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bennyistanto/longterm_chirps_dekad.py

Last active June 6, 2020 00:48
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Longterm MEAN, MAXIMUM, MINIMUM, STD for CHIRPS dekad data using arcpy
Raw
longterm_chirps_dekad.py
#!/usr/bin/env python
# Import system modules
import optparse, sys, os, traceback, errno
import ast
import re
import json
import ConfigParser
import re
import logging
import arcpy
from arcpy.sa import *
#function
class VampireDefaults:
def __init__(self):
# set up logging
self.logger = logging.getLogger('Vampire')
logging.basicConfig(filename='vampire.log', format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
level=logging.DEBUG, filemode='w')
# load default values from .ini file
self.config = ExtParser()
cur_dir = os.path.join(os.getcwd(), 'vampire.ini')
ini_files = ['vampire.ini',
os.path.join(os.getcwd(), 'vampire.ini'),
cur_dir]
dataset = self.config.read(ini_files)
if len(dataset) == 0:
msg = "Failed to open/find vampire.ini in {0}, {1} and {2}".format(ini_files[0], ini_files[1], ini_files[2])
raise ValueError, msg
self.countries = dict(self.config.items('country'))
self.countries = dict((k.title(), v) for k, v in self.countries.iteritems())
self.country_codes_l = []
self.country_codes = {}
for c in self.countries:
cc = ast.literal_eval(self.countries[c].replace("\n", ""))
if 'chirps_boundary_file' in ast.literal_eval(self.countries[c].replace("\n", "")):
_chirps_boundary_file = ast.literal_eval(self.countries[c].replace("\n", ""))['chirps_boundary_file']
p = re.match(r'.*\$\{(?P<param>.*)\}.*', _chirps_boundary_file)
if p:
# has a reference
_chirps_boundary_file = _chirps_boundary_file.replace('${'+p.group('param')+'}',
self.config.get('CHIRPS', p.group('param')))
cc['chirps_boundary_file'] = _chirps_boundary_file
if 'modis_boundary_file' in ast.literal_eval(self.countries[c].replace("\n", "")):
_modis_boundary_file = ast.literal_eval(self.countries[c].replace("\n", ""))['modis_boundary_file']
p = re.match(r'.*\$\{(?P<param>.*)\}.*', _modis_boundary_file)
if p:
# has a reference
_modis_boundary_file = _modis_boundary_file.replace('${'+p.group('param')+'}',
self.config.get('MODIS', p.group('param')))
cc['modis_boundary_file'] = _modis_boundary_file
self.countries[c] = cc
self.country_codes[cc['abbreviation']] = c
self.country_codes_l.append(cc['abbreviation'])
return
def get(self, section, item):
return self.config.get(section, item)
def get_home_country(self):
return self.config.get('vampire', 'home_country')
def get_country(self, country=None):
if not country:
return self.countries
return self.countries[country]
def get_country_code(self, country=None):
if country is None:
return self.country_codes
if country in self.countries:
return self.countries[country]['abbreviation']
return
def get_country_name(self, country_code):
for c in self.countries:
if country_code.lower() == self.countries[c]['abbreviation'].lower():
return c
return None
def print_defaults(self):
print self.config._sections
#function
class ExtParser(ConfigParser.SafeConfigParser):
#implementing extended interpolation
def __init__(self, *args, **kwargs):
self.cur_depth = 0
ConfigParser.SafeConfigParser.__init__(self, *args, **kwargs)
def get(self, section, option, raw=False, vars=None):
r_opt = ConfigParser.SafeConfigParser.get(self, section, option, raw=True, vars=vars)
if raw:
return r_opt
ret = r_opt
re_oldintp = r'%\((\w*)\)s'
re_newintp = r'\$\{(\w*):(\w*)\}'
m_new = re.findall(re_newintp, r_opt)
if m_new:
for f_section, f_option in m_new:
self.cur_depth = self.cur_depth + 1
if self.cur_depth < ConfigParser.MAX_INTERPOLATION_DEPTH:
sub = self.get(f_section, f_option, vars=vars)
ret = ret.replace('${{{0}:{1}}}'.format(f_section, f_option), sub)
else:
raise ConfigParser.InterpolationDepthError, (option, section, r_opt)
m_old = re.findall(re_oldintp, r_opt)
if m_old:
for l_option in m_old:
self.cur_depth = self.cur_depth + 1
if self.cur_depth < ConfigParser.MAX_INTERPOLATION_DEPTH:
sub = self.get(section, l_option, vars=vars)
ret = ret.replace('%({0})s'.format(l_option), sub)
else:
