R2 - 0.942) < 0.01\n", ")\n", "print(\n", " \"Adj.R² approx. 0.938 :\",\n", " abs(ADJ_R2 - 0.938) < 0.01\n", ")\n", "print(\n", " \"Cell-out Q² ~ 0.937 :\",\n", " abs(Q2_CELL_OUT - 0.937) < 0.01\n", ")\n", "print(\n", " \"LOF non-significant :\",\n", " P_LOF > 0.05\n", ")\n", "\n", "\n", "print(\"\\nSAVED OUTPUTS\")\n", "\n", "for file in sorted(\n", " OUTPUT_DIR.iterdir()\n", "):\n", " print(\" -\", file.name)\n", "\n", "\n", "print(\"=\" * 76)" ] }, { "cell_type": "markdown", "id": "a88fc620-9c02-4672-906c-61582024d5e4", "metadata": {}, "source": [ "#STEP8" ] }, { "cell_type": "code", "execution_count": 181, "id": "cf0af76f-4579-44bd-8823-36b4a0bf378f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "===========================================================================\n", "MODEL COEFFICIENTS\n", "===========================================================================\n" ] }, { "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>Term</th>\n", " <th>Coefficient</th>\n", " <th>SE</th>\n", " <th>t</th>\n", " <th>p</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Intercept</td>\n", " <td>21.347898</td>\n", " <td>0.356338</td>\n", " <td>59.9090</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>RCA</td>\n", " <td>-0.049624</td>\n", " <td>0.021230</td>\n", " <td>-2.3375</td>\n", " <td>0.021793</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Temperature</td>\n", " <td>-0.003857</td>\n", " <td>0.001889</td>\n", " <td>-2.0415</td>\n", " <td>0.044340</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>RCA²</td>\n", " <td>-0.000901</td>\n", " <td>0.000376</td>\n", " <td>-2.3952</td>\n", " <td>0.018835</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Temperature²</td>\n", " <td>-0.000022</td>\n", " <td>0.000003</td>\n", " <td>-7.9073</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>RCA × Temperature</td>\n", " <td>0.000084</td>\n", " <td>0.000027</td>\n", " <td>3.1244</td>\n", " <td>0.002446</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Term Coefficient SE t p\n", "0 Intercept 21.347898 0.356338 59.9090 0.000000\n", "1 RCA -0.049624 0.021230 -2.3375 0.021793\n", "2 Temperature -0.003857 0.001889 -2.0415 0.044340\n", "3 RCA² -0.000901 0.000376 -2.3952 0.018835\n", "4 Temperature² -0.000022 0.000003 -7.9073 0.000000\n", "5 RCA × Temperature 0.000084 0.000027 3.1244 0.002446" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "===========================================================================\n", "MODEL VALIDATION STATISTICS\n", "===========================================================================\n" ] }, { "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>Validation metric</th>\n", " <th>Value</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>R²</td>\n", " <td>0.941531</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Adjusted R²</td>\n", " <td>0.938051</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>RMSE</td>\n", " <td>0.860296</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>MAE</td>\n", " <td>0.658515</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Cell-out Q²</td>\n", " <td>0.936522</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>Cell-out RMSE</td>\n", " <td>0.896390</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>Cell-out MAE</td>\n", " <td>0.690432</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>R² − Q²</td>\n", " <td>0.005009</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>Lack-of-fit F</td>\n", " <td>0.519718</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>Lack-of-fit p</td>\n", " <td>0.960670</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>Pure-error df</td>\n", " <td>60.000000</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>Lack-of-fit df</td>\n", " <td>24.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Validation metric Value\n", "0 R² 0.941531\n", "1 Adjusted R² 0.938051\n", "2 RMSE 0.860296\n", "3 MAE 0.658515\n", "4 Cell-out Q² 0.936522\n", "5 Cell-out RMSE 0.896390\n", "6 Cell-out MAE 0.690432\n", "7 R² − Q² 0.005009\n", "8 Lack-of-fit F 0.519718\n", "9 Lack-of-fit p 0.960670\n", "10 Pure-error df 60.000000\n", "11 Lack-of-fit df 24.000000" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "✓ MODEL VALIDATION TABLES SAVED\n" ] } ], "source": [ "# ============================================================\n", "# CELL 73 — TABLE: MODEL COEFFICIENTS AND VALIDATION STATISTICS\n", "# ============================================================\n", "\n", "import numpy as np\n", "import pandas as pd\n", "\n", "\n", "# ============================================================\n", "# PART A — NATURAL-UNIT MODEL COEFFICIENTS\n", "# ============================================================\n", "\n", "coef_table_final = pd.DataFrame({\n", " \"Term\": [\n", " \"Intercept\",\n", " \"RCA\",\n", " \"Temperature\",\n", " \"RCA²\",\n", " \"Temperature²\",\n", " \"RCA × Temperature\"\n", " ],\n", "\n", " \"Coefficient\": [\n", " natural_model.params[\"Intercept\"],\n", " natural_model.params[\"RCA\"],\n", " natural_model.params[\"Temperature\"],\n", " natural_model.params[\"RCA2\"],\n", " natural_model.params[\"Temperature2\"],\n", " natural_model.params[\"RCA_x_T\"]\n", " ],\n", "\n", " \"SE\": [\n", " natural_model.bse[\"Intercept\"],\n", " natural_model.bse[\"RCA\"],\n", " natural_model.bse[\"Temperature\"],\n", " natural_model.bse[\"RCA2\"],\n", " natural_model.bse[\"Temperature2\"],\n", " natural_model.bse[\"RCA_x_T\"]\n", " ],\n", "\n", " \"t\": [\n", " natural_model.tvalues[\"Intercept\"],\n", " natural_model.tvalues[\"RCA\"],\n", " natural_model.tvalues[\"Temperature\"],\n", " natural_model.tvalues[\"RCA2\"],\n", " natural_model.tvalues[\"Temperature2\"],\n", " natural_model.tvalues[\"RCA_x_T\"]\n", " ],\n", "\n", " \"p\": [\n", " natural_model.pvalues[\"Intercept\"],\n", " natural_model.pvalues[\"RCA\"],\n", " natural_model.pvalues[\"Temperature\"],\n", " natural_model.pvalues[\"RCA2\"],\n", " natural_model.pvalues[\"Temperature2\"],\n", " natural_model.pvalues[\"RCA_x_T\"]\n", " ]\n", "})\n", "\n", "\n", "# ============================================================\n", "# PART B — MODEL VALIDATION\n", "# ============================================================\n", "\n", "validation_table_final = pd.DataFrame({\n", " \"Validation metric\": [\n", " \"R²\",\n", " \"Adjusted R²\",\n", " \"RMSE\",\n", " \"MAE\",\n", " \"Cell-out Q²\",\n", " \"Cell-out RMSE\",\n", " \"Cell-out MAE\",\n", " \"R² − Q²\",\n", " \"Lack-of-fit F\",\n", " \"Lack-of-fit p\",\n", " \"Pure-error df\",\n", " \"Lack-of-fit df\"\n", " ],\n", "\n", " \"Value\": [\n", " R2,\n", " ADJ_R2,\n", " RMSE,\n", " MAE,\n", " Q2_CELL_OUT,\n", " RMSE_CELL_OUT,\n", " MAE_CELL_OUT,\n", " R2 - Q2_CELL_OUT,\n", " F_LOF,\n", " P_LOF,\n", " DF_PE,\n", " DF_LOF\n", " ]\n", "})\n", "\n", "\n", "print(\"=\" * 75)\n", "print(\"MODEL COEFFICIENTS\")\n", "print(\"=\" * 75)\n", "\n", "display(\n", " coef_table_final.round({\n", " \"Coefficient\": 8,\n", " \"SE\": 8,\n", " \"t\": 4,\n", " \"p\": 6\n", " })\n", ")\n", "\n", "\n", "print(\"\\n\" + \"=\" * 75)\n", "print(\"MODEL VALIDATION STATISTICS\")\n", "print(\"=\" * 75)\n", "\n", "display(\n", " validation_table_final.round(6)\n", ")\n", "\n", "\n", "# ============================================================\n", "# SAVE\n", "# ============================================================\n", "\n", "coef_table_final.to_csv(\n", " OUTPUT_DIR / \"Table_Model_Coefficients.csv\",\n", " index=False\n", ")\n", "\n", "validation_table_final.to_csv(\n", " OUTPUT_DIR / \"Table_Model_Validation_Statistics.csv\",\n", " index=False\n", ")\n", "\n", "print(\"\\n✓ MODEL VALIDATION TABLES SAVED\")" ] }, { "cell_type": "code", "execution_count": 183, "id": "d8d9ff83-fcd2-4afc-aa4b-34b06f825cdd", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "============================================================================\n", "MODEL ADEQUACY RELATIVE TO EXPERIMENTAL ERROR\n", "============================================================================\n", "Pure-error SD : 0.9587 MPa\n", "Lack-of-fit RMS component : 0.6911 MPa\n", "Lack-of-fit F : 0.519718\n", "Lack-of-fit p : 0.960670\n", "\n", "R² : 0.941531\n", "Adjusted R² : 0.938051\n", "Cell-out Q² : 0.936522\n", "R² − Q² : 0.005009\n", "\n", "✓ No statistically detectable lack of fit relative to replicate-based pure error.\n", "✓ Cell-out predictive performance remains close to the fitted performance.\n", "✓ Strong cell-wise cross-validated predictive ability.\n", "============================================================================\n" ] } ], "source": [ "# ============================================================\n", "# MODEL ADEQUACY RELATIVE TO EXPERIMENTAL ERROR\n", "# ============================================================\n", "\n", "SD_PURE_ERROR = np.sqrt(MS_PE)\n", "SD_LOF = np.sqrt(MS_LOF)\n", "\n", "R2_Q2_GAP = R2 - Q2_CELL_OUT\n", "\n", "\n", "print(\"=\" * 76)\n", "print(\"MODEL ADEQUACY RELATIVE TO EXPERIMENTAL ERROR\")\n", "print(\"=\" * 76)\n", "\n", "print(f\"Pure-error SD : {SD_PURE_ERROR:.4f} MPa\")\n", "print(f\"Lack-of-fit RMS component : {SD_LOF:.4f} MPa\")\n", "print(f\"Lack-of-fit F : {F_LOF:.6f}\")\n", "print(f\"Lack-of-fit p : {P_LOF:.6f}\")\n", "\n", "print()\n", "\n", "print(f\"R² : {R2:.6f}\")\n", "print(f\"Adjusted R² : {ADJ_R2:.6f}\")\n", "print(f\"Cell-out Q² : {Q2_CELL_OUT:.6f}\")\n", "print(f\"R² − Q² : {R2_Q2_GAP:.6f}\")\n", "\n", "print()\n", "\n", "if P_LOF >= 0.05:\n", " print(\n", " \"✓ No statistically detectable lack of fit \"\n", " \"relative to replicate-based pure error.\"\n", " )\n", "else:\n", " print(\n", " \"⚠ Statistically significant lack of fit detected.\"\n", " )\n", "\n", "if R2_Q2_GAP <= 0.05:\n", " print(\n", " \"✓ Cell-out predictive performance remains \"\n", " \"close to the fitted performance.\"\n", " )\n", "else:\n", " print(\n", " \"⚠ Noticeable R²–Q² separation detected.\"\n", " )\n", "\n", "if Q2_CELL_OUT >= 0.90:\n", " print(\n", " \"✓ Strong cell-wise cross-validated predictive ability.\"\n", " )\n", "\n", "print(\"=\" * 76)" ] }, { "cell_type": "code", "execution_count": 185, "id": "619e6113-0abb-4f62-9478-28e48d801439", "metadata": {}, "outputs": [ { "data": { "image/png": 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gDH6fHhUTG0dCQmKaeno+8t/Y2zPjumbHq5Rrmi9vqX8/gh900+akjqHh6Vu3ITmgzY3U//ZT18Unm3+DOblWGR9P88+GVOMvU74YAOau29RfQr7/eROzFq7MsuyM/nu7uTil6Y5PPbkm4cEXwMCQMPOxiuUz/zduqc9EIUThyPUYxdRUhtyvI1iUqdVq2jSpx4atyWPOvlu9ic5tm2Y4TtGYlIRWo8HV2YGAoFBUKhXb13xhHsyd2qMtlY/L1dnR/HPK4Hjz+3uB6fIZ9A+D+di4ePPPGXV5Jcu+JfVRbs5O5p8vXruVo3Mymj3q6pL5vaVuOUnJ52Bny9efjCMiKpoLV25y624gm/7Zw38HT7Bp217aNKtP57ZN8PF0Z8XnUwkODefi1VvcvHuftRv/5cS5yyxd8ycdWzeiTrWKWdbXoNfzQvvm/PDrZs5eus53qzeZ/+Clbk3cd+S0OUj09/Nl4cdj8fF05+LVm7z42sQcPZvMODvac+N2AAB3A4Op4l82+dk88qxSbPj7YYvn5x+OolXjuuh1Wrq/MZkzF6+ly59VI7pbqt+7Oe8No3PbJunyKIqCSqVKMy7v3iNd+3fuZVzX7Ny7H2wuH9L+frg6OZjrmPJ8Vi2YluF/08z+PeZ25nHKv/2UulS2S26lu53Bv8HcXkub6djorPdISBkvCzBpWD9eeq4tVgY9I6Z+yt+pWvrSl6tJ8z6j3hR3Fyfzs7149VamwW5hfCYKISwnVzuzqKxs0rxX4mMzyVl8DXm1m3lW4PkrNxj23jzOXrpGQqKRsIgotu0+zIAx07l49SYA7Vo0AJI/+CbO+JrL12+TaDQSGBLG5h37GTx+ZpruyZxKGTOW+pWyXEqLhrXQPWhN2LH3KFv/O0RMbBx/7zrIjn3HANDptDRvmNzlm7qVcdeB4yQlmbgfFMrSNX9meG0nx4eD66/fumfuQsxKy0a1zQHp/qNn+Py7tQSFhBMVHcP+o6fTTYDITN3qFXFySL7+2YvXWP37VqJj4zhw7CzrU3V1tmlSD0ieQPPzxm0EhYRT1b8sHVs1pE71h8HB3QfBxJJVG9jw926iYmKpXc2fZ1o3onKFMqnyJQczjz7zs//+mKaF6KVUAeGXD8bgWRn0vNChufm4JtUfXa1GnbzMSkgY85f8nKNnkJXU3fgLl6/j1t37hIRFMPeRCT0Z1cXWxpqkpCR+3rCNs5euZ5g/5dkDnLt0Pc0f9NTrac7/5icOHDtLfEICkVExHDh2hskzF/Php0sBqFujovnf0ekLV1m7aTvRsXHs3H+Mv1N1D+dGQFAoX69YT1R0DMfOXEzTMtzsqVrJdWzxsI7/+3Qpp89fNbdw7th3jFHTPmPxj+m7o/OiTdP65p/nLl6VPDkqOJS5i1anylMvX66VU6n/e9tYW6FSwT//HTJ/LjyOlM86gHnfrGbn/mNEx8YREBjCqt+2cuvu/TT58vszUQhROHLVoqh2difp9jXze1PgPZTEBFS6/F1XrDD5+Xry1fQxjP7gC0LDI9m+9yjb9x7NNP+wAd3Zf/QMl67dYs+hUzw3YHy6PM+2bZyvdfR0d2XsGy8zY8EKEo1JDH9vfro877zRG88HXZOtGtXG2dHe/Mey4XOvE5eQgJU+4/9uZX08cHFyICQsgqOnL1L/2eRZj1ntolHKzZlJw/ubZz0v/GEdC39YZ07v170Tz2bQAvUoaysDH4x5jbH/+xJjUhIfzP+eD+Z/nyZP/x6dqFW1AgAnzl7mlz+2Z1iWVqOhWYPkwGr3oZPmCS2PsrWxon6tytnWDR50MVevyNHTF81LJHVs1SjNOMd6NSvh5uJEUEgY5y7foHm3IQD4PRhz9jj693iGdZt3cjcgmJPnrtC+z2gguRs/Ix1aNeSn3/8B4LV3PgGSn7GHmzP3AkPS5a9bvRJbdia3PA178HuVMuShfcsGPPd0Uzb+s4fb9wJ5dfRH6c7v0rEFkByUDh/Q3Tzr+b053/DenG/MdQ0Jy/3aqi5ODixcvo7Pv1ub5niLRrXN/537dunA9r1HOXDsLGcuXqPHW1PSlZOTSWo5MXxgd/YeOfVg6MMJWr00LE16WV9Phg/skS/XyqkOrRpy6nzyKhNTZn/DlNnfoFar8PUsxY07AY9Vdp8u7dm5/xh7D58iMDiMN9+dnSb912+Sh8oUxmeiEMJyctWiqPWvlvZAkpGE4/vysz5FQqO61dm0bDYjBvWgVtUKONjZoNNq8HBzplHdakwa1o+yD8YQOTnY8dNXHzBiUA+qVvTDxsqAXqfD28ONpk/VYMLbfWnRsE6+1/HVHs/w7ZyJtGpcJ3k2q1qNk4MdLRvVYcnsd+mfar06O1sbvpk5gfo1K2NjbYW1tYGXOrfJdDazQa9n/vvDqVWlQq62Kez5XFtWfjGNZ9o0xsPNGZ1Wg4OdDbWqVKB+zUo5LqdDq4asWjCNTq0a4ebihFajwd7WhoZ1qjL3vWFMHNrPnLd9iwZ0at2IMt4e2NpYoVGrcXa0p0Wj2nw7Z6J5wkDXTi1p27Qe3h5u2FgZ0KjVuLs60aFlA1Z8NjXD7rGs7jPt+zZp3jvY2bJk1gQa16uOrY0VTg52dH+2NfOmpg0k8sLR3pYVn02lQ8sG2FhbYWNtRdum9Vj55fsZ5p/wdl/69+hEKTdnDHoddapX5NvZ71La2yPD/L27tKNPl/Z4uDmjVqftflSpVMyaPISZk96mUd1qONrbotVocHNxolbVCrz5yosM7NXZnH9Az2eZNnoQZX090Wk1lPHxYOLQfvR+sV2e7r1CWR+WzH6XOtUrotfpcHV2oF/3Tnw2baS5q1Sv1/HtnIlMGfEqdapXxM7WGp1Oi6e7C0/VqsLI114yB7OPy8XJgTUL/8ebfV+kQlkfDHodep2OCmW9eaPPC6xZ+L8Ml8axpEE9OzN8YA98PN3R63RUqVCGLz4cTb1c/PvLjF6n5ZuZE5g6aiD1alTC3jb5c9Hd1Yk2TeuZu9kL6zNRCGEZqtjosBwPFjHFxhA5Z3yaJXJUtvbYDZmKxjnzAerGm1eKzRI5Qoii49EFt1MvTyOEEMLyctWiqLa2wapD9zTHlOhIor76H/EHd2CKfTjZxRQRRvzh/4hc8AGxfz3+2CwhhBBCCFGwcr00vqFRG0zBAcT/93C/YyU6gth1S4ldv+zhXs8JD2fXasrlbPyXEEIIIYQoOnLV9ZxawtE9xP7xE0p09mtmaSvWwG7g2LxcRgghhBBCFJJcdT2npq/bFIfxc7Du/hq6Wo1Qu3qAlTWo1WCwRu3pi75+c2z6DMW238j8rLMQQgghRJG2cdMfNGrcHFd3b8pVqMI3S74DIDExkVFjxuHt64e3rx+jx47DaDSaz8suvaDluUVRCCGEEEKkt2XLVt4eOoLvv11Es2ZNiYiI5P79+1SuXIn/ffQxGzf+wfp1yUt9denagxdffJ5JEycAZJte0CRQFEIIIYTIR81atGHQwFd5bdCAdGn+laoza+bHdOv6IgC//LqeiZOmcOHcqRylF7RcT2YRQgghhCgq4uLiSMjhnvV5lXr70BQGgwGDIf12nNHR0Rw9eoyI7t2oXbcB4WHhNG/RjDmzZmAwGLh9+za1az3cZat2rZrcvHmL8PBwTCYly3RHx4y3BbUkCRSFEEIIUSzFxcVRtWpp7t3P/W5PuWFra0t0dHSaY5MnTWDK5Inp8oaGhaEoCqtWreb3337B1cWF4SPG8Nrrb/L1118CpAn4Un6OjIoyb5uaWboEikIIIYQQOZSQkMC9+4lcP1IHB3tN9ifkQURkEmXrHePShdPY2z/cbSmj1kQAO9vkLV2HDHmTsmXKAPDelInUqFUPtTp5DnFERARubq7mnwHs7ewwmZQs0wuDBIpCCCGEKNbs7FXY2auyz5gHJpLLtbe3x8HBIdv8Tk5OlC7ti4r09XF0cMDHx4fjJ05Qvnw5AI6fOImvr6+5tTC79IKW5+VxhBBCCCFEeq8NGsBXCxdx+84dYmNj+fiTmbRp3Qo7Ozv69+vDrFlzuXcvgHv3Apg9ey4DB/Qzn5tdekGTFkUhhBBCFGtJKCRhmUVc8lLuO2NHExISSqPGzQFo2bIF3y5ZBMDEd8cTEhJK3foNAejVqyfjxz3clCS79IImy+MIIYQQoliKiIjAw6sM9y9adoxiqYrHCLh7I0ddz08aaVEUQgghRLFmQsFkoRZFS5VbXEigKIQQQohizYTJPOnEEmWXZDKZRQghhBBCZEhaFIUQQghRrJkUBZNioa5nC5VbXEiL4gOKohAREWFeFV0IIYQQoqSTQPGByMhIPLzKEBkZWdhVKRCxcXGFXYUiSZ5LxuS5ZEyeS8bkuaQnzyRZSFgEb0yYyTP938nXclMms1jqVZJJoFhSSctpxuS5ZEyeS8bkuWRMnkt68kwICYvg3U8WcvNuIFX9yxZ2dUQOyRhFIYQQQljcur92moPEj8YNzteyi9qC208SCRSFEEIIYXEDXnoWK4Oerp1aYmNtVdjVETkkgaIQQgghLCIkLAKVSoWzoz0ajZq+XTtY5DqKBccSKiW8RVHGKAohhBAi36WMSZzwyUJCw0vGRNEnkbQoCiGEECJfPTpxxaDXWfR6SYpCkoUmDFmq3OJCWhSFEEIIkW8eDRI/GjdYxiQWY9KiKIQQQoh8UVhBovLgZamySzJpURRCCCHEY0tKMjF1zhJpSXzCSKAohBBCiMem0agZ0PNZalf1L/AgMWUdRUu9SjLpehZCCCFEniUajei0yeHEU7WqUL9mZVQqVSHXSuQXaVEUQgghRJ6EhEUwbMp8/vnvkPlYYQSJSYplXyWZBIpCCCGEyLWUiSs37gTw1/b9mEymwq6SsADpehZCCCFErjw6u/mDsa+hVhde25PpwctSZZdkEigKIYQQJUxUdDTb/9tHcGgo1lZWNGlQn9I+Xjk6V9ZJLFkkUBRCCCFKiLj4eOYt+IZf1v+JLkGLLkGHSWPiI+3nNHqqLu9NGEEZX59Mzw8NjyySQaIJFSYsMzbSUuUWFxIoCiGEECVAfEICg4dP4MKRq/iGeGOXZGtOS1AlcH7bFXqdHcqP335O+bJlMixDr9dhZ2tTpIJEYVkSKAohhBAlwKLvV3DhyFXKBZZF88hcVr2ip3SUD7fv3GPU+Gn8tvrbDGcv21pb8b9xg1FBkQoSTUryy1Jll2Qy61kIIYR4wsUnJLDq599xD3VLFySm5hVdips37nLkxCnzsZCwCD77bg1x8QlAcrBYlIJEYVnSoiiEEEI84Q4dPUFijBEHo12W+dSosQu3488t/1K/ds00E1dsrK0Y3Pv5Aqpx7phQkSRjFC1CAsUCoigKcXGxJCYmoBSBtaYSExNJiI8t7GoUOYX1XNRqDQYrK3Q6vexoIITId2HhERgUPaocBD06o5ag4NB0s5v7dmlfADUVRY0EihamKArhYSFER0ehmEyoNRrUak2hfz9RgCSjsZBrUfQUxnNRUEhKSkIJM6HVanFwdMbGNutv/UIIkRu2tjYkqowoKNkGi0aNEStrqyI5uzkzSRZsUbRUucWFBIoWpCgKIcGBxMbG4OTsjp29I3q9VZFoMcrJh0VJVFjPRVEUYmOiiAgPJiQ4EAUFW1v7Aq+HEOLJ1LBebUx6E9GamDSznR9lwkSkYzQXbwYSk0CxCBKFZclkFguKiY4kNiYaD8/SuLp5YjBYF4kgURQ9KpUKG1t7PLzKYu/oQmhwkLT4CiHyjY21Nd1e6ESAY/IX0cwEWgVhbW9NdLxSrIJEk6Ky6KskkxZFC4qJicbaxg47e6fCroooJlQqFa5uXkRFhBITG429vWNhV0kI8YQY9sYA9h04yrXzN/EO88Sg6M1pSZi4bxVIuEcE382fTVSskbo1KhWLIFFYlgSKFmIymYiPi8OtVOYr3AuREY1Gg7WtPXExMRIoCiHyjYO9HT98M5/3PprL9l37sI+3RRWtBr1CpG00Hp5uLP14LjWrVSnsquaa7MxiORIoWkhSUhIAer3hscq5dO0mk2YsAJLHsU0Z+Tq1q1V87PqJok2vMxAVF1PY1RBCPGEcHRz4fNYH3LkXwOZtOwgKDgGVmkMnrxBvBL3BprCrKIoYCRQtRFGSl8BRqR9vGKizowPfzJqCo4MdF6/eYOLHX7L2m1n5UUVRhKnUahSlhG8HIISwGG9PDwb26WleAici1khV/7KU9i5V2FXLkyTUJFlo2kVSFmM6SwIJFC0suwbr1j3e4MbtewDY2lhTp3olpk8YShkfTwBcnR92Peq0WjRay80/+mrZGpav3UREZBQtGtVl+oShuLk4ZZo/IjKKT75cyrbdB4mJjaN1k/p8OO4tnB0d0uV9a8LHbNm5j5ULptO4Xk3z8QVLf+aPbbu5dvMOjvZ2dGzdhHfe6oetjbUlblEIIcQDj66TWFwmroiCJbOeC1FUdAw37wTwzez32L9xGT8vmkFEVDRTZy9MlzfRaOT9uYsY+Vpvi9RlzcatLFy+lg/GvsnaxbOIiIxm5NQ5WZ4zfvrnXLhynSVz3mPNopmER0ZleM66P/8lJi4uwzIOnzzHG327sWHpfD79YCy79h9l2rzF+XJPQgghMvakBYky69lyJFAsRGcuXkWr0dDsqVq4uzpT1b8crRrV49L1W2nymUwmxn4wnw4tG9P0qdoWqcvyNRsZ9PKLdGzdhGqVyjNz8gj2Hj7B+cvXM8wfFxfP1l0HmDR8EDWr+FPF349pY97gvwPHuHj1hjnfvfvBzF28gk8mDs+wnO/mTuXFjq0oX9aXhnVrMOaNvmzdud8i9yiEECLZZ9+ueWKCRGFZ0vVciM5evErViuWwsjKgKAonzlxk7aZ/qFU17WSVSTMWULlCWfp2eybL8ibP/IrfNm/PNP3Fjq2ZPmFIuuPxCYmcvXSNKSNfNx8r4+OJr1cpjp0+T+UKZdOdk5iUhMlkwmB4uLyCwZA8cefoqfNULFcGgImffMGQ/i/h4+meZd1ThIRH4GCf+WKwQgghHt/QAd2w/fkPhg3o/kQEibIzi+VIoFiIzly4wukLl6nRtidGo5GERCN+pb2ZOHygOc+OvYdZ/9e/1K1Rhf8OHMPRwY6vZ0zKsLzRg/swuE+XTK9nZ5vxbLaw8AhMJhOuLmmXYnFxciQ4NDzDc+xtbahZ1Z8vvlvN7Ckj0Wq1zF+8AoCgkDAAVq77i0RjEn26dsq0TqlFREWzZOV6XnquXY7yCyGEyLmIqGhsra3RaNSUcnVm/Nt9C7tKohiQQLEQnb14lbFv9qNT6ybcvBvA5BkLmDl5BGV9vcx5WjWpz7mdv+aoPDcXpywnn2Qmr5Nr500dw+hpc6nbsS8atZo+XTvh7uqMWq3m9r1APv9uNWsXz8xRWfEJiQyZOIPS3h681a9H3iokhBAiQyljEqv4l2XkoJ5oNE/WyDOToiLJQmMJS/oYRQkUC4nRmMSFqzd4v3Y1/Ep741fam37dOzPn6+X8tHBGnsrMa9ezs5MDarWa4JBw/P1Km4+HhIWnmXX9qAp+vvy+dD7hEVGoVKDX6Vjx65/4epXi9PnLBAaH0ualN9Oc88rw9+j+bFtmTh5hPmY0JjHyvdlEx8Sy4ov/odVqcnHXQgghspJ64oqdrQ2JRiMajT77E4sRBTWKhaZdZLXlYUkggWIhuXTtJgkJiVQs9zAwe7p5A2YsWEpAYDAe7q65LjOvXc8GvY6q/n7sPXKCRvVqAHDzzj1u3b1PneqVs72uo4MdAL9v2YFWq6HpU7XR67T88cPnafI988pwPpk4jBYN65qPmUwmxn44n2u37rL6q49lWRwhhMhHGc1utjI8WUGisCwJFAvJ2YtXKeXmgoO9nflY+bK+lHJ15p//DuZ4XF9qee16BujXozMffbqE6pXK4+vlwUefLaFJ/ZppJrLM+moZAYEhzH1/NADb9x7GoNfh7eHOoeNn+GDeYoYN6ImLU/I6ihlNgint7YFnqYdB8KQZC9h/9BTfzZ1KotFIYHAoAC5ODmg00rIohBB59aQtgZOVJCw36STJIqUWHxIoFpKzF6+m6eZN0aheTbb+dyBPgeLj6Pl8e4JCwpgyayGRUdE0b1iH6e8OTZMnMDiUOwGB5vehYRHMXbyCwKBQvD3cGDW4D4NefjFX1/15w98APPfqqDTHd/76Db5eHnm7GSGEKOGiY2JLTJAoLEsVGx1WsjvfH4iIiMDDqwwBd2/g4JB+Z5HcSkiI5/69O/iW8cdgVfT2zlRQUJXwKf8ZKSrPJSQ4gIiwILx8yhR2VQCIjY3F2lqGBTxKnkvG5LmkV9DPRFEUlqzeyNmL157oIDHlb/fGkw2wtbdM21d0pJHnah7Mt/iguJEWRSGEEOIJo1KpeP3l50hITMSglzGJIu+erPnxQgghRAkVEhbBhI8XcvPOfSA5WCwpQaIJlUVfJZm0KAohhBDFXOqJK8vW/smUEa9mmC8x0cjOvfu5ePkqABUrlKNlk0bodBIOiIzJb4YQQghRjD06u3nM4F4Z5lv7+yY+/+p7YsLisIqxQgXE2sRh5WBg+NsD6NX1+YKteD4yocZkoU5SS5VbXEigKIQQQhRTOV0CZ8nyVSz4ajlegR74Gn3Mk/aUcIWwwHBmzPyKkNAw3h7Ur6BvQRRxEigKIYQQxVBOg8SzFy7xxcKllA/ww8aUdua1ChXORicMAQYWffMjLZo0pEbV7DdaKGqSFDVJimVa/ixVbnFRsu9eCCGEKKZCwyMJDY/Kdp3ElWvW4xDpkC5ITM3GZI1DhD0rf15vodqK4koCRSGEEKIYqlDWh9lThma7TuLmrTtwiXHKtjyXWGc2/7MzH2tYcJQHYxQt8bLUHtLFhXQ9FwOXrt1k0owFQPIiqlNGvk7tahULuVZCCCEKWkhYBHsPn6Lz000B8PP1zDK/oijExMaiM+myLVtn0hGfEE9iolFmQQsz+U0oBpwdHfhm1hQcHey4ePUGEz/+krXfzCrsagkhhChAqcck6vU62rdokO05KpUKG2sbEtQJGJKyXlMxQZ2AQW8olkFikpL8slTZJVnx+214Au3Ye5glq9Zz4uwlEhMTqVnVn7Fv9qNhneoAuDo7mvPqtFo02pLTDP7VsjUsX7uJiMgoWjSqy/QJQ3Fzcco0f0RkFJ98uZRtuw8SExtH6yb1+XDcWzg7Jm+7NP+blWz4eyd3A4Kws7WmWYM6TB4xCHdXZwBadh3M7Xv305W7bskcalevZJF7FEKI7Dw6caXZUzVzfG6n9q3Yvmof9hF2WeYLtQmjY7tWj1tV8YQpORFHEfXZt6sYNmUWrRrX45dvZvHLktmUL+NL/xHvcer85TR5E41G3p+7iJGv9S6k2hasNRu3snD5Wj4Y+yZrF88iIjKakVPnZHnO+Omfc+HKdZbMeY81i2YSHhmV5pwKZX2YPmEIf69ewJLZ73EnIJB3/vepOX3dd3PYv3GZ+TVsQE883V2pWdXfUrcphChBFEXh6MnTTJk+l1eHjOON0ZP54adfCI+IyPScnM5uzkyfl7oQbh9JjDom0zwx6ljC7SPp0/PFXN1PUZGE2qKvkqxk330h+/PfPSz4/meWzp/G63264u9Xmqr+5fj43aFU8CvN4h9/Nec1mUyM/WA+HVo2pulTtQux1gVn+ZqNDHr5RTq2bkK1SuWZOXkEew+f4Pzl6xnmj4uLZ+uuA0waPoiaVfyp4u/HtDFv8N+BY1y8egOAFzq0okn9Wvh6eVC7eiXe7NuNIyfPmctwdXbE3dXZ/Ppn90Fe6NgKtVr+qQghHs/9wCB6Dx7JwGHv8u9/V7l5W8+Fy0ks+HY9rZ7vzYqf16U753GDRIAqFSsw/O0BXPO4SYg2DIWHfakKCqHaMK553GDIm/2oUaX4LY0jLEu6nguJyWRi1lfL6N2lI/VrVU2TplKpqF+zKgeOnTIfmzRjAZUrlKVvt2cKuqqPZfLMr/ht8/ZM01/s2JrpE4akOx6fkMjZS9eYMvJ187EyPp74epXi2OnzVK5QNt05iUlJmEwmDIaH43AMBgMAR0+dp2K5MmnyR0RF89vfO3jqkeef4vzl65y9eJX508ZmeY9CCJGdsPAIBgwbT2SUFf7luqBWp/7zW5Wo6PvMWfA9JkWhf69u5pRf/tj+WEFiitf7vYyrixOffvkdgaFBWMdZg6IQax2HjZM1k4cMo8cLnR/zLguPoqhRLLTeoaXKLS4kUCwkJ85e5Pqtu/SaPiHD9Lj4eLQaDZA8hnH9X/9St0YV/jtwDEcHO76eMakgq5tnowf3YXCfLpmm29naZHg8LDwCk8mEq4tjmuMuTo4Eh4ZneI69rQ01q/rzxXermT1lJFqtlvmLVwAQFBJmzrd+83amzPyKmNg46tWownfzpmZY3ro/t1G9UnkqlS+TYboQQuTUDz+vIywcyvg2QaVSpUu3sy2Ft2dL5i5YwnMd2uLi7ATAwF6dsTLo6f5s6zwHiSm6du7E8x3b8d/+Q1y6chVFUfAv50eLJg3RaiUcEBmT34xCcvbiVTQaNZXKp28ZAzh36RqVHrSatWpSn3M7f80wX1Hn5uKU5eSTzCh5nGU2b+oYRk+bS92OfdGo1fTp2gl3V+c0XcftmjekdrVK3A0I5NMlq5g0YwFfPhKwm0wmft+yk9ezCHKFECIn4hMS+GXjZlycG2UYJKaws3XH2sqdH9f+xis9u+HsaI9Wo6Ff9075VhetVkvrZo1p3axxvpVZFFhyLGFJH6MogWIhSUw0otVo0WjS/wKeOn+ZU+cvM/qNvoVQs/yV165nZycH1Go1wSHh+PuVNh8PCQtPMwv8URX8fPl96XzCI6JQqUCv07Hi1z/x9SplzmNna4OdrQ3lSntTvowvTV8cyMWrN9J0Te8+dJygkDCe79Ayl3cshBBpXbh0hfj4JOxss17zEMDKyoflP2/g9JX7zJj4Nk4OWc9UFsLSJFAsJJUqlCU+IYGT5y5Rq+rDxbPj4uKZMvMrWjd9ijZNn8p1uSt+/QNFAZUquVWuX/dn87Pa6Zy9dJWo6Fga1K6WYXpeu54Neh1V/f3Ye+QEjerVAODmnXvcunufOtWzH2zt+ODD9fctO9BqNZlOAEoZ1K15ZLLKuj//pXmjuri7OGd7LSGEyEpCQiJajS7L1kQAk0khMdFETGwcNtZW6IvheoaFJQkVSWT9fB+n7JJMfgsLSaO6NahXowpjP5jP1DGD8fP14sKVm3z27So0ajVf/G9cnso9ff4KLz3XjjUbt/LSc+3yudbpbd6+l4p+mY/hy2vXM0C/Hp356NMlVK9UHl8vDz76bAlN6tc0T2SZ9dUyAgJDmPv+aPM52/cexqDX4e3hzqHjZ/hg3mKGDeiJi5MDiUYjny1ZRfuWjXFzduTO/SDmL/6RGpUr4Ffa21xGTGwcW3bs45OJw/JUbyGESK2UuyvxCdEYjfFotYYM85hMCtExscTHh+Pq4vRYE1eEyE8SKBYSlUrFd/OmMnPBMsZMm0dIWASKojDu7f4M7tMVrVZjzrv/yCk+/241SUlJ2NpY8+3cqezcd4RFK34l0WikfBkfZkwaDiSPbaxasZz5/3Prr+17+Hr5L2g0ampVrcj7Y94AoP3LQ9iyagEqlYrPvl2Ff9nSODvZs+znjVQo68u/ew4yZ+robErPnZ7PtycoJIwpsxYSGRVN84Z1mP7uUHN6YHAodwIC05wTGhbB3MUrCAwKxdvDjVGD+zDo5eR1wVSouHTtJms2biU8IhI3V2daNKzL6MF90oxh3Lx9L2q1mvYtGuXr/QghSqbSPt5UreTP/cDLlHJP3/uSEiQmJRmJjrnGzGnvSZCYSyZUmCw0ltAkLYqisDjY2zH93aFMf3cocXHxdHltLLfv3U8TJN64fY8PP/2GH7/4CCdHexISE7l28w5ffLeaZZ99iI21FWM+mMfp85epVKEsRqMRrVaD0WjE2irjb66ZuXL9Ft+uXM+PX36ErY01fYZO5sr1W9jb2+Lu6mzuNjl59hIvdmiFX2lvvDzc+OnrT9BoNNmUnjdDXn2JIa++lGHa7PdGpTvW9Zk2dH2mTYb5tVpNjmaLZ1WGEELkxYDe3Zj00ac42PtgZfVwnLWiQHRsHElJSYSEnaJcWS/aNH+yJpqI4k0CxSLCysrAVx+/y4atuwgLj8TJ0R6A3zZvp3+Pzub3ep2ODX/vJDQ8ktfGfghAeGQUKpWKi1du4F+utPn/U0tKSqLr66m7sxVARc3KFcytdH9s203fbs9ga2MNgEajxsrKwKlzl6leubz5zOu37lLW14vwiCjsbG0sFiQKIcSTok3zJrz+yk2+XfELTo7VcXEuj0ajR6UCkymCwOCT2NslsGjefFngPw9MihqThdY7tFS5xYUEikVI+bK+6bbnS0g0kpRkApK3fjKZTCQkGhk2sCddOiW3eiUajei0Wn7e8DfVK1fg1PnLVK9cIU05Go2G37+fZ36voKB6pDk90ZhEfEIiAMdPX8BoTMLbw50/t+3Bzzd5DN/ug8fNrYtnL12VNQaFECKHhg1+lcoVy7N42U+cv7QOKyt7TElGFBLo2rk9bw3oQyl3t8KuphBpSKBYxL38QgdGvT+H37ZsR4WKRTMn06drJ8ZMm8fq37ag1Wp5oUNLej7fnjMXrtCpTVP++ncPndo0zfW1er/YkZHvz2btpn+ws7UxTxJpWLc6781ayJ2AQAKDQ82ti6W9PTl0/Ay9h0xi2WcfoNfp8vXehRDiSdO+dQvq16nNiPfm0KReVRrWqUbVSv7Y2doWdtWKNQUwWbDskkwVGx1W0p8BABEREXh4lSHg7g0cHBweu7yEhHju37uDbxl/DFYZLwFTmDJqURRF57mEBAcQERaEl0/RaLGNjY3F2tq6sKtR5MhzyVhJfy5xcfFcunqNhMREPEu54+3pYX4mqfdurlm5PDMmviVdzY8h5W/34qMdsLa3TGNFbGQib9Tdkm/xQXFTbFoU4+PjGT1mHNv+3U5wcAje3l6MGTWCV1/tB0BiYiLjJkzi55/XANCr10vMnvmJbEskhBCiQASFhPDtDz/xy/o/McYZ0ag0xJniqVenBq/26U6dWjXNQWJV/7JMGzNIgsR8kqSoSbLQWEJLlVtcFJsoymg04unpwR8bf6NcOT8OHDxEl6498PHxoV27tsyYOZu9e/Zy+OA+ALp07cGs2XOZNDHjvZSFEEKI/HLrzl36vzGa2FvxeEaUwi7JFhUqElWJ3Nh2l1HHPsTbzw+V3p6q/mVlnURRbBSbMNnW1pap702mfPlyqFQqGjVsQMuWLdizdy8Ay5b/yIQJ4/Dy8sTLy5Px499h6bIfCrnWQgghnnRJSUm8PWoyxmsmyoWWwT7JzjyERafo8Iorhcd1Dy6fO4+bo5UEiRaQstezpV4lWbG9+7i4OA4dOkKNGjUIDQ3j9u3b1K5V05xeu1ZNbt68RXh4eIbnx8fHExERYX5FRkYWVNWFEEI8QfYePMLtm/fwifTMeIyzCmyM1riHu6I1xkqQKIqVYtP1nJqiKLw9ZAT+/uXp8uLz3L5zBwBHx4eLmKb8HBkVleZ4itlz5jH945npjsfGxaHLh9m7RmMiSqr/FTWKolAE5mwUOUXnuSiYFIXY2NjCrgiQPDhfpCfPJWMl7bmsXvM79qH2qFBn8mmvwsbKgI/Jg0NHT3D56nVsrK2wMhiwyuXGCE+S/JzwZEKFSbHMh7fszFLMKIrCiJFjuHDxIn9sXI9arcbO1g5Inv3k5uZq/hnA3s4uw3LGvTOGEcMfbgcXGRmJf6XqWFtZ5csvb0KCGlWq/2Wk95BJ7D96CgBrKwN+pb15q193nm/fMl2eTz8YywsdWpmP370fRIuur2Mymbiy93cAAgKDmfnVMnYfPE54ZBQebi7Uq1mFmZNHoNfp0lwvte/nvU+rJvUf+57z01fL1rB87SYiIqNo0agu0ycMzXTP6IjIKD75cinbdh8kJjaO1k3q8+G4t3B2dMh9markrf5y89xTP1cbayv8/Uoz5o2+tGxc7zGegAq1SlWkZo4WpboUJfJcMlaSnsuduwHYGK3SfNIrikJcQgLWhgeBoFqNxqTFFK/Qtf+b5vVxa1arQv9eXejQpqVMvhRFUrHqelYUhVGj3+HQocNs/H2duaXQ2dkJHx8fjp84Yc57/MRJfH19M2xNBDAYDDg4OJhf9vb2BXIPj3q9dxf2b1zGXz9+wQsdWjJ62jzOXLiSJo+nuyvr/tqe5thvm3fg4eaS5tiQSTMIDA5l0cxJ/L1qAdMnDMXG2hpT0sPVpVKut2/jUvZvXMb+jcto8lQti91fXqzZuJWFy9fywdg3Wbt4FhGR0YycOifT/OOnf86FK9dZMuc91iyaSXhkVLr8uS0Tcv7c4eFz/X3pfKpXrsAbE6Zz7eadnN+0EKLYMhj0JKVaxU9RFKJj4khIMBIXnwBAQkIC4ZERJCQZKeXakCqVelKpQlfu3bfmvU8W8PY775W4ltj8ZEJt0VdJVqzufvSYcezdu4+NG9bj7OyUJq1/vz7MmjWXe/cCuHcvgNmz5zJwQL/CqWgu2NhY4e7qTGlvT958pTsOdrbpWv06tWnKwWOnCQoJMx/7bfN2nu/wsOUxPCKKo6fOM2nEIOpUr0xpb0+aN6zD9AlD0nRtpFwv9auoLZS9fM1GBr38Ih1bN6FapfLMnDyCvYdPcP7y9XR54+Li2brrAJOGD6JmFX+q+Psxbcwb/HfgGBev3shTmSly8txTpDzX8mV8+GDsm2jUanYfPA7AT79v4dl+I6jWugfNu77GvEUrMBqTHuMJCSGKkmZNGxDtEAM8DBKTTCa0GjUGgx6j0UhMbCxx1omodXqcnPzQqHXodDaUcquGX+lOHD91g/c+mZfNlURxMPiNt3FwcsetlI/5tW//AXN6YmIio8aMw9vXD29fP0aPHYfRaMxxekErNoHi9Rs3WLR4CRcuXqJy1Zrmhz98RPLuIRPfHU+jRg2pWz/51ahxI8aPG1vItc45k8nEX9v3EB4Zla77wcHeluYN67Bx6y4Azl68SlBIGM0b1DHnsbGxwtrKwN879pOUVLhByOSZX1Gjbc9MX5NnfpXpufEJiZy9dI2m9R+2cpbx8cTXqxTHTp9Plz8xKQmTyYTBoDcfMzzo6jl66nyeykyRk+eeEa1Wg1arJfHBP2xFUZg0fBCbV37JR+OHsGbjVlb9tjnLMoQQxUePF54lyiqKaFVMmiDRxsYaFRAbFw8qDSGOYbi5V0at1qQ5X6PR4+PVnL/+2cmNW7cL5yaKuZS9ni31yq03Br9G0P3b5lfjRg3NaamX8zt8cB97du9l1uy5OU4vaMVmQETZMmWIjQ7LNF2n0/Hp/Dl8Oj/r7sSiZuGytSxZuZ6EhESMSUl4urvybNtm6fJ16dSar5f/woCez7P+r+08374lWu3DDxudVsv0CUOYMmshS1auo27NKjRvUIcezz2dZqxeyvVS27VuSbrxfI9j9OA+DO7TJdN0O9vMd6oJC4/AZDLh6pJ2yICLkyPBoelnsNvb2lCzqj9ffLea2VNGotVqmb94BYC5JTC3ZaaW3XN/VKLRyJKV64mKjqFB7WoAvPxiR3N6aW9PBr38In/9u5t+3Z/N8tpCiOLB08Odfn168O03q/GJ8cZOZWMOEk0mE4nGRO67h5LooMKjVI0My9DpbLC3K83a3/9kzJDXC/YGngBJqEiy0KSTlHIfXR3FYDCYGyZyY9nyH5k182O8vDwBGD/+HSZOmmJe9zm79IJWbALFJ1Wfrp149aXnCAwJ46PPljBi4Mu4OqcfV9m2WQMmfvwll67dZMPfO1k4YxKxcXFp8nTp1IanWzRiz8HjHDpxhu9/+p0lq9bz23fz8CzlmuZ6Cg8n9zraP5zwk2g08tmSVew9fAK1Wo1ep2XEoN5899NvLJo52Zzvh1/+4OqN20wdPThdXd1cnDKdeJIdJQ8TxOdNHcPoaXOp27EvGrWaPl074e7qbN7xIC9lpsjJc4eHAXh8QgJ2NjZ88M6bVK9cAYCjp87x6ZJVXLh8ncjoGIxJSXiVcst7pYQQRU6d2nWw9/yHG9qbuCnO2EbaolHURKljCHAKxMrWhUr+HdFqM18ax6B35dzFqwVYa5Eb/pWqp3k/edIEpkyemGHelatWs3LVajw9Penfry8jhg9FrVZnu5yfyaRkmZ7ZvAtLkkCxkDk62OFX2hu/0t7MmzqGnm9N4K8fv8Td1TlNPr1OxzNtmzJpxgJsrK2oXa0i+46cTFeeva0NHVs3oWPrJgwf9DJP93yLVev/YvQbfdNcL7M9jd+btRAPNxfWLJqJWq3m2s07xMbFc+X6w+6Q6JhYlq/ZyJpF6ZcXguSu5982b8/0nl/s2JrpE4ZkmObs5IBarSY4JBx/v9Lm4yFh4RkG0AAV/Hz5fel8wiOiUKmSn9WKX//E16tUnstMkdPnnhKA29pYp/lvFx0Ty6AxH/Jc+xaMer03jg72bNq6i583bM3yukKI4qVdiwYY/jeRMl5ubPp7G3v3HCY+IYFS1i5En0ukQrkXst2uT0FBoyk2I8KKFJNiweVxHpR76cLpNBNfM2tNHPL2m3w8/X+4uDhz6PARXuk3ELVazYjhQ4mKjgIyX85PedCykZvl/ixNAsUipIKfLw3qVOfL73/ig3feSpfepWNrXh4yidGD++SoPAc7W9xdnYnJoAUsI6fPX+bMhSt8MnEYKlXyPwy/0t5A8ji7+IREDHodS1atp+fz7XFyzHim+ON0PRv0Oqr6+7H3yAka1Uvuorl55x637t6nTvXKWdbf0SG5ZfT3LTvQajU0far2Y5cJOXvuKQH4oy5fv0V4ZBTvDh2ArU3yciH37gdne00hRNEXEhZBQFAIVf39AGjRMPkzZ8igfgwZlDyZ8n5gEO269SMhMRorQ9ara8THB1C7+tMWrbPIO3t7exwcsh+mVbduHfPPjRo24J2xo/hx5WpGDB+a7XJ+JpOSZXphkECxiHn1pecYNOZDhg3sla5VsWHdGhz6cwX2dukDrcCQUN758FN6Pd+eyhX8UKlg/V/bOX/5OpNHvGbOFxMTR2BwaJoWRXtbG6ysDGzevpdnn25uDhJTq+Dny5Ubt3B3ceavf/ew/rvMZ+c9TtczQL8enfno0yVUr1QeXy8PPvpsCU3q16RyhbIAzPpqGQGBIcx9P3ki0/a9hzHodXh7uHPo+Bk+mLeYYQN64uLkkOMys5LVc8+Ot4c7Oq2WZWs28ly7Fuw+eIy/tu/BpgStMSfEkygkLIJ3P1lIYHAYMycPoVK50hnmK+XuRsumDTl64jw+Xk9lWl5cXDjRMffo9vwzlqryE81kwTGKj7vgduqW5NTL+ZUvXw5Iv5xfdukFTQLFIqZJ/Vr4lfbi21XreXfYwHTpqYOf1OxtbKhSwY8vvv+JW3fvo1arKV/Ghy/+N45mDWqb8y1ZtZ4lq9anOfd/496mb7dniIqJpbSPZ4blVyxXhktXb7Jq3WaGDuiJQW+5JXV6Pt+eoJAwpsxaSGRUdPIyP+8+XBw9MDiUOwGB5vehYRHMXbyCwKBQvD3cGDW4D4NefjFXZWYns+eeHTcXJ6a/O5T5i3/ky+9/omXjerz96kss+3ljnsoTQhS+lCDx5t1AqvqXxdfTPcv8w17vT5/BIwkOccLVxT9dekJCNLfv7eKVl7pQ6kErkii+1v6yjg7tn8be3p4jR48xZ+583nzj4Xj+lOX8mjRuDJBuOb/s0guaKjY6rOjtL1cIIiIi8PAqQ8DdGzlqWs5OQkI89+/dwbeMPwar3LdEWVpGYxR/37KDzdv38sVH41Gr1YRHRHEnIJCqFcvx+5YdbP3vAPfuB/Pz1zMKqdaWl9nYzYIWEhxARFgQXj5lCrsqAMTGxpaonTZySp5Lxp7k5/JokPjRuME52rt55579vDP1E9QaR+xty2NlcCTJlEB4xHWio6/TpXM73ntnOBpN5qsqiPRS/nZ/fLAHVnaWacCIi0pkUoO1OY4P2nV4hlOnTmM0JuHt7cWr/fsxetRwc8tiYmIi48ZP5Kef1wDQq1dP5sz6xLw0XnbpBU0CxQckUEwehzjn6x/Ye/gEGo0GtVrFpOGvUbtaRc5eukrnfiP5+esZPPVg2ZcnkQSKGXuS//A/DnkuGXtSn0teg0RIfiZx8Qms27SZNb/9SVBwCHq9nhZNnqJ3t+epVb1qhsN+RNaKYqD4pJGuZ2GmUqkY93b/DNOq+pcz720shBAl0bxvfspTkJjC2cmRQX17MqhvTwvVsORKUtQk5WFh7JyWXZKV7LsXQgghcmj4gO60blwnT0GiEMWVtCgKIYQQmYiMisHWxgq1Wo2HuwsThrxS2FUSGTDx+LOTsyq7JJMWRSGEECIDIWERjP3fF3z+/VpMppIeLoiSSloUhRBCiEeknrhiZ2tDQqIRK4O+sKslMiFjFC2nZN+9EEII8YiMZjdLkChKKmlRFEIIIR54nCVwROEpiL2eSyppURRCCCGAqOgYCRKFeIS0KIpc+2rZGpav3UREZBQtGtVl+oShWe7tHBEZxSdfLmXb7oPExMbRukl9Phz3Fs6ODxcuPXX+Mp988T1HT51Dr9fRvEEdvpw+wZz+1/Y9/LD2D06eu0RUdIys6SiEyHe2NtbUq1kZO1sbCRKLGRNqTBZq+7JUucVFyb57kWtrNm5l4fK1fDD2TdYunkVEZDQjp87J8pzx0z/nwpXrLJnzHmsWzSQ8MirNOZeu3aTvsCk0qFON376fx9rFs3iufYs0ZcTFxdP0qVq81a+7Re5LCCFUKhVv9n2RT959U4JEIR6QQLEQnb14lcoturH05w207PY61du8xJsTphMZHVPYVcvU8jUbGfTyi3Rs3YRqlcozc/II9h4+wfnL1zPMHxcXz9ZdB5g0fBA1q/hTxd+PaWPe4L8Dx7h49QYAcxetoEOrxox6vQ8Vy5XB3680nVo3TVNOl05tGDqgJ/VqVrH4PQohSo6QsAgmzvia2/cCgeRg0aCXiSvFjUmx7Kskk0CxEJ2/fA2TYuKPbbv5esYkvp/3PkdOnuO71b9Z9LqTZ35Fzba9qNG2Z4avyTO/yvC8+IREzl66RtP6tczHyvh44utVimOnz2d4TmJSEiaTCUOqGYMGgwGAo6fOk5SUxM59Ryjt5UHvIZNo2Lk//UdOzTTwFEKI/JIyceXYmUssW/NnvpSpKAoRkVEEBoeQkJiYL2UKUZhkjGIhOnfpGk4O9iydP83czdH92bYcO33BotcdPbgPr/fpkuka9na2NhkeDwuPwGQy4erimOa4i5MjwaHhGZ5jb2tDzar+fPHdamZPGYlWq2X+4hUABIWEERIWQWxcPN+sXMfEYQOpVa0iy9dspN+I99j289eZ1kUIIR7Ho7ObR73+ePsvJyQm8seWbSxbvY4Ll6+gKAp6vZ7OHdrQv1dXyvh451PNRUYUC45RVEp4m5oEioXo/OXrvPRcuzRjYXQ6nfnnjz//jsoVytK989PmY4lGI58tWcX+IydRa9S4ODny8btDcXZ0YMaCpezYexgnB3sARr7em8b1aqa7rpuLE64ujqhyud2Rksfm93lTxzB62lzqduyLRq2mT9dOuLs6o1arzbsddGrTlD5dOwEw/d2hNH1hINt2H+SFDq3ydlEhhMhEfi+BEx0Tw9tjp3Dmwi3s7fyp7F8PtUZLXFw4O/67xKYtI3jvnaF0f/7ZfLwLIQqGBIqF6Nyla3R7tm2aY6cvXKFy+TIAnL10lS6dWqdJnzRjAeXL+LBm8SwAvv/pd2YuWMaMScM5e/Eqc6eOplql8lled/LMr/ht8/ZM01/s2JrpE4akO+7s5IBarSY4JBx/v9Lm4yFh4bg6O6bLn6KCny+/L51PeEQUKhXodTpW/Ponvl6lcHZyQKNRU76Mjzm/TqultLcHd+8HZXkfQgiRW5ZYJ3H8tBmcvxRE2dId0Kgfftm3sXbBxrohkVGl+XD2Asr6+vJU3VpZlCTyKklRkWSh9Q4tVW5xIYFiIQkLjyQgKARTqlGyN27f47/9Rxn3Vj8ALl29iX+5hwHZ8TMXuXL9FrOnjDQf69CqCT/+mjy25sLl61Qo65vttfPa9WzQ66jq78feIydoVK8GADfv3OPW3fvUqV452+s6OtgB8PuWHWi1Gpo+VRu9Tke1SuW5fuuuOZ/RmMStu/fx9nDPtkwhhMiN4NAIQsIi8y1IvHTlGjv3HKRi+S5pgsTU7O28cLCvzMLvV/KtBIqimJFAsZCcu3wNaysDS3/eQBV/P6JjYpkw/XN6vtCeqhXLERAYjJOjPfpUXdF/bd+TbjZweEQktrbWBAaHEh4ZxYDR0wDo0qk1vV7okOG189r1DNCvR2c++nQJ1SuVx9fLg48+W0KT+jWpXKGsOc+sr5YREBjC3PdHA7B972EMeh3eHu4cOn6GD+YtZtiAnrg4Ja+jOKjXi7z78Rc0rl+TWlUrsuznDWjUato2a2AuMyw8kjsBgeaA8syFKwD4lyud5hkJIURWKpbzZfbkIXi4u+TLEjg/r9+Eg31ZtNqsy3J1rsjBo79z514A3p4ej31dkZYJFaY8/E3LadklmQSKheT85evUqV6Zlo3r0uvtd9Go1fR/6TmGD+wFwJmLV6lasVyac2JiYtN00QJs+uc/nm3bjNMXrtCuZSM+/3CcRevd8/n2BIWEMWXWQiKjomnesA7T3x2aJk9gcCh3AgLN70PDIpi7eAWBQaF4e7gxanAfBr38ojn9xY6tCA4NY/ZXy4mIiqZW1Yos//xDbG2szXm2/neA8R99Zn7/3KujANj56zf4esmHrhAicyFhEew/eoZn2jQGoFyZ/JtYcvHKdQx612zz6XTWWBnsuXbzlgSKoliRQLGQnLt0jcoVyvLmK91585X0i0ifvXiVao8EivVqVmXzjr306Pw0KpWK/UdOsWPvYX75Zjbfrv6NmpX9C6TuQ159iSGvvpRp+uz3RqV53/WZNnR9pk2WZQ56+cU0weOjenR+mh6pJvUIIUROpB6TaNDraNusfpp0o9HIrn0HWb3md65cvYFGo6Fener06dmF6lUqoVJl3ZqkUqtRMOWoLgoKGnXJnkFrKYqiRlEsNOvZQuUWFxIoFpLzl6/R8/n2maafu3SNi1dvsH3vEQC6dmpNt2fbcvbSVXq8MR61Wo2nuyvfzXsfg0HPuUvX6N2lY0FVXwghirxHJ640rlc9TXrA/UDeHDmRm1fv4hBqj63RBgWF/y4eZtOf/9K2TRNmfDAxywW469eqytnzO4CsNwOIi48gISGaiuXLZZlPiKLGooFidEwsW3YeYP/Rs1y9eYfIqBjs7Wzw8/WiUd1qdGjZoESuk6coChev3qRS+bKZ5vn8fxl3Ib87dECu8gshREmU3ezmyKhoBrz9DpEXoqkUUQF1qrXynCId8YwqxZ6/jzBB+YT5n0zNtGWx+/PPsmjZauLiw7EyZL76Q3DIOTq0aYGLs1O+3aN4KAkVSRYaS2ipcosLiwSKwaHhfLV8Hes37yIuPh5IuwbfqfNX2PjPbqZ/vowunVoypH/XLJdXedKoVCpO/vNTYVdDCCGeSDlZAmfNb5sIvR5G+Qi/DCf26RQtZYNKs33HPo6dOkPdmtXT5QHw9HCnT48XWPPbP5T2boteb5suT2DwORISbvHWwAn5c4NCFCCLBIodXxlDbFw8igIuTvbUquqPr6c7trbWREXFcDsgiJNnLxMcFsGq37by+9//cXDjEktUpdCkfPDkdZFqUdLJL44QefXzxm1ZBokmk4kfV63DOcwpy9UfdIoWhwh7fvxpXaaBIsC4YW8QH5/Arxs3YW/rh719GdRqLXFxYUTFXEWtimHBrA+o4Jd5L5J4PCZFhclC6x1aqtziwiKBYkKika6dWtGtU0vq1cx8fb0jJ8/z61872fD3bktUo1CpHgxYTjIZC7kmojhKSkpCLYPehciT115+DmsrAy91bpPhEjjBoWHcC7xPrcTMg78UjrH2HD5yMss8Go2G98ePpPsLnVi59nd27z9CQkICpdxcee2VHjzXsR06rSbP9yNEYbJIoLjlx3l4ume/XEC9mpWpV7Mywwekn/Vb3Gk0GjQaDbExUdjaOhR2dUQxoigKsTGR6PWGwq6KEMVGaHgkarUaR3tbdFotr/Z4JtO8RqMRRSFHa8mqUJm3Gs1OjSqV+XhKxuPFY2Njc1SGyBuTosZkodnJliq3uLDI3eckSEzNw93FEtUoVCqVCitrG6Ijw3P8ISMEQHxcDIkJCVjbpB/rJIRILyQsggkff8WkGV8TERWdbX4XZyesra2I1sRkmzdaE0Np3/xbd1GI4qZAlscxJiVx5OR57geFYkxKGzR16diiIKpQKOzsHIiJieLenWt4epdFrZauB5G1+PhY7t29jk6nx2Blnf0JQpRwj05c0Wqy/5w16PW82LkDf6/YhV145l/IFBQiXCMZ0/P1/KyysADZmcVyLB4o3r4XyKCxn3Dr3v10aSpUT3SgqNPrcXP3JCjwHtevnMXG1gFra1vUOfggszQFSvivfsYK47koikJSkpHo6AjiYqLR6fS4lfLMdqFfIUq6nMxuzky/Xt1Yv2EzwTGhuCY6p0tXULhlexdnT0fat3ly/04JkR2LB4qfLvmZm3fTB4kAqJ78mZ0GgxUeHj7ExEQRGxNNVGRYYVcJSP4QzMtez0+6QnsuquTfFWcXN6xtbGUiixDZeJwgEaBc2dJ8PvsDRox7n+iwGNyinLExJS+4Ha6NJMQxFGtvA0u+nCn7yRcDJgWSLBRSmJ78UCVLFg8UDxw/i1qlYtqY15g6dwkVyvrwfLumLF3zJ9PGvGbpyxcJWp0OB0dnHBydURQFRVEo7OVPYmPjsM7Fh2pJUTjPRYVKpZIWRCFyKCnJxORZi/McJKZo3rgBa35YyLKVa9nwx1aMxiQURcHR0Z6+L71Inx5dZIFsUeJZPFAMDYvAr7QnPTq3ZurcJdjaWPFG3xdZv3kXW3bsp0PLBpauQpFSVAICtVotYyYzIM9FiKJPo1HTr3sn1m/eybTRg/IUJKao4FeWDyeN5d3RQwgMCkaj0eDh7o5OJzvcFieKorLgXs+F/ze7MFn8X4LBoDfvk2ll0HP3fjCJRiNRMbHs2H/M0pcXQgjxhEhKMqHRJAcDTevXoEm96vn2xdvG2pqypX3zpSxR8GTBbcux+EAoFycHgkLDAfDxdCcoJIymXd4iKCRcxn0IIYTIkZCwCIZOmcuuA8fNx4pC74wQTzqLB4qVy5chMDiMC1dv0ql1IxQFomPiAGhfwrqdhRBC5F7KxJXrtwPYsHW3rE0r0klZHsdSr5LM4l3P898fQVJSEjqdlop+vuj1Oo6eukBV/7K82fdFS19eCCFEMfbo7OZpowfJqgBCFCCLBoqxcfHsO3IagMb1qmNtZWBw7+cteUkhhBBPiMddAkeUHDJG0XIsFijeDw6l7/APuBMQBIC3hxs/fvE+pVzTL2wqhBCi6AgJDWP9H5v5c8d/RMfE4u7qQrdO7ejYphVWVgWzB3loeKQEiUIUARYLFL9dvZHb94LM7+8EBPHt6o1MHNrPUpcUQgjxmH7d+BfT5n6OplQpVOX8UHsZuBMZyYmvv2PGl4v5asYH1K1ZPdflJgefWzh97iKKAlUqlafLsx0o5eaaYX6dVoPBoJcgUeSISVFjstDyOJYqt7iwWKC4c/9xVCpo1/wpFAW2/neInfuPS6AohBBF1MbN/zDt0wUY2rXF4OWVJk2pXo2Ys+d4bcxEVn41nyoVK+SozKSkJD79+juW/7QOayt39Hp3APYe3MKX3yynV5fnGD/izXTrFtrZ2vDJhDdRq9USJApRiCwWKAYEhuDp7spnH4xCURSe7j2SgMAQS11OCCHEY0hITOSjzxeia9o4XZAIyUvR2FarSmR0NLO++obv5s/ItkxFUfjfnC/Y8Nd/lPHpgLV12qFHcfHhrNu0g7CIcGZNm0hoeCSrftvK4N7Po9frsLO1ybf7E082GaNoORYLFOPiEyjllvyhoFKpKOXqLIGiEEIUUf/s+I84tRrbsmWzzGdTvRoH1v7Kzdt3KO3jnWXeIydOsf6Pf/Ar/Qx6vW26dCuDI6V92rJ1x5/8sXU7v209wM27gdhYGxjYs/Nj3Y8QIn9YdNZzSFgE6zfvAiA0LBKA37bsQkm1zXGXji0sWQUhhBA5cOj4SUzeXtkuYq2xscFQqhRHT57ONlD8cc1v2Nn6ZRgkptDprLGzLc8Hc77C1aMsVf3L0uv5p/N0D6LkUlChWGi9Q0uVW1xYNFC8dfc+k2ctSnNs0syH71WoJFAUQogiID4hETQ5+5Og0mowGo3Z5tu17xAebi2zzGMyKeh1Xty5d5RmTVvIxBUhihiLBoqpWw4zpMougxBCiILg5+uN5tSZbPMpSUkkhoTi7emZbd74+Dg0Gn2m6SaTQnRMLCqVFrVKxdSRAyRIFHliwoJjFKVF0TI+Hv+GpYoWQgiRz57r0I5Pv12OMTISrb19pvnibtzE2daGBnVrZVums6MT8QkRGAwZl2c0JmEymUhKisHR0R4nx8yvK4QoHBYLFLt0yrq7QQghRP65HxjE/aBgDHo9Zcv4otfpcnW+p4c7z7Rtxd//7cG2/dOotOn/PBgjI0k8eIi33hiIRqPJtsxuz3Xgp/V7cLD3yTBdr9eCyorIqJt07dwh2/GRQmRGZj1bjsX3ehZCCGE5ew4e5tulqzlw+Dh6tQ6jyYidgy29X3qBfr264+iQ81a6D8eN4t64yZzY+AeaGtWxLueHSqPBFB9PzIWLmM6cpVen9vTqmrOtWF/q0pmlq38lLOImTg6lgeTuZkUxmQPN+PhAYmJv8XK3Sbm/eSGExVksUGzfZ1S2eVQqFVt+nG+pKgghxBNt6co1fPrFtziHOFElviJ6RYeCQnhgJCu+Xs8fm/9l2aL5uLu65Kg8a2srvp0/gzW/bWLpmnXc2bMXrV6PMT6emlUrM+jdMbRv3SLHLX/enh7M+XAiY6d8TFxsKC7OFYlPUDApClYGNWHhlwkLP8P/Jo2igl/Wy/IIkRXFgi2KirQoWsbte0GoVFlPaJFeBiGEyJtdew/w6Rff4nevDLamhwtTq1DhZHTAMciem4m3GT72PVZ9/2WOgzu9TkffHl3o3e0Fbty6Q3RMDC7OTnh5lMpTPZ9u2Yxvv5jB3C+/5eCxNeh0Dmg1GhQlmhpVK/HJ1A9p2qB+nsoWQliexbuedVotVSvKN0UhhMhPi777EZdg5zRBYmoqVPiGe3P2/EWOnjxNvVo1clW+Wq3Gr4xvflSVcmXLYufiRZnyibg729GnS3uqVfLHv7xfvpQvhOnBy1Jll2QWCxR9PN24fS8IY5KRqOgY+vd4hi4dWqDX526AtRBCiLRu3r7L8ZNnqZpQKct8atQ4hNuzdt0m6tWqQWxsHLFxcdjZ2eZ6sktehYRF8O4nC7l5N5Da1avIOolCFDMWCxQ3r5jH37sOsnTNnxw/c4kP5n/H59+tofeL7enTpT3OsgyCEELkyd2AAKzVVuiU7D/CrRIMHD1+mleGjeXI8ZMoCuh1Wjq0aUn/Hl2oWa2KRes6d/Fqbt4NpKp/WQkShcWYUFlsvcOSvo6i2mIFq9V0bNWIVV9O48fPp9Ku+VOEhUfx1fJfaffySKKiYyx1aSGEeKLpdDqSlKQc5Y1NjOPyjVuc0mpx7NkDl/59ser8DH/fukPfYWNZsWadRes6bEB3WjaqLUGiEMWUxQLF1Lw8XPH1KoWVlR5Fgdj4BEzZbtuS3sKvF9OseWscnUvxUq8+adJu37nDS7364FO6HL5lytPnlVcJCLifX7cghBBFRqUK5UAHUZroLPPFxMYSZh2BVf1a2NWqhcbWFpVWi87FBbvGjbDq2J5Zi77jn52787V+UTGxmEzJI7u8SrkycWg/CRKFRSmKyqKvksyigeKZi9cYP/0rOvQdw9I1f5CQaOSF9s1Z983HONhlvkl8Zry8PJkw4R0GDnw1XdqoUWMBOH/2JGdPHychPp53xr/72PcghBBFja2NDV1f6ESgfTAKGX/pNikK4UokMbYJ2NasnmEevbs7mrp1+Ozb5Sh5+PKekeQxiYv4avk6c7AohCi+LDZGccCY6Rw8fhYAe1sbej3/NK9074i7i1Oey+zy4gsAnDhxktu3b6dJu3btOmPHjsbOzg6A7t27MWdO5ms0xsfHEx8fb34fGRmZ53oJIURBe+PV3vz9z05uJ97FJ9oL1SPjqMKTIrjldRe7RvXQ2GQ8MxrA2r8CV48e4/T5C9SoUvmx6pQyceX2vUAcHexISDRiZch8r2ch8ovszGI5FgsUDxxLDhJ1Wi31albm9r1AZi5YkSaPSqVi9pSh+XK9EcOH8uu69TzTqQOKorBmzS906tQh0/yz58xj+scz0x2PjYtDV0CzAQtTXFx89plKIHkuGZPnkrHCfC72drYs/nwGI8a/z7m7F3EIdcDKaIVJlUSUQzRBhKDy9cS2Xp0sWwtVWi16FxfOX7xMhbJl8lyf0PBI3pvzLbfvBeLv58uU4f1QTEnExsbmucwnifwbSs/a2rqwqyBywKLrKKpUYEwysmPf0Uzz5Feg2KRJY75buhwvHz8AGjZswLsT3sk0/7h3xjBi+MNrR0ZG4l+pOtZWViXml7ek3GduyXPJmDyXjBXmc6lcsQKb1ixl176DrF23idu376HX2/BCi6e5eP0G20PDUatzNsLocT77QsIimDb/e+7cD6ZapXJMGd4PF2enPJX1JJN/Q5YjLYqWY9FAMZ+GvGTLZDLR+fkudO/WlU0bkmfwfTR9Bs+/2J3t27ZkeI7BYMBgMBRMBYUQwkK0Wi1tmjehTfMmaY6v/vV3dq78OdvzTXFxxN+/T5VK/nm6flR0jHmdxJQlcFSZjJsUQhQ/FgsUz2xbkX2mfBISEsqNGzcZMuRNbB6MxXn77TeY/+nnBAUF4+bmWmB1EUKIoqBzh6eZsWAxCQH30Wex/V7M2XPUqVGN8nnsdra1saZWVX/sbG3MS+BId7MoaNKiaDkFsjxOfjEajcTFxWE0JqGYTMTFxZGQkICbmysVKpRn0aIlxMXFERcXx6JFS/Dx8ZEgUQhRItnb2TJkwCvE79yFMSwswzyxV6+hnD7DmDcG5vk6KpWKoa924+MJb8oSOEI8gSwSKJ46f8Ui+WfMnI2zqyczZ81h0x9/4ezqyXMvdAVgzU8rOXbsOBUqVqVchcocOnSYtT+vzHXdhRDiSTH4lV4M7PIcURs2EfXfbuLv3CExOJjYq9eI/nsrpr37+PTDKbneBzokLILJsxZzJyAISA4WZXazKEyyjqLlqGKjw/J9MEn1p1+hSb0adH2mFa2b1MU2g2+ZMbFx/LvnCL/+tYP9R85w6p8f8rsauRIREYGHVxkC7t7AwcGhUOtSEGJjY2VgdQbkuWRMnkvGistzuXDpCivXbWDrf3uJi4vD2cmRl57tSNfnOuHu6pKrslLv3dyiYW0mDeuXLk9xeS4FSZ6JZaT87X7mjwnobC0z7yAxOp4/n51ZYuKDR1lkjKK7ixN7Dp9i75FTqFVqKpT1wde7FLbWVkTHxHLrbiCXr9/GpJhQFPBwc7ZENYQQQgCV/MszbdxIpo0b+VjlpA4Sq/qXZdRrL+VTDYV4PLLXs+VYJFDc/OM8fvhlMyt/+5t790O4cPUmF67eRKVKOxO6lKsTfbt1pH/3jpaohhBCiHzyaJAoezcLUTJYJFA06PW83vt5BvXqzIFjZzlw7CxXb9whIjoae1sb/Hy9aFC7Co3r1UCjKVbzaYQQosSRIFEUdYqCxcYSFtRSf0WVRddRVKvVNK5Xncb1Mt5nVAghRNEXFBJGUGiEBIlClEAWDRSFEEIUf5XKl2HWpLfx9nCTIFEUSZacnVzSZz1Lv68QQoh0QsIi2LLjgPm9v5+vBIlClEDSoiiEECKN1GMSdXotbZrUK+wqCZElxYI7s5T0FkUJFIUQ4gkTFh7Bln93cj8wCCsrKxo3qEuNKpVzdO6jE1ca1alm4doK8fik69lyJFAUQohiKCQ0jPV/bObQsdMYjUn4lyvDcx3asHL9Rn7fvBWtuztJ9naoExP59PsfqFSuLB+OG5llwCizm4UQj5JAUQghihFFUfjqux9YvGw11lbuWBk8UanUnDp7hMXLV6O2tsKxc0cMpdzN55gSErhy+gz9hr3D0k9nUrtG+lZCCRJFcWZSkl+WKrskK5BAcf3mXew5dJLAkDCUVAsSqVDx/bxJBVEFIYR4Inz69Xf88PMmSvu0x8b64fZ7sbFxWFlVJCD0MJG796N/4RlUGg0Aar0eu7p1iNbrGDn1I7b+vBytNu3H/+rf/5EgUQiRjsUDxfnf/MSS1RvSHVcUUJXsbn8hhMiVK9dv8N2PaylX9hmsDI7m44oCcQkJaAy2eHk159btrcScv4BttappzrepWpXQM+fYtfcAbVo0TZP2+svPYW2lp9fzT0uQKIqd5AW3LVd2SWbxQHH9ll0oCmi1GlydHNA8+IYrhBAid1b/sgF7+zJpgkQAY5IRAJVajUqlxsmhEqEnzmJTtQqqVN/IVWo1+JXlz3930qZFU0LDI9FqNNjb2aDX6xjYs3OB3o8QouizeKAYHROLs6M9G76fiYuTg6UvJ4QQT6wdew5gb1sp3XElZRDVg5jQzr4M968cxBQbi8bGJk1etY0NIRER5jGJBoOej8e/ib2dDUIUVyZUmLBMN+XjlBsbG8tTDZsSHBzMvTs3AEhMTGTchEn8/PMaAHr1eonZMz8xDwfJLr2gWXzB7ZaN6mBl0EuQKIQQjykuPgGNRpfuuEr94A9ZSryo0qBChWI0pstrionBztrm4TqJWi0ajey9IIQlfPi/j/Hx8U5zbMbM2ezds5fDB/dx+OA+9uzey6zZc3OcXtAs8ulw8PhZ8+vp5k8RGh7J8Knz+ee/Q2nSDh4/a4nLCyHEE8nD3ZW4uPB0x7Wa5JYGxWQCwJgYBSpQW6Uda6iYTChXrnHjTqhMXBFPFAWVRV95cfToMTZv+ZtxY8ekOb5s+Y9MmDAOLy9PvLw8GT/+HZYu+yHH6QXNIu2Yr46enm6iyrbdh9m2+3CaYypUnPqn8G5eCCGKkx4vdGLugpW4uabtflapwMqgJy4+AdR6wsMvYyjnh1qvT5Mv+tRpjHHxxCRAtYoSJAqRG5GRkWneGwwGDAZDhnmNRiNDh43k03mz0xwPDQ3j9u3b1K5V03ysdq2a3Lx5i/DwcEwmJct0R8e045MLgsX6G1JmIGX5ooRPJRJCiFzo3KEtGk0894PS98ZYGQxo1GpiIwMIj7iEXe3q5jRTfDyRh44Qc+AwpdzLUK2inwSJ4omSsjOLpV4A/pWq4+FVxvyaPWdepvX59LMvqVGjOi1btkhzPCo6CiBNwJfyc2RUVLbphcEiLYpL5022RLFCCFGi2drYsHDO/3h91CRu343EzaUKBkPy+O+kpARiY68TGHgUvUbBePgICfb2qBONJNy+TbVK/nR5ZzTHzl1j2uhBEiQKkUuXLpzG3t7e/D6z1sQrV66yaPES9u3ZmS7NztYOgIiICNzcXM0/A9jb2WF6MDEts/TCYJFAsWGdh2t33QkIQq/T4eZS8M2lQgjxpKldoxqrl3zGgiU/sHXHH1hZOaBWqYmJDad6lYp8NHk6tapX5Z+d/xEYFIJOp6NZo/pU9q8AQG9FSbNkjhBPgoJYR9He3h4Hh+wn5u7evYegoCDqP9UYgITEBCIiIvErV4k1a1bh4+PD8RMnKF++HADHT5zE19fX3HKYXXpBs/hc63a9R1Gnmj8rv5yW5vjQyXM5e/k621Z/bukqCCHEE6WCX1nmfTSFoJAQzl+8QqLRSBlfb8qXLWPO0+25ZwgJi2DyrMVUrlyVlB2eJUgUwrJ69OhG+/btzO/37d/Pm28NY9/eXbi6utC/Xx9mzZpLk8bJgeTs2XMZOKCfOX926QWtQBblySjIDwoJJyAwpCAuL4QQTyQ3FxfcGrlkmJZ67+bftuyiaf0aEiSKJ1bqsYSWKDs3rK2tsba2Nr93cXZGpQJPTw8AJr47npCQUOrWbwhAr149GT9urDl/dukFzWKB4vrNu8w/h4RFpHkfHRPLpWu30OvSrwcmhBDi8aQOEqv6l2Xa6EESJApRSFq2bGFebBtAp9Px6fw5fDp/Tob5s0svaBYLFCfNXGReIufW3ftMnrUoXZ6KfqUtdXkhhCiRHg0SZXazKAmKUovik8aiXc+Kkry+V0YDTF2dHRk/pK8lLy+EECVKaHikBIlCiHxlsUDx75XzAWjfZzRV/cvy+YejzGnWVgbZ0k8IIfKZRqNGp9VKkChKHBMqTBZq+bPUHtLFhcUCRR9PdwA+Hv8Gzk4O5vdCCCEsw8HOlk8mvoVWo5EgUQiRLyw+67lh3WpA8nqKj7Iy6KVlUQghHkNIWAQ/b9zGoF7PoddpcbCzLewqCVHgCmIdxZLK4oFi+z6jskz3dHdl/Nt96NiqkaWrIoQQT5TUE1esDAYGvPRMYVdJCPGEsdhezymy2+/57v1gxn74JYdPnrd0VYQQ4onx6Ozmns+1KewqCVFoCmKv55LK4oHiG31fwNrKgK+XO/27d6J/906U9i6FtZWBV7p2oHwZL0yKwvc//2HpqgghxBNBlsARQhQUi3c9x8cnoNdpWffNx9jaJK9UPmxAN9r3GY1Op2XN1x/Rvs8ojp++aOmqCCFEsSdBohDpyTqKyaJjYjl25hI3bgcQFR2DrY01ZX09qVPN3xyD5ZbFA8Xf/96No4Ndmgra2drg5GjPb1t2Me6tPtSoXJ49h05auipCCFHsHTh2VoJEIUQauw+eYNXv/7Bz/zGSkpLSpWs0Glo1rkPvF9vTtH6NXJVt8UAxLi6esIhIpn++jBc6NEetUvPHv3u5fuse1lYGABKNRvmwE0KIHOjUuhEGvY5GdavJ56YQDygPXpYquyh7ZcSHHD19wTw728HeFi93F2xtrImOieXu/WAiomL457/DbNt9mLo1KrHis6k5Lt/igWLLRnXYvPMAK3/7m5W//f1IWm0SEo2cvXidsj6elq6KEEIUSyFhEQSHRlCxnC8AbZrWA0BRFI4cP8WhYydISEzAw92djm1b4uggy44JUVIcOXWB+jUr06l1I1o2qkNp71Lp8ty8c5+d+4/x57/7OHrqQq7Kt3igOHX0QILDIjh04lya4w1qV2HqqIEEBofS64WnqVGpnKWrIoQQxU7KmMSQsEhmTR5C+TLeABw8epz353zOrfuBqH28UTQaNBERTP98IT2ee4YJw99Er9MVcu2FKCCWnJ1cxMco/rL4I6r6+2WZp7R3Kfp27UDfrh04d+l6rsq3eKDo7GjP8k+ncOjEOc5fvgFAFf+y1K9Z2Zw+ctBLlq6GEEIUO49OXPF0dwFgz4FDvP3u+6jq1Ma+VUtU2ocf5YkhIfyyey/Xbt3i65kfodNZ/GNeCFGIsgsSH1XFv2yu8hfYJ8hTtarwVK0qBXU5IYQo1jKb3RwXF8/oaR+jadgAm0oV052nc3FB074dh/78i1W//kb/Xt0LofZCFLCSPEgR2HXgOD9v3EaHFg15vn2zNGkb/t7Nll0H6PlcW1o0rJ3rsgskUFy/eRd7Dp0kMCQMJdVeOCpUfD9vUkFUQQghio2slsDZvG0H8To9thX9Mz1frdejrVGDpWvW88pLXVGrLb5krhCiEP3w62b2HDzJxKH90qU1qFOFiTO+Jj4+oWgGivO/+YklqzekO64ooCra3f5CCFEo5ixalekSOBu3bYdyfqiy+QC18itL4L79XLh8lSoVK1i4xkIUrpK+juK5S9fx8nDF28MtXZqnuyvenm6cvZi7sYkpLB4ort+yC0UBrVaDq5MDGo3G0pcUQohibeir3Vi25k8G9XqWXzf+ydatu4iJjcOjlBvXrt9CVaF8tmWoNBq01tZERkUVQI2FEIUpPCIaz1IumaarVCoiomLyVLbFA8Wo6FicHe3Z8P1MXJxkyQYhhMhIyi4KKpUKH093qlfw5Jnu/bCKM2AbZotW0XBfE8J9q0ASbtxB7+qKzjXzPwxKUhLG2Fgc7e0L8C6EKBwlfIgi7q5O3L4byOkLV6n+yCoypy9c5dbd+3i6u+apbIsPXGneoCZWBr0EiUIIkYmQsAjGfPgFX69Yj6IobNi8lff/Nw+fO16Uv++HR4I7roku+MR5Ui2wMq73bAn+dQPG8PBMy4y9chVPNzcqVpClx4R40jWuVx2TovDWxDms+m0rZy9d49yl66z6bStvT5oDQNOncrcjSwqLtyi2bVaff/ceZfjU+XTp0AIHe9s06Q1qV7V0FYQQoshKPXHFztaGiMgops/6Au/7njga03/BNuj0uEW4kqgLJHLvQZw7tUuXxxQfT9KpUwx8tW+2YxmFeCIoKsutd1gMxii+0ecF/tq+j5CwcD76fGmaNEUBWxsrBvd+IU9lWzxQnDhjESoVbNudvHVMaipUnPrnB0tXQQghiqSMZjdv3bELU5SCk9Ex0/PsbGxwCXPkysUr2DUOQ+fkBCTv1JIYGEj8vv00q1aVnl2eK6A7EUIUpjI+Hnw1/R3GT1/A/eCwNGmlXJ2YPWUoZXw88lR2gSyPo2TWwa8qDj3/QgiR/zJbAmff/iNYh1qhIvNWDK1Wi6vKmXvGACJ+XY91OT8UjRbCw1FHRdG/6/OMGDwQrVYW2xYlREkfpAg0rFOVLT/OZ/fhk1y5fgeA8mW9aVa/Jnp93ndpsvinyNJ5ky19CSGEKFYio2IyXScxLi4BdQ6Gj2s1Ghxt7en/enesrAwkJiZSyt2Np1s2w9bGxtK3IIQogvR6HW2a1KNNk3r5VqbFA8WGdWQMohBCpGZrY0X1yuWxs7VJt05imTJeHLA+DvFZl2HCRKwqjqfq1qJ+7ZoWrrEQRZuiZNF7mQ9lF2WRUTF8u3oj56/coFK50rze+3ns7fLvy2KBLNcfFR3DN6s2MHTyXD76fBkXrt5k/eZd3AkIKojLCyFEkaJWqxk+oDsfT3gzTZAI8MKzHQi3jiBRlZhlGaG6MNxKOVO3ZnVLVlWI4kGx8KsIGzd9AaHhkfTp0p6wiCjGTV+Qr+VbPFAMCgmn+5tT+HTJT2zfd5TTF64SFh7FpJmLWPXbVktfXgghioSQsAimzP6GgMAQIDlYtDLo0+UrX7YMTRvX55bTHUyYMiwrXpVAoFswgwf1ke35hCjhjp66wOjBvWjRsDZj3niZIycv5Gv5Fv+Emf/NT9y8cx+NRmNuvm1QuwrWVgb2Hj5l6csLIUShS5m4cvjkeZau+SPb/LM+nIhndXeuuF0nTBuO8qBJI4kkAvSBXPG8RrcenejxwrOWrroQxULKFn6WehVlXTq15LV3PmH+Nz/xxviZdOnYIl/Lt/gYxZ37j2FrY8WG72fSttdIIHkrGa9SrtL1LIR44j06u3n4wB7ZnuPo4MAP33zKslVrWfXz79yJuIdWpSXeFE9F/3KM7v8az7ZvI2skCiGYOLQf/x04wfkrNxg6oBstG9XJ1/ItHiiGR0Xj7+ebbusYtVpFdEycpS8vhBCFJrMlcHLC1saGIa/1Z3D/Ply4fIW4uHhcXZzxK+Nr4VoLUQyV8OVxmjesRfOGtSxStsUDRXcXJ67dvMvte4HmY+cuXefK9Tt4e7pZ+vJCCFEoHg0SX3/5Gb7/8Sfu3Q/G1saK5o0b0KRBPTQaTZbl6HRaqlepVEC1FkIUN6HhkTg75nxP99zmt3ig2KpxHVb//g/PD5yASgXnL9+g15D3UVBolc/No0IIUVQEBIUQFBJO+dKehAbepNeg4djZ+aJR22IyJfLz+i04O9vx8ZSxNKpft7CrK0SxZsmxhEV9jGLbXiN4tk0TnmnTmMb1q6PN4MunMSmJ/UdO88e2ffy5fR9H/vwux+VbPFAcPrAHuw+e5Obd+wDExScA4OvlzpBXu1n68kIIUSiq+vsxZUR/Jn74CRGRevzLd0GnfdjtbFKSCA65xJtjprBwzv9o0iD/FsgVQpQcLk4OrNu8k/VbdqLX6fD388WrlCs21lZEx8ZyNyCYy9dvk5CYiKKATy57cy0eKDo72rNm0f/4cd0Wjp+5BECtqv707doBR3tbS19eCCEKTEhYBEdOXaBd86cA+PPvrYSFQRnfpqhUaReZUKs0uLtWRoWKCR/M5J91P6LTyZZ7QuRJCR6j+NeKufzyx3ZW/baVi1dvcfrCVU5fuJouX6Vypen9Yju6PdsqV+Vb9FMpIdHIvMWr0Wo1jH3jZZmhJ4R4YqUek6jXaalfsxLrNm7Bo1TLdEFiaq4u/ly5fo6de/fzdMtmBVhjIcSTQKfV8vIL7Xj5hXZcunaLIycvcP32PSKjYrCztaasjyf1a1XG3y9vE+EsGijqdVp+2vAPfr6evPNmb0teSgghCs2jE1eeqlWFw8dOoFIZsLHOuptHpVJjbfDlnx17JFAUIs9UD16WKrt48PfzzXNAmBmLL7hds0oFgkLCSUrKeIcBIYQozjJbAic6NhatzipHPSlarRURUdEFUFshhMgdiweKA156hsjoGN756EuOnbnI7XuB3AkIMr9yY+HXi2nWvDWOzqV4qVefdOkbN/1Bo8bNcXX3plyFKnyzJOezeoQQIrdCwyMzXSfR2dGB+PgoFCX7L8mJxihKuTpburpCPLlK8F7PlmbxkdPD3puPSgVbdh5gy84DadJUqDj1zw85LsvLy5MJE95h2787uH37dpq0LVu2MnLUO3z/7SKaNWtKREQk9+/fz5d7EEKIjPy0YVumi2nXq1UTWxs9EZF3cHTIvCvIZDISHXODzh3fKogqCyFErhTIFDsls2hclbswvcuLLwBw4sTJdIHiB/+bzqSJ42nZMnmPQ2dnJ5ydnTItKz4+nvj4ePP7yMjIXNVFCCEG9nwWB3s7er/YLt2OKzqdlv69urBo2XrsbEuh0ejTna8oCgH3j+NXxpt6tWoUVLWFePKU4FnPlmbxQPHj8W9Y+hJER0dz9OgxIrp3o3bdBoSHhdO8RTPmzJqBp6dHhufMnjOP6R/PTHc8Ni4OnU5n6SoXuri4+OwzlUDyXDJW0p5LXFw8/+zazYXLV0GB8n5laNeqObY21oSGR6LTabGzsUYxmejz4tOAQmxsbLpyenV9jgNHTnD0xFbcXOtgZ+tpHrMYnxBJYNAp1OpgZr0/k7i4J2dL05L2+5IT8kzSs7a2LuwqiBxQxUaHFbtY+aPpn3D8xEnW/LQSgFu3b1OxUnVq1qjOmjWrcHVxYfiIMdy/f59NG9dnWEZGLYr+laoTcPcGDg4OBXEbhSo2Nlb+kWZAnkvGSspzURSFFWvW8fl3P2A0GEgq5Q4qFZrAIFRRUbzSrQtnr97HxtqK6ePfQKNWZftcEhONLFr2Iz+u+R2jUY1eb/eguzmI1s0a8+6ot/DO5AttcVVSfl9yQ56JZURERODhVYZa38xEY2OZ55sUE8uJwROKTXxw4NhZFv6wjlPnr1CpXGmGD+zBhr930/3ZVtSrWTnX5Vm8RbH6069Qu6o/K7+club4tHnfcvn6HX747L3HvoadbfLC3UOGvEnZMmUAeG/KRGrUqkd0dDS2tukX9jYYDBgMhse+thDiyfHFkmUsWbseQ/Nm2Hh7mVv/FEUhPiCAhT//gr3OhlbNWqBWq8lJn5ROp2XY668yuH9v9hw4TFBwCNZWVjSsV5tS7rLfvRAi//x34ARvT5pDkil5Ep1JUfD1cmfd5p0kJhmLZqCoKBl/lJ69dJ1T56/kyzWcnJwoXdoXVQZrHSmZDpAUQoiHzpy/yDer1mDT+Vl0j4xvVlBItHdA3+5pIv/aTNcOjbGxtsqwuzkzBr2eNs2b5HOthRAgQxRTfP79GkyKiT5d2rNy/d8A+HqVopSrE8dOX8xTmRZbHufg8bMcPH4WgKjoWPP7g8fPsn3vUa7dvIsmg42rs2I0GomLi8NoTEIxmYiLiyMhIXnv6NcGDeCrhYu4fecOsbGxfPzJTNq0boWdnV2+35sQ4smz8tff0ZQrly5INCkmomPiMJlM6BwdsKlejTUb/iycSgohRBYuXLlJudLeTBnxaprjLs6O3A8KzVOZFmtRfHX0dFLWmb1y4zYDxkxPl6eMj2euypwxc3aaCSjOrp60aNGMLX9t4p2xowkJCaVR4+YAtGzZgm+XLMr7DQghSpS/d+1B3+LRnVEUc5Co0WiwtbbCWNGf7Rs2YjLJJgJCFBnSpAiAtZWB8IgoEo1G87Go6Bhu3gnANo9jOC3a9awooFJlvDyOQa9j2KvdclXelMkTmTJ5YoZpGo2GmTOmM3NG+oBUCCGyExsTi43Vox+kKqwMehISErGxTt5lRWNtRVJSEvHxCYVSTyGEyEzjutXZsusAvd6eCsDNOwG89PZUYmLjaNmoTp7KtFiguHTeZEBhwJiPqVDWh/dGDjCnWVvpKevriYNd+kkmQghRGOzs7UiMikLr6EByE0Jyl4hOq0Wn1ZjfGyOj0Ov1WFkZnqglbYQo3lSgyF7Powf34uCJs5y7fAOAkLBIQsIicXGyZ9RrPfNUpsUCxYZ1qgIw9NWueLi5mt8LIURR9GKHtqw+dAydtycxMXEYDHp02pSPyId/KBIuXuTZtq1ytIezEEIUpDI+HqxfMoOV6//m9IWrAFSr5EefLu1xd3HKU5kWn/U89NXuad6fuXiNg8fPUtW/LA3rVLP05YUQIkde7vIcK379nfBrpVG7uZGQkJgqUEyWEBSE6cpV+r4zopBqKYTIiErJ9WZvuSq7OEg0Gln84+9oNGqGD+yeb19mLTbrOcXUOUuo22kgh0+e58TZy7w8ZCqzFv7IwLEfs2nbXktfXgghcsTe3oEyvuWJ2/ovSVeuYq17GCQqSUnEXLxE7JatjBk8gOpVKhViTYUQIj2dVsuSVRv48999+drjYfEWxeNnL6FWqahTrSIzFvyAMcmErY0V0TFx/PDLX3RuK+uKCSEKV0hYBO9+shCTyoqGdZ4i5H4Ad0+fQedZClQqEgPu4+HkxOgJo3m2fdvCrq4Q4lEy6xmAJvVrcOTkeaJj47B9ZP/5vLJ4oHjvfjBeHq5oNGrOXb6Bj6cbm5bNoV3vkVy7dc/SlxdCiCylBIk37wZS1b8sH40bjLWVgaMnT3Ph0hUURaG8Xxka1K39YDcWIYQomurVqMTugyfpPfR9XuzQAldnxzTpXTq2yHWZFg8UExKN5nE+N+4EUKNSOfQ6LaVcnbl07ZalLy+EEFlSq9Wo1WpzkGjz4Ft4vVo1qFerRiHXTgiRI4oFZz1bbDZ1/pv3zU+oVHD5+m3mfbM6TZoKVdEMFN1cHLl8/TZTZn9DUEgY5cp4AxASHoGLU9HfXFsI8WRzcrBjxsS30eu05iBRCCGKI69Srvm+IoPFA8W2zerzwy+b+fXPHahU0LZpPULCIrgfGErTp+TbuhCi4IWERbB2078M7NUZnVaLk4Ns9SlEsSZjFAH4Z/Vn+V6mxQPFMYN7odVouHbzLk83f4p6NStz8txlXujQnFaN61j68kIIkUbqMYlWBgP9e3Qq7CoJIUSRZfFA0aDXM+6tPmmO1axSgZpVKlj60kIIkcajE1d6dG5d2FUSQuQHaVEEoH2fUZmmqVQqtvw4P9dlWjxQFEKIoiCj2c0yJlEI8SS5fS8o07S8Dl2UQFEI8cSTIFGIJ5y0KALw8fg30ryPioll+96j7D96hhGDeuSpTAkUhRBPvL2HT0mQKIR44nXp1DLdsVe6daT7G5M5ee5KnsqUQFEI8cTr/HRT9HodzZ6qKUGiEE8iWUcxQ4qicONOAKERkVy7eTdPZUigKIR4IoWERRAaHkmFsj4AtG/RoJBrJIQQllX96VcyTfP1KpWnMi0SKN4JyHww5aO8PdwsUQUhRAmWMiYxNDyK2VOG4ufraU6Lio4mLj4eB3t79DpdIdZSCJFfVEryy1JlFxdKJnW1Nuh5583eeSrTIoFiu96jcjS7RoWKU//8YIkqCCFKqEcnrpRydSIx0cjmbTtYse5XTp29gIKCXqvjmXZt6Ne9G1Ur+Rd2tYUQj0MmswDpJ7OgUuHiZE/NKhVwdrTPU5kW63rOLKpNoziF6UKIIi+j2c1qlYohEydz4to5PFq702xAA7TWWqIDYjj830n+HPovk4cPp8cLzxZ29YUQ4vE8CAxbNqqT5vDte4EEh4bj7+eb6yItEigOfbWr+edEYxIrft2Mq7MjbZvWQ1Fg+76j3A8KpX932RFBCJE/MlsCZ9yHH3M2+BL13q2J1vrhR569jx32vexwr+/K9C+/wNuzFE0bPlWIdyCEEI9n0sxF1Knmny5QHPu/Lzl17kqeenEtFCh2N/88Z9Eq9Dodv37zMbYPZhsOH9id9n1GExMXb4nLCyFKoJlf/ZguSLx5+w5bduyg4Yf10gSJqTn7O+HzjBcLlv8ggaIQotjLqK82PDIaJY996Baf9bx+804cHezMQSKArY01jg52bNy6m0nD+lm6CkKIEmD4wO4sW/Mnowf3Mi+B88vGP3Gt5YKVkyHLc32aebFv0yGuXL9B+bJlCqK6Qoh8VNIns6Teuu/sxWtp3sfFJRASHoGLk2OeyrZ4oBgbG09oeCSzFv7Is22boELFpm17uH7rHtZWWX94CyFEVqJjYrGxtkKlUuHrVYrJI14FIDIqmhOnz/LfgUOofdQoJgWVOvMZdnpbHQ5edly/cUsCRSFEsZOydZ9KBQmJxgy38uv5XJs8lW3xQLF5w1r8vesQy9b+ybK1f5qPq1TQvEFNS19eCPGEShmT2KB2VV7v/TwqlYqgkBAWfLec37dsQeeoJ0FJIOZaLEGngyn7dGlKt/RGlcmSDKYkBbVGXcB3IYTIH6oHL0uVXbSlzHaeNGsxpb1L8fYrXcxpBoOeCmW9qVQ+b1+CLR4oTh01kODQCI6cupDmeN3qFZk6aqClLy+EeAKlnrhiZ2tDQmIiwSGh9B8xmiSvJGqMropDGXvi4uOJjY0l9Fw4l3+7RsSNSKq/UjldsBgbHEfUvWgqV6hQSHckhBB5l7J13+2AQDzcXDPcyi+vLB4oujo7suLzqRw8fpYLV26iKAqVypehYZ2qlr60EOIJlNHsZr1Ox6ip01BVVFG9dxVzIGjQ64iNi8O9viuOFR04MecMt3beoXQrnzRl3tx+mxaNG+Lp4V4YtySEeFyyjiLwcDLxpWu3CAwOw2QypUlv1qBWrssssC38GtSuSqXyZTAajbg6521ApRCiZMtsCZyjJ09z6eZ1Gr9dP01roUqlxspgID42HoODnrIv+HLtz5v4puqCvr33LsG7Q/jsy2mFdFdCCJE/bt8LZMjkuVy6ditdWl43OSmQQPHfPUeYs2gV127dpXZVf17v/TzLf/mLQb06p1vrRwghMhIZFZNhkAjw++a/cWvggkavSXeetZUVKApxMQk4V3Pk8k/XubPvHiqNmsDdQcTfieerT6ZTpaJ0OwtRbEmLIgCzv17Jxavpg0Qgz9O3LT5ye/ehk4yY+ilXb95FUZKfd+UKZThw7Cwbt+6x9OWFEE8IWxsrqvr7pQsSAe4EBmDtYZXpudbW1jjY2WHQ69HZaLm6+gYJ2+N5rV0vNq9aQcN6dQrgDoQQwrIOHDuLTqtl6bxJAFT1L8un74/A2dGexTPG56lMi7coLvxhHSbFRMtGtdm5/zgAPp7uuDo7cuLcZUtfXghRxJw6d56Vv27g6OmzJJlM+JctTZ8uz9OkQT00mvQtginUajUjX3uJhEQjVgZ9mjQbK2uMsUlZXlej0WBjbYOtzpp5n0ylaYP6+XI/QojCV9LXUUwRFR2Df7nSNKxTDZUKNBo1HVo1ZMHyX1n4w/o8jVG0eIvi2QvX8CrlxtefjEtz3N3VicCgUEtfXghRRMQnJDD6vY/oPWQMf1y5TmDFioRUqcKeyBiGvv8xLw0eTmBwSJpzQsIimDpnCfcffFao1ep0QSJAq4aNCD0ShpLNJvPh1yIwRiZRq1qV/LsxIYR4xOix4/CvVJ1SnqUp71+Vd8a9S0JCAgCJiYmMGjMOb18/vH39GD12HEaj0XxudulZsbO1ISEhEQB7O1suXbvN+s27uHnnPmcuXsvTvVg8UFRInnn4qODQ8OTFFIUQTzxFURj3wSdsO3MW+x7dsGvaGKsypbEq7Ytd/brY9ejKlSQTA0eOJzomBng4ceXgiXMsXfNHluV3ero1pnATgSeCs6zDjb9u80KH9tjZ2ubr/QkhCpli4VcuvTn4dY4fPcD9ezfZv3cXJ0+dYt78zwCYMXM2e/fs5fDBfRw+uI89u/cya/Zc87nZpWeljI8HdwKCiI2Lp1aV8sTFJzB51iLiExLw8/XM/Y1QAIFi+TLeXLt1l29W/g5ATGwccxatIjA4jIrlfC19eSFEEXDkxCm27z+M7dNtUVulH0uo0miwbdGMWzGxrP9jS7rZzcMGdM+g1IesrAxMGzOaC0svE3AsMF3LojE+ifMrL6EN0DB0YP98vTchhHhUlSqVsU31hVStUnPpUvJwu2XLf2TChHF4eXni5eXJ+PHvsHTZw9nI2aVn5e1+XXm7f1dCwyMZ/3ZffDzdUBRwc3HivTyuXW3xMYq9Xnia9+d+y6ff/oxKlby2z6Vrt1CpoMezedtORghRNMXFxXPvfiBqtQqPUu4Y9MndxCt++Q11hXIZBokpVGo1miqV+Xb1L2w/cI5b94IynLiSmU5PtwZg2rz53Np0B6d6TmitNMTdjyNwfzCV/Mrx2Rcf/L+9+46OomoDOPzb3fReSUICJJRAqCJSpCNSFIHQROnYUBAFpINYAWmCDZTPQhEVUCkCIiAiKEV6lyYlpIf0kLbZ+f4IWQgphJDJJtn3OWfO2czMzn33ZrP75s4tuLu5lshrFUKUIaUw6jkpKSnXbmtra6ytC16KeN78hcydt4Dk5GTc3d14//13iIuLJzQ0lEYNb69M16hhA0JCrpOQkIDBoBR63Nm54OkFDQYDtQL8qBXgh08ldzQaDdu/W0RcQhKuzo7FfPGlkCj269aBS1dC+fbn3zDc+i9fo4GBvbrQt1t7tYsXQpSCkNBwVqz9ifVbt5Kemd0Px87Glr5PPsmgvr05dOI0lk0fued1LKv6cXXHH+is3GhQp2aRk8QcXTu2p32rR/lt55/8+c8BUmNS8fGsRM8FnWlYL6jA5fuEEOJeagbWy/XztKmTmD5tSoHnTxg/lgnjx/Lvv+f4YfVavLwqkZySDJAr4ct5nJScbLwbUtDxwhJFgM4DxlLJw5Wdqz827nuQJBFKaR7FyaMGMaRvV07eGuVcv3Z1fL1lBQQhKoIjJ07xyuSpONSzp86rgTj7Z38oxV1MYPMff7Duxd/IyFDQFGEdZQUNCgq1/H3vO0nMYWNjTc8nO9Pzyc73/VwhRDlVCi2KF8+fxtHxdtJVWGvinerUqU2DBvV56aWRrPp2OQCJiYl4eLgbHwM4OjhgMCiFHi+MVqulqq8XWXetxvKgVO+jOG3OUr74dgOVvTzo0q45Xdo1x9fbkz/2HeGnLbvULl4IoaKo6BhGTpmGT3cv6g6rjUuAExqNBo1Gg1stF+q/VAfXds4kJieRERV9z+sp8fE4OTowa/IrxUoShRBCLY6Ojjg5ORm3oiaKkD2S+eKlS7i6uuDr68vxEyeMx46fOImfnx/Ozs73PH4vk0YOIiIqlk+++ZHY+MT7e4EFUD1RXPfbbnbtP5pn/xffbmDGgi/VLl4IoaI1GzZhG2BDlba+BZ7j36Uq9u5WZJz9N9/pawyKgczM7KkfMv49z9Pdn8DF6cFulQghzIyiUXe7D8nJyaxY8S3x8dlTdp06dZo5c+fT6fGOAAwZPIC5cxcQERFJREQk8+YtYPiwwcbn3+t4YUZOm48+S8/n366nTZ+R1Os4yLjV71i0a9yt1NZ6vltcQtK9TxJClFmKorB60yb8BvgUep5Go6FaNz/OLP+PlMNHsG/ysLGvoEExkHIzDYPBgPZKBNrr1xn43rTSCF8IIVSh0WhYveZHpkx7k/T0DDw9PQju2YM3p2f3Z5wyeSKxsXE0btIMgP79n2bihDeMz7/X8cIUOpVsMWcOVy1R7DRgjPHx2QtXcv2cmpb+wKNwhBCmlZ6eQVxcAnWr1b7nua41XLC20uIUFkZCXDxWdYOw8PHiZmo6mbGxGC5cxCY8nE9mvkW1KjJtlhCiGMrICir29vZs3rS+wOOWlpYsWjifRQvnF+t4YZZ9WPL/aKuWKIZGxADZI5wzMvXGn+/UrnljtYoXQqhMe2twiiHz3h2nDXoD1tbW/Pz1Yr5du55V634hKiERRQELrYbeT3bmhfemUTPAX+WohRCi4mr2UFCJX1O1RDG4SxsA1v+2BzcXR9o2f8h4zMbaisDqVej9RHu1ihdCqMzK0pLataoTfSKGKu0K7qMIEHPyBo3qBeHh5saQ/n05dTGSy9dCqV7Vh1mTR+LpLnMbCiGKT9Z6vu2fY2dZsnIdp879R2BAFUYP78sv2/+mz5PteLjBve8A3U21RHHWpBEAHDh6hrqB/safhRAVx5DeffhgxWJ8W/mgtch/bJw+TU/kXzGMn5D9GfDtz9sIjbxBo3q1iz0FjhBCiLz++ucEr0ydb5wix6Ao+Pl4su633WRm6ctWopjj9x8+UrsIIYSJdH28PavWr+fMV+cIGh6IzkqX67g+Vc/ppedoEFCHNi2aAvDSwB7Y2lozMLiTJIlCiJJRCvMolgcff7MWg2JgQHAnvlu/HQA/n0pUcnfh2OkLxbqm6tPjTJy1mBY9R3D6/GXjvjMXrtCi5wgmzVqidvFCCBVZW1mxdN4HVM7y4sCbR7i44TIxp2OJOX2D8z9e4sCMI9RxqsF7EyeQlpEJZHc9efHZ7pIkCiFECTv/XwgBVSoz/bWhufa7uToTFRNXrGuq3qJ44OgZbK2tqBcYYNxXt5Y/drbW7D96Wu3ihRAqc3F2YuUnizhw+Bjfb9jAhY2X0Wg0NKxZh2c/6Em1KlWY8sHn2NvZ8v7El7CXBFEIUcKkj2I2WxtrEhKTydTrjfuSU24SEhaJvZ1tsa6peqIYl5CEv593nv32trZcDY1Qu3ghRCnQarU82vRhHm36cK79sfGJTJ69hJDwaIJqVkNWWhZCCPW0aFyPbXv+of8rMwAICYuk3yszuJmalmtQ8f1Q/dazo70dV65HcOlqqHHfpauhXL0ejpODndrFCyFM5O4kUQauCCGEusa99Ayuzo78e+kaALHxSVy9HoGrsyNjnn+6WNdUvUXxobo1+WPfUQaOfofObbNnGd+25yBZBgMP1QtUu3ghhAlIkiiEEKWvSuVKrP/yA1at38aZ81cAqBvoz4DgTni6uRTrmqonii8M6MHuA8dJSrnJT7/uArKXmLHQaXnx2e5qFy+EKGWZej1T53whSaIQovTIqGcjDzdnXn+uX4ldT/VEsXG9Wnz83hjmLF7FtdBIAKr6ejF55CAa1a2pdvFCiFJmaWHB0089xq9/7OedN56XJFEIIUrRzr8P882aLVy8ch2Amv5+DOv3BB1bP1Ks66meKAJ0ePRhOjz6MHEJSQCyxrMQFVBWlgHdrWX9HmvVhPaPNkarVb0btBBCgIqjnstTi+KyNVuY98V3QPbdW4DDJ89x5NQ5xo94luFPd7vva5bqp7irs6MkiUJUQLHxibw2YyEHj5817pMkUQghStdXP2xCUaBx/UDGj3iW8SOepUmD2igKfP3D5mJdU5UWxXodB9EoqCbfffo2dR8bhKaAOTE0aDj1+0o1QhBClJI7B678tOVPHmlYB01Bf/RCCCFUk5aRgXclN1YsnG78Z31I3650HjiW5OTUYl1TlX/5FSV3S62iFLCVp/ZcIUQed49unjFmmCSJQghhIp3bNiMzMyvXhNt6fRaZGXqeerxlsa6pSoviqKG98PZ0Nz4WQlQMySkpbNn+B0dOnyLlZipnL1wHrR0N6wbK6GYhhOnIqGcgu4tfUnIKfV6aRrsWjQH4c/8xbqal4+Rgz2fLfzaeO2po7yJdU6VEsU++j4UQ5ZOiKCxf/SMff/0Ntt42ONZzINNBT6JzMglnE3koyButVloShRCmIUv4Zft69WY0GvjvWjiXQ8KB24Na/vf9xlznmjRRFEJULN98v4Zv1v9I0Cu1cK3lgqIopNxMRfO4Bm0a7P/mCKOmTOfzObOxtJSPFSGEMIXsPuIle03VBrMUhQxmEaLsCwkNY+l339N4UgMcfR0A0Gg02NvZZn8g2WloMCqIo3NO8stv2+n91BOmDVgIYX7k1jMAKxZNL/FrqjeYpSjbfdb+ks+X0qp1e5xdK9Gv/4B8z0lNTaVeg8Z4V65aEi9FCLO3ev0m3Bu64lDZnrT0DOP+7EEr2f+6WthY4P2YFyt+/hlFKUefqkIIUUElJKUQn5icaysOVVoUg7u0yfXztt3/oNNqad64HgoKB4+dJSNTT9f2ze/ruj4+3kyaNJ6df/xJaGhovue8+94sfH0rc+PGjWLHL4S4bef+v3Hv4kbKzVQMBgMaDVhbWeU5z6dpJXZ/v58bcXF4uLmZIFIhhNmSFkUAQsKieHvhVxw6fg59lj7XseLexVUlUZw1aYTx8Vc/bEKr0fLLsrlUcncFIOpGHN2GTsDX2/O+rhvcswcAJ06czDdRPHr0GL9t286c2TMZPHT4A7wCIUSOlJRUbCxsMRgM6HQ6rK0s8z3PwsYCnU5Lamp6KUcohBACYPLsJRw9fSH/g8UclaN6r/Nvf/6NSh6uxiQRoJK7K14ebqzZtLPIo27uRa/XM+rV11n04bwinZ+enk56+u0vtKSkpBKJQ4iKJDY+keSbadhGpeJSwxl7OxtybjffLfVGGooBXJydSjdIIYTZk1HP2f69eBVbayvGvtgfXx9PNAV8Xt8P1RPF+MRkom/Es2rdNp587FEAtuzcx+WQMKws82+ZKI5FH31K/fr1aNu2Dbt377nn+fPmf8jMWXPy7E9NS8OyBOMqq9LSpNUnP1Ivt8UlJPHm/K+w1DoRvfcGNR/zv3Uk/0/N0L/Cad38ESx0WlJTi7cCQHkj75f8Sb3kJXWSl62tralDqHCaNKzN5ZBwBgR3KrFlVFVPFB9uEMi+w6eZ9ekKZn26Is+xkvDff5f5YumX7N+7u8jPmTB+HK+NHmX8OSkpiZqB9bC1sTGbN6+5vM77JfWSbcdfRwiLukHTJg9z4NAuQvdG4NfKJ99zk64nE7k7mndnjzO7+jO311tUUi95SZ2oSPooAjBr0ssMGfMe/V5+kxYP18uzCEJx7uKqnijOGDOcFyfO4Xp4dK79fj6evPl6yfQj/PvvvcTExNDkkRYAZGRmkJiYhH9AIGvXfk/TR5rkeY61tTXW1tYlUr4QFVH3Tq2wtrKkdbOGHDnemjFvvUNqVCpVOvhi45L9t6NPzyL8n0iubbjOK4MG07RxIxNHLYQQ5uvXP/Zx5XoEGg38e+lqnuNlMlGs5uvN5uXz2LxjL+cvh6AoCrWrV+XJji2xus+JefV6/a0tC8VgIC0tDa1WS9++venU6XHjefsPHGDEy6+yf98e3N1l9KUQRRUbn0hCYjIBVSsD0LldMwCaNGrAyk8+YtGXX7F/xhGcqzih0WpIDEumqrcPsydMostj7UwZuhDCjEkfxWxLVq4HQKvR4uhgh06ne+BrlsoSCpYWFgR3bfvA1/lgzrxc/Qpd3b1p06YV27ZuztWk7+bqikYD3t5eD1ymEOYiNj6RybOXEJ+YzLzpo6jm653reFBgTb6YO5vQ8AhO/3sevV5PVT9f6tUJvDWnohBCCFPSabX4VHJn49cfYG9XMl0dSiVRPH3uMh99vYajpy8QGFCFUcP6sHnHXvp2a0/j+kXvpzh92hSmT5tyz/Patm1DRNi1BwlZCLOSkySGhEcTVLManm4uBZ7r6+ONr493gceFEKLUSR9FAEY/15d5n3/H5ZBw6teuXiLXVD1RPH3uMoNef5eMzEwUBQyKQiV3F9b9thudTntfiaIQouTdnSS+P+HFPB2ghRBClH1LV20kM1NP/5EzcHZ0uDWlWTaNRsO2VQvv+5qqJ4offb2G9IxMalevwrn/QgCo6e+Hi5MDR06dU7t4IUQhJEkUQlQI0qIIQFhkjPHx3cv2FbeHkOqJ4tHTF/Bwc2bN5+/RqPMw435vTzdCwqPULl4IUYgPFn8rSaIQQlQQo4b2KvFrqp4oZmbqqezlgaVF7qISk2+SlWVQu3ghRCFGDe3Nih+38sZLz0iSKIQotzQUtGZUyVy7vBg1tE+JX1P1RLGqrxeXroSyeec+APT6LL5bv52wyBhqV6+idvFCiLukpKZhZ2ONRqOhmq83b74+zNQhCSGEKCFxCUn8sHEHp/79Dx8vD/p378jp85dp2qgOvt6e9329klnfpRA9O7fBoChMnPkZGg2cuXCZmZ8sR6OB7p1aq128EOIOsfGJjH37I75ZswVFKUcdb4QQojCKyls5ceV6BD2fn8yny35i1/6jnD5/mZupaUyd8wUrftparGuqnigO6/ckj7d+BEUh19ahZROG9n1C7eKFELfcOXDl1Ln/yMzUmzokIYQQJWjuklXExCZQu0Y1ctoCGtWtiZOjPfsOnyrWNVW/9azTafn43TEcPnmO42cuAtAwqAaPNKyjdtFCiFvyG91sZWVp6rCEEKJkyKhnAP45dgZvTzfWLnmPBp2GGPdXruTOtbDIYl1T1UQxI1PPs6Pewt7OluULp9GkQW01ixNC5EOmwBFCCPOgGBSsrCzR6W7fMFYUhejYhGJfU9Vbz1aWFlwPjyI55aYs8SWECSQmp0iSKISo8HLWelZrKy+CAv0JCYvk7YVfA9kNBRNnLuZGXEKxV2pRvY9iuxaNuRYWSVxCktpFCSHuYm9rS62AKpIkCiGEGRg1pDcaNKzdtBONBq6HR7F55z60Gg0jBvYs1jVV76NYt5Y/23b/w4DR79C7a9vsNWTvaF0M7tJG7RCEMFs6nZZxLz5Dpl6PjbWVqcMRQgj1lKOWv5JWr+MgGgXV5LtP32bp3EksXv4zZy5cAbLzsFeG9OLRJvWLdW3VE8W5n3+HRgPXQiNY9NWaXMc0aCRRFKKExcYn8vE3P/Lq0D54uDmj02nR6SRJFEKIikpRbufJLZvUp2Uxk8L8qJ4oAhQ4XVt5uvEvRDlw58AVe1sbJrw8wNQhCSGE6tTsS2juqYrqieL27xaqXYQQgryjm0cN7W3qkIQQQpSSS1dCGTZuZoHHNWj45sOp931d1RPF4iwXI4S4PyU9BU58QiLrNm9l7ZbNRERGY2lpQbPGDzEgOJgWjzSWWQyEEGWLzKNISmoq/xw7m2e/RpN9Z7e4H9ulcuv574MnWLxiHecvh6AoCnVqVOPlQcG0btawNIoXokIr6STx6MnTjJw8DcvKlnh18qSRXyWy0rO4eOwqr77zJm0aN2PujKlYWcqE3UIIUVbY29oSVKtaiV9X9URx/dbdTJu3FLjdV/HIqfOMmDKXmRNeIrhrW7VDEKJCC4uMISomvkSSxMtXQxgxcTKVu3tTpZ3vHUcUnKs5UbWjH/98eowZcxfwwbTJDx68EEKUBGlRpIa/L8sXTi/x66o+j+LiletQFHCws+Xx1o/QsXUTnBzsUJTsY0KIB1O/dnVmTxlRIvMk/m/Vdzg1drorSbzNysGSei/XYcvOP/jv6rUHKksIIUTZp3qLYlRMHA52tvyybC6V3F2z992Io9vQCUTFxKldvBAVUmx8Iif//Y92LR4CIKim/wNfMyExkV937qLRpMKnVbBxscbzYXfWrP+Fya+PeuByhRDiQWlubWpdu6zzqeSePU+1ClRPFAOrVyE1LcOYJAJUcnfFy8MNRwc7tYsXosK5s0+ipaVFic2XdeXadSzsLXCsbH/Pc13qOHHy6LkSKVcIIcSD+f2Hj1S7tuq3nieNHERoRDQ/bNxBys1UUlLTWL3xd6Ji4pg2eojaxQtRodw9cOWhujVL7NrZo+KK+L+zBgwGQ4mVLYQQD0RReTNjqrcoDhnzHgDvfbSM9z5alutY/5EzjI81aDj1+0q1wxGi3Crp0c13q+pXmYykDG5Gp2LnaVvouYkXk2kV0KTEyhZCCFE2qd6iqChF3Mw9ZReiEGoniQBuri50aNWS63+GFXpeZkomUQdj6N+ze4mWL4QQxZWzMotamzlTvUVx1NBeahchRIW34setqiaJOV4aNICBr76Gc3UnvB7OO1m+Pj2LM1+ep1WTJgQFltxtbyGEEGVTKSSKfdQuQogK7+XBwdjZWjOodxfVkkSAoMCaLHx7BuPefo8bR2PxbuOFo689WRlZRB6NIXJ3NHV8ajBvxjTVYhBCiPsm8yiqplRWZhFC3L+4hCSsrSyxs7XBxtqKlwb2LJVy27VswfpvvmT1+o38+OWvJCcngwL169Zhxouv06lDG1mVRQhRtkiiqBpJFIUog3L6JDo52PPu+BdUbUXMTxVfH8aPGsEbI1/iZmoqer0eZyenUo1BCCGE6ak+mEUIcX/uHLhiUEz7r6xGo8Hezk5aEIUQZZpG5c2cSaIoRBlSGqObhRBCiKKSW89ClBGSJAohRDFJH0XVqN6iOGzcTN7/eHme/Ws27eTjr39Uu3ghyoVMvZ4pH3whSaIQQogyRfVE8Z9jZzl9/nKe/T//+idfrFqvdvFClAuWFhb0eaId9QMDJEkUQoj7JUv4qcYkt55vpqYRFROHxuy7iApzZzAY0Gqz/1/r3K4ZHVs/gk4nXYeFEEKUDaolivU6DgJAo4ETZy8af76Tp5uLWsULUebFxicyY8FXDH/6SZo0qA0gSaIQQhSH9FFUjWrfSjlrON/5+O6tW8eWahUvRJmWM3Dl0tVQftz8B4qJp8ERQggh8qNai2LOGs+fLV+Ht6cbfZ5sZzxmY21NYPUqtGnWSK3ihSiz7h7d/Obrw9BopBuGEEIUl0bJ3tS6tjlTMVHMXuP5wNGz1PT3lTWfhUCmwBFCCFG+qD6YZcWi6WoXIUS5IEmiEEKoRPooqkb1RHHYuJkFHtOg4ZsPp6odghBlhsGgSJIohBCi3FA9Ufzn2Fk0mtsDW3K6YinK7cdCmAM3FyfmTR+FtZWlJIlCCFGSpEVRNaonio80rJMrIUxKvsmFy9dBAw83CFS7eCFMKjY+kfW/7WFo3yfQ6bS4OjuaOiQhhBCiyEzSR/HC5esMHP0Ondo0Vbt4IUzmzj6JNtZWDAjuZOqQhBCiQpJRz+oxyey+tQL8qFc7gJU//WaK4oVQ3d0DV4K7tDF1SEIIIcR9U71F8eDxs7l+NhgMXA4J5/iZi7KAnyi3FEXh6MnTXAsJRavTUjewFjWr+wMyulkIIUqd9FFUjeqJ4tCxMwsctNIwqKbaxQtR4rZs38mny5cTGReDUxVHlCwDcf8lUD+oDi8+O4DvNu6SJFEIIUSFoHqiCLdHPN+pik8lZowZVhrFC1Fi/vft9yz5biX+vavQ/JEm6Cyze29kpGRyfXcYr06fjqtLVZo2fkiSRCGEKCUaRUGj0lKoal23vFA9UVz24bRcP2s04OHqTFVfb3Q6k3SRFKJYDh8/yZJvV9JwbF0c/RxyHbOyt6T6E9Ww97Lj368v8saLsyRJFEIIUe6pnqk1eygo19a0URABVStLkijKnRU//oRXa888SaKiKBgMBgC8HvbENdCZrb//YYoQhRDCPCkqb/chPT2dkaNeo07dhnh6+dGocVOWL19pPJ6ZmcmYcROo7OdPZT9/xr4xAb1eX+Tjpa1UsrWT/17iufGzeaTb8zzS7XmeGz+b42culkbRQpSItLR0/vhrH5VbeeXarygKKTdTSbmZakwWPVt6sH77NlOEKYQQwsT0ej3e3l5s2bSBqIgQln6xmMlTp7Njx04APpgzj31793H44H4OH9zP3r/3MXfeAuPz73W8tKmeKJ69eIXBr7/HgaOnuZmazs3UdPYfOc3Qse9z+vxltYsXokQkJCaSZcjC1sPWuC8nSTQYDGi1WrTa7FFbdh42xMbFmyhSIYQwPxpuz6VY4tutMpKSkkhMTDRu6enp+cZib2/PjDenUb16ABqNhubNmtK2bRv27tsHwPIVq5g0aQI+Pt74+HgzceJ4lt3R4niv46VN9UTxs+XryMjUY2NtTZtmjWjdrCG2NtZkZOpZsnK92sULUSLs7OzQkD1oBXIniTqdDns7G3I+TjJTMrGV/olCCFGh1Aysh5dPVeM2b/6HRXpeWloahw4doX79+sTFxRMaGkqjhg2Mxxs1bEBIyHUSEhLuedwUVB/McuTkOexsrdn49Rwqe3kAEBoRTY/nJnPk5Dm1ixeiRDg62NOwXhDhByKp1tGvwCQRIPqfGzzesrXpghVCCHNTCvMoXjx/GkfH28uwWltb3/upisIrI1+jZs3qBPfsTmhYGADOzs7Gc3IeJyUno9waYV3Q8Tv3lxbVWxSTb6ZS2cvDmCQC+Hp7UtnLg+SbqWoXL0SJGdKnD5G7okmISSowSUwKTSb66A36B3c3XaBCCCFKnKOjI05OTsbtXomioii89vo4zl+4wJofVqHVanGwzx4MmZiYaDwv57Gjg8M9j5uC6omip5sLV0LC2fHXIeO+bbsPciUkHA+30s+MhSiuTu3b0LL+w1xYeomMG5m5kkRFUYg9F8epT84ycsgQAqpVMW2wQghhRjQqb/dLURTGjB3PoUOH2bRxnbEl0NXVBV9fX46fOGE89/iJk/j5+eHs7HzP46ag+q3nNs0bseaXnbz+1iJsrKxAoyHtVgfQds0bq128ECXiZmoatjbWzJ0xjXmffs7aDzfjUssJm2q2KAaF5LPJ6GP1jH3uBQb0CTZ1uEIIIUxo7LgJ7Nu3n1+3/IKrq0uuY0MGD2Du3AU82qIFAPPmLWD4sMFFPl7aVE8UXxvel78PniA0IobU9Azjfl9vD14d1lvt4oV4YDlrN7du2ojBfbowbexoXh46iE3bfufS1atYWllQ/9nadH2svQxiEUIIUyhDaz1fvXaNL5Z+ibW1NbWDbg9KefaZp/nk44VMmTyR2Ng4GjdpBkD//k8zccIbxvPudby0aVJT4lVfmyYhKYVV67Zx4mz23IkNg2oyILgTLk6mud+en8TERLx8qhIZfg0nJydTh6O61NRUbG1t732imbm7XnKSxJy1mz+Y8gpWlqWy8mWZIu+X/Em95E/qJS+pE3XkfHd3eHEBFlbq1K8+I5U//veG2eQHdyuVbzxnR3tGDulVGkUJUWLuThLfn/CiWSaJQghR1uXMeajWtc1Zqa6jN3rGQjoPHFuaRQpRLPklibJ2sxBCCHNTqoli9I14QiOii/38JZ8vpVXr9ji7VqJf/wHG/fdaV1GI+5GQlCJJohBClCdlaK3niqZc3Ufz8fFm0qTx7PzjT0JDQ43771xXMSDAn38OHiK4V198fX15/PHHTBixKI8c7GypXtUXB3s7SRKFEEKYtXKVKAb37AHAiRMncyWKOesq5rhzXUVJFMX90um0THh5ABmZmdja3HvmfSGEEKYlfRTVU6q3nts2f4ienduoXs6d6yoWJD09Pdfi3klJSarHJcqu2PhE3ln4DXEJ2e8DnU4rSaIQQgizV6otiqUx8vnudRULMm/+h8ycNSfP/tS0NCwtLdUMsUxIS0s3dQhlRlxCEm/O/4rQiGgsdBrGvdjf1CGVOfJ+yZ/US/6kXvKSOsmrRKcLUpTsTQ1qXbecUCVRXP/bniKfG9yl5FoY71xXccum9Wi1BTeYThg/jtdGjzL+nJSURM3Aetja2JjNXFfm8joLExufyNsLvyEs6gZ1AwN4ZXCw1EsBpF7yJ/WSP6mXvKRO1CO3ntWjSqI4dc4XaIqwOKIGTYklineuq7hl88Z7rolobW19zwW9RcWW3xQ4GnMf3iaEEELcQbVbz0Vqqb3PNF2v19/aslAMBtLS0tBqtVhZWRW6rqIQdytonsTU1FRThyaEEOJ+laEl/CoaVRLF7d8tVOOyfDBnXq5+ha7u3rRp04r/LV1S6LqKQtzteng0EdGxMk+iEEIIUQhVEkVfb081Lsv0aVOYPm1KvsdSU+JVKVNUTA2DajB78ssEVPGRJFEIIco7A2gM6l3bnFWowSxCFCY2PpEz56/QullDAOoFBpg4IiGEEKJsqzCDWYQoTE6fxOvh0cwYM5wWD9czdUhCCCFKivRRVE25GswiRHHcPXClYVANU4ckhBBClAvlajCLEPeroNHNQgghKg6ZR1E95WowixD3Q5JEIYQQ4sGUyhJ+YZExLP1uI8dOXaCanxfDnu7G3kMn6dy2GbUC/EojBGGGlq3dIkmiEEKYBemkqBbVE8WroRE8O+ptEpKSURSwtbXGQqfls+U/k5CYzNTRQ9QOQZiplwcFY2drw5A+XSVJFEIIIYqh4MWQS8ii/60hPjEZN5fbS+o1qFMDR3tb/jl2Vu3ihZmJS0giLT0DADtbG2OyKIQQouLK6aOo1mbOVE8U9x09jbOjPb+umJdrv3cldyKib6hdvDAjsfGJTJq1mBkLvjQmi0IIIYQoPtUTxdTUdHwqueNgb5drf0amXr7MRYm5c+CKXp+FwWDmU+kLIYQ5UVTezJjqiaKPlzsXr1zn2JkLxn1/7j/G1esRMjpalAgZ3SyEEEKoQ/VEsUvbZuizDAwc/Q4aDZw8e4mR0+aj0UCntk3VLl5UcJIkCiGEkBZF9aieKL48OJgGdaqjKNmrtRgUBUWB+oHVGTGwp9rFiwosI1PP5A8+lyRRCCGEUInq0+PY2liz6pO32LJzH8fPXASgYVANnnzsUSwtSmUaR1FBWVla0KtLW37/6xDvjn9BkkQhhDBXOa1Ral3bjJVKpmah09GjU2t6dGpdGsWJCs5gMKDVZjeGP9GhBZ3bNkOnU71xXAghhDA7qn27btm5j94vTeP7DTvyHPt+ww56vzSNzTv3qVW8qKBi4xN5/e2Pcg2OkiRRCCHMm8yjqB7VvmHXb9vDuUtX6dCycZ5jj7V8mHOXrrJ+6261ihcVUM7AlYtXQln7yx8oZn47QAghhFCbareeL/wXQiV3V7w93fMc8/J0w8vTjfOXQ9QqXlQAySkpxMUnYGVpiYWlFVPuGLgy7bUhaDQaU4cohBCirJC2A1WolijeiE/E38+7wOP2trZcDY1Qq3hRjh0/dYbvflzDzj1/odEoZGYasLKyQmPlSfNHmsjoZiGEEKKUqJYoOtjZci00kuSUm3lWZUlKvsm10Egc7GzVKl6UU2s3bGLRko94uqueDYu1VPXWEBufzo596azYmMqNCHv0er2pwxRCCFGGqNmXUPooqqR29apk6vW89eHXuZbqS0vPYMaCL8nU66ldo6paxYtyaO/Bw3y05COWvpvF+OctqFZZiwJYWWno1l7LTx9b4uF4kSnvvmPqUIUQQgizoFqLYreOLTlw7Axbd+3n4PGzNKhdHYAT//5HbHwCGg081bGlWsWLcmjZdyt48elMHgqyNO7TajXY29mi0YBGo2H2Gwodhx3l7PmLBAXWNGG0QgghygyDkr2pdW0zplqLYu8n2vJw/UAUBW7EJbBr/1F27T/KjbgEFAUa1w+kV9e2ahUvypmQ0HCOnzpN784WKAYluxX61t+mVqsxDlxxctDQrZ3C+s2bcz0/IzOTXX/vZ/W6X1i/+TdCQsNK+yUIIYQQFY5qLYparZYv5kxk1scr+OX3v9Hrs7ILtNDR/fFWTB09xDhpshAhYWH4VtLh6phF8s1UDAYFDRqsrS3znNsg0MDWfZcByMrKYtn3q/n+pzXYWt+kZlW4maZh9qIsHnnoIca+8io1q/uX8qsRQghRqtRck9m8GxTVXZnF3taGmZNeYurowVwOCUdRFKpXrYy9DGIRd7HQ6UjPVIxJok6nxcoq/7dnpl5Bq7PAYDDw5qzZnD37J7PGZPHoQ1q0Wg2gEBMHy9cd4rnRo1i84EPq16ldui9ICCGEqABKZQk/eztb6t/qoygqtviERDZs+Y1fd2wm+kYcdrbWtGzemn49ehTasufp4UlIeBqXrinUrKbF3s6mwHkS/zxoScOGD/HTL79y8tSffDdfwc1Fl+scD1cNbzxnibvrTSbOmM6GVd9jaSlriwshREUko57VI/d+RYnZd/AI3Z99lr///h/PBV/ls+lJvPlyDBmJGxg04gU+/d/X+a6mEhufyOzPvsXa1o3vNxeeJF68auDACQh+siurf/6BV57R4+ZS8MTbg3vo0GkS2L3vQIm9TiGEEMJcSBOLKBGn/j3H+Den8ObIDLp3sODOt1arh+G5vgZemvE9trY2PD9oQK7n7tp3lJDwaB5u3Jw9R/5m2c8pDOuty5MshoQbePU9LYOffpaExCQiIsPp0iZ3S+LddDoNvR7P4NftW+nYtlWJvV4hhBBljZk3/alEEkVRIpZ89T8GdU+ne4e8g08AqlfR8uHkLJ6bupx+PXvg5OhgPNara1usrSzp0PJhLl/tw5ipk9n0ZzJPd8mgelUtqWnw218atu7R8HRwb0Y+P4yDR4/j7qrDxtpwz9gqe2nYe/JGib1WIYQQwlxIoigeWEhoOIeOHWPW6MLfTg1r66hTXWHTbzt4olNHklNSqVK5EhqNhm635tSsVyeQjau+47edf7Lx1w1ERkdjbW1Js4cfZdX/elDDvxoA9nZ2JCYbyMpS0OkKX/M5PlHBzs6h0HOEEEKUX9JHUT2SKIoHdvb8BeoEWODhmnXPc9s0yeDoyWPsOniO5JRU5k0fha+3Z65zbG1tCO7WheBuXQq8Tp1aNbCycuSvw3G0a1bw21hRFDbutKJPrw5Ff0FCCCGEAGQwiygBBoOBok6JqdUqHDx+hpDwaLwruePq7FisMi0sLOjTozdfrNaRnlHwv3u/78siJMKSJx5vX6xyhBBClAOKypsZk0RRPLCqfr5cvKrnZmrhf02KQeHAcQOp6VqCalbj/QkvYmdrU+xyh/TvBxaBjH4PwqJy91XU6xU27NAzdaEFb0+eip2tzN0phBBC3C+59SweWFBgTar4+fPLzov075b/YBbFoHDh6k32HNbSuk3jB04SAWxsrFk8fz7vz1/AUy//ScvGCrWqZXLzpobfD1ih0boy8803aN+qxQOVI4QQomzTKAqafKZfK6lrmzNJFMUD02g0PD94OO/MeZv6tbOoVzPvlDVRMalMmKelcuUAPnzr9QdOEnPY29kxe8abRES+zObtO4mIDMfKxpqJYx6i7aPN0OkKnz5HCCFEBWC4tal1bTMmiaIoER3btiI84mWem/I5fbvo6dtVS1UfDYkp8OufepattyEjy5O1X39WYkninby9PHl+UP8Sv64QQghhziRRFCVm0NN9aFA3iO9/WkuvV/8iK0sPaKgfVJuXn+/HEx3bY2EhbzkhhBAlS249q0e+tUWJalS/Lo3qv0VYZDSTZ31G2xaNeeHZnqYOSwghhBDFIImimVAUhX+OHOPnXzZw+eolFEWhbu0G9O3Zk/pBtQtcW7k4YuMTefvDr4mOS+bMhWtkZOqxspS3mhBCCJWoOY2NeTcoSqJoDuITEhk3bSpXrp2jdyc9wW01ZBkU9h8NY+Qb22na5FFmTZ+OjY31A5cVG5/I5NlLCAmPNk6BI0miEEIIUT7JPIoVXFp6Oq9OHI+741m2fa0wdpgFbZvqaN9Mx5SXdWz9ChJi9zHx7bcwGB5saFd+SaIaA1eEEEKIXBRF3c2MSaJYwW3ZvpOM1MvMnaDB1jrv7WUXRw2fzYB/zx1m/+GjxS4nPjFZkkQhhBCigpFEsYJbs24tA7tnYmVZcB9ER3sNfTpnsnbdT8Uux8Helmq+3pIkCiGEKHUaRd3NnEnnsQosLS2d85eu0KH5vX/NHVpoWf3ryWKXZaHTMWnUIDIz9diWQF9HIYQQQpietChWYJl6PQBWVvc+18pCQ6Y+676uH5eQxMyPlxOXkARkJ4uSJAohhCh1isqbGZNEsQKzt7PFxcmBMxfvPUjlzCUDvj6VinztuIQkJs1azF+HTrJszZYHCVMIIYQQZZQkihWYVqulx5PdWb2l8F+zoiis2WpJcLfeRbpuTpKYM3BlxCCZUFsIIYTpSB9F9UiiWME9HdyDvces2PC7vsBzvlybRWiUA906d7zn9e5OEmXgihBCCFFxyWCWCq6ytxfz3p3J+DencuR0OgO6a6gdoEVRFI6dNbByg4ZDp534bP48HB3sC72WJIlCCCHKJDXnOzTzeRQlUTQDLR5pzMrPl/LtmjUMmrAdnVZPlgEsLW3p0fUpvn29Dz5e9+6feOV6BOFRsZIkCiGEEGZCEkUzEVCtCm9OeINxo14hPDKKjIwMqvtXw8a66KOUG9erxaxJL1Gjmq8kiUIIIcoOWetZNdJH0czY29lRM8CfgKpVipQkxsYnsvfwKePPDerUkCRRCCGEMBOSKIoC5azdPPPj5Rw8ftbU4QghhBD50qCgUVTazLxJURJFka+cJDEkPJraNapSLzDA1CEJIYQQopRJH0WRx51JogxcEUIIUebJqGfVSIuiyEWSRCGEEELkkBZFkctXP2ySJFEIIUT5Yri1qXVtMyaJoshl5JBe2NvZMqzfE5IkCiGEEGZOEkVBXEIStjbW2FhbYW9ny8ghvUwdkhBCCFFkOSOU1bq2OZM+imYuZ1m+dxd9Q1p6hqnDEUIIIUQZUq4SxSWfL6VV6/Y4u1aiX/8BuY5lZmYyZtwEKvv5U9nPn7FvTECv15so0vLhzrWb09IzMBjMvCOGEEKI8iln1LNamxkrV4mij483kyaNZ/jwoXmOfTBnHvv27uPwwf0cPrifvX/vY+68BSaIsvRkZWURFhHJ1ZDrpNy8eV/PvTNJDKpZjfdk4IoQQggh7lKu+igG9+wBwIkTJwkNDc11bPmKVcydMwsfH28AJk4cz5Sp05k6ZVKpx6m2hMQk1q7/hZ9++YnomDh0FhpAR5fHHmNgv37Urlmj0OfHJSTx5vyvCIu6YUwS7SVJFEIIUV7JPIqqKVctigWJi4snNDSURg0bGPc1atiAkJDrJCQk5Puc9PR0EhMTjVtSUlJphftAIiKjGTbyFQ4c+JppI2I5/LOWIz9rWbMoC0eLbQwf9Qrb/thd4PMzMjKZNHsJoRHRkiQKIYQQolDlqkWxIMkpyQA4Ozsb9+U8TkpOzrU/x7z5HzJz1pw8+1PT0rC0tFQp0geTlZXFa5Mn0rh2GG+9qkGn0wGgKAo1qmiYMkJD6yaZjJv9Pp7uH1KnVv4ti13bNuOPfUeY9uogtCikpqaW5sso09LS0k0dQpkk9ZI/qZf8Sb3kJXWSl62tbcldrAzNo7jk86V8++13nDp9hs6dH2ft6u+MxzIzM5kwaSpr1qwFoH//fsybMxsLC4siHTeFCpEoOtg7AJCYmIiHh7vxMYCjg0O+z5kwfhyvjR5l/DkpKYmagfWwtbEp2TdvCdq99wAJCdeZ8aoWCwtNvue0bWpB3y561m5Yz6w3pxv3K4qCRpP9nN5Ptqdz26Y4ONiXStzlTVn9/Zua1Ev+pF7yJ/WSl9SJecgZT7Hzjz/zdJO7czwFQHCvvsydt8DYTe5ex02hQtx6dnV1wdfXl+MnThj3HT9xEj8/v3xbEwGsra1xcnIybo6OjqUVbrGt37yBfl30WBaQJOZ4ppuG33f/SUJi9u302PhEXn/rI07+e8l4jk5XIX71QgghBBoUVbf7EdyzBz26P4WHu1ueY8tXrGLSpAn4+Hjj4+PNxInjWbZ8ZZGPm0K5yhb0ej1paWno9VkoBgNpaWlkZGTP/Tdk8ADmzl1AREQkERGRzJu3gOHDBps44pIVEnqNerUKTxIB/H21WFsqREXHGNduvnDlOj9s/B3FzDvlCiGEEMWRlJSUa2xDevr9dSe413iK4oy3KA3l6tbzB3Pm5epX6OruTZs2rdi2dTNTJk8kNjaOxk2aAdC//9NMnPCGqUJVhU5