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Copy pathEmissions_netCDF_plots_multi_v5.py
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Emissions_netCDF_plots_multi_v5.py
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#!/usr/bin/python
#
# Python module to derive emissions from Biomass burning
#
# Garry Hayman
# Centre for Ecology and Hydrology
# July 2013
#
# python Emissions_netCDF_plots_multi.py Y Y 2000 2000 NNNNNNNNNNNYNNN 1 Contour0 1 ~/Work/CODE/GOOGLE_EARTH/ YY
# python Emissions_netCDF_plots_multi.py Y N 2000 2000 NNNNNNNNNNNYNNN 1 Map 1 ~/Work/CODE/GOOGLE_EARTH/ YY
#
# ./Emissions_netCDF_plots_multi.py N N 1997 2009 NNNNNNNNNNNNNNN 1 Map 1 ~/Work/CODE/GOOGLE_EARTH/ NNN Y Y 0 4 > /prj/wetlands_africa/Sciamachy/a_PLOTS/Wetland_Emissions_Sudd_JULES_20140327.out
#
# ./Emissions_netCDF_plots_multi.py N N 1997 2009 NNNNNNNNNNNNNNN 1 Map 1 ~/Work/CODE/GOOGLE_EARTH/ NNN Y Y 0 5 > /prj/wetlands_africa/Sciamachy/a_PLOTS/Wetland_Emissions_Sudd_JULES_GIEMS_20140327.out
#
# PLOTS[0]: Annual/monthly emission maps
# PLOTS[1]: Emission time series
# PLOTS[2]: Emission annual cycle
# PLOTS[3]: Latitudinal (zonal) plot (single/multi)
# PLOTS[4]: Longitudinal (meridional) plot (single/multi)
# PLOTS[5]: Use wetland fraction
# PLOTS[6]: Emission time series (multi)
# PLOTS[7]: Emission annual cycle (multi)
# PLOTS[9]: Maps of wetland fraction
# PLOTS[10]: Emission anomaly time series (multi)
# PLOTS[11]: Images and kmz files for Google Earth
# PLOTS[12]: Maps of wetland fraction and emissions
# PLOTS[13]: CarbonSAT plots
# PLOTS[14]: Site-specific plots
#
# Contains
#
import os
import sys
import numpy as np
from numpy import array,arange,dtype
#
import data_info
import data_netCDF
import data_regrid_new
import merge_Data
import plot_map
import plot_functions
import Emissions_Wetlands_Zonal
#
iINTER = sys.argv[1]
DEBUG = sys.argv[2]
START_YEAR = int(sys.argv[3])
END_YEAR = int(sys.argv[4])
PLOTS = sys.argv[5]
PLOT_OPT = sys.argv[6]
MAP_TYPE = sys.argv[7]
iMASS = sys.argv[8]
KMZ_DIR = sys.argv[9]
KMZ_CODES = sys.argv[10]
iFIG_PAPER = sys.argv[11]
iUSE_STORED = sys.argv[12]
iLEAP = sys.argv[13]
#
NETCDF_DIR = '/prj/ALANIS/UM_Modelling/EMISSIONS/'
TIME_NAME = 'time'
#
DAYS_MONTH = [ 31 , 31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31 ]
SMONTHS = ['January','February','March','April','May','June', \
'July','August','September','October','November','December' ]
#
ORIENTATION = 'Portrait'
FONTSIZES = [ 12,12,14,16 ]
NROWS_0 = 4
NCOLUMNS_0 = 3
#
PLOT_TEXT = '' # Not used
SET_UNDER = '#ffffff' # white
SET_OVER = '#800000' # maroon
SET_UNDER_DIFF = '#191970'
PLOT_CODES_GR = ['ko-','ro-','bo-','go-','mo-','yo-','co-' ]
PLOT_CODES_AN = ['k-' ,'r-' ,'b-' ,'g-' ,'m-' ,'y-' ,'c-' ]
PLOT_CODES_ZL = ['r:' ,'r-' ,'b-' ,'g-' ,'k-' ,'m-' ,'y-' ,'c-' ]
MAX_TREND_0 = 100
MAX_CLIM_0 = 50
MAX_SITE_0 = 400
MAX_ANOM_0 = 50
MIN_EMISS = 1.00E-20
PLOT_SCALE = [1.00,0.50,0.20,0.10,0.05,0.02,0.01]
PLOT_MAX = [0.1,0.2,0.5,1.0,2.0,5.0,10.0,20.0,50.0,100.0,200.0,500.0,1000.0,2000.0,5000.0]
DATA_NAMES = ['ch4_surf_emiss','fch4wetl','fch4wetl_wet','FCH4WETL','flux', \
'agriwaste','fossil','bbur','wetlands','others','soils','total','Prior']
JULES_DATES = ['20120203','20120224','20120405','20120522','20121003','20130319']
LEGENDS_MULTI = []
CLEVELS_C = [ 0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0 ]
