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

"""

Created on Fri Oct 26 23:48:57 2012

@author: benoitparmentier

"""

#!/usr/bin/python

#

# Early version of assorted Python code to download MODIS 11A1 (daily)

# data associated with specified dates and tiles, then interface with

# GRASS to calculate temporally averaged LST in a way that accounts for

# QC flags.

#

# TODO:

#  - functionalize to encapsulate high level procedural steps

#  - create a 'main' function to allow script to be run as a command

#    that can accept arguments?

#  - put downloaded data somewhere other than working directory?

#  - write code to set up and take down a temporary GRASS location/mapset

#  - write code to export climatology rasters to file (geotiff? compressed?)

#  - calculate other aggregate stats? (stddev, ...)

#  - deal with nightly LST too?

#  - deal with more than just first two bits of QC flags?

#     e.g.: r.mapcalc 'qc_level2 = (qc>>2) & 0x03'   

#     0x03?? that means 0000_0011 and >>2 means shit twice on the right for 

#  - record all downloads to a log file?

#

# Jim Regetz

# NCEAS

# Created on 16-May-2012

# Modified by Benoit Parmentier

# NCEAS on 18-October-2012

# 

import os, glob

import datetime, calendar

import ftplib

import grass.script as gs

#from datetime import date

#from datetime import datetime

#------------------

# helper functions

#------------------

def yj_to_ymd(year, doy):

    """Return date as e.g. '2000.03.05' based on specified year and

    numeric day-of-year (doy) """

    date = datetime.datetime.strptime('%d%03d' % (year, doy), '%Y%j')

    return date.strftime('%Y.%m.%d')

def get_doy_range(year, month):

    """Determine starting and ending numeric day-of-year (doy)

    asscociated with the specified month and year.

    Arguments:

    year -- four-digit integer year

    month -- integer month (1-12)

    Returns tuple of start and end doy for that month/year.

    """

    last_day_of_month = calendar.monthrange(year, month)[1]

    start_doy = int(datetime.datetime.strptime('%d.%02d.%02d' % (year,

        month, 1), '%Y.%m.%d').strftime('%j'))

    end_doy = int(datetime.datetime.strptime('%d.%02d.%02d' % (year,

        month, last_day_of_month), '%Y.%m.%d').strftime('%j'))

    return (int(start_doy), int(end_doy))

#added by Benoit, to create a list of raster images for particular month (2001-2010)

def date_sequence(year_start,month_start,day_start,year_end,month_end,day_end):

    """Create a date sequence for a start date and end date defined by year, month, date

    Arguments:

    year_start -- starting year

    Returns list of absolute paths to the downloaded HDF files.Note the format yyyydoy

    """

    from datetime import date

    from dateutil.rrule import rrule, DAILY

    a = date(year_start, month_start,day_start)

    b = date(year_end, month_end, day_end)

    date_seq=list()

    for dt in rrule(DAILY, dtstart=a, until=b):

        print dt.strftime("%Y%j")    

        date_seq.append(dt.strftime("%Y%j"))

    return(date_seq)

#Added on January 16, 2012 by Benoit NCEAS

def list_raster_per_month(listmaps):

    """Determine starting and ending numeric day-of-year (doy)

    asscociated with the specified month and year.

    Arguments:

    maplist -- list of raster grass-digit integer year

    month -- integer month (1-12)

    year_range -- list of years

    Returns tuple of start and end doy for that month/year.

    """

    list_maps_month=list()

    nb_month=12

    for i in range(1,nb_month+1):

        #i=i+1

        #filename = listfiles_wd2[i] #list the files of interest

        #monthlist[:]=[] #empty the list

        monthlist=list()

        #monthlist2[:]=[] #empty the list

        j=0  #counter

        for mapname in listmaps:

            #tmp_str=LST[0]

            date_map=mapname.split('_')[1][1:8]

            #date = filename[filename.rfind('_MOD')-8:filename.rfind('_MOD')]

            d=datetime.datetime.strptime(date_map,'%Y%j') #This produces a date object from a string extracted from the file name.

            month=d.strftime('%m') #Extract the month of the current map

            if int(month)==i:

                #monthlist.append(listmaps[j])

                monthlist.append(mapname)

