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 them
if gridsizetype=’length’ then gridsize is a distance between grid points
if 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 interpolated
All z<zmin would be set to zmin
zmax=nanmax(z)
Maximum z (elevation) of domain to be interpolated
All z>zmax would be set to zmax
RetainRatio=’all’
Define to down sample input data or not
‘all’: data are not down sampled
value between 0 and 1: percentage of retaining data
RetainRatio=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