COLOURS_C = [ '#1e3cff', '#00a0ff', '#00c8c8', '#00d28c', '#00dc00', '#a0e632', '#e6dc32', '#e6af2d', \
'#f08228', '#fa3c3c', '#f00082' ]
#
COLOURS_D = [ '#1e3cff', '#4169e1', '#00bfff', '#87cefa', '#ffc0cb', '#fa8072', '#fa3c3c', '#ff0000' ]
CLEVELS_D = [ -1.00,-0.75,-0.50,-0.25,0.0,0.25,0.50,0.75,1.00 ]
#
SITE_DATA = [ \
# [ 'Salmisuo' , '31.00', '63.00'], \
[ 'Salmisuo' , '30.93', '62.75'], \
# [ 'Degero' , '19.55', '64.18'], \
[ 'Degero' , '19.55', '64.30'], \
# [ 'BOREAS-NSA', '-99.00', '56.00'], \
[ 'BOREAS-NSA', '-99.00', '56.00'], \
# [ 'Minnesota' , '-93.47', '47.53'], \
[ 'Minnesota' , '-93.47', '47.53'], \
# [ 'Michigan' , '-84.02', '42.45'], \
[ 'Michigan' , '-84.02', '42.45'], \
# [ 'Panama' , '-79.63', '9.31'] \
[ 'Panama' , '-80.00', '9.00'] \
]
#
if PLOT_OPT == '1':
PLOT_MAP = 'Y'
else:
PLOT_MAP = 'N'
#
NSITES = len(SITE_DATA)
NYEARS = END_YEAR-START_YEAR+1
NMONTHS = 12
NTIMES_1 = 1
NTIMES = NMONTHS*NYEARS
NTRANS = 12
iNAME = 0
#
LAT_ZONAL_ALL = []
LON_MERID_ALL = []
DATA_MONTH_ALL = []
DATA_MONTH_ALL2= []
DATA_ANN_ALL = []
EMISS_FACTORS = []
SOURCE_ALL = []
SOURCE_FILES = []
SOURCE_KMZ = []
SRESOL_KMZ = []
COLOURS_KMZ = []
CLEVELS_KMZ = []
EMISS_KMZ = []
NAMES_KMZ = []
NDIMS_KMZ = []
LATS_KMZ = []
LONGS_KMZ = []
LATS_ALL = []
LONGS_ALL = []
RESOL_LAT_ALL = []
RESOL_LONG_ALL = []
EMISS_SITE = []
AREAS_ALL = []
UNITS_KMZ = []
SDATE_TIMES_KMZ= []
EDATE_TIMES_KMZ= []
WET_FRACT_KMZ = []
MISS_DATA_KMZ = []
MON_EMISS_ALL = []
NTIMES_ALL = []
#
if PLOTS[9] == 'Y':
print('\nMap wetland fraction (Y/N) : ')
MAP_FRACT = input()
else:
MAP_FRACT = 'N'
#
DOMAIN = data_info.data_DOMAIN_info()
#
for i in range(len(DOMAIN)):
print DOMAIN[i][0]
iDOMAIN = int(input())
#
SDOMAIN = DOMAIN[iDOMAIN][1]
LONG_DOMS = float(DOMAIN[iDOMAIN][2])
LONG_DOME = float(DOMAIN[iDOMAIN][3])
LAT_DOMS = float(DOMAIN[iDOMAIN][5])
LAT_DOME = float(DOMAIN[iDOMAIN][6])
LAT_START = -90.0
LAT_END = 90.0
DEL_LAT = int(DOMAIN[iDOMAIN][7])
DEL_LONG = int(DOMAIN[iDOMAIN][4])
DLAT = LAT_END-LAT_START
WIDTH0 = float(DOMAIN[iDOMAIN][8])
HEIGHT0 = float(DOMAIN[iDOMAIN][9])
RESOLUTION = DOMAIN[iDOMAIN][10]
ASPECT = DOMAIN[iDOMAIN][11]
#
SRESOL_OUT = '5.0'
O_RESOL_LONG = float(SRESOL_OUT)
O_RESOL_LAT = float(SRESOL_OUT)
NLONG_OUT = int(360.0/O_RESOL_LONG)
NLAT_OUT = int(180.0/O_RESOL_LAT)
NLONG_AREA = int((LONG_DOME-LONG_DOMS)/O_RESOL_LONG)
NLAT_AREA = int((LAT_DOME-LAT_DOMS)/O_RESOL_LAT)
#
if NYEARS > 14:
XINC = 5
elif NYEARS > 6:
XINC = 2
elif NYEARS < 6:
XINC = 1
#
if iUSE_STORED == 'Y':
DATA_SOURCES,DATA_STORED = data_info.data_DATA_SOURCES(iUSE_STORED)
else:
DATA_SOURCES = data_info.data_DATA_SOURCES(iUSE_STORED)
#
FIG_PLOT_SCALES= data_info.data_PLOT_SCALES_info()
#
print('Input number of files: ')
NFILES = int(input())
#
XDATA_MULTI = np.zeros((NFILES,NTRANS,NYEARS))
YDATA_MULTI = np.zeros((NFILES,NTRANS,NYEARS))
XCLIM_MULTI = np.zeros((NFILES,NTRANS,NMONTHS))
YCLIM_MULTI = np.zeros((NFILES,NTRANS,NMONTHS))
EMISS_ANNUAL = np.zeros((NYEARS,NFILES))
EMISS_DOMAIN = np.zeros((NYEARS,NFILES))
EMISS_DOMAIN_M = np.zeros((NMONTHS*NYEARS,NFILES))
TRANS_EMISS = np.zeros((NYEARS,NTRANS))
TRANS_EMISS_CL = np.zeros((NMONTHS,NTRANS))
TRANS_EMISS_VL = np.zeros((NMONTHS,NTRANS))
YSCALE = np.ones((NTRANS))
#
TRANS_EMISS_MN = np.zeros((NFILES,NTRANS,NMONTHS*NYEARS))
