From 123c76f51a83d469f781ba3e1b40aa5a3dd0df45 Mon Sep 17 00:00:00 2001
From: Luis Roberto Mercado Diaz <luis.mercado_diaz@uconn.edu>
Date: Thu, 7 Dec 2023 15:27:33 -0500
Subject: [PATCH] error in file names ..._STFT.csv

Nothing important
---
 option.py       | 95 +++++++++++++++++++++++++++++++++++++++++++++++++
 project_NVIL.py |  2 +-
 2 files changed, 96 insertions(+), 1 deletion(-)
 create mode 100644 option.py

diff --git a/option.py b/option.py
new file mode 100644
index 0000000..c1d8352
--- /dev/null
+++ b/option.py
@@ -0,0 +1,95 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Dec  6 22:07:18 2023
+
+@author: lrm22005
+"""
+
+import os
+import pandas as pd
+import numpy as np
+import torch
+from torch.utils.data import Dataset, DataLoader
+from torchvision.transforms import ToTensor
+from sklearn.preprocessing import StandardScaler
+from PIL import Image
+
+class CustomDataset(Dataset):
+    def __init__(self, data_path, labels_path, UIDs, standardize=True, data_format='csv'):
+        self.data_path = data_path
+        self.labels_path = labels_path
+        self.UIDs = UIDs
+        self.standardize = standardize
+        self.data_format = data_format
+        self.transforms = ToTensor()
+
+        # Extract unique segment names and their corresponding labels
+        self.segment_names, self.labels = self.extract_segment_names_and_labels()
+
+    def __len__(self):
+        return len(self.segment_names)
+
+    def __getitem__(self, idx):
+        segment_name = self.segment_names[idx]
+        label = self.labels[segment_name]
+
+        # Load data on-the-fly based on the segment_name
+        time_freq_tensor = self.load_data(segment_name)
+
+        return {'data': time_freq_tensor.unsqueeze(0), 'label': label, 'segment_name': segment_name}
+
+    def extract_segment_names_and_labels(self):
+        segment_names = []
+        labels = {}
+
+        for UID in self.UIDs:
+            label_file = os.path.join(self.labels_path, UID + "_final_attemp_4_1_Dong.csv")
+            if os.path.exists(label_file):
+                label_data = pd.read_csv(label_file, sep=',', header=0, names=['segment', 'label'])
+                label_segment_names = label_data['segment'].apply(lambda x: x.split('.')[0])
+                for idx, segment_name in enumerate(label_segment_names):
+                    if segment_name not in segment_names:
+                        segment_names.append(segment_name)
+                        labels[segment_name] = label_data['label'].values[idx]
+
+        return segment_names, labels
+
+    def load_data(self, segment_name):
+        data_path_UID = os.path.join(self.data_path, segment_name.split('_')[0])
+        seg_path = os.path.join(data_path_UID, segment_name + '_filt.csv')
+
+        try:
+            if self.data_format == 'csv' and seg_path.endswith('.csv'):
+                time_freq_plot = np.array(pd.read_csv(seg_path, header=None))
+                time_freq_tensor = torch.Tensor(time_freq_plot).reshape(1, 128, 128)
+            elif self.data_format == 'png' and seg_path.endswith('.png'):
+                img = Image.open(seg_path)
+                img_data = np.array(img)
+                time_freq_tensor = torch.Tensor(img_data).unsqueeze(0)
+            else:
+                raise ValueError("Unsupported file format")
+
+            if self.standardize:
+                time_freq_tensor = self.standard_scaling(time_freq_tensor)  # Standardize the data
+
+            return time_freq_tensor.clone()
+
+        except Exception as e:
+            print(f"Error processing segment: {segment_name}. Exception: {str(e)}")
+            return torch.zeros((1, 128, 128))  # Return zeros in case of an error
+
+    def standard_scaling(self, data):
+        scaler = StandardScaler()
+        data = scaler.fit_transform(data.reshape(-1, data.shape[-1])).reshape(data.shape)
+        return torch.Tensor(data)
+
+def load_data_split_batched(data_path, labels_path, UIDs, batch_size, standardize=False, data_format='csv'):
+    dataset = CustomDataset(data_path, labels_path, UIDs, standardize, data_format)
+    dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False)
+    return dataloader
+
+# To validate the len of the dataloader
+# number of examples
+# len(train_loader)
+# number of batches
+# len(train_loader)
diff --git a/project_NVIL.py b/project_NVIL.py
index eddc23a..d54789e 100644
--- a/project_NVIL.py
+++ b/project_NVIL.py
@@ -146,7 +146,7 @@ def extract_segment_names_and_labels(self):
 
     def load_data(self, segment_name):
         data_path_UID = os.path.join(self.data_path, segment_name.split('_')[0])
-        seg_path = os.path.join(data_path_UID, segment_name + '_filt.csv')
+        seg_path = os.path.join(data_path_UID, segment_name + '_filt_STFT.csv')
 
         try:
             if self.data_format == 'csv' and seg_path.endswith('.csv'):