                #monthlist2.append(listmaps[j])

            j=j+1

        list_maps_month.append(monthlist)   #This is a list of a list containing a list for each month

        #list_files2.append(monthlist2)

    return(list_maps_month)    

# quick function to return list of dirs in wd

def list_contents(ftp):

    """Parse ftp directory listing into list of names of the files

    and/or directories contained therein. May or may not be robust in

    general, but seems to work fine for LP DAAP ftp server."""

    listing = []

    ftp.dir(listing.append)

    contents = [item.split()[-1] for item in listing[1:]]

    return contents

def download_mod11a1(destdir, tile, start_doy, end_doy, year, ver=5):

    """Download into destination directory the MODIS 11A1 HDF files

    matching a given tile, year, and day range. If for some unexpected

    reason there are multiple matches for a given day, only the first is

    used. If no matches, the day is skipped with a warning message.

    Arguments:

    destdir -- path to local destination directory

    tile -- tile identifier (e.g., 'h08v04')

    start_doy -- integer start of day range (0-366)

    end_doy -- integer end of day range (0-366)

    year -- integer year (>=2000)

    ver -- MODIS version (4 or 5)

    Returns list of absolute paths to the downloaded HDF files.

    """

    # connect to data pool and change to the right directory

    ftp = ftplib.FTP('e4ftl01.cr.usgs.gov')

    ftp.login('anonymous', '')

    ftp.cwd('MOLT/MOD11A1.%03d' % ver)

    # make list of daily directories to traverse

    available_days = list_contents(ftp)

    desired_days = [yj_to_ymd(year, x) for x in range(start_doy, end_doy+1)]

    days_to_get = filter(lambda day: day in desired_days, available_days)

    if len(days_to_get) < len(desired_days):

        missing_days = [day for day in desired_days if day not in days_to_get]

        print 'skipping %d day(s) not found on server:' % len(missing_days)

        print '\n'.join(missing_days)

    # get each tile in turn

    hdfs = list()

    for day in days_to_get:

        ftp.cwd(day)

        files_to_get = [file for file in list_contents(ftp)

            if tile in file and file[-3:]=='hdf']

        if len(files_to_get)==0:

            # no file found -- print message and move on

            print 'no hdf found on server for tile', tile, 'on', day

            ftp.cwd('..') #added by Benoit

            continue

        elif 1<len(files_to_get):

            # multiple files found! -- just use the first...

            print 'multiple hdfs found on server for tile', tile, 'on', day

        file = files_to_get[0]

        local_file = os.path.join(destdir, file)

        ftp.retrbinary('RETR %s' % file, open(local_file, 'wb').write)

        hdfs.append(os.path.abspath(local_file))

        ftp.cwd('..')

    # politely disconnect

    ftp.quit()

    # return list of downloaded paths

    return hdfs

def get_hdf_paths(hdfdir, tile, start_doy, end_doy, year):

    """Look in supplied directory to find the MODIS 11A1 HDF files

    matching a given tile, year, and day range. If for some unexpected

    reason there are multiple matches for a given day, only the first is

    used. If no matches, the day is skipped with a warning message.

    Arguments:

    hdfdir -- path to directory containing the desired HDFs

    tile -- tile identifier (e.g., 'h08v04')

    start_doy -- integer start of day range (0-366)

    end_doy -- integer end of day range (0-366)

    year -- integer year (>=2000)

    Returns list of absolute paths to the located HDF files.

    """

    hdfs = list()

    for doy in range(start_doy, end_doy+1):

        fileglob = 'MOD11A1.A%d%03d.%s*hdf' % (year, doy, tile)

        pathglob = os.path.join(hdfdir, fileglob)

        files = glob.glob(pathglob)

        if len(files)==0:

            # no file found -- print message and move on

            print 'cannot access %s: no such file' % pathglob

            continue

        elif 1<len(files):

            # multiple files found! -- just use the first...

            print 'multiple hdfs found for tile', tile, 'on', day

        hdfs.append(os.path.abspath(files[0]))

    return hdfs

def calc_clim(maplist, name, overwrite=False):

    """Generate some climatalogies in GRASS based on the input list of

    maps. As usual, current GRASS region settings apply. Produces the

    following output rasters:

      * nobs: count of number of (non-null) values over the input maps

      * mean: arithmetic mean of (non-null) values over the input maps

    Arguments:

    maplist -- list of names of GRASS maps to be aggregated

    name -- template (suffix) for GRASS output map names

    Returns list of names of the output maps created in GRASS.

