Public Member Functions
def	__init__
def	variogram
def	vario_model
def	int_vario
def	krige
def	block_krige
Public Attributes
	x
	y
	z
	D
	Zg
	s2_k

Detailed Description

This performs the ordinary kriging
Input:
    x: x vector of location
    Y: y vector of location
    z: data vector at location (x,y)

Output:
    None
    
Methods:
    variogram: estimate the variogram

Definition at line 15 of file krige.py.

Constructor & Destructor Documentation

def ambhas.krige.OK.__init__	(	self,
		x,
		y,
		z
	)

Definition at line 30 of file krige.py.

Member Function Documentation

def ambhas.krige.OK.block_krige	(	self,
		Xg,
		Yg,
		model_par,
		model_type
	)

Input:
    Xg:     x location where krigged data is required
    Yg:     y location whre krigged data is required
    model_par: see the vario_model
    model_type: see the vario_model
    
Attributes:
    self.Zg : krigged data
    self.s2_k = variance in the data

Definition at line 199 of file krige.py.

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def ambhas.krige.OK.int_vario	(	self,
		Xg,
		Yg,
		model_par,
		model_type
	)

this computes the integral of the variogram over a square
using the Monte Carlo integration method

this works only for two dimensional grid

Input:
    Xg:     x location where krigged data is required
    Yg:     y location whre kirgged data is required
    model_par: see the vario_model
    model_type: see the vario_model

Definition at line 124 of file krige.py.

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def ambhas.krige.OK.krige	(	self,
		Xg,
		Yg,
		model_par,
		model_type
	)

Input:
    Xg:     x location where krigged data is required
    Yg:     y location whre kirgged data is required
    model_par: see the vario_model
    model_type: see the vario_model
    
Attributes:
    self.Zg : krigged data
    self.s2_k = variance in the data

Definition at line 151 of file krige.py.

def ambhas.krige.OK.vario_model	(	self,
		lags,
		model_par,
		model_type = `'linear'`
	)

Input:
    model_type : the type of variogram model 
             spherical
             linear
             exponential
    model_par:  parameters of variogram model
        this should be a dictionary 
        e.g. for shperical and exponential
            model_par = {'nugget':0, 'range':1, 'sill':1}
        for linear
            model_par = {'nugget':0, 'slope':1}
Output:
    G:  The fitted variogram model

Definition at line 82 of file krige.py.

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