nQUbmvRM9g0EhU6+QfDOVeUuXGKfAmTZ6iPH2sxBCCFFhlMKo55qB9XLtnjZ1EtOnTSnyZe41niKnIed+xluUhnKVKE6fNqXAX4qlpSWLFs5n0cL5pRxV6WlY7yF2Hwzh8ZaFn3fguAEba3s+W7GB0MjsKXDel3kShRBCiGK7eP50rm5q1tbW9/X8e42nMBiUQo+bSrm69Wzu+vXsyZbdGiJjCh6CpSgKy9ZpsLDxkCRRCCGEeSiFlVkcHR1zjW2430TxXuMpijPeojRIoliO1KoRQJeOnXnlbQ1RN/ImiwaDwtwvDVwIcaGSV1VJEoUQQohS9iDjKcrieItydetZwLRx43h/vkK3Edt5qr2BDi0UrCzg7CUDa7ZaodV58eVH87CxtcPe1kaSRCGEEBVfGVqZ5UHGU5TF8Raa1JR4GQZLdj8AL5+qRIZfw8nJydTh3NPZ8xdZu2E9x08eJVOvx8vTi0reNXhz3MvYFKE5PDU1Veb0yofUS/6kXvIn9ZI/qZe8pE7UkfPd3av5dCwt1GkYydSnse7A++UmPyhpcuu5nAoKrMmMCeP5acUqli/5Eo2NL0fOhvDLjr2mDk0IIYQQFYTcei7n4hKSmDRrsXEKnCcfe9TUIQkhhBClqwzdeq5opEWxHLs7SXxvwovYS59EIYQQQpQQaVEspyRJFEIIIW5Rbm1qXduMSYtiObXjr0OSJAohhBBCVdKiWE71fbI91laWdGz9iCSJQgghzJtByd7UurYZk0SxHImNTyQ1LR1fb080Gg09OrU2dUhCCCGEqMAkUSwnYuMTmTx7CSk305g3fRSVvTxMHZIQQghRRqg46tnMOylKH8VyICdJDAmPxsvTDRcn0y0OLoQQQgjzIS2KZdydSaKs3SyEEELkQ+ZRVI20KJZhkiQKIYQQwpSkRbGMUhSF9z9eLkmiEEIIcS8KKrYoqnPZ8kJaFMsojUbDK4N70fyhIEkShRBCCGES0qJYxqSlZ2BjbQVArQA/3h73vIkjEkIIIco46aOoGmlRLEPiEpJ4bcZCVm/83dShCCGEEEJIolhW3Ll284FjZ8jU600dkhBCCFE+KAZ1NzMmiWIZcGeSmLN2s6WF9AoQQgghhGlJNmJi+SWJsnazEEIIcR+kj6JqpEXRhOITkyVJFEIIIUSZJS2KJmRvZ4OfTyUc7O0kSRRCCCGKy6CARqWWP4N5tyhKomhClhYWTHl1MJmZepknUQghhBBljiSKpSw0PIqpsxaRlhLO9bBwtFotdWsH8nSvfrRr2RwLGcQihBBC3B/po6gayUpK0bFT//LC66Op7JHKkGBo2tAaQxb8dfgYCz89w7drAlk0axbOTk6mDlUIIYQQQgazlJbL10J54bVX6dc5hTWLFJ7pZkPNqloCA7Q819eSDYsNuNieZezUqWRlZZk6XCGEEKL8yGlRVGszY5IoloLY+ERenvAWQdXTeH2IBgd7WzQaTa5zbKw1zJ+kITTsHH8fOGSiSIUQQgghbpNEUWWx8YlMmrWYyIhLDAlW8k0Sc9hYa+jXRc/aDT+VcpRCCCHMyYsvvcL4CZMBuBYSgkclXxISEkwc1QNQVN7MmCSKKrscEk5IWCR6vZ7HWtgUmCTmeLSxhvMXL5RSdEIIIcqiJZ8vpVXr9ji7VqJf/wH3PP/PP3fT5Ymn8PKpinflqvdVVtUqVYiJCsXZ2bm44YoKTBJFlTVpUJuprw7GytICrbbwJBFAqwWDmc/ZJIQQ5s7Hx5tJk8YzfPjQIp1vZ2/P0MGDmPPB+ypHVkYZDOpuZkwSRRXEJSSx/8hp488tH2mIq7MTh0/d+812+JSB6v7+KkYnhBCirAvu2YMe3Z/Cw92tSOc3faQJAwY8Q/WAgPsu6+rVq9jauxAfHw9k35YeOeo1Bg99Dk8vPxo+9Ai7d+8xnp+Zmcm7782kbv2H8K0SQN9+zxAWHn7f5YryQabHKWE5azeHRcbwzhsv0KRBbSwsLAju1oPvNn/Po40Lfm6mXmHNViteHdG79AIWQghB9+GTCjy28K3R1PT3A+C9j5bxz7Gz+Z73bM/HGRDcCYBtf/7DJ8tu9zdXFAObls0rsXjnzV/Ivn37+fmn1SV2zTut/fFn1q5exbKv/8e8+R/y4oiRnDt7EoC33n6Po0eP8fv2rbi7uzHjrXcZMvQ5dmz7VZVYikTmUVSNtCiWoJwkMSQ8msDqValTs5rx2NPBPTh21o5l6/T5PlevV3jnEwM2dn60b/1oaYUshBCiHJowfqxqSSJA166dad++HTqdjsGDB3LtWgg3bsSiKApL//cVcz6YiY+PN1ZWVrz91nT27TtAyPXrqsUjTEdaFEvInUliUM1qedZu9qrkycdz5/HaxAnsP5bKs930NKyjuzXhdharNlmQkVWVz+bNxcrS0oSvRAghzM8v38wp0nlvvj6sSOd1bteMzu2aGX9OTU0tTlgm41WpkvGxvZ0dAMnJSRgMBlJSUujUpRt3js20srLi+vVQqvj5lXaot6g5PNm8WxQlUSwB90oSc9SvU5s1y5bx08bNvLN4HdE34gGo4V+FfsFP81SXjtjZ2pZy9EIIIUTRuLu7YWdnx+5dO6hdO9DU4YhSIIliCfjfdxvvmSTm8HBzY8Swwbw0dBCpaWnodDqsraxKMVohhBBlnV6vv7VloRgMpKWlodVqsSrg+8JgMJCRkUFGRiYAaWlpANjYFPx9VBxarZYXnh/O5CnT+fjjD6ni58eNG7Hs/GMX/fqasH+9QQGNSi1/Zj4TiSSKJWDkkF7Y2dowvH+3QpPEO2k0Gmk9FEIIka8P5sxj5qzbt8Nd3b1p06YV27ZuBmDuvAX8/fc+Nqz/EYC//vqbLk90z3U+QGpKfInH9t67b/Hhwo944skeREZG4ebmSof27UybKArVaFJT4s07Vb4lMTERL5+qRIZfw8nJ6Z7nxyUkYW9rg5VV+exPmJqaiq0kqnlIveRP6iV/Ui/5k3rJS+pEHTnf3b1qjMRSZ61KGZlZ6ay7tLjI+UFFI6OeiyFnWb53Fn1jbOYXQgghhKho5NbzfYqNT2Ty7CWEhEfjYG+HPisLK8pnq6IQQghRISiKen0JzXweRUkU78OdSWJQzWq8P+FF7IrYJ1EIIYQQKpEJt1Ujt56LSJJEIYQQQpgbaVEsgvSMDEkShRBCiLLKoAAGFa9tvqRFsQisrax48rGW1K3lL0miEEIIIcyGtCgWQlEUNLfWKAru0obuj7dCp5PcWgghhChTpI+iaiTrKUBcQhJj3/mYMxeuGPdJkiiEEEIIcyKZTz5y1m4+918Iq9ZtM3U4QgghhCiEYlBU3cyZ3Hq+S3xiMu9/stI4cGXq6CGmDkkIIYQQwiQkUbzLjAVfEnkjkaCa1XhvwotFXrtZCCGEEKai3NrUurb5klvPdwmNiJEkUQghhBACaVE0Um6NagrwrcTEl58hKzODxMwME0elntS0NDIzZZ3qu0m95E/qJX9SL/mTeslL6iR/jo6OxtlFHohBxRZF6aMoAJKTkwFYuWQ+K5fMN3E0QgghRMUXGX4NJycnU4chCiGJ4i0+Pj5cPH8aBweHkvnvpgxLSkqiZmA9Lp4/jaOjo6nDKTOkXvIn9ZI/qZf8Sb3kJXVSsBKrD8WQvalBreuWE5Io3qLVavH19TV1GKXK0dFR/pPLh9RL/qRe8if1kj+pl7ykTkR5JImiEEIIIco1xaCgqNRH0dznUZRRz0IIIYQQIl/SomiGrK2tmTZ1EtbW1qYOpUyResmf1Ev+pF7yJ/WSl9RJKZA+iqrRpKbEm3ebqhBCCCHKpcTERLx8qtKz0hAstVaqlJFpyGBD1AqzHaEtLYpCCCGEKNekj6J6pI+iEEIIIYTIl7QoCiGEEKJ8kz6KqpFEUQghhBDlWqaSCSrlc5mKeS+9KIliBbfk86V8++13nDp9hs6dH2ft6u+MxzIzM5kwaSpr1qwFoH//fsybMxsLi4r9tiioTtLT0xk7bgI7/9jFjRuxVK7sw7gxrzF06GATR1w6Cnuv5EhNTeWRZi25ceMGEWHXTBBl6btXvWzavIX33pvFxUv/4eTkxNQpE3nxhedMFG3pKaxeQsPCGDN2PHv37kOj0dC2bRsWLpiHl1clE0asvnt9hpjrZ66arKys8Pby4tfINaqW4+3lhZWVOoNlyjp5d1ZwPj7eTJo0np1//EloaGiuYx/Mmce+vfs4fHA/AMG9+jJ33gKmTplkilBLTUF1otfr8fb2YsumDQQE+PPPwUME9+qLr68vjz/+mAkjLh2FvVdyvPveLHx9K3Pjxo1Sjs50CquXbdt28PqY8Xzz1Re0atWSxMQkoqKiTBRp6SqsXsaMeQM0Gs6dPYmiKAx/7kXGT5zMyuVfmyja0nGvzxBz/cxVk42NDWfPHCcjI0PVcqysrLCxsVG1jLJKBrNUcME9e9Cj+1N4uLvlObZ8xSomTZqAj483Pj7eTJw4nmXLV5ogytJVUJ3Y29sz481pVK8egEajoXmzprRt24a9+/aZKNLSVdh7BeDo0WP8tm07E94YV8qRmVZh9fLOezOZOmUibdu2QafT4erqQu3agSaIsvQVVi9XrlylT+9eODg44OjoSJ8+vTlz+qwJoixd9/oMMdfPXLXZ2Njg5OSk6mauSSJIomi24uLiCQ0NpVHDBsZ9jRo2ICTkOgkJCSaMrOxIS0vj0KEj1K9f39ShmJxer2fUq6+z6MN5WFub5+2Xu6WkpHD06DESE5No1Lgp/gGBDBoynIiISFOHZnKvjR7Fz+vWk5CQQHx8PGvX/kTXrp1NHVapu/MzRD5zRXkliaKZSk5JBsDZ2dm4L+dxUnKySWIqSxRF4ZWRr1GzZnWCe3Y3dTgmt+ijT6lfvx5t27YxdShlRlx8PIqi8P33P7Bxw0+cOnkESwtLnn9hhKlDM7lHH21BdHQMPr7+VPYLIDYujsmTxps6rFJ192eIfOaK8koSRTPlYO8AZM9qnyPnsaODg0liKisUReG118dx/sIF1vywCq3WvP9M/vvvMl8s/ZLZs943dShlioO9PQAjR46gWtWqODg48Ob0Kfyx609SUlJMHJ3pGAwGunUP5tEWzYmJCiUmKpSWj7age88+pg6t1OT3GSKfuaK8Mu9vQDPm6uqCr68vx0+cMO47fuIkfn5+uf7jNTeKojBm7HgOHTrMpo3rzLoucvz9915iYmJo8kgL/AMCeWbAYBITk/APCOTgocOmDs9kXFxcqFLFDw2aPMcUxXxXcoiNjePatRBGjhyBnZ0ddnZ2vPLKSxw48A8xMRV/EFRBnyHymSvKK0kUKzi9Xk9aWhp6fRaKwUBaWppxdNiQwQOYO3cBERGRREREMm/eAoYPq/hTwRRWJ2PHTWDfvv1s+mU9rq4upg20lBVUL3379ubs6ePs37eH/fv2sPizj3F0dGT/vj081KihqcNWXWHvl+efG8biJV8QGhZGamoqs2bPoUP7djiYQQtRQfXi4eFOjRrV+eKLL0lLSyMtLY0vvvgSX19fPDzcTR226gr7DDHXz1xRvsn0OBXcB3PmMXPWHOPPru7etGnTim1bNzNl8kRiY+No3KQZAP37P83ECW+YKtRSU1Cd/G/pEr5Y+iXW1tbUDrrd4fzZZ57mk48XmiLUUlXYe8XW1ta4383VFY0GvL29TBFmqSusXsa/MZbY2Diat2gNQNu2bfjqyy9MFWqpKqxe1q7+jomTplKjVhAGg4FGDRvy45q883JWNFevXSv0M8RcP3NF+aZJTYk333skQgghhBCiQHLrWQghhBBC5EsSRSGEEEIIkS9JFIUQQgghRL4kURRCCCGEEPmSRFEIIYQQQuRLEkUhhBBCCJEvSRSFEEIIIUS+JFEUJvXPsTMEdRhIUIeB/HPsjKnDKRV3vuZ1W/80dTgVkjm+r0xlyJj3CeowkCFjir8W+MqfthLUYSAjJs8rwchKztsLvyaow0DmLlll6lCEKHWyMosocWnpGXz7829s2/0P/10LIyMjE3dXZx5uUJshfbrSqG5NU4coKjh7O1saBtUwPi5Lhox5n4PHz9K0URArFk03dThFMuWDz1n/2x4qe3nw+w8flei1b6am8fm3GwB4aWAP4/5Pl/3EZ8t/Nv68fOE0mj1U1/jzT1t2MX3e/4w/z5r0Er26tgOg4zOvExYZYzym0Whwd3EioGplBgR3omv75vcV44vPdmftpp2sWredwX264lOp4i9FKEQOSRRFiYpLSGLo2Pe5cPk6AC5ODnh5uHH1egRbdu5j6679TBs9lAHBnUwcaV4ZGZlYWVmaOowKJ1Ovx0KnQ6PRlFqZ9QIDWL343VIrT02mqL/StGnHXmLjE6nq60WTBrULPO+bNVtyJYrLf/z1nte2tLQgqGY1MvVZXLoSysHjZzl4/CwxsUMY1LtLkWP09fak2UN12X/kNKs3/s6YF54u8nOFKO8kURQl6t1F3xiTxJcG9GDMC0+j0Wi4fC2M5yd+QHjkDWZ9soKmjYKoFeCX67nRN+J5/a1F/PXPCaytrQju0oZxLz2DhU4HwJad+1i2dgtXr0eQlp6Ju5sTtatXZfTwvtSt5Q9kJ6qffPMju/YfJeZGPE6O9rRt/hBjX+yPp5sLkH1bcujYmQDMnjSCbXsOcuDIaR5tUp8r1yO4dDWUx1o+zGczb6/BOnLaAv7Ye4SAKj5sWTH/1nXOsnTVBk7+e4nU9AyqVq7EMz0eZ2CvzsYvdX1WFou+XMO6rbtJTUunTbOGdG7brMj1mZ6RwZffb+LXP/ZzPTwKnU5HNV8v3hjxLK0eyV5L9syFKyxesY4jJ8+RnHITDzcX2jZvxOjhfXF3dQZg3dY/mTpnKQDvT3iRTTv2cvT0efx8KjH9taF4ebjy9sKvOXH2En4+lZjy6mBaNqmfp74+mPIy23cfZO/hU9jk8zvKacnp0ak1fj6e/LRlF1E34tm/8QucHOzZvucgy3/8lbMXrqLXZ1HT35fn+nejW8eWxte89/ApPl+5nguXQ0hJTcPN2ZEa/r481/8p42tetW4bazbtJDQiGoNBwcPNmTo1qjF51CAqe3nkinn5wmn4+/nwWP/XyDIYmDRyEMP6PQFkJ2Ft+4wiPjGZp596jHfeeB6AtZv/YPXG37l0NRSNRkPdWv68MjiYVk0bFvr7ulfsQR0GGs89ePys8edRQ3vz6rA+JVJ/d7ZYPtWxJV+t3kRUTBy1a1TlrbHDCarpbzz3l+1/8+myn4iIjiWwehWmjh7CgFffBiC4SxtmT345V+tcWGSMMeac43da/9sevli1gchb17u7vPxs3P4XAI+3fqTAcywtdPy5/xj/XQujetXK/PXPCS5cvo6lhY5MfVaBz/N0czH+w3Du0jWCX5gCwM+//smg3l3YuusAK378lWthkSQmpWBpYYF/FR+efuox+vfomOtaj7d+hP1HTrNx+1+SKAqzIn0URYlJTE5h++6DAFT28uC15/oZE6aAqpV5ZXAvALIMBtb/tjvP82fM/5JT5y5jZ2dLXEIS36zZwkdfrQXg/H/XGP/+Z5z89z/sbG2oUa0yGRmZ7Np3lMsh4QAkJKXwzMi3+H7DDmLjEqlRzZfUtAzWbd3NwNHvkJickqfMtz78mkPH/6VKZS+srCzp+2R7APb8c5y4hCQA4hOT+euf4wD07dYBgB1/HWL4GzP5+9BJrKysqObrzaWrYcz8ZEWufkyfLvuJr37YRGx8Iva2Npw6d5kZC74qUn3qs7J47o3ZfLrsJy5dDcXezhZfbw/+uxbGxSvZyfixMxd4dtTb/P7XIdLSM6jq601UTByrf9nJM6PeIiEp72t+d9EywqJi0Gq1XLoaymszFvHc+NlcD4/GwkLHxSvXGfv2R/nW14z5X3H24lXs8/kd3WnrrgMsXbURRwd73Fwcgex+aK/NWMThE+dwcXLAx8udMxeuMP79z/j259+A7ER/5NT5HDx+Fp1OS01/PxRg76FTnDl/GYBd+47y/sfLOf9fCO4uzlSt7EViUgrb9xwk6kZcvnVZycOVti0eAm4nJgB//XOC+MRkAPo9lf27/eCzb5kx/0tOn7+Ml6cbLs4OHD55jpcmz+X3vw4V+PsqSuwNg2pgb2cDgL2dDQ2DatAwqAZenm4lUn93Onb6PO9/shwLnY70jEyOn7nImLc/JivLAMCBo6eZOGsx18IisdBpuZmaxosTP8hznaBa/rg6Z8dgaWlhjLlKZa9c55389xJvLfgKrUZDWnpGnvLyk5aewYmzFwFoFFRwl5SuHVqgKArL12a3In6zdjMAT3R4tMDn3M2g5I3j9PnLnD5/BXs7W2oFVMHCQseZC1d4e+HXrNm0M9e5D9WtBUB41A2uhUYWuVwhyjtpURQl5kpIBFmG7A/joJrV0Oly/x9Sv3aA8fHFK6F5nt+2xUN8OGM0mfosho+byZFT51nx41ZeHtSTK9cjURQFO1sbNi+fh51t9pft5ZBwrG/dLl61bhvXwiKxtrLkl2/mUqVyJcKjbtBl4FhCwqL4YcMOXhrYM1eZ9QL9+d+8ydjb2pCVZSAxOYUPv1xNZqaeLTv3MbBXZ7bu2k+mPgtLCx09O7cGYN6S7zAYFNo0b8SSmePR6bT87/tf+HDpD6z86TeG9nsCFydH4xdbw6AarFz0JpaWFox79xO27jpwz/r8ded+jpw6D0D/7o8x/fVhWOh0JCXf5EZ8IgCLvlxDRmYmdjbWbPxmDr7enmz78x9ef/sjrodHs2rdNkYO6ZXrul3bN2fO1FdYs2knby34iqSUm9QLDOCr+ZPZd/gUL0ycQ2LyTU6evZSnBe2xVg8X+Du6sy+gwWBg1Sdv0TCoBgaDgYxMPYtuJZR3tty9vfBrVm/8nY++XsvTTz1GWGQM6RmZAKxZ8h6VvTyA7C/ntPQMAK7c+segmp83m5fNM77P/r14NU/Cdad+3Trwx94jnL1whQuXr1MrwI9fdvwNQJ0aValfuzqhEdGs/HkrAGOef5oRg3qiKAovT5nP7gPHmL/0BzoW0PJVlNhXL37X2OJXt1ZAgX0Ui1t/d3ad0GcZ+OGzd2gYVINZn6xg5c+/cS00kmthkQRU8WHJyvUAud473/78GzM/WZErlk/fG2vso3hnC93d0jMyCy0vPyFhUcYWQb/KlfI9B+DZHo+zY88hNmzbQ9f2zdl76BQ+ldzp2qF5rsT/btGx8fQfOQO9PivXZ07vJ7L7Mvbt1p6XBwdjf+vzJD0jgx7DJ3MtLJKN2//i6aceMz7H18fT+PhySDhVfXMnykJUVJIoCnXk059KUQo9zFMdW6LRaLCytKBr++YcOXWejMxMLoeE83D9QFydHYlLSKJN75FU9fWiejVfWj3SgO6dWgFw/Ex2y0R6RiadB47Nc/2jpy/k2TcguJPxS0Kn0+Lq7EjHVk3YuusAv+z4m4G9OrNpx14AHmvVBHdXZ2LjE7kWlt2isOfAceo/PjjXNbMMBk6cuYR/FW9jgtCpTVPjl3j3x1sVKVE8duZ2vC8PDjbe3nV0sMPRwQ6AE2cvAdCkUR18vbO/yDq3a4aNtVWu1po7dW7bFMB4fvZrexitVks1P2/jvujYhDzPLex3VL92deN5zRoHGQeTaLVaLlwO4WZqGgBrNu3M01qTnJLKhcvXqRngSzU/b65ej+CJweOp6utFQBUfmj0UZPxyb92sIYu+WsPV6xE82nME1fy8qVHNlw4tG1O7RtUC67Nt84eo5OFKVEwcv+z4ixEDe/LH3iPA7Zbik//+h8GQ/UZd9NUaFn21Jtc1roSEE5eQZGxhu1NN/3vHXlTFrb96d/wzFhhQxXiNWtWrGPfHxMYTUMWHfy9dA6BJw9rG90LPzm3yJIpFda/y8pN0R6t1TktrfpydHAju0obvN+zgtbcWATCodxfj30RBMjP1nDh7CY1Gg6uzI9Wr1mBgr87GwSw3U9N4491POPnvf8QnJhl/9wDRMfG5ruVgf/sfoaR8WtuFqKgkURQlxr+KNzqtliyDgbMXrpCVZcjVqnj61u03gBrVfPM8/87O+ncmlQAebs6s/3I2m37fy5nzl7kWFslvuw6wZec+zv13jSmjbidr1laW+SYM+Y1U9HR3ybOv31OPsXXXAY6fucjew6eMrXp9n+yQ7zXzu4a1tdVdifGdr03Jc35+inre/cpJMi0tbn/J5nwJ5o4z7626wn5Hd/J0cy3wWFVfL1ycHPJeW6vB2sqK1YvfZdOOvzn573+EhEXy5/5jbN9zkP1Hz/Dpe2Op6e/Hhq8/yP7dX7rG1esRbNi2hw3b9jBl1GCG9O2ab7k6nZZeXdvyxbcb2LRjLwFVfEhLz8DG2sr4z8adavr7YWdrnWe/Piv/PnFFib2oilt/d3JytDc+trjj7/Du95VGUzI9kIpa3p0cbr0XITvZLczQfk+w+pffSU5Jxd7Ohn7dOuT6Zyo/hY3STk1L57k3ZhOfmIxOq6WWfxWsrS25dDWUlJtpeX7PSck3jY8dHezvvpwQFZYkiqLEODnY06ltU7buOkBYZAwff7329mCWkHCWrFwHgFarIbhL2zzP37xzHx1aPow+K4ttu/8BwMrSEn8/b6JuxBEdG89z/bsZz58x/0vWbv6D/UdOA9m3d3cfOIbBYOCtsc8ZB7gYDAYOnfgXZ8d8vlzzadp89OF6+Pl4cj08mikffI6iKFT28qDlI9mDO9xcnKhSuRIhYVFU9vbgy7mTsbG2ArL7Se78+xDtWjxkTELS0jPYvucgg/t0xdJCx5Y/9hepPhvXC+T7DTsA+OLbjUx/bSg6nZaUm6nExCVQzdebhkE1OHD0DIeP/0toRDS+3p789ucBY0tmgzo1ilRWURX2O7rT3dVaK6AKdjbW3ExLp37t6syZ+oqxNSjqRhwHj52lbi1/Um6mcvHKdZ7t+TgDe2lvvfYNLPpqDfuPnALgamgEWVkGY59XgOcnzGbvoVPsP3q6wEQRoO+T7Vm6aiPhUTeMt3I7t22G060v/gZ1qqPRaFAUhbbNGzF+xLPG98j18Cj+vXTNOCjqbkWJHcDGxsp4fkGKW3/3I6hGNfYfPc3hk/8SGR2Ll6dbgbdxbayzE+bUtHQMBgNabckkl37enljodOizsggNj6ZeYECB51bz9aZDyyb8/tch+nXrYPyHp7j+uxpm7J86enhfRgzqSVxCEj2fn0zKzbQ854dF3J5ux7+Kd57jQlRUkiiKEjVjzHAuXQ3lwuXrLP1uI2s27cTNxYmr17P7L2q1GqaNHppnxDNkD1J4/NkxZOr1RN+IB2BQ78442Ntx5sIVho6dibOTA5XcXTAYFP67FgZgHFU5sFdn1m3dTWhENP1enk71qr4oioHwyBvcTEtn1qSXCr01mUOj0dDnyfZ89NVaomKyB0f0fqJdri/H8S89y5h3PubwiXO06TOSKpW9iE9MIio6jiyDgV5d22FjbcWQPl1Z+t1Gjp+5SMdnXsfaypKY2Pgi1eUTj7Xgh407OHLqPD9s3MH2Pf/g5uJESFgUY154mqF9n+D15/oxdNxMbqal0334JHy9Pbl8q14qe3kwsFfnIpVVVIX9jgpjY23F6Of6MWfxt2zZuY99h0/hXcmd2LgEom7E41PJnW4dWxKfmMyg197Fwd4WLw9XLC0sjH3Lcn7PR06eY+qcpbi7OuHp5kJqegZXr0fcOqdaoXH4+VSixcP12Hf4lPF327dbe+NxX29PBvXqzMqff+Pr1Zv5ZcffeLg6E30jnpi4BJo2CipwdG5RYofsW7R7DhznzIUrPDVsIvZ2Nkx/bWihSX1R6+9+vDy4J/uPniblZhpPDp2ATyU3wqNi8z038Nat5LiEJJ4YPB4XZwdGDOzJY62a3FeZd7OztaFe7QCOn7nI8bMX6dyu8BkBFrw5ipup6SUyN2ZVXy/s7WxIuZnGZ8t/YvPOvURExaLV5j8NUU7rpbenG9V8JVEU5kNGPYsS5ersyOrF7zLupWeoX7s6GZl6roVG4uHmzBMdWvDdJ28XOIfiu+NfoH7tAFJSUnF2cmBo3yeM01D4envS/fFWuDk7EhoRw/XwKPx8PBncpytvvj4UyJ6zcfXidxgQ3AmfSh5cvR5OTFwi1ar48Fz/brnmYLuX3l3bobuVGGq1Gvo8mbuPWed2zfhmwRTaNGuITqvl4uUQFIPCo03qM/XV27fBRz/Xl+f6d8PFyYHklJvUCvDj7XHPFykGC52Or+ZP4dVhfahRzZek5FRCw6MJqOJDTf/sRLtx/UC+/+RtOrZqgpWVJVdCwvF0d6Fftw78sPidfG9RPojCfkf3MqzfE3z0zhiaNgoiIzOTS1dC0el0PNbyYV5/vh+Q3Srd58n2+FRyJyomnkvXwvB0d6F313YsmPEqALVrVKNLu2bY2dpwNTSS6Jg4Aqr4MHJIL165a+BOfvp1u92FwL+KD00bBeU6PuXVwbzzxvM0rFOD5JRULoeEY2drw5OPPZqrRftuRYkd4PlnnqJj60dwcrDj0tVQTpy9RNI9brsWtf7uR/PG9Zg7dSRVK3uRqddja2PDx++8bjxufauVHKDPE+0I7tIGV2dHroVFcuLsJeOAqgfVs3MbAH7/+/A9z7W2ssLV2RErywdv43B0sOOTd8cSVLMaWq2WjEw9b40dTmD1/P+Z3HFrxHuPTq0fuGwhyhNNakq8Oh2hhBAVwt1zEt5Pwi3KLkVRuBISTkDVysZ9P27exZvzs1c7eW/8i7laW9WScjOVTgPGEpeQxLcfzyh00m1TCY2IpsvAceh0On5dOd84ol0IcyC3noUQwgxlGQw8OXQCQTWrUdnLg+jYeOMo+qBa/vTIZ4CPGuztbHllcDCzPl3JF6s2sPSDiaVS7v348vtNZBkMDO7TRZJEYXakRVEIUShpUayYDAYDY9/5mJP//seNuES0Wg1+PpXo1KYpzz/7lHHaKCGEeZNEUQghhBBC5EsGswghhBBCiHxJoiiEEEIIIfIliaIQQgghhMiXJIpCCCGEECJfkigKIYQQQoh8SaIohBBCCCHyJYmiEEIIIYTIlySKQgghhBAiX5IoCiGEEEKIfP0fm8Fi/Vi281oAAAAASUVORK5CYII=", "text/plain": [ "<Figure size 660x610 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "✓ FIGURE 9C SAVED\n", "Cell-out Q² : 0.936522\n", "CV RMSE : 0.8964 MPa\n" ] } ], "source": [ "# ============================================================\n", "# — FIGURE 9C\n", "# CELL-OUT CROSS-VALIDATED OBSERVED–PREDICTED\n", "# ============================================================\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "\n", "# ============================================================\n", "# AGGREGATE CELL-OUT PREDICTIONS AT CELL