# -*- coding: utf-8 -*-
import plotly.plotly as py
import plotly.graph_objs as go
import pandas as pd
import matplotlib.pyplot as plt

#headers = ['Pixel Number','Wavelength (nm)','Sum Spectrum','Averaged Spectrum','Background Spectrum']
headers = ['Pixel Number','Wavelength (nm)','Sum Spectrum','Averaged Spectrum']
df = pd.read_csv('agua.CSV',names=headers, index_col='Pixel Number', header = 0)
#bg = pd.read_csv('backgrounsic8s3avg.CSV',names=headers, index_col='Pixel Number', header = 0)



#df['Wavelength (nm)'] = df['Wavelength (nm)'].map(lambda x: datetime.strptime(str(x), '%Y/%m/%d %H:%M:%S.%f'))
x = df['Wavelength (nm)']
#w = bg['Wavelength (nm)']
#t = bg['Averaged Spectrum']

r = df['Averaged Spectrum'] 

resto=0

O = r.astype(float)

contador=0
parameter = 5400
while contador < len(O):
    
    if (O[contador])<parameter: #Homogenizador de datos
        
       resto = parameter - O[contador] 
       
       O[contador] = O[contador]-resto
       
       if(O[contador]<0):
           
           O[contador]=0
       
      
       
      
       
    contador += 1

z=((1/533)-(1/x))*1e7

trace0 = go.Scatter(
   x = z,
   y = (O )/290000,
 #  y = df['Averaged Spectrum'],
   mode ='lines+markers',
   marker = dict(
        size = 10,
        color = 'rgba(155, 182, 193, .9)',
        line = dict(
            width = 2,
        )
    )
   
   
)

data=[trace0]
# plot

layout = dict(title = 'Raman Spectrum',
              yaxis = dict(zeroline = False, range = (0,1), title = 'Intensity'),
              xaxis = dict(zeroline = False, range = (1000,2700), title = 'Wavenumber (cm^(-1))'),
              
             )

fig = dict(data=data, layout=layout)



py.plot(fig, filename = 'agua', auto_open=True)
#plt.plot(x,y)
# beautify the x-labels
#plt.gcf().autofmt_xdate()
p=O/1000
datos = pd.DataFrame({'wavenumber':z,'averaged_spectrum':p})
writer = pd.ExcelWriter('palmitico.xlsx',engine='xlsxwriter')
datos.to_excel(writer,sheet_name='Sheet1')


#plt.show()