"""
Get mask from objectively analyzed data
Construct tensors
Get sample means and standard deviations
Make random subset of sample means (with new mask)
Subtract annual mean from sample means
Fit using tensor-guided kriging
"""
from shared import *
import gph.Woa as woa
from interp import *
pngDir = None
#pngDir = "../../png/woa/"
slon = woa.getLonSampling()
slat = woa.getLatSampling()
def main(args):
#goShowData()
goKriging()
def goKriging():
namet = "s00an1" # trend
namea = "s10an1" # NODC gridded
namem = "s10mn1" # sample mean
named = "s10sd1" # sample std dev
namee = "s10se1" # sample std err
namen = "s10dd1" # number of samples
""" Get mask from objectively analyzed data Construct tensors Get sample means and standard deviations Make random subset of sample means (with new mask) Subtract annual mean from sample means Fit using tensor-guided kriging """ from shared import * import gph.Woa as woa from interp import * pngDir = None #pngDir = "../../png/woa/" slon = woa.getLonSampling() slat = woa.getLatSampling() def main(args): #goShowData() goKriging() def goKriging(): namet = "s00an1" # trend namea = "s10an1" # NODC gridded namem = "s10mn1" # sample mean named = "s10sd1" # sample std dev namee = "s10se1" # sample std err namen = "s10dd1" # number of samples namek = "s10kg1" # Kriging gridded depth = 100
