def regionDailyTimeSeries(C, region): """" Returns a summed time series the length of axis 0 for C subet by the passed region name """ minLat, maxLat, minLon, maxLon, resolution = cnm.getRegionBounds(region) C_PNW, ynew, xnew = cnm.mask2dims(C, longitude, latitude, 0, minLon, maxLon, minLat, maxLat) C_PNW_daily = np.sum(C_PNW, axis=(1,2)) return C_PNW_daily
print 'Final merged var size: ' + str(varBase.shape) print 'Final merged time array: ' + str(len(tBase)) ####################################################################### # Handle making the spatial subset ####################################################################### if region != '_': # Get the chosen region bounds minLat, maxLat, minLon, maxLon, resolution = cnm.getRegionBounds(region) # Subset the data based on the bounds of this region. varBase, latitude, longitude = cnm.mask2dims(varBase, longitude[:], latitude[:], 0,\ xmin=minLon, xmax=maxLon,\ ymin=minLat, ymax=maxLat) print 'Data subset to region ' + region ####################################################################### # Write the merged file ####################################################################### if ncVARType == 'era_interim': outputFile = outDir + ncVAR + region + \ str(startYear) + '_' + str(endYear) + '.nc' elif ncVARType == 'GFED4s': outputFile = dataDir + 'GFED4.1s_ecmwf_' + ncVAR + region + \
figureDir = '../Figures/GFED_era_interm_analysis/' # always relativ to /Python
# Get emissions, use this to get dimensions
ncFile = drive + "rebuild/GFED4.1s_METGrid_C_NA_2003_2016.nc"
nc = Dataset(ncFile, 'r')
latitude = nc.variables['latitude'][:]
longitude = nc.variables['longitude'][:]
time = nc.variables['time'][:]
C = nc.variables['C'][:]
nc.close()
# Make time into datetime arrays
time, month, year = cnm.get_era_interim_time(time)
# Spatially subset the data
C, ynew, xnew = cnm.mask2dims(C, longitude, latitude, 0, minLon, maxLon, minLat, maxLat)
################################################################################
# Show emissions time series for the domain
################################################################################
C_daily_total = np.sum(C,axis=(1,2)) # sums daily value for all lon lat
C_cumulative = np.cumsum(C_daily_total)
fig = plt.figure(figsize=(12,8))
ax = plt.subplot(111)
plt.plot(time, C_daily_total)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.tick_params(axis='y', labelsize=20)
ax.tick_params(axis='x', labelsize=20)
plt.xlabel("date", fontsize=26)
lon = lon1
###############################################################################
# Handle making the spatial subset
###############################################################################
x1 = GFED_nc.variables['C'][:]
x2 = FINN_nc.variables['CO2'][:] * gramsCarbonPerCO2
if region != '_':
# Get the chosen region bounds
minLat, maxLat, minLon, maxLon, resolution = cnm.getRegionBounds(region)
# Subset the data based on the bounds of this region.
x1, latNew, lonNew = cnm.mask2dims(x1, lon, lat, 0,\
xmin=minLon, xmax=maxLon,\
ymin=minLat, ymax=maxLat)
x2, latNew, lonNew = cnm.mask2dims(x2, lon, lat, 0,\
xmin=minLon, xmax=maxLon,\
ymin=minLat, ymax=maxLat)
# Need to use the subset dimensions moving forward
lon = lonNew
lat = latNew
###############################################################################
# Begin comparisons
###############################################################################
# x1_ = x1.ravel()
############################################################################### # Handle spatially subsetting the data to be written. ############################################################################### if domain == "_NA_": # These are the same _NA_ "North America" subset region used by # merge_yearly_nc.py minLat = 30. maxLat = 50. minLon = 234. maxLon = 259. dailyVAR, lat, lon = cnm.mask2dims(dailyVAR, lon[:], lat[:], tDim=0, xmin=minLon, xmax=maxLon, ymin=minLat, ymax=maxLat) outputFile = os.path.join(outputDir, 'z_all_NA_daily.nc') else: outputFile = os.path.join(outputDir, 'z_all_daily.nc') print '----------------------------------------------------------------------' print 'outputFile used:' print outputFile print '----------------------------------------------------------------------' ############################################################################### # Write the daily averaged netCDF data
print '-----------------------------------------------------------------------'
print 'It took ' + str(dt) + ' minutes to regrid (lons) of the emissions array'
print '-----------------------------------------------------------------------'
######################################################################################
# Subset this grid, by the maximum and minimum bounds of fires, for HMS inventory. No
# need to loop all over the world when there are only recorded fires in North America.
# This dramatically improves performance when HMS fires are those being regridded.
######################################################################################
if inventory == 'HMS':
minLat = 10.
maxLat = 85.
minLon = 190.
maxLon = 325.
emissions, fire_lat, fire_lon = cnm.mask2dims(emissions, fire_lon, fire_lat,\
0, minLon, maxLon, minLat, maxLat)
######################################################################################
# Sanity plot for interactive analysis mode
######################################################################################
if sanityCheck:
emissions_new_sum = np.sum(emissions, axis=0)
emissions_ma = np.ma.masked_where(emissions_new_sum == 0,
emissions_new_sum,
copy=True)
m = Basemap(projection='robin', lon_0=0, resolution='c')
lons, lats = np.meshgrid(fire_lon, fire_lat)
x, y = m(lons, lats)
m = Basemap(projection='robin', lon_0=0, resolution='c')
m_f = str(endMonth) # Get region lat lon range minLat, maxLat, minLon, maxLon, resolution = cnm.getRegionBounds(region) # Get emissions, use this to get dimensions ncFile = drive + "GFED4s/GFED4.1s_ecmwf_C_2003_2016.nc" nc = Dataset(ncFile, 'r') latitude = nc.variables['latitude'][:] longitude = nc.variables['longitude'][:] time = nc.variables['time'][:] C = nc.variables['C'][:] nc.close() # Spatially subset the GFED4s data C, ynew, xnew = cnm.mask2dims(C, longitude, latitude, 0, minLon, maxLon, minLat, maxLat) # Get burn area ncFile = drive + "GFED4s/GFED4.1s_ecmwf_burned_area_2003_2016.nc" nc = Dataset(ncFile, 'r') latitude = nc.variables['latitude'][:] longitude = nc.variables['longitude'][:] time = nc.variables['time'] time, month, year = cnm.get_era_interim_time(time) BA = nc.variables['burned_area'][:] nc.close() BA, ynew, xnew = cnm.mask2dims(BA, longitude, latitude, 0, minLon, maxLon, minLat, maxLat) ################################################################################