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{
"cells": [
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Excel file 'all_large_data.xlsx' created with 570 rows.\n",
" Trial SoakingTime Condition Bag Measurement Sample \\\n",
"0 1 0 Initial Moisture (Trial1) 1 1 1 \n",
"1 1 0 Initial Moisture (Trial1) 1 1 2 \n",
"2 1 0 Initial Moisture (Trial1) 1 2 1 \n",
"3 1 0 Initial Moisture (Trial1) 1 2 2 \n",
"4 1 0 Initial Moisture (Trial1) 1 3 1 \n",
"5 1 0 Initial Moisture (Trial1) 1 3 2 \n",
"6 1 0 Initial Moisture (Trial1) 1 4 1 \n",
"7 1 0 Initial Moisture (Trial1) 1 4 2 \n",
"8 1 0 Initial Moisture (Trial1) 1 5 1 \n",
"9 1 0 Initial Moisture (Trial1) 1 5 2 \n",
"10 1 0 Initial Moisture (Trial1) 2 1 1 \n",
"11 1 0 Initial Moisture (Trial1) 2 1 2 \n",
"12 1 0 Initial Moisture (Trial1) 2 2 1 \n",
"13 1 0 Initial Moisture (Trial1) 2 2 2 \n",
"14 1 0 Initial Moisture (Trial1) 2 3 1 \n",
"15 1 0 Initial Moisture (Trial1) 2 3 2 \n",
"16 1 0 Initial Moisture (Trial1) 2 4 1 \n",
"17 1 0 Initial Moisture (Trial1) 2 4 2 \n",
"18 1 0 Initial Moisture (Trial1) 2 5 1 \n",
"19 1 0 Initial Moisture (Trial1) 2 5 2 \n",
"\n",
" InitialTemp Temp DeltaTemp InitialMC MC% DeltaMC \n",
"0 65.2 65 -0.2 3.28 3.3 0.02 \n",
"1 65.2 66 0.8 3.28 3.3 0.02 \n",
"2 65.2 65 -0.2 3.28 3.1 -0.18 \n",
"3 65.2 65 -0.2 3.28 3.3 0.02 \n",
"4 65.2 65 -0.2 3.28 3.3 0.02 \n",
"5 65.2 64 -1.2 3.28 3.4 0.12 \n",
"6 65.2 66 0.8 3.28 3.3 0.02 \n",
"7 65.2 64 -1.2 3.28 3.5 0.22 \n",
"8 65.2 67 1.8 3.28 3.0 -0.28 \n",
"9 65.2 65 -0.2 3.28 3.3 0.02 \n",
"10 65.7 67 1.3 3.27 3.2 -0.07 \n",
"11 65.7 66 0.3 3.27 3.1 -0.17 \n",
"12 65.7 67 1.3 3.27 3.3 0.03 \n",
"13 65.7 63 -2.7 3.27 3.4 0.13 \n",
"14 65.7 66 0.3 3.27 3.2 -0.07 \n",
"15 65.7 63 -2.7 3.27 3.3 0.03 \n",
"16 65.7 67 1.3 3.27 3.3 0.03 \n",
"17 65.7 66 0.3 3.27 3.3 0.03 \n",
"18 65.7 65 -0.7 3.27 3.4 0.13 \n",
"19 65.7 67 1.3 3.27 3.2 -0.07 \n"
]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import statsmodels.api as sm\n",
"import statsmodels.formula.api as smf\n",
"\n",
"# ------------------------------------------------------------------\n",
"# 1. Manually define the data in a nested dictionary.\n",
"# Each key is a condition block.\n",
"# For each block, we have data for each bag (keys 1, 2, 3).\n",
"# Each bag’s value is a list of 5 measurement rows.\n",
"# In each measurement row, there are 2 sample readings as tuples: (Temp, MC%).\n",
"# ------------------------------------------------------------------\n",
"data = {\n",
" \"Initial Moisture (Trial1)\": {\n",
" 1: [\n",
" [(65, 3.30), (66, 3.30)],\n",
" [(65, 3.10), (65, 3.30)],\n",
" [(65, 3.30), (64, 3.40)],\n",
" [(66, 3.30), (64, 3.50)],\n",
" [(67, 3.00), (65, 3.30)],\n",
" ],\n",
" 2: [\n",
" [(67, 3.20), (66, 3.10)],\n",
" [(67, 3.30), (63, 3.40)],\n",
" [(66, 3.20), (63, 3.30)],\n",
" [(67, 3.30), (66, 3.30)],\n",
" [(65, 3.40), (67, 3.20)],\n",
" ],\n",
" 3: [\n",
" [(71, 3.00), (68, 3.40)],\n",
" [(68, 3.20), (69, 3.20)],\n",
" [(68, 3.30), (70, 3.20)],\n",
" [(69, 3.20), (70, 3.40)],\n",
" [(68, 3.20), (70, 3.20)],\n",
" ],\n",
" },\n",
" \"6 hours\": {\n",
" 1: [\n",
" [(69, 4.30), (68, 4.40)],\n",
" [(69, 4.20), (67, 4.70)],\n",
" [(69, 4.20), (67, 4.50)],\n",
" [(68, 4.30), (68, 4.40)],\n",
" [(68, 4.20), (67, 4.30)],\n",
" ],\n",
" 2: [\n",
" [(71, 4.30), (69, 4.20)],\n",
" [(69, 4.60), (69, 4.30)],\n",
" [(69, 4.30), (69, 4.50)],\n",
" [(68, 4.50), (69, 4.40)],\n",
" [(68, 4.60), (68, 4.40)],\n",
" ],\n",
" 3: [\n",
" [(69, 4.40), (69, 4.60)],\n",
" [(69, 4.50), (69, 4.70)],\n",
" [(70, 4.30), (69, 5.00)],\n",
" [(69, 4.40), (69, 4.50)],\n",
" [(68, 4.50), (69, 4.50)],\n",
" ],\n",
" },\n",
" \"7 hours\": {\n",
" 1: [\n",