raise ConfigParser.InterpolationDepthError, (option, section, r_opt)
self.cur_depth = self.cur_depth - 1
return ret
vp = VampireDefaults()
nmonthly = ['01','02','03','04', '05', '06', '07', '08', '09', '10', '11', '12']
ndekad = ['1','2','3']
data_folder = "Z:\\Temp\\DryWetSeason\\Month3"
lta_folder = "Z:\\Temp\\DryWetSeason\\Statistics"
dekad_pattern = vp.get('CHIRPS', 'global_dekad_pattern') # global_monthly_pattern, global_seasonal_pattern, global_dekad_pattern
Moregex_dekad = re.compile(dekad_pattern)
def dekadLT():
dictionary = {}
for i in nmonthly:
for j in ndekad:
index = i+j
content = []
for file_dekad in os.listdir(data_folder):
if file_dekad.endswith(".tif") or file_dekad.endswith(".tiff"):
Moresult_dekad = Moregex_dekad.match(file_dekad)
Dmonth = Moresult_dekad.group('month')
Ddekad = Moresult_dekad.group('dekad')
if Ddekad == j and Dmonth == i:
content.append(os.path.join(data_folder, file_dekad))
dictionary[index] = content
for k in nmonthly:
for l in ndekad:
index = k + l
listoffile = dictionary[index]
ext = ".tif"
newfilename_dekad_avg = 'chirps-v2.0.1981-2019.{0}.{1}.dekad.39yrs.avg{2}'.format(k, l, ext)
newfilename_dekad_std = 'chirps-v2.0.1981-2019.{0}.{1}.dekad.39yrs.std{2}'.format(k, l, ext)
newfilename_dekad_max = 'chirps-v2.0.1981-2019.{0}.{1}.dekad.39yrs.max{2}'.format(k, l, ext)
newfilename_dekad_min = 'chirps-v2.0.1981-2019.{0}.{1}.dekad.39yrs.min{2}'.format(k, l, ext)
print(newfilename_dekad_avg)
if arcpy.Exists(os.path.join(lta_folder, newfilename_dekad_avg)):
print(newfilename_dekad_avg + " exists")
else:
arcpy.CheckOutExtension("spatial")
outCellStatistics_avg = CellStatistics(listoffile, "MEAN", "DATA")
outCellStatistics_avg.save(os.path.join(lta_folder, newfilename_dekad_avg))
arcpy.CheckInExtension("spatial")
if arcpy.Exists(os.path.join(lta_folder, newfilename_dekad_std)):
print(newfilename_dekad_std + " exists")
else:
arcpy.CheckOutExtension("spatial")
outCellStatistics_std = CellStatistics(listoffile, "STD", "DATA")
outCellStatistics_std.save(os.path.join(lta_folder, newfilename_dekad_std))
arcpy.CheckInExtension("spatial")
if arcpy.Exists(os.path.join(lta_folder, newfilename_dekad_max)):
print(newfilename_dekad_max + " exists")
else:
arcpy.CheckOutExtension("spatial")
outCellStatistics_max = CellStatistics(listoffile, "MAXIMUM", "DATA")
outCellStatistics_max.save(os.path.join(lta_folder, newfilename_dekad_max))
arcpy.CheckInExtension("spatial")
if arcpy.Exists(os.path.join(lta_folder, newfilename_dekad_min)):
print(newfilename_dekad_min + " exists")
else:
arcpy.CheckOutExtension("spatial")
outCellStatistics_min = CellStatistics(listoffile, "MINIMUM", "DATA")
outCellStatistics_min.save(os.path.join(lta_folder, newfilename_dekad_min))
arcpy.CheckInExtension("spatial")
dekadLT()
Raw
vampire.ini
[vampire]
home_country = LKA
base_data_dir = C:\PRIMA\data\Download
base_product_dir = C:\PRIMA\data
base_boundary_dir = C:\PRIMA\data\Shapefiles\Boundaries
base_national_boundary_dir = C:\PRIMA\data\Shapefiles\Boundaries\National
[directories]
gdal_dir = C:\OSGeo4W\bin
mrt_dir = C:\Program Files\MRT\bin
temp_dir = C:\PRIMA\data\temp
default_download = C:\PRIMA\data\Download
default_product = C:\PRIMA\data
default_output = C:\PRIMA\data
geoserver_data = C:\Program Files (x86)\GeoServer 2.11.0\data_dir\data\
[database]
default_host = localhost
default_port = 5432
default_user = prima_user
default_pw = prima_user
default_schema = public
impact_db = prima_impact
impact_area_table = vhi_area
impact_popn_table = vhi_popn
impact_crops_table = vhi_crops
rainfall_anomaly_db = prima_ra
rainfall_anomaly_table = rainfall_anomaly
vhi_db = prima_vhi_250m
vhi_table = vhi
spi_db = prima_spi_10day
spi_table = spi
[geodatabase]
config = {
'country': 'idn',
'storingConfig': 'file', #file for file gedatabase, ent for enterprise geodatabase
'product': ['spi_1_month'],
'template': {'rainfall_anomaly_1_month': 'D:\\SharedFolder\\DIO\\vampire\\rainfall_anomaly_avg.rft.xml',
'vhi_crop_1_month': 'D:\\SharedFolder\\DIO\\vampire\\VHI_Classified.rft.rft.xml',
'idn_vhi_monthly2': 'D:\\SharedFolder\\DIO\\idn_vhi_monthly2.rft.rft.xml',
