*******************************************************
*	   Storm Surge Calculation System (SSCS)		 *
*******************************************************
Now is :  2019-04-23 12:11:49.802040  UTC

inp1= 20190422.12
ncores= 5
var= wind
submitting calc 2019-04-22 12:00:00 2019-04-22 12:00:00 delft3d 19.4 59.72 -36.0 -3.0 4.0 ECMWF 72 15 True GDACS/1000559/3_ECMWF 6 1 False False 5  20190422.12   wind False
*************---------------------******************
ndt: 1
it:  0
ndt: 1
idate:  2019-04-22 12:00:00
running case from  2019-04-22 12:00:00  for  72  h   start= 1
var wind
**** gometeo:  72
listWindows
rundate:20190422.12
RUNNING  2019-04-22 12:00:00  for  72 hours
prevCalcDate 2019-04-22 00:00:00

============================================
process meteo
============================================
1. process meteo, starting at  2019-04-23 12:11:49.821807

..1.1 calling ECMWF_2_netcdf...
...file netcdf already existing : /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/netcdf/20190422.12.tropical_cyclone.nc
ret= 0
removing submitted
ret= 0 newcase= False  forceFinal= False  forceBulletin= False
home dir /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/
wind and rainfall classification
============================================
1. Set INPUT/OUTPUT
============================================
startdate 2019-04-22 12:00:00
currdate 2019-04-22 12:00:00
ndt:  0 delta: 12
nt 72
alldate:  DatetimeIndex(['2019-04-22 12:00:00'], dtype='datetime64[ns]', freq='12H')
============================================
2. Processing meteo files: nc2tif
============================================

 >> 2.1. Process curr + past files
processing curr bull... 2019-04-22 12:00:00
forcing ECMWF
verifying that input file is present
start reading nc...
wind
ntNC:  73   ntmax 73
use all data in nc file
...create velAll
73 469 573
...start calculating velAll
...end calculating velAll
73 469 573
nt,nx,ny, ntmax 73 469 573 73
[19.399999618530273, 0.07, 0, -3.0, 0, -0.071]
*********** 12 1 12
varMAX.shape  (469, 573)
19.3999996185 59.7200012207 -36.0 -3.0
lon 573
lat 469
savemap /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190422.12/20190422.12_wind.jpg
dtk,nt,ntmax 12 73 73 61
k1, k2, ht 0 12 0


k1, k2, ht 12 24 12


k1, k2, ht 24 36 24


k1, k2, ht 36 48 36


k1, k2, ht 48 60 48


k1, k2, ht 60 72 60


processing all past bull only if Past=True... False
no past

 >> 2.2. Process final data files
compfile /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/20190422.12_Final_completed_wind.txt
FINAL
alldate:  DatetimeIndex(['2019-04-22 12:00:00'], dtype='datetime64[ns]', freq='12H')
1
**FIRST
cp  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190422.12/20190422.12_wind.tif /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_final.tif
max file created /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_final.tif
19.4349996185 59.4749996185 -35.795 -3.035
lon 573
lat 469
savemap /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_FINAL.jpg
ret:  0
============================================
3. Classify meteo + GDACS index score
============================================

 >> 3.1. Classify curr + past forecast
Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190422.12/20190422.12_wind_t0.tif.
Using internal nodata values (e.g. -999) for image /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190422.12/20190422.12_wind_t0.tif.
Copying nodata values from source /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190422.12/20190422.12_wind_t0.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190422.12//wind10m_res_t0.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/GDACS/DATA/lspop20141.tif.
Using internal nodata values (e.g. -2.14748e+09) for image /mnt/output/GDACS/DATA/lspop20141.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/lspop20141.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190422.12//wind10m_popfile_t0_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/GDACS/DATA/countries.tif.
Using internal nodata values (e.g. -32768) for image /mnt/output/GDACS/DATA/countries.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/countries.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190422.12//wind10m_countryfile_t0_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
input var: wind10m
Input File: /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190422.12/20190422.12_wind_t0.tif
hurName:  
hdate:  
var:  wind10m
description:  wind10m: _ (ECMWF)
OutDir:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190422.12//
PopFile:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190422.12//wind10m_popfile_t0_clipped.tif
country:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190422.12//wind10m_countryfile_t0_clipped.tif
outxml file:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190422.12/wind_popDensValues_t0.xml
outDir created
popfile: LandScan
popCellSize= 0.00833333333333
projection= GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433],AUTHORITY["EPSG","4326"]]


>>  1.  resample the tif file to the resolution and proj of pop density  0.00833333333333  deg


>>  2.  read the charactristics of the input file


>>  3a. extract a piece of pop. file corresponding to the required bounding box
>>  3b. extract a piece of countries corresponding to the required bounding box and resolution/proj of pop density
>>  4.  classify the vmax file creating another array of values classified
cellsize 0.00833333333333
cellsize 0.00833333333333
cellsize 0.00833333333333
>>  6.  print / save output in  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190422.12/wind_popDensValues_t0.xml
>>  7.  remove files 
done
t0 completed 

Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190422.12/20190422.12_wind.tif.
Using internal nodata values (e.g. -999) for image /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190422.12/20190422.12_wind.tif.
Copying nodata values from source /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190422.12/20190422.12_wind.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190422.12//wind10m_res_all.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/GDACS/DATA/lspop20141.tif.
Using internal nodata values (e.g. -2.14748e+09) for image /mnt/output/GDACS/DATA/lspop20141.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/lspop20141.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190422.12//wind10m_popfile_all_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/GDACS/DATA/countries.tif.
Using internal nodata values (e.g. -32768) for image /mnt/output/GDACS/DATA/countries.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/countries.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190422.12//wind10m_countryfile_all_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
input var: wind10m
Input File: /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190422.12/20190422.12_wind.tif
hurName:  
hdate:  
var:  wind10m
description:  wind10m: _ (ECMWF)
OutDir:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190422.12//
PopFile:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190422.12//wind10m_popfile_all_clipped.tif
country:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190422.12//wind10m_countryfile_all_clipped.tif
outxml file:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190422.12/wind_popDensValues_all.xml
popfile: LandScan
popCellSize= 0.00833333333333
projection= GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433],AUTHORITY["EPSG","4326"]]


>>  1.  resample the tif file to the resolution and proj of pop density  0.00833333333333  deg


>>  2.  read the charactristics of the input file


>>  3a. extract a piece of pop. file corresponding to the required bounding box
>>  3b. extract a piece of countries corresponding to the required bounding box and resolution/proj of pop density
>>  4.  classify the vmax file creating another array of values classified
cellsize 0.00833333333333
cellsize 0.00833333333333
cellsize 0.00833333333333
>>  5.  count the popolation in each cell and assign to the class and write to output
>>  6.  print / save output in  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190422.12/wind_popDensValues_all.xml
>>  7.  remove files 
done
Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_final.tif.
Using internal nodata values (e.g. -999) for image /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_final.tif.
Copying nodata values from source /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_final.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//wind10m_res_final.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/GDACS/DATA/lspop20141.tif.
Using internal nodata values (e.g. -2.14748e+09) for image /mnt/output/GDACS/DATA/lspop20141.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/lspop20141.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//wind10m_popfile_final_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/GDACS/DATA/countries.tif.
Using internal nodata values (e.g. -32768) for image /mnt/output/GDACS/DATA/countries.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/countries.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//wind10m_countryfile_final_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
t0 completed 

copy:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190422.12/wind_popDensValues_all.xml in:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/delft3d/20190422.12/wind_popDensValues_all.xml

 >> 3.2. Classify final folder
/mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final/20190422.12_final_completed_wind.txt
input var: wind10m
Input File: /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_final.tif
hurName:  
hdate:  
var:  wind10m
description:  wind10m: _ (ECMWF)
OutDir:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//
PopFile:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//wind10m_popfile_final_clipped.tif
country:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//wind10m_countryfile_final_clipped.tif
outxml file:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final/wind_popDensValues_final.xml
outDir created
popfile: LandScan
popCellSize= 0.00833333333333
projection= GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433],AUTHORITY["EPSG","4326"]]


>>  1.  resample the tif file to the resolution and proj of pop density  0.00833333333333  deg


>>  2.  read the charactristics of the input file


>>  3a. extract a piece of pop. file corresponding to the required bounding box
>>  3b. extract a piece of countries corresponding to the required bounding box and resolution/proj of pop density
>>  4.  classify the vmax file creating another array of values classified
cellsize 0.00833333333333
cellsize 0.00833333333333
cellsize 0.00833333333333
>>  5.  count the popolation in each cell and assign to the class and write to output
>>  6.  print / save output in  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final/wind_popDensValues_final.xml
>>  7.  remove files 
done
copy:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final/wind_popDensValues_final.xml in:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/delft3d/final/wind_popDensValues_final.xml
*******************************************************
*  Case Completed									 *
*******************************************************
Case starterd at:  2019-04-23 12:11:49.802040  UTC

Case completed at:  2019-04-23 12:15:07.728600  UTC

**********E  N  D   O F	J O B***********************
*******************************************************
*	   Storm Surge Calculation System (SSCS)		 *
*******************************************************
Now is :  2019-04-23 12:36:12.732371  UTC

inp1= 20190422.12
ncores= 5
var= rain
submitting calc 2019-04-22 12:00:00 2019-04-22 12:00:00 delft3d 19.4 59.72 -36.0 -3.0 4.0 ECMWF 72 15 True GDACS/1000559/3_ECMWF 6 1 False False 5  20190422.12   rain False
*************---------------------******************
ndt: 1
it:  0
ndt: 1
idate:  2019-04-22 12:00:00
running case from  2019-04-22 12:00:00  for  72  h   start= 1
var rain
**** gometeo:  72
listWindows
rundate:20190422.12
RUNNING  2019-04-22 12:00:00  for  72 hours
prevCalcDate 2019-04-22 00:00:00
Nothing to do, case already completed
ret= -3 newcase= False  forceFinal= False  forceBulletin= False
ret=
*******************************************************
*	   Storm Surge Calculation System (SSCS)		 *
*******************************************************
Now is :  2019-04-23 12:37:00.383413  UTC

inp1= 20190422.12
ncores= 5
var= wind
submitting calc 2019-04-22 12:00:00 2019-04-22 12:00:00 delft3d 19.4 59.72 -36.0 -3.0 4.0 ECMWF 72 15 True GDACS/1000559/3_ECMWF 6 1 False False 5  20190422.12   wind False
*************---------------------******************
ndt: 1
it:  0
ndt: 1
idate:  2019-04-22 12:00:00
running case from  2019-04-22 12:00:00  for  72  h   start= 1
var wind
**** gometeo:  72
listWindows
rundate:20190422.12
RUNNING  2019-04-22 12:00:00  for  72 hours
prevCalcDate 2019-04-22 00:00:00

============================================
process meteo
============================================
1. process meteo, starting at  2019-04-23 12:37:00.408137

..1.1 calling ECMWF_2_netcdf...
...file netcdf already existing : /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/netcdf/20190422.12.tropical_cyclone.nc
ret= 0
removing submitted
ret= 0 newcase= False  forceFinal= False  forceBulletin= False
home dir /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/
wind and rainfall classification
============================================
1. Set INPUT/OUTPUT
============================================
startdate 2019-04-22 12:00:00
currdate 2019-04-22 12:00:00
ndt:  0 delta: 12
nt 72
alldate:  DatetimeIndex(['2019-04-22 12:00:00'], dtype='datetime64[ns]', freq='12H')
============================================
2. Processing meteo files: nc2tif
============================================

 >> 2.1. Process curr + past files
processing curr bull... 2019-04-22 12:00:00
processing all past bull only if Past=True... False
no past

 >> 2.2. Process final data files
compfile /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/20190422.12_Final_completed_wind.txt
============================================
3. Classify meteo + GDACS index score
============================================

 >> 3.1. Classify curr + past forecast
t0 completed 

t0 completed 


 >> 3.2. Classify final folder
/mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final/20190422.12_final_completed_wind.txt
*******************************************************
*  Case Completed									 *
*******************************************************
Case starterd at:  2019-04-23 12:37:00.383413  UTC

Case completed at:  2019-04-23 12:37:00.454849  UTC

**********E  N  D   O F	J O B***********************
*******************************************************
*	   Storm Surge Calculation System (SSCS)		 *
*******************************************************
Now is :  2019-04-23 12:37:09.669152  UTC

inp1= 20190422.12
ncores= 5
var= rain
submitting calc 2019-04-22 12:00:00 2019-04-22 12:00:00 delft3d 19.4 59.72 -36.0 -3.0 4.0 ECMWF 72 15 True GDACS/1000559/3_ECMWF 6 1 False False 5  20190422.12   rain False
*************---------------------******************
ndt: 1
it:  0
ndt: 1
idate:  2019-04-22 12:00:00
running case from  2019-04-22 12:00:00  for  72  h   start= 1
var rain
**** gometeo:  72
listWindows
rundate:20190422.12
RUNNING  2019-04-22 12:00:00  for  72 hours
prevCalcDate 2019-04-22 00:00:00
Nothing to do, case already completed
ret= -3 newcase= False  forceFinal= False  forceBulletin= False
home dir /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/
wind and rainfall classification
============================================
1. Set INPUT/OUTPUT
============================================
startdate 2019-04-22 12:00:00
currdate 2019-04-22 12:00:00
ndt:  0 delta: 12
nt 72
alldate:  DatetimeIndex(['2019-04-22 12:00:00'], dtype='datetime64[ns]', freq='12H')
============================================
2. Processing meteo files: nc2tif
============================================

 >> 2.1. Process curr + past files
processing curr bull... 2019-04-22 12:00:00
forcing ECMWF
verifying that input file is present
start reading nc...
rain
use all data in nc file
nt,nx,ny, ntmax 72 469 574 73
nt,nx,ny, ntmax 72 469 574 73
[19.406266848993972, 0.07, 0, -3.0579964267022164, 0, -0.07]
*********** 12 1 12
varMAX.shape  (469, 574)
19.406266849 59.695364329 -35.9578199457 -3.0579964267
lon 574
lat 469
savemap /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190422.12/20190422.12_rain.jpg
dtk,nt,ntmax 12 72 73 61
k1, k2, ht 0 12 0


k1, k2, ht 12 24 12


k1, k2, ht 24 36 24


k1, k2, ht 36 48 36


k1, k2, ht 48 60 48


k1, k2, ht 60 72 60


processing all past bull only if Past=True... False
no past

 >> 2.2. Process final data files
compfile /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/20190422.12_Final_completed_rain.txt
FINAL
alldate:  DatetimeIndex(['2019-04-22 12:00:00'], dtype='datetime64[ns]', freq='12H')
1
**FIRST
cp  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190422.12/20190422.12_rain.tif /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_final.tif
max file created /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_final.tif
19.441266849 59.551266849 -35.8529964267 -3.0929964267
lon 574
lat 469
savemap /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_FINAL.jpg
ret:  0
============================================
3. Classify meteo + GDACS index score
============================================

 >> 3.1. Classify curr + past forecast
Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190422.12/20190422.12_rain_t0.tif.
Using internal nodata values (e.g. -999) for image /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190422.12/20190422.12_rain_t0.tif.
Copying nodata values from source /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190422.12/20190422.12_rain_t0.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190422.12//rain_res_t0.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/GDACS/DATA/lspop20141.tif.
Using internal nodata values (e.g. -2.14748e+09) for image /mnt/output/GDACS/DATA/lspop20141.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/lspop20141.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190422.12//rain_popfile_t0_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/GDACS/DATA/countries.tif.
Using internal nodata values (e.g. -32768) for image /mnt/output/GDACS/DATA/countries.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/countries.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190422.12//rain_countryfile_t0_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
input var: rain
Input File: /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190422.12/20190422.12_rain_t0.tif
hurName:  
hdate:  
var:  rain
description:  rain: _ (ECMWF)
OutDir:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190422.12//
PopFile:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190422.12//rain_popfile_t0_clipped.tif
country:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190422.12//rain_countryfile_t0_clipped.tif
outxml file:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190422.12/rain_popDensValues_t0.xml
popfile: LandScan
popCellSize= 0.00833333333333
projection= GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433],AUTHORITY["EPSG","4326"]]


>>  1.  resample the tif file to the resolution and proj of pop density  0.00833333333333  deg


>>  2.  read the charactristics of the input file


>>  3a. extract a piece of pop. file corresponding to the required bounding box
>>  3b. extract a piece of countries corresponding to the required bounding box and resolution/proj of pop density
>>  4.  classify the vmax file creating another array of values classified
cellsize 0.00833333333333
cellsize 0.00833333333333
cellsize 0.00833333333333
>>  5.  count the popolation in each cell and assign to the class and write to output
>>  6.  print / save output in  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190422.12/rain_popDensValues_t0.xml
>>  7.  remove files 
done
t0 completed 

Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190422.12/20190422.12_rain.tif.
Using internal nodata values (e.g. -999) for image /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190422.12/20190422.12_rain.tif.
Copying nodata values from source /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190422.12/20190422.12_rain.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190422.12//rain_res_all.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/GDACS/DATA/lspop20141.tif.
Using internal nodata values (e.g. -2.14748e+09) for image /mnt/output/GDACS/DATA/lspop20141.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/lspop20141.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190422.12//rain_popfile_all_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/GDACS/DATA/countries.tif.
Using internal nodata values (e.g. -32768) for image /mnt/output/GDACS/DATA/countries.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/countries.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190422.12//rain_countryfile_all_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
input var: rain
Input File: /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190422.12/20190422.12_rain.tif
hurName:  
hdate:  
var:  rain
description:  rain: _ (ECMWF)
OutDir:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190422.12//
PopFile:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190422.12//rain_popfile_all_clipped.tif
country:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190422.12//rain_countryfile_all_clipped.tif
outxml file:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190422.12/rain_popDensValues_all.xml
popfile: LandScan
popCellSize= 0.00833333333333
projection= GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433],AUTHORITY["EPSG","4326"]]


>>  1.  resample the tif file to the resolution and proj of pop density  0.00833333333333  deg


>>  2.  read the charactristics of the input file


>>  3a. extract a piece of pop. file corresponding to the required bounding box
>>  3b. extract a piece of countries corresponding to the required bounding box and resolution/proj of pop density
>>  4.  classify the vmax file creating another array of values classified
cellsize 0.00833333333333
cellsize 0.00833333333333
cellsize 0.00833333333333
>>  5.  count the popolation in each cell and assign to the class and write to output
>>  6.  print / save output in  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190422.12/rain_popDensValues_all.xml
>>  7.  remove files 
done
Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_final.tif.
Using internal nodata values (e.g. -999) for image /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_final.tif.
Copying nodata values from source /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_final.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//rain_res_final.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/GDACS/DATA/lspop20141.tif.
Using internal nodata values (e.g. -2.14748e+09) for image /mnt/output/GDACS/DATA/lspop20141.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/lspop20141.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//rain_popfile_final_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/GDACS/DATA/countries.tif.
Using internal nodata values (e.g. -32768) for image /mnt/output/GDACS/DATA/countries.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/countries.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//rain_countryfile_final_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
t0 completed 

copy:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190422.12/rain_popDensValues_all.xml in:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/delft3d/20190422.12/rain_popDensValues_all.xml

 >> 3.2. Classify final folder
/mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final/20190422.12_final_completed_rain.txt
input var: rain
Input File: /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_final.tif
hurName:  
hdate:  
var:  rain
description:  rain: _ (ECMWF)
OutDir:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//
PopFile:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//rain_popfile_final_clipped.tif
country:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//rain_countryfile_final_clipped.tif
outxml file:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final/rain_popDensValues_final.xml
popfile: LandScan
popCellSize= 0.00833333333333
projection= GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433],AUTHORITY["EPSG","4326"]]


>>  1.  resample the tif file to the resolution and proj of pop density  0.00833333333333  deg


