#import types # to add precip conversion fct. to datasets #from importlib import import_module # internal imports from geodata.base import Variable, Axis from geodata.netcdf import DatasetNetCDF from geodata.gdal import addGDALtoDataset, GridDefinition, loadPickledGridDef, addGeoLocator from geodata.misc import DatasetError from utils.nctools import writeNetCDF from datasets.common import getRootFolder, grid_folder, transformPrecip, timeSlice from datasets.common import translateVarNames, loadObservations, addLandMask, addLengthAndNamesOfMonth, getFileName from processing.process import CentralProcessingUnit ## GPCC Meta-data dataset_name = 'GPCC' root_folder = getRootFolder(dataset_name=dataset_name) # get dataset root folder based on environment variables # 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)
import numpy as np
import pandas as pd
import scipy.interpolate as si
import os
from warnings import warn
# internal imports
from datasets.common import BatchLoad, getRootFolder
from geodata.base import Dataset, Variable, Axis, concatDatasets
from geodata.misc import ArgumentError, VariableError, DataError, isNumber, DatasetError
from datasets.WSC import getGageStation, GageStationError, loadWSC_StnTS, updateScalefactor
import datetime as dt
## HGS Meta-data
dataset_name = 'HGS'
root_folder = getRootFolder(dataset_name=dataset_name, fallback_name='WRF') # get dataset root folder based on environment variables
prefix_file = 'batch.pfx' # text file that contians the HGS problem prefix (also HGS convention)
# variable attributes and name
variable_attributes = dict(sfroff = dict(name='sfroff', units='kg/m^2/s', atts=dict(long_name='Surface Runoff')), # surface flow rate over area
ugroff = dict(name='ugroff', units='kg/m^2/s', atts=dict(long_name='Subsurface Runoff')), # subsurface flow rate over area
runoff = dict(name='runoff', units='kg/m^2/s', atts=dict(long_name='Total Runoff')), # total flow rate over area
discharge = dict(name='discharge', units='kg/s', atts=dict(long_name='Surface Flow Rate')), # surface flow rate
seepage = dict(name='seepage' , units='kg/s', atts=dict(long_name='Subsurface Flow Rate')), # subsurface flow rate
flow = dict(name='flow' , units='kg/s', atts=dict(long_name='Total Flow Rate')), ) # total flow rate
# list of variables to load
variable_list = variable_attributes.keys() # also includes coordinate fields
flow_to_flux = dict(discharge='sfroff', seepage='ugroff', flow='runoff') # relationship between flux and flow variables
# N.B.: computing surface flux rates from gage flows also requires the drainage area
hgs_varlist = ['time','discharge','seepage','flow'] # internal use only; needs to have 'time'
hgs_variables = {'surface':'discharge','porous media':'seepage','total':'flow'} # mapping of HGS variable names GeoPy conventions
#external imports
import numpy as np
import os
# from atmdyn.properties import variablePlotatts
from geodata.base import Axis
from geodata.netcdf import DatasetNetCDF
from geodata.gdal import addGDALtoDataset, getProjFromDict, GridDefinition
from geodata.misc import DatasetError
from datasets.common import translateVarNames, name_of_month, getRootFolder, loadObservations, grid_folder
from processing.process import CentralProcessingUnit
## NARR Meta-data
dataset_name = 'NARR'
root_folder = getRootFolder(dataset_name=dataset_name) # get dataset root folder based on environment variables
# 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