'DSLR_01mm': 'D:\\IDN_GIS\\05_Analysis\\03_Early_Warning\\Days_Since_Last_Rain\\idn_cli_dslr_imerg.rft.xml',
'DSLR_05mm': 'D:\\IDN_GIS\\05_Analysis\\03_Early_Warning\\Days_Since_Last_Rain\\idn_cli_dslr_imerg.rft.xml',
'spi_dekad': 'D:\\SharedFolder\\DIO\\vampire\\standard_precipitation_index.rft.xml',
'spi_dekad': 'D:\\SharedFolder\\DIO\\vampire\\standard_precipitation_index.rft.xml',
'spi_3_month': 'D:\\SharedFolder\\DIO\\vampire\\standard_precipitation_index.rft.xml'},
'summary': {'rainfall_anomaly_1_month': 'Monthly rainfall anomaly, percent of average 1981 - 2015',
'rainfall_anomaly_dekad': 'Dekad rainfall anomaly, percent of average 1981 - 2015',
'rainfall_anomaly_3_month': '3-Monthly rainfall anomaly, percent of average 1981 - 2015',
'spi_1_month': 'Monthly standard precipitation index, percent of average 1981 - 2015',
'spi_3_month': '3-Monthly standard precipitation index, percent of average 1981 - 2015',
'spi_dekad': 'Dekad standard precipitation index, percent of average 1981 - 2015',
'idn_vhi_monthly2': 'Monthly vegetation health index',
'vhi_crop_1_month': 'Monthly vegetation health index',
'DSLR_01mm': 'Days Since Last Rain with 1 mm Threshold',
'DSLR_05mm': 'Days Since Last Rain with 5 mm Threshold',},
'tags': {'rainfall_anomaly_1_month': 'Rainfall, Anomaly, Indonesia, CHIRPS',
'rainfall_anomaly_dekad': 'Rainfall, Anomaly, Indonesia, CHIRPS',
'rainfall_anomaly_3_month': 'Rainfall, Anomaly, Indonesia, CHIRPS',
'spi_1_month': 'Rainfall, SPI, Indonesia, CHIRPS',
'spi_3_month': 'Rainfall, SPI, Indonesia, CHIRPS',
'spi_dekad': 'Rainfall, SPI, Indonesia, CHIRPS',
'vhi_crop_1_month': 'VHI, Indonesia, MODIS',
'DSLR_01mm': 'DSLR, Indonesia, IMERG, daily',
'DSLR_05mm': 'DSLR, Indonesia, IMERG, daily',
'idn_vhi_monthly2': 'VHI, Indonesia, MODIS'},
'description': {'rainfall_anomaly_1_month': 'Rainfall anomaly derived from the monthly rainfall compare to the long term average (1981 - 2015). Rainfall data come from CHIRPS of University of California Santa Barbara',
'rainfall_anomaly_dekad': 'Rainfall anomaly derived from the dekad rainfall compare to the long term average (1981 - 2015). Rainfall data come from CHIRPS of University of California Santa Barbara',
'rainfall_anomaly_3_month': 'Rainfall anomaly derived from the 3-monthly rainfall compare to the long term average (1981 - 2015). Rainfall data come from CHIRPS of University of California Santa Barbara',
'vhi_crop_1_month': 'Monthly vegetation health index in crop area. EVI data and temperature data come from MODIS data',
'DSLR_01mm': 'Days Since Last Rain with 1 mm threshold. Data processed from IMERG daily data',
'DSLR_05mm': 'Days Since Last Rain with 1 mm threshold. Data processed from IMERG daily data',
'idn_vhi_monthly2': 'Monthly Vegetation Health Index (VHI) for Indonesia. Data processed from MODIS Enhanced Vegetation Index (EVI) and Land Surface Temperature (LST)',
'spi_1_month': 'Standard Precipitation Index derived from the difference between monthly rainfall to long term average compare to the long term standard deviation (1981 - 2015). Rainfall data come from CHIRPS of University of California Santa Barbara',
'spi_3_month': 'Standard Precipitation Index derived from the difference between monthly rainfall to long term average compare to the long term standard deviation (1981 - 2015). Rainfall data come from CHIRPS of University of California Santa Barbara',
'spi_dekad': 'Standard Precipitation Index derived from the difference between monthly rainfall to long term average compare to the long term standard deviation (1981 - 2015)Rainfall data come from CHIRPS of University of California Santa Barbara'},
'credits': 'Dio Dafrista and Benny Istanto',
'ws': 'D:/SharedFolder/DIO/vampire/',
'keycode': 'D:/SharedFolder/DIO/vampire/keycodes',
'gdbpath': 'D:\\IDN_GIS\\05_Analysis\\03_Early_Warning\\Rainfall_Anomaly',
'sourcedata': 'D:/IDN_GIS/01_Data/03_Regional',
'postgreHost': 'localhost',
'dba': 'postgres',
'dbapass': 'Indonesia',
'sdeuser': 'sde',
'sdepass': 'sde_indonesia',
