--- a
+++ b/ecgtoHR/create_dataset.py
@@ -0,0 +1,53 @@
+import os
+import glob
+import wfdb as wf
+import matplotlib.pyplot as plt
+from tqdm import tqdm
+import numpy as np
+
+from scipy.signal import resample
+from sklearn.preprocessing import StandardScaler
+from sklearn.model_selection import train_test_split
+
+import torch
+
+from utils import dist_transform,getWindow
+
+def data_preprocess(args):
+    
+    PATH = args.data_path
+    all_paths = sorted(glob.glob(PATH + '/*.dat'))
+    all_paths = [paths[:-4] for paths in all_paths]
+
+    windowed_data,windowed_beats = getWindow(all_paths)
+
+    scaler = StandardScaler()
+    mod_windowed_data = []
+    dist_tran_data = []
+
+    for window in range(len(windowed_data)):
+        
+        beats = ((windowed_beats[window] * 500/360).astype(int))
+        if(len(beats) != 0):
+            mod_windowed_data.append(scaler.fit_transform(resample(windowed_data[window],5000).reshape(-1,1)))
+            dist_tran_data.append(dist_transform(5000,beats))
+
+    X_train,X_test,y_train,y_test = train_test_split(np.array(mod_windowed_data)[:,:,0],np.array(dist_tran_data)[:,:,0],test_size = 0.1, random_state = 42)
+
+    X_train_toTensor = torch.Tensor(X_train).type(torch.float)
+    print(X_train_toTensor.size())
+    X_test_toTensor = torch.Tensor(X_test).type(torch.float)
+    print(X_test_toTensor.size())
+    y_train_toTensor = torch.Tensor(y_train).type(torch.float)
+    print(y_train_toTensor.size())
+    y_test_toTensor = torch.Tensor(y_test).type(torch.float)
+    print(y_test_toTensor.size())
+
+    if not(os.path.exists('data')):
+        os.mkdir('data')
+
+    torch.save(X_train_toTensor, "data/ecgtoHR_train_data.pt")
+    torch.save(y_train_toTensor, "data/ecgtoHR_train_labels.pt")
+    torch.save(X_test_toTensor, "data/ecgtoHR_test_data.pt")
+    torch.save(y_test_toTensor, "data/ecgtoHR_test_labels.pt")
+