2016-03-07 Scott Havens
Distributed forcing data over a grid using interpolation
smrf.spatial.grid.
GRID
Bases: object
object
Inverse distance weighting class - Standard IDW - Detrended IDW
calculateInterpolation
Interpolate over the grid
data – data to interpolate
mx – x locations for the points
my – y locations for the points
X – x locations in grid to interpolate over
Y – y locations in grid to interpolate over
detrendedInterpolation
Interpolate using a detrended approach
grid_method – scipy.interpolate.griddata interpolation method
detrendedInterpolationLocal
detrendedInterpolationMask
smrf.spatial.idw.
IDW
Inverse distance weighting class for distributing input data. Availables options are:
Standard IDW
Detrended IDW
calculateDistances
Calculate the distances from the measurement locations to the grid locations
calculateIDW
Calculate the IDW of the data at mx,my over GridX,GridY Inputs: data - is the same size at mx,my
calculateWeights
Calculate the weights for
detrendData
Detrend the data in val using the heights zmeas data - is the same size at mx,my flag - 1 for positive, -1 for negative, 0 for any trend imposed
detrendedIDW
Calculate the detrended IDW of the data at mx,my over GridX,GridY Inputs: data - is the same size at mx,my
retrendData
Retrend the IDW values
smrf.spatial.kriging.
KRIGE
Kriging class based on the pykrige package
calculate
Estimate the variogram, calculate the model, then apply to the grid
data: numpy array same length as m* config: configuration for dk
If style was specified as ‘masked’, zvalues will be a numpy masked array.
at the specified set of points. If style was specified as ‘masked’, sigmasq will be a numpy masked array.
v
Detrend the data in val using the heights zmeas
data – is the same size at mx,my
flag –
1 for positive, -1 for negative, 0 for any trend imposed
data minus the elevation trend