from netCDF4 import Dataset
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns

import xarray as xr
from Process_Spectra import process_spectra

#fn = '/home/s/Sukanya.Patra/TEST/2023/08/07/20230807_010005.znc'
fn='/project/meteo/data/miraMACS/mom/2023/08/30/20230830_0500.znc'
#fn='20230823_0400.znc'


dataset = xr.open_dataset(fn)
data = process_spectra(dataset,'o')
data.time.attrs['units'] = 'seconds since {0}'.format('1970-01-01 00:00:00 UTC')
data = xr.decode_cf(data)

plt.figure(1)
plt.plot(data.doppler.values,10*np.log10(data.SPCco_cal[1,ind2f[0][0],:]).values.transpose())

for i in range(1,len(data.range),50):
	threshold=10*np.log10(np.nanmax(np.nanmax(data.SPCco_cal[1,ind2f[0][i],:].values)))
	final_val=10*np.log10(data.SPCco_cal[1,ind2f[0][i],:]).values+threshold
	plt.plot(data.doppler.values,final_val.transpose())
	plt.legend(data.range[i].values/1000)





threshold=10*np.log10(np.nanmax(np.nanmax(data.SPCco_cal[1,0,:].values)))
final_val=10*np.log10(data.SPCco_cal[1,1,:]).values+threshold

a=[]

a.append(final_val)

for i in range(1,100):
          threshold=np.nanmax(np.nanmax(final_val))
          final_val=10*np.log10(data.SPCco_cal[1,i,:]).values+threshold+10
          a.append(final_val)
          
     
          
spectra_F=np.array(a)

plt.figure(1)
plt.plot(data.doppler.values,10*np.log10(data.SPCco_cal[1,0,:]).values.transpose())
plt.plot(data.doppler.values,spectra_F.transpose())
plt.legend(data.range[0:10].values/1000)  
plt.xlim(-10,10)  
plt.xlabel('Doppler',fontsize=18,fontweight='bold')
plt.ylabel('Ze',fontsize=18,fontweight='bold')