"""
 Used by Xiao-Ming Hu (xhu@ou.edu) to plot MODIS cloud aerosol around the world
 Apr. 17, 2020
"""
from oco2_modis_vistool import do_modis_overlay_plot
from oco2_modis_vistool import load_OCO2_Lite_overlay_data 
import numpy as np
from datetime import timedelta, date


#odat = np.loadtxt('OCO-2_9_LITE_LEVEL2_extracted_2016only.nc_20sMean_fromStempede2.txt', skiprows=1)
odat = np.loadtxt('OCO-2_9_LITE_LEVEL2_extracted_2019only.nc_2sMean.txt', skiprows=1)
#odat = np.loadtxt('OCO-2_9_LITE_LEVEL2_extracted_2019only.nc_20sMean.txt', skiprows=1)
print(odat.shape)
Date = odat[:,0]/1000000
Date = Date.astype(int)
print(Date.shape)

def daterange(start_date, end_date):
    for n in range(int ((end_date - start_date).days)):
        yield start_date + timedelta(n)

#start_date = date(2019, 1, 1)
#i_infile = 0
#end_date = date(2019, 12, 31)
start_date = date(2019, 6, 1)
i_infile = 0
end_date = date(2019, 7, 11)
#start_date = date(2019, 12, 1)
#i_infile = 335 
#end_date = date(2019, 1, 3) # test
for single_date in daterange(start_date, end_date):
    date_to_findstr = single_date.strftime("%Y%m%d")
    date_to_find = int(date_to_findstr) #.astype(int)
    print(type(date_to_find))
    index_select = np.argwhere(Date==date_to_find)
    print("going to find ",date_to_find)
    print(index_select.shape)
#    print(index_select)
    lat_data = odat[index_select,2]
    lon_data = odat[index_select,3]
    variable_data = odat[index_select,1]
    figurename = "wrfout_d01_MODIScloud_xhu_%d" % (i_infile)
#    figurename = "wrfout_d01_MODIScloud_xhu_sub_%d" % (i_infile)
    print(figurename)
#    do_modis_overlay_plot([55, -138],    # CONUS
#                          [24, -59.05], 
#    do_modis_overlay_plot([55, -127.],    # CONUS
#                          [24, -67.05], 
    do_modis_overlay_plot([20, -89.],    # SouthAmerica Peru` 
                          [-55, -25.05], 
#    do_modis_overlay_plot([55, 75],  # China
#                          [17, 135], 
#    do_modis_overlay_plot([44, 110],  #Eastern China
#                          [27, 128], 
	         	  single_date.strftime("%Y-%m-%d"), lat_data, lon_data, variable_data,
                          out_plot=figurename,var_label="$XCO_2$\n ppm")
    i_infile = i_infile + 1