'proj': "PROJCS['WGS_1984_UTM_Zone_49S',GEOGCS['GCS_WGS_1984',DATUM['D_WGS_1984',SPHEROID['WGS_1984',6378137.0,298.257223563]],PRIMEM['Greenwich',0.0],UNIT['Degree',0.0174532925199433]],PROJECTION['Transverse_Mercator'],PARAMETER['False_Easting',500000.0],PARAMETER['False_Northing',10000000.0],PARAMETER['Central_Meridian',111.0],PARAMETER['Scale_Factor',0.9996],PARAMETER['Latitude_Of_Origin',0.0],UNIT['Meter',1.0]];-5120900 1900 10000;-100000 10000;-100000 10000;0,001;0,001;0,001;IsHighPrecision",
'server_url': 'http://localhost:6080/arcgis/admin',
'sdeCatalog': 'C:/Users/dio.dafrista/AppData/Roaming/ESRI/Desktop10.3/ArcCatalog/',
'server_admin': '[email protected]',
'use_arcgis_desktop_staging_folder': 'False',
'username': '[email protected]',
'password': 'Semangat@2017'
}
[CHIRPS]
base_data_name = chirps-v2.0
base_product_name = _cli_chirps-v2.0
# FTP data directories
ftp_address = chg-ftpout.geog.ucsb.edu
ftp_user = anonymous
ftp_password = @anonymous
ftp_dir_daily = /pub/org/chg/products/CHIRPS-2.0/global_daily/tifs/p25/
ftp_dir_monthly = pub/org/chg/products/CHIRPS-2.0/global_monthly/tifs/
ftp_dir_dekad = pub/org/chg/products/CHIRPS-2.0//global_dekad/tifs/
ftp_dir_pentad = pub/org/chg/products/CHIRPS-2.0/global_pentad/tifs/
ftp_dir_seasonal = pub/org/chg/products/CHIRPS-2.0/global_3-monthly/tifs/
# patterns MUST contain (?P<year>\d{4}) and (?P<month>\d{2}), (?P<dekad>\d{1}) or (?P<season>\d{6}) as appropriate
global_monthly_pattern = ^(?P<base_name>chirps)-(?P<version>v\d{1}.\d).(?P<year>\d{4}).(?P<month>\d{2})(?P<extension>\.tiff?)(?P<gzip>.*)?$
global_seasonal_pattern = ^(?P<base_name>chirps)-(?P<version>v\d{1}.\d).(?P<year>\d{4}).(?P<season>\d{6})(?P<extension>\.tiff?)(?P<gzip>.*)?$
global_dekad_pattern = ^(?P<base_name>chirps)-(?P<version>v\d{1}.\d).(?P<year>\d{4}).(?P<month>\d{2}).(?P<dekad>\d{1})(?P<extension>\.tiff?)?(?P<gzip>.*)?$
global_daily_pattern = ^(?P<base_name>chirps)-(?P<version>v\d{1}.\d).(?P<year>\d{4}).(?P<month>\d{2}).(?P<day>\d{2})(?P<extension>\.tiff?)(?P<gzip>.*)?$
regional_monthly_pattern = ^(?P<country>\w{3})_cli_(?P<base_name>chirps)-(?P<version>v\d{1}.\d).(?P<year>\d{4}).(?P<month>\d{2})(?P<extension>\.tif)(?P<gzip>.*)?$
regional_seasonal_pattern = ^(?P<country>\w{3})_cli_(?P<base_name>chirps)-(?P<version>v\d{1}.\d).(?P<year>\d{4}).(?P<season>\d{6})(?P<extension>\.tif)(?P<gzip>.*)?$
regional_dekad_pattern = ^(?P<country>\w{3})_cli_(?P<base_name>chirps)-(?P<version>v\d{1}.\d).(?P<year>\d{4}).(?P<month>\d{2}).(?P<dekad>\d{1})(?P<extension>\.tif)(?P<gzip>.*)?$
regional_daily_pattern = ^(?P<country>\w{3})_cli_(?P<base_name>chirps)-(?P<version>v\d{1}.\d).(?P<year>\d{4}).(?P<month>\d{2}).(?P<day>\d{2})(?P<extension>\.tif)(?P<gzip>.*)?$
crop_regional_output_monthly_pattern = _cli_{base_name}-{version}.{year}.{month}{extension}
crop_regional_output_seasonal_pattern = _cli_{base_name}-{version}.{year}.{season}{extension}
crop_regional_output_dekad_pattern = _cli_{base_name}-{version}.{year}.{month}.{dekad}{extension}
crop_regional_output_daily_pattern = _cli_{base_name}-{version}.{year}.{month}.{day}{extension}
data_dir = ${vampire:base_data_dir}\CHIRPS
home_country_product_dir = ${vampire:base_product_dir}\Rasters\Climate\Precipitation\CHIRPS
regional_product_dir_prefix = ${vampire:base_product_dir}
regional_product_dir_suffix = Rasters\Climate\Precipitation\CHIRPS
global_product_dir = ${vampire:base_product_dir}\Rasters\Climate\Precipitation\CHIRPS
home_country_chirps_boundary = ${vampire:base_product_dir}\Shapefiles\Boundaries\Subset\CHIRPS\idn_cli_chirps_20_005_deg_grid_diss_a.shp
regional_boundary_prefix = ${vampire:base_product_dir}\
regional_boundary_suffix = Shapefiles\Boundaries\Subset\CHIRPS\
regional_boundary_file = _bnd_subset_chirps_20_005_deg_grid_diss_a.shp
[CHIRPS_Rainfall_Anomaly]
output_dir = ${vampire:base_product_dir}\Analysis\Rainfall_Anomaly\
ra_global_output_monthly_pattern = {base_name}-{version}.{year}.{month}.ratio_anom{extension}
ra_global_output_seasonal_pattern = {base_name}-{version}.{year}.{season}.ratio_anom{extension}
ra_global_output_dekad_pattern = {base_name}-{version}.{year}.{month}.{dekad}.ratio_anom{extension}
ra_regional_monthly_pattern = ^(?P<country>\w{3})_cli_(?P<base_name>chirps)-(?P<version>v\d{1}.\d).(?P<year>\d{4}).(?P<month>\d{2}).ratio_anom(?P<extension>\.tif)$