TRANS_EMISS_RM = np.zeros((NFILES,NTRANS,NMONTHS*NYEARS))
TRANS_EMISS_AN = np.zeros((NFILES,NTRANS,NMONTHS*NYEARS))
#
for iFILE in range(NFILES):
#
TRANS_EMISS[:,:] = 0.0
TRANS_EMISS_CL[:,:] = 0.0
TRANS_EMISS_VL[:,:] = 0.0
#
SDATE_TIMES_KMZ.append([])
EDATE_TIMES_KMZ.append([])
#
print
print 'Data Source'
for i in range(len(DATA_SOURCES)):
print DATA_SOURCES[i][0]
TEXT = 'Input dependent on value of iUSE_STORED (X or X.YY) for file %3d:' % (iFILE+1)
print TEXT
#
if iUSE_STORED == 'N':
iSOURCE = int(input())
else:
dSOURCE = str(input())
iSOURCE = int((dSOURCE.split('.'))[0])
#
DATA_SOURCE = DATA_SOURCES[iSOURCE][1]
DATA_LABEL = DATA_SOURCES[iSOURCE][2]
SRESOL = DATA_SOURCES[iSOURCE][3]
START_DATA = int(DATA_SOURCES[iSOURCE][4])
END_DATA = int(DATA_SOURCES[iSOURCE][5])
MISS_DATA = float(DATA_SOURCES[iSOURCE][6])
LONG_END = DATA_SOURCES[iSOURCE][7]
GRIDDED = DATA_SOURCES[iSOURCE][8]
#
if iUSE_STORED == 'N':
SOURCE_FILES.append(DATA_LABEL)
print 'Provide run details (e.g., RCP scenario): '
DETAILS = str(input())
else:
SOURCE_FILES.append(DATA_STORED[dSOURCE][0])
DETAILS = DATA_STORED[dSOURCE][0]
#
print DATA_LABEL
if 'XXXX' in DATA_LABEL:
DATA_LABEL = DETAILS
print DETAILS
print DATA_LABEL
#
SOURCE_ALL.append(DATA_LABEL)
SOURCE_KMZ.append(DATA_SOURCE)
SRESOL_KMZ.append(SRESOL)
MISS_DATA_KMZ.append(MISS_DATA)
#
if LONG_END == 360.0:
LONG_START = 0.0
LONG_PLOTS = -180.0
LONG_PLOTE = 180.0
else:
LONG_START = -180.0
LONG_PLOTS = -180.0
LONG_PLOTE = 180.0
#
DLONG = LONG_END-LONG_START
#
# Get area of grid squares
#
FILE_CDF_A = NETCDF_DIR + '/GridCell_Area_'+SRESOL+'.nc'
print(FILE_CDF_A)
DATA_NAME = 'area'
DIMS,AREA = data_netCDF.data_netCDF_array2D(FILE_CDF_A,DATA_NAME)
AREA = np.squeeze(AREA)
if DEBUG == 'Y':
print(AREA.min(),AREA.max(),AREA.sum())
#
# Get TRANSCOM regions
#
DATA_NAME = 'transcom_regions'
FILE_TRANS = '/prj/ALANIS/UM_Modelling/TRANSCOM_Regions_'+SRESOL+'.nc'
print FILE_TRANS
DIMS,TRANSCOM= data_netCDF.data_netCDF_array2D(FILE_TRANS,DATA_NAME)
TRANSCOM = np.squeeze(TRANSCOM)
#
# Switch E-W hemisphere
#
TRANSCOM = plot_map.switch_long(TRANSCOM)
#
# Correct AREA and TRANSCOM if SREOLS is UM or MACC
#
if SRESOL == 'UM' or SRESOL == 'MACC':
AREA = AREA[:-1,:]
TRANSCOM = TRANSCOM[:-1,:]
#
AREAS_ALL.append(AREA)
#
TRANS_REGS = data_info.data_TRANSCOM_info()
#
# Start for wetland fraction
#
wTIME = (START_YEAR-START_DATA)*12
#
if 'JULES_Wetlands' in DATA_SOURCE:
#
START_DATA = START_YEAR
END_DATA = END_YEAR
#
MODIFIER = [ \
['0: original',''], \
['1: masked by EO inundation','+Masked'], \
['2: driven with EO inundation','+GIEMS'], \
]
#
print("\nJULES Wetland option")
for i in range(len(MODIFIER)):
print(MODIFIER[i][0])
#
JULES_OPT,OPT_CORR=data_info.data_WETLAND_EMISS_info()
FILE_CORR = ''
iOPT_CORR = -1
#
if iUSE_STORED == 'N':
#
# Input from keyboard
#
print('\nInput option : ')
jFILE = input()
#
print("\nJULES Run Dates")
for i in range(len(JULES_DATES)):
print(i,JULES_DATES[i])
print('\nInput index for date of JULES run (0-'+str(len(JULES_DATES)-1)+'): ')
iDATA = int(input())
#
print('\nInput JULES run (e.g., m43, m45): ')
JULES_RUN = input()
#
print('\nMean annual wetland emission (Tg per annum): ')
WET_TOTAL = float(input())
if iDATA < 4 and JULES_RUN != 'm43':
print 'STOP - incompatible Jules run and data option'
quit()
#
if iINTER == 'Y':
print("\nJULES Parameterisation")
