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 |
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
def ambhas.krige.OK.__init__ | ( | self, | |
x, | |||
y, | |||
z | |||
) |
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.
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.
def ambhas.krige.OK.krige | ( | self, | |
Xg, | |||
Yg, | |||
model_par, | |||
model_type | |||
) |
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.
def ambhas.krige.OK.variogram | ( | self, | |
var_type = 'averaged' , |
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n_lag = 9 |
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) |