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Add Chapter 3 Methodology #79

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nuluh merged 18 commits from latex/methodology into main 2025-05-22 20:25:23 +00:00
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4 changed files with 97 additions and 53 deletions
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86
code/notebooks/stft.ipynb
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@@ -121,8 +121,9 @@
"signal_sensor2_test1 = []\n",
"\n",
"for data in df:\n",
" signal_sensor1_test1.append(data['sensor 1'].values)\n",
" signal_sensor2_test1.append(data['sensor 2'].values)\n",
" if not data.empty and 'sensor 1' in data.columns and 'sensor 2' in data.columns:\n",
" signal_sensor1_test1.append(data['sensor 1'].values)\n",
" signal_sensor2_test1.append(data['sensor 2'].values)\n",
"\n",
"print(len(signal_sensor1_test1))\n",
"print(len(signal_sensor2_test1))"
@@ -156,8 +157,6 @@
"from scipy.signal import stft, hann\n",
"from multiprocessing import Pool\n",
"\n",
"\n",
"\n",
"# Function to compute and append STFT data\n",
"def process_stft(args):\n",
" # Define STFT parameters\n",
@@ -199,23 +198,22 @@
" # Compute STFT\n",
" frequencies, times, Zxx = stft(sensor_data, fs=Fs, window=window, nperseg=window_size, noverlap=window_size - hop_size)\n",
" magnitude = np.abs(Zxx)\n",
" flattened_stft = magnitude.flatten()\n",
" df_stft = pd.DataFrame(magnitude, index=frequencies, columns=times).T\n",
" df_stft.columns = [f\"Freq_{i}\" for i in frequencies]\n",
" \n",
" # Define the output CSV file path\n",
" stft_file_name = f'stft_data{sensor_num}_{damage_num}.csv'\n",
" sensor_output_dir = os.path.join(damage_base_path, sensor_name.lower())\n",
" os.makedirs(sensor_output_dir, exist_ok=True)\n",
" stft_file_path = os.path.join(sensor_output_dir, stft_file_name)\n",
" print(stft_file_path)\n",
" # Append the flattened STFT to the CSV\n",
" try:\n",
" flattened_stft_df = pd.DataFrame([flattened_stft])\n",
" if not os.path.isfile(stft_file_path):\n",
" # Create a new CSV\n",
" flattened_stft_df.to_csv(stft_file_path, index=False, header=False)\n",
" df_stft.to_csv(stft_file_path, index=False, header=False)\n",
" else:\n",
" # Append to existing CSV\n",
" flattened_stft_df.to_csv(stft_file_path, mode='a', index=False, header=False)\n",
" df_stft.to_csv(stft_file_path, mode='a', index=False, header=False)\n",
" print(f\"Appended STFT data to {stft_file_path}\")\n",
" except Exception as e:\n",
" print(f\"Error writing to {stft_file_path}: {e}\")"
@@ -295,7 +293,7 @@
"\n",
"# get current y ticks in list\n",
"print(len(frequencies))\n",
"print(len(times))\n"
"print(len(times))"
]
},
{
@@ -326,7 +324,6 @@
"ready_data1 = []\n",
"for file in os.listdir('D:/thesis/data/converted/raw/sensor1'):\n",
" ready_data1.append(pd.read_csv(os.path.join('D:/thesis/data/converted/raw/sensor1', file)))\n",
"ready_data1[0]\n",
"# colormesh give title x is frequency and y is time and rotate/transpose the data\n",
"# Plotting the STFT Data"
]
@@ -337,8 +334,8 @@
"metadata": {},
"outputs": [],
"source": [
"ready_data1[1]\n",
"plt.pcolormesh(ready_data1[1])"
"ready_data1[0]\n",
"plt.pcolormesh(ready_data1[0])"
]
},
{
@@ -363,8 +360,7 @@
"source": [
"ready_data2 = []\n",
"for file in os.listdir('D:/thesis/data/converted/raw/sensor2'):\n",
" ready_data2.append(pd.read_csv(os.path.join('D:/thesis/data/converted/raw/sensor2', file)))\n",
"ready_data2[5]"
" ready_data2.append(pd.read_csv(os.path.join('D:/thesis/data/converted/raw/sensor2', file)))"
]
},
{
@@ -384,10 +380,25 @@
"outputs": [],
"source": [
"x1 = 0\n",
"\n",
"print(type(ready_data1[0]))\n",
"ready_data1[0].iloc[:,0]\n",
"# x1 = x1 + ready_data1[0].shape[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"x1 = 0\n",
"print(type(x1))\n",
"for i in range(len(ready_data1)):\n",
" print(ready_data1[i].shape)\n",
" # print(ready_data1[i].shape)\n",
" # print(ready_data1[i].)\n",
" print(type(ready_data1[i].shape[0]))\n",
" x1 = x1 + ready_data1[i].shape[0]\n",
" print(type(x1))\n",
"\n",
"print(x1)"
]
@@ -407,13 +418,6 @@
"print(x2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Appending"
]
},
{
"cell_type": "code",
"execution_count": null,
@@ -455,10 +459,15 @@
]
},
{
"cell_type": "markdown",
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"### Labeling"
"y_1 = [1,1,1,1]\n",
"y_2 = [0,1,1,1]\n",
"y_3 = [1,0,1,1]\n",
"y_4 = [1,1,0,0]"
]
},
{
@@ -494,16 +503,6 @@
" print(ready_data1[i].shape[0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for i in range(len(y_data)):\n",
" print(ready_data2[i].shape[0])"
]
},
{
"cell_type": "code",
"execution_count": null,
@@ -521,8 +520,7 @@
"metadata": {},
"outputs": [],
"source": [
"# len(y_data[0])\n",
"y_data[0]"
"y_data"
]
},
{
@@ -806,6 +804,7 @@
"\n",
" # df1['s1'] = sensor1[sensor1.columns[-1]]\n",
" # df1['s2'] = sensor2[sensor2.columns[-1]]\n",
"ed\n",
" # # Combined Plot for sensor 1 and sensor 2 from data1 file in which motor is operated at 800 rpm\n",
"\n",
" # plt.plot(df1['s2'], label='sensor 2')\n",
@@ -835,19 +834,14 @@
" # plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Test with Outside of Its Training Data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
"source": [
"spectograph('D:/thesis/data/converted/raw')"
]
}
],
"metadata": {

