content: # -*- coding: utf-8 -*-
  """
  Authors: Tim Hessels
           UNESCO-IHE 2016
  Contact: t.hessels@unesco-ihe.org
  Repository: https://github.com/wateraccounting/wa
  Module: Collect/MOD9
  """
  
  # import general python modules
  import os
  import numpy as np
  import pandas as pd
  import gdal
  import urllib
  import urllib2
  from bs4 import BeautifulSoup
  import re
  import urlparse
  import glob
  import requests
  from joblib import Parallel, delayed
  
  # Water Accounting modules
  import wa
  import wa.General.raster_conversions as RC
  import wa.General.data_conversions as DC
  from wa import WebAccounts
  
  def DownloadData(Dir, Startdate, Enddate, latlim, lonlim, Waitbar, cores, hdf_library, remove_hdf):
      """
      This function downloads MOD9 daily data
  
      Keyword arguments:
      Dir -- 'C:/file/to/path/'
      Startdate -- 'yyyy-mm-dd'
      Enddate -- 'yyyy-mm-dd'
      latlim -- [ymin, ymax] (values must be between -90 and 90)
      lonlim -- [xmin, xmax] (values must be between -180 and 180)
      cores -- The number of cores used to run the routine. It can be 'False'
               to avoid using parallel computing routines.
      Waitbar -- 1 (Default) will print a waitbar
      """
  
      # Check start and end date and otherwise set the date to max
      if not Startdate:
          Startdate = pd.Timestamp('2000-02-24')
      if not Enddate:
          Enddate = pd.Timestamp('Now')
  
      # Make an array of the days of which the NDVI is taken
      Dates = pd.date_range(Startdate, Enddate, freq = 'D')
  
      # Create Waitbar
      if Waitbar == 1:
          import wa.Functions.Start.WaitbarConsole as WaitbarConsole
          total_amount = len(Dates)
          amount = 0
          WaitbarConsole.printWaitBar(amount, total_amount, prefix = 'Progress:', suffix = 'Complete', length = 50)
  
      # Check the latitude and longitude and otherwise set lat or lon on greatest extent
      if latlim[0] < -90 or latlim[1] > 90:
          print 'Latitude above 90N or below 90S is not possible. Value set to maximum'
          latlim[0] = np.max(latlim[0], -90)
          latlim[1] = np.min(latlim[1], 90)
      if lonlim[0] < -180 or lonlim[1] > 180:
          print 'Longitude must be between 180E and 180W. Now value is set to maximum'
          lonlim[0] = np.max(lonlim[0], -180)
          lonlim[1] = np.min(lonlim[1], 180)
  
      # Make directory for the MODIS NDVI data
      Dir = Dir.replace("/", os.sep)
      output_folder = os.path.join(Dir, 'Reflectance', 'MOD9')
      if not os.path.exists(output_folder):
          os.makedirs(output_folder)
  
      TilesVertical, TilesHorizontal = wa.Collect.MOD15.DataAccess.Get_tiles_from_txt(output_folder, hdf_library, latlim, lonlim)
  
      # Pass variables to parallel function and run
      args = [output_folder, TilesVertical, TilesHorizontal, lonlim, latlim, hdf_library]
      if not cores:
          for Date in Dates:
              RetrieveData(Date, args)
              if Waitbar == 1:
                  amount += 1
                  WaitbarConsole.printWaitBar(amount, total_amount, prefix = 'Progress:', suffix = 'Complete', length = 50)
          results = True
      else:
          results = Parallel(n_jobs=cores)(delayed(RetrieveData)(Date, args)
                                           for Date in Dates)
      if remove_hdf == 1:
          # Remove all .hdf files
          os.chdir(output_folder)
          files = glob.glob("*.hdf")
          for f in files:
              os.remove(os.path.join(output_folder, f))
  
          # Remove all .txt files
          files = glob.glob("*.txt")
          for f in files:
              os.remove(os.path.join(output_folder, f))
  
  	return results
  
  def RetrieveData(Date, args):
      """
      This function retrieves MOD9 Reflectance data for a given date from the
      http://e4ftl01.cr.usgs.gov/ server.
  