ra_regional_seasonal_pattern = ^(?P<country>\w{3})_cli_(?P<base_name>chirps)-(?P<version>v\d{1}.\d).(?P<year>\d{4}).(?P<season>\d{6}).ratio_anom(?P<extension>\.tif)$
ra_regional_dekad_pattern = ^(?P<country>\w{3})_cli_(?P<base_name>chirps)-(?P<version>v\d{1}.\d).(?P<year>\d{4}).(?P<month>\d{2}).(?P<dekad>\d{1}).ratio_anom(?P<extension>\.tif)$
ra_regional_output_monthly_pattern = {country}_cli_{base_name}-{version}.{year}.{month}.ratio_anom{extension}
ra_regional_output_seasonal_pattern = {country}_cli_{base_name}-{version}.{year}.{season}.ratio_anom{extension}
ra_regional_output_dekad_pattern = {country}_cli_{base_name}-{version}.{year}.{month}.{dekad}.ratio_anom{extension}
output_file_suffix = ratio_anom
interval = 10
[CHIRPS_SPI]
output_dir = ${vampire:base_product_dir}\Analysis\Standardized_Precipitation_Index\
spi_global_output_monthly_pattern = {base_name}-{version}.{year}.{month}.spi{extension}
spi_global_output_seasonal_pattern = {base_name}-{version}.{year}.{season}.spi{extension}
spi_global_output_dekad_pattern = {base_name}-{version}.{year}.{month}.{dekad}.spi{extension}
spi_regional_monthly_pattern = ^(?P<country>\w{3})_cli_(?P<base_name>chirps)-(?P<version>v\d{1}.\d).(?P<year>\d{4}).(?P<month>\d{2}).spi(?P<extension>\.tif)$
spi_regional_seasonal_pattern = ^(?P<country>\w{3})_cli_(?P<base_name>chirps)-(?P<version>v\d{1}.\d).(?P<year>\d{4}).(?P<season>\d{6}).spi(?P<extension>\.tif)$
spi_regional_dekad_pattern = ^(?P<country>\w{3})_cli_(?P<base_name>chirps)-(?P<version>v\d{1}.\d).(?P<year>\d{4}).(?P<month>\d{2}).(?P<dekad>\d{1}).spi(?P<extension>\.tif)$
spi_regional_output_monthly_pattern = {country}_cli_{base_name}-{version}.{year}.{month}.spi{extension}
spi_regional_output_seasonal_pattern = {country}_cli_{base_name}-{version}.{year}.{season}.spi{extension}
spi_regional_output_dekad_pattern = {country}_cli_{base_name}-{version}.{year}.{month}.{dekad}.spi{extension}
output_file_suffix = spi
interval = 10
[CHIRPS_Longterm_Average]
global_lta_monthly_pattern = ^(?P<base_name>chirps)-(?P<version>v\d{1}.\d).(?P<yr_range>\d{4}-\d{4}).(?P<month>\d{2}).(?P<interval>\w*).(?P<num_yrs>\d*yrs).(?P<type>avg)(?P<extension>.tif$)
global_lta_seasonal_pattern = ^(?P<base_name>chirps)-(?P<version>v\d{1}.\d).(?P<yr_range>\d{4}-\d{4}).(?P<season>\d{6}).(?P<interval>\w*).(?P<num_yrs>\d*yrs).(?P<type>avg)(?P<extension>.tif$)
global_lta_dekad_pattern = ^(?P<base_name>chirps)-(?P<version>v\d{1}.\d).(?P<yr_range>\d{4}-\d{4}).(?P<month>\d{2}).(?P<dekad>\d{1}).(?P<interval>\w*).(?P<num_yrs>\d*yrs).(?P<type>avg)(?P<extension>.tif$)
global_lta_output_monthly_pattern = {base_name}-{version}.{yr_range}.{month}.{interval}.{num_yrs}.{type}{extension}
global_lta_output_seasonal_pattern = {base_name}-{version}.{yr_range}.{season}.{interval}.{num_yrs}.{type}{extension}
global_lta_output_dekad_pattern = {base_name}-{version}.{yr_range}.{month}.{dekad}.{interval}.{num_yrs}.{type}{extension}
regional_lta_monthly_pattern = ^(?P<country>\w{3})_(?P<base_name>chirps)-(?P<version>v\d{1}.\d).(?P<yr_range>\d{4}-\d{4}).(?P<month>\d{2}).(?P<interval>\w*).(?P<num_yrs>\d*yrs).(?P<type>avg)(?P<extension>.tif$)
regional_lta_seasonal_pattern = ^(?P<country>\w{3})_(?P<base_name>chirps)-(?P<version>v\d{1}.\d).(?P<yr_range>\d{4}-\d{4}).(?P<season>\d{6}).(?P<interval>\w*).(?P<num_yrs>\d*yrs).(?P<type>avg)(?P<extension>.tif$)
regional_lta_dekad_pattern = ^(?P<country>\w{3})_(?P<base_name>chirps)-(?P<version>v\d{1}.\d).(?P<yr_range>\d{4}-\d{4}).(?P<month>\d{02}).(?P<dekad>\d{1}).(?P<interval>\w*).(?P<num_yrs>\d*yrs).(?P<type>avg)(?P<extension>.tif$)
lta_date_range = 1981-2016
[CHIRPS_Longterm_Standard_Deviation]
global_ltsd_monthly_pattern = ^(?P<base_name>chirps)-(?P<version>v\d{1}.\d).(?P<yr_range>\d{4}-\d{4}).(?P<month>\d{2}).(?P<interval>\w*).(?P<num_yrs>\d*yrs).(?P<type>std)(?P<extension>.tif$)
global_ltsd_seasonal_pattern = ^(?P<base_name>chirps)-(?P<version>v\d{1}.\d).(?P<yr_range>\d{4}-\d{4}).(?P<season>\d{6}).(?P<interval>\w*).(?P<num_yrs>\d*yrs).(?P<type>std)(?P<extension>.tif$)