    """

    denominator = '(%s)' % '+'.join(['if(!isnull(%s))' % m

        for m in maplist])

    gs.mapcalc('nobs_%s = %s' % (name, denominator), overwrite=overwrite)

    numerator = '(%s)' % '+'.join(['if(isnull(%s), 0, %s)' % (m, m)

        for m in maplist])

    gs.mapcalc('mean_%s = round(float(%s)/nobs_%s)' % (name, numerator, name),

        overwrite=overwrite)

    return ['%s_%s' % (s, name) for s in ['nobs', 'mean']]

def load_qc_adjusted_lst(hdf):

    """Load LST_Day_1km into GRASS from the specified hdf file, and

    nullify any cells for which QA flags indicate anything other than

    high quality.

    Argument:

    hdf -- local path to the 11A1 HDF file

    Returns the name of the QC-adjusted LST map created in GRASS.

    """

    lstname =  'LST_' + '_'.join(os.path.basename(hdf).split('.')[1:3])               

    # read in lst grid

    gs.run_command('r.in.gdal',

        input='HDF4_EOS:EOS_GRID:%s:MODIS_Grid_Daily_1km_LST:LST_Day_1km' % hdf,

        output=lstname)            # No new location created since it has already been done with r.in.gdal before!!!

    # read in qc grid

    gs.run_command('r.in.gdal',

        input = 'HDF4_EOS:EOS_GRID:%s:MODIS_Grid_Daily_1km_LST:QC_Day' % hdf,

        output = 'qc_this_day')

    gs.run_command('g.region', rast=lstname)

    # null out any LST cells for which QC is not high [00]  #THIS WHERE WE MIGHT NEED TO CHANGE...

    gs.mapcalc('${lst} = if((qc_this_day & 0x03)==0, ${lst}, null())',

        lst=lstname, overwrite=True)

    # clean up

    gs.run_command('g.remove', rast='qc_this_day')

    # return name of qc-adjusted LST raster in GRASS

    return lstname

def main():

    #--------------------------------------------

    # Download and Calculate monthly climatology for daily LST time series for a specific area

    #--------------------------------------------

    # TODO: set up a (temporary?) GRASS database to use for processing? code

    # currently assumes it's being run within an existing GRASS session

    # using an appropriately configured LOCATION...

    #

    # note the following trick to fix datum for modis sinu;

    # TODO: check that this works properly...compare to MRT output?

    # gs.run_command('g.proj', flags='c',

    #     proj4='+proj=sinu +a=6371007.181 +b=6371007.181 +ellps=sphere')

    ##    proj4='+proj=sinu +R=6371007.181 +nadgrids=@null +wktext')

    #### Added by Benoit on 18 October 2012

    #Parameters

    tiles = ['h11v08','h11v07','h12v08'] #These tiles correspond to Venezuela.

    start_year = 2001

    end_year = 2010

    end_month=12

    start_month=1

    hdfdir =  '/home/parmentier/Data/benoit_test' #destination file where hdf files are stored locally after download.

    ################## First step: download data ###################

    # Added tile loop 

    year_list=range(start_year,end_year+1) #list of year to loop through

    year_list=[2001,2002]

    for tile in tiles:	

        for year in year_list:

            		if year==end_year:

            				start_doy, end_doy = get_doy_range(year, end_month)

                			start_doy=1  #Fix problem later

                			hdfs = download_mod11a1(hdfdir, tile, start_doy, end_doy, year)

            		elif year==start_year:

                			start_doy, end_doy = get_doy_range(year, start_month)

                			end_doy=365

                	if calendar.isleap(year):

                			end_doy=366

                			hdfs = download_mod11a1(hdfdir, tile, start_doy, end_doy, year)

            		elif (year!=start_year and year!=end_year):

                			start_doy=1

                			end_doy=365

                			if calendar.isleap(year):

                					end_doy=366

                			hdfs = download_mod11a1(hdfdir, tile, start_doy, end_doy, year)

    # modify loop to take into account "hdfs", list of hdf files needed in the next steps…  

    ################# Second step: compute climatology #################

    # Set up a temporary GRASS data base 

    tmp_location = 'tmp' + str(os.getpid())        # Name of the temporary GRASS data base 

    orig_location = gs.gisenv()['LOCATION_NAME']

    orig_mapset = gs.gisenv()['MAPSET']

    gs.os.environ['GRASS_OVERWRITE'] = '1'         # Allow for overwrite?? GRASS data base  

    ##NEED TO LOOP OVER DIFFERENT TILES!!!

    ##start loop for tile

    #load a file that will define the region of interest and location at the same time

    tile= 'h12v08'

    tile=tiles[2]

    #tile= 'h12v08'

    path_file = '/home/parmentier/Data/benoit_test'

    os.chdir(path_file)    #set working directory

    os.getcwd()            #get current working directory

    listfiles_wd2 = glob.glob('*'+tile+'*'+'.hdf') #list the all the LST files of interest

    name_of_file = listfiles_wd2[0]    

    #name_of_file = 'MOD11A1.A2003364.h12v08.005.2008163211649.hdf'

     # first load daytime LST, by accessing specific layers in the hdd??

    #SUBDATASET_1_NAME=HDF4_EOS:EOS_GRID:"MOD11A1.A2003364.h12v08.005.2008163211649.hdf":MODIS_Grid_Daily_1km_LST:LST_Day_1km

    #path_file = os.getcwd() 

    #Create the name of the layer to extract from the hdf file used for definition of the database

    lst1day = 'HDF4_EOS:EOS_GRID:%s/%s:%s' % (

    path_file,

    name_of_file,

     'MODIS_Grid_Daily_1km_LST:LST_Day_1km')

    #now import one image and set the location, mapset and database !!create name per tile

    gs.run_command('r.in.gdal', input=lst1day, output='LST_1day_h12v08',

           location=tmp_location)

    # Now that the new location has been create, switch to new location

    gs.run_command('g.gisenv', set='LOCATION_NAME=%s' % tmp_location)

    gs.run_command('g.gisenv', set='MAPSET=PERMANENT')

    # set GRASS the projection system and GRASS region to match the extent of the file

    gs.run_command('g.proj', flags='c',

    proj4='+proj=sinu +a=6371007.181 +b=6371007.181 +ellps=sphere')

    gs.run_command('g.region', rast='LST_1day_h12v08')

    #generate monthly pixelwise mean & count of high-quality daytime LST

    gs.os.environ['GRASS_OVERWRITE'] = '1'

    #Provide a list of file "hdfs" to process…

    #tile= 'h12v08'

    fileglob = 'MOD11A1.A*.%s*hdf' % (tile)

    pathglob = os.path.join(path_file, fileglob)

    files = glob.glob(pathglob)

    hdfs = files

    ### LST values in images must be masked out if they have low quality: this is done using QC flags from the layer subset in hdf files

    LST = [load_qc_adjusted_lst(hdf) for hdf in hdfs]

    ### LST is a list that contains the name of the new lst files in the GRASS format. The files are stored in the temporary GRASS database.

    list_maps_month=list_raster_per_month(LST)    

    list_maps_name=['jan','feb','mar','apr','may','jun','jul','aug','sep','oct','nov','dec']

    ##Now calculate clim per month, do a test for year1

    nb_month=12

    for i in range(1,nb_month+1):

        #clims = calc_clim(LST, 'LST_%s_%d_%02d' % (tile, year, month))

        clims = calc_clim(list_maps_month[i-1],list_maps_name[i-1]+'_'+str(i-1))

    #clims = calc_clim(LST, 'LST_%s_%d_%02d' % (tile, year, month))

    # clean up  if necessary

    gs.run_command('g.remove', rast=','.join(LST))

    gs.os.environ['GRASS_OVERWRITE'] = '0'

    # clean up

    gs.run_command('g.gisenv', set='LOCATION_NAME=%s' % orig_location)

    gs.run_command('g.gisenv', set='MAPSET=%s' % orig_mapset)

    shutil.rmtree(os.path.join(gs.gisenv()['GISDBASE'], tmp_location))

    return None