      Keyword arguments:
      Date -- 'yyyy-mm-dd'
      args -- A list of parameters defined in the DownloadData function.
      """
      # Argument
      [output_folder, TilesVertical, TilesHorizontal, lonlim, latlim, hdf_library] = args
  
      # Collect the data from the MODIS webpage and returns the data and lat and long in meters of those tiles
      try:
          Collect_data(TilesHorizontal, TilesVertical, Date, output_folder, hdf_library)
      except:
          print "Was not able to download the file"
  
        # Define the output name of the collect data function
      name_collect = os.path.join(output_folder, 'Merged.tif')
  
      # Reproject the MODIS product to epsg_to
      epsg_to ='4326'
      name_reprojected = RC.reproject_MODIS(name_collect, epsg_to)
  
      # Clip the data to the users extend
      data, geo = RC.clip_data(name_reprojected, latlim, lonlim)
  
      # Save results as Gtiff
      ReffileName = os.path.join(output_folder, 'Reflectance_MOD09GQ_-_daily_' + Date.strftime('%Y') + '.' + Date.strftime('%m') + '.' + Date.strftime('%d') + '.tif')
      DC.Save_as_tiff(name=ReffileName, data=data, geo=geo, projection='WGS84')
  
      # remove the side products
      os.remove(os.path.join(output_folder, name_collect))
      os.remove(os.path.join(output_folder, name_reprojected))
  
      return True
  
  
  def Collect_data(TilesHorizontal,TilesVertical,Date,output_folder, hdf_library):
      '''
      This function downloads all the needed MODIS tiles from http://e4ftl01.cr.usgs.gov/MOLT/MOD13Q1.006/ as a hdf file.
  
      Keywords arguments:
      TilesHorizontal -- [TileMin,TileMax] max and min horizontal tile number
      TilesVertical -- [TileMin,TileMax] max and min vertical tile number
      Date -- 'yyyy-mm-dd'
      output_folder -- 'C:/file/to/path/'
      '''
  
      # Make a new tile for the data
      sizeX = int((TilesHorizontal[1] - TilesHorizontal[0] + 1) * 4800)
      sizeY = int((TilesVertical[1] - TilesVertical[0] + 1) * 4800)
      DataTot = np.zeros((sizeY, sizeX))
  
      # Load accounts
      username, password = WebAccounts.Accounts(Type = 'NASA')
  
      # Create the Lat and Long of the MODIS tile in meters
      for Vertical in range(int(TilesVertical[0]), int(TilesVertical[1])+1):
          Distance = 231.65635826395834 # resolution of a MODIS pixel in meter
          countY=(TilesVertical[1] - TilesVertical[0] + 1) - (Vertical - TilesVertical[0])
  
          for Horizontal in range(int(TilesHorizontal[0]), int(TilesHorizontal[1]) + 1):
              countX=Horizontal - TilesHorizontal[0] + 1
  
              # Download the MODIS NDVI data
              url = 'https://e4ftl01.cr.usgs.gov/MOLT/MOD09GQ.006/' + Date.strftime('%Y') + '.' + Date.strftime('%m') + '.' + Date.strftime('%d') + '/'
  
  		      # Reset the begin parameters for downloading
              downloaded = 0
              N=0
  
  	         # Check the library given by user
              if hdf_library is not None:
                  os.chdir(hdf_library)
                  hdf_name = glob.glob("MOD09GQ.A%s%03s.h%02dv%02d.*" %(Date.strftime('%Y'), Date.strftime('%j'), Horizontal, Vertical))
  
                  if len(hdf_name) == 1:
                      hdf_file = os.path.join(hdf_library, hdf_name[0])
  
                      if os.path.exists(hdf_file):
                          downloaded = 1
                          file_name = hdf_file
  
  
              if not downloaded == 1:
                  # Get files on FTP server
                  f = urllib2.urlopen(url)
  
                  # Sum all the files on the server
                  soup = BeautifulSoup(f, "lxml")
                  for i in soup.findAll('a', attrs = {'href': re.compile('(?i)(hdf)$')}):
  
                      # Find the file with the wanted tile number
                      Vfile=str(i)[30:32]
                      Hfile=str(i)[27:29]
                      if int(Vfile) is int(Vertical) and int(Hfile) is int(Horizontal):
  
