#!/usr/bin/env python2

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

"""

Created on Sat Jul 7 13:21:25 2018

@author: wangxufeng

"""

#from netCDF4 import Dataset as NCfile

import matplotlib.pyplot as plt

import numpy as np

from glob import glob

import os

import pandas as pd

import savitzky_golay

from scipy.interpolate import interp1d

import phenology_estimate as Phen_Est

import Geotiff_read_write as geotif_RW

############### set the path for GIMMS3g v0 geotiff format ##########################

inpath = '/home/wangxufeng/wxf1/GIMMS3g_V0_phen/NDVI_GIMMS_3g_1982_2013/AVHRR_3g_tiff'

os.chdir(inpath)

start_row = 000

end_row = 1080

start_year = 1982

end_year = 2013

for yr in range(start_year,end_year+1):

for m in range(1,13):

infile_a = "%s/GIMMS3g_NDVI_%d_%02d_1.tif" %(inpath, yr,m)

infile_b = "%s/GIMMS3g_NDVI_%d_%02d_2.tif" %(inpath, yr,m)

print(infile_a)

NDVI_a,Ysize, Xsize = geotif_RW.ReadGeoTiff(infile_a)

NDVI_b,Ysize, Xsize = geotif_RW.ReadGeoTiff(infile_b)

NDVI_a = NDVI_a[start_row:end_row,:]

NDVI_b = NDVI_b[start_row:end_row,:]

rows,cols = np.shape(NDVI_a)

NDVI_a = np.reshape(NDVI_a,[1,rows,cols])

NDVI_b = np.reshape(NDVI_b,[1,rows,cols])

NDVI_yr = np.vstack((NDVI_a,NDVI_b))

if((yr == 1982) and (m==1)):

NDVI_all = NDVI_yr

else:

NDVI_all = np.vstack((NDVI_all,NDVI_yr))

SOS_dbl_2rd=[]

EOS_dbl_2rd=[]

SOS_Gmax20 = []

EOS_Gmax20 = []

SOS_Gmax50 = []

EOS_Gmax50 = []

SOS_poly = []

EOS_poly = []

SOS_dbl_1st = []

EOS_dbl_1st = []

VI_SOS_dbl_2rd=[]

VI_EOS_dbl_2rd=[]

VI_SOS_Gmax20 = []

VI_EOS_Gmax20 = []

VI_SOS_Gmax50 = []

VI_EOS_Gmax50 = []

VI_SOS_poly = []

VI_EOS_poly = []

VI_SOS_dbl_1st = []

VI_EOS_dbl_1st = []

para_phen = []

ndata_year = 24 # records number for a year

nyears = 34 # years of the data

# start_row,end_row

for r in range(end_row-start_row):

print r+start_row

for c in range(4320):

multi_year_vi = np.asarray(NDVI_all[:,r,c])/10000.0

# exclude non vegetated area

ind = multi_year_vi<0

### no NDVI pixels ##########################

if(len(multi_year_vi[ind])>(len(multi_year_vi)/2)):

for yr in range(start_year,end_year+1):

SOS_dbl_2rd.append(-9999)

EOS_dbl_2rd.append(-9999)

SOS_Gmax20.append(-9999)

EOS_Gmax20.append(-9999)

SOS_Gmax50.append(-9999)

EOS_Gmax50.append(-9999)

SOS_poly.append(-9999)

EOS_poly.append(-9999)

SOS_dbl_1st.append(-9999)

EOS_dbl_1st.append(-9999)

continue

### replace negative NDVIS

multi_year_vi[ind] = np.min(multi_year_vi[~ind])

multi_year_vi_sg=savitzky_golay.savitzky_golay(multi_year_vi,15,4)

# multiyear average NDVI and gs NDVI/ nongs NDVI

avg_multi = np.nanmean(np.reshape(multi_year_vi_sg,[len(multi_year_vi_sg)/24,24]),axis=0)

avg_vi = np.nanmean(avg_multi)

gs_avg_vi = np.nanmean(avg_multi[6:18])

nongs_avg_vi = (np.nanmean(avg_multi[0:6])+np.nanmean(avg_multi[18:24]))/2

if((avg_vi>0.1) and (gs_avg_vi > (nongs_avg_vi + 0.1))):

if(~np.isnan(np.sum(multi_year_vi_sg))):

[onset_ndvi,dormacy_ndvi] = Phen_Est.get_onset_dormancy_ndvi(multi_year_vi_sg,ndata_year)

else:

onset_ndvi = np.nan

dormacy_ndvi = np.nan

multi_year_vi_sg = np.squeeze(multi_year_vi_sg)

'''

plt.plot(multi_year_vi_sg)

plt.show()

