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Revision a05f49eb

Added by Jim Regetz almost 13 years ago

  • ID a05f49eb9917be5c8d91e262609c631410e57c81

automated determination of which days to aggregate for any given month

View differences:

climate/extra/aggregate-daily-lst.py
23 23 
# Created on 16-May-2012
24 24 

  		




  25
  25
  
    
import os, glob


  		




  26
  
  
    
import datetime


  		




  
  26
  
    
import datetime, calendar


  		




  27
  27
  
    
import ftplib


  		




  28
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import grass.script as gs


  		




  29
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  ......




  36
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    date = datetime.datetime.strptime('%d%d' % (year, doy), '%Y%j')


  		




  37
  37
  
    
    return date.strftime('%Y.%m.%d')


  		




  38
  38
  
    

  		




  
  39
  
    
def get_doy_range(year, month):


  		




  
  40
  
    
    last_day_of_month = calendar.monthrange(year, month)[1]


  		




  
  41
  
    
    start_doy = int(datetime.datetime.strptime('%d.%02d.%02d' % (year,


  		




  
  42
  
    
        month, 1), '%Y.%m.%d').strftime('%j'))


  		




  
  43
  
    
    end_doy = int(datetime.datetime.strptime('%d.%02d.%02d' % (year,


  		




  
  44
  
    
        month, last_day_of_month), '%Y.%m.%d').strftime('%j'))


  		




  
  45
  
    
    return (int(start_doy), int(end_doy))


  		




  
  46
  
    

  		




  39
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# quick function to return list of dirs in wd


  		




  40
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def list_contents(ftp):


  		




  41
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    listing = []


  		




  ......




  178
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    # return name of qc-adjusted LST raster in GRASS


  		




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    return lstname


  		




  180
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  181
  
  
    
#---------------------------------------------


  		




  182
  
  
    
# test: download then aggregate for one month


  		




  183
  
  
    
#---------------------------------------------


  		




  
  189
  
    

  		




  
  190
  
    
#--------------------------------------------


  		




  
  191
  
    
# test procedures mostly for timing purposes


  		




  
  192
  
    
#--------------------------------------------


  		




  184
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# TODO: set up a (temporary?) GRASS database to use for processing? code


  		




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


  		




  ......




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


  		




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


  		




  194
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  204
  
    
# (1) download then aggregate for one month


  		




  
  205
  
    

  		




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tile = 'h09v04'


  		




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year = 2005


  		




  197
  
  
    
start_doy = 1


  		




  198
  
  
    
end_doy = 31


  		




  
  208
  
    
month = 1


  		




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hdfdir = '.'


  		




  200
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  211
  
    
# determine range of doys for the specified month


  		




  
  212
  
    
start_doy, end_doy = get_doy_range(year, month)


  		




  201
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# download data


  		




  202
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### [atlas 17-May-2012] Wall time: 111.62 s


  		




  203
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hdfs = download_mod11a1(hdfdir, tile, start_doy, end_doy, year)


  		




  204
  
  
    

  		




  205
  
  
    
# note: now that we've downloaded the files into local directory


  		




  206
  
  
    
# 'hdfdir', this should yield the same list...


  		




  207
  
  
    
#hdfs = get_hdf_paths(hdfdir, tile, start_doy, end_doy, year)


  		




  208
  
  
    

  		




  209
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# generate monthly pixelwise mean & count of high-quality daytime LST


  		




  210
  
  
    
# values


  		




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### [atlas 17-May-2012] Wall time: 53.79 s


  		




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


  		




  213
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LST = [load_qc_adjusted_lst(hdf) for hdf in hdfs]


  		




  214
  
  
    
calc_clim(LST, 'LST_%s_%d_%03d_%03d' % (tile, year, start_doy, end_doy))


  		




  
  220
  
    
clims = calc_clim(LST, 'LST_%s_%d_%02d' % (tile, year, month))


  		




  215
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# clean up


  		




  216
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gs.run_command('g.remove', rast=','.join(LST))


  		




  217
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gs.os.environ['GRASS_OVERWRITE'] = '0'


  		




  
  224
  
    

  		




  
  225
  
    

  		




  
  226
  
    
# (2) aggregate all 12 months in one year, using local data


  		




  
  227
  
    

  		




  
  228
  
    
tile = 'h09v04'


  		




  
  229
  
    
year = 2005


  		




  
  230
  
    
hdfdir = '/home/layers/data/climate/MOD11A1.004-OR-orig'


  		




  
  231
  
    

  		




  
  232
  
    
### [atlas 17-May-2012] Wall time: 802.86 s


  		




  
  233
  
    
gs.os.environ['GRASS_OVERWRITE'] = '1'


  		




  
  234
  
    
for month in range(1, 12+1):


  		




  
  235
  
    
    start_doy, end_doy = get_doy_range(year, month)


  		




  
  236
  
    
    hdfs = get_hdf_paths(hdfdir, tile, start_doy, end_doy, year)


  		




  
  237
  
    
    LST = [load_qc_adjusted_lst(hdf) for hdf in hdfs]


  		




  
  238
  
    
    clims = calc_clim(LST, 'LST_%s_%d_%02d' % (tile, year, month))


  		




  
  239
  
    
    gs.run_command('g.remove', rast=','.join(LST))


  		




  
  240
  
    
gs.os.environ['GRASS_OVERWRITE'] = '0'


  		












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