text: # Imports
  
  import numpy as np
  import tensorflow as tf
  
  def load_params(file_list,Np,M):
  	data_file = 'data_tmp/params_'+str(file_list[0])+'.dat'
  	load_data = np.genfromtxt(data_file, delimiter='  ', defaultfmt='%e')
  
  	gamma_down = load_data[0]
  	gamma_up = load_data[1]
  	omega = load_data[2]
  
  
  	gamma_downs = np.linspace(1,6,num=Np)
  	gamma_ups = np.linspace(1,6,num=Np)
  	omegas = np.linspace(4,10,num=Np)
  
  	gamma_down_index = np.argmin(abs(gamma_down-gamma_downs))
  	gamma_downs_onehot = np.full(Np,0)
  	gamma_downs_onehot[gamma_down_index] = 1
  
  	gamma_up_index = np.argmin(abs(gamma_up-gamma_ups))
  	gamma_ups_onehot = np.full(Np,0)
  	gamma_ups_onehot[gamma_up_index] = 1
  
  	omega_index = np.argmin(abs(omega-omegas))
  	omegas_onehot = np.full(Np,0)
  	omegas_onehot[omega_index] = 1
  
  	full_data=np.array([[gamma_downs_onehot,gamma_ups_onehot, omegas_onehot]])
  
  	for i in range(2,M):
  		data_string = 'data_tmp/params_'+str(file_list[i])+'.dat'
  		load_data = np.genfromtxt(data_string, delimiter='  ', defaultfmt='%e')
  
  		gamma_down = load_data[0]
  		gamma_up = load_data[1]
  		omega = load_data[2]
  
  
  		gamma_downs = np.linspace(1,6,num=Np)
  		gamma_ups = np.linspace(1,6,num=Np)
  		omegas = np.linspace(4,10,num=Np)
  
  		gamma_down_index = np.argmin(abs(gamma_down-gamma_downs))
  		gamma_downs_onehot = np.full(Np,0)
  		gamma_downs_onehot[gamma_down_index] = 1
  
  		gamma_up_index = np.argmin(abs(gamma_up-gamma_ups))
  		gamma_ups_onehot = np.full(Np,0)
  		gamma_ups_onehot[gamma_up_index] = 1
  
  		omega_index = np.argmin(abs(omega-omegas))
  		omegas_onehot = np.full(Np,0)
  		omegas_onehot[omega_index] = 1
  	
  		onehot_data = np.array([gamma_downs_onehot,gamma_ups_onehot, omegas_onehot])
  
  		full_data = np.append(full_data, [onehot_data], axis=0)
  
  	#full_data = np.transpose(full_data,axes=(0,2,1))
  
  	return np.float32(full_data)
  