" [(69, 4.50), (69, 4.50)],\n",
" [(68, 4.60), (69, 4.60)],\n",
" [(68, 4.50), (69, 4.50)],\n",
" [(68, 4.60), (68, 4.80)],\n",
" [(69, 4.40), (68, 4.60)],\n",
" ],\n",
" 2: [\n",
" [(69, 4.70), (69, 4.90)],\n",
" [(69, 4.70), (69, 5.10)],\n",
" [(68, 4.70), (69, 4.80)],\n",
" [(68, 4.70), (69, 5.00)],\n",
" [(68, 4.80), (68, 4.60)],\n",
" ],\n",
" 3: [\n",
" [(70, 4.50), (70, 4.80)],\n",
" [(69, 4.50), (68, 4.80)],\n",
" [(69, 4.60), (67, 4.90)],\n",
" [(69, 4.50), (69, 4.80)],\n",
" [(69, 4.90), (68, 4.80)],\n",
" ],\n",
" },\n",
" \"8 hours\": {\n",
" 1: [\n",
" [(68, 5.20), (67, 4.80)],\n",
" [(69, 4.90), (67, 4.60)],\n",
" [(69, 5.00), (68, 4.60)],\n",
" [(68, 5.20), (68, 4.70)],\n",
" [(68, 5.00), (68, 4.80)],\n",
" ],\n",
" 2: [\n",
" [(69, 4.90), (68, 4.70)],\n",
" [(68, 5.00), (68, 4.80)],\n",
" [(68, 5.10), (68, 5.10)],\n",
" [(67, 5.00), (68, 4.60)],\n",
" [(68, 5.10), (68, 5.00)],\n",
" ],\n",
" 3: [\n",
" [(70, 5.30), (69, 5.20)],\n",
" [(69, 5.40), (67, 5.20)],\n",
" [(69, 5.00), (69, 4.80)],\n",
" [(69, 4.90), (69, 4.90)],\n",
" [(68, 5.10), (68, 4.90)],\n",
" ],\n",
" },\n",
" \"9 hours\": {\n",
" 1: [\n",
" [(68, 5.30), (68, 5.00)],\n",
" [(68, 5.60), (68, 5.30)],\n",
" [(68, 5.20), (68, 5.10)],\n",
" [(68, 5.30), (68, 4.90)],\n",
" [(68, 5.10), (68, 5.00)],\n",
" ],\n",
" 2: [\n",
" [(71, 5.20), (69, 5.30)],\n",
" [(70, 5.40), (69, 5.10)],\n",
" [(70, 5.30), (69, 5.00)],\n",
" [(70, 5.10), (69, 5.10)],\n",
" [(69, 5.20), (68, 5.00)],\n",
" ],\n",
" 3: [\n",
" [(71, 5.30), (68, 5.30)],\n",
" [(71, 5.40), (69, 5.20)],\n",
" [(70, 5.50), (68, 5.40)],\n",
" [(68, 5.70), (68, 5.20)],\n",
" [(68, 5.40), (68, 5.40)],\n",
" ],\n",
" },\n",
" \"10 hours\": {\n",
" 1: [\n",
" [(68, 5.30), (68, 5.00)],\n",
" [(68, 5.60), (68, 5.30)],\n",
" [(68, 5.20), (68, 5.10)],\n",
" [(68, 5.30), (68, 4.90)],\n",
" [(68, 5.10), (68, 5.00)],\n",
" ],\n",
" 2: [\n",
" [(71, 5.20), (69, 5.30)],\n",
" [(70, 5.40), (69, 5.10)],\n",
" [(70, 5.30), (69, 5.00)],\n",
" [(70, 5.10), (69, 5.10)],\n",
" [(69, 5.20), (68, 5.00)],\n",
" ],\n",
" 3: [\n",
" [(71, 5.30), (68, 5.30)],\n",
" [(71, 5.40), (69, 5.20)],\n",
" [(70, 5.50), (68, 5.40)],\n",
" [(68, 5.70), (68, 5.20)],\n",
" [(68, 5.40), (68, 5.40)],\n",
" ],\n",
" },\n",
" \"Initial Moisture (Trial2)\": {\n",
" 1: [\n",
" [(67, 3.20), (66, 3.50)],\n",
" [(65, 3.30), (63, 3.50)],\n",
" [(67, 3.10), (63, 3.50)],\n",
" [(67, 3.20), (65, 3.50)],\n",
" [(68, 3.30), (64, 3.40)],\n",
" ],\n",
" 2: [\n",
" [(68, 3.20), (68, 3.30)],\n",
" [(69, 3.30), (68, 3.30)],\n",
" [(68, 3.40), (68, 3.30)],\n",
" [(68, 3.30), (68, 3.30)],\n",
" [(67, 3.30), (67, 3.30)],\n",
" ],\n",
" 3: [\n",
" [(67, 3.30), (68, 3.30)],\n",
" [(66, 3.40), (68, 3.30)],\n",
" [(67, 3.40), (69, 3.30)],\n",
" [(67, 3.50), (69, 3.40)],\n",
" [(68, 3.30), (68, 3.00)],\n",
" ],\n",
" },\n",
" \"14 hours\": {\n",
" 1: [\n",
" [(69, 6.80), (68, 6.70)],\n",
" [(67, 7.00), (68, 6.60)],\n",
" [(68, 6.80), (68, 6.70)],\n",
" [(67, 6.70), (68, 6.40)],\n",
" [(67, 6.90), (67, 6.60)],\n",
" ],\n",
" 2: [\n",
" [(68, 6.10), (68, 6.60)],\n",
" [(67, 6.00), (67, 6.70)],\n",
" [(66, 6.40), (67, 6.90)],\n",
" [(67, 6.30), (67, 6.70)],\n",
" [(67, 6.00), (68, 6.40)],\n",
" ],\n",
" 3: [\n",
" [(70, 5.70), (70, 6.50)],\n",
" [(70, 6.00), (69, 6.30)],\n",
" [(70, 5.90), (68, 6.40)],\n",
" [(70, 5.60), (68, 6.70)],\n",
" [(69, 5.80), (68, 6.60)],\n",
" ],\n",
" },\n",
" \"15 hours\": {\n",
" 1: [\n",
" [(68, 7.20), (69, 7.30)],\n",
" [(68, 7.20), (69, 6.80)],\n",
" [(67, 7.10), (68, 7.00)],\n",
" [(68, 7.50), (68, 6.90)],\n",
" [(68, 7.10), (68, 7.20)],\n",
" ],\n",
" 2: [\n",
" [(70, 6.60), (68, 6.60)],\n",
" [(68, 6.70), (67, 6.60)],\n",
" [(67, 6.70), (67, 6.80)],\n",
