# coding: utf-8

from lab import *
import pickle
import numpy as np
import matplotlib.pyplot as plt


def plot((name, points), n):
  plt.figure(n+1)

  if name == 'azoto':
    plt.ylim(-5,-5.5)
  elif name == 'acqua':
    plt.ylim(4, 4.5)
  elif name =='stearico':
    name = 'acido stearico'
  elif name == 'glicole':
    plt.ylim(-0.7,-0.4)

  plt.title(name.capitalize())
  plt.xlabel('tempo')
  plt.ylabel('differenza di potenziale (mV)')
  plt.plot(filter(lambda x: x!=0, points), '#44b34e')
  plt.show()

  
set = pickle.load(open('termocoppia.pickle'))
for i, curve in enumerate(set.iteritems()):
  #plot(curve, i)
  pass

V = map(np.mean,
    [ set['stagno'][150:250]  # Tf
    , set['indio'][125:250]   # Tf
    , set['acqua'][150:225]   # Te
    , set['stearico'][80:120] # Tf
    , [0]                     # miscela acqua-ghiaccio
    , set['glicole'][130:170] # Tf
    , set['etere'][50:150]    # Tf
    , set['azoto']            # Te
    ])

T = [ 505.08 # Tf Sn
    , 429.75 # Tf In
    , 373.15 # Te H2O
    , 342.40 # Tf CH3-(CH2)16-COOH
    , 273.15 # Tf H2O
    , 260.20 # Tf HO-(CH2)2-OH
    , 156.80 # Tf CH3CH2-O-CH2CH3
    , 77.36  # Te N2
    ]

x = np.linspace(-6, 6, 100)
a,b,c,d,e = polynomial(V, T, 4)
f = lambda x: a.mean*x**4 + b.mean*x**3 + c.mean*x**2 + d.mean*x + e.mean
g = lambda n,x: sum(a*x**i for i, a in enumerate(polyfit(V,T,n)))
# q(np.sum((np.polyval(np.polyfit(V,T,7), V) - T) ** 2), 1)*100

plt.figure(8)
plt.xlabel('Differenza di potenziale (mV)')
plt.ylabel('Temperatura (K)')
plt.scatter(V, T, color='#6972cc')
plt.plot(x, g(5,x), '#6972cc')
plt.show()
print '''
  T(V)={a.n:.3f}V^4+{b.n:.3f}V^3{c.n:.3f}V^2+{d.n:.3f}V+{e.n:.3f}
'''.format(**dict(a=a,b=b,c=c,d=d,e=e))