for i in range(len(JULES_OPT)):
print(JULES_OPT[i][0])
#
print('\nInput version : ')
iNPP = input()
else:
iNPP = int(sys.argv[14])
#
if iDATA >= 4:
#
print("Correction option")
for i in range(len(OPT_CORR)):
print(OPT_CORR[i][0])
#
if iINTER == 'Y':
print('\nInput correction option: ')
iOPT_CORR = input()
else:
iOPT_CORR = int(sys.argv[15])
#
FILE_CORR = OPT_CORR[iOPT_CORR][1]
#
else:
#
# Get parameters from stored values
#
jFILE = int(DATA_STORED[dSOURCE][9])
iDATA = int(DATA_STORED[dSOURCE][10])
JULES_RUN = DATA_STORED[dSOURCE][11]
WET_TOTAL = float(DATA_STORED[dSOURCE][12])
#
if iDATA < 4 and JULES_RUN != 'm43':
print 'STOP - incompatible Jules run and data option'
quit()
#
iNPP = int(DATA_STORED[dSOURCE][13])
#
if iDATA >= 4:
iOPT_CORR = int(DATA_STORED[dSOURCE][14])
FILE_CORR = OPT_CORR[iOPT_CORR][1]
#
# Correct iDATA for iNPP option
#
FILE_PART = JULES_OPT[iNPP][1]
#
# Filenames
#
TEXT_NAMES = [ 'CH4 Wetlands' ]
#
FILE_DATA = [ \
[ 'M','CH4','fch4wetl', \
'/Wetland_Emissions_CH4_'+FILE_PART+'_'+JULES_RUN, \
'20120203','20120224','20120405','20120522','20121003','20130319','20130402'], \
[ 'M','CH4','fch4wetl', \
'/Wetland_Emissions_CH4_'+FILE_PART+'_'+JULES_RUN+'_Masked', \
'20120203','20120224','20120405','20120522','20121003','20130319','20130402'], \
[ 'M','CH4','fch4wetl', \
'/Wetland_Emissions_CH4_'+FILE_PART+'_'+JULES_RUN+'_EO', \
'20120203','20120224','20120405','20120522','20121003','20130319','20130402'] \
]
#
# Get data - wetland fraction
#
if PLOTS[5] == 'Y' or PLOTS[11] == 'Y' or PLOTS[14] == 'Y' or DATA_SOURCE == 'JULES_Wetlands_Area':
#
SET_OVER = 'k' # black
SDATE = FILE_DATA[jFILE][4+iDATA]
FILENAME = NETCDF_DIR+'a_WETLANDS_'+SDATE+FILE_DATA[jFILE][3]+'_1993_2009'+FILE_CORR+'.nc'
FILENAME = FILENAME.replace('Wetland_Emissions_CH4','Wetland_Fraction')
print(' ')
print(FILENAME)
#
DATA_NAME = 'fwetl'
DIMS,WET_FRACT = data_netCDF.data_netCDF_array_var(FILENAME,DATA_NAME)
WET_FRACT = np.squeeze(WET_FRACT)
#
if DATA_SOURCE == 'JULES_Wetlands_Area':
EMISS = WET_FRACT.copy()
else:
#
EMISS = []
iTIME = 0
iREAD = 0
sCODE = FILE_DATA[jFILE][0]
FSPECIES = FILE_DATA[jFILE][1]
SDATE = FILE_DATA[jFILE][4+iDATA]
#
# Get data - wetland emissions
#
for iYEAR in range(NYEARS):
#
YEAR = START_YEAR+iYEAR
SYEAR = '%4d' % (YEAR)
FILENAME = NETCDF_DIR+'a_WETLANDS_'+SDATE+FILE_DATA[jFILE][3]+'_'+SYEAR+FILE_CORR+'.nc'
FILE_PLOT_PART= FILENAME[:-3].replace('.','_')
print(' ')
#
DATA_NAME = FILE_DATA[jFILE][2]
TEXT_NAME = TEXT_NAMES[iNAME]
#
if DATA_SOURCE == 'JULES_Wetlands' and os.path.exists(FILENAME):
print(FILENAME)
DIMS,EMISS_IN= data_netCDF.data_netCDF_array_var(FILENAME,DATA_NAME)
EMISS_IN = np.squeeze(EMISS_IN)
#
# On first pass, need to define multi-year emission array
#
print YEAR,iREAD,iTIME
if iREAD == 0:
iREAD = 1
NTIMES_JUL = (min(END_YEAR,END_DATA)-max(START_YEAR,START_DATA)+1) \
*NMONTHS
print NTIMES_JUL
EMISS = np.zeros((NTIMES_JUL,EMISS_IN.shape[1],EMISS_IN.shape[2]))
if PLOTS[5] == 'Y':
DATA_NAME = DATA_NAME+'_wet'
WET_FRACT_ANN = WET_FRACT[wTIME:wTIME+12,:,:]
print wTIME,WET_FRACT_ANN.shape,EMISS.shape
INDICES = WET_FRACT_ANN[:,:,:] > 0
print len(INDICES[INDICES]),WET_FRACT_ANN.size
#
EMISS_WET = np.zeros(EMISS_IN.shape)
EMISS_WET[:,:,:] = 0.0
EMISS_WET[INDICES] = EMISS_IN[INDICES]/ \
WET_FRACT_ANN[INDICES]
WET_FRACT_ANN[WET_FRACT_ANN <= 0.0] = MISS_DATA