>>  2.  read the charactristics of the input file


>>  3a. extract a piece of pop. file corresponding to the required bounding box
>>  3b. extract a piece of countries corresponding to the required bounding box and resolution/proj of pop density
>>  4.  classify the vmax file creating another array of values classified
cellsize 0.00833333333333
cellsize 0.00833333333333
cellsize 0.00833333333333
>>  5.  count the popolation in each cell and assign to the class and write to output
>>  6.  print / save output in  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final/rain_popDensValues_final.xml
>>  7.  remove files 
done
copy:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final/rain_popDensValues_final.xml in:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/delft3d/final/rain_popDensValues_final.xml
==============================================================
*******************************************************
*	   Storm Surge Calculation System (SSCS)		 *
*******************************************************
Now is :  2019-04-23 12:57:54.113838  UTC

inp1= 20190422.12
ncores= 5
var= wind
submitting calc 2019-04-22 12:00:00 2019-04-23 00:00:00 delft3d 19.4 59.72 -36.0 -3.0 4.0 ECMWF 72 15 True GDACS/1000559/3_ECMWF 6 1 False False 5  20190422.12   wind False
*************---------------------******************
ndt: 2
it:  0
ndt: 2
idate:  2019-04-22 12:00:00
running case from  2019-04-22 12:00:00  for  72  h   start= 1
var wind
**** gometeo:  72
listWindows
rundate:20190422.12
RUNNING  2019-04-22 12:00:00  for  72 hours
prevCalcDate 2019-04-22 00:00:00

============================================
process meteo
============================================
1. process meteo, starting at  2019-04-23 12:57:54.172999

..1.1 calling ECMWF_2_netcdf...
...file netcdf already existing : /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/netcdf/20190422.12.tropical_cyclone.nc
ret= 0
removing submitted
ret= 0 newcase= False  forceFinal= False  forceBulletin= False
it:  1
ndt: 2
idate:  2019-04-23 00:00:00
running case from  2019-04-23 00:00:00  for  72  h   start= 0
var wind
**** gometeo:  72
listWindows
rundate:20190423.00
RUNNING  2019-04-23 00:00:00  for  72 hours
prevCalcDate 2019-04-22 12:00:00

============================================
process meteo
============================================
1. process meteo, starting at  2019-04-23 12:57:54.182606
0 .. 10 .. 20 .. 30 .. 40 .. 50 .. 60 .. 70 .. 80 .. 90 .. 100 - Done

..1.1 calling ECMWF_2_netcdf...
...file netcdf already existing : /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/netcdf/20190423.00.tropical_cyclone.nc
ret= 0
removing submitted
ret= 0 newcase= True  forceFinal= False  forceBulletin= False
home dir /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/
ret 0
classifications
wind and rainfall classification
============================================
1. Set INPUT/OUTPUT
============================================
startdate 2019-04-22 12:00:00
currdate 2019-04-23 00:00:00
ndt:  12 delta: 12
nt1=delta 12
nt 72
alldate:  DatetimeIndex(['2019-04-22 12:00:00', '2019-04-23 00:00:00'], dtype='datetime64[ns]', freq='12H')
============================================
2. Processing meteo files: nc2tif
============================================

 >> 2.1. Process curr + past files
processing curr bull... 2019-04-23 00:00:00
forcing ECMWF
verifying that input file is present
start reading nc...
wind
ntNC:  73   ntmax 73
use all data in nc file
...create velAll
73 469 573
...start calculating velAll
...end calculating velAll
73 469 573
nt,nx,ny, ntmax 73 469 573 73
[19.399999618530273, 0.07, 0, -3.0, 0, -0.071]
*********** 12 1 12
varMAX.shape  (469, 573)
19.3999996185 59.7200012207 -36.0 -3.0
lon 573
lat 469
savemap /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.00/20190423.00_wind.jpg
dtk,nt,ntmax 12 73 73 61
k1, k2, ht 0 12 0


k1, k2, ht 12 24 12


k1, k2, ht 24 36 24


k1, k2, ht 36 48 36


k1, k2, ht 48 60 48


k1, k2, ht 60 72 60


processing all past bull only if Past=True... True

 itdate, istime 2019-04-22 12:00:00 20190422.12
meteo-processing past forecast already completed

 >> 2.2. Process final data files
compfile /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/20190423.00_Final_completed_wind.txt
FINAL
remove maxtif
alldate:  DatetimeIndex(['2019-04-22 12:00:00', '2019-04-23 00:00:00'], dtype='datetime64[ns]', freq='12H')
2


date:  2019-04-23 00:00:00
python  /mnt/output/SSCS/scripts_dev/gdal_calc.py -A /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190422.12/20190422.12_wind_stept0.tif -B /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.00/20190423.00_wind.tif --outfile=/mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_final.tif --calc="maximum(A,B)"
max file created /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_final.tif
19.4349996185 59.4749996185 -35.795 -3.035
lon 573
lat 469
savemap /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_FINAL.jpg
ret:  0
============================================
3. Classify meteo + GDACS index score
============================================

 >> 3.1. Classify curr + past forecast
Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.00/20190423.00_wind_t0.tif.
Using internal nodata values (e.g. -999) for image /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.00/20190423.00_wind_t0.tif.
Copying nodata values from source /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.00/20190423.00_wind_t0.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.00//wind10m_res_t0.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/GDACS/DATA/lspop20141.tif.
Using internal nodata values (e.g. -2.14748e+09) for image /mnt/output/GDACS/DATA/lspop20141.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/lspop20141.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.00//wind10m_popfile_t0_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/GDACS/DATA/countries.tif.
Using internal nodata values (e.g. -32768) for image /mnt/output/GDACS/DATA/countries.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/countries.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.00//wind10m_countryfile_t0_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
input var: wind10m
Input File: /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.00/20190423.00_wind_t0.tif
hurName:  
hdate:  
var:  wind10m
description:  wind10m: _ (ECMWF)
OutDir:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.00//
PopFile:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.00//wind10m_popfile_t0_clipped.tif
country:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.00//wind10m_countryfile_t0_clipped.tif
outxml file:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.00/wind_popDensValues_t0.xml
outDir created
popfile: LandScan
popCellSize= 0.00833333333333
projection= GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433],AUTHORITY["EPSG","4326"]]


>>  1.  resample the tif file to the resolution and proj of pop density  0.00833333333333  deg


>>  2.  read the charactristics of the input file


>>  3a. extract a piece of pop. file corresponding to the required bounding box
>>  3b. extract a piece of countries corresponding to the required bounding box and resolution/proj of pop density
>>  4.  classify the vmax file creating another array of values classified
cellsize 0.00833333333333
cellsize 0.00833333333333
cellsize 0.00833333333333
>>  6.  print / save output in  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.00/wind_popDensValues_t0.xml
>>  7.  remove files 
done
t0 completed 

Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.00/20190423.00_wind.tif.
Using internal nodata values (e.g. -999) for image /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.00/20190423.00_wind.tif.
Copying nodata values from source /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.00/20190423.00_wind.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.00//wind10m_res_all.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/GDACS/DATA/lspop20141.tif.
Using internal nodata values (e.g. -2.14748e+09) for image /mnt/output/GDACS/DATA/lspop20141.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/lspop20141.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.00//wind10m_popfile_all_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/GDACS/DATA/countries.tif.
Using internal nodata values (e.g. -32768) for image /mnt/output/GDACS/DATA/countries.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/countries.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.00//wind10m_countryfile_all_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
input var: wind10m
Input File: /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.00/20190423.00_wind.tif
hurName:  
hdate:  
var:  wind10m
description:  wind10m: _ (ECMWF)
OutDir:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.00//
PopFile:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.00//wind10m_popfile_all_clipped.tif
country:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.00//wind10m_countryfile_all_clipped.tif
outxml file:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.00/wind_popDensValues_all.xml
popfile: LandScan
popCellSize= 0.00833333333333
projection= GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433],AUTHORITY["EPSG","4326"]]


>>  1.  resample the tif file to the resolution and proj of pop density  0.00833333333333  deg


>>  2.  read the charactristics of the input file


>>  3a. extract a piece of pop. file corresponding to the required bounding box
>>  3b. extract a piece of countries corresponding to the required bounding box and resolution/proj of pop density
>>  4.  classify the vmax file creating another array of values classified
cellsize 0.00833333333333
cellsize 0.00833333333333
cellsize 0.00833333333333
>>  5.  count the popolation in each cell and assign to the class and write to output
>>  6.  print / save output in  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.00/wind_popDensValues_all.xml
>>  7.  remove files 
done
Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_final.tif.
Using internal nodata values (e.g. 3.40282e+38) for image /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_final.tif.
Copying nodata values from source /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_final.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//wind10m_res_final.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/GDACS/DATA/lspop20141.tif.
Using internal nodata values (e.g. -2.14748e+09) for image /mnt/output/GDACS/DATA/lspop20141.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/lspop20141.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//wind10m_popfile_final_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/GDACS/DATA/countries.tif.
Using internal nodata values (e.g. -32768) for image /mnt/output/GDACS/DATA/countries.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/countries.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//wind10m_countryfile_final_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
t0 completed 

copy:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.00/wind_popDensValues_all.xml in:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/delft3d/20190423.00/wind_popDensValues_all.xml

 >> 3.2. Classify final folder
/mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final/20190423.00_final_completed_wind.txt
input var: wind10m
Input File: /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_final.tif
hurName:  
hdate:  
var:  wind10m
description:  wind10m: _ (ECMWF)
OutDir:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//
PopFile:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//wind10m_popfile_final_clipped.tif
country:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//wind10m_countryfile_final_clipped.tif
outxml file:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final/wind_popDensValues_final.xml
xml file exists...REMOVE
popfile: LandScan
popCellSize= 0.00833333333333
projection= GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433],AUTHORITY["EPSG","4326"]]


>>  1.  resample the tif file to the resolution and proj of pop density  0.00833333333333  deg


>>  2.  read the charactristics of the input file


>>  3a. extract a piece of pop. file corresponding to the required bounding box
>>  3b. extract a piece of countries corresponding to the required bounding box and resolution/proj of pop density
>>  4.  classify the vmax file creating another array of values classified
cellsize 0.00833333333333
cellsize 0.00833333333333
cellsize 0.00833333333333
>>  5.  count the popolation in each cell and assign to the class and write to output
>>  6.  print / save output in  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final/wind_popDensValues_final.xml
>>  7.  remove files 
done
*******************************************************
*  Case Completed									 *
*******************************************************
Case starterd at:  2019-04-23 12:57:54.113838  UTC

Case completed at:  2019-04-23 13:00:52.308899  UTC

**********E  N  D   O F	J O B***********************
*******************************************************
*	   Storm Surge Calculation System (SSCS)		 *
*******************************************************
Now is :  2019-04-23 13:20:19.611851  UTC

inp1= 20190422.12
ncores= 5
var= rain
submitting calc 2019-04-22 12:00:00 2019-04-23 00:00:00 delft3d 19.4 59.72 -36.0 -3.0 4.0 ECMWF 72 15 True GDACS/1000559/3_ECMWF 6 1 False False 5  20190422.12   rain False
*************---------------------******************
ndt: 2
it:  0
ndt: 2
idate:  2019-04-22 12:00:00
running case from  2019-04-22 12:00:00  for  72  h   start= 1
var rain
**** gometeo:  72
listWindows
rundate:20190422.12
RUNNING  2019-04-22 12:00:00  for  72 hours
prevCalcDate 2019-04-22 00:00:00
Nothing to do, case already completed
ret= -3 newcase= False  forceFinal= False  forceBulletin= False
netcdf already created
it:  1
ndt: 2
idate:  2019-04-23 00:00:00
running case from  2019-04-23 00:00:00  for  72  h   start= 0
var rain
**** gometeo:  72
listWindows
rundate:20190423.00
RUNNING  2019-04-23 00:00:00  for  72 hours
prevCalcDate 2019-04-22 12:00:00
0 .. 10 .. 20 .. 30 .. 40 .. 50 .. 60 .. 70 .. 80 .. 90 .. 100 - Done
Nothing to do, case already completed
ret= -3 newcase= False  forceFinal= False  forceBulletin= False
netcdf already created
home dir /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/
ret -3
classifications
wind and rainfall classification
============================================
1. Set INPUT/OUTPUT
============================================
startdate 2019-04-22 12:00:00
currdate 2019-04-23 00:00:00
ndt:  12 delta: 12
nt1=delta 12
nt 72
alldate:  DatetimeIndex(['2019-04-22 12:00:00', '2019-04-23 00:00:00'], dtype='datetime64[ns]', freq='12H')
============================================
2. Processing meteo files: nc2tif
============================================

 >> 2.1. Process curr + past files
processing curr bull... 2019-04-23 00:00:00
forcing ECMWF
verifying that input file is present
start reading nc...
rain
use all data in nc file
nt,nx,ny, ntmax 72 469 574 73
nt,nx,ny, ntmax 72 469 574 73
[19.406266848993972, 0.07, 0, -3.0579964267022164, 0, -0.07]
*********** 12 1 12
varMAX.shape  (469, 574)
19.406266849 59.695364329 -35.9578199457 -3.0579964267
lon 574
lat 469
savemap /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.00/20190423.00_rain.jpg
dtk,nt,ntmax 12 72 73 61
k1, k2, ht 0 12 0


k1, k2, ht 12 24 12


k1, k2, ht 24 36 24


k1, k2, ht 36 48 36


k1, k2, ht 48 60 48


k1, k2, ht 60 72 60


processing all past bull only if Past=True... True

 itdate, istime 2019-04-22 12:00:00 20190422.12
meteo-processing past forecast already completed

 >> 2.2. Process final data files
compfile /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/20190423.00_Final_completed_rain.txt
FINAL
remove maxtif
alldate:  DatetimeIndex(['2019-04-22 12:00:00', '2019-04-23 00:00:00'], dtype='datetime64[ns]', freq='12H')
2


date:  2019-04-23 00:00:00
python  /mnt/output/SSCS/scripts_dev/gdal_calc.py -A /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190422.12/20190422.12_rain_stept0.tif -B /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.00/20190423.00_rain.tif --outfile=/mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_final.tif --calc="A+B"
max file created /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_final.tif
19.441266849 59.551266849 -35.8529964267 -3.0929964267
lon 574
lat 469
savemap /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_FINAL.jpg
ret:  0
============================================
3. Classify meteo + GDACS index score
============================================

 >> 3.1. Classify curr + past forecast
Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.00/20190423.00_rain_t0.tif.
Using internal nodata values (e.g. -999) for image /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.00/20190423.00_rain_t0.tif.
Copying nodata values from source /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.00/20190423.00_rain_t0.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.00//rain_res_t0.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/GDACS/DATA/lspop20141.tif.
Using internal nodata values (e.g. -2.14748e+09) for image /mnt/output/GDACS/DATA/lspop20141.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/lspop20141.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.00//rain_popfile_t0_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/GDACS/DATA/countries.tif.
Using internal nodata values (e.g. -32768) for image /mnt/output/GDACS/DATA/countries.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/countries.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.00//rain_countryfile_t0_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
input var: rain
Input File: /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.00/20190423.00_rain_t0.tif
hurName:  
hdate:  
var:  rain
description:  rain: _ (ECMWF)
OutDir:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.00//
PopFile:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.00//rain_popfile_t0_clipped.tif
country:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.00//rain_countryfile_t0_clipped.tif
outxml file:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.00/rain_popDensValues_t0.xml
popfile: LandScan
popCellSize= 0.00833333333333
projection= GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433],AUTHORITY["EPSG","4326"]]


>>  1.  resample the tif file to the resolution and proj of pop density  0.00833333333333  deg


>>  2.  read the charactristics of the input file


>>  3a. extract a piece of pop. file corresponding to the required bounding box
>>  3b. extract a piece of countries corresponding to the required bounding box and resolution/proj of pop density
>>  4.  classify the vmax file creating another array of values classified
cellsize 0.00833333333333
cellsize 0.00833333333333
cellsize 0.00833333333333
>>  5.  count the popolation in each cell and assign to the class and write to output
>>  6.  print / save output in  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.00/rain_popDensValues_t0.xml
>>  7.  remove files 
done
t0 completed 

Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.00/20190423.00_rain.tif.
Using internal nodata values (e.g. -999) for image /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.00/20190423.00_rain.tif.
Copying nodata values from source /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.00/20190423.00_rain.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.00//rain_res_all.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/GDACS/DATA/lspop20141.tif.
Using internal nodata values (e.g. -2.14748e+09) for image /mnt/output/GDACS/DATA/lspop20141.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/lspop20141.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.00//rain_popfile_all_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/GDACS/DATA/countries.tif.
Using internal nodata values (e.g. -32768) for image /mnt/output/GDACS/DATA/countries.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/countries.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.00//rain_countryfile_all_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
input var: rain
Input File: /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.00/20190423.00_rain.tif
hurName:  
hdate:  
var:  rain
description:  rain: _ (ECMWF)
OutDir:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.00//
PopFile:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.00//rain_popfile_all_clipped.tif
country:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.00//rain_countryfile_all_clipped.tif
outxml file:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.00/rain_popDensValues_all.xml
popfile: LandScan
popCellSize= 0.00833333333333
projection= GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433],AUTHORITY["EPSG","4326"]]


>>  1.  resample the tif file to the resolution and proj of pop density  0.00833333333333  deg


>>  2.  read the charactristics of the input file


>>  3a. extract a piece of pop. file corresponding to the required bounding box
>>  3b. extract a piece of countries corresponding to the required bounding box and resolution/proj of pop density
>>  4.  classify the vmax file creating another array of values classified
cellsize 0.00833333333333
cellsize 0.00833333333333
cellsize 0.00833333333333
>>  5.  count the popolation in each cell and assign to the class and write to output
>>  6.  print / save output in  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.00/rain_popDensValues_all.xml
>>  7.  remove files 
done
Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_final.tif.
Using internal nodata values (e.g. 3.40282e+38) for image /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_final.tif.
Copying nodata values from source /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_final.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//rain_res_final.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/GDACS/DATA/lspop20141.tif.
Using internal nodata values (e.g. -2.14748e+09) for image /mnt/output/GDACS/DATA/lspop20141.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/lspop20141.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//rain_popfile_final_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/GDACS/DATA/countries.tif.
Using internal nodata values (e.g. -32768) for image /mnt/output/GDACS/DATA/countries.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/countries.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//rain_countryfile_final_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
t0 completed 

copy:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.00/rain_popDensValues_all.xml in:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/delft3d/20190423.00/rain_popDensValues_all.xml

 >> 3.2. Classify final folder
/mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final/20190423.00_final_completed_rain.txt
input var: rain
Input File: /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_final.tif
hurName:  
hdate:  
var:  rain
description:  rain: _ (ECMWF)
OutDir:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//
PopFile:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//rain_popfile_final_clipped.tif
country:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//rain_countryfile_final_clipped.tif
outxml file:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final/rain_popDensValues_final.xml
xml file exists...REMOVE
popfile: LandScan
popCellSize= 0.00833333333333
projection= GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433],AUTHORITY["EPSG","4326"]]


>>  1.  resample the tif file to the resolution and proj of pop density  0.00833333333333  deg


>>  2.  read the charactristics of the input file


>>  3a. extract a piece of pop. file corresponding to the required bounding box
>>  3b. extract a piece of countries corresponding to the required bounding box and resolution/proj of pop density
>>  4.  classify the vmax file creating another array of values classified
cellsize 0.00833333333333
cellsize 0.00833333333333
cellsize 0.00833333333333
>>  5.  count the popolation in each cell and assign to the class and write to output
>>  6.  print / save output in  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final/rain_popDensValues_final.xml
>>  7.  remove files 
done
==============================================================
*******************************************************
*	   Storm Surge Calculation System (SSCS)		 *
*******************************************************
Now is :  2019-04-23 13:34:39.535201  UTC

inp1= 20190422.12
ncores= 5
var= wind
submitting calc 2019-04-22 12:00:00 2019-04-23 00:00:00 delft3d 19.4 59.72 -36.0 -3.0 4.0 ECMWF 72 15 True GDACS/1000559/3_ECMWF 6 1 False False 5  20190422.12   wind False
*************---------------------******************
ndt: 2
it:  0
ndt: 2
idate:  2019-04-22 12:00:00
running case from  2019-04-22 12:00:00  for  72  h   start= 1
var wind
**** gometeo:  72
listWindows
rundate:20190422.12
RUNNING  2019-04-22 12:00:00  for  72 hours
prevCalcDate 2019-04-22 00:00:00

============================================
process meteo
============================================
1. process meteo, starting at  2019-04-23 13:34:39.558283