" [(68, 6.50), (68, 6.70)],\n",
" [(68, 6.80), (68, 6.60)],\n",
" ],\n",
" 3: [\n",
" [(69, 5.90), (69, 6.30)],\n",
" [(67, 6.10), (69, 6.20)],\n",
" [(68, 6.10), (68, 6.40)],\n",
" [(68, 5.90), (69, 6.40)],\n",
" [(68, 6.20), (69, 6.30)],\n",
" ],\n",
" },\n",
" \"16 hours\": {\n",
" 1: [\n",
" [(68, 6.90), (69, 6.90)],\n",
" [(68, 6.70), (69, 6.90)],\n",
" [(68, 7.00), (68, 6.70)],\n",
" [(68, 6.80), (68, 7.00)],\n",
" [(68, 6.80), (67, 7.20)],\n",
" ],\n",
" 2: [\n",
" [(67, 6.60), (68, 7.00)],\n",
" [(67, 6.80), (68, 6.90)],\n",
" [(69, 6.40), (68, 7.30)],\n",
" [(68, 6.60), (68, 7.50)],\n",
" [(68, 6.60), (67, 7.40)],\n",
" ],\n",
" 3: [\n",
" [(67, 6.30), (68, 6.70)],\n",
" [(68, 6.20), (68, 6.80)],\n",
" [(67, 6.30), (68, 6.90)],\n",
" [(68, 6.20), (68, 6.80)],\n",
" [(68, 6.20), (68, 6.70)],\n",
" ],\n",
" },\n",
" \"17 hours\": {\n",
" 1: [\n",
" [(70, 6.90), (68, 7.00)],\n",
" [(68, 7.20), (68, 7.10)],\n",
" [(67, 6.90), (68, 7.00)],\n",
" [(68, 7.30), (68, 7.00)],\n",
" [(67, 6.90), (68, 7.00)],\n",
" ],\n",
" 2: [\n",
" [(70, 7.00), (69, 7.00)],\n",
" [(69, 6.90), (69, 6.90)],\n",
" [(69, 7.10), (68, 7.00)],\n",
" [(69, 7.00), (69, 7.00)],\n",
" [(68, 7.00), (68, 7.00)],\n",
" ],\n",
" 3: [\n",
" [(71, 6.70), (70, 6.50)],\n",
" [(70, 7.00), (68, 6.50)],\n",
" [(68, 7.00), (67, 6.60)],\n",
" [(67, 7.10), (68, 6.60)],\n",
" [(68, 7.00), (67, 6.60)],\n",
" ],\n",
" },\n",
" \"18 hours\": {\n",
" 1: [\n",
" [(69, 7.40), (68, 7.10)],\n",
" [(69, 7.10), (68, 7.10)],\n",
" [(68, 7.20), (68, 7.20)],\n",
" [(67, 7.40), (67, 7.10)],\n",
" [(68, 7.30), (67, 7.20)],\n",
" ],\n",
" 2: [\n",
" [(70, 7.40), (71, 7.20)],\n",
" [(70, 7.10), (70, 7.70)],\n",
" [(70, 7.10), (68, 7.80)],\n",
" [(69, 7.30), (68, 7.50)],\n",
" [(69, 7.20), (68, 7.40)],\n",
" ],\n",
" 3: [\n",
" [(67, 7.00), (68, 7.00)],\n",
" [(67, 6.80), (68, 6.90)],\n",
" [(67, 6.70), (67, 7.10)],\n",
" [(68, 6.60), (67, 6.90)],\n",
" [(68, 6.70), (67, 6.90)],\n",
" ],\n",
" },\n",
" \"Initial Moisture (Trial3)\": {\n",
" 1: [\n",
" [(66, 2.90), (68, 2.90)],\n",
" [(66, 3.00), (68, 2.90)],\n",
" [(64, 3.10), (69, 2.90)],\n",
" [(65, 2.90), (69, 3.00)],\n",
" [(65, 3.00), (69, 2.80)],\n",
" ],\n",
" 2: [\n",
" [(70, 2.90), (70, 2.90)],\n",
" [(70, 3.00), (70, 3.00)],\n",
" [(68, 2.90), (69, 2.90)],\n",
" [(69, 2.90), (70, 3.00)],\n",
" [(68, 3.00), (70, 3.00)],\n",
" ],\n",
" 3: [\n",
" [(69, 2.80), (70, 2.90)],\n",
" [(70, 2.90), (68, 3.00)],\n",
" [(70, 2.90), (70, 3.00)],\n",
" [(67, 3.00), (68, 3.00)],\n",
" [(67, 3.00), (69, 3.00)],\n",
" ],\n",
" },\n",
" \"26 hours\": {\n",
" 1: [\n",
" [(69, 7.80), (68, 8.70)],\n",
" [(68, 8.00), (67, 8.80)],\n",
" [(68, 7.70), (67, 8.40)],\n",
" [(68, 7.80), (67, 8.80)],\n",
" [(68, 7.80), (67, 8.40)],\n",
" ],\n",
" 2: [\n",
" [(77, 7.80), (70, 8.10)],\n",
" [(77, 7.80), (69, 8.00)],\n",
" [(69, 7.70), (69, 8.30)],\n",
" [(68, 7.70), (69, 8.00)],\n",
" [(68, 7.90), (68, 8.00)],\n",
" ],\n",
" 3: [\n",
" [(69, 8.40), (68, 8.30)],\n",
" [(68, 8.10), (68, 8.00)],\n",
" [(68, 8.60), (69, 8.10)],\n",
" [(68, 8.20), (69, 8.10)],\n",
" [(68, 8.40), (69, 8.00)],\n",
" ],\n",
" },\n",
" \"27 hours\": {\n",
" 1: [\n",
" [(69, 8.10), (69, 8.80)],\n",
" [(68, 8.10), (68, 8.30)],\n",
" [(67, 8.60), (68, 8.60)],\n",
" [(68, 8.50), (68, 8.40)],\n",
" [(68, 8.10), (67, 8.70)],\n",
" ],\n",
" 2: [\n",
" [(70, 8.10), (68, 8.30)],\n",
" [(68, 8.00), (68, 8.60)],\n",
" [(69, 8.30), (68, 8.60)],\n",
" [(68, 8.20), (69, 8.50)],\n",
" [(68, 8.60), (68, 8.50)],\n",
" ],\n",
" 3: [\n",
" [(70, 8.20), (69, 8.50)],\n",
" [(70, 8.20), (69, 8.50)],\n",
" [(70, 8.20), (69, 8.60)],\n",
" [(70, 8.20), (69, 8.50)],\n",
" [(70, 8.30), (69, 8.10)],\n",
" ],\n",
" },\n",