42
data/QUGS/convert.py
View File

@@ -2,6 +2,7 @@ import pandas as pd
import os
import re
import sys
import numpy as np
from colorama import Fore, Style, init
from typing import TypedDict, Dict, List
from joblib import load
@@ -225,25 +226,56 @@ class DataProcessor:
"""
idx = self._create_vector_column_index()
# if overwrite:
for i in range(len(self.data)):
for j in range(len(self.data[i])):
for i in range(len(self.data)): # damage(s)
for j in range(len(self.data[i])): # col(s)
# Get the appropriate indices for slicing from idx
indices = idx[j]
# Get the current DataFrame
df = self.data[i][j]
# Keep the 'Time' column and select only specified 'Real' columns
# First, we add 1 to all indices to account for 'Time' being at position 0
# Keep the 'Time' column and select only specifid 'Real' colmns
# First, we add 1 to all indices to acount for 'Time' being at positiion 0
real_indices = [index + 1 for index in indices]
# Create list with Time column index (0) and the adjusted Real indices
# Create list with Time column index (0) and the adjustedd Real indices
all_indices = [0] + [real_indices[0]] + [real_indices[-1]]
# Apply the slicing
self.data[i][j] = df.iloc[:, all_indices]
# TODO: if !overwrite:
def export_to_csv(self, output_dir: str, file_prefix: str = "DAMAGE"):
"""
Export the processed data to CSV files in the required folder structure.
:param output_dir: Directory to save the CSV files.
:param file_prefix: Prefix for the output filenames.
"""
for group_idx, group in enumerate(self.data, start=1):
group_folder = os.path.join(output_dir, f"{file_prefix}_{group_idx}")
os.makedirs(group_folder, exist_ok=True)
for test_idx, df in enumerate(group, start=1):
# Ensure columns are named uniquely if duplicated
df = df.copy()
df.columns = ["Time", "Real_0", "Real_1"] # Rename
# Export first Real column
out1 = os.path.join(
group_folder, f"{file_prefix}_{group_idx}_TEST{test_idx}_01.csv"
)
df[["Time", "Real_0"]].rename(columns={"Real_0": "Real"}).to_csv(
out1, index=False
)
# Export last Real column
out2 = os.path.join(
group_folder, f"{file_prefix}_{group_idx}_TEST{test_idx}_02.csv"
)
df[["Time", "Real_1"]].rename(columns={"Real_1": "Real"}).to_csv(
out2, index=False
)
def create_damage_files(base_path, output_base, prefix):
# Initialize colorama

21
data/QUGS/test.py
View File

@@ -4,5 +4,22 @@ from joblib import dump, load
# a = generate_damage_files_index(
# num_damage=6, file_index_start=1, col=5, base_path="D:/thesis/data/dataset_A"
# )
# dump(DataProcessor(file_index=a), "D:/cache.joblib")
a = load("D:/cache.joblib")
b = generate_damage_files_index(
num_damage=6,
file_index_start=1,
col=5,
base_path="D:/thesis/data/dataset_B",
prefix="zzzBD",
)
# data_A = DataProcessor(file_index=a)
# # data.create_vector_column(overwrite=True)
# data_A.create_limited_sensor_vector_column(overwrite=True)
# data_A.export_to_csv("D:/thesis/data/converted/raw")
data_B = DataProcessor(file_index=b)
# data.create_vector_column(overwrite=True)
data_B.create_limited_sensor_vector_column(overwrite=True)
data_B.export_to_csv("D:/thesis/data/converted/raw_B")
# a = load("D:/cache.joblib")
# breakpoint()

1
latex/thesis.cls
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@@ -20,6 +20,7 @@
\RequirePackage{etoolbox}
\RequirePackage{tocloft}
\RequirePackage{tocbibind}
\RequirePackage{amsmath,amsfonts,amssymb}
% Polyglossia set language
\setmainlanguage{bahasai}