..1.1 calling ECMWF_2_netcdf...
...file netcdf already existing : /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/netcdf/20190422.12.tropical_cyclone.nc
ret= 0
removing submitted
ret= 0 newcase= False  forceFinal= False  forceBulletin= False
it:  1
ndt: 2
idate:  2019-04-23 00:00:00
running case from  2019-04-23 00:00:00  for  72  h   start= 0
var wind
**** gometeo:  72
listWindows
rundate:20190423.00
RUNNING  2019-04-23 00:00:00  for  72 hours
prevCalcDate 2019-04-22 12:00:00

============================================
process meteo
============================================
1. process meteo, starting at  2019-04-23 13:34:39.570202

..1.1 calling ECMWF_2_netcdf...
...file netcdf already existing : /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/netcdf/20190423.00.tropical_cyclone.nc
ret= 0
removing submitted
ret= 0 newcase= True  forceFinal= False  forceBulletin= False
home dir /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/
ret 0
classifications
============================================
1. Set INPUT/OUTPUT
============================================
startdate 2019-04-22 12:00:00
currdate 2019-04-23 00:00:00
ndt:  12 delta: 12
nt1=delta 12
nt 72
alldate:  DatetimeIndex(['2019-04-22 12:00:00', '2019-04-23 00:00:00'], dtype='datetime64[ns]', freq='12H')
============================================
2. Processing meteo files: nc2tif
============================================

 >> 2.1. Process curr + past files
processing curr bull... 2019-04-23 00:00:00
processing all past bull only if Past=True... True

 itdate, istime 2019-04-22 12:00:00 20190422.12
meteo-processing past forecast already completed

 >> 2.2. Process final data files
compfile /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/20190423.00_Final_completed_wind.txt
============================================
3. Classify meteo + GDACS index score
============================================

 >> 3.1. Classify curr + past forecast
t0 completed 

t0 completed 


 >> 3.2. Classify final folder
/mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final/20190423.00_final_completed_wind.txt
*******************************************************
*  Case Completed									 *
*******************************************************
Case starterd at:  2019-04-23 13:34:39.535201  UTC

Case completed at:  2019-04-23 13:34:39.607682  UTC

**********E  N  D   O F	J O B***********************
*******************************************************
*	   Storm Surge Calculation System (SSCS)		 *
*******************************************************
Now is :  2019-04-23 13:34:43.526189  UTC

inp1= 20190422.12
ncores= 5
var= rain
submitting calc 2019-04-22 12:00:00 2019-04-23 00:00:00 delft3d 19.4 59.72 -36.0 -3.0 4.0 ECMWF 72 15 True GDACS/1000559/3_ECMWF 6 1 False False 5  20190422.12   rain False
*************---------------------******************
ndt: 2
it:  0
ndt: 2
idate:  2019-04-22 12:00:00
running case from  2019-04-22 12:00:00  for  72  h   start= 1
var rain
**** gometeo:  72
listWindows
rundate:20190422.12
RUNNING  2019-04-22 12:00:00  for  72 hours
prevCalcDate 2019-04-22 00:00:00
Nothing to do, case already completed
ret= -3 newcase= False  forceFinal= False  forceBulletin= False
netcdf already created
it:  1
ndt: 2
idate:  2019-04-23 00:00:00
running case from  2019-04-23 00:00:00  for  72  h   start= 0
var rain
**** gometeo:  72
listWindows
rundate:20190423.00
RUNNING  2019-04-23 00:00:00  for  72 hours
prevCalcDate 2019-04-22 12:00:00
Nothing to do, case already completed
ret= -3 newcase= False  forceFinal= False  forceBulletin= False
netcdf already created
home dir /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/
ret -3
classifications
============================================
1. Set INPUT/OUTPUT
============================================
startdate 2019-04-22 12:00:00
currdate 2019-04-23 00:00:00
ndt:  12 delta: 12
nt1=delta 12
nt 72
alldate:  DatetimeIndex(['2019-04-22 12:00:00', '2019-04-23 00:00:00'], dtype='datetime64[ns]', freq='12H')
============================================
2. Processing meteo files: nc2tif
============================================

 >> 2.1. Process curr + past files
processing curr bull... 2019-04-23 00:00:00
processing all past bull only if Past=True... True

 itdate, istime 2019-04-22 12:00:00 20190422.12
meteo-processing past forecast already completed

 >> 2.2. Process final data files
compfile /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/20190423.00_Final_completed_rain.txt
============================================
3. Classify meteo + GDACS index score
============================================

 >> 3.1. Classify curr + past forecast
t0 completed 

t0 completed 


 >> 3.2. Classify final folder
/mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final/20190423.00_final_completed_rain.txt
==============================================================
*******************************************************
*	   Storm Surge Calculation System (SSCS)		 *
*******************************************************
Now is :  2019-04-24 06:18:13.160937  UTC

inp1= 20190422.12
ncores= 5
var= wind
submitting calc 2019-04-22 12:00:00 2019-04-23 12:00:00 delft3d 19.4 59.72 -36.0 -3.0 4.0 ECMWF 72 15 True GDACS/1000559/3_ECMWF 6 1 False False 5  20190422.12   wind False
*************---------------------******************
ndt: 3
it:  0
ndt: 3
idate:  2019-04-22 12:00:00
running case from  2019-04-22 12:00:00  for  72  h   start= 1
var wind
**** gometeo:  72
listWindows
rundate:20190422.12
RUNNING  2019-04-22 12:00:00  for  72 hours
prevCalcDate 2019-04-22 00:00:00

============================================
process meteo
============================================
1. process meteo, starting at  2019-04-24 06:18:13.235210

..1.1 calling ECMWF_2_netcdf...
...file netcdf already existing : /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/netcdf/20190422.12.tropical_cyclone.nc
ret= 0
removing submitted
ret= 0 newcase= False  forceFinal= False  forceBulletin= False
it:  1
ndt: 3
idate:  2019-04-23 00:00:00
running case from  2019-04-23 00:00:00  for  72  h   start= 0
var wind
**** gometeo:  72
listWindows
rundate:20190423.00
RUNNING  2019-04-23 00:00:00  for  72 hours
prevCalcDate 2019-04-22 12:00:00

============================================
process meteo
============================================
1. process meteo, starting at  2019-04-24 06:18:13.282946

..1.1 calling ECMWF_2_netcdf...
...file netcdf already existing : /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/netcdf/20190423.00.tropical_cyclone.nc
ret= 0
removing submitted
ret= 0 newcase= True  forceFinal= False  forceBulletin= False
it:  2
ndt: 3
idate:  2019-04-23 12:00:00
running case from  2019-04-23 12:00:00  for  72  h   start= 0
var wind
**** gometeo:  72
listWindows
rundate:20190423.12
RUNNING  2019-04-23 12:00:00  for  72 hours
prevCalcDate 2019-04-23 00:00:00

============================================
process meteo
============================================
1. process meteo, starting at  2019-04-24 06:18:13.331908
0 .. 10 .. 20 .. 30 .. 40 .. 50 .. 60 .. 70 .. 80 .. 90 .. 100 - Done
0 .. 10 .. 20 .. 30 .. 40 .. 50 .. 60 .. 70 .. 80 .. 90 .. 100 - Done

..1.1 calling ECMWF_2_netcdf...
...file netcdf already existing : /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/netcdf/20190423.12.tropical_cyclone.nc
ret= 0
removing submitted
ret= 0 newcase= True  forceFinal= False  forceBulletin= False
home dir /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/
ret 0
classifications
============================================
1. Set INPUT/OUTPUT
============================================
startdate 2019-04-22 12:00:00
currdate 2019-04-23 12:00:00
ndt:  24 delta: 12
nt1=delta 12
nt 72
alldate:  DatetimeIndex(['2019-04-22 12:00:00', '2019-04-23 00:00:00',
               '2019-04-23 12:00:00'],
              dtype='datetime64[ns]', freq='12H')
============================================
2. Processing meteo files: nc2tif
============================================

 >> 2.1. Process curr + past files
processing curr bull... 2019-04-23 12:00:00
forcing ECMWF
verifying that input file is present
start reading nc...
wind
ntNC:  73   ntmax 73
use all data in nc file
...create velAll
73 469 573
...start calculating velAll
...end calculating velAll
73 469 573
nt,nx,ny, ntmax 73 469 573 73
[19.399999618530273, 0.07, 0, -3.0, 0, -0.071]
*********** 12 1 12
varMAX.shape  (469, 573)
19.3999996185 59.7200012207 -36.0 -3.0
lon 573
lat 469
savemap /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.12/20190423.12_wind.jpg
dtk,nt,ntmax 12 73 73 61
k1, k2, ht 0 12 0


k1, k2, ht 12 24 12


k1, k2, ht 24 36 24


k1, k2, ht 36 48 36


k1, k2, ht 48 60 48


k1, k2, ht 60 72 60


processing all past bull only if Past=True... True

 itdate, istime 2019-04-22 12:00:00 20190422.12
meteo-processing past forecast already completed

 itdate, istime 2019-04-23 00:00:00 20190423.00
meteo-processing past forecast already completed

 >> 2.2. Process final data files
compfile /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/20190423.12_Final_completed_wind.txt
FINAL
remove maxtif
alldate:  DatetimeIndex(['2019-04-22 12:00:00', '2019-04-23 00:00:00',
               '2019-04-23 12:00:00'],
              dtype='datetime64[ns]', freq='12H')
3


date:  2019-04-23 00:00:00
python  /mnt/output/SSCS/scripts_dev/gdal_calc.py -A /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190422.12/20190422.12_wind_stept0.tif -B /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.00/20190423.00_wind_stept0.tif --outfile=/mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_final.tif --calc="maximum(A,B)"


date:  2019-04-23 12:00:00
python  /mnt/output/SSCS/scripts_dev/gdal_calc.py -A /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_final.tif -B /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.12/20190423.12_wind.tif --outfile=/mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_final.tif --calc="maximum(A,B)"
max file created /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_final.tif
19.4349996185 59.4749996185 -35.795 -3.035
lon 573
lat 469
savemap /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_FINAL.jpg
ret:  0
============================================
3. Classify meteo + GDACS index score
============================================

 >> 3.1. Classify curr + past forecast
Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.12/20190423.12_wind_t0.tif.
Using internal nodata values (e.g. -999) for image /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.12/20190423.12_wind_t0.tif.
Copying nodata values from source /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.12/20190423.12_wind_t0.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.12//wind10m_res_t0.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/GDACS/DATA/lspop20141.tif.
Using internal nodata values (e.g. -2.14748e+09) for image /mnt/output/GDACS/DATA/lspop20141.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/lspop20141.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.12//wind10m_popfile_t0_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/GDACS/DATA/countries.tif.
Using internal nodata values (e.g. -32768) for image /mnt/output/GDACS/DATA/countries.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/countries.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.12//wind10m_countryfile_t0_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
input var: wind10m
Input File: /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.12/20190423.12_wind_t0.tif
hurName:  
hdate:  
var:  wind10m
description:  wind10m: _ (ECMWF)
OutDir:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.12//
PopFile:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.12//wind10m_popfile_t0_clipped.tif
country:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.12//wind10m_countryfile_t0_clipped.tif
outxml file:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.12/wind_popDensValues_t0.xml
outDir created
popfile: LandScan
popCellSize= 0.00833333333333
projection= GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433],AUTHORITY["EPSG","4326"]]


>>  1.  resample the tif file to the resolution and proj of pop density  0.00833333333333  deg


>>  2.  read the charactristics of the input file


>>  3a. extract a piece of pop. file corresponding to the required bounding box
>>  3b. extract a piece of countries corresponding to the required bounding box and resolution/proj of pop density
>>  4.  classify the vmax file creating another array of values classified
cellsize 0.00833333333333
cellsize 0.00833333333333
cellsize 0.00833333333333
>>  5.  count the popolation in each cell and assign to the class and write to output
>>  6.  print / save output in  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.12/wind_popDensValues_t0.xml
>>  7.  remove files 
done
t0 completed 

Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.12/20190423.12_wind.tif.
Using internal nodata values (e.g. -999) for image /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.12/20190423.12_wind.tif.
Copying nodata values from source /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.12/20190423.12_wind.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.12//wind10m_res_all.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/GDACS/DATA/lspop20141.tif.
Using internal nodata values (e.g. -2.14748e+09) for image /mnt/output/GDACS/DATA/lspop20141.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/lspop20141.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.12//wind10m_popfile_all_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/GDACS/DATA/countries.tif.
Using internal nodata values (e.g. -32768) for image /mnt/output/GDACS/DATA/countries.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/countries.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.12//wind10m_countryfile_all_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
input var: wind10m
Input File: /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.12/20190423.12_wind.tif
hurName:  
hdate:  
var:  wind10m
description:  wind10m: _ (ECMWF)
OutDir:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.12//
PopFile:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.12//wind10m_popfile_all_clipped.tif
country:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.12//wind10m_countryfile_all_clipped.tif
outxml file:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.12/wind_popDensValues_all.xml
popfile: LandScan
popCellSize= 0.00833333333333
projection= GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433],AUTHORITY["EPSG","4326"]]


>>  1.  resample the tif file to the resolution and proj of pop density  0.00833333333333  deg


>>  2.  read the charactristics of the input file


>>  3a. extract a piece of pop. file corresponding to the required bounding box
>>  3b. extract a piece of countries corresponding to the required bounding box and resolution/proj of pop density
>>  4.  classify the vmax file creating another array of values classified
cellsize 0.00833333333333
cellsize 0.00833333333333
cellsize 0.00833333333333
>>  5.  count the popolation in each cell and assign to the class and write to output
>>  6.  print / save output in  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.12/wind_popDensValues_all.xml
>>  7.  remove files 
done
Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_final.tif.
Using internal nodata values (e.g. 3.40282e+38) for image /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_final.tif.
Copying nodata values from source /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_final.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//wind10m_res_final.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/GDACS/DATA/lspop20141.tif.
Using internal nodata values (e.g. -2.14748e+09) for image /mnt/output/GDACS/DATA/lspop20141.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/lspop20141.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//wind10m_popfile_final_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/GDACS/DATA/countries.tif.
Using internal nodata values (e.g. -32768) for image /mnt/output/GDACS/DATA/countries.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/countries.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//wind10m_countryfile_final_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
t0 completed 

copy:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.12/wind_popDensValues_all.xml in:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/delft3d/20190423.12/wind_popDensValues_all.xml

 >> 3.2. Classify final folder
/mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final/20190423.12_final_completed_wind.txt
input var: wind10m
Input File: /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_final.tif
hurName:  
hdate:  
var:  wind10m
description:  wind10m: _ (ECMWF)
OutDir:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//
PopFile:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//wind10m_popfile_final_clipped.tif
country:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//wind10m_countryfile_final_clipped.tif
outxml file:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final/wind_popDensValues_final.xml
xml file exists...REMOVE
popfile: LandScan
popCellSize= 0.00833333333333
projection= GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433],AUTHORITY["EPSG","4326"]]


>>  1.  resample the tif file to the resolution and proj of pop density  0.00833333333333  deg


>>  2.  read the charactristics of the input file


>>  3a. extract a piece of pop. file corresponding to the required bounding box
>>  3b. extract a piece of countries corresponding to the required bounding box and resolution/proj of pop density
>>  4.  classify the vmax file creating another array of values classified
cellsize 0.00833333333333
cellsize 0.00833333333333
cellsize 0.00833333333333
>>  5.  count the popolation in each cell and assign to the class and write to output
>>  6.  print / save output in  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final/wind_popDensValues_final.xml
>>  7.  remove files 
done
*******************************************************
*  Case Completed									 *
*******************************************************
Case starterd at:  2019-04-24 06:18:13.160937  UTC

Case completed at:  2019-04-24 06:21:51.795436  UTC

**********E  N  D   O F	J O B***********************
*******************************************************
*	   Storm Surge Calculation System (SSCS)		 *
*******************************************************
Now is :  2019-04-24 06:43:12.590023  UTC

inp1= 20190422.12
ncores= 5
var= rain
submitting calc 2019-04-22 12:00:00 2019-04-23 12:00:00 delft3d 19.4 59.72 -36.0 -3.0 4.0 ECMWF 72 15 True GDACS/1000559/3_ECMWF 6 1 False False 5  20190422.12   rain False
*************---------------------******************
ndt: 3
it:  0
ndt: 3
idate:  2019-04-22 12:00:00
running case from  2019-04-22 12:00:00  for  72  h   start= 1
var rain
**** gometeo:  72
listWindows
rundate:20190422.12
RUNNING  2019-04-22 12:00:00  for  72 hours
prevCalcDate 2019-04-22 00:00:00
Nothing to do, case already completed
ret= -3 newcase= False  forceFinal= False  forceBulletin= False
netcdf already created
it:  1
ndt: 3
idate:  2019-04-23 00:00:00
running case from  2019-04-23 00:00:00  for  72  h   start= 0
var rain
**** gometeo:  72
listWindows
rundate:20190423.00
RUNNING  2019-04-23 00:00:00  for  72 hours
prevCalcDate 2019-04-22 12:00:00
Nothing to do, case already completed
ret= -3 newcase= False  forceFinal= False  forceBulletin= False
netcdf already created
it:  2
ndt: 3
idate:  2019-04-23 12:00:00
running case from  2019-04-23 12:00:00  for  72  h   start= 0
var rain
**** gometeo:  72
listWindows
rundate:20190423.12
RUNNING  2019-04-23 12:00:00  for  72 hours
prevCalcDate 2019-04-23 00:00:00
0 .. 10 .. 20 .. 30 .. 40 .. 50 .. 60 .. 70 .. 80 .. 90 .. 100 - Done
0 .. 10 .. 20 .. 30 .. 40 .. 50 .. 60 .. 70 .. 80 .. 90 .. 100 - Done
Nothing to do, case already completed
ret= -3 newcase= False  forceFinal= False  forceBulletin= False
netcdf already created
home dir /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/
ret -3
classifications
============================================
1. Set INPUT/OUTPUT
============================================
startdate 2019-04-22 12:00:00
currdate 2019-04-23 12:00:00
ndt:  24 delta: 12
nt1=delta 12
nt 72
alldate:  DatetimeIndex(['2019-04-22 12:00:00', '2019-04-23 00:00:00',
               '2019-04-23 12:00:00'],
              dtype='datetime64[ns]', freq='12H')
============================================
2. Processing meteo files: nc2tif
============================================

 >> 2.1. Process curr + past files
processing curr bull... 2019-04-23 12:00:00
forcing ECMWF
verifying that input file is present
start reading nc...
rain
use all data in nc file
nt,nx,ny, ntmax 72 469 574 73
nt,nx,ny, ntmax 72 469 574 73
[19.406266848993972, 0.07, 0, -3.0579964267022164, 0, -0.07]
*********** 12 1 12
varMAX.shape  (469, 574)
19.406266849 59.695364329 -35.9578199457 -3.0579964267
lon 574
lat 469
savemap /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.12/20190423.12_rain.jpg
dtk,nt,ntmax 12 72 73 61
k1, k2, ht 0 12 0


k1, k2, ht 12 24 12


k1, k2, ht 24 36 24


k1, k2, ht 36 48 36


k1, k2, ht 48 60 48


k1, k2, ht 60 72 60


processing all past bull only if Past=True... True

 itdate, istime 2019-04-22 12:00:00 20190422.12
meteo-processing past forecast already completed

 itdate, istime 2019-04-23 00:00:00 20190423.00
meteo-processing past forecast already completed

 >> 2.2. Process final data files
compfile /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/20190423.12_Final_completed_rain.txt
FINAL
remove maxtif
alldate:  DatetimeIndex(['2019-04-22 12:00:00', '2019-04-23 00:00:00',
               '2019-04-23 12:00:00'],
              dtype='datetime64[ns]', freq='12H')
3


date:  2019-04-23 00:00:00
python  /mnt/output/SSCS/scripts_dev/gdal_calc.py -A /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190422.12/20190422.12_rain_stept0.tif -B /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.00/20190423.00_rain_stept0.tif --outfile=/mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_final.tif --calc="A+B"


date:  2019-04-23 12:00:00
python  /mnt/output/SSCS/scripts_dev/gdal_calc.py -A /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_final.tif -B /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.12/20190423.12_rain.tif --outfile=/mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_final.tif --calc="A+B"
max file created /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_final.tif
19.441266849 59.551266849 -35.8529964267 -3.0929964267
lon 574
lat 469
savemap /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_FINAL.jpg
ret:  0
============================================
3. Classify meteo + GDACS index score
============================================