#
if DEBUG == 'Y':
print oLAT,oLONG
print WET_FRACT_ANN[:,oLAT,oLONG]
print EMISS_IN[:,oLAT,oLONG]
print EMISS_WET[:,oLAT,oLONG]
#
if LONG_END == 360.0:
EMISS_IN = plot_map.switch_long_time(EMISS_IN)
if PLOTS[5] == 'Y':
EMISS_WET = plot_map.switch_long_time(EMISS_WET)
WET_FRACT_ANN = plot_map.switch_long_time(WET_FRACT_ANN)
#
EMISS[iTIME:iTIME+NMONTHS,:,:] = EMISS_IN
#
iTIME += NMONTHS
#
if len(EMISS) != 0:
EMISS[EMISS < 0] = 0.0
else:
EMISS = np.zeros((NTIMES,AREA.shape[0],AREA.shape[1]))
#
PLOT_DIR = NETCDF_DIR+'a_WETLANDS_SUMMARY/'
#
LEGEND_JULES = (SOURCE_ALL[iFILE]+MODIFIER[jFILE][1]+OPT_CORR[iOPT_CORR][2]).replace('_',' ')
LEGENDS_MULTI.append(LEGEND_JULES)
#
else:
PLOTS = list(PLOTS)
PLOTS[5] = 'N'
PLOTS = ''.join(PLOTS)
#
LEGENDS_MULTI.append(SOURCE_ALL[iFILE])
#
if iUSE_STORED == 'N':
print('Input number of netCDF files: ')
NSUB_FILES = int(input())
else:
NSUB_FILES = int(DATA_STORED[dSOURCE][1])
#
FILE_CDF = []
#
for iSUB_FILE in range(NSUB_FILES):
if iUSE_STORED == 'N':
print('Input netCDF filename: '+NETCDF_DIR)
FILENAME = NETCDF_DIR+input()
else:
FILENAME = NETCDF_DIR+DATA_STORED[dSOURCE][2][iSUB_FILE]
#
print(FILENAME)
FILE_CDF.append(FILENAME)
#
PLOT_DIR = NETCDF_DIR
#
# Input data
#
# Get Variable Names
#
if GRIDDED == 'LAND_MON':
SDATE = str(START_DATA)+'01'
FILE_VAR = FILENAME.replace('XXXXXX',SDATE)
else:
FILE_VAR = FILENAME
#
print FILE_VAR
VAR_NAMES=data_netCDF.data_netCDF_getVARNAMES(FILE_VAR)
#
if iUSE_STORED == 'N':
for iVAR in range(len(VAR_NAMES)):
TEXT = ('%4d %s' % (iVAR,VAR_NAMES[iVAR]))
print(TEXT)
# Select variable
print('Input index for variable: ')
sVAR = str(input()).split(':')
else:
sVAR = DATA_STORED[dSOURCE][3].split(':')
#
if SRESOL=='UM':
RESOL_LONG = 1.875
RESOL_LAT = 1.250
NLAT = int((LAT_END-LAT_START)/RESOL_LAT)
NLONG = int((LONG_END-LONG_START)/RESOL_LONG)
DATA_IN = np.zeros((NTIMES_1,NLAT,NLONG))
LONG_ALL = LONG_PLOTS+RESOL_LONG*arange(NLONG)
LAT_ALL = LAT_START +RESOL_LAT/2.0 +RESOL_LAT *arange(NLAT)
elif SRESOL=='MACC':
RESOL_LONG = 3.75
RESOL_LAT = 2.50
NLAT = int((LAT_END-LAT_START)/RESOL_LAT)
NLONG = int((LONG_END-LONG_START)/RESOL_LONG)
DATA_IN = np.zeros((NTIMES_1,NLAT,NLONG))
LONG_ALL = LONG_PLOTS+RESOL_LONG*arange(NLONG)
LAT_ALL = LAT_START +RESOL_LAT/2.0 +RESOL_LAT *arange(NLAT)
else:
RESOL_LONG = float(SRESOL)
RESOL_LAT = float(SRESOL)
NLAT = int((LAT_END-LAT_START)/RESOL_LAT)
NLONG = int((LONG_END-LONG_START)/RESOL_LONG)
DATA_IN = np.zeros((NTIMES_1,NLAT,NLONG))
LONG_ALL = LONG_PLOTS+RESOL_LONG/2.0+RESOL_LONG*arange(NLONG)
LAT_ALL = LAT_START +RESOL_LAT/2.0 +RESOL_LAT *arange(NLAT)
#
iDOM_LONGS = int((LONG_DOMS-LONG_PLOTS)/RESOL_LONG)
iDOM_LONGE = int((LONG_DOME-LONG_PLOTS)/RESOL_LONG)
iDOM_LATS = int((LAT_DOMS-LAT_START)/RESOL_LAT)
iDOM_LATE = int((LAT_DOME-LAT_START)/RESOL_LAT)
#
iDOM_LONGSR = int((LONG_DOMS-LONG_PLOTS)/O_RESOL_LONG)
iDOM_LONGER = int((LONG_DOME-LONG_PLOTS)/O_RESOL_LONG)
iDOM_LATSR = int((LAT_DOMS-LAT_START)/O_RESOL_LAT)
iDOM_LATER = int((LAT_DOME-LAT_START)/O_RESOL_LAT)
#
print iDOM_LONGS,iDOM_LONGE,iDOM_LATS,iDOM_LATE, \
iDOM_LONGSR,iDOM_LONGER,iDOM_LATSR,iDOM_LATER
#
LONG = np.arange(LONG_DOMS,LONG_DOME+DEL_LONG,DEL_LONG)
LAT = np.arange(LAT_DOMS,LAT_DOME+DEL_LAT,DEL_LAT )
#
LONG_CSAT = LONG_DOMS+O_RESOL_LONG*(0.5+arange(NLONG_AREA))
LAT_CSAT = LAT_DOMS +O_RESOL_LAT*(0.5+arange(NLAT_AREA))
