import matplotlib.pylab as mp
import pandas as pd
from lmfit import minimize, Parameters, report_fit

#path = "/Users/paulgauthier/Documents/Postdoc/Princeton/Results/Vo_Calculations/For LmFit MichMenten.csv"
# x=np.linspace(-100,300,500)
data = pd.read_csv( path )
y = data[0.037849903,0.033348401,0.025744915,0.018811544,0.015011589,0.011411627,0.008685033	-0.000378251,0]
x = data[1,0.5,0.25,0.125,0.0625,0.03125,0.015625,0.0078125,0]


def res(params, x, y):
    Vmax = params['Vmax'].value
    Km = params['Km'].value
    model = Vmax * x / (Km + x)
    return model - y


params = Parameters( )
params.add( 'Vmax', value=15 )
params.add( 'Km', value=200 )

result = minimize( res, params, args=(x, y) )
final = y + result.residual
report_fit( params )

Vmax = params['Vmax'].value
Km = params['Km'].value
data['fitted'] = Vmax * x / (Km + x)
data.to_csv( path )
fitted = data['fitted']

mp.plot( x, y, 'k', label='Raw' )
mp.plot( x, final, 'r', label='final' )
mp.plot( x, fitted, 'r', label='fitted' )
mp.legend( loc=4 )
mp.xlabel( 'Light intensity', fontsize=16 )
mp.ylabel( 'Net Oxygen Prodcution (umol m-2 s-1)', fontsize=16 )