 >> 3.1. Classify curr + past forecast
Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.12/20190423.12_rain_t0.tif.
Using internal nodata values (e.g. -999) for image /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.12/20190423.12_rain_t0.tif.
Copying nodata values from source /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.12/20190423.12_rain_t0.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.12//rain_res_t0.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/GDACS/DATA/lspop20141.tif.
Using internal nodata values (e.g. -2.14748e+09) for image /mnt/output/GDACS/DATA/lspop20141.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/lspop20141.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.12//rain_popfile_t0_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/GDACS/DATA/countries.tif.
Using internal nodata values (e.g. -32768) for image /mnt/output/GDACS/DATA/countries.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/countries.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.12//rain_countryfile_t0_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
input var: rain
Input File: /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.12/20190423.12_rain_t0.tif
hurName:  
hdate:  
var:  rain
description:  rain: _ (ECMWF)
OutDir:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.12//
PopFile:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.12//rain_popfile_t0_clipped.tif
country:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.12//rain_countryfile_t0_clipped.tif
outxml file:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.12/rain_popDensValues_t0.xml
popfile: LandScan
popCellSize= 0.00833333333333
projection= GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433],AUTHORITY["EPSG","4326"]]


>>  1.  resample the tif file to the resolution and proj of pop density  0.00833333333333  deg


>>  2.  read the charactristics of the input file


>>  3a. extract a piece of pop. file corresponding to the required bounding box
>>  3b. extract a piece of countries corresponding to the required bounding box and resolution/proj of pop density
>>  4.  classify the vmax file creating another array of values classified
cellsize 0.00833333333333
cellsize 0.00833333333333
cellsize 0.00833333333333
>>  5.  count the popolation in each cell and assign to the class and write to output
>>  6.  print / save output in  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.12/rain_popDensValues_t0.xml
>>  7.  remove files 
done
t0 completed 

Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.12/20190423.12_rain.tif.
Using internal nodata values (e.g. -999) for image /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.12/20190423.12_rain.tif.
Copying nodata values from source /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.12/20190423.12_rain.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.12//rain_res_all.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/GDACS/DATA/lspop20141.tif.
Using internal nodata values (e.g. -2.14748e+09) for image /mnt/output/GDACS/DATA/lspop20141.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/lspop20141.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.12//rain_popfile_all_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/GDACS/DATA/countries.tif.
Using internal nodata values (e.g. -32768) for image /mnt/output/GDACS/DATA/countries.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/countries.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.12//rain_countryfile_all_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
input var: rain
Input File: /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.12/20190423.12_rain.tif
hurName:  
hdate:  
var:  rain
description:  rain: _ (ECMWF)
OutDir:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.12//
PopFile:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.12//rain_popfile_all_clipped.tif
country:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.12//rain_countryfile_all_clipped.tif
outxml file:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.12/rain_popDensValues_all.xml
popfile: LandScan
popCellSize= 0.00833333333333
projection= GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433],AUTHORITY["EPSG","4326"]]


>>  1.  resample the tif file to the resolution and proj of pop density  0.00833333333333  deg


>>  2.  read the charactristics of the input file


>>  3a. extract a piece of pop. file corresponding to the required bounding box
>>  3b. extract a piece of countries corresponding to the required bounding box and resolution/proj of pop density
>>  4.  classify the vmax file creating another array of values classified
cellsize 0.00833333333333
cellsize 0.00833333333333
cellsize 0.00833333333333
>>  5.  count the popolation in each cell and assign to the class and write to output
>>  6.  print / save output in  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.12/rain_popDensValues_all.xml
>>  7.  remove files 
done
Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_final.tif.
Using internal nodata values (e.g. 3.40282e+38) for image /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_final.tif.
Copying nodata values from source /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_final.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//rain_res_final.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/GDACS/DATA/lspop20141.tif.
Using internal nodata values (e.g. -2.14748e+09) for image /mnt/output/GDACS/DATA/lspop20141.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/lspop20141.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//rain_popfile_final_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/GDACS/DATA/countries.tif.
Using internal nodata values (e.g. -32768) for image /mnt/output/GDACS/DATA/countries.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/countries.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//rain_countryfile_final_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
t0 completed 

copy:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190423.12/rain_popDensValues_all.xml in:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/delft3d/20190423.12/rain_popDensValues_all.xml

 >> 3.2. Classify final folder
/mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final/20190423.12_final_completed_rain.txt
input var: rain
Input File: /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_final.tif
hurName:  
hdate:  
var:  rain
description:  rain: _ (ECMWF)
OutDir:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//
PopFile:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//rain_popfile_final_clipped.tif
country:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//rain_countryfile_final_clipped.tif
outxml file:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final/rain_popDensValues_final.xml
xml file exists...REMOVE
popfile: LandScan
popCellSize= 0.00833333333333
projection= GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433],AUTHORITY["EPSG","4326"]]


>>  1.  resample the tif file to the resolution and proj of pop density  0.00833333333333  deg


>>  2.  read the charactristics of the input file


>>  3a. extract a piece of pop. file corresponding to the required bounding box
>>  3b. extract a piece of countries corresponding to the required bounding box and resolution/proj of pop density
>>  4.  classify the vmax file creating another array of values classified
cellsize 0.00833333333333
cellsize 0.00833333333333
cellsize 0.00833333333333
>>  5.  count the popolation in each cell and assign to the class and write to output
>>  6.  print / save output in  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final/rain_popDensValues_final.xml
>>  7.  remove files 
done
==============================================================
*******************************************************
*	   Storm Surge Calculation System (SSCS)		 *
*******************************************************
Now is :  2019-04-24 10:46:42.330569  UTC

inp1= 20190422.12
ncores= 5
var= rain
submitting calc 2019-04-22 12:00:00 2019-04-24 00:00:00 delft3d 19.4 59.72 -36.0 -3.0 4.0 ECMWF 72 15 True GDACS/1000559/3_ECMWF 6 1 False False 5  20190422.12   rain False
*************---------------------******************
ndt: 4
it:  0
ndt: 4
idate:  2019-04-22 12:00:00
running case from  2019-04-22 12:00:00  for  72  h   start= 1
var rain
**** gometeo:  72
listWindows
rundate:20190422.12
RUNNING  2019-04-22 12:00:00  for  72 hours
prevCalcDate 2019-04-22 00:00:00
Nothing to do, case already completed
ret= -3 newcase= False  forceFinal= False  forceBulletin= False
netcdf already created
it:  1
ndt: 4
idate:  2019-04-23 00:00:00
running case from  2019-04-23 00:00:00  for  72  h   start= 0
var rain
**** gometeo:  72
listWindows
rundate:20190423.00
RUNNING  2019-04-23 00:00:00  for  72 hours
prevCalcDate 2019-04-22 12:00:00
Nothing to do, case already completed
ret= -3 newcase= False  forceFinal= False  forceBulletin= False
netcdf already created
it:  2
ndt: 4
idate:  2019-04-23 12:00:00
running case from  2019-04-23 12:00:00  for  72  h   start= 0
var rain
**** gometeo:  72
listWindows
rundate:20190423.12
RUNNING  2019-04-23 12:00:00  for  72 hours
prevCalcDate 2019-04-23 00:00:00
Nothing to do, case already completed
ret= -3 newcase= False  forceFinal= False  forceBulletin= False
netcdf already created
it:  3
ndt: 4
idate:  2019-04-24 00:00:00
running case from  2019-04-24 00:00:00  for  72  h   start= 0
var rain
**** gometeo:  72
listWindows
rundate:20190424.00
RUNNING  2019-04-24 00:00:00  for  72 hours
prevCalcDate 2019-04-23 12:00:00
0 .. 10 .. 20 .. 30 .. 40 .. 50 .. 60 .. 70 .. 80 .. 90 .. 100 - Done
0 .. 10 .. 20 .. 30 .. 40 .. 50 .. 60 .. 70 .. 80 .. 90 .. 100 - Done
0 .. 10 .. 20 .. 30 .. 40 .. 50 .. 60 .. 70 .. 80 .. 90 .. 100 - Done
Nothing to do, case already completed
ret= -3 newcase= False  forceFinal= False  forceBulletin= False
netcdf already created
home dir /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/
ret -3
classifications
============================================
1. Set INPUT/OUTPUT
============================================
startdate 2019-04-22 12:00:00
currdate 2019-04-24 00:00:00
ndt:  36 delta: 12
nt1=delta 12
nt 72
alldate:  DatetimeIndex(['2019-04-22 12:00:00', '2019-04-23 00:00:00',
               '2019-04-23 12:00:00', '2019-04-24 00:00:00'],
              dtype='datetime64[ns]', freq='12H')
============================================
2. Processing meteo files: nc2tif
============================================

 >> 2.1. Process curr + past files
processing curr bull... 2019-04-24 00:00:00
forcing ECMWF
verifying that input file is present
start reading nc...
rain
use all data in nc file
nt,nx,ny, ntmax 72 469 574 73
nt,nx,ny, ntmax 72 469 574 73
[19.406266848993972, 0.07, 0, -3.0579964267022164, 0, -0.07]
*********** 12 1 12
varMAX.shape  (469, 574)


lonmin 19.406266849
latmin -35.9578199457
lonmax 59.695364329
latmax -3.0579964267
<type 'netCDF4.Variable'>
float64 latitude(latitude)
    units: degrees_north
    point_spacing: even
unlimited dimensions: 
current shape = (469,)
filling off

<type 'netCDF4.Variable'>
float64 longitude(longitude)
    units: degrees_east
    point_spacing: even
unlimited dimensions: 
current shape = (574,)
filling off

19.406266849 59.695364329 -35.9578199457 -3.0579964267
lon 574
lat 469
savemap /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190424.00/20190424.00_rain.jpg
dtk,nt,ntmax 12 72 73 61
k1, k2, ht 0 12 0


k1, k2, ht 12 24 12


k1, k2, ht 24 36 24


k1, k2, ht 36 48 36


k1, k2, ht 48 60 48


k1, k2, ht 60 72 60


processing all past bull only if Past=True... True

 itdate, istime 2019-04-22 12:00:00 20190422.12
meteo-processing past forecast already completed

 itdate, istime 2019-04-23 00:00:00 20190423.00
meteo-processing past forecast already completed

 itdate, istime 2019-04-23 12:00:00 20190423.12
meteo-processing past forecast already completed

 >> 2.2. Process final data files
compfile /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/20190424.00_Final_completed_rain.txt
FINAL
remove maxtif
alldate:  DatetimeIndex(['2019-04-22 12:00:00', '2019-04-23 00:00:00',
               '2019-04-23 12:00:00', '2019-04-24 00:00:00'],
              dtype='datetime64[ns]', freq='12H')
4


date:  2019-04-23 00:00:00
python  /mnt/output/SSCS/scripts_dev/gdal_calc.py -A /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190422.12/20190422.12_rain_stept0.tif -B /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.00/20190423.00_rain_stept0.tif --outfile=/mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_final.tif --calc="A+B"


date:  2019-04-23 12:00:00
python  /mnt/output/SSCS/scripts_dev/gdal_calc.py -A /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_final.tif -B /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.12/20190423.12_rain_stept0.tif --outfile=/mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_final.tif --calc="A+B"


date:  2019-04-24 00:00:00
python  /mnt/output/SSCS/scripts_dev/gdal_calc.py -A /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_final.tif -B /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190424.00/20190424.00_rain.tif --outfile=/mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_final.tif --calc="A+B"
max file created /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_final.tif


lonminH 19.441266849
latminH -35.8529964267
lonmaxH 59.551266849
latmaxH -3.0929964267
[[ -3.09299643  -3.09299643  -3.09299643 ...,  -3.09299643  -3.09299643
   -3.09299643]
 [ -3.16299643  -3.16299643  -3.16299643 ...,  -3.16299643  -3.16299643
   -3.16299643]
 [ -3.23299643  -3.23299643  -3.23299643 ...,  -3.23299643  -3.23299643
   -3.23299643]
 ..., 
 [-35.71299643 -35.71299643 -35.71299643 ..., -35.71299643 -35.71299643
  -35.71299643]
 [-35.78299643 -35.78299643 -35.78299643 ..., -35.78299643 -35.78299643
  -35.78299643]
 [-35.85299643 -35.85299643 -35.85299643 ..., -35.85299643 -35.85299643
  -35.85299643]]
[[ 19.44126685  19.51126685  19.58126685 ...,  59.41126685  59.48126685
   59.55126685]
 [ 19.44126685  19.51126685  19.58126685 ...,  59.41126685  59.48126685
   59.55126685]
 [ 19.44126685  19.51126685  19.58126685 ...,  59.41126685  59.48126685
   59.55126685]
 ..., 
 [ 19.44126685  19.51126685  19.58126685 ...,  59.41126685  59.48126685
   59.55126685]
 [ 19.44126685  19.51126685  19.58126685 ...,  59.41126685  59.48126685
   59.55126685]
 [ 19.44126685  19.51126685  19.58126685 ...,  59.41126685  59.48126685
   59.55126685]]
19.441266849 59.551266849 -35.8529964267 -3.0929964267
lon 574
lat 469
savemap /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_FINAL.jpg
ret:  0
============================================
3. Classify meteo + GDACS index score
============================================

 >> 3.1. Classify curr + past forecast
Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190424.00/20190424.00_rain_t0.tif.
Using internal nodata values (e.g. -999) for image /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190424.00/20190424.00_rain_t0.tif.
Copying nodata values from source /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190424.00/20190424.00_rain_t0.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.00//rain_res_t0.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/GDACS/DATA/lspop20141.tif.
Using internal nodata values (e.g. -2.14748e+09) for image /mnt/output/GDACS/DATA/lspop20141.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/lspop20141.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.00//rain_popfile_t0_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/GDACS/DATA/countries.tif.
Using internal nodata values (e.g. -32768) for image /mnt/output/GDACS/DATA/countries.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/countries.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.00//rain_countryfile_t0_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
input var: rain
Input File: /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190424.00/20190424.00_rain_t0.tif
hurName:  
hdate:  
var:  rain
description:  rain: _ (ECMWF)
OutDir:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.00//
PopFile:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.00//rain_popfile_t0_clipped.tif
country:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.00//rain_countryfile_t0_clipped.tif
outxml file:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.00/rain_popDensValues_t0.xml
outDir created
popfile: LandScan
popCellSize= 0.00833333333333
projection= GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433],AUTHORITY["EPSG","4326"]]


>>  1.  resample the tif file to the resolution and proj of pop density  0.00833333333333  deg


>>  2.  read the charactristics of the input file


>>  3a. extract a piece of pop. file corresponding to the required bounding box
>>  3b. extract a piece of countries corresponding to the required bounding box and resolution/proj of pop density
>>  4.  classify the vmax file creating another array of values classified
cellsize 0.00833333333333
cellsize 0.00833333333333
cellsize 0.00833333333333
>>  5.  count the popolation in each cell and assign to the class and write to output
>>  6.  print / save output in  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.00/rain_popDensValues_t0.xml
>>  7.  remove files 
done
t0 completed 

Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190424.00/20190424.00_rain.tif.
Using internal nodata values (e.g. -999) for image /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190424.00/20190424.00_rain.tif.
Copying nodata values from source /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190424.00/20190424.00_rain.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.00//rain_res_all.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/GDACS/DATA/lspop20141.tif.
Using internal nodata values (e.g. -2.14748e+09) for image /mnt/output/GDACS/DATA/lspop20141.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/lspop20141.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.00//rain_popfile_all_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/GDACS/DATA/countries.tif.
Using internal nodata values (e.g. -32768) for image /mnt/output/GDACS/DATA/countries.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/countries.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.00//rain_countryfile_all_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
input var: rain
Input File: /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190424.00/20190424.00_rain.tif
hurName:  
hdate:  
var:  rain
description:  rain: _ (ECMWF)
OutDir:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.00//
PopFile:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.00//rain_popfile_all_clipped.tif
country:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.00//rain_countryfile_all_clipped.tif
outxml file:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.00/rain_popDensValues_all.xml
popfile: LandScan
popCellSize= 0.00833333333333
projection= GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433],AUTHORITY["EPSG","4326"]]


>>  1.  resample the tif file to the resolution and proj of pop density  0.00833333333333  deg


>>  2.  read the charactristics of the input file


>>  3a. extract a piece of pop. file corresponding to the required bounding box
>>  3b. extract a piece of countries corresponding to the required bounding box and resolution/proj of pop density
>>  4.  classify the vmax file creating another array of values classified
cellsize 0.00833333333333
cellsize 0.00833333333333
cellsize 0.00833333333333
>>  5.  count the popolation in each cell and assign to the class and write to output
>>  6.  print / save output in  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.00/rain_popDensValues_all.xml
>>  7.  remove files 
done
Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_final.tif.
Using internal nodata values (e.g. 3.40282e+38) for image /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_final.tif.
Copying nodata values from source /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_final.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//rain_res_final.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/GDACS/DATA/lspop20141.tif.
Using internal nodata values (e.g. -2.14748e+09) for image /mnt/output/GDACS/DATA/lspop20141.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/lspop20141.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//rain_popfile_final_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/GDACS/DATA/countries.tif.
Using internal nodata values (e.g. -32768) for image /mnt/output/GDACS/DATA/countries.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/countries.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//rain_countryfile_final_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
t0 completed 

copy:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.00/rain_popDensValues_all.xml in:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/delft3d/20190424.00/rain_popDensValues_all.xml

 >> 3.2. Classify final folder
/mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final/20190424.00_final_completed_rain.txt
input var: rain
Input File: /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_final.tif
hurName:  
hdate:  
var:  rain
description:  rain: _ (ECMWF)
OutDir:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//
PopFile:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//rain_popfile_final_clipped.tif
country:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//rain_countryfile_final_clipped.tif
outxml file:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final/rain_popDensValues_final.xml
xml file exists...REMOVE
popfile: LandScan
popCellSize= 0.00833333333333
projection= GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433],AUTHORITY["EPSG","4326"]]


>>  1.  resample the tif file to the resolution and proj of pop density  0.00833333333333  deg


>>  2.  read the charactristics of the input file


>>  3a. extract a piece of pop. file corresponding to the required bounding box
>>  3b. extract a piece of countries corresponding to the required bounding box and resolution/proj of pop density
>>  4.  classify the vmax file creating another array of values classified
cellsize 0.00833333333333
cellsize 0.00833333333333
cellsize 0.00833333333333
>>  5.  count the popolation in each cell and assign to the class and write to output
>>  6.  print / save output in  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final/rain_popDensValues_final.xml
>>  7.  remove files 
done
==============================================================
*******************************************************
*	   Storm Surge Calculation System (SSCS)		 *
*******************************************************
Now is :  2019-04-24 14:40:56.999968  UTC

inp1= 20190422.12
ncores= 5
var= wind
submitting calc 2019-04-22 12:00:00 2019-04-24 00:00:00 delft3d 19.4 59.72 -36.0 -3.0 4.0 ECMWF 72 15 True GDACS/1000559/3_ECMWF 6 1 False False 5  20190422.12   wind False
*************---------------------******************
ndt: 4
it:  0
ndt: 4
idate:  2019-04-22 12:00:00
running case from  2019-04-22 12:00:00  for  72  h   start= 1
var wind
**** gometeo:  72
listWindows
rundate:20190422.12
RUNNING  2019-04-22 12:00:00  for  72 hours
prevCalcDate 2019-04-22 00:00:00

============================================
process meteo
============================================
1. process meteo, starting at  2019-04-24 14:40:57.083862

..1.1 calling ECMWF_2_netcdf...
...file netcdf already existing : /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/netcdf/20190422.12.tropical_cyclone.nc
ret= 0
removing submitted
ret= 0 newcase= False  forceFinal= False  forceBulletin= False
it:  1
ndt: 4
idate:  2019-04-23 00:00:00
running case from  2019-04-23 00:00:00  for  72  h   start= 0
var wind
**** gometeo:  72
listWindows
rundate:20190423.00
RUNNING  2019-04-23 00:00:00  for  72 hours
prevCalcDate 2019-04-22 12:00:00

============================================
process meteo
============================================
1. process meteo, starting at  2019-04-24 14:40:57.102555