#
LONG_MAP = LONG_ALL[(LONG_ALL >= LONG_DOMS ) & (LONG_ALL <= LONG_DOME)]
LAT_MAP = LAT_ALL[(LAT_ALL >= LAT_DOMS) & (LAT_ALL <= LAT_DOME)]
#
if DEBUG == 'Y':
print LONG_DOMS,LONG_DOME,LAT_DOMS,LAT_DOME
print LONG
print LAT
print LONG_ALL
print LAT_ALL
print LONG_MAP
print LAT_MAP
print LONG_CSAT
print LAT_CSAT
#
if PLOTS[11] == 'Y':
#
MLAT = iDOM_LATE -iDOM_LATS
MLONG = iDOM_LONGE-iDOM_LONGS
MTIMES = NYEARS*NMONTHS
#
NDIMS_KMZ.append([MTIMES,MLAT,MLONG])
LATS_KMZ.append([LAT_DOMS, LAT_DOME ])
LONGS_KMZ.append([LONG_DOMS,LONG_DOME])
#
# Emissions will be from 180W to 180 E, hence use LONG_PLOTS
#
if PLOTS[16] == 'Y':
LATS_ALL.append( LAT_START +RESOL_LAT/2.0 +RESOL_LAT *arange(NLAT))
LONGS_ALL.append(LONG_PLOTS+RESOL_LONG/2.0+RESOL_LONG*arange(NLONG))
#
ANN_EMISS_FILE= np.zeros((NYEARS,NLAT,NLONG))
MON_EMISS_FILE= np.zeros((NTIMES,NLAT,NLONG))
MON_EMISS = np.zeros((NLAT,NLONG))
TOTAL_EMISS = np.zeros((NLAT,NLONG))
TOTAL_EMISS_1 = np.zeros((NLAT,NLONG))
TOTAL_EMISS_2 = np.zeros((NLAT,NLONG))
DATA_FRACT = np.zeros((NLAT,NLONG))
NUM_IN = np.ones((NTIMES_1,NLAT,NLONG))
#
SOPTIONS = [ 'Annual in months', \
'Annual in seconds', \
'Monthly in months', \
'Monthly in seconds',
'Monthly in hours',
'Monthly in days' ]
#
if iUSE_STORED == 'N':
print('Input scaling factor: ')
FACTOR = float(input())
#
print('Input N for numpy array or M for masked array: ')
iMASK = input()
#
for iOPT in range(len(SOPTIONS)):
TEXT = ('%4d %s' % (iOPT,SOPTIONS[iOPT]))
print(TEXT)
#
print('Input timebase for variable: ')
iOPT = int(input())
#
else:
FACTOR = float(DATA_STORED[dSOURCE][4])
iMASK = DATA_STORED[dSOURCE][5]
iOPT = int(DATA_STORED[dSOURCE][6])
#
if PLOTS[3] == 'Y':
#
if iFILE == 0:
#
NLAT_INT2 = 0
while NLAT_INT2 == 0:
print('Input latitude interval: ')
dLAT = float(input())
NLAT_INT = int(180/dLAT)
NLAT_INT2 = int(dLAT/RESOL_LAT)
#
LAT_ZONAL = LAT_START + (dLAT/2.0) + dLAT*arange(NLAT_INT,dtype='float32')
LAT_TICKS = int(LAT_START)+10*arange(19)
#
EMISS_LAT = np.zeros((NFILES,NYEARS,NLAT_INT))
XZONAL = np.zeros((NFILES,NLAT_INT))
YZONAL = np.zeros((NFILES,NLAT_INT))
#
else:
NLAT_INT2 = int(dLAT/RESOL_LAT)
#
if PLOTS[4] == 'Y':
#
if iFILE == 0:
#
NLON_INT2 = 0
while NLON_INT2 == 0:
print('Input longitude interval: ')
dLON = float(input())
NLON_INT = int(360/dLON)
NLON_INT2 = int(dLON/RESOL_LONG)
#
LON_MERID = LONG_PLOTS + (dLON/2.0) + dLON*arange(NLON_INT,dtype='float32')
LON_TICKS = int(LONG_PLOTS)+30*arange(13)
#
EMISS_LON = np.zeros((NFILES,NYEARS,NLON_INT))
XMERID = np.zeros((NFILES,NLON_INT))
YMERID = np.zeros((NFILES,NLON_INT))
#
else:
NLON_INT2 = int(dLON/RESOL_LONG)
#
# Input lat,long for debug
#
if DEBUG == 'Y' and PLOTS[5] == 'Y':
#
print('Input grid square latitude index: ')
oLAT = int(input())
#
print('Input grid square longitude index: ')
oLONG = int(input())
#
# Need to have second longitude
#
if LONG_END == 360.0:
if oLONG < NLONG/2:
oLONG2 = oLONG+NLONG/2
else:
oLONG2 = oLONG-NLONG/2
#
# Select threshold if PLOTS[13] == 'Y'
#
if PLOTS[13] == 'Y':
print('Input threshold: ')
THRESHOLD = float(input())
#
# Get data
#
if not 'JULES_Wetlands' in DATA_SOURCE:
#
for iSUB_FILE in range(NSUB_FILES):
#
iVAR = int(sVAR[iSUB_FILE])
DATA_NAME = VAR_NAMES[iVAR]
#
FILENAME = FILE_CDF[iSUB_FILE]
FILE_PLOT_PART= FILENAME[:-3].replace('.','_')
print GRIDDED
if GRIDDED == 'GRID':