global_ltsd_dekad_pattern = ^(?P<base_name>chirps)-(?P<version>v\d{1}.\d).(?P<yr_range>\d{4}-\d{4}).(?P<month>\d{2}).(?P<dekad>\d{1}).(?P<interval>\w*).(?P<num_yrs>\d*yrs).(?P<type>std)(?P<extension>.tif$)
global_ltsd_output_monthly_pattern = {base_name}-{version}.{yr_range}.{month}.{interval}.{num_yrs}.{type}{extension}
global_ltsd_output_seasonal_pattern = {base_name}-{version}.{yr_range}.{season}.{interval}.{num_yrs}.{type}{extension}
global_ltsd_output_dekad_pattern = {base_name}-{version}.{yr_range}.{month}.{dekad}.{interval}.{num_yrs}.{type}{extension}
regional_ltsd_monthly_pattern = ^(?P<country>\w{3})_(?P<base_name>chirps)-(?P<version>v\d{1}.\d).(?P<yr_range>\d{4}-\d{4}).(?P<month>\d{2}).(?P<interval>\w*).(?P<num_yrs>\d*yrs).(?P<type>std)(?P<extension>.tif$)
regional_ltsd_seasonal_pattern = ^(?P<country>\w{3})_(?P<base_name>chirps)-(?P<version>v\d{1}.\d).(?P<yr_range>\d{4}-\d{4}).(?P<season>\d{6}).(?P<interval>\w*).(?P<num_yrs>\d*yrs).(?P<type>std)(?P<extension>.tif$)
regional_ltsd_dekad_pattern = ^(?P<country>\w{3})_(?P<base_name>chirps)-(?P<version>v\d{1}.\d).(?P<yr_range>\d{4}-\d{4}).(?P<month>\d{02}).(?P<dekad>\d{1}).(?P<interval>\w*).(?P<num_yrs>\d*yrs).(?P<type>std)(?P<extension>.tif$)
ltsd_date_range = 1981-2016
[CHIRPS_Days_Since_Last_Rain]
default_threshold = 0.5
default_max_days = 30
output_dir = ${vampire:base_product_dir}\Analysis\Days_Since_Last_Rain
regional_dslr_output_pattern = {country}_cli_{base_name}-{version}.{year}.{month}.{day}.dslr{extension}
regional_dsld_output_pattern = {country}_cli_{base_name}-{version}.{year}.{month}.{day}.dsld{extension}
regional_wet_accum_output_pattern = {country}_cli_{base_name}-{version}.{year}.{month}.{day}.num_wet{extension}
regional_accum_output_pattern = {country}_cli_{base_name}-{version}.{year}.{month}.{day}.total_accum{extension}
[CHIRPS_subset]
idn = D:/IDN_GIS/01_Data/02_IDN/ShapeFiles/Boundaries/Subset/CHIRPS/idn_bnd_subset_chirps_20_005_deg_grid_diss_a.shp
mng = D:/IDN_GIS/01_Data/02_IDN/ShapeFiles/Boundaries/Subset/CHIRPS/mng_bnd_subset_chirps_20_005_deg_grid_diss_a.shp
[IMERG_subset]
idn = D:/IDN_GIS/01_Data/02_IDN/ShapeFiles/Boundaries/Subset/GPM/idn_bnd_subset_imerg_01_deg_grid_diss_a.shp
[MODIS]
user = elninoksp
password = elninoKSP2016
vegetation_product = MOD13Q1.006
land_surface_temperature_product = MOD11A2.006
data_dir = ${vampire:base_data_dir}\MODIS
vegetation_download_dir = ${MODIS:data_dir}\${MODIS:vegetation_product}\HDF_MOD
vegetation_mosaic_dir = ${MODIS:data_dir}\${MODIS:vegetation_product}\Processed\HDF_MOD
temperature_download_dir = ${MODIS:data_dir}\${MODIS:land_surface_temperature_product}\HDF_MOD
modis_tile_pattern = ^(?P<product>MOD\d{2}[A,C,Q]\d{1}).A(?P<year>\d{4})(?P<dayofyear>\d{3}).(?P<tile>h\d{2}v\d{2}).(?P<version>\d{3})?.*.(?P<extension>\.hdf$)
modis_mosaic_output_pattern = {product}.{year}.{month}.{day}.{version}{extension}
modis_monthly_pattern = ^(?P<product>MOD\d{2}[A,C,Q]\d{1}).A?(?P<year>\d{4}).(?P<month>\d{2}).(?P<day>\d{2}).(?P<version>\d{3})(?P<extension>\.hdf$)
tiles_IDN = h27v08,h27v09,h27v10,h28v08,h28v09,h28v10,h29v08,h29v09,h29v10,h30v08,h30v09,h30v10,h31v08,h31v09,h31v10,h32v08,h32v09,h32v10
tiles_RBB = h00v09,h00v10,h01v09,h01v10,h01v11,h02v08,h02v09,h03v09,h03v10,h22v05,h23v05,h23v06,h24v05,h24v06,h24v07,h25v05,h25v06,h25v07,h25v08,h26v06,h26v07,h26v08,h27v04,h27v05,h27v06,h27v07,h27v08,h27v09,h28v05,h28v06,h28v07,h28v08,h28v09,h29v06,h29v07,h29v08,h29v09,h29v10,h30v07,h30v08,h30v09,h30v10,h31v08,h31v09,h32v09,h32v10,h33v09,h33v10,h33v11,h34v07,h34v08,h34v09,h34v10,h35v08,h35v09,h35v10
home_country_vegetation_boundary = ${vampire:base_product_dir}\Shapefiles\Boundaries\Subset\MODIS\idn_phy_modis_1km_grid_diss_a.shp
regional_vegetation_boundary_file = _phy_modis_1km_grid_diss_a.shp
global_prefix = ${vampire:base_product_dir}\Global
home_country_prefix = ${vampire:base_product_dir}\
regional_prefix = ${vampire:base_product_dir}\
boundary_dir_suffix = Shapefiles\Boundaries\Subset\MODIS
[MODIS_PRODUCTS]
MOD13A3.005 = 1 1
MOD13A3.005.EVI = [2]
MOD13A3.005.EVI_Name = 1_km_monthly_EVI
MOD13A3.005.interval = monthly
MOD13A3.005.boundary_filename = _bnd_subset_modis_1km_grid_diss_a.shp