..1.1 calling ECMWF_2_netcdf...
...file netcdf already existing : /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/netcdf/20190423.00.tropical_cyclone.nc
ret= 0
removing submitted
ret= 0 newcase= True  forceFinal= False  forceBulletin= False
it:  2
ndt: 4
idate:  2019-04-23 12:00:00
running case from  2019-04-23 12:00:00  for  72  h   start= 0
var wind
**** gometeo:  72
listWindows
rundate:20190423.12
RUNNING  2019-04-23 12:00:00  for  72 hours
prevCalcDate 2019-04-23 00:00:00

============================================
process meteo
============================================
1. process meteo, starting at  2019-04-24 14:40:57.122857

..1.1 calling ECMWF_2_netcdf...
...file netcdf already existing : /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/netcdf/20190423.12.tropical_cyclone.nc
ret= 0
removing submitted
ret= 0 newcase= True  forceFinal= False  forceBulletin= False
it:  3
ndt: 4
idate:  2019-04-24 00:00:00
running case from  2019-04-24 00:00:00  for  72  h   start= 0
var wind
**** gometeo:  72
listWindows
rundate:20190424.00
RUNNING  2019-04-24 00:00:00  for  72 hours
prevCalcDate 2019-04-23 12:00:00

============================================
process meteo
============================================
1. process meteo, starting at  2019-04-24 14:40:57.142042
0 .. 10 .. 20 .. 30 .. 40 .. 50 .. 60 .. 70 .. 80 .. 90 .. 100 - Done
0 .. 10 .. 20 .. 30 .. 40 .. 50 .. 60 .. 70 .. 80 .. 90 .. 100 - Done
0 .. 10 .. 20 .. 30 .. 40 .. 50 .. 60 .. 70 .. 80 .. 90 .. 100 - Done

..1.1 calling ECMWF_2_netcdf...
...file netcdf already existing : /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/netcdf/20190424.00.tropical_cyclone.nc
ret= 0
removing submitted
ret= 0 newcase= True  forceFinal= False  forceBulletin= False
home dir /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/
ret 0
classifications
============================================
1. Set INPUT/OUTPUT
============================================
startdate 2019-04-22 12:00:00
currdate 2019-04-24 00:00:00
ndt:  36 delta: 12
nt1=delta 12
nt 72
alldate:  DatetimeIndex(['2019-04-22 12:00:00', '2019-04-23 00:00:00',
               '2019-04-23 12:00:00', '2019-04-24 00:00:00'],
              dtype='datetime64[ns]', freq='12H')
============================================
2. Processing meteo files: nc2tif
============================================

 >> 2.1. Process curr + past files
processing curr bull... 2019-04-24 00:00:00
forcing ECMWF
verifying that input file is present
start reading nc...
wind
ntNC:  73   ntmax 73
use all data in nc file
...create velAll
73 469 573
...start calculating velAll
...end calculating velAll
73 469 573
nt,nx,ny, ntmax 73 469 573 73
[19.399999618530273, 0.07, 0, -3.0, 0, -0.071]
*********** 12 1 12
varMAX.shape  (469, 573)


lonmin 19.3999996185
latmin -36.0
lonmax 59.7200012207
latmax -3.0
<type 'netCDF4.Variable'>
float64 latitude(lat)
    units: degrees_north
    point_spacing: even
unlimited dimensions: 
current shape = (469,)
filling off

<type 'netCDF4.Variable'>
float64 longitude(lon)
    units: degrees_east
    point_spacing: even
unlimited dimensions: 
current shape = (573,)
filling off

19.3999996185 59.7200012207 -36.0 -3.0
lon 573
lat 469
savemap /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190424.00/20190424.00_wind.jpg
dtk,nt,ntmax 12 73 73 61
k1, k2, ht 0 12 0


k1, k2, ht 12 24 12


k1, k2, ht 24 36 24


k1, k2, ht 36 48 36


k1, k2, ht 48 60 48


k1, k2, ht 60 72 60


processing all past bull only if Past=True... True

 itdate, istime 2019-04-22 12:00:00 20190422.12
meteo-processing past forecast already completed

 itdate, istime 2019-04-23 00:00:00 20190423.00
meteo-processing past forecast already completed

 itdate, istime 2019-04-23 12:00:00 20190423.12
meteo-processing past forecast already completed

 >> 2.2. Process final data files
compfile /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/20190424.00_Final_completed_wind.txt
FINAL
remove maxtif
alldate:  DatetimeIndex(['2019-04-22 12:00:00', '2019-04-23 00:00:00',
               '2019-04-23 12:00:00', '2019-04-24 00:00:00'],
              dtype='datetime64[ns]', freq='12H')
4


date:  2019-04-23 00:00:00
python  /mnt/output/SSCS/scripts_dev/gdal_calc.py -A /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190422.12/20190422.12_wind_stept0.tif -B /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.00/20190423.00_wind_stept0.tif --outfile=/mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_final.tif --calc="maximum(A,B)"


date:  2019-04-23 12:00:00
python  /mnt/output/SSCS/scripts_dev/gdal_calc.py -A /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_final.tif -B /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.12/20190423.12_wind_stept0.tif --outfile=/mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_final.tif --calc="maximum(A,B)"


date:  2019-04-24 00:00:00
python  /mnt/output/SSCS/scripts_dev/gdal_calc.py -A /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_final.tif -B /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190424.00/20190424.00_wind.tif --outfile=/mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_final.tif --calc="maximum(A,B)"
max file created /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_final.tif


lonminH 19.4349996185
latminH -35.795
lonmaxH 59.4749996185
latmaxH -3.035
[[ -3.035  -3.035  -3.035 ...,  -3.035  -3.035  -3.035]
 [ -3.105  -3.105  -3.105 ...,  -3.105  -3.105  -3.105]
 [ -3.175  -3.175  -3.175 ...,  -3.175  -3.175  -3.175]
 ..., 
 [-35.655 -35.655 -35.655 ..., -35.655 -35.655 -35.655]
 [-35.725 -35.725 -35.725 ..., -35.725 -35.725 -35.725]
 [-35.795 -35.795 -35.795 ..., -35.795 -35.795 -35.795]]
[[ 19.43499962  19.50499962  19.57499962 ...,  59.33499962  59.40499962
   59.47499962]
 [ 19.43499962  19.50499962  19.57499962 ...,  59.33499962  59.40499962
   59.47499962]
 [ 19.43499962  19.50499962  19.57499962 ...,  59.33499962  59.40499962
   59.47499962]
 ..., 
 [ 19.43499962  19.50499962  19.57499962 ...,  59.33499962  59.40499962
   59.47499962]
 [ 19.43499962  19.50499962  19.57499962 ...,  59.33499962  59.40499962
   59.47499962]
 [ 19.43499962  19.50499962  19.57499962 ...,  59.33499962  59.40499962
   59.47499962]]
19.4349996185 59.4749996185 -35.795 -3.035
lon 573
lat 469
savemap /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_FINAL.jpg
ret:  0
============================================
3. Classify meteo + GDACS index score
============================================

 >> 3.1. Classify curr + past forecast
Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190424.00/20190424.00_wind_t0.tif.
Using internal nodata values (e.g. -999) for image /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190424.00/20190424.00_wind_t0.tif.
Copying nodata values from source /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190424.00/20190424.00_wind_t0.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.00//wind10m_res_t0.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/GDACS/DATA/lspop20141.tif.
Using internal nodata values (e.g. -2.14748e+09) for image /mnt/output/GDACS/DATA/lspop20141.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/lspop20141.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.00//wind10m_popfile_t0_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/GDACS/DATA/countries.tif.
Using internal nodata values (e.g. -32768) for image /mnt/output/GDACS/DATA/countries.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/countries.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.00//wind10m_countryfile_t0_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
input var: wind10m
Input File: /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190424.00/20190424.00_wind_t0.tif
hurName:  
hdate:  
var:  wind10m
description:  wind10m: _ (ECMWF)
OutDir:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.00//
PopFile:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.00//wind10m_popfile_t0_clipped.tif
country:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.00//wind10m_countryfile_t0_clipped.tif
outxml file:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.00/wind_popDensValues_t0.xml
popfile: LandScan
popCellSize= 0.00833333333333
projection= GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433],AUTHORITY["EPSG","4326"]]


>>  1.  resample the tif file to the resolution and proj of pop density  0.00833333333333  deg


>>  2.  read the charactristics of the input file


>>  3a. extract a piece of pop. file corresponding to the required bounding box
>>  3b. extract a piece of countries corresponding to the required bounding box and resolution/proj of pop density
>>  4.  classify the vmax file creating another array of values classified
cellsize 0.00833333333333
cellsize 0.00833333333333
cellsize 0.00833333333333
>>  5.  count the popolation in each cell and assign to the class and write to output
>>  6.  print / save output in  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.00/wind_popDensValues_t0.xml
>>  7.  remove files 
done
t0 completed 

Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190424.00/20190424.00_wind.tif.
Using internal nodata values (e.g. -999) for image /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190424.00/20190424.00_wind.tif.
Copying nodata values from source /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190424.00/20190424.00_wind.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.00//wind10m_res_all.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/GDACS/DATA/lspop20141.tif.
Using internal nodata values (e.g. -2.14748e+09) for image /mnt/output/GDACS/DATA/lspop20141.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/lspop20141.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.00//wind10m_popfile_all_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/GDACS/DATA/countries.tif.
Using internal nodata values (e.g. -32768) for image /mnt/output/GDACS/DATA/countries.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/countries.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.00//wind10m_countryfile_all_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
input var: wind10m
Input File: /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190424.00/20190424.00_wind.tif
hurName:  
hdate:  
var:  wind10m
description:  wind10m: _ (ECMWF)
OutDir:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.00//
PopFile:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.00//wind10m_popfile_all_clipped.tif
country:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.00//wind10m_countryfile_all_clipped.tif
outxml file:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.00/wind_popDensValues_all.xml
popfile: LandScan
popCellSize= 0.00833333333333
projection= GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433],AUTHORITY["EPSG","4326"]]


>>  1.  resample the tif file to the resolution and proj of pop density  0.00833333333333  deg


>>  2.  read the charactristics of the input file


>>  3a. extract a piece of pop. file corresponding to the required bounding box
>>  3b. extract a piece of countries corresponding to the required bounding box and resolution/proj of pop density
>>  4.  classify the vmax file creating another array of values classified
cellsize 0.00833333333333
cellsize 0.00833333333333
cellsize 0.00833333333333
>>  5.  count the popolation in each cell and assign to the class and write to output
>>  6.  print / save output in  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.00/wind_popDensValues_all.xml
>>  7.  remove files 
done
Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_final.tif.
Using internal nodata values (e.g. 3.40282e+38) for image /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_final.tif.
Copying nodata values from source /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_final.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//wind10m_res_final.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/GDACS/DATA/lspop20141.tif.
Using internal nodata values (e.g. -2.14748e+09) for image /mnt/output/GDACS/DATA/lspop20141.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/lspop20141.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//wind10m_popfile_final_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/GDACS/DATA/countries.tif.
Using internal nodata values (e.g. -32768) for image /mnt/output/GDACS/DATA/countries.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/countries.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//wind10m_countryfile_final_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
t0 completed 

copy:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.00/wind_popDensValues_all.xml in:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/delft3d/20190424.00/wind_popDensValues_all.xml

 >> 3.2. Classify final folder
/mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final/20190424.00_final_completed_wind.txt
input var: wind10m
Input File: /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_final.tif
hurName:  
hdate:  
var:  wind10m
description:  wind10m: _ (ECMWF)
OutDir:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//
PopFile:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//wind10m_popfile_final_clipped.tif
country:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//wind10m_countryfile_final_clipped.tif
outxml file:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final/wind_popDensValues_final.xml
xml file exists...REMOVE
popfile: LandScan
popCellSize= 0.00833333333333
projection= GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433],AUTHORITY["EPSG","4326"]]


>>  1.  resample the tif file to the resolution and proj of pop density  0.00833333333333  deg


>>  2.  read the charactristics of the input file


>>  3a. extract a piece of pop. file corresponding to the required bounding box
>>  3b. extract a piece of countries corresponding to the required bounding box and resolution/proj of pop density
>>  4.  classify the vmax file creating another array of values classified
cellsize 0.00833333333333
cellsize 0.00833333333333
cellsize 0.00833333333333
>>  5.  count the popolation in each cell and assign to the class and write to output
>>  6.  print / save output in  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final/wind_popDensValues_final.xml
>>  7.  remove files 
done
*******************************************************
*  Case Completed									 *
*******************************************************
Case starterd at:  2019-04-24 14:40:56.999968  UTC

Case completed at:  2019-04-24 14:44:18.517476  UTC

**********E  N  D   O F	J O B***********************
*******************************************************
*	   Storm Surge Calculation System (SSCS)		 *
*******************************************************
Now is :  2019-04-29 12:51:24.522807  UTC

inp1= 20190422.12
ncores= 5
var= wind
submitting calc 2019-04-22 12:00:00 2019-04-26 00:00:00 delft3d 19.4 59.72 -36.0 -3.0 4.0 ECMWF 72 15 True GDACS/1000559/3_ECMWF 6 1 False False 5  20190422.12   wind False
*************---------------------******************
ndt: 8
it:  0
ndt: 8
idate:  2019-04-22 12:00:00
running case from  2019-04-22 12:00:00  for  72  h   start= 1
var wind
**** gometeo:  72
listWindows
rundate:20190422.12
RUNNING  2019-04-22 12:00:00  for  72 hours
prevCalcDate 2019-04-22 00:00:00

============================================
process meteo
============================================
1. process meteo, starting at  2019-04-29 12:51:24.596252

..1.1 calling ECMWF_2_netcdf...
...file netcdf already existing : /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/netcdf/20190422.12.tropical_cyclone.nc
ret= 0
removing submitted
ret= 0 newcase= False  forceFinal= False  forceBulletin= False
it:  1
ndt: 8
idate:  2019-04-23 00:00:00
running case from  2019-04-23 00:00:00  for  72  h   start= 0
var wind
**** gometeo:  72
listWindows
rundate:20190423.00
RUNNING  2019-04-23 00:00:00  for  72 hours
prevCalcDate 2019-04-22 12:00:00

============================================
process meteo
============================================
1. process meteo, starting at  2019-04-29 12:51:24.639629

..1.1 calling ECMWF_2_netcdf...
...file netcdf already existing : /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/netcdf/20190423.00.tropical_cyclone.nc
ret= 0
removing submitted
ret= 0 newcase= True  forceFinal= False  forceBulletin= False
it:  2
ndt: 8
idate:  2019-04-23 12:00:00
running case from  2019-04-23 12:00:00  for  72  h   start= 0
var wind
**** gometeo:  72
listWindows
rundate:20190423.12
RUNNING  2019-04-23 12:00:00  for  72 hours
prevCalcDate 2019-04-23 00:00:00

============================================
process meteo
============================================
1. process meteo, starting at  2019-04-29 12:51:24.680862

..1.1 calling ECMWF_2_netcdf...
...file netcdf already existing : /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/netcdf/20190423.12.tropical_cyclone.nc
ret= 0
removing submitted
ret= 0 newcase= True  forceFinal= False  forceBulletin= False
it:  3
ndt: 8
idate:  2019-04-24 00:00:00
running case from  2019-04-24 00:00:00  for  72  h   start= 0
var wind
**** gometeo:  72
listWindows
rundate:20190424.00
RUNNING  2019-04-24 00:00:00  for  72 hours
prevCalcDate 2019-04-23 12:00:00

============================================
process meteo
============================================
1. process meteo, starting at  2019-04-29 12:51:24.720808

..1.1 calling ECMWF_2_netcdf...
...file netcdf already existing : /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/netcdf/20190424.00.tropical_cyclone.nc
ret= 0
removing submitted
ret= 0 newcase= True  forceFinal= False  forceBulletin= False
it:  4
ndt: 8
idate:  2019-04-24 12:00:00
running case from  2019-04-24 12:00:00  for  72  h   start= 0
var wind
**** gometeo:  72
listWindows
rundate:20190424.12
RUNNING  2019-04-24 12:00:00  for  72 hours
prevCalcDate 2019-04-24 00:00:00

============================================
process meteo
============================================
1. process meteo, starting at  2019-04-29 12:51:24.765929

..1.1 calling ECMWF_2_netcdf...
...file netcdf already existing : /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/netcdf/20190424.12.tropical_cyclone.nc
ret= 0
removing submitted
ret= 0 newcase= True  forceFinal= False  forceBulletin= False
it:  5
ndt: 8
idate:  2019-04-25 00:00:00
running case from  2019-04-25 00:00:00  for  72  h   start= 0
var wind
**** gometeo:  72
listWindows
rundate:20190425.00
RUNNING  2019-04-25 00:00:00  for  72 hours
prevCalcDate 2019-04-24 12:00:00

============================================
process meteo
============================================
1. process meteo, starting at  2019-04-29 12:51:24.805160

..1.1 calling ECMWF_2_netcdf...
...file netcdf already existing : /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/netcdf/20190425.00.tropical_cyclone.nc
ret= 0
removing submitted
ret= 0 newcase= True  forceFinal= False  forceBulletin= False
it:  6
ndt: 8
idate:  2019-04-25 12:00:00
running case from  2019-04-25 12:00:00  for  72  h   start= 0
var wind
**** gometeo:  72
listWindows
rundate:20190425.12
RUNNING  2019-04-25 12:00:00  for  72 hours
prevCalcDate 2019-04-25 00:00:00

============================================
process meteo
============================================
1. process meteo, starting at  2019-04-29 12:51:24.844669

..1.1 calling ECMWF_2_netcdf...
...file netcdf already existing : /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/netcdf/20190425.12.tropical_cyclone.nc
ret= 0
removing submitted
ret= 0 newcase= True  forceFinal= False  forceBulletin= False
it:  7
ndt: 8
idate:  2019-04-26 00:00:00
running case from  2019-04-26 00:00:00  for  72  h   start= 0
var wind
**** gometeo:  72
listWindows
rundate:20190426.00
RUNNING  2019-04-26 00:00:00  for  72 hours
prevCalcDate 2019-04-25 12:00:00

============================================
process meteo
============================================
1. process meteo, starting at  2019-04-29 12:51:24.882600
0 .. 10 .. 20 .. 30 .. 40 .. 50 .. 60 .. 70 .. 80 .. 90 .. 100 - Done
0 .. 10 .. 20 .. 30 .. 40 .. 50 .. 60 .. 70 .. 80 .. 90 .. 100 - Done
0 .. 10 .. 20 .. 30 .. 40 .. 50 .. 60 .. 70 .. 80 .. 90 .. 100 - Done
0 .. 10 .. 20 .. 30 .. 40 .. 50 .. 60 .. 70 .. 80 .. 90 .. 100 - Done
0 .. 10 .. 20 .. 30 .. 40 .. 50 .. 60 .. 70 .. 80 .. 90 .. 100 - Done
0 .. 10 .. 20 .. 30 .. 40 .. 50 .. 60 .. 70 .. 80 .. 90 .. 100 - Done
0 .. 10 .. 20 .. 30 .. 40 .. 50 .. 60 .. 70 .. 80 .. 90 .. 100 - Done

..1.1 calling ECMWF_2_netcdf...
...file netcdf already existing : /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/netcdf/20190426.00.tropical_cyclone.nc
ret= 0
removing submitted
ret= 0 newcase= True  forceFinal= False  forceBulletin= False
home dir /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/
ret 0
classifications
============================================
1. Set INPUT/OUTPUT
============================================
startdate 2019-04-22 12:00:00
currdate 2019-04-26 00:00:00
ndt:  84 delta: 12
nt1=delta 12
nt 72
alldate:  DatetimeIndex(['2019-04-22 12:00:00', '2019-04-23 00:00:00',
               '2019-04-23 12:00:00', '2019-04-24 00:00:00',
               '2019-04-24 12:00:00', '2019-04-25 00:00:00',
               '2019-04-25 12:00:00', '2019-04-26 00:00:00'],
              dtype='datetime64[ns]', freq='12H')
============================================
2. Processing meteo files: nc2tif
============================================

 >> 2.1. Process curr + past files
processing curr bull... 2019-04-26 00:00:00
forcing ECMWF
verifying that input file is present
start reading nc...
wind
ntNC:  73   ntmax 73
use all data in nc file
...create velAll
73 469 573
...start calculating velAll
...end calculating velAll
73 469 573
nt,nx,ny, ntmax 73 469 573 73
[19.399999618530273, 0.07, 0, -3.0, 0, -0.071]
*********** 12 1 12
varMAX.shape  (469, 573)


lonmin 19.3999996185
latmin -36.0
lonmax 59.7200012207
latmax -3.0
<type 'netCDF4.Variable'>
float64 latitude(lat)
    units: degrees_north
    point_spacing: even
unlimited dimensions: 
current shape = (469,)
filling off

