def averageRasters(filelist=None, projection=None, shape=None, lfeedback=True):
''' load a list of rasters into an array and average over a shape; return timeseries '''
# read rasters
data, geotransform = readRasterArray(file_pattern='{FILENAME}',
lfeedback=lfeedback,
lgeotransform=True,
lskipMissing=False,
lna=False,
axes=('FILENAME', ),
FILENAME=filelist)
if lfeedback: print((data.shape, geotransform))
# create GridDef to rasterize shape
griddef = GridDefinition(name='raster',
projection=projection,
geotransform=geotransform,
size=(data.shape[-1], data.shape[-2]),
convention='proj4')
if lfeedback: print(griddef)
# create mask
mask = shape.rasterize(griddef=griddef, invert=False)
assert mask.shape == data.shape[1:], griddef
mask = mask.reshape((1, ) + mask.shape).repeat(data.shape[0], axis=0)
assert mask.shape == data.shape, mask
#print(mask)
# extract and average data
#print(mask[1,:].sum(),)
data.mask = mask
#print(data[1,:])
#print(data[1,:].sum(),data[1,:].mean())
ts_data = data.mean(axis=-1).mean(axis=-1)
assert len(filelist) == data.shape[0], data.shape
# return timeseries of shape averages
return ts_data
# NARR projection
projdict = dict(proj = 'lcc', # Lambert Conformal Conic
lat_1 = 50., # Latitude of first standard parallel
lat_2 = 50., # Latitude of second standard parallel
lat_0 = 50., # Latitude of natural origin
lon_0 = -107.) # Longitude of natural origin
#
# x_0 = 5632642.22547, # False Origin Easting
# y_0 = 4612545.65137) # False Origin Northing
# NARR grid definition
projection = getProjFromDict(projdict)
geotransform = (-5648873.5, 32463.0, 0.0, -4628776.5, 0.0, 32463.0)
size = (349, 277) # (x,y) map size of NARR grid
# make GridDefinition instance
NARR_grid = GridDefinition(name=dataset_name, projection=projdict, geotransform=geotransform, size=size)
# variable attributes and name
varatts = dict(air = dict(name='T2', units='K'), # 2m Temperature
prate = dict(name='precip', units='kg/m^2/s'), # total precipitation rate (kg/m^2/s)
# LTM-only variables (currently...)
prmsl = dict(name='pmsl', units='Pa'), # sea-level pressure
pevap = dict(name='pet', units='kg/m^2'), # monthly accumulated PET (kg/m^2)
pr_wtr = dict(name='pwtr', units='kg/m^2'), # total precipitable water (kg/m^2)
# axes (don't have their own file; listed in axes)
lon = dict(name='lon2D', units='deg E'), # geographic longitude field
lat = dict(name='lat2D', units='deg N'), # geographic latitude field
time = dict(name='time', units='days', offset=-1569072, scalefactor=1./24.), # time coordinate
# N.B.: the time coordinate is only used for the monthly time-series data, not the LTM
# the time offset is chose such that 1979 begins with the origin (time=0)
x = dict(name='x', units='m', offset=-5632642), # projected west-east coordinate
# PRISM grid definition
dlat = dlon = 1. / 120. # 0.0083333333
dlat2 = dlon2 = 1. / 240. # half step
nlat = 1680 # slat = 14 deg
nlon = 3241 # slon = 27 deg
# N.B.: coordinates refer to grid points (CF convention), commented values refer to box edges (GDAL convention)
llclat = 48. # 48.0000000000553
# llclat = 48.0000000000553 # 48.
llclon = -140. # -140.0
geotransform = (llclon - dlon2, dlon, 0.0, llclat - dlat2, 0.0, dlat)
size = (nlon, nlat) # (x,y) map size of PRISM grid
# make GridDefinition instance
PCIC_grid = GridDefinition(name=dataset_name,
projection=None,
geotransform=geotransform,
size=size)
## Functions that handle access to the original PCIC NetCDF files
# variable attributes and names in original PCIC files
ltmvaratts = dict(
tmin=dict(name='Tmin',
units='K',
atts=dict(long_name='Minimum 2m Temperature'),
offset=273.15), # 2m minimum temperature
tmax=dict(name='Tmax',
units='K',
atts=dict(long_name='Maximum 2m Temperature'),
offset=273.15), # 2m maximum temperature
pr=dict(name='precip',
dataset_name = 'CFSR'
# CFSR grid definition
# geotransform_031 = (-180.15625, 0.3125, 0.0, 89.915802001953125, 0.0, -0.30960083)
geotransform_031 = (-0.15625, 0.3125, 0.0, 89.915802001953125, 0.0,
-0.30960083)
size_031 = (1152, 576) # (x,y) map size
geotransform_05 = (-180.0, 0.5, 0.0, -90.0, 0.0, 0.5)
geotransform_05 = (-0.25, 0.5, 0.0, 90.25, 0.0, -0.5
) # this grid actually has a grid point at the poles!
size_05 = (720, 361) # (x,y) map size
# make GridDefinition instance
CFSR_031_grid = GridDefinition(name='CFSR_031',
projection=None,
geotransform=geotransform_031,
size=size_031,
lwrap360=True)
CFSR_05_grid = GridDefinition(name='CFSR_05',
projection=None,
geotransform=geotransform_05,
size=size_05,
lwrap360=True)
CFSR_grid = CFSR_031_grid # default
# variable attributes and name
varatts = dict(
TMP_L103_Avg=dict(name='T2', units='K'), # 2m average temperature
TMP_L1=dict(name='Ts', units='K'), # average skin temperature
PRATE_L1=dict(name='precip', units='kg/m^2/s'), # total precipitation
PRES_L1=dict(name='ps', units='Pa'), # surface pressure
dataset_name = 'GPCC'
# GPCC grid definition
geotransform_025 = (-180.0, 0.25, 0.0, -90.0, 0.0, 0.25)
size_025 = (1440, 720) # (x,y) map size
geotransform_05 = (-180.0, 0.5, 0.0, -90.0, 0.0, 0.5)
size_05 = (720, 360) # (x,y) map size
geotransform_10 = (-180.0, 1.0, 0.0, -90.0, 0.0, 1.0)
size_10 = (360, 180) # (x,y) map size
geotransform_25 = (-180.0, 2.5, 0.0, -90.0, 0.0, 2.5)
size_25 = (144, 72) # (x,y) map size
# make GridDefinition instance
GPCC_025_grid = GridDefinition(name='GPCC_025',
projection=None,
geotransform=geotransform_025,
size=size_025)
GPCC_05_grid = GridDefinition(name='GPCC_05',
projection=None,
geotransform=geotransform_05,
size=size_05)
GPCC_10_grid = GridDefinition(name='GPCC_10',
projection=None,
geotransform=geotransform_10,
size=size_10)
GPCC_25_grid = GridDefinition(name='GPCC_25',
projection=None,
geotransform=geotransform_25,
size=size_25)
# variable attributes and name