DIMS,EMISS_IN = data_netCDF.data_netCDF_array_var(FILENAME,DATA_NAME)
elif GRIDDED == 'LAND_MON':
SDATE = str(START_YEAR)+'_'+str(END_YEAR)
FILE_PLOT_PART= FILE_PLOT_PART.replace('XXXXXX',SDATE)
LAT_NAME = 'latitude'
LONG_NAME = 'longitude'
DIMS,EMISS_IN = data_netCDF.data_netCDF_array_land_var \
(FILENAME,DATA_NAME,LAT_NAME,LONG_NAME, \
START_DATA,END_DATA,NLONG,NLAT,LONG_START,LAT_START,MISS_DATA,GRIDDED,DEBUG)
EMISS_IN = np.squeeze(EMISS_IN)
#
# Need to invert both latitude and longitude for Bloom inventory
#
if 'Bloom' in FILENAME:
EMISS_IN = EMISS_IN[:,-1::-1,-1::-1]
print EMISS_IN.shape
if 'fch4.ref' in FILENAME:
SOURCE_ALL[iFILE] = SOURCE_ALL[iFILE]+' Fung'
LEGENDS_MULTI[iFILE] = LEGENDS_MULTI[iFILE]+' Fung'
FILE_PLOT_PART = FILE_PLOT_PART+'_'+DATA_NAME
elif 'fch4.kaplan' in FILENAME:
SOURCE_ALL[iFILE] = SOURCE_ALL[iFILE]+' Kaplan'
LEGENDS_MULTI[iFILE] = LEGENDS_MULTI[iFILE]+' Kaplan'
FILE_PLOT_PART = FILE_PLOT_PART+'_'+DATA_NAME
elif 'fung' in FILENAME:
if 'rice' in FILENAME:
SOURCE_ALL[iFILE] = SOURCE_ALL[iFILE]+'+Rice'
LEGENDS_MULTI[iFILE] = LEGENDS_MULTI[iFILE]+'+Rice'
elif 'A06' in FILENAME:
SOURCE_ALL[iFILE] = SOURCE_ALL[iFILE]+' JULES'
LEGENDS_MULTI[iFILE] = LEGENDS_MULTI[iFILE]+' JULES'
elif 'C06' in FILENAME:
SOURCE_ALL[iFILE] = SOURCE_ALL[iFILE]+' JULES+GIEMS'
LEGENDS_MULTI[iFILE] = LEGENDS_MULTI[iFILE]+' JULES+GIEMS'
print SOURCE_ALL[iFILE]
#
if 'M' in iMASK:
import numpy.ma as ma
if iMASK == 'M':
MASK_FACTOR = 1.0
else:
MASK_FACTOR = float(iMASK.split('_')[1])
TEMP = EMISS_IN
EMISS_IN = MASK_FACTOR*ma.getdata(TEMP)
EMISS_IN[EMISS_IN == MISS_DATA] = 0.0
EMISS_IN[EMISS_IN != MISS_DATA] = EMISS_IN[EMISS_IN != MISS_DATA]/MASK_FACTOR
print TEMP.min(),TEMP.max(),TEMP.sum(),EMISS_IN.min(),EMISS_IN.max(),EMISS_IN.sum()
else:
print EMISS_IN.min(),EMISS_IN.max(),EMISS_IN.sum()
#
# Set missing data to zero
#
EMISS_IN[(EMISS_IN == MISS_DATA) | (EMISS_IN < 0)] = 0.0
#
# Need to invert latitude dimension if DATA_SOURCE = 'Inverse_N2O'
#
if DATA_SOURCE == 'Inverse_N2O':
EMISS_IN = EMISS_IN[:,::-1,:]
#
# Need to correct latitude array if SRESOL is UM or MACC
#
if SRESOL == 'UM' or SRESOL == 'MACC':
EMISS_IN = EMISS_IN[:,:-1,:]
#
if iSUB_FILE == 0:
if len(EMISS_IN.shape) == 2:
EMISS = np.zeros((NYEARS,EMISS_IN.shape[0],EMISS_IN.shape[1]))
for iYEAR in range(NYEARS):
EMISS[iYEAR,:,:] = EMISS_IN
else:
EMISS = EMISS_IN
else:
if len(EMISS_IN.shape) == 2:
for iYEAR in range(NYEARS):
EMISS[iYEAR,:,:] = EMISS[iYEAR,:,:] + EMISS_IN
else:
EMISS = EMISS + EMISS_IN
#
if LONG_END == 360.0:
EMISS = plot_map.switch_long_time(EMISS)
print 'Emission dataset: E-W hemispheres switched'
#
#
if 'JULES' in DATA_SOURCE and (PLOTS[11] == 'Y' or PLOTS[14] == 'Y'):
#
# Switch E-W hemisphere as needed
#
if LONG_END == 360.0:
WET_FRACT = plot_map.switch_long_time(WET_FRACT)
#
WET_FRACT_KMZ.append(WET_FRACT[wTIME:wTIME+NYEARS*NMONTHS,iDOM_LATS:iDOM_LATE,iDOM_LONGS:iDOM_LONGE])
#
# Calculate annual emissions
#
if iOPT == 0:
NSECS = 1.0
# NSECS = 24.0*60.0*60.0*365.25/12.0
NSTEP = 1
elif iOPT == 1:
NSECS = 24.0*60.0*60.0*365.25
NSTEP = 1
elif iOPT == 2:
NSECS = 1.0
NSTEP = 12
elif iOPT == 3 or iOPT == 6:
NSECS = 24.0*60.0*60.0
NSTEP = 12
elif iOPT == 4:
NSECS = 24.0
NSTEP = 12
elif iOPT == 5:
NSECS = 1.0
NSTEP = 12
#
print(NSECS)
#
# Assign to master emission array
#
EMISS_ALL = np.zeros((NTIMES,EMISS.shape[1],EMISS.shape[2]))