MOD13A3.005.download_dir = ${MODIS:data_dir}\MOD13A3.005\HDF_MOD
MOD13A3.005.mosaic_dir = ${MODIS:data_dir}\MOD13A3.005\Mosaic
MOD13Q1.005 = 1 1
MOD13Q1.005.EVI = [2]
MOD13Q1.005.EVI_Name = 250_m_16_days_EVI
MOD13Q1.005.interval = 16Days
MOD13Q1.005.boundary_filename = _bnd_subset_modis_250m_grid_diss_a.shp
MOD13Q1.005.download_dir = ${MODIS:data_dir}\MOD13Q1.005\HDF_MOD
MOD13Q1.005.mosaic_dir = ${MODIS:data_dir}\MOD13Q1.005\Mosaic
MOD13Q1.006 = 1 1
MOD13Q1.006.EVI = [2]
MOD13Q1.006.EVI_Name = 250_m_16_days_EVI
MOD13Q1.006.interval = 16Days
MOD13Q1.006.boundary_filename = _bnd_subset_modis_250m_grid_diss_a.shp
MOD13Q1.006.download_dir = ${MODIS:data_dir}\MOD13Q1.005\HDF_MOD
MOD13Q1.006.mosaic_dir = ${MODIS:data_dir}\MOD13Q1.005\Mosaic
MOD11C3.005 =
MOD11C3.005.LST_Day = [1]
MOD11C3.005.LST_Day_Name = LST_Day
MOD11C3.005.LST_Night = [6]
MOD11C3.005.LST_Night_Name = LST_Night
MOD11C3.005.interval = monthly
MOD11C3.005.boundary_filename = _bnd_subset_modis_lst_005_grid_diss_a.shp
MOD11C3.005.download_dir = ${MODIS:data_dir}\MOD11C3.005\HDF_MOD
MOD11C3.005.mosaic_dir =
MOD11A2.005 = 1 1 1 1 1
MOD11A2.005.LST_Day = [1]
MOD11A2.005.LST_Day_Name = LST_Day
MOD11A2.005.LST_Night = [5]
MOD11A2.005.LST_Night_Name = LST_Night
MOD11A2.005.interval = 8Days
MOD11A2.005.boundary_filename = _bnd_subset_modis_1km_grid_diss_a.shp
MOD11A2.005.download_dir = ${MODIS:data_dir}\MOD11A2.005\HDF_MOD
MOD11A2.005.mosaic_dir = ${MODIS:data_dir}\MOD11A2.005\Mosaic
MOD11A2.006 = 1 1 1 1 1
MOD11A2.006.LST_Day = [1]
MOD11A2.006.LST_Day_Name = LST_Day
MOD11A2.006.LST_Night = [5]
MOD11A2.006.LST_Night_Name = LST_Night
MOD11A2.006.interval = 8Days
MOD11A2.006.boundary_filename = _bnd_subset_modis_1km_grid_diss_a.shp
MOD11A2.006.download_dir = ${MODIS:data_dir}\MOD11A2.005\HDF_MOD
MOD11A2.006.mosaic_dir = ${MODIS:data_dir}\MOD11A2.005\Mosaic
[MODIS_EVI]
evi_spectral_subset = [2]
evi_subset_name = 1_km_monthly_EVI
evi_extract_dir = ${MODIS:data_dir}\${MODIS:vegetation_product}\Processed\EVI
evi_dir_suffix = Rasters\Physical\Vegetation\${MODIS:vegetation_product}.EVI\By16Days
evi_pattern = ^(?P<product>MOD\d{2}[A,Q]\d{1}).(?P<year>\d{4}).(?P<month>\d{2}).(?P<day>\d{2}).(?P<version>\d{3}).(?P<subset>.*)(?P<extension>\.tif$)
evi_output_pattern = {product}.{year}.{month}.{day}.{version}.tif
evi_regional_pattern = ^(?P<country>\w{3})(?P<type>_\w{3}_)(?P<product>MOD\d{2}[A,Q]\d{1}).(?P<year>\d{4}).(?P<month>\d{2}).(?P<day>\d{2}).(?P<version>\d{3}).(?P<subset>.*)(?P<extension>\.tif$)
evi_regional_output_pattern = {country}_phy_{product}.{year}.{month}.{day}.{version}.{subset}{extension}
[MODIS_EVI_Long_Term_Average]
# idn_phy_MOD13A3.2000-2015.001.1_km_monthly_EVI.15yrs.avg
lta_pattern = ^(?P<country>\w{3})_phy_(?P<product>MOD\d{2}[A,Q]\d{1}).(?P<yr_range>\d{4}-\d{4}).(?P<day_of_yr>\d{3}).(?P<subset>.*).(?P<num_yrs>.*yrs).(?P<statistic>.*)(?P<extension>\.tif$)
lta_output_pattern = {country}{type}{product}.{yr_range}.{day_of_yr}.{subset}.{num_yrs}.{statistic}{extension}
lta_dir_suffix = Rasters\Physical\Vegetation\${MODIS:vegetation_product}.EVI\StatisticsBy16Days
[MODIS_NDVI]
ndvi_spectral_subset = [1]
ndvi_subset_name = 1_km_monthly_NDVI
ndvi_extract_dir = ${MODIS:data_dir}\${MODIS:vegetation_product}\Processed\NDVI
ndvi_input_pattern = ^(?P<product>MOD\d{2}[A,Q]\d{1}).(?P<year>\d{4}).(?P<month>\d{2}).(?P<day>\d{2}).(?P<version>\d{3})(?P<extension>\.hdf$)
ndvi_output_pattern = {product}.{year}.{month}.{day}.{version}.tif
[MODIS_LST]
lst_dir_suffix = Rasters\Climate\Temperature\MODIS\${MODIS:land_surface_temperature_product}\By8Days
lst_download_dir = ${MODIS:data_dir}\${MODIS:land_surface_temperature_product}\HDF_MOD
lst_day_dir = ${MODIS:data_dir}\${MODIS:land_surface_temperature_product}\Processed\Day
lst_night_dir = ${MODIS:data_dir}\${MODIS:land_surface_temperature_product}\Processed\Night
lst_dir = ${MODIS:data_dir}\${MODIS:land_surface_temperature_product}\Processed\Average
lst_pattern = ^(?P<product>MOD\d{2}[C,A]\d{1}).A(?P<year>\d{4})(?P<dayofyear>\d{3}).(?P<version>\d{3})?.*(?P<extension>\.hdf$)
lst_mosaic_pattern = ^(?P<product>MOD\d{2}A\d{1}).(?P<year>\d{4}).(?P<month>\d{2}).(?P<day>\d{2}).(?P<version>\d{3})?.*(?P<extension>\.hdf$)