'''

doy = np.arange(8,365,15)

print r+start_row,onset_ndvi,dormacy_ndvi

for yr in range(start_year,end_year+1):

t_axis=np.asarray(doy)

start_ind=(yr-start_year)*ndata_year

end_ind=(yr-start_year+1)*ndata_year

ndvi_1year=multi_year_vi_sg[start_ind:end_ind]

avg_vi_1year = np.nanmean(ndvi_1year)

gs_avg_vi_1year = np.nanmean(ndvi_1year[6:18])

nongs_avg_vi_1year = (np.nanmean(ndvi_1year[0:6])+np.nanmean(ndvi_1year[18:24]))/2

if((avg_vi_1year>0.1) and (gs_avg_vi_1year>nongs_avg_vi_1year+0.1) and ~np.isnan(dormacy_ndvi) and ~np.isnan(onset_ndvi)):

t_axis= t_axis[~np.isnan(ndvi_1year)]

ndvi_1year = ndvi_1year[~np.isnan(ndvi_1year)]

# using polynomial function to fit

[sos_poly, eos_poly,spr_days,spr_ndvi,fall_days,fall_ndvi] = \

Phen_Est.fit_phenology_model_poly (ndvi_1year, t_axis,onset_ndvi,dormacy_ndvi)

SOS_poly.append(sos_poly)

EOS_poly.append(eos_poly)

# using logistic function to fit

xinit=None

pheno_model="dbl_logistic"

#### raw NDVI data

[para, msg, result]=Phen_Est.fit_phenology_model_double_logistic( ndvi_1year, t_axis, pheno_model, xinit )

para_phen.append(para[3])

para_phen.append(para[5])

doys = range(1,len(result)+1)

[s_sos, s_eos] = Phen_Est.get_phen_date_model_double_logistic(result,doys)

SOS_dbl_2rd.append(s_sos)

EOS_dbl_2rd.append(s_eos)

#### Gmax20 to get SOS and EOS ########################

ndvi_norm = Phen_Est.VI_normalize_yearly(result)

SOS20,EOS20 = Phen_Est.Gmax_SOS_EOS(ndvi_norm,0.2)

SOS_Gmax20.append(SOS20)

EOS_Gmax20.append(EOS20)

#### Gmax50 to get SOS and EOS ########################

SOS50,EOS50 = Phen_Est.Gmax_SOS_EOS(ndvi_norm,0.5)

SOS_Gmax50.append(SOS50)

EOS_Gmax50.append(EOS50)

#### first derive SOS and EOS ########################

[s_sos_slog, s_eos_slog] = Phen_Est.get_phen_date_model_double_logistic_first_derive(result,doys)

SOS_dbl_1st.append(s_sos_slog)

EOS_dbl_1st.append(s_eos_slog)

#### using Dbl_log Zhang to get SOS and EOS ####################

'''

plt.plot(doys,result)

plt.plot([s_sos,s_sos],[0,1],'r-',label='dbl_log')

plt.plot([s_eos,s_eos],[0,1],'r-',label='dbl_log')

plt.plot([SOS20,SOS20],[0,1],'g-',label='p20')

plt.plot([EOS20,EOS20],[0,1],'g-',label='p20')

plt.plot([SOS50,SOS50],[0,1],'y-',label='p50')

plt.plot([EOS50,EOS50],[0,1],'y-',label='p50')

plt.plot([sos_poly,sos_poly],[0,1],'k-',label='poly')

plt.plot([eos_poly,eos_poly],[0,1],'k-',label='poly')

plt.show()

'''

else:

SOS_dbl_2rd.append(-9999)

EOS_dbl_2rd.append(-9999)

SOS_Gmax20.append(-9999)

EOS_Gmax20.append(-9999)

SOS_Gmax50.append(-9999)

EOS_Gmax50.append(-9999)

SOS_poly.append(-9999)

EOS_poly.append(-9999)

SOS_dbl_1st.append(-9999)

EOS_dbl_1st.append(-9999)

else:

for yr in range(start_year,end_year+1):

SOS_dbl_2rd.append(-9999)

EOS_dbl_2rd.append(-9999)

SOS_Gmax20.append(-9999)

EOS_Gmax20.append(-9999)

SOS_Gmax50.append(-9999)

EOS_Gmax50.append(-9999)

SOS_poly.append(-9999)

EOS_poly.append(-9999)

SOS_dbl_1st.append(-9999)

EOS_dbl_1st.append(-9999)

cols = (end_row - start_row)*4320

### output results ###################################

SOS_dbl_2rd = np.reshape(SOS_dbl_2rd,[cols,32])

EOS_dbl_2rd = np.reshape(EOS_dbl_2rd,[cols,32])

SOS_Gmax20 = np.reshape(SOS_Gmax20,[cols,32])

EOS_Gmax20 = np.reshape(EOS_Gmax20,[cols,32])