LEVEL\n", "# ============================================================\n", "\n", "cv_cell = (\n", " cellout\n", " .groupby(\n", " [\"RCA_pct\", \"Temperature_C\"],\n", " as_index=False\n", " )\n", " .agg(\n", " Observed_MPa=(\"Observed_MPa\", \"mean\"),\n", " Predicted_MPa=(\"Predicted_CellOut_MPa\", \"mean\")\n", " )\n", ")\n", "\n", "\n", "assert len(cv_cell) == 30\n", "\n", "\n", "# ============================================================\n", "# PLOT LIMITS\n", "# ============================================================\n", "\n", "plot_min = min(\n", " cv_cell[\"Observed_MPa\"].min(),\n", " cv_cell[\"Predicted_MPa\"].min()\n", ")\n", "\n", "plot_max = max(\n", " cv_cell[\"Observed_MPa\"].max(),\n", " cv_cell[\"Predicted_MPa\"].max()\n", ")\n", "\n", "margin = 0.05 * (plot_max - plot_min)\n", "\n", "plot_min -= margin\n", "plot_max += margin\n", "\n", "\n", "# ============================================================\n", "# FIGURE\n", "# ============================================================\n", "\n", "fig, ax = plt.subplots(\n", " figsize=(6.6, 6.1),\n", " facecolor=\"#F7F5F0\"\n", ")\n", "\n", "ax.set_facecolor(\"#F7F5F0\")\n", "\n", "\n", "# Points colored by temperature\n", "sc = ax.scatter(\n", " cv_cell[\"Observed_MPa\"],\n", " cv_cell[\"Predicted_MPa\"],\n", " c=cv_cell[\"Temperature_C\"],\n", " cmap=\"viridis\",\n", " s=62,\n", " edgecolor=\"#17252A\",\n", " linewidth=0.8,\n", " alpha=0.92,\n", " zorder=3\n", ")\n", "\n", "\n", "# 1:1 reference line\n", "ax.plot(\n", " [plot_min, plot_max],\n", " [plot_min, plot_max],\n", " linestyle=\"--\",\n", " linewidth=1.4,\n", " color=\"#20364B\",\n", " alpha=0.85,\n", " label=\"1:1 line\",\n", " zorder=2\n", ")\n", "\n", "\n", "ax.set_xlim(\n", " plot_min,\n", " plot_max\n", ")\n", "\n", "ax.set_ylim(\n", " plot_min,\n", " plot_max\n", ")\n", "\n", "\n", "ax.set_aspect(\n", " \"equal\",\n", " adjustable=\"box\"\n", ")\n", "\n", "\n", "ax.set_xlabel(\n", " \"Observed compressive strength (MPa)\",\n", " fontsize=10.5,\n", " fontweight=\"bold\",\n", " color=\"#20364B\"\n", ")\n", "\n", "ax.set_ylabel(\n", " \"Cell-out predicted strength (MPa)\",\n", " fontsize=10.5,\n", " fontweight=\"bold\",\n", " color=\"#20364B\"\n", ")\n", "\n", "\n", "ax.set_title(\n", " \"Cell-out cross-validated performance\",\n", " loc=\"left\",\n", " fontsize=12,\n", " fontweight=\"bold\",\n", " color=\"#20364B\",\n", " pad=14\n", ")\n", "\n", "\n", "# Panel identifier\n", "ax.text(\n", " -0.12,\n", " 1.03,\n", " \"C\",\n", " transform=ax.transAxes,\n", " fontsize=17,\n", " fontweight=\"bold\",\n", " color=\"#E76F51\"\n", ")\n", "\n", "\n", "# ============================================================\n", "# METRICS INSIDE EMPTY REGION\n", "# ============================================================\n", "\n", "metric_text = (\n", " f\"$R^2$ = {R2:.3f}\\n\"\n", " f\"$Q^2_{{cell-out}}$ = {Q2_CELL_OUT:.3f}\\n\"\n", " f\"RMSE$_{{CV}}$ = {RMSE_CELL_OUT:.2f} MPa\\n\"\n", " f\"$p_{{LOF}}$ = {P_LOF:.3f}\"\n", ")\n", "\n", "ax.text(\n", " 0.055,\n", " 0.945,\n", " metric_text,\n", " transform=ax.transAxes,\n", " va=\"top\",\n", " ha=\"left\",\n", " fontsize=9.5,\n", " color=\"#20364B\",\n", " bbox=dict(\n", " boxstyle=\"round,pad=0.45\",\n", " facecolor=\"white\",\n", " edgecolor=\"#D8D4CB\",\n", " alpha=0.90\n", " )\n", ")\n", "\n", "\n", "# ============================================================\n", "# COLORBAR\n", "# ============================================================\n", "\n", "cbar = fig.colorbar(\n", " sc,\n", " ax=ax,\n", " fraction=0.048,\n", " pad=0.035\n", ")\n", "\n", "cbar.set_label(\n", " \"Temperature (°C)\",\n", " fontsize=9.5,\n", " fontweight=\"bold\",\n", " color=\"#20364B\"\n", ")\n", "\n", "\n", "ax.legend(\n", " loc=\"lower right\",\n", " frameon=False,\n", " fontsize=9\n", ")\n", "\n", "\n", "ax.spines[\"top\"].set_visible(False)\n", "ax.spines[\"right\"].set_visible(False)\n", "\n", "ax.grid(\n", " alpha=0.15,\n", " linewidth=0.6\n", ")\n", "\n", "\n", "plt.tight_layout()\n", "\n", "\n", "# ============================================================\n", "# SAVE\n", "# ============================================================\n", "\n", "figure_base = (\n", " OUTPUT_DIR\n", " / \"Figure_9c_CellOut_Observed_Predicted\"\n", ")\n", "\n", "\n", "plt.savefig(\n", " str(figure_base) + \".png\",\n", " dpi=600,\n", " bbox_inches=\"tight\",\n", " facecolor=fig.get_facecolor()\n", ")\n", "\n", "plt.savefig(\n", " str(figure_base) + \".pdf\",\n", " bbox_inches=\"tight\",\n", " facecolor=fig.get_facecolor()\n", ")\n", "\n", "plt.savefig(\n", " str(figure_base) + \".tiff\",\n", " dpi=600,\n", " bbox_inches=\"tight\",\n", " facecolor=fig.get_facecolor(),\n", " pil_kwargs={\"compression\": \"tiff_lzw\"}\n", ")\n", "\n", "\n", "plt.show()\n", "\n", "print(\"✓ FIGURE 9C SAVED\")\n", "print(f\"Cell-out Q² : {Q2_CELL_OUT:.6f}\")\n", "print(f\"CV RMSE : {RMSE_CELL_OUT:.4f} MPa\")" ] }, { "cell_type": "code", "execution_count": 187, "id": "65107765-fd26-4f35-bf96-31a10482644c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "==============================================================================\n", "STEP 8 — MODEL VALIDATION\n", "==============================================================================\n", "\n", "FIT\n", "R² : 0.941531\n", "Adjusted R² : 0.938051\n", "RMSE : 0.8603 MPa\n", "\n", "CELL-WISE VALIDATION\n", "Cell-out Q² : 0.936522\n", "Cell-out RMSE : 0.8964 MPa\n", "Cell-out MAE : 0.6904 MPa\n", "R² − Q² : 0.005009\n", "\n", "EXPERIMENTAL-ERROR TEST\n", "Pure-error SD : 0.9587 MPa\n", "Lack-of-fit F : 0.519718\n", "Lack-of-fit p : 0.960670\n", "\n", "DECISION\n", "Non-significant lack of fit : True\n", "Strong cell-out validation : True\n", "Small R²–Q² gap : True\n", "\n", "MODEL VALIDATED WITHIN EXPERIMENTAL DOMAIN: True\n", "==============================================================================\n" ] } ], "source": [ "# ============================================================\n", "#— STEP 8 FINAL CHECK\n", "# ============================================================\n", "\n", "print(\"=\" * 78)\n", "print(\"STEP 8 — MODEL VALIDATION\")\n", "print(\"=\" * 78)\n", "\n", "print(\"\\nFIT\")\n", "print(f\"R² : {R2:.6f}\")\n", "print(f\"Adjusted R² : {ADJ_R2:.6f}\")\n", "print(f\"RMSE : {RMSE:.4f} MPa\")\n", "\n", "print(\"\\nCELL-WISE VALIDATION\")\n", "print(f\"Cell-out Q² : {Q2_CELL_OUT:.6f}\")\n", "print(f\"Cell-out RMSE : {RMSE_CELL_OUT:.4f} MPa\")\n", "print(f\"Cell-out MAE : {MAE_CELL_OUT:.4f} MPa\")\n", "print(f\"R² − Q² : {R2-Q2_CELL_OUT:.6f}\")\n", "\n", "print(\"\\nEXPERIMENTAL-ERROR TEST\")\n", "print(f\"Pure-error SD : {SD_PURE_ERROR:.4f} MPa\")\n", "print(f\"Lack-of-fit F : {F_LOF:.6f}\")\n", "print(f\"Lack-of-fit p : {P_LOF:.6f}\")\n", "\n", "print(\"\\nDECISION\")\n", "\n", "print(\n", " \"Non-significant lack of fit :\",\n", " P_LOF >= 0.05\n", ")\n", "\n", "print(\n", " \"Strong cell-out validation :\",\n", " Q2_CELL_OUT >= 0.90\n", ")\n", "\n", "print(\n", " \"Small R²–Q² gap :\",\n", " (R2-Q2_CELL_OUT) <= 0.05\n", ")\n", "\n", "MODEL_VALIDATED = (\n", " (P_LOF >= 0.05)\n", " and\n", " (Q2_CELL_OUT >= 0.90)\n", " and\n", " ((R2-Q2_CELL_OUT) <= 0.05)\n", ")\n", "\n", "print()\n", "print(\"MODEL VALIDATED WITHIN EXPERIMENTAL DOMAIN:\", MODEL_VALIDATED)\n", "\n", "print(\"=\" * 78)" ] }, { "cell_type": "markdown", "id": "c2dc1b58-8b0c-4881-a57b-8d17fe7e1d2c", "metadata": {}, "source": [ "#STEP9" ] }, { "cell_type": "code", "execution_count": 189, "id": "575ab5b6-1097-4121-b2d7-d2ec7cfa333a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "==================================================================================\n", "ABSOLUTE vs RELATIVE THERMAL RESPONSE\n", "==================================================================================\n", "Reference temperature: 24 °C\n" ] }, { "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>RCA_pct</th>\n", " <th>Temperature_C</th>\n", " <th>Mean_strength_MPa</th>\n", " <th>SD_strength_MPa</th>\n", " <th>n</th>\n", " <th>Reference_strength_MPa</th>\n", " <th>Retention_pct</th>\n", " <th>Thermal_loss_pct</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>24</td>\n", " <td>21.094</td>\n", " <td>1.281</td>\n", " <td>3</td>\n", " <td>21.094</td>\n", " <td>100.00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0</td>\n", " <td>150</td>\n", " <td>20.152</td>\n", " <td>0.990</td>\n", " <td>3</td>\n", " <td>21.094</td>\n", " <td>95.53</td>\n", " <td>4.47</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0</td>\n", " <td>300</td>\n", " <td>17.650</td>\n", " <td>0.738</td>\n", " <td>3</td>\n", " <td>21.094</td>\n", " <td>83.67</td>\n", " <td>16.33</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0</td>\n", " <td>450</td>\n", " <td>15.500</td>\n", " <td>0.275</td>\n", " <td>3</td>\n", " <td>21.094</td>\n", " <td>73.48</td>\n", " <td>26.52</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0</td>\n", " <td>600</td>\n", " <td>11.560</td>\n", " <td>0.311</td>\n", " <td>3</td>\n", " <td>21.094</td>\n", " <td>54.80</td>\n", " <td>45.20</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>10</td>\n", " <td>24</td>\n", " <td>21.047</td>\n", " <td>0.655</td>\n", " <td>3</td>\n", " <td>21.047</td>\n", " <td>100.00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>10</td>\n", " <td>150</td>\n", " <td>20.122</td>\n", " <td>0.929</td>\n", " <td>3</td>\n", " <td>21.047</td>\n", " <td>95.60</td>\n", " <td>4.40</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>10</td>\n", " <td>300</td>\n", " <td>17.306</td>\n", " <td>0.760</td>\n", " <td>3</td>\n", " <td>21.047</td>\n", " <td>82.22</td>\n", " <td>17.78</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>10</td>\n", " <td>450</td>\n", " <td>15.328</td>\n", " <td>0.514</td>\n", " <td>3</td>\n", " <td>21.047</td>\n", " <td>72.82</td>\n", " <td>27.18</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>10</td>\n", " <td>600</td>\n", " <td>11.215</td>\n", " <td>0.470</td>\n", " <td>3</td>\n", " <td>21.047</td>\n", " <td>53.29</td>\n", " <td>46.71</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>20</td>\n", " <td>24</td>\n", " <td>19.908</td>\n", " <td>1.303</td>\n", " <td>3</td>\n", " <td>19.908</td>\n", " <td>100.00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>20</td>\n", " <td>150</td>\n", " <td>19.881</td>\n", " <td>0.704</td>\n", " <td>3</td>\n", " <td>19.908</td>\n", " <td>99.86</td>\n", " <td>0.14</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>20</td>\n", " <td>300</td>\n", " <td>17.172</td>\n", " <td>0.215</td>\n", " <td>3</td>\n", " <td>19.908</td>\n", " <td>86.26</td>\n", " <td>13.74</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>20</td>\n", " <td>450</td>\n", " <td>14.344</td>\n", " <td>0.944</td>\n", " <td>3</td>\n", " <td>19.908</td>\n", " <td>72.05</td>\n", " <td>27.95</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>20</td>\n", " <td>600</td>\n", " <td>10.726</td>\n", " <td>0.285</td>\n", " <td>3</td>\n", " <td>19.908</td>\n", " <td>53.88</td>\n", " <td>46.12</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>30</td>\n", " <td>24</td>\n", " <td>19.354</td>\n", " <td>2.398</td>\n", " <td>3</td>\n", " <td>19.354</td>\n", " <td>100.00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>30</td>\n", " <td>150</td>\n", " <td>18.241</td>\n", " <td>1.666</td>\n", " <td>3</td>\n", " <td>19.354</td>\n", " <td>94.25</td>\n", " <td>5.75</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>30</td>\n", " <td>300</td>\n", " <td>17.016</td>\n", " <td>0.143</td>\n", " <td>3</td>\n", " <td>19.354</td>\n", " <td>87.92</td>\n", " <td>12.08</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>30</td>\n", " <td>450</td>\n", " <td>14.148</td>\n", " <td>0.101</td>\n", " <td>3</td>\n", " <td>19.354</td>\n", " <td>73.10</td>\n", " <td>26.90</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>30</td>\n", " <td>600</td>\n", " <td>9.990</td>\n", " <td>0.474</td>\n", " <td>3</td>\n", " <td>19.354</td>\n", " <td>51.62</td>\n", " <td>48.38</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>40</td>\n", " <td>24</td>\n", " <td>17.059</td>\n", " <td>1.514</td>\n", " <td>3</td>\n", " <td>17.059</td>\n", " <td>100.00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>40</td>\n", " <td>150</td>\n", " <td>16.984</td>\n", " <td>0.850</td>\n", " <td>3</td>\n", " <td>17.059</td>\n", " <td>99.56</td>\n", " <td>0.44</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>40</td>\n", " <td>300</td>\n", " <td>15.807</td>\n", " <td>1.337</td>\n", " <td>3</td>\n", " <td>17.059</td>\n", " <td>92.66</td>\n", " <td>7.34</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>40</td>\n", " <td>450</td>\n", " <td>13.995</td>\n", " <td>0.226</td>\n", " <td>3</td>\n", " <td>17.059</td>\n", " <td>82.04</td>\n", " <td>17.96</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>40</td>\n", " <td>600</td>\n", " <td>9.624</td>\n", " <td>1.213</td>\n", " <td>3</td>\n", " <td>17.059</td>\n", " <td>56.42</td>\n", " <td>43.58</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>50</td>\n", " <td>24</td>\n", " <td>16.790</td>\n", " <td>0.677</td>\n", " <td>3</td>\n", " <td>16.790</td>\n", " <td>100.00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>50</td>\n", " <td>150</td>\n", " <td>16.398</td>\n", " <td>1.375</td>\n", " <td>3</td>\n", " <td>16.790</td>\n", " <td>97.67</td>\n", " <td>2.33</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>50</td>\n", " <td>300</td>\n", " <td>14.634</td>\n", " <td>0.810</td>\n", " <td>3</td>\n", " <td>16.790</td>\n", " <td>87.16</td>\n", " <td>12.84</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>50</td>\n", " <td>450</td>\n", " <td>12.366</td>\n", " <td>0.665</td>\n", " <td>3</td>\n", " <td>16.790</td>\n", " <td>73.65</td>\n", " <td>26.35</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>50</td>\n", " <td>600</td>\n", " <td>9.130</td>\n", " <td>0.331</td>\n", " <td>3</td>\n", " <td>16.790</td>\n", " <td>54.38</td>\n", " <td>45.62</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " RCA_pct