lst_output_pattern = {product}.{year}{dayofyear}.{version}.tif
lst_day_night_pattern = ^(?P<product>MOD\d{2}[C,A]\d{1}).(?P<year>\d{4})(?P<dayofyear>\d{3}).(?P<version>\d{3}).(?P<subset>\w*)(?P<extension>\.tif$)
lst_average_pattern = ^(?P<product>MOD\d{2}[C,A]\d{1}).(?P<year>\d{4}).(?P<month>\d{2}).(?P<day>\d{2}).(?P<version>\d{3})(?P<extension>\.tif$)
lst_average_output_pattern = {product}.{year}{dayofyear}.{version}.avg{extension}
lst_regional_pattern = ^(?P<country>\w{3})_cli_(?P<product>MOD\d{2}[C,A]\d{1}).(?P<year>\d{4}).(?P<month>\d{2}).(?P<day>\d{2}).(?P<version>\d{3})(?P<extension>\.tif$)
lst_regional_output_pattern = {country}_cli_{product}.{year}.{month}.{day}.{version}{extension}
[MODIS_LST_Long_Term_Average]
lta_pattern = ^(?P<country>\w{3})(?P<type>_cli_)(?P<product>MOD\d{2}[C,A]\d{1}).(?P<yr_range>\d{4}-\d{4}).(?P<day_of_yr>\d{3}).(?P<subset>.*).(?P<num_yrs>.*yrs).(?P<statistic>.*)(?P<extension>\.tif$)
lta_output_pattern = {product}.{yr_range}.{dayofyear}.{subset}.{num_yrs}.{statistic}{extension}
lta_regional_output_pattern = {country}{type}{product}.{yr_range}.{day_of_yr}.{subset}.{num_yrs}.{statistic}{extension}
lta_dir_suffix = Rasters\Climate\Temperature\MODIS\${MODIS:land_surface_temperature_product}\StatisticsBy8Days
[MODIS_VCI]
vci_product_dir = ${vampire:base_product_dir}\Analysis\Vegetation_Condition_Index
vci_pattern = ^(?P<country>\w{3})_phy_(?P<product>MOD\d{2}[Q,C,A]\d{1}).(?P<year>\d{4}).(?P<month>\d{2}).(?P<day>\d{2}).(?P<subset>\w*).VCI(?P<extension>\.tif$)
vci_output_pattern = {country}_phy_{product}.{year}.{month}.{day}.{subset}_VCI{extension}
[MODIS_TCI]
tci_product_dir = ${vampire:base_product_dir}\Analysis\Temperature_Condition_Index
tci_pattern = ^(?P<country>\w{3})_cli_(?P<product>MOD\d{2}[C,A]\d{1}).(?P<year>\d{4}).(?P<month>\d{2}).(?P<day>\d{2}).(?P<version>\d{3}).TCI(?P<extension>\.tif$)
tci_output_pattern = {country}_cli_{product}.{year}.{month}.{day}.{version}.TCI{extension}
[MODIS_VHI]
interval = 16
vhi_product_dir = ${vampire:base_product_dir}\Analysis\Vegetation_Health_Index
vhi_output_pattern = {country}_phy_{product}.{year}.{month}.{day}.{subset}_EVI_VCI_VHI{extension}
vhi_crop_output_pattern = {country}_phy_{product}.{year}.{month}.{day}.{subset}_EVI_VCI_VHI_cropmask_iwmi{extension}
vhi_crop_pattern = ^(?P<country>\w{3})_phy_(?P<product>MOD\d{2}[Q,C,A]\d{1}).(?P<year>\d{4}).(?P<month>\d{2}).(?P<day>\d{2}).(?P<subset>\w*)_EVI_VCI_VHI_cropmask(?P<extension>\.tif$)
vhi_pattern = ^(?P<country>\w{3})_phy_(?P<product>MOD\d{2}[Q,C,A]\d{1}).(?P<year>\d{4}).(?P<month>\d{2}).(?P<day>\d{2}).(?P<subset>\w*)_EVI_EVI_VCI_VHI(?P<extension>\.tif$)
[DSLR]
DSLR_output_pattern = ^(?P<country>\w{3})_cli_dslr_(?P<threshold>\d{2})mm_threshold_(?P<year>\d{4})(?P<month>\d{2})(?P<day>\d{2})(?P<extension>\.tif$)
[hazard_impact]
vhi_interval = 16
vhi_threshold = 20
vhi_output_dir = ${vampire:base_product_dir}\Tabular\Vegetation_Health_Index
vhi_area_pattern = ^(?P<country>\w{3})_phy_(?P<product>MOD\d{2}[Q,C,A]\d{1}).(?P<year>\d{4}).(?P<month>\d{2}).(?P<day>\d{2})_vhi_impact_area_dsd(?P<extension>\.csv$)
vhi_popn_pattern = ^(?P<country>\w{3})_phy_(?P<product>MOD\d{2}[Q,C,A]\d{1}).(?P<year>\d{4}).(?P<month>\d{2}).(?P<day>\d{2})_vhi_impact_popn_dsd(?P<extension>\.csv$)
vhi_crops_pattern = ^(?P<country>\w{3})_phy_(?P<product>MOD\d{2}[Q,C,A]\d{1}).(?P<year>\d{4}).(?P<month>\d{2}).(?P<day>\d{2})_vhi_impact_crops_dsd(?P<extension>\.csv$)
#vhi_area_pattern = ^(?P<country>\w{3})_phy_(?P<product>MOD\d{2}[Q,C,A]\d{1}).(?P<year>\d{4}).(?P<month>\d{2}).(?P<day>\d{2}).(?P<subset>\w*)_vhi_area_dsd(?P<extension>\.csv$)
#vhi_popn_pattern = ^(?P<country>\w{3})_phy_(?P<product>MOD\d{2}[Q,C,A]\d{1}).(?P<year>\d{4}).(?P<month>\d{2}).(?P<day>\d{2}).(?P<subset>\w*)_vhi_popn_dsd(?P<extension>\.csv$)
vhi_area_output_pattern = {country}_phy_{product}.{year}.{month}.{day}_vhi_impact_area_dsd.csv
vhi_popn_output_pattern = {country}_phy_{product}.{year}.{month}.{day}_vhi_impact_popn_dsd.csv
vhi_crops_output_pattern = {country}_phy_{product}.{year}.{month}.{day}_vhi_impact_crops_dsd.csv
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