<type 'netCDF4.Variable'>
float64 longitude(lon)
    units: degrees_east
    point_spacing: even
unlimited dimensions: 
current shape = (573,)
filling off

19.3999996185 59.7200012207 -36.0 -3.0
lon 573
lat 469
savemap /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190426.00/20190426.00_wind.jpg
dtk,nt,ntmax 12 73 73 61
k1, k2, ht 0 12 0


k1, k2, ht 12 24 12


k1, k2, ht 24 36 24


k1, k2, ht 36 48 36


k1, k2, ht 48 60 48


k1, k2, ht 60 72 60


processing all past bull only if Past=True... True

 itdate, istime 2019-04-22 12:00:00 20190422.12
meteo-processing past forecast already completed

 itdate, istime 2019-04-23 00:00:00 20190423.00
meteo-processing past forecast already completed

 itdate, istime 2019-04-23 12:00:00 20190423.12
meteo-processing past forecast already completed

 itdate, istime 2019-04-24 00:00:00 20190424.00
meteo-processing past forecast already completed

 itdate, istime 2019-04-24 12:00:00 20190424.12
forcing ECMWF
verifying that input file is present
start reading nc...
wind
ntNC:  73   ntmax 73
use all data in nc file
...create velAll
73 469 573
...start calculating velAll
...end calculating velAll
73 469 573
nt,nx,ny, ntmax 73 469 573 73
[19.399999618530273, 0.07, 0, -3.0, 0, -0.071]
*********** 12 1 12
varMAX.shape  (469, 573)


lonmin 19.3999996185
latmin -36.0
lonmax 59.7200012207
latmax -3.0
<type 'netCDF4.Variable'>
float64 latitude(lat)
    units: degrees_north
    point_spacing: even
unlimited dimensions: 
current shape = (469,)
filling off

<type 'netCDF4.Variable'>
float64 longitude(lon)
    units: degrees_east
    point_spacing: even
unlimited dimensions: 
current shape = (573,)
filling off

19.3999996185 59.7200012207 -36.0 -3.0
lon 573
lat 469
savemap /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190424.12/20190424.12_wind.jpg
dtk,nt,ntmax 12 73 73 61
k1, k2, ht 0 12 0


k1, k2, ht 12 24 12


k1, k2, ht 24 36 24


k1, k2, ht 36 48 36


k1, k2, ht 48 60 48


k1, k2, ht 60 72 60



 itdate, istime 2019-04-25 00:00:00 20190425.00
forcing ECMWF
verifying that input file is present
start reading nc...
wind
ntNC:  73   ntmax 73
use all data in nc file
...create velAll
73 469 573
...start calculating velAll
...end calculating velAll
73 469 573
nt,nx,ny, ntmax 73 469 573 73
[19.399999618530273, 0.07, 0, -3.0, 0, -0.071]
*********** 12 1 12
varMAX.shape  (469, 573)


lonmin 19.3999996185
latmin -36.0
lonmax 59.7200012207
latmax -3.0
<type 'netCDF4.Variable'>
float64 latitude(lat)
    units: degrees_north
    point_spacing: even
unlimited dimensions: 
current shape = (469,)
filling off

<type 'netCDF4.Variable'>
float64 longitude(lon)
    units: degrees_east
    point_spacing: even
unlimited dimensions: 
current shape = (573,)
filling off

19.3999996185 59.7200012207 -36.0 -3.0
lon 573
lat 469
savemap /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190425.00/20190425.00_wind.jpg
dtk,nt,ntmax 12 73 73 61
k1, k2, ht 0 12 0


k1, k2, ht 12 24 12


k1, k2, ht 24 36 24


k1, k2, ht 36 48 36


k1, k2, ht 48 60 48


k1, k2, ht 60 72 60



 itdate, istime 2019-04-25 12:00:00 20190425.12
forcing ECMWF
verifying that input file is present
start reading nc...
wind
ntNC:  73   ntmax 73
use all data in nc file
...create velAll
73 469 573
...start calculating velAll
...end calculating velAll
73 469 573
nt,nx,ny, ntmax 73 469 573 73
[19.399999618530273, 0.07, 0, -3.0, 0, -0.071]
*********** 12 1 12
varMAX.shape  (469, 573)


lonmin 19.3999996185
latmin -36.0
lonmax 59.7200012207
latmax -3.0
<type 'netCDF4.Variable'>
float64 latitude(lat)
    units: degrees_north
    point_spacing: even
unlimited dimensions: 
current shape = (469,)
filling off

<type 'netCDF4.Variable'>
float64 longitude(lon)
    units: degrees_east
    point_spacing: even
unlimited dimensions: 
current shape = (573,)
filling off

19.3999996185 59.7200012207 -36.0 -3.0
lon 573
lat 469
savemap /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190425.12/20190425.12_wind.jpg
dtk,nt,ntmax 12 73 73 61
k1, k2, ht 0 12 0


k1, k2, ht 12 24 12


k1, k2, ht 24 36 24


k1, k2, ht 36 48 36


k1, k2, ht 48 60 48


k1, k2, ht 60 72 60



 >> 2.2. Process final data files
compfile /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/20190426.00_Final_completed_wind.txt
FINAL
remove maxtif
alldate:  DatetimeIndex(['2019-04-22 12:00:00', '2019-04-23 00:00:00',
               '2019-04-23 12:00:00', '2019-04-24 00:00:00',
               '2019-04-24 12:00:00', '2019-04-25 00:00:00',
               '2019-04-25 12:00:00', '2019-04-26 00:00:00'],
              dtype='datetime64[ns]', freq='12H')
8


date:  2019-04-23 00:00:00
python  /mnt/output/SSCS/scripts_dev/gdal_calc.py -A /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190422.12/20190422.12_wind_stept0.tif -B /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.00/20190423.00_wind_stept0.tif --outfile=/mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_final.tif --calc="maximum(A,B)"


date:  2019-04-23 12:00:00
python  /mnt/output/SSCS/scripts_dev/gdal_calc.py -A /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_final.tif -B /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.12/20190423.12_wind_stept0.tif --outfile=/mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_final.tif --calc="maximum(A,B)"


date:  2019-04-24 00:00:00
python  /mnt/output/SSCS/scripts_dev/gdal_calc.py -A /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_final.tif -B /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190424.00/20190424.00_wind_stept0.tif --outfile=/mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_final.tif --calc="maximum(A,B)"


date:  2019-04-24 12:00:00
python  /mnt/output/SSCS/scripts_dev/gdal_calc.py -A /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_final.tif -B /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190424.12/20190424.12_wind_stept0.tif --outfile=/mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_final.tif --calc="maximum(A,B)"


date:  2019-04-25 00:00:00
python  /mnt/output/SSCS/scripts_dev/gdal_calc.py -A /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_final.tif -B /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190425.00/20190425.00_wind_stept0.tif --outfile=/mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_final.tif --calc="maximum(A,B)"


date:  2019-04-25 12:00:00
python  /mnt/output/SSCS/scripts_dev/gdal_calc.py -A /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_final.tif -B /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190425.12/20190425.12_wind_stept0.tif --outfile=/mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_final.tif --calc="maximum(A,B)"


date:  2019-04-26 00:00:00
python  /mnt/output/SSCS/scripts_dev/gdal_calc.py -A /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_final.tif -B /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190426.00/20190426.00_wind.tif --outfile=/mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_final.tif --calc="maximum(A,B)"
max file created /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_final.tif


lonminH 19.4349996185
latminH -35.795
lonmaxH 59.4749996185
latmaxH -3.035
[[ -3.035  -3.035  -3.035 ...,  -3.035  -3.035  -3.035]
 [ -3.105  -3.105  -3.105 ...,  -3.105  -3.105  -3.105]
 [ -3.175  -3.175  -3.175 ...,  -3.175  -3.175  -3.175]
 ..., 
 [-35.655 -35.655 -35.655 ..., -35.655 -35.655 -35.655]
 [-35.725 -35.725 -35.725 ..., -35.725 -35.725 -35.725]
 [-35.795 -35.795 -35.795 ..., -35.795 -35.795 -35.795]]
[[ 19.43499962  19.50499962  19.57499962 ...,  59.33499962  59.40499962
   59.47499962]
 [ 19.43499962  19.50499962  19.57499962 ...,  59.33499962  59.40499962
   59.47499962]
 [ 19.43499962  19.50499962  19.57499962 ...,  59.33499962  59.40499962
   59.47499962]
 ..., 
 [ 19.43499962  19.50499962  19.57499962 ...,  59.33499962  59.40499962
   59.47499962]
 [ 19.43499962  19.50499962  19.57499962 ...,  59.33499962  59.40499962
   59.47499962]
 [ 19.43499962  19.50499962  19.57499962 ...,  59.33499962  59.40499962
   59.47499962]]
19.4349996185 59.4749996185 -35.795 -3.035
lon 573
lat 469
savemap /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_FINAL.jpg
ret:  0
============================================
3. Classify meteo + GDACS index score
============================================

 >> 3.1. Classify curr + past forecast
Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190426.00/20190426.00_wind_t0.tif.
Using internal nodata values (e.g. -999) for image /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190426.00/20190426.00_wind_t0.tif.
Copying nodata values from source /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190426.00/20190426.00_wind_t0.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190426.00//wind10m_res_t0.tif.
0Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190424.12/20190424.12_wind_t0.tif.
Using internal nodata values (e.g. -999) for image /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190424.12/20190424.12_wind_t0.tif.
Copying nodata values from source /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190424.12/20190424.12_wind_t0.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.12//wind10m_res_t0.tif.
0Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190425.00/20190425.00_wind_t0.tif.
Using internal nodata values (e.g. -999) for image /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190425.00/20190425.00_wind_t0.tif.
Copying nodata values from source /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190425.00/20190425.00_wind_t0.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.00//wind10m_res_t0.tif.
0Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190425.12/20190425.12_wind_t0.tif.
Using internal nodata values (e.g. -999) for image /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190425.12/20190425.12_wind_t0.tif.
Copying nodata values from source /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190425.12/20190425.12_wind_t0.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.12//wind10m_res_t0.tif.
0..........1010....1010........20.20..20.20........30.30..30.30........40.40..40.40........50.50.5050...........6060.60..60.........7070.70..70.........808080.....80.....90..90.90....90........100 - done.
100 - done.
100 - done.
100 - done.
Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/GDACS/DATA/lspop20141.tif.
Using internal nodata values (e.g. -2.14748e+09) for image /mnt/output/GDACS/DATA/lspop20141.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/lspop20141.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.12//wind10m_popfile_t0_clipped.tif.
0.Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/GDACS/DATA/lspop20141.tif.
Using internal nodata values (e.g. -2.14748e+09) for image /mnt/output/GDACS/DATA/lspop20141.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/lspop20141.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190426.00//wind10m_popfile_t0_clipped.tif.
0Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/GDACS/DATA/lspop20141.tif.
Using internal nodata values (e.g. -2.14748e+09) for image /mnt/output/GDACS/DATA/lspop20141.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/lspop20141.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.00//wind10m_popfile_t0_clipped.tif.
0Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/GDACS/DATA/lspop20141.tif.
Using internal nodata values (e.g. -2.14748e+09) for image /mnt/output/GDACS/DATA/lspop20141.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/lspop20141.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.12//wind10m_popfile_t0_clipped.tif.
0........10....101010........20....202020........30.....303030....40.......404040........50...505050..........6060....6060........7070......7070......8080....80.80.....9090........9090........100 - done.
100 - done.
100 - done.
100 - done.
Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/GDACS/DATA/countries.tif.
Using internal nodata values (e.g. -32768) for image /mnt/output/GDACS/DATA/countries.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/countries.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.00//wind10m_countryfile_t0_clipped.tif.
0..Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/GDACS/DATA/countries.tif.
Using internal nodata values (e.g. -32768) for image /mnt/output/GDACS/DATA/countries.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/countries.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.12//wind10m_countryfile_t0_clipped.tif.
0Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/GDACS/DATA/countries.tif.
Using internal nodata values (e.g. -32768) for image /mnt/output/GDACS/DATA/countries.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/countries.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.12//wind10m_countryfile_t0_clipped.tif.
0Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/GDACS/DATA/countries.tif.
Using internal nodata values (e.g. -32768) for image /mnt/output/GDACS/DATA/countries.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/countries.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190426.00//wind10m_countryfile_t0_clipped.tif.
0....10.........10101020............30202020.........40....303030........50...404040.........505050...60............70606060.........80....707070........90.....808080..........909090.........100 - done.
100 - done.
100 - done.
100 - done.
input var: wind10m
Input File: /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190426.00/20190426.00_wind_t0.tif
hurName:  
hdate:  
var:  wind10m
description:  wind10m: _ (ECMWF)
OutDir:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190426.00//
PopFile:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190426.00//wind10m_popfile_t0_clipped.tif
country:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190426.00//wind10m_countryfile_t0_clipped.tif
outxml file:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190426.00/wind_popDensValues_t0.xml
outDir created
popfile: LandScan
popCellSize= 0.00833333333333
projection= GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433],AUTHORITY["EPSG","4326"]]


>>  1.  resample the tif file to the resolution and proj of pop density  0.00833333333333  deg


>>  2.  read the charactristics of the input file


>>  3a. extract a piece of pop. file corresponding to the required bounding box
>>  3b. extract a piece of countries corresponding to the required bounding box and resolution/proj of pop density
>>  4.  classify the vmax file creating another array of values classified
cellsize 0.00833333333333
cellsize 0.00833333333333
cellsize 0.00833333333333
>>  6.  print / save output in  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190426.00/wind_popDensValues_t0.xml
>>  7.  remove files 
done
input var: wind10m
Input File: /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190425.12/20190425.12_wind_t0.tif
hurName:  
hdate:  
var:  wind10m
description:  wind10m: _ (ECMWF)
OutDir:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.12//
PopFile:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.12//wind10m_popfile_t0_clipped.tif
country:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.12//wind10m_countryfile_t0_clipped.tif
outxml file:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.12/wind_popDensValues_t0.xml
outDir created
popfile: LandScan
popCellSize= 0.00833333333333
projection= GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433],AUTHORITY["EPSG","4326"]]


>>  1.  resample the tif file to the resolution and proj of pop density  0.00833333333333  deg


>>  2.  read the charactristics of the input file


>>  3a. extract a piece of pop. file corresponding to the required bounding box
>>  3b. extract a piece of countries corresponding to the required bounding box and resolution/proj of pop density
>>  4.  classify the vmax file creating another array of values classified
cellsize 0.00833333333333
cellsize 0.00833333333333
cellsize 0.00833333333333
>>  5.  count the popolation in each cell and assign to the class and write to output
>>  6.  print / save output in  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.12/wind_popDensValues_t0.xml
>>  7.  remove files 
done
input var: wind10m
Input File: /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190424.12/20190424.12_wind_t0.tif
hurName:  
hdate:  
var:  wind10m
description:  wind10m: _ (ECMWF)
OutDir:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.12//
PopFile:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.12//wind10m_popfile_t0_clipped.tif
country:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.12//wind10m_countryfile_t0_clipped.tif
outxml file:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.12/wind_popDensValues_t0.xml
outDir created
popfile: LandScan
popCellSize= 0.00833333333333
projection= GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433],AUTHORITY["EPSG","4326"]]


>>  1.  resample the tif file to the resolution and proj of pop density  0.00833333333333  deg


>>  2.  read the charactristics of the input file


>>  3a. extract a piece of pop. file corresponding to the required bounding box
>>  3b. extract a piece of countries corresponding to the required bounding box and resolution/proj of pop density
>>  4.  classify the vmax file creating another array of values classified
cellsize 0.00833333333333
cellsize 0.00833333333333
cellsize 0.00833333333333
>>  5.  count the popolation in each cell and assign to the class and write to output
>>  6.  print / save output in  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.12/wind_popDensValues_t0.xml
>>  7.  remove files 
done
input var: wind10m
Input File: /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190425.00/20190425.00_wind_t0.tif
hurName:  
hdate:  
var:  wind10m
description:  wind10m: _ (ECMWF)
OutDir:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.00//
PopFile:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.00//wind10m_popfile_t0_clipped.tif
country:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.00//wind10m_countryfile_t0_clipped.tif
outxml file:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.00/wind_popDensValues_t0.xml
outDir created
popfile: LandScan
popCellSize= 0.00833333333333
projection= GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433],AUTHORITY["EPSG","4326"]]


>>  1.  resample the tif file to the resolution and proj of pop density  0.00833333333333  deg


>>  2.  read the charactristics of the input file


>>  3a. extract a piece of pop. file corresponding to the required bounding box
>>  3b. extract a piece of countries corresponding to the required bounding box and resolution/proj of pop density
>>  4.  classify the vmax file creating another array of values classified
cellsize 0.00833333333333
cellsize 0.00833333333333
cellsize 0.00833333333333
>>  5.  count the popolation in each cell and assign to the class and write to output
>>  6.  print / save output in  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.00/wind_popDensValues_t0.xml
>>  7.  remove files 
done
t0 completed 

Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190424.12/20190424.12_wind.tif.
Using internal nodata values (e.g. -999) for image /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190424.12/20190424.12_wind.tif.
Copying nodata values from source /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190424.12/20190424.12_wind.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.12//wind10m_res_all.tif.
0Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190426.00/20190426.00_wind.tif.
Using internal nodata values (e.g. -999) for image /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190426.00/20190426.00_wind.tif.
Copying nodata values from source /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190426.00/20190426.00_wind.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190426.00//wind10m_res_all.tif.
0.Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190425.12/20190425.12_wind.tif.
Using internal nodata values (e.g. -999) for image /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190425.12/20190425.12_wind.tif.
Copying nodata values from source /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190425.12/20190425.12_wind.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.12//wind10m_res_all.tif.
0Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190425.00/20190425.00_wind.tif.
Using internal nodata values (e.g. -999) for image /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190425.00/20190425.00_wind.tif.
Copying nodata values from source /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190425.00/20190425.00_wind.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.00//wind10m_res_all.tif.
0........10....101010......20.......202020....30........30.303040............40504040..........50.5050.60........70.....60.6060.....80.......70.7070.90..........80.8080.........909090.........100 - done.
100 - done.
100 - done.
100 - done.
Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/GDACS/DATA/lspop20141.tif.
Using internal nodata values (e.g. -2.14748e+09) for image /mnt/output/GDACS/DATA/lspop20141.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/lspop20141.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.00//wind10m_popfile_all_clipped.tif.
0.Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/GDACS/DATA/lspop20141.tif.
Using internal nodata values (e.g. -2.14748e+09) for image /mnt/output/GDACS/DATA/lspop20141.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/lspop20141.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.12//wind10m_popfile_all_clipped.tif.
0..Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/GDACS/DATA/lspop20141.tif.
Using internal nodata values (e.g. -2.14748e+09) for image /mnt/output/GDACS/DATA/lspop20141.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/lspop20141.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.12//wind10m_popfile_all_clipped.tif.
0Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/GDACS/DATA/lspop20141.tif.
Using internal nodata values (e.g. -2.14748e+09) for image /mnt/output/GDACS/DATA/lspop20141.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/lspop20141.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190426.00//wind10m_popfile_all_clipped.tif.
0.10..........10..101020........20...2020......30....3030....4030........4040....5040......5050.50......60.........60606070...........707070.......80......908080.80..........9090...90.......100 - done.
100 - done.
100 - done.
100 - done.
Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/GDACS/DATA/countries.tif.
Using internal nodata values (e.g. -32768) for image /mnt/output/GDACS/DATA/countries.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/countries.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.12//wind10m_countryfile_all_clipped.tif.
0.Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/GDACS/DATA/countries.tif.
Using internal nodata values (e.g. -32768) for image /mnt/output/GDACS/DATA/countries.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/countries.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.00//wind10m_countryfile_all_clipped.tif.
0.Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/GDACS/DATA/countries.tif.
Using internal nodata values (e.g. -32768) for image /mnt/output/GDACS/DATA/countries.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/countries.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190426.00//wind10m_countryfile_all_clipped.tif.
0..Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/GDACS/DATA/countries.tif.
Using internal nodata values (e.g. -32768) for image /mnt/output/GDACS/DATA/countries.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/countries.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.12//wind10m_countryfile_all_clipped.tif.
0.10........10..2010.10..........2030.20..20.......40..30.30.....30.50....40.40....40....50.50.50....60........60....706060.........70..80.7070.........9080....8080........90...9090........100 - done.
100 - done.
100 - done.
100 - done.
input var: wind10m
Input File: /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190425.00/20190425.00_wind.tif
hurName:  
hdate:  
var:  wind10m
description:  wind10m: _ (ECMWF)
OutDir:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.00//
PopFile:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.00//wind10m_popfile_all_clipped.tif
country:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.00//wind10m_countryfile_all_clipped.tif
outxml file:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.00/wind_popDensValues_all.xml
popfile: LandScan
popCellSize= 0.00833333333333
projection= GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433],AUTHORITY["EPSG","4326"]]