#
iTIME = 0
for iYEAR in range(NYEARS):
#
YEAR = START_YEAR+iYEAR
#
if EMISS.shape[0] == 12:
#
# If single year, replicate this for other years
#
EMISS_ALL[iTIME:iTIME+NMONTHS] = EMISS[:,:,:]
else:
#
# Identify array elements and assign annual emissions
#
iSTART_ALL = iTIME
iEND_ALL = iSTART_ALL+NSTEP
#
if YEAR >= START_DATA and YEAR <= END_DATA:
#
iSTART_DAT = (YEAR-START_DATA)*NSTEP
iEND_DAT = iSTART_DAT+NSTEP
print iYEAR,YEAR,iTIME,iSTART_ALL,iEND_ALL,iSTART_DAT,iEND_DAT
#
EMISS_ALL[iSTART_ALL:iEND_ALL,:,:] = \
EMISS[iSTART_DAT:iEND_DAT,:,:]
#
iTIME += NSTEP
#
iTIME = 0
jTIME = 0
iSTART = START_YEAR-START_DATA
iEND = iSTART+(END_YEAR-START_YEAR)
dTIME = iSTART
#
for iYEAR in range(NYEARS):
#
YEAR = START_YEAR+iYEAR
SYEAR = '%4d' % (YEAR)
#
# Check for leap year
#
if ('Bloom' in DATA_SOURCE or iLEAP == 'Y') and YEAR == 4*int(YEAR/4):
DAYS_MONTH[2] = 29
else:
DAYS_MONTH[2] = 28
#
print YEAR,DAYS_MONTH[2]
#
TOTAL_EMISS[:,:] = 0.0
TOTAL_EMISS_1[:,:] = 0.0
TOTAL_EMISS_2[:,:] = 0.0
#
MON_EMISS_2 = np.zeros((NMONTHS,NLAT,NLONG))
print MON_EMISS_2.shape
print iYEAR,NYEARS,iTIME,jTIME,dTIME
#
if iOPT == 0 or iOPT == 1: # Annual dataset
CONV_FACTOR = FACTOR*NSECS/1.0E+09
elif DATA_SOURCE == 'JULES_Wetlands_Area':
CONV_FACTOR = FACTOR
#
if iOPT == 0 or iOPT == 1 or DATA_SOURCE == 'JULES_Wetlands_Area':
#
# Convert from kg m-2 s-1 to Tg per annum
#
ANN_EMISS_FILE[iYEAR,:,:] = EMISS_ALL[iYEAR,:,:]*AREA[:,:]*CONV_FACTOR
TOTAL_EMISS[:,:] = TOTAL_EMISS[:,:] + ANN_EMISS_FILE[iYEAR,:,:]
#
TOTAL_EMISS_1[:,:] = TOTAL_EMISS_1[:,:] + \
EMISS_ALL[iYEAR,:,:]*1.00E+12*CONV_FACTOR
#
TOTAL_EMISS_2[:,:] = TOTAL_EMISS_2[:,:] + \
EMISS_ALL[iYEAR,:,:]*1.00E+12*CONV_FACTOR
#
if iOPT >= 2: # Loop over months
#
# Reset iTIME if only single year present in datafile - replicate this annual cycle
#
if EMISS.shape[0] == 12:
iTIME = 0
#
for iMONTH in range(NMONTHS):
#
if iOPT == 2: CONV_FACTOR = FACTOR*NSECS/1.0E+09
elif iOPT >= 3: CONV_FACTOR = FACTOR*NSECS*DAYS_MONTH[iMONTH+1]/1.0E+09
# elif iOPT == 4: CONV_FACTOR = FACTOR*NSECS*30.0/1.0E+09
elif DATA_SOURCE == 'JULES_Wetlands_Area':
CONV_FACTOR = FACTOR
#
# Convert from kg m-2 s-1 to Tg per annum
#
if iOPT == 6: # Daily data
print iMONTH+1,YEAR,DAYS_MONTH[iMONTH+1],dTIME
MON_EMISS[:,:] = 0.0
for iDAY in range(DAYS_MONTH[iMONTH+1]):
MON_EMISS[:,:] = MON_EMISS[:,:] \
+EMISS_ALL[dTIME,:,:]*AREA[:,:] \
*CONV_FACTOR/DAYS_MONTH[iMONTH+1]
dTIME += 1
else:
MON_EMISS[:,:] = EMISS_ALL[iTIME,:,:]*AREA[:,:]*CONV_FACTOR
#
MON_EMISS_FILE[jTIME,:,:] = MON_EMISS
TOTAL_EMISS = TOTAL_EMISS + MON_EMISS
#
if PLOTS[5] != 'Y':
TOTAL_EMISS_1[:,:] = TOTAL_EMISS_1[:,:] + \
EMISS_ALL[iTIME,:,:]*CONV_FACTOR*1.00E+12
else:
TOTAL_EMISS_1[:,:] = TOTAL_EMISS_1[:,:] + \
EMISS_WET[iTIME,:,:]*CONV_FACTOR*1.00E+12
#
if DEBUG == 'Y':
print iTIME,oLONG2,oLAT,EMISS_WET[iTIME,oLAT,oLONG2],CONV_FACTOR, \
EMISS_WET[iTIME,oLAT,oLONG2]*CONV_FACTOR*1.00E+12, \
TOTAL_EMISS_1[oLAT,oLONG2]
#
if PLOTS[0] == 'Y' or PLOTS[13] == 'Y':
print EMISS_ALL.shape,MON_EMISS_2.shape
MON_EMISS_2[iMONTH,:,:] = EMISS_ALL[iTIME,:,:]*CONV_FACTOR*1.00E+15/(DAYS_MONTH[iMONTH+1])
TOTAL_EMISS_2[:,:] = TOTAL_EMISS_2[:,:] + MON_EMISS_2[iMONTH,:,:]/12.0
# TOTAL_EMISS_2[:,:] = TOTAL_EMISS_2[:,:] + \
# EMISS_ALL[iTIME,:,:]*CONV_FACTOR*1.00E+15/(12*30.0)
#
if DEBUG == 'Y':