" \"28 hours\": {\n",
" 1: [\n",
" [(69, 8.60), (68, 8.80)],\n",
" [(68, 8.50), (68, 9.20)],\n",
" [(68, 8.50), (69, 8.90)],\n",
" [(68, 8.70), (69, 8.90)],\n",
" [(69, 8.30), (69, 8.90)],\n",
" ],\n",
" 2: [\n",
" [(70, 8.00), (68, 9.40)],\n",
" [(69, 8.90), (69, 8.50)],\n",
" [(69, 8.00), (69, 9.30)],\n",
" [(69, 9.20), (68, 8.30)],\n",
" [(68, 8.30), (69, 9.20)],\n",
" ],\n",
" 3: [\n",
" [(68, 8.40), (68, 8.70)],\n",
" [(69, 8.50), (68, 8.80)],\n",
" [(69, 8.00), (68, 8.20)],\n",
" [(69, 8.30), (67, 8.80)],\n",
" [(68, 8.20), (68, 8.70)],\n",
" ],\n",
" },\n",
" \"29 hours\": {\n",
" 1: [\n",
" [(70, 8.40), (69, 8.80)],\n",
" [(68, 8.50), (68, 9.00)],\n",
" [(68, 8.60), (68, 8.50)],\n",
" [(68, 8.30), (68, 8.70)],\n",
" [(69, 8.60), (68, 8.80)],\n",
" ],\n",
" 2: [\n",
" [(70, 8.70), (67, 9.10)],\n",
" [(69, 8.90), (68, 8.70)],\n",
" [(69, 8.10), (68, 8.60)],\n",
" [(70, 8.10), (68, 8.70)],\n",
" [(69, 8.10), (68, 8.60)],\n",
" ],\n",
" 3: [\n",
" [(70, 8.70), (67, 9.00)],\n",
" [(69, 8.90), (68, 8.80)],\n",
" [(68, 8.10), (69, 8.80)],\n",
" [(67, 8.80), (68, 8.80)],\n",
" [(68, 8.10), (69, 8.80)],\n",
" ],\n",
" },\n",
" \"30 hours\": {\n",
" 1: [\n",
" [(68, 9.20), (68, 8.90)],\n",
" [(68, 9.40), (68, 9.30)],\n",
" [(67, 9.30), (68, 8.80)],\n",
" [(68, 9.40), (67, 8.50)],\n",
" [(68, 9.40), (66, 8.90)],\n",
" ],\n",
" 2: [\n",
" [(69, 9.80), (68, 9.60)],\n",
" [(68, 9.40), (67, 9.50)],\n",
" [(68, 9.60), (68, 9.30)],\n",
" [(68, 9.60), (67, 9.40)],\n",
" [(68, 9.30), (68, 9.50)],\n",
" ],\n",
" 3: [\n",
" [(69, 8.50), (68, 8.70)],\n",
" [(69, 8.50), (68, 8.60)],\n",
" [(68, 9.80), (68, 9.60)],\n",
" [(69, 8.50), (68, 8.60)],\n",
" [(67, 8.20), (67, 8.50)],\n",
" ],\n",
" },\n",
" \"31 hours\": {\n",
" 1: [\n",
" [(69, 9.30), (67, 9.30)],\n",
" [(66, 9.50), (66, 9.30)],\n",
" [(67, 9.30), (66, 9.10)],\n",
" [(67, 9.50), (67, 9.30)],\n",
" [(67, 9.50), (67, 9.30)],\n",
" ],\n",
" 2: [\n",
" [(67, 8.50), (68, 9.00)],\n",
" [(67, 8.70), (67, 9.00)],\n",
" [(67, 9.20), (67, 9.20)],\n",
" [(68, 9.10), (68, 8.60)],\n",
" [(67, 8.90), (67, 8.90)],\n",
" ],\n",
" 3: [\n",
" [(68, 8.90), (69, 8.80)],\n",
" [(68, 8.80), (67, 8.90)],\n",
" [(68, 8.60), (67, 8.90)],\n",
" [(68, 8.60), (66, 8.70)],\n",
" [(67, 8.90), (67, 8.70)],\n",
" ],\n",
" },\n",
"}\n",
"\n",
"# ------------------------------------------------------------------\n",
"# 2. Convert the nested dictionary into a list of tidy rows.\n",
"# Here each row will contain:\n",
"# Condition, SoakingTime, Trial, Bag, Measurement, Sample, Temp, MC%\n",
"# ------------------------------------------------------------------\n",
"rows = []\n",
"\n",
"\n",
"def get_trial(condition):\n",
" # Determine trial based on the condition key\n",
" if \"Trial1\" in condition or condition in [\"6 hours\", \"7 hours\", \"8 hours\", \"9 hours\", \"10 hours\"]:\n",
" return 1\n",
" elif \"Trial2\" in condition or condition in [\"14 hours\", \"15 hours\", \"16 hours\", \"17 hours\", \"18 hours\"]:\n",
" return 2\n",
" elif \"Trial3\" in condition or condition in [\"26 hours\", \"27 hours\", \"28 hours\", \"29 hours\", \"30 hours\", \"31 hours\"] or \"Initial Moisture (Trial3)\" in condition:\n",
" return 3\n",
" else:\n",
" return None\n",
"\n",
"\n",
"def get_soaking_time(condition):\n",
" # For initial measurements, use 0; otherwise, extract the first token as the numeric time\n",
" if \"Initial Moisture\" in condition:\n",
" return 0\n",
" try:\n",
" return int(condition.split()[0])\n",
" except:\n",
" return None\n",
"\n",
"\n",
"for condition, bags in data.items():\n",
" trial = get_trial(condition)\n",
" soaking_time = get_soaking_time(condition)\n",
" for bag, meas_list in bags.items():\n",