>>  1.  resample the tif file to the resolution and proj of pop density  0.00833333333333  deg


>>  2.  read the charactristics of the input file


>>  3a. extract a piece of pop. file corresponding to the required bounding box
>>  3b. extract a piece of countries corresponding to the required bounding box and resolution/proj of pop density
>>  4.  classify the vmax file creating another array of values classified
cellsize 0.00833333333333
cellsize 0.00833333333333
cellsize 0.00833333333333
>>  5.  count the popolation in each cell and assign to the class and write to output
>>  6.  print / save output in  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.00/wind_popDensValues_all.xml
>>  7.  remove files 
done
input var: wind10m
Input File: /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190426.00/20190426.00_wind.tif
hurName:  
hdate:  
var:  wind10m
description:  wind10m: _ (ECMWF)
OutDir:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190426.00//
PopFile:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190426.00//wind10m_popfile_all_clipped.tif
country:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190426.00//wind10m_countryfile_all_clipped.tif
outxml file:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190426.00/wind_popDensValues_all.xml
popfile: LandScan
popCellSize= 0.00833333333333
projection= GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433],AUTHORITY["EPSG","4326"]]


>>  1.  resample the tif file to the resolution and proj of pop density  0.00833333333333  deg


>>  2.  read the charactristics of the input file


>>  3a. extract a piece of pop. file corresponding to the required bounding box
>>  3b. extract a piece of countries corresponding to the required bounding box and resolution/proj of pop density
>>  4.  classify the vmax file creating another array of values classified
cellsize 0.00833333333333
cellsize 0.00833333333333
cellsize 0.00833333333333
>>  6.  print / save output in  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190426.00/wind_popDensValues_all.xml
>>  7.  remove files 
done
input var: wind10m
Input File: /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190424.12/20190424.12_wind.tif
hurName:  
hdate:  
var:  wind10m
description:  wind10m: _ (ECMWF)
OutDir:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.12//
PopFile:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.12//wind10m_popfile_all_clipped.tif
country:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.12//wind10m_countryfile_all_clipped.tif
outxml file:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.12/wind_popDensValues_all.xml
popfile: LandScan
popCellSize= 0.00833333333333
projection= GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433],AUTHORITY["EPSG","4326"]]


>>  1.  resample the tif file to the resolution and proj of pop density  0.00833333333333  deg


>>  2.  read the charactristics of the input file


>>  3a. extract a piece of pop. file corresponding to the required bounding box
>>  3b. extract a piece of countries corresponding to the required bounding box and resolution/proj of pop density
>>  4.  classify the vmax file creating another array of values classified
cellsize 0.00833333333333
cellsize 0.00833333333333
cellsize 0.00833333333333
>>  5.  count the popolation in each cell and assign to the class and write to output
>>  6.  print / save output in  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.12/wind_popDensValues_all.xml
>>  7.  remove files 
done
input var: wind10m
Input File: /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190425.12/20190425.12_wind.tif
hurName:  
hdate:  
var:  wind10m
description:  wind10m: _ (ECMWF)
OutDir:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.12//
PopFile:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.12//wind10m_popfile_all_clipped.tif
country:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.12//wind10m_countryfile_all_clipped.tif
outxml file:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.12/wind_popDensValues_all.xml
popfile: LandScan
popCellSize= 0.00833333333333
projection= GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433],AUTHORITY["EPSG","4326"]]


>>  1.  resample the tif file to the resolution and proj of pop density  0.00833333333333  deg


>>  2.  read the charactristics of the input file


>>  3a. extract a piece of pop. file corresponding to the required bounding box
>>  3b. extract a piece of countries corresponding to the required bounding box and resolution/proj of pop density
>>  4.  classify the vmax file creating another array of values classified
cellsize 0.00833333333333
cellsize 0.00833333333333
cellsize 0.00833333333333
>>  5.  count the popolation in each cell and assign to the class and write to output
>>  6.  print / save output in  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.12/wind_popDensValues_all.xml
>>  7.  remove files 
done
Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_final.tif.
Using internal nodata values (e.g. 3.40282e+38) for image /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_final.tif.
Copying nodata values from source /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_final.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//wind10m_res_final.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/GDACS/DATA/lspop20141.tif.
Using internal nodata values (e.g. -2.14748e+09) for image /mnt/output/GDACS/DATA/lspop20141.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/lspop20141.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//wind10m_popfile_final_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4816P x 3996L.
Processing input file /mnt/output/GDACS/DATA/countries.tif.
Using internal nodata values (e.g. -32768) for image /mnt/output/GDACS/DATA/countries.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/countries.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//wind10m_countryfile_final_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
t0 completed 

copy:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.12/wind_popDensValues_all.xml in:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/delft3d/20190424.12/wind_popDensValues_all.xml
copy:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.00/wind_popDensValues_all.xml in:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/delft3d/20190425.00/wind_popDensValues_all.xml
copy:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.12/wind_popDensValues_all.xml in:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/delft3d/20190425.12/wind_popDensValues_all.xml
copy:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190426.00/wind_popDensValues_all.xml in:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/delft3d/20190426.00/wind_popDensValues_all.xml

 >> 3.2. Classify final folder
/mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final/20190426.00_final_completed_wind.txt
input var: wind10m
Input File: /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/wind_final.tif
hurName:  
hdate:  
var:  wind10m
description:  wind10m: _ (ECMWF)
OutDir:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//
PopFile:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//wind10m_popfile_final_clipped.tif
country:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//wind10m_countryfile_final_clipped.tif
outxml file:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final/wind_popDensValues_final.xml
xml file exists...REMOVE
popfile: LandScan
popCellSize= 0.00833333333333
projection= GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433],AUTHORITY["EPSG","4326"]]


>>  1.  resample the tif file to the resolution and proj of pop density  0.00833333333333  deg


>>  2.  read the charactristics of the input file


>>  3a. extract a piece of pop. file corresponding to the required bounding box
>>  3b. extract a piece of countries corresponding to the required bounding box and resolution/proj of pop density
>>  4.  classify the vmax file creating another array of values classified
cellsize 0.00833333333333
cellsize 0.00833333333333
cellsize 0.00833333333333
>>  5.  count the popolation in each cell and assign to the class and write to output
>>  6.  print / save output in  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final/wind_popDensValues_final.xml
>>  7.  remove files 
done
*******************************************************
*  Case Completed									 *
*******************************************************
Case starterd at:  2019-04-29 12:51:24.522807  UTC

Case completed at:  2019-04-29 12:56:38.592899  UTC

**********E  N  D   O F	J O B***********************
*******************************************************
*	   Storm Surge Calculation System (SSCS)		 *
*******************************************************
Now is :  2019-04-29 15:39:21.155385  UTC

inp1= 20190422.12
ncores= 5
var= rain
submitting calc 2019-04-22 12:00:00 2019-04-26 00:00:00 delft3d 19.4 59.72 -36.0 -3.0 4.0 ECMWF 72 15 True GDACS/1000559/3_ECMWF 6 1 False False 5  20190422.12   rain False
*************---------------------******************
ndt: 8
it:  0
ndt: 8
idate:  2019-04-22 12:00:00
running case from  2019-04-22 12:00:00  for  72  h   start= 1
var rain
**** gometeo:  72
listWindows
rundate:20190422.12
RUNNING  2019-04-22 12:00:00  for  72 hours
prevCalcDate 2019-04-22 00:00:00
Nothing to do, case already completed
ret= -3 newcase= False  forceFinal= False  forceBulletin= False
netcdf already created
it:  1
ndt: 8
idate:  2019-04-23 00:00:00
running case from  2019-04-23 00:00:00  for  72  h   start= 0
var rain
**** gometeo:  72
listWindows
rundate:20190423.00
RUNNING  2019-04-23 00:00:00  for  72 hours
prevCalcDate 2019-04-22 12:00:00
Nothing to do, case already completed
ret= -3 newcase= False  forceFinal= False  forceBulletin= False
netcdf already created
it:  2
ndt: 8
idate:  2019-04-23 12:00:00
running case from  2019-04-23 12:00:00  for  72  h   start= 0
var rain
**** gometeo:  72
listWindows
rundate:20190423.12
RUNNING  2019-04-23 12:00:00  for  72 hours
prevCalcDate 2019-04-23 00:00:00
Nothing to do, case already completed
ret= -3 newcase= False  forceFinal= False  forceBulletin= False
netcdf already created
it:  3
ndt: 8
idate:  2019-04-24 00:00:00
running case from  2019-04-24 00:00:00  for  72  h   start= 0
var rain
**** gometeo:  72
listWindows
rundate:20190424.00
RUNNING  2019-04-24 00:00:00  for  72 hours
prevCalcDate 2019-04-23 12:00:00
Nothing to do, case already completed
ret= -3 newcase= False  forceFinal= False  forceBulletin= False
netcdf already created
it:  4
ndt: 8
idate:  2019-04-24 12:00:00
running case from  2019-04-24 12:00:00  for  72  h   start= 0
var rain
**** gometeo:  72
listWindows
rundate:20190424.12
RUNNING  2019-04-24 12:00:00  for  72 hours
prevCalcDate 2019-04-24 00:00:00
Nothing to do, case already completed
ret= -3 newcase= False  forceFinal= False  forceBulletin= False
netcdf already created
it:  5
ndt: 8
idate:  2019-04-25 00:00:00
running case from  2019-04-25 00:00:00  for  72  h   start= 0
var rain
**** gometeo:  72
listWindows
rundate:20190425.00
RUNNING  2019-04-25 00:00:00  for  72 hours
prevCalcDate 2019-04-24 12:00:00
Nothing to do, case already completed
ret= -3 newcase= False  forceFinal= False  forceBulletin= False
netcdf already created
it:  6
ndt: 8
idate:  2019-04-25 12:00:00
running case from  2019-04-25 12:00:00  for  72  h   start= 0
var rain
**** gometeo:  72
listWindows
rundate:20190425.12
RUNNING  2019-04-25 12:00:00  for  72 hours
prevCalcDate 2019-04-25 00:00:00
Nothing to do, case already completed
ret= -3 newcase= False  forceFinal= False  forceBulletin= False
netcdf already created
it:  7
ndt: 8
idate:  2019-04-26 00:00:00
running case from  2019-04-26 00:00:00  for  72  h   start= 0
var rain
**** gometeo:  72
listWindows
rundate:20190426.00
RUNNING  2019-04-26 00:00:00  for  72 hours
prevCalcDate 2019-04-25 12:00:00
0 .. 10 .. 20 .. 30 .. 40 .. 50 .. 60 .. 70 .. 80 .. 90 .. 100 - Done
0 .. 10 .. 20 .. 30 .. 40 .. 50 .. 60 .. 70 .. 80 .. 90 .. 100 - Done
0 .. 10 .. 20 .. 30 .. 40 .. 50 .. 60 .. 70 .. 80 .. 90 .. 100 - Done
0 .. 10 .. 20 .. 30 .. 40 .. 50 .. 60 .. 70 .. 80 .. 90 .. 100 - Done
0 .. 10 .. 20 .. 30 .. 40 .. 50 .. 60 .. 70 .. 80 .. 90 .. 100 - Done
0 .. 10 .. 20 .. 30 .. 40 .. 50 .. 60 .. 70 .. 80 .. 90 .. 100 - Done
0 .. 10 .. 20 .. 30 .. 40 .. 50 .. 60 .. 70 .. 80 .. 90 .. 100 - Done
Nothing to do, case already completed
ret= -3 newcase= False  forceFinal= False  forceBulletin= False
netcdf already created
home dir /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/
ret -3
classifications
============================================
1. Set INPUT/OUTPUT
============================================
startdate 2019-04-22 12:00:00
currdate 2019-04-26 00:00:00
ndt:  84 delta: 12
nt1=delta 12
nt 72
alldate:  DatetimeIndex(['2019-04-22 12:00:00', '2019-04-23 00:00:00',
               '2019-04-23 12:00:00', '2019-04-24 00:00:00',
               '2019-04-24 12:00:00', '2019-04-25 00:00:00',
               '2019-04-25 12:00:00', '2019-04-26 00:00:00'],
              dtype='datetime64[ns]', freq='12H')
============================================
2. Processing meteo files: nc2tif
============================================

 >> 2.1. Process curr + past files
processing curr bull... 2019-04-26 00:00:00
forcing ECMWF
verifying that input file is present
start reading nc...
rain
use all data in nc file
nt,nx,ny, ntmax 72 469 574 73
nt,nx,ny, ntmax 72 469 574 73
[19.406266848993972, 0.07, 0, -3.0579964267022164, 0, -0.07]
*********** 12 1 12
varMAX.shape  (469, 574)


lonmin 19.406266849
latmin -35.9578199457
lonmax 59.695364329
latmax -3.0579964267
<type 'netCDF4.Variable'>
float64 latitude(latitude)
    units: degrees_north
    point_spacing: even
unlimited dimensions: 
current shape = (469,)
filling off

<type 'netCDF4.Variable'>
float64 longitude(longitude)
    units: degrees_east
    point_spacing: even
unlimited dimensions: 
current shape = (574,)
filling off

19.406266849 59.695364329 -35.9578199457 -3.0579964267
lon 574
lat 469
savemap /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190426.00/20190426.00_rain.jpg
dtk,nt,ntmax 12 72 73 61
k1, k2, ht 0 12 0


k1, k2, ht 12 24 12


k1, k2, ht 24 36 24


k1, k2, ht 36 48 36


k1, k2, ht 48 60 48


k1, k2, ht 60 72 60


processing all past bull only if Past=True... True

 itdate, istime 2019-04-22 12:00:00 20190422.12
meteo-processing past forecast already completed

 itdate, istime 2019-04-23 00:00:00 20190423.00
meteo-processing past forecast already completed

 itdate, istime 2019-04-23 12:00:00 20190423.12
meteo-processing past forecast already completed

 itdate, istime 2019-04-24 00:00:00 20190424.00
meteo-processing past forecast already completed

 itdate, istime 2019-04-24 12:00:00 20190424.12
forcing ECMWF
verifying that input file is present
start reading nc...
rain
use all data in nc file
nt,nx,ny, ntmax 72 469 574 73
nt,nx,ny, ntmax 72 469 574 73
[19.406266848993972, 0.07, 0, -3.0579964267022164, 0, -0.07]
*********** 12 1 12
varMAX.shape  (469, 574)


lonmin 19.406266849
latmin -35.9578199457
lonmax 59.695364329
latmax -3.0579964267
<type 'netCDF4.Variable'>
float64 latitude(latitude)
    units: degrees_north
    point_spacing: even
unlimited dimensions: 
current shape = (469,)
filling off

<type 'netCDF4.Variable'>
float64 longitude(longitude)
    units: degrees_east
    point_spacing: even
unlimited dimensions: 
current shape = (574,)
filling off

19.406266849 59.695364329 -35.9578199457 -3.0579964267
lon 574
lat 469
savemap /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190424.12/20190424.12_rain.jpg
dtk,nt,ntmax 12 72 73 61
k1, k2, ht 0 12 0


k1, k2, ht 12 24 12


k1, k2, ht 24 36 24


k1, k2, ht 36 48 36


k1, k2, ht 48 60 48


k1, k2, ht 60 72 60



 itdate, istime 2019-04-25 00:00:00 20190425.00
forcing ECMWF
verifying that input file is present
start reading nc...
rain
use all data in nc file
nt,nx,ny, ntmax 72 469 574 73
nt,nx,ny, ntmax 72 469 574 73
[19.406266848993972, 0.07, 0, -3.0579964267022164, 0, -0.07]
*********** 12 1 12
varMAX.shape  (469, 574)


lonmin 19.406266849
latmin -35.9578199457
lonmax 59.695364329
latmax -3.0579964267
<type 'netCDF4.Variable'>
float64 latitude(latitude)
    units: degrees_north
    point_spacing: even
unlimited dimensions: 
current shape = (469,)
filling off

<type 'netCDF4.Variable'>
float64 longitude(longitude)
    units: degrees_east
    point_spacing: even
unlimited dimensions: 
current shape = (574,)
filling off

19.406266849 59.695364329 -35.9578199457 -3.0579964267
lon 574
lat 469
savemap /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190425.00/20190425.00_rain.jpg
dtk,nt,ntmax 12 72 73 61
k1, k2, ht 0 12 0


k1, k2, ht 12 24 12


k1, k2, ht 24 36 24


k1, k2, ht 36 48 36


k1, k2, ht 48 60 48


k1, k2, ht 60 72 60



 itdate, istime 2019-04-25 12:00:00 20190425.12
forcing ECMWF
verifying that input file is present
start reading nc...
rain
use all data in nc file
nt,nx,ny, ntmax 72 469 574 73
nt,nx,ny, ntmax 72 469 574 73
[19.406266848993972, 0.07, 0, -3.0579964267022164, 0, -0.07]
*********** 12 1 12
varMAX.shape  (469, 574)


lonmin 19.406266849
latmin -35.9578199457
lonmax 59.695364329
latmax -3.0579964267
<type 'netCDF4.Variable'>
float64 latitude(latitude)
    units: degrees_north
    point_spacing: even
unlimited dimensions: 
current shape = (469,)
filling off

<type 'netCDF4.Variable'>
float64 longitude(longitude)
    units: degrees_east
    point_spacing: even
unlimited dimensions: 
current shape = (574,)
filling off

