new_contents: import pandas as pd
  import glob
  import matplotlib.pyplot as plt
  import numpy as np
  
  # index -> max_loss_scale value
  mapping = {1: 1e5, 2: 2e5, 3: 5e5, 4: 5e5, 5: 5e4,
         6: 1e4, 7: 5e3, 8: 1e3, 9: 5e2, 10:1e2}
  data = {}
  for file in glob.glob('../model_output_4/*_loss_trend.csv'):
  	data[int(file.split('_')[4])] = pd.read_csv(file)
  
  max_loss_scale = []; loss = []
  for i, mls in mapping.items():
  	if i != 4:
  		# plt.loglog(data[i]['epoch'], data[i]['train_scaled_l1_metric'],label=str(mls))
  		# plt.loglog(data[i]['epoch'], data[i]['eval_scaled_l1_metric'],label=str(mls))
  		max_loss_scale.append(mls)
  		loss.append(
  			[data[i]['train_scaled_l1_metric'],
  			 data[i]['eval_scaled_l1_metric'],
  			 data[i]['train_l1_metric'],
  			 data[i]['eval_l1_metric']])
  
  sort_idx = np.argsort(max_loss_scale)
  max_loss_scale = np.array(max_loss_scale)[sort_idx]
  loss = np.array(loss)[sort_idx]
  for i in [0, 2]:
  	plt.loglog(max_loss_scale, loss[:,i, 999])
  
  # plt.legend()
  plt.show()
subject: add analysis folder