" for m_idx, meas in enumerate(meas_list, start=1):\n",
" # We assume each measurement row has 2 sample readings (for that bag)\n",
" for s_idx, (temp, mc) in enumerate(meas, start=1):\n",
" rows.append({\"Condition\": condition, \"Trial\": trial, \"SoakingTime\": soaking_time, \"Bag\": bag, \"Measurement\": m_idx, \"Sample\": s_idx, \"Temp\": temp, \"MC%\": mc})\n",
"\n",
"# Create a DataFrame from the collected rows.\n",
"df = pd.DataFrame(rows)\n",
"\n",
"# ------------------------------------------------------------------\n",
"# 3. Compute baseline values (initial measurements) for each trial and bag.\n",
"# For each trial, use the rows where Condition contains \"Initial Moisture\" to get the baseline.\n",
"# ------------------------------------------------------------------\n",
"baseline = {} # structure: { trial: { bag: { \"InitialTemp\": value, \"InitialMC\": value } } }\n",
"\n",
"for trial in df[\"Trial\"].unique():\n",
" trial = int(trial)\n",
" # Filter for initial measurements for this trial.\n",
" init_df = df[df[\"Condition\"].str.contains(\"Initial Moisture\") & (df[\"Trial\"] == trial)]\n",
" # Compute the average baseline for each bag.\n",
" for bag in sorted(init_df[\"Bag\"].unique()):\n",
" bag_df = init_df[init_df[\"Bag\"] == bag]\n",
" baseline.setdefault(trial, {})[bag] = {\"InitialTemp\": bag_df[\"Temp\"].mean(), \"InitialMC\": bag_df[\"MC%\"].mean()}\n",
"\n",
"\n",
"# ------------------------------------------------------------------\n",
"# 4. For each non-initial measurement row, add the baseline values and compute deltas.\n",
"# (For initial rows, delta will be zero.)\n",
"# ------------------------------------------------------------------\n",
"def assign_baseline(row):\n",
" trial = int(row[\"Trial\"])\n",
" bag = row[\"Bag\"]\n",
" base = baseline.get(trial, {}).get(bag, {\"InitialTemp\": None, \"InitialMC\": None})\n",
" return pd.Series({\"InitialTemp\": base[\"InitialTemp\"], \"InitialMC\": base[\"InitialMC\"]})\n",
"\n",
"\n",
"df_baseline = df.apply(assign_baseline, axis=1)\n",
"df = pd.concat([df, df_baseline], axis=1)\n",
"\n",
"# Compute delta columns.\n",
"df[\"DeltaTemp\"] = df[\"Temp\"] - df[\"InitialTemp\"]\n",
"df[\"DeltaMC\"] = df[\"MC%\"] - df[\"InitialMC\"]\n",
"\n",
"# ------------------------------------------------------------------\n",
"# 5. Rearrange columns for clarity.\n",
"# ------------------------------------------------------------------\n",
"df = df[[\"Trial\", \"SoakingTime\", \"Condition\", \"Bag\", \"Measurement\", \"Sample\", \"InitialTemp\", \"Temp\", \"DeltaTemp\", \"InitialMC\", \"MC%\", \"DeltaMC\"]]\n",
"\n",
"# ------------------------------------------------------------------\n",
"# 6. Save the tidy data to an Excel file.\n",
"# ------------------------------------------------------------------\n",
"output_filename = \"all_large_data.xlsx\"\n",
"df.to_excel(output_filename, index=False)\n",
"print(f\"Excel file '{output_filename}' created with {len(df)} rows.\")\n",
"print(df.head(20))"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"First 10 rows:\n",
" Trial SoakingTime Condition Bag Measurement Sample \\\n",
"0 1 0 Initial Moisture (Trial1) 1 1 1 \n",
"1 1 0 Initial Moisture (Trial1) 1 1 2 \n",
"2 1 0 Initial Moisture (Trial1) 1 2 1 \n",
"3 1 0 Initial Moisture (Trial1) 1 2 2 \n",
"4 1 0 Initial Moisture (Trial1) 1 3 1 \n",
"5 1 0 Initial Moisture (Trial1) 1 3 2 \n",
"6 1 0 Initial Moisture (Trial1) 1 4 1 \n",
"7 1 0 Initial Moisture (Trial1) 1 4 2 \n",
"8 1 0 Initial Moisture (Trial1) 1 5 1 \n",
"9 1 0 Initial Moisture (Trial1) 1 5 2 \n",
"\n",
" InitialTemp Temp DeltaTemp InitialMC MC% DeltaMC \n",
"0 65.2 65 -0.2 3.28 3.3 0.02 \n",
"1 65.2 66 0.8 3.28 3.3 0.02 \n",
