scientimate.interpxyz2grid#
xgrid, ygrid, zgrid = scientimate.interpxyz2grid(x, y, z, gridsize=100, gridsizetype='points', xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, RetainRatio='all', interpMethod='nearest', dispout='no')
Description#
Interpolate x (longitude), y (latitude) and z (elevation) data into a defined mesh
Inputs#
- x
x (longitude) data extracted from xyz file
- y
y (latitude) data extracted from xyz file
- z
z (elevation) data extracted from xyz file
- gridsize=100
- Grid size in x (longitude) and y (latitude) directions to interpolate elevation data on themif gridsizetype=’length’ then gridsize is a distance between grid pointsif gridsizetype=’points’ then gridsize is number of grid points in each direction
- gridsizetype=’points’
- Grid size type‘points’: gridsize is considered as number of grid points in each direction‘length’: gridsize is considered as length between grid points
- xmin=nanmin(x)
Minimum x (longitude) of domain to be interpolated
- xmax=nanmax(x)
Maximum x (longitude) of domain to be interpolated
- ymin=nanmin(y)
Minimum y (latitude) of domain to be interpolated
- ymax=nanmax(y)
Maximum y (latitude) of domain to be interpolated
- zmin=nanmin(z)
- Minimum z (elevation) of domain to be interpolatedAll z<zmin would be set to zmin
- zmax=nanmax(z)
- Maximum z (elevation) of domain to be interpolatedAll z>zmax would be set to zmax
- RetainRatio=’all’
- Define to down sample input data or not‘all’: data are not down sampledvalue between 0 and 1: percentage of retaining dataRetainRatio=0.8 : 80% of data are retained
- interpMethod=’nearest’
- Interpolation method‘linear’: Use default or ‘linear’ method to interpolate‘nearest’: Use nearest neighbor method to interpolate
- dispout=’no’
Define to display outputs or not (‘yes’: display, ‘no’: not display)
Outputs#
- xgrid
Interpolated x (longitude) data on defined mesh
- ygrid
Interpolated y (latitude) data on defined mesh
- zgrid
Interpolated z (elevation) data on defined mesh
Examples#
import scientimate as sm
import numpy as np
x=10.*np.random.rand(1000)
y=10.*np.random.rand(1000)
z=x**2+y**2
xgrid,ygrid,zgrid=sm.interpxyz2grid(x,y,z,100,'points',np.nanmin(x),np.nanmax(x),np.nanmin(y),np.nanmax(y),np.nanmin(z),np.nanmax(z),'all','nearest','yes')
x=(-90-(-91))*np.random.rand(1000)+(-91)
y=(31-(30))*np.random.rand(1000)+(30)
z=x**2+y**2
xgrid,ygrid,zgrid=sm.interpxyz2grid(x,y,z,0.005,'length',np.nanmin(x),np.nanmax(x),np.nanmin(y),np.nanmax(y),np.nanmin(z),np.nanmax(z),'all','linear','yes')
References#
Geospatial data