                          # Define the whole url name
                          full_url = urlparse.urljoin(url, i['href'])
  
                          # if not downloaded try to download file
                          while downloaded == 0:
  
                              try:# open http and download whole .hdf
                                  nameDownload = full_url
                                  file_name = os.path.join(output_folder,nameDownload.split('/')[-1])
                                  if os.path.isfile(file_name):
                                      downloaded = 1
                                  else:
                                      x = requests.get(nameDownload, allow_redirects = False)
                                      try:
                                          y = requests.get(x.headers['location'], auth = (username, password))
                                      except:
                                          from requests.packages.urllib3.exceptions import InsecureRequestWarning
                                          requests.packages.urllib3.disable_warnings(InsecureRequestWarning)
                                          y = requests.get(x.headers['location'], auth = (username, password), verify = False)
                                      z = open(file_name, 'wb')
                                      z.write(y.content)
                                      z.close()
                                      statinfo = os.stat(file_name)
                                      # Say that download was succesfull
                                      if int(statinfo.st_size) > 10000:
                                           downloaded = 1
  
                              # If download was not succesfull
                              except:
  
                                  # Try another time
                                  N = N + 1
  
      				         # Stop trying after 10 times
                          if N == 10:
                              print 'Data from ' + Date.strftime('%Y-%m-%d') + ' is not available'
                              downloaded = 1
              try:
                  # Open .hdf only band with NDVI and collect all tiles to one array
                  dataset = gdal.Open(file_name)
                  sdsdict = dataset.GetMetadata('SUBDATASETS')
                  sdslist = [sdsdict[k] for k in sdsdict.keys() if '_2_NAME' in k]
                  sds = []
  
                  for n in sdslist:
                      sds.append(gdal.Open(n))
                      full_layer = [i for i in sdslist if 'sur_refl_b01_1' in i]
                      idx = sdslist.index(full_layer[0])
                      if Horizontal == TilesHorizontal[0] and Vertical == TilesVertical[0]:
                          geo_t = sds[idx].GetGeoTransform()
  
                          # get the projection value
                          proj = sds[idx].GetProjection()
  
                      data = sds[idx].ReadAsArray()
                      countYdata = (TilesVertical[1] - TilesVertical[0] + 2) - countY
                      DataTot[int((countYdata - 1) * 4800):int(countYdata * 4800), int((countX - 1) * 4800):int(countX * 4800)]=data
                  del data
  
              # if the tile not exists or cannot be opened, create a nan array with the right projection
              except:
                  if Horizontal==TilesHorizontal[0] and Vertical==TilesVertical[0]:
                       x1 = (TilesHorizontal[0] - 19) * 4800 * Distance
                       x4 = (TilesVertical[0] - 9) * 4800 * -1 * Distance
                       geo = 	[x1, Distance, 0.0, x4, 0.0, -Distance]
                       geo_t=tuple(geo)
  
                  proj='PROJCS["unnamed",GEOGCS["Unknown datum based upon the custom spheroid",DATUM["Not specified (based on custom spheroid)",SPHEROID["Custom spheroid",6371007.181,0]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433]],PROJECTION["Sinusoidal"],PARAMETER["longitude_of_center",0],PARAMETER["false_easting",0],PARAMETER["false_northing",0],UNIT["Meter",1]]'
                  data=np.ones((4800,4800)) * (-9999)
                  countYdata=(TilesVertical[1] - TilesVertical[0] + 2) - countY
                  DataTot[(countYdata - 1) * 4800:countYdata * 4800,(countX - 1) * 4800:countX * 4800] = data
  
      # Make geotiff file
      name2 = os.path.join(output_folder, 'Merged.tif')
      driver = gdal.GetDriverByName("GTiff")
      dst_ds = driver.Create(name2, DataTot.shape[1], DataTot.shape[0], 1, gdal.GDT_Float32, ['COMPRESS=LZW'])
      try:
           dst_ds.SetProjection(proj)
      except:
          proj='PROJCS["unnamed",GEOGCS["Unknown datum based upon the custom spheroid",DATUM["Not specified (based on custom spheroid)",SPHEROID["Custom spheroid",6371007.181,0]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433]],PROJECTION["Sinusoidal"],PARAMETER["longitude_of_center",0],PARAMETER["false_easting",0],PARAMETER["false_northing",0],UNIT["Meter",1]]'
          x1 = (TilesHorizontal[0] - 18) * 4800 * Distance
          x4 = (TilesVertical[0] - 9) * 4800 * -1 * Distance
          geo = [x1, Distance, 0.0, x4, 0.0, -Distance]
          geo_t = tuple(geo)
          dst_ds.SetProjection(proj)
  
      dst_ds.GetRasterBand(1).SetNoDataValue(-9999)
      dst_ds.SetGeoTransform(geo_t)
      dst_ds.GetRasterBand(1).WriteArray(DataTot*0.0001)
      dst_ds = None
      sds = None
      return()
  