"2 65.2 65 -0.2 3.28 3.1 -0.18 \n",
"3 65.2 65 -0.2 3.28 3.3 0.02 \n",
"4 65.2 65 -0.2 3.28 3.3 0.02 \n",
"5 65.2 64 -1.2 3.28 3.4 0.12 \n",
"6 65.2 66 0.8 3.28 3.3 0.02 \n",
"7 65.2 64 -1.2 3.28 3.5 0.22 \n",
"8 65.2 67 1.8 3.28 3.0 -0.28 \n",
"9 65.2 65 -0.2 3.28 3.3 0.02 \n",
"\n",
"DataFrame info:\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 570 entries, 0 to 569\n",
"Data columns (total 12 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Trial 570 non-null int64 \n",
" 1 SoakingTime 570 non-null int64 \n",
" 2 Condition 570 non-null object \n",
" 3 Bag 570 non-null int64 \n",
" 4 Measurement 570 non-null int64 \n",
" 5 Sample 570 non-null int64 \n",
" 6 InitialTemp 570 non-null float64\n",
" 7 Temp 570 non-null int64 \n",
" 8 DeltaTemp 570 non-null float64\n",
" 9 InitialMC 570 non-null float64\n",
" 10 MC% 570 non-null float64\n",
" 11 DeltaMC 570 non-null float64\n",
"dtypes: float64(5), int64(6), object(1)\n",
"memory usage: 53.6+ KB\n",
"None\n",
"\n",
"Summary statistics:\n",
" Trial SoakingTime Bag Measurement Sample \\\n",
"count 570.000000 570.000000 570.000000 570.000000 570.000000 \n",
"mean 2.052632 15.315789 2.000000 3.000000 1.500000 \n",
"std 0.826219 10.406767 0.817214 1.415456 0.500439 \n",
"min 1.000000 0.000000 1.000000 1.000000 1.000000 \n",
"25% 1.000000 7.000000 1.000000 2.000000 1.000000 \n",
"50% 2.000000 15.000000 2.000000 3.000000 1.500000 \n",
"75% 3.000000 27.000000 3.000000 4.000000 2.000000 \n",
"max 3.000000 31.000000 3.000000 5.000000 2.000000 \n",
"\n",
" InitialTemp Temp DeltaTemp InitialMC MC% DeltaMC \n",
"count 570.000000 570.000000 570.000000 570.000000 570.000000 570.000000 \n",
"mean 67.408772 68.171930 0.763158 3.164561 6.310877 3.146316 \n",
"std 1.516666 1.311803 1.710880 0.169317 2.002868 2.096736 \n",
"min 65.200000 63.000000 -2.900000 2.940000 2.800000 -0.320000 \n",
"25% 65.700000 68.000000 -0.700000 2.950000 4.800000 1.520000 \n",
"50% 67.700000 68.000000 0.300000 3.270000 6.600000 3.300000 \n",
"75% 68.800000 69.000000 2.300000 3.300000 8.200000 5.250000 \n",
"max 69.400000 77.000000 7.600000 3.350000 9.800000 6.850000 \n"
]
}
],
"source": [
"# -------------------------------\n",
"# 1. Load the tidy data from Excel\n",
"# -------------------------------\n",
"df = pd.read_excel(\"all_large_data.xlsx\")\n",
"\n",
"# Display first few rows and info\n",
"print(\"First 10 rows:\")\n",
"print(df.head(10))\n",
"print(\"\\nDataFrame info:\")\n",
"print(df.info())\n",
"print(\"\\nSummary statistics:\")\n",
"print(df.describe())"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 1603.21x500 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1603.21x500 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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7u+vatWtaunSptm3bpqtXr2rgwIFaunSpLBaLGjdurBUrVmjBggW2J8KnTp2qwoULy9XVNQ2ZS92KFSsUExOj9u3bq23btrp//7527dplu7kbEhKiLl266OzZs7JYLHrttdfUrFkzFS5cWHfv3tW2bdu0aNEiBQQEqHv37lq0aJHKly9vVwxxT8l7eHioSpUq6fbe1q9fr4EDByomJkaurq7q0KGDGjduLC8vL50/f17z58/XkSNH9Ndff6lLly5auHBhsr1ChgwZosDAQL344otq3769ihcvritXrmjmzJn6559/dO/ePQ0ePFirVq2yXZsrVqxQQECAbcifN998U2+//bYkJbjRvnv3br3//vuKjo5WgQIF1KlTJz3//PPKlSuX/P39tWTJEu3bt0+bNm1Sv379NGPGjGSLAB988IECAwP16quvqlWrVipQoIDOnTunKVOmyN/fX9evX9eIESM0ffp0SVLhwoW1YsUKHT16VMOGDZMk9e/f39bLonDhwobzfejQId26dUuSVLduXcP7xRUqYmJitH79eluOHhXXW6pFixYpHu/8+fMKCwuTJFWvXt1wwSQ1NWrUULFixXT9+nX17NlTH374odzd3TVlyhTdvXtXefPm1csvv2zb/qeffpIU26Mjrb2NpNihq1xcXBQVFaU///yTQgUAAMjxKFQAAIAcLyYmRjVq1NDEiRNtk7IauaFvtVo1btw4SbHDh/zyyy8qVaqUbX21atXUsmVL+fr6atSoUZKktWvXZmqh4uLFi7Yba5LUvXv3TDu3Ec2bN9e6desUEBCgAwcO2ObSiC/uRubLL7+c6rBPq1atshUpnnvuOU2bNi3Bk+o1a9bU66+/rs6dO+vq1auaOHGiGjZsqGeffTbVWDdu3Ki7d+9Kih3uJ36sPj4+at68uT766COtXbtWx44d07Fjx1S1alXly5dP+fLlSzCUU7ly5TLk6fc4MTExevXVVxNMRh1/HpOxY8fq7NmzcnFx0YQJExL1eHjppZfUpk0bde7cWSEhIfr888+1aNEiw+d/8OCBbdicIkWKpNtN5QcPHmj48OGKiYmRh4eHpk+frueff9623tfXV23atNHw4cO1ZMkSnTlzRmPHjrXdrH9UYGCg3nvvvQRP2/v4+Ojll19Whw4ddPLkSZ09e1ZHjhyRr6+vJKlSpUoJeiUVKlRIlSpVSnDcsLAwDRo0SNHR0apQoYLmzJmToGdHXJw//vijpk+frt27d2vRokXJ3swPDAzU119/nWB+CF9fX7388stq2bKlrTdPXI8ANzc3VapUKUFPjuLFiyeK04g9e/bYfrZn//Lly6tcuXI6d+5csoWKc+fO2XqzpFaouHr1qu3n4sWLG44jNc7Ozvr666/13nvv6eTJk+rbt2+idXF/Q3bs2KE9e/aoZMmSevPNNx/rvJ6ennryySd1/vx5W08RAACAnIzJtAEAABT7VHRckcKoq1ev6oknnlDevHnVrl27BEWK+Fq3bm37+ebNm48Vpz1u376tPn362G5WdujQQT4+Ppl2fiMaNmxom89h3bp1idaHhoba5mNIbaJdSZoxY4ak2DHgx44dm+RwOiVKlNDo0aMlxRab4vZJTWBgoO3nuMmeH/Xee+/p7bff1qeffmp46LCMktxN73v37mnx4sWSYq+J5IZlevbZZ9WzZ09J0uHDh+2a1Dr+5MNpGcM/OUuWLLEVi/r375+gSBHHyclJX375pe13tGjRIts+jypUqFCSPWrc3Nz0+uuv25ZPnjxpV5wrV660XS/ffPNNssNPffzxx7b5Zfz8/JI93rPPPpvkJNZeXl62G/xWqzXRnCXp4Z9//pEkubq62j2cWVyvtH379unOnTuJ1scVIatVq6YSJUqkeKyMuqak2ML0vHnzVL9+fXl6esrT01O1a9fWnDlzEkwMHtd7rn///rai6fnz5zV8+HB16NBBnTp10oQJE5Kc7yIpcb2Uzp49q9DQ0HR9TwAAAFkNhQoAAADF3iizV8mSJbVy5Ur9/fffGjx4cLLb5c2b13YzPiIiIq0h2iUwMFBdu3bVhQsXJEmVK1dO9qlyM3l6etomE96wYUOi9Vu2bFFISIjy5cunOnXqpHiswMBA2w3lhg0bqmjRosluW7NmTT399NOSpJ07dyomJibVWMuWLWv7uV+/fjpy5EiibSpVqqQRI0aoR48eyRauMoOLi0uyvUT27dtnuyma2gTvL730ku1ne576jl/0S89rfseOHZJin3RP6sZ9HDc3N9v68PBw7du3L8ntateuLReXpDuZxx/GKf5NciPiimt58+ZNsTjo4uJi+x2cP38+2UJmSr+nx4nTiIsXL0qKHdrO6ET2ceJu8scNb/Qoo8M+SUrQKycj/o76+vpqxowZOnjwoA4ePKg5c+YkGI5pzZo1OnbsmCpUqGArPh8+fFjt2rXTokWLdOTIEf39998aP368Xn/9dUNF6bhiWnR0tC5fvpzu7wkAACArYegnAAAAScWKFXus/eNuzD548ECXL1/WpUuXdO7cOZ04cUL79++3ja1utVofO9bUXLp0ST169NClS5ckSWXKlNG0adNsxRJHEzf807Vr13T48GHbEDvS/25kNmvWLNkbynHOnj1r+zn+MZLj6+urs2fP6t69e7p+/XqqT3Q3bNhQFStW1KlTp3TgwAF16NBBhQsXVr169VSnTh3Vq1dPBQsWTPW8mSF//vxyd3dPct3x48dtP/fr18/wMe25kerl5SWLxSKr1Zpsb4a0OHPmjKTYa9rLyyvFbeMXH0+fPq1XXnkl0TYp/c49PT1tP0dFRdkVZ9z8HPfv308wEXNqLl++nGDC8zgpDRMWP87o6Gg7ojQmrmdIavlOSoUKFVS2bFmdP39e69atS1BcOnPmjG2OFCOFivg9lNLzmjIiKipK//3vfyVJAwYMkJOTk2JiYjR06FCFhoaqWbNm+vzzzxUWFqYhQ4bowIEDGjZsmKZNm5biceMPIRYQEGCb2BsAACAnolABAAByPFdXV7ufFI7v3LlzmjVrlrZt25bkU7RJTRCdUQ4ePKi+ffsqKChIUuzQIjNnzkzzDfSzZ88qMjIy2fVPPvnkY00oK/1v+KewsDCtXbvWVmQICQnRtm3bJBl74jr+0DIFChRIdfv4OQkODk61UOHi4qJp06Zp2LBhtrgCAgK0fPlyLV++XBaLRT4+PmrTpo06dOjwWNfU40rpd5LUEDxGxJ/vIDXu7u4qWrSorl+/rlu3bikqKirVQpMRcTeokxtKKb7410ByN7ZTytPjfG7TO8fxixGPih9nRhRCQ0JCJKWcq5Q0b95cP//8s/bs2aN79+7ZCh5xRcgaNWokWZx5VNwQWVLmDqEnxQ455u/vr+rVq9smJN+7d6/OnTunvHnz6ocffrAVgseOHatGjRpp27ZtunTpUoIeL4+KX6iIyzMAAEBORaECAADkeI9zQ3Lp0qX64osvEjxxnS9fPpUtW1bly5eXr6+v6tWrpxYtWmT4jag1a9Zo8ODBCg8PlxTbY2DKlCmPNZ577969E0xi+yg/Pz/VqlUrzceXYm/CvvTSS1q/fr3Wr19vG0Zr8+bNCg0NVcGCBVWzZs3HOkdS4j99bnR+kiJFimjatGk6ffq01q1bpy1btuj48eOKiYmR1Wq1zeWwcOFCzZ4929ANdXsZGaYqpWs6/vueOHFiqgWaOEnN95ESX19fXb9+XeHh4Tp8+HCSE6UnJyAgQBMmTFCtWrVUs2ZN20Tk9tyIj58ne+efeVxxfw8qVqyo77//3vB+GTnBelrFXUtpnRA9rlARGRmpTZs2qW3btpL+V6gwMveMJJUqVUoFChTQ7du3dejQIUVERNhVDNy5c6d27dqlWrVqqUaNGoYLL2FhYZo4caIkaeDAgbbX9+/fLyl2/pD4vdWKFi2q0qVL68KFCzpw4ECKhYr412VmFrQBAAAcEYUKAACANDp16pStSJE7d271799fTZs2TXSzMSYmxjb0U0aZN2+eRo0aZbuR27BhQ/3nP/+Rh4dHhp43vTRv3lzr16/XlStXdOzYMVWpUkVr166VFDvsk5GbpPGHhrl9+3aq28ffxt6JrytUqKAKFSqof//+Cg4O1t69e7Vt2zatXbtW9+/f16lTpzRmzBh9++23ho9p9Mn4+/fv2xXro+K/V29vb1WqVOmxjpecBg0a2H6HO3futKtQsWXLFi1cuFALFy5U69at9eOPP0qKjT0gIMDWYyglt27dsv2c2ROb58uXT4GBgQoPD8+w/GaW3Llz6+7du2n+G1axYkWVKVNGFy5c0Nq1a9W2bVudOnVK58+fl7Ozs5o1a2b4WC+99JKWL1+ukJAQHThwQLVr1za877Jly7Rq1SpNnz5dX3/9dYpznMTn5+engIAAvfTSSwnmrAgICJCUdO+t/Pnz68KFC7Zhs5ITv3j9uD3TAAAAsjom0wYAAEijhQsX2p6c/uKLL9StW7ckn4i+ceOGoafg02r+/PkaOXKk7eZ2x44d9fPPP6dLkWLTpk06depUsv8etzdFnIYNG9rmVFi/fn2CYZ+MPnEdfy6ApCa6ftShQ4ckxfboMDL0TEREhM6cOaOjR48meP2JJ57QK6+8oq+//lq//fabbWibuAmVjYpfjEnppvD169ftOu6jypcvb/v58OHDKW574cIFTZo0SStXrpS/v79d53nllVdsN18XL15s+Ea31WrVvHnzbMutWrWy/Rz3O75w4UKqQ1HFf2/xJ0LPDHE5vnjxYqpFldWrV2vOnDn6888/HXL4n8KFC0v63435tIibVHvXrl16+PChrYD1wgsv2DUs3euvv277ee7cuYb3CwgI0Pr16yXFDkv28ssvG9rv3r17mj59uiwWi/7v//7P8Pniei2l9nc/fjHNyN8gAACA7IxCBQAAQBpdvHjR9nOVKlWS3W7lypW2n+2dlDc1u3bt0qhRo2zL7733nkaNGpXmYVrMkjt3br300kuSpD///FN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",