SOS_Gmax50 = np.reshape(SOS_Gmax50,[cols,32])

EOS_Gmax50 = np.reshape(EOS_Gmax50,[cols,32])

SOS_poly = np.reshape(SOS_poly,[cols,32])

EOS_poly = np.reshape(EOS_poly,[cols,32])

SOS_dbl_1st = np.reshape(SOS_dbl_1st,[cols,32])

EOS_dbl_1st = np.reshape(EOS_dbl_1st,[cols,32])

######## set the path for output the remote sensing phenology results #########

outpath = '/home/wangxufeng/wxf1/GIMMS3g_V0_phen/NDVI_GIMMS_3g_1982_2013/phen_v0_out/r%03d_%03d_v0' %(start_row,end_row-1)

Ysize = end_row-start_row

Xsize = 4320

vi='NDVI'

for n in range(end_year-start_year+1):

# output dbl_2rd result

outdata = SOS_dbl_2rd[:,n]

outdata = np.reshape(outdata,[Ysize,Xsize])

year = start_year+n

outfile = '%s/GIMMS_%d_%s_SOS_dbl_2rd_sr_%d.txt' %(outpath,year,vi,start_row)

NoData_value = -9999

np.savetxt(outfile,outdata, delimiter = ',',fmt='%7d')

outdata = EOS_dbl_2rd[:,n]

outdata = np.reshape(outdata,[Ysize,Xsize])

year = start_year+n

outfile = '%s/GIMMS_%d_%s_EOS_dbl_2rd_sr_%d.txt' %(outpath,year,vi,start_row)

NoData_value = -9999

np.savetxt(outfile,outdata, delimiter = ',',fmt='%7d')

# output GMAX20 result

outdata = SOS_Gmax20[:,n]

outdata = np.reshape(outdata,[Ysize,Xsize])

year = start_year+n

outfile = '%s/GIMMS_%d_%s_SOS_Gmax20_sr_%d.txt' %(outpath,year,vi,start_row)

NoData_value = -9999

np.savetxt(outfile,outdata, delimiter = ',',fmt='%7d')

outdata = EOS_Gmax20[:,n]

outdata = np.reshape(outdata,[Ysize,Xsize])

year = start_year+n

outfile = '%s/GIMMS_%d_%s_EOS_Gmax20_sr_%d.txt' %(outpath,year,vi,start_row)

NoData_value = -9999

np.savetxt(outfile,outdata, delimiter = ',',fmt='%7d')

# output GMAX50 result

outdata = SOS_Gmax50[:,n]

outdata = np.reshape(outdata,[Ysize,Xsize])

year = start_year+n

outfile = '%s/GIMMS_%d_%s_SOS_Gmax50_sr_%d.txt' %(outpath,year,vi,start_row)

NoData_value = -9999

np.savetxt(outfile,outdata, delimiter = ',',fmt='%7d')

outdata = EOS_Gmax50[:,n]

outdata = np.reshape(outdata,[Ysize,Xsize])

year = start_year+n

outfile = '%s/GIMMS_%d_%s_EOS_Gmax50_sr_%d.txt' %(outpath,year,vi,start_row)

NoData_value = -9999

np.savetxt(outfile,outdata, delimiter = ',',fmt='%7d')

# output poly result

outdata = SOS_poly[:,n]

outdata = np.reshape(outdata,[Ysize,Xsize])

year = start_year+n

outfile = '%s/GIMMS_%d_%s_SOS_poly_sr_%d.txt' %(outpath,year,vi,start_row)

NoData_value = -9999

np.savetxt(outfile,outdata, delimiter = ',',fmt='%7d')

outdata = EOS_poly[:,n]

outdata = np.reshape(outdata,[Ysize,Xsize])

year = start_year+n

outfile = '%s/GIMMS_%d_%s_EOS_poly_sr_%d.txt' %(outpath,year,vi,start_row)

NoData_value = -9999

np.savetxt(outfile,outdata, delimiter = ',',fmt='%7d')

# output dbl_1st result

outdata = SOS_dbl_1st[:,n]

outdata = np.reshape(outdata,[Ysize,Xsize])

year = start_year+n

outfile = '%s/GIMMS_%d_%s_SOS_dbl_1st_sr_%d.txt' %(outpath,year,vi,start_row)

NoData_value = -9999

np.savetxt(outfile,outdata, delimiter = ',',fmt='%7d')

outdata = EOS_dbl_1st[:,n]

outdata = np.reshape(outdata,[Ysize,Xsize])

year = start_year+n

outfile = '%s/GIMMS_%d_%s_EOS_dbl_1st_sr_%d.txt' %(outpath,year,vi,start_row)

NoData_value = -9999

np.savetxt(outfile,outdata, delimiter = ',',fmt='%7d')