19.406266849 59.695364329 -35.9578199457 -3.0579964267
lon 574
lat 469
savemap /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190425.12/20190425.12_rain.jpg
dtk,nt,ntmax 12 72 73 61
k1, k2, ht 0 12 0


k1, k2, ht 12 24 12


k1, k2, ht 24 36 24


k1, k2, ht 36 48 36


k1, k2, ht 48 60 48


k1, k2, ht 60 72 60



 >> 2.2. Process final data files
compfile /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/20190426.00_Final_completed_rain.txt
FINAL
remove maxtif
alldate:  DatetimeIndex(['2019-04-22 12:00:00', '2019-04-23 00:00:00',
               '2019-04-23 12:00:00', '2019-04-24 00:00:00',
               '2019-04-24 12:00:00', '2019-04-25 00:00:00',
               '2019-04-25 12:00:00', '2019-04-26 00:00:00'],
              dtype='datetime64[ns]', freq='12H')
8


date:  2019-04-23 00:00:00
python  /mnt/output/SSCS/scripts_dev/gdal_calc.py -A /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190422.12/20190422.12_rain_stept0.tif -B /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.00/20190423.00_rain_stept0.tif --outfile=/mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_final.tif --calc="A+B"


date:  2019-04-23 12:00:00
python  /mnt/output/SSCS/scripts_dev/gdal_calc.py -A /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_final.tif -B /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190423.12/20190423.12_rain_stept0.tif --outfile=/mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_final.tif --calc="A+B"


date:  2019-04-24 00:00:00
python  /mnt/output/SSCS/scripts_dev/gdal_calc.py -A /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_final.tif -B /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190424.00/20190424.00_rain_stept0.tif --outfile=/mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_final.tif --calc="A+B"


date:  2019-04-24 12:00:00
python  /mnt/output/SSCS/scripts_dev/gdal_calc.py -A /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_final.tif -B /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190424.12/20190424.12_rain_stept0.tif --outfile=/mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_final.tif --calc="A+B"


date:  2019-04-25 00:00:00
python  /mnt/output/SSCS/scripts_dev/gdal_calc.py -A /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_final.tif -B /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190425.00/20190425.00_rain_stept0.tif --outfile=/mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_final.tif --calc="A+B"


date:  2019-04-25 12:00:00
python  /mnt/output/SSCS/scripts_dev/gdal_calc.py -A /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_final.tif -B /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190425.12/20190425.12_rain_stept0.tif --outfile=/mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_final.tif --calc="A+B"


date:  2019-04-26 00:00:00
python  /mnt/output/SSCS/scripts_dev/gdal_calc.py -A /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_final.tif -B /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190426.00/20190426.00_rain.tif --outfile=/mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_final.tif --calc="A+B"
max file created /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_final.tif


lonminH 19.441266849
latminH -35.8529964267
lonmaxH 59.551266849
latmaxH -3.0929964267
[[ -3.09299643  -3.09299643  -3.09299643 ...,  -3.09299643  -3.09299643
   -3.09299643]
 [ -3.16299643  -3.16299643  -3.16299643 ...,  -3.16299643  -3.16299643
   -3.16299643]
 [ -3.23299643  -3.23299643  -3.23299643 ...,  -3.23299643  -3.23299643
   -3.23299643]
 ..., 
 [-35.71299643 -35.71299643 -35.71299643 ..., -35.71299643 -35.71299643
  -35.71299643]
 [-35.78299643 -35.78299643 -35.78299643 ..., -35.78299643 -35.78299643
  -35.78299643]
 [-35.85299643 -35.85299643 -35.85299643 ..., -35.85299643 -35.85299643
  -35.85299643]]
[[ 19.44126685  19.51126685  19.58126685 ...,  59.41126685  59.48126685
   59.55126685]
 [ 19.44126685  19.51126685  19.58126685 ...,  59.41126685  59.48126685
   59.55126685]
 [ 19.44126685  19.51126685  19.58126685 ...,  59.41126685  59.48126685
   59.55126685]
 ..., 
 [ 19.44126685  19.51126685  19.58126685 ...,  59.41126685  59.48126685
   59.55126685]
 [ 19.44126685  19.51126685  19.58126685 ...,  59.41126685  59.48126685
   59.55126685]
 [ 19.44126685  19.51126685  19.58126685 ...,  59.41126685  59.48126685
   59.55126685]]
19.441266849 59.551266849 -35.8529964267 -3.0929964267
lon 574
lat 469
savemap /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_FINAL.jpg
ret:  0
============================================
3. Classify meteo + GDACS index score
============================================

 >> 3.1. Classify curr + past forecast
Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190426.00/20190426.00_rain_t0.tif.
Using internal nodata values (e.g. -999) for image /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190426.00/20190426.00_rain_t0.tif.
Copying nodata values from source /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190426.00/20190426.00_rain_t0.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190426.00//rain_res_t0.tif.
0Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190425.00/20190425.00_rain_t0.tif.
Using internal nodata values (e.g. -999) for image /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190425.00/20190425.00_rain_t0.tif.
Copying nodata values from source /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190425.00/20190425.00_rain_t0.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.00//rain_res_t0.tif.
0Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190425.12/20190425.12_rain_t0.tif.
Using internal nodata values (e.g. -999) for image /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190425.12/20190425.12_rain_t0.tif.
Copying nodata values from source /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190425.12/20190425.12_rain_t0.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.12//rain_res_t0.tif.
0Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190424.12/20190424.12_rain_t0.tif.
Using internal nodata values (e.g. -999) for image /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190424.12/20190424.12_rain_t0.tif.
Copying nodata values from source /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190424.12/20190424.12_rain_t0.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.12//rain_res_t0.tif.
0............10101010............20202020............30303030............40404040............50505050............60606060............70707070............80808080............90909090............100 - done.
100 - done.
100 - done.
100 - done.
Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/GDACS/DATA/lspop20141.tif.
Using internal nodata values (e.g. -2.14748e+09) for image /mnt/output/GDACS/DATA/lspop20141.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/lspop20141.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190426.00//rain_popfile_t0_clipped.tif.
0Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/GDACS/DATA/lspop20141.tif.
Using internal nodata values (e.g. -2.14748e+09) for image /mnt/output/GDACS/DATA/lspop20141.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/lspop20141.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.12//rain_popfile_t0_clipped.tif.
0Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/GDACS/DATA/lspop20141.tif.
Using internal nodata values (e.g. -2.14748e+09) for image /mnt/output/GDACS/DATA/lspop20141.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/lspop20141.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.00//rain_popfile_t0_clipped.tif.
0.Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/GDACS/DATA/lspop20141.tif.
Using internal nodata values (e.g. -2.14748e+09) for image /mnt/output/GDACS/DATA/lspop20141.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/lspop20141.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.12//rain_popfile_t0_clipped.tif.
0........10....101010........20....202020...........303030.30...........40404040............50505050.........60....606060........70....70.7070.......80....8080.80......90....9090....90........100 - done.
100 - done.
100 - done.
100 - done.
Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/GDACS/DATA/countries.tif.
Using internal nodata values (e.g. -32768) for image /mnt/output/GDACS/DATA/countries.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/countries.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.12//rain_countryfile_t0_clipped.tif.
0Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/GDACS/DATA/countries.tif.
Using internal nodata values (e.g. -32768) for image /mnt/output/GDACS/DATA/countries.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/countries.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.12//rain_countryfile_t0_clipped.tif.
0Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/GDACS/DATA/countries.tif.
Using internal nodata values (e.g. -32768) for image /mnt/output/GDACS/DATA/countries.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/countries.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190426.00//rain_countryfile_t0_clipped.tif.
0Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/GDACS/DATA/countries.tif.
Using internal nodata values (e.g. -32768) for image /mnt/output/GDACS/DATA/countries.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/countries.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.00//rain_countryfile_t0_clipped.tif.
0............10101010............20202020............30303030............40404040............50505050........6060..........70706060........8080..........70907090............8080......9090......100 - done.
100 - done.
100 - done.
100 - done.
input var: rain
Input File: /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190424.12/20190424.12_rain_t0.tif
hurName:  
hdate:  
var:  rain
description:  rain: _ (ECMWF)
OutDir:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.12//
PopFile:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.12//rain_popfile_t0_clipped.tif
country:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.12//rain_countryfile_t0_clipped.tif
outxml file:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.12/rain_popDensValues_t0.xml
popfile: LandScan
popCellSize= 0.00833333333333
projection= GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433],AUTHORITY["EPSG","4326"]]


>>  1.  resample the tif file to the resolution and proj of pop density  0.00833333333333  deg


>>  2.  read the charactristics of the input file


>>  3a. extract a piece of pop. file corresponding to the required bounding box
>>  3b. extract a piece of countries corresponding to the required bounding box and resolution/proj of pop density
>>  4.  classify the vmax file creating another array of values classified
cellsize 0.00833333333333
cellsize 0.00833333333333
cellsize 0.00833333333333
>>  5.  count the popolation in each cell and assign to the class and write to output
>>  6.  print / save output in  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.12/rain_popDensValues_t0.xml
>>  7.  remove files 
done
input var: rain
Input File: /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190425.12/20190425.12_rain_t0.tif
hurName:  
hdate:  
var:  rain
description:  rain: _ (ECMWF)
OutDir:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.12//
PopFile:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.12//rain_popfile_t0_clipped.tif
country:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.12//rain_countryfile_t0_clipped.tif
outxml file:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.12/rain_popDensValues_t0.xml
popfile: LandScan
popCellSize= 0.00833333333333
projection= GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433],AUTHORITY["EPSG","4326"]]


>>  1.  resample the tif file to the resolution and proj of pop density  0.00833333333333  deg


>>  2.  read the charactristics of the input file


>>  3a. extract a piece of pop. file corresponding to the required bounding box
>>  3b. extract a piece of countries corresponding to the required bounding box and resolution/proj of pop density
>>  4.  classify the vmax file creating another array of values classified
cellsize 0.00833333333333
cellsize 0.00833333333333
cellsize 0.00833333333333
>>  5.  count the popolation in each cell and assign to the class and write to output
>>  6.  print / save output in  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.12/rain_popDensValues_t0.xml
>>  7.  remove files 
done
input var: rain
Input File: /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190425.00/20190425.00_rain_t0.tif
hurName:  
hdate:  
var:  rain
description:  rain: _ (ECMWF)
OutDir:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.00//
PopFile:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.00//rain_popfile_t0_clipped.tif
country:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.00//rain_countryfile_t0_clipped.tif
outxml file:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.00/rain_popDensValues_t0.xml
popfile: LandScan
popCellSize= 0.00833333333333
projection= GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433],AUTHORITY["EPSG","4326"]]


>>  1.  resample the tif file to the resolution and proj of pop density  0.00833333333333  deg


>>  2.  read the charactristics of the input file


>>  3a. extract a piece of pop. file corresponding to the required bounding box
>>  3b. extract a piece of countries corresponding to the required bounding box and resolution/proj of pop density
>>  4.  classify the vmax file creating another array of values classified
cellsize 0.00833333333333
cellsize 0.00833333333333
cellsize 0.00833333333333
>>  5.  count the popolation in each cell and assign to the class and write to output
>>  6.  print / save output in  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.00/rain_popDensValues_t0.xml
>>  7.  remove files 
done
input var: rain
Input File: /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190426.00/20190426.00_rain_t0.tif
hurName:  
hdate:  
var:  rain
description:  rain: _ (ECMWF)
OutDir:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190426.00//
PopFile:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190426.00//rain_popfile_t0_clipped.tif
country:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190426.00//rain_countryfile_t0_clipped.tif
outxml file:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190426.00/rain_popDensValues_t0.xml
popfile: LandScan
popCellSize= 0.00833333333333
projection= GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433],AUTHORITY["EPSG","4326"]]


>>  1.  resample the tif file to the resolution and proj of pop density  0.00833333333333  deg


>>  2.  read the charactristics of the input file


>>  3a. extract a piece of pop. file corresponding to the required bounding box
>>  3b. extract a piece of countries corresponding to the required bounding box and resolution/proj of pop density
>>  4.  classify the vmax file creating another array of values classified
cellsize 0.00833333333333
cellsize 0.00833333333333
cellsize 0.00833333333333
>>  5.  count the popolation in each cell and assign to the class and write to output
>>  6.  print / save output in  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190426.00/rain_popDensValues_t0.xml
>>  7.  remove files 
done
t0 completed 

Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190426.00/20190426.00_rain.tif.
Using internal nodata values (e.g. -999) for image /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190426.00/20190426.00_rain.tif.
Copying nodata values from source /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190426.00/20190426.00_rain.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190426.00//rain_res_all.tif.
0Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190424.12/20190424.12_rain.tif.
Using internal nodata values (e.g. -999) for image /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190424.12/20190424.12_rain.tif.
Copying nodata values from source /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190424.12/20190424.12_rain.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.12//rain_res_all.tif.
0Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190425.12/20190425.12_rain.tif.
Using internal nodata values (e.g. -999) for image /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190425.12/20190425.12_rain.tif.
Copying nodata values from source /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190425.12/20190425.12_rain.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.12//rain_res_all.tif.
0Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190425.00/20190425.00_rain.tif.
Using internal nodata values (e.g. -999) for image /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190425.00/20190425.00_rain.tif.
Copying nodata values from source /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190425.00/20190425.00_rain.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.00//rain_res_all.tif.
0...........10..101010.......20.....2020.20....30........3030.30..40..........40405040..........505050..60........70....6060....60....80......7070.70..90..........808080.........909090.........100 - done.
100 - done.
100 - done.
100 - done.
Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/GDACS/DATA/lspop20141.tif.
Using internal nodata values (e.g. -2.14748e+09) for image /mnt/output/GDACS/DATA/lspop20141.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/lspop20141.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.12//rain_popfile_all_clipped.tif.
0Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/GDACS/DATA/lspop20141.tif.
Using internal nodata values (e.g. -2.14748e+09) for image /mnt/output/GDACS/DATA/lspop20141.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/lspop20141.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.00//rain_popfile_all_clipped.tif.
0Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/GDACS/DATA/lspop20141.tif.
Using internal nodata values (e.g. -2.14748e+09) for image /mnt/output/GDACS/DATA/lspop20141.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/lspop20141.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.12//rain_popfile_all_clipped.tif.
0Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/GDACS/DATA/lspop20141.tif.
Using internal nodata values (e.g. -2.14748e+09) for image /mnt/output/GDACS/DATA/lspop20141.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/lspop20141.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190426.00//rain_popfile_all_clipped.tif.
0............10101010............20202020.........30....3030.30......40.....40.4040....50......505050............60606060............70707070...........808080....80........909090.....90........100 - done.
100 - done.
100 - done.
100 - done.
Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/GDACS/DATA/countries.tif.
Using internal nodata values (e.g. -32768) for image /mnt/output/GDACS/DATA/countries.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/countries.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.00//rain_countryfile_all_clipped.tif.
0..Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/GDACS/DATA/countries.tif.
Using internal nodata values (e.g. -32768) for image /mnt/output/GDACS/DATA/countries.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/countries.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190426.00//rain_countryfile_all_clipped.tif.
0Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/GDACS/DATA/countries.tif.
Using internal nodata values (e.g. -32768) for image /mnt/output/GDACS/DATA/countries.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/countries.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.12//rain_countryfile_all_clipped.tif.
0Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/GDACS/DATA/countries.tif.
Using internal nodata values (e.g. -32768) for image /mnt/output/GDACS/DATA/countries.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/countries.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.12//rain_countryfile_all_clipped.tif.
0...10..........20101010...........30.2020.20........40....303030.....50......404040.........505050.........60....606060......70.......707070....80........80.808090............909090.........100 - done.
100 - done.
100 - done.
100 - done.
input var: rain
Input File: /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190425.00/20190425.00_rain.tif
hurName:  
hdate:  
var:  rain
description:  rain: _ (ECMWF)
OutDir:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.00//
PopFile:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.00//rain_popfile_all_clipped.tif
country:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.00//rain_countryfile_all_clipped.tif
outxml file:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.00/rain_popDensValues_all.xml
popfile: LandScan
popCellSize= 0.00833333333333
projection= GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433],AUTHORITY["EPSG","4326"]]


>>  1.  resample the tif file to the resolution and proj of pop density  0.00833333333333  deg


>>  2.  read the charactristics of the input file


>>  3a. extract a piece of pop. file corresponding to the required bounding box
>>  3b. extract a piece of countries corresponding to the required bounding box and resolution/proj of pop density
>>  4.  classify the vmax file creating another array of values classified
cellsize 0.00833333333333
cellsize 0.00833333333333
cellsize 0.00833333333333
>>  5.  count the popolation in each cell and assign to the class and write to output
>>  6.  print / save output in  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.00/rain_popDensValues_all.xml
>>  7.  remove files 
done
input var: rain
Input File: /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190424.12/20190424.12_rain.tif
hurName:  
hdate:  
var:  rain
description:  rain: _ (ECMWF)
OutDir:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.12//
PopFile:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.12//rain_popfile_all_clipped.tif
country:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.12//rain_countryfile_all_clipped.tif
outxml file:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.12/rain_popDensValues_all.xml
popfile: LandScan
popCellSize= 0.00833333333333
projection= GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433],AUTHORITY["EPSG","4326"]]


>>  1.  resample the tif file to the resolution and proj of pop density  0.00833333333333  deg


>>  2.  read the charactristics of the input file


>>  3a. extract a piece of pop. file corresponding to the required bounding box
>>  3b. extract a piece of countries corresponding to the required bounding box and resolution/proj of pop density
>>  4.  classify the vmax file creating another array of values classified
cellsize 0.00833333333333
cellsize 0.00833333333333
cellsize 0.00833333333333
>>  5.  count the popolation in each cell and assign to the class and write to output
>>  6.  print / save output in  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.12/rain_popDensValues_all.xml
>>  7.  remove files 
done
input var: rain
Input File: /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190426.00/20190426.00_rain.tif
hurName:  
hdate:  
var:  rain
description:  rain: _ (ECMWF)
OutDir:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190426.00//
PopFile:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190426.00//rain_popfile_all_clipped.tif
country:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190426.00//rain_countryfile_all_clipped.tif
outxml file:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190426.00/rain_popDensValues_all.xml
popfile: LandScan
popCellSize= 0.00833333333333
projection= GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433],AUTHORITY["EPSG","4326"]]


>>  1.  resample the tif file to the resolution and proj of pop density  0.00833333333333  deg


>>  2.  read the charactristics of the input file


>>  3a. extract a piece of pop. file corresponding to the required bounding box
>>  3b. extract a piece of countries corresponding to the required bounding box and resolution/proj of pop density
>>  4.  classify the vmax file creating another array of values classified
cellsize 0.00833333333333
cellsize 0.00833333333333
cellsize 0.00833333333333
>>  5.  count the popolation in each cell and assign to the class and write to output
>>  6.  print / save output in  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190426.00/rain_popDensValues_all.xml
>>  7.  remove files 
done
input var: rain
Input File: /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/20190425.12/20190425.12_rain.tif
hurName:  
hdate:  
var:  rain
description:  rain: _ (ECMWF)
OutDir:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.12//
PopFile:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.12//rain_popfile_all_clipped.tif
country:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.12//rain_countryfile_all_clipped.tif
outxml file:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.12/rain_popDensValues_all.xml
popfile: LandScan
popCellSize= 0.00833333333333
projection= GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433],AUTHORITY["EPSG","4326"]]


>>  1.  resample the tif file to the resolution and proj of pop density  0.00833333333333  deg


>>  2.  read the charactristics of the input file


>>  3a. extract a piece of pop. file corresponding to the required bounding box
>>  3b. extract a piece of countries corresponding to the required bounding box and resolution/proj of pop density
>>  4.  classify the vmax file creating another array of values classified
cellsize 0.00833333333333
cellsize 0.00833333333333
cellsize 0.00833333333333
>>  5.  count the popolation in each cell and assign to the class and write to output
>>  6.  print / save output in  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.12/rain_popDensValues_all.xml
>>  7.  remove files 
done
Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_final.tif.
Using internal nodata values (e.g. 3.40282e+38) for image /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_final.tif.
Copying nodata values from source /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_final.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//rain_res_final.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/GDACS/DATA/lspop20141.tif.
Using internal nodata values (e.g. -2.14748e+09) for image /mnt/output/GDACS/DATA/lspop20141.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/lspop20141.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//rain_popfile_final_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
Creating output file that is 4822P x 3940L.
Processing input file /mnt/output/GDACS/DATA/countries.tif.
Using internal nodata values (e.g. -32768) for image /mnt/output/GDACS/DATA/countries.tif.
Copying nodata values from source /mnt/output/GDACS/DATA/countries.tif to destination /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//rain_countryfile_final_clipped.tif.
0...10...20...30...40...50...60...70...80...90...100 - done.
t0 completed 

copy:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190424.12/rain_popDensValues_all.xml in:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/delft3d/20190424.12/rain_popDensValues_all.xml
copy:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.00/rain_popDensValues_all.xml in:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/delft3d/20190425.00/rain_popDensValues_all.xml
copy:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190425.12/rain_popDensValues_all.xml in:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/delft3d/20190425.12/rain_popDensValues_all.xml
copy:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/20190426.00/rain_popDensValues_all.xml in:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/delft3d/20190426.00/rain_popDensValues_all.xml

 >> 3.2. Classify final folder
/mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final/20190426.00_final_completed_rain.txt
input var: rain
Input File: /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/tif/final/rain_final.tif
hurName:  
hdate:  
var:  rain
description:  rain: _ (ECMWF)
OutDir:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//
PopFile:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//rain_popfile_final_clipped.tif
country:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final//rain_countryfile_final_clipped.tif
outxml file:  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final/rain_popDensValues_final.xml
xml file exists...REMOVE
popfile: LandScan
popCellSize= 0.00833333333333
projection= GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433],AUTHORITY["EPSG","4326"]]


>>  1.  resample the tif file to the resolution and proj of pop density  0.00833333333333  deg


>>  2.  read the charactristics of the input file


>>  3a. extract a piece of pop. file corresponding to the required bounding box
>>  3b. extract a piece of countries corresponding to the required bounding box and resolution/proj of pop density
>>  4.  classify the vmax file creating another array of values classified
cellsize 0.00833333333333
cellsize 0.00833333333333
cellsize 0.00833333333333
>>  5.  count the popolation in each cell and assign to the class and write to output
>>  6.  print / save output in  /mnt/output/SSCS/2019/GDACS/1000559/3_ECMWF/class/final/rain_popDensValues_final.xml
>>  7.  remove files 
done
==============================================================