# -*- coding: utf-8 -*-

  
  import csv, time, glob, datetime, os
  import matplotlib.pyplot as plt
  import matplotlib.dates as md
  
  #from scipy import signal
  #from scipy.optimize import curve_fit
  
  tic = time.time()

  os.chdir('/home/bma/server/data/2016/2016-03')

  
  def getColumn(filename, column):

  	results = []
  	for dat_file in sorted(glob.glob(filename)):
  		file_result = csv.reader(open(dat_file), delimiter='\t')
  		results = results + map(float,[result[column] for result in file_result])
  	return results

  
  t = getColumn('*-lakeshore.dat', 0)
  T1 = getColumn('*-lakeshore.dat', 2)
  T2 = getColumn('*-lakeshore.dat', 3)
  T3 = getColumn('*-lakeshore.dat', 4)
  T4 = getColumn('*-lakeshore.dat', 5)
  
  """
  n = 400
  t = [t[n*i] for i in range(len(t)/n)]
  T1 = [T1[n*i] for i in range(len(T1)/n)]
  T2 = [T2[n*i] for i in range(len(T2)/n)]
  T3 = [T3[n*i] for i in range(len(T3)/n)]
  T4 = [T4[n*i] for i in range(len(T4)/n)]
  
  def func(U, a0, b1, b2, y0):

  	sys = signal.lti([a0],[b2, b1, 1])
  	y = sys.output(U, t, y0)
  	return y[1]

  
  print('Fitting...')
  popt, cov = curve_fit(func, T1, T4)
  print(popt)
  Yfit = func(T1, *popt)
  """
  timetamps = [datetime.datetime.fromtimestamp(ti) for ti in t]
  datenums=md.date2num(timetamps)
  
  plt.subplots_adjust(bottom=0.35)
  plt.xticks(rotation=90)
  ax=plt.gca()
  xfmt = md.DateFormatter('%Y-%m-%d %H:%M:%S')
  ax.xaxis.set_major_formatter(xfmt)
  
  plt.plot(datenums, T1, label = 'Table')
  plt.plot(datenums, T2, label = 'Link st.')
  plt.plot(datenums, T3, label = 'PT2')
  plt.plot(datenums, T4, label = 'Reg. st.')
  #plt.plot(datenums, Yfit, label = 'Fit')
  
  plt.legend()
  plt.grid()
  plt.show()
  
  toc = time.time() - tic
  print(toc)