Temperature_C Mean_strength_MPa SD_strength_MPa n \\\n", "0 0 24 21.094 1.281 3 \n", "1 0 150 20.152 0.990 3 \n", "2 0 300 17.650 0.738 3 \n", "3 0 450 15.500 0.275 3 \n", "4 0 600 11.560 0.311 3 \n", "5 10 24 21.047 0.655 3 \n", "6 10 150 20.122 0.929 3 \n", "7 10 300 17.306 0.760 3 \n", "8 10 450 15.328 0.514 3 \n", "9 10 600 11.215 0.470 3 \n", "10 20 24 19.908 1.303 3 \n", "11 20 150 19.881 0.704 3 \n", "12 20 300 17.172 0.215 3 \n", "13 20 450 14.344 0.944 3 \n", "14 20 600 10.726 0.285 3 \n", "15 30 24 19.354 2.398 3 \n", "16 30 150 18.241 1.666 3 \n", "17 30 300 17.016 0.143 3 \n", "18 30 450 14.148 0.101 3 \n", "19 30 600 9.990 0.474 3 \n", "20 40 24 17.059 1.514 3 \n", "21 40 150 16.984 0.850 3 \n", "22 40 300 15.807 1.337 3 \n", "23 40 450 13.995 0.226 3 \n", "24 40 600 9.624 1.213 3 \n", "25 50 24 16.790 0.677 3 \n", "26 50 150 16.398 1.375 3 \n", "27 50 300 14.634 0.810 3 \n", "28 50 450 12.366 0.665 3 \n", "29 50 600 9.130 0.331 3 \n", "\n", " Reference_strength_MPa Retention_pct Thermal_loss_pct \n", "0 21.094 100.00 0.00 \n", "1 21.094 95.53 4.47 \n", "2 21.094 83.67 16.33 \n", "3 21.094 73.48 26.52 \n", "4 21.094 54.80 45.20 \n", "5 21.047 100.00 0.00 \n", "6 21.047 95.60 4.40 \n", "7 21.047 82.22 17.78 \n", "8 21.047 72.82 27.18 \n", "9 21.047 53.29 46.71 \n", "10 19.908 100.00 0.00 \n", "11 19.908 99.86 0.14 \n", "12 19.908 86.26 13.74 \n", "13 19.908 72.05 27.95 \n", "14 19.908 53.88 46.12 \n", "15 19.354 100.00 0.00 \n", "16 19.354 94.25 5.75 \n", "17 19.354 87.92 12.08 \n", "18 19.354 73.10 26.90 \n", "19 19.354 51.62 48.38 \n", "20 17.059 100.00 0.00 \n", "21 17.059 99.56 0.44 \n", "22 17.059 92.66 7.34 \n", "23 17.059 82.04 17.96 \n", "24 17.059 56.42 43.58 \n", "25 16.790 100.00 0.00 \n", "26 16.790 97.67 2.33 \n", "27 16.790 87.16 12.84 \n", "28 16.790 73.65 26.35 \n", "29 16.790 54.38 45.62 " ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "RELATIVE STRENGTH RETENTION (%)\n" ] }, { "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>Temperature_C</th>\n", " <th>24</th>\n", " <th>150</th>\n", " <th>300</th>\n", " <th>450</th>\n", " <th>600</th>\n", " </tr>\n", " <tr>\n", " <th>RCA_pct</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>100.0</td>\n", " <td>95.53</td>\n", " <td>83.67</td>\n", " <td>73.48</td>\n", " <td>54.80</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>100.0</td>\n", " <td>95.60</td>\n", " <td>82.22</td>\n", " <td>72.82</td>\n", " <td>53.29</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>100.0</td>\n", " <td>99.86</td>\n", " <td>86.26</td>\n", " <td>72.05</td>\n", " <td>53.88</td>\n", " </tr>\n", " <tr>\n", " <th>30</th>\n", " <td>100.0</td>\n", " <td>94.25</td>\n", " <td>87.92</td>\n", " <td>73.10</td>\n", " <td>51.62</td>\n", " </tr>\n", " <tr>\n", " <th>40</th>\n", " <td>100.0</td>\n", " <td>99.56</td>\n", " <td>92.66</td>\n", " <td>82.04</td>\n", " <td>56.42</td>\n", " </tr>\n", " <tr>\n", " <th>50</th>\n", " <td>100.0</td>\n", " <td>97.67</td>\n", " <td>87.16</td>\n", " <td>73.65</td>\n", " <td>54.38</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "Temperature_C 24 150 300 450 600\n", "RCA_pct \n", "0 100.0 95.53 83.67 73.48 54.80\n", "10 100.0 95.60 82.22 72.82 53.29\n", "20 100.0 99.86 86.26 72.05 53.88\n", "30 100.0 94.25 87.92 73.10 51.62\n", "40 100.0 99.56 92.66 82.04 56.42\n", "50 100.0 97.67 87.16 73.65 54.38" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "THERMAL STRENGTH LOSS (%)\n" ] }, { "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>Temperature_C</th>\n", " <th>24</th>\n", " <th>150</th>\n", " <th>300</th>\n", " <th>450</th>\n", " <th>600</th>\n", " </tr>\n", " <tr>\n", " <th>RCA_pct</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0.0</td>\n", " <td>4.47</td>\n", " <td>16.33</td>\n", " <td>26.52</td>\n", " <td>45.20</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>0.0</td>\n", " <td>4.40</td>\n", " <td>17.78</td>\n", " <td>27.18</td>\n", " <td>46.71</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>0.0</td>\n", " <td>0.14</td>\n", " <td>13.74</td>\n", " <td>27.95</td>\n", " <td>46.12</td>\n", " </tr>\n", " <tr>\n", " <th>30</th>\n", " <td>0.0</td>\n", " <td>5.75</td>\n", " <td>12.08</td>\n", " <td>26.90</td>\n", " <td>48.38</td>\n", " </tr>\n", " <tr>\n", " <th>40</th>\n", " <td>0.0</td>\n", " <td>0.44</td>\n", " <td>7.34</td>\n", " <td>17.96</td>\n", " <td>43.58</td>\n", " </tr>\n", " <tr>\n", " <th>50</th>\n", " <td>0.0</td>\n", " <td>2.33</td>\n", " <td>12.84</td>\n", " <td>26.35</td>\n", " <td>45.62</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "Temperature_C 24 150 300 450 600\n", "RCA_pct \n", "0 0.0 4.47 16.33 26.52 45.20\n", "10 0.0 4.40 17.78 27.18 46.71\n", "20 0.0 0.14 13.74 27.95 46.12\n", "30 0.0 5.75 12.08 26.90 48.38\n", "40 0.0 0.44 7.34 17.96 43.58\n", "50 0.0 2.33 12.84 26.35 45.62" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "✓ MECHANISTIC NORMALIZATION COMPLETED\n" ] } ], "source": [ "# ============================================================\n", "# — MECHANISTIC NORMALIZATION\n", "# ABSOLUTE STRENGTH vs RELATIVE THERMAL DEGRADATION\n", "# ============================================================\n", "\n", "import numpy as np\n", "import pandas as pd\n", "\n", "\n", "# ============================================================\n", "# MEANS\n", "# ============================================================\n", "\n", "mechanism_df = (\n", " df_rsm\n", " .groupby(\n", " [\"RCA_pct\", \"Temperature_C\"],\n", " as_index=False\n", " )\n", " .agg(\n", " Mean_strength_MPa=(\n", " \"CompressiveStrength_MPa\",\n", " \"mean\"\n", " ),\n", " SD_strength_MPa=(\n", " \"CompressiveStrength_MPa\",\n", " \"std\"\n", " ),\n", " n=(\n", " \"CompressiveStrength_MPa\",\n", " \"size\"\n", " )\n", " )\n", ")\n", "\n", "\n", "# ============================================================\n", "# REFERENCE TEMPERATURE\n", "# Automatically uses lowest experimental temperature\n", "# ============================================================\n", "\n", "T_REFERENCE = mechanism_df[\"Temperature_C\"].min()\n", "\n", "baseline_strength = (\n", " mechanism_df[\n", " mechanism_df[\"Temperature_C\"] == T_REFERENCE\n", " ]\n", " [\n", " [\"RCA_pct\", \"Mean_strength_MPa\"]\n", " ]\n", " .rename(\n", " columns={\n", " \"Mean_strength_MPa\":\n", " \"Reference_strength_MPa\"\n", " }\n", " )\n", ")\n", "\n", "\n", "mechanism_df = mechanism_df.merge(\n", " baseline_strength,\n", " on=\"RCA_pct\",\n", " how=\"left\"\n", ")\n", "\n", "\n", "# ============================================================\n", "# RELATIVE STRENGTH RETENTION AND LOSS\n", "# ============================================================\n", "\n", "mechanism_df[\"Retention_pct\"] = (\n", " 100\n", " * mechanism_df[\"Mean_strength_MPa\"]\n", " / mechanism_df[\"Reference_strength_MPa\"]\n", ")\n", "\n", "mechanism_df[\"Thermal_loss_pct\"] = (\n", " 100\n", " - mechanism_df[\"Retention_pct\"]\n", ")\n", "\n", "\n", "# ============================================================\n", "# OUTPUT\n", "# ============================================================\n", "\n", "print(\"=\" * 82)\n", "print(\"ABSOLUTE vs RELATIVE THERMAL RESPONSE\")\n", "print(\"=\" * 82)\n", "\n", "print(\n", " f\"Reference temperature: \"\n", " f\"{T_REFERENCE:.0f} °C\"\n", ")\n", "\n", "display(\n", " mechanism_df.round({\n", " \"Mean_strength_MPa\": 3,\n", " \"SD_strength_MPa\": 3,\n", " \"Reference_strength_MPa\": 3,\n", " \"Retention_pct\": 2,\n", " \"Thermal_loss_pct\": 2\n", " })\n", ")\n", "\n", "\n", "# ============================================================\n", "# PIVOT — RELATIVE RETENTION\n", "# ============================================================\n", "\n", "retention_table = mechanism_df.pivot(\n", " index=\"RCA_pct\",\n", " columns=\"Temperature_C\",\n", " values=\"Retention_pct\"\n", ")\n", "\n", "loss_table = mechanism_df.pivot(\n", " index=\"RCA_pct\",\n", " columns=\"Temperature_C\",\n", " values=\"Thermal_loss_pct\"\n", ")\n", "\n", "\n", "print(\"\\nRELATIVE STRENGTH RETENTION (%)\")\n", "display(\n", " retention_table.round(2)\n", ")\n", "\n", "print(\"\\nTHERMAL STRENGTH LOSS (%)\")\n", "display(\n", " loss_table.round(2)\n", ")\n", "\n", "\n", "# ============================================================\n", "# SAVE\n", "# ============================================================\n", "\n", "mechanism_df.to_csv(\n", " OUTPUT_DIR\n", " / \"Table_Mechanism_Relative_Thermal_Degradation.csv\",\n", " index=False\n", ")\n", "\n", "retention_table.to_csv(\n", " OUTPUT_DIR\n", " / \"Table_Relative_Strength_Retention.csv\"\n", ")\n", "\n", "print(\"\\n✓ MECHANISTIC NORMALIZATION COMPLETED\")" ] }, { "cell_type": "code", "execution_count": 191, "id": "25fbebb2-b37f-409c-b00c-45bbc00de7fd", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "==============================================================================\n", "BASELINE STRENGTH vs RELATIVE THERMAL DEGRADATION\n", "==============================================================================\n", "Maximum temperature : 600 °C\n", "Number of RCA levels : 6\n", "\n" ] }, { "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>RCA_pct</th>\n", " <th>Reference_strength_MPa</th>\n", " <th>Mean_strength_MPa</th>\n", " <th>Retention_pct</th>\n", " <th>Thermal_loss_pct</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>4</th>\n", " <td>0</td>\n", " <td>21.094</td>\n", " <td>11.560</td>\n", " <td>54.800</td>\n", " <td>45.200</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>10</td>\n", " <td>21.047</td>\n", " <td>11.215</td>\n", " <td>53.286</td>\n", " <td>46.714</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>20</td>\n", " <td>19.908</td>\n", " <td>10.726</td>\n", " <td>53.880</td>\n", " <td>46.120</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>30</td>\n", " <td>19.354</td>\n", " <td>9.990</td>\n", " <td>51.617</td>\n", " <td>48.383</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>40</td>\n", " <td>17.059</td>\n", " <td>9.624</td>\n", " <td>56.419</td>\n", " <td>43.581</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>50</td>\n", " <td>16.790</td>\n", " <td>9.130</td>\n", " <td>54.377</td>\n", " <td>45.623</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " RCA_pct Reference_strength_MPa Mean_strength_MPa Retention_pct \\\n", "4 0 21.094 11.560 54.800 \n", "9 10 21.047 11.215 53.286 \n", "14 20 19.908 10.726 53.880 \n", "19 30 19.354 9.990 51.617 \n", "24 40 17.059 9.624 56.419 \n", "29 50 16.790 9.130 54.377 \n", "\n", " Thermal_loss_pct \n", "4 45.200 \n", "9 46.714 \n", "14 46.120 \n", "19 48.383 \n", "24 43.581 \n", "29 45.623 " ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "ASSOCIATION\n", "Pearson r : 0.4188\n", "Pearson p : 0.4085\n", "Spearman rho : 0.1429\n", "Spearman p : 0.7872\n", "\n", "NOTE: Because the number of RCA levels is small, these correlations are interpreted descriptively rather than as standalone mechanistic proof.\n", "==============================================================================\n" ] } ], "source": [ "# ============================================================\n", "# — BASELINE STRENGTH vs RELATIVE THERMAL LOSS\n", "# ============================================================\n", "\n", "from scipy.stats import pearsonr, spearmanr\n", "\n", "\n", "T_MAXIMUM = mechanism_df[\"Temperature_C\"].max()\n", "\n", "\n", "highest_T = (\n", " mechanism_df[\n", " mechanism_df[\"Temperature_C\"] == T_MAXIMUM\n", " ]\n", " .copy()\n", ")\n", "\n", "\n", "x = highest_T[\"Reference_strength_MPa\"].values\n", "y = highest_T[\"Thermal_loss_pct\"].values\n", "\n", "\n", "# ============================================================\n", "# CORRELATIONS\n", "# ============================================================\n", "\n", "pearson_r, pearson_p = pearsonr(\n", " x,\n", " y\n", ")\n", "\n", "spearman_rho, spearman_p = spearmanr(\n", " x,\n", " y\n", ")\n", "\n", "\n", "print(\"=\" * 78)\n", "print(\"BASELINE STRENGTH vs RELATIVE THERMAL DEGRADATION\")\n", "print(\"=\" * 78)\n", "\n", "print(\n", " f\"Maximum temperature : \"\n", " f\"{T_MAXIMUM:.0f} °C\"\n", ")\n", "\n", "print(\n", " f\"Number of RCA levels : \"\n", " f\"{len(highest_T)}\"\n", ")\n", "\n", "print()\n", "\n", "display(\n", " highest_T[\n", " [\n", " \"RCA_pct\",\n", " \"Reference_strength_MPa\",\n", " \"Mean_strength_MPa\",\n", " \"Retention_pct\",\n", " \"Thermal_loss_pct\"\n", " ]\n", " ].round(3)\n", ")\n", "\n", "print(\"\\nASSOCIATION\")\n", "\n", "print(\n", " f\"Pearson r : \"\n", " f\"{pearson_r:.4f}\"\n", ")\n", "\n", "print(\n", " f\"Pearson p : \"\n", " f\"{pearson_p:.4f}\"\n", ")\n", "\n", "print(\n", " f\"Spearman rho : \"\n", " f\"{spearman_rho:.4f}\"\n", ")\n", "\n", "print(\n", " f\"Spearman p : \"\n", " f\"{spearman_p:.4f}\"\n", ")\n", "\n", "print()\n", "print(\n", " \"NOTE: Because the number of RCA levels is small, \"\n", " \"these correlations are interpreted descriptively \"\n", " \"rather than as standalone mechanistic proof.\"\n", ")\n", "\n", "print(\"=\" * 78)" ] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:anaconda3] *", "language": "python", "name": "conda-env-anaconda3-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.5" } }, "nbformat": 4, "nbformat_minor": 5}