"text/plain": [
"<Figure size 1603.21x500 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1603.21x500 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1603.21x500 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1603.21x500 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import pandas as pd\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"\n",
"\n",
"def analyze_raw_moisture_temperature(excel_file=\"all_data_with_delta.xlsx\"):\n",
" \"\"\"\n",
" Reads tidy data (with columns Trial, SoakingTime, Bag, Sample, MC%, Temp, etc.),\n",
" and generates bar charts (catplots) for each Trial, comparing:\n",
" - MC% by SoakingTime and Bag, separate bars for Sample1 vs Sample2\n",
" - Temp by SoakingTime and Bag, separate bars for Sample1 vs Sample2\n",
" \"\"\"\n",
" # ------------------------------------------------------\n",
" # 1) Load the tidy data\n",
" # ------------------------------------------------------\n",
" df = pd.read_excel(excel_file)\n",
"\n",
" # We'll assume columns:\n",
" # Trial, SoakingTime, Bag, Measurement, Sample, Temp, MC%, etc.\n",
" # If your columns differ, rename them accordingly.\n",
"\n",
" # Set a nice style\n",
" sns.set(style=\"whitegrid\", context=\"talk\")\n",
"\n",
" # Sort so the bar charts go in ascending SoakingTime order\n",
" df = df.sort_values([\"Trial\", \"Bag\", \"SoakingTime\", \"Sample\"])\n",
"\n",
" # Unique Trials\n",
" trials = df[\"Trial\"].unique()\n",
"\n",
" for trial in trials:\n",
" # Subset for just this trial\n",
" sub = df[df[\"Trial\"] == trial]\n",
"\n",
" # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
" # A) MC% bar chart, comparing SoakingTime (x-axis) and Sample (hue)\n",
" # across columns for each Bag\n",
" # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
" g1 = sns.catplot(data=sub, x=\"SoakingTime\", y=\"MC%\", hue=\"Sample\", col=\"Bag\", kind=\"bar\", palette=\"Set2\", height=5, aspect=1) # x-axis: SoakingTime # y-axis: moisture content # separate bars for Sample 1 vs. 2 # separate subplot column for each Bag # choose a pleasing color palette # adjust size\n",
" g1.fig.suptitle(f\"Trial {trial} - Moisture Content (MC%)\", y=1.02)\n",
" g1.set_axis_labels(\"Soaking Time (hours) [0=Initial]\", \"MC%\")\n",
" g1._legend.set_title(\"Sample\") # rename legend title\n",
"\n",
" # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
" # B) Temp bar chart, comparing SoakingTime (x-axis) and Sample (hue)\n",
" # across columns for each Bag\n",
" # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
" g2 = sns.catplot(data=sub, x=\"SoakingTime\", y=\"Temp\", hue=\"Sample\", col=\"Bag\", kind=\"bar\", palette=\"Set1\", height=5, aspect=1) # a different color palette if you like\n",
" g2.fig.suptitle(f\"Trial {trial} - Temperature (°F)\", y=1.02)\n",
" g2.set_axis_labels(\"Soaking Time (hours) [0=Initial]\", \"Temperature (°F)\")\n",
" g2._legend.set_title(\"Sample\")\n",
"\n",
" plt.show()\n",
"\n",
"\n",
"# --------------------------------------------------------------------\n",
"# Example usage:\n",
"# --------------------------------------------------------------------\n",
"if __name__ == \"__main__\":\n",
" analyze_raw_moisture_temperature(\"all_data_with